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Bai Z, Osman M, Brendel M, Tangen CM, Flaig TW, Thompson IM, Plets M, Scott Lucia M, Theodorescu D, Gustafson D, Daneshmand S, Meeks JJ, Choi W, Dinney CPN, Elemento O, Lerner SP, McConkey DJ, Faltas BM, Wang F. Predicting response to neoadjuvant chemotherapy in muscle-invasive bladder cancer via interpretable multimodal deep learning. NPJ Digit Med 2025; 8:174. [PMID: 40121304 PMCID: PMC11929913 DOI: 10.1038/s41746-025-01560-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2024] [Accepted: 03/11/2025] [Indexed: 03/25/2025] Open
Abstract
Building accurate prediction models and identifying predictive biomarkers for treatment response in Muscle-Invasive Bladder Cancer (MIBC) are essential for improving patient survival but remain challenging due to tumor heterogeneity, despite numerous related studies. To address this unmet need, we developed an interpretable Graph-based Multimodal Late Fusion (GMLF) deep learning framework. Integrating histopathology and cell type data from standard H&E images with gene expression profiles derived from RNA sequencing from the SWOG S1314-COXEN clinical trial (ClinicalTrials.gov NCT02177695 2014-06-25), GMLF uncovered new histopathological, cellular, and molecular determinants of response to neoadjuvant chemotherapy. Specifically, we identified key gene signatures that drive the predictive power of our model, including alterations in TP63, CCL5, and DCN. Our discovery can optimize treatment strategies for patients with MIBC, e.g., improving clinical outcomes, avoiding unnecessary treatment, and ultimately, bladder preservation. Additionally, our approach could be used to uncover predictors for other cancers.
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Affiliation(s)
- Zilong Bai
- Weill Cornell Medicine, New York, NY, USA
| | | | | | | | - Thomas W Flaig
- University of Colorado Comprehensive Cancer Center, Aurora, CO, USA
| | - Ian M Thompson
- Children's Hospital of San Antonio, San Antonio, TX, USA
| | - Melissa Plets
- SWOG Statistics and Data Management Center, Seattle, WA, USA
| | - M Scott Lucia
- University of Colorado Comprehensive Cancer Center, Aurora, CO, USA
| | | | - Daniel Gustafson
- University of Colorado Comprehensive Cancer Center, Aurora, CO, USA
| | - Siamak Daneshmand
- USC Institute of Urology, USC/Norris Comprehensive Cancer Center, Los Angeles, CA, USA
| | | | | | | | | | | | | | | | - Fei Wang
- Weill Cornell Medicine, New York, NY, USA.
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2
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Sharma A, Steger RF, Li JM, Baude JA, Heom KA, Dey SS, Stowers RS. Sp1 mechanotransduction regulates breast cancer cell invasion in response to multiple tumor-mimicking extracellular matrix cues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.18.643983. [PMID: 40166320 PMCID: PMC11957027 DOI: 10.1101/2025.03.18.643983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
Breast cancer progression is marked by extracellular matrix (ECM) remodeling, including increased stiffness, faster stress relaxation, and elevated collagen levels. In vitro experiments have revealed a role for each of these factors to individually promote malignant behavior, but their combined effects remain unclear. To address this, we developed alginate-collagen hydrogels with independently tunable stiffness, stress relaxation, and collagen density. We show that these combined tumor-mimicking ECM cues reinforced invasive morphologies and promoted spheroid invasion in breast cancer and mammary epithelial cells. High stiffness and low collagen density in slow-relaxing matrices led to the greatest cell migration speed and displacement. RNA-seq revealed Sp1 target gene enrichment in response to both individual and combined ECM cues, with a greater enrichment observed under multiple cues. Notably, high expression of Sp1 target genes upregulated by fast stress relaxation correlated with poor patient survival. Mechanistically, we found that phosphorylated-Sp1 (T453) was increasingly located in the nucleus in stiff and/or fast relaxing matrices, which was regulated by PI3K and ERK1/2 signaling, as well as actomyosin contractility. This study emphasizes how multiple ECM cues in complex microenvironments reinforce malignant traits and supports an emerging role for Sp1 as a mechanoresponsive transcription factor.
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Affiliation(s)
- Abhishek Sharma
- Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Rowan F Steger
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Jen M Li
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Jane A Baude
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Kellie A Heom
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Siddharth S Dey
- Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA
- Department of Bioengineering, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Ryan S Stowers
- Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA
- Department of Bioengineering, University of California, Santa Barbara, Santa Barbara, CA, USA
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3
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Aghova T, Lhotska H, Lizcova L, Svobodova K, Hodanova L, Janeckova K, Vucinic K, Gregor M, Konecna D, Kramar F, Soukup J, Netuka D, Zemanova Z. Diagnostic challenges in complicated case of glioblastoma. Pathol Oncol Res 2024; 30:1611875. [PMID: 39534304 PMCID: PMC11554483 DOI: 10.3389/pore.2024.1611875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Accepted: 10/14/2024] [Indexed: 11/16/2024]
Abstract
Glioblastoma is the commonest primary malignant brain tumor, with a very poor prognosis and short overall survival. It is characterized by its high intra- and intertumoral heterogeneity, in terms of both the level of single-nucleotide variants, copy number alterations, and aneuploidy. Therefore, routine diagnosis can be challenging in some cases. We present a complicated case of glioblastoma, which was characterized with five cytogenomic methods: interphase fluorescence in situ hybridization, multiplex ligation-dependent probe amplification, comparative genomic hybridization array and single-nucleotide polymorphism, targeted gene panel, and whole-genome sequencing. These cytogenomic methods revealed classical findings associated with glioblastoma, such as a lack of IDH and TERT mutations, gain of chromosome 7, and loss of chromosome 10. At least three pathological clones were identified, including one with whole-genome duplication, and one with loss of 1p and suspected loss of 19q. Deletion and mutation of the TP53 gene were detected with numerous breakends on 17p and 20q. Based on these findings, we recommend a combined approach to the diagnosis of glioblastoma involving the detection of copy number alterations, mutations, and aneuploidy. The choice of the best combination of methods is based on cost, time required, staff expertise, and laboratory equipment. This integrated strategy could contribute directly to tangible improvements in the diagnosis, prognosis, and prediction of the therapeutic responses of patients with brain tumors.
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Affiliation(s)
- Tatiana Aghova
- Center of Oncocytogenomics, Institute of Medical Biochemistry and Laboratory Diagnostics, General University Hospital and 1st Faculty of Medicine of Charles University in Prague, Prague, Czechia
| | - Halka Lhotska
- Center of Oncocytogenomics, Institute of Medical Biochemistry and Laboratory Diagnostics, General University Hospital and 1st Faculty of Medicine of Charles University in Prague, Prague, Czechia
| | - Libuse Lizcova
- Center of Oncocytogenomics, Institute of Medical Biochemistry and Laboratory Diagnostics, General University Hospital and 1st Faculty of Medicine of Charles University in Prague, Prague, Czechia
| | - Karla Svobodova
- Center of Oncocytogenomics, Institute of Medical Biochemistry and Laboratory Diagnostics, General University Hospital and 1st Faculty of Medicine of Charles University in Prague, Prague, Czechia
| | - Lucie Hodanova
- Center of Oncocytogenomics, Institute of Medical Biochemistry and Laboratory Diagnostics, General University Hospital and 1st Faculty of Medicine of Charles University in Prague, Prague, Czechia
| | - Karolina Janeckova
- Center of Oncocytogenomics, Institute of Medical Biochemistry and Laboratory Diagnostics, General University Hospital and 1st Faculty of Medicine of Charles University in Prague, Prague, Czechia
| | - Kim Vucinic
- Laboratory of Genomics and Bioinformatics, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czechia
| | - Martin Gregor
- Laboratory of Integrative Biology, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czechia
| | - Dora Konecna
- Department of Neurosurgery, 1st Faculty of Medicine of Charles University and Military University Hospital Prague, Prague, Czechia
| | - Filip Kramar
- Department of Neurosurgery, 1st Faculty of Medicine of Charles University and Military University Hospital Prague, Prague, Czechia
| | - Jiri Soukup
- Department of Pathology, 1st Faculty of Medicine of Charles University and Military University Hospital Prague, Prague, Czechia
- The Fingerland Department of Pathology, Charles University, Faculty of Medicine in Hradec Králové and University Hospital Hradec Králové, Hradec Králové, Czechia
- Department of Pathology, Charles University, First Faculty of Medicine and General University Hospital in Prague, Prague, Czechia
| | - David Netuka
- Department of Neurosurgery, 1st Faculty of Medicine of Charles University and Military University Hospital Prague, Prague, Czechia
| | - Zuzana Zemanova
- Center of Oncocytogenomics, Institute of Medical Biochemistry and Laboratory Diagnostics, General University Hospital and 1st Faculty of Medicine of Charles University in Prague, Prague, Czechia
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4
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Kasperski A, Heng HH. The Spiral Model of Evolution: Stable Life Forms of Organisms and Unstable Life Forms of Cancers. Int J Mol Sci 2024; 25:9163. [PMID: 39273111 PMCID: PMC11395208 DOI: 10.3390/ijms25179163] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2024] [Revised: 08/19/2024] [Accepted: 08/20/2024] [Indexed: 09/15/2024] Open
Abstract
If one must prioritize among the vast array of contributing factors to cancer evolution, environmental-stress-mediated chromosome instability (CIN) should easily surpass individual gene mutations. CIN leads to the emergence of genomically unstable life forms, enabling them to grow dominantly within the stable life form of the host. In contrast, stochastic gene mutations play a role in aiding the growth of the cancer population, with their importance depending on the initial emergence of the new system. Furthermore, many specific gene mutations among the many available can perform this function, decreasing the clinical value of any specific gene mutation. Since these unstable life forms can respond to treatment differently than stable ones, cancer often escapes from drug treatment by forming new systems, which leads to problems during the treatment for patients. To understand how diverse factors impact CIN-mediated macroevolution and genome integrity-ensured microevolution, the concept of two-phased cancer evolution is used to reconcile some major characteristics of cancer, such as bioenergetic, unicellular, and multicellular evolution. Specifically, the spiral of life function model is proposed, which integrates major historical evolutionary innovations and conservation with information management. Unlike normal organismal evolution in the microevolutionary phase, where a given species occupies a specific location within the spiral, cancer populations are highly heterogenous at multiple levels, including epigenetic levels. Individual cells occupy different levels and positions within the spiral, leading to supersystems of mixed cellular populations that exhibit both macro and microevolution. This analysis, utilizing karyotype to define the genetic networks of the cellular system and CIN to determine the instability of the system, as well as considering gene mutation and epigenetics as modifiers of the system for information amplification and usage, explores the high evolutionary potential of cancer. It provides a new, unified understanding of cancer as a supersystem, encouraging efforts to leverage the dynamics of CIN to develop improved treatment options. Moreover, it offers a historically contingent model for organismal evolution that reconciles the roles of both evolutionary innovation and conservation through macroevolution and microevolution, respectively.
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Affiliation(s)
- Andrzej Kasperski
- Department of Biotechnology, Laboratory of Bioinformatics and Control of Bioprocesses, Institute of Biological Sciences, University of Zielona Góra, Szafrana 1, 65-516 Zielona Góra, Poland
| | - Henry H Heng
- Center for Molecular Medicine and Genetics, Department of Pathology, Wayne State University School of Medicine, Detroit, MI 48201, USA
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Li T, Chen YC, Ao P. Heterogeneous Evolution of Breast Cancer Cells-An Endogenous Molecular-Cellular Network Study. BIOLOGY 2024; 13:564. [PMID: 39194502 DOI: 10.3390/biology13080564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2024] [Revised: 07/23/2024] [Accepted: 07/25/2024] [Indexed: 08/29/2024]
Abstract
Breast cancer heterogeneity presents a significant challenge in clinical therapy, such as over-treatment and drug resistance. These challenges are largely due to its obscure normal epithelial origins, evolutionary stability, and transitions on the cancer subtypes. This study aims to elucidate the cellular emergence and maintenance of heterogeneous breast cancer via quantitative bio-process modeling, with potential benefit to therapeutic strategies for the disease. An endogenous molecular-cellular hypothesis posits that both pathological and physiological states are phenotypes evolved from and shaped by interactions among a number of conserved modules and cellular factors within a biological network. We hereby developed a model of core endogenous network for breast cancer in accordance with the theory, quantifying its intrinsic dynamic properties with dynamic modeling. The model spontaneously generates cell states that align with molecular classifications at both the molecular and modular level, replicating four widely recognized molecular subtypes of the cancer and validating against data extracted from the TCGA database. Further analysis shows that topologically, a singular progression gateway from normal breast cells to cancerous states is identified as the Luminal A-type breast cancer. Activated positive feedback loops are found to stabilize cellular states, while negative feedback loops facilitate state transitions. Overall, more routes are revealed on the cellular transition between stable states, and a traceable count explains the origin of breast cancer heterogeneity. Ultimately, the research intended to strength the search for therapeutic targets.
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Affiliation(s)
- Tianqi Li
- Center for Quantitative Life Sciences & Physics Department, Shanghai University, Shanghai 200444, China
| | - Yong-Cong Chen
- Center for Quantitative Life Sciences & Physics Department, Shanghai University, Shanghai 200444, China
| | - Ping Ao
- School of Biomedical Engineering, Sichuan University, Chengdu 610065, China
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6
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Ali HR, West RB. Spatial Biology of Breast Cancer. Cold Spring Harb Perspect Med 2024; 14:a041335. [PMID: 38110242 PMCID: PMC11065165 DOI: 10.1101/cshperspect.a041335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
Spatial findings have shaped on our understanding of breast cancer. In this review, we discuss how spatial methods, including spatial transcriptomics and proteomics and the resultant understanding of spatial relationships, have contributed to concepts regarding cancer progression and treatment. In addition to discussing traditional approaches, we examine how emerging multiplex imaging technologies have contributed to the field and how they might influence future research.
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Affiliation(s)
- H Raza Ali
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, United Kingdom
| | - Robert B West
- Department of Pathology, Stanford University Medical Center, Stanford, California 94305, USA
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Wang J, Li B, Luo M, Huang J, Zhang K, Zheng S, Zhang S, Zhou J. Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance. Signal Transduct Target Ther 2024; 9:83. [PMID: 38570490 PMCID: PMC10991592 DOI: 10.1038/s41392-024-01779-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 04/05/2024] Open
Abstract
Ductal carcinoma in situ (DCIS) represents pre-invasive breast carcinoma. In untreated cases, 25-60% DCIS progress to invasive ductal carcinoma (IDC). The challenge lies in distinguishing between non-progressive and progressive DCIS, often resulting in over- or under-treatment in many cases. With increasing screen-detected DCIS in these years, the nature of DCIS has aroused worldwide attention. A deeper understanding of the biological nature of DCIS and the molecular journey of the DCIS-IDC transition is crucial for more effective clinical management. Here, we reviewed the key signaling pathways in breast cancer that may contribute to DCIS initiation and progression. We also explored the molecular features of DCIS and IDC, shedding light on the progression of DCIS through both inherent changes within tumor cells and alterations in the tumor microenvironment. In addition, valuable research tools utilized in studying DCIS including preclinical models and newer advanced technologies such as single-cell sequencing, spatial transcriptomics and artificial intelligence, have been systematically summarized. Further, we thoroughly discussed the clinical advancements in DCIS and IDC, including prognostic biomarkers and clinical managements, with the aim of facilitating more personalized treatment strategies in the future. Research on DCIS has already yielded significant insights into breast carcinogenesis and will continue to pave the way for practical clinical applications.
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Affiliation(s)
- Jing Wang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Baizhou Li
- Department of Pathology, the Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Meng Luo
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
- Department of Plastic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jia Huang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Kun Zhang
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shu Zheng
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Suzhan Zhang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China.
| | - Jiaojiao Zhou
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China.
- Cancer Center, Zhejiang University, Hangzhou, China.
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8
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Ludwik KA, Greathouse FR, Han S, Stauffer K, Brenin DR, Stricker TP, Lannigan DA. Identifying the effectiveness of 3D culture systems to recapitulate breast tumor tissue in situ. Cell Oncol (Dordr) 2024; 47:481-496. [PMID: 37776423 PMCID: PMC11090829 DOI: 10.1007/s13402-023-00877-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/16/2023] [Indexed: 10/02/2023] Open
Abstract
PURPOSE Breast cancer heterogeneity contributes to chemotherapy resistance and decreased patient survival. To improve patient outcomes it is essential to develop a technology that is able to rapidly select the most efficacious therapy that targets the diverse phenotypes present within the tumor. Breast cancer organoid technologies are proposed as an attractive approach for evaluating drug responses prior to patient therapy. However, there remain challenges in evaluating the effectiveness of organoid cultures to recapitulate the heterogeneity present in the patient tumor in situ. METHOD Organoids were generated from seven normal breast and nineteen breast cancer tissues diagnosed as estrogen receptor positive or triple negative. The Jensen-Shannon divergence index, a measure of the similarity between distributions, was used to compare and evaluate heterogeneity in starting tissue and their resultant organoids. Heterogeneity was analyzed using cytokeratin 8 and cytokeratin 14, which provided an easily scored readout. RESULTS In the in vitro culture system HER1 and FGFR were able to drive intra-tumor heterogeneity to generate divergent phenotypes that have different sensitivities to chemotherapies. CONCLUSION Our methodology, which focuses on quantifiable cellular phenotypes, provides a tractable system that complements omics approaches to provide an unprecedented view of heterogeneity and will enhance the identification of novel therapies and facilitate personalized medicine.
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Affiliation(s)
- Katarzyna A Ludwik
- Department Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Frances R Greathouse
- Department Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | | | - Kimberly Stauffer
- Department Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - David R Brenin
- Department Surgery, University of Virginia, Charlottesville, VA, 22908, USA
| | - Thomas P Stricker
- Department Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Deborah A Lannigan
- Department Pathology, Microbiology & Immunology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
- Department Biomedical Engineering, Vanderbilt University, Nashville, TN, 37235, USA.
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9
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Abstract
Molecular abnormalities that shape human neoplasms dissociate their phenotypic landscape from that of the healthy counterpart. Through the lens of a microscope, tumour pathology optically captures such aberrations projected onto a tissue slide and has categorized human epithelial neoplasms into distinct histological subtypes based on the diverse morphogenetic and molecular programmes that they manifest. Tumour histology often reflects tumour aggressiveness, patient prognosis and therapeutic vulnerability, and thus has been used as a de facto diagnostic tool and for making clinical decisions. However, it remains elusive how the diverse histological subtypes arise and translate into pleiotropic biological phenotypes. Molecular analysis of clinical tumour tissues and their culture, including patient-derived organoids, and add-back genetic reconstruction of tumorigenic pathways using gene engineering in culture models and rodents further elucidated molecular mechanisms that underlie morphological variations. Such mechanisms include genetic mutations and epigenetic alterations in cellular identity codes that erode hard-wired morphological programmes and histologically digress tumours from the native tissues. Interestingly, tumours acquire the ability to grow independently of the niche-driven stem cell ecosystem along with these morphological alterations, providing a biological rationale for histological diversification during tumorigenesis. This Review comprehensively summarizes our current understanding of such plasticity in the histological and lineage commitment fostered cooperatively by molecular alterations and the tumour environment, and describes basic and clinical implications for future cancer therapy.
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Affiliation(s)
- Masayuki Fujii
- Department of Integrated Medicine and Biochemistry, Keio University School of Medicine, Tokyo, Japan.
| | - Shigeki Sekine
- Division of Pathology and Clinical Laboratories, National Cancer Center Hospital, Tokyo, Japan
| | - Toshiro Sato
- Department of Integrated Medicine and Biochemistry, Keio University School of Medicine, Tokyo, Japan.
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10
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Lu Y, Chen QM, An L. Semi-reference based cell type deconvolution with application to human metastatic cancers. NAR Genom Bioinform 2023; 5:lqad109. [PMID: 38143958 PMCID: PMC10748484 DOI: 10.1093/nargab/lqad109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 11/01/2023] [Accepted: 12/13/2023] [Indexed: 12/26/2023] Open
Abstract
Bulk RNA-seq experiments, commonly used to discern gene expression changes across conditions, often neglect critical cell type-specific information due to their focus on average transcript abundance. Recognizing cell type contribution is crucial to understanding phenotype and disease variations. The advent of single-cell RNA sequencing has allowed detailed examination of cellular heterogeneity; however, the cost and analytic caveat prohibits such sequencing for a large number of samples. We introduce a novel deconvolution approach, SECRET, that employs cell type-specific gene expression profiles from single-cell RNA-seq to accurately estimate cell type proportions from bulk RNA-seq data. Notably, SECRET can adapt to scenarios where the cell type present in the bulk data is unrepresented in the reference, thereby offering increased flexibility in reference selection. SECRET has demonstrated superior accuracy compared to existing methods using synthetic data and has identified unknown tissue-specific cell types in real human metastatic cancers. Its versatility makes it broadly applicable across various human cancer studies.
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Affiliation(s)
- Yingying Lu
- Interdisciplinary Program in Statistics and Data Science, University of Arizona, Tucson, AZ, USA
| | - Qin M Chen
- College of Pharmacy, University of Arizona, Tucson, AZ, USA
- Cancer Biology Program, University of Arizona, Tucson, AZ, USA
| | - Lingling An
- Interdisciplinary Program in Statistics and Data Science, University of Arizona, Tucson, AZ, USA
- Department of Biosystems Engineering, University of Arizona, Tucson, AZ, USA
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, USA
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11
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Kurkalang S, Roy S, Acharya A, Mazumder P, Mazumder S, Patra S, Ghosh S, Sarkar S, Kundu S, Biswas NK, Ghose S, Majumder PP, Maitra A. Single-cell transcriptomic analysis of gingivo-buccal oral cancer reveals two dominant cellular programs. Cancer Sci 2023; 114:4732-4746. [PMID: 37792582 PMCID: PMC10728019 DOI: 10.1111/cas.15979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 09/02/2023] [Accepted: 09/13/2023] [Indexed: 10/06/2023] Open
Abstract
Oral squamous cell carcinoma of the gingivo-buccal region (OSCC-GB) is the most common cancer among men in India, and is associated with poor prognosis and frequent recurrence. Cellular heterogeneity in OSCC-GB was investigated by single-cell RNA sequencing of tumors derived from the oral cavity of 12 OSCC-GB patients, 3 of whom had concomitant presence of a precancerous lesion (oral submucous fibrosis [OSMF]). Unique malignant cell types, features, and phenotypic shifts in the stromal cell population were identified in oral tumors with associated submucous fibrosis. Expression levels of FOS, ATP1A, and DUSP1 provided robust discrimination between tumors with or without the concomitant presence of OSMF. Malignant cell populations shared between tumors with and without OSMF were enriched with the expression of partial epithelial-mesenchymal transition (pEMT) or fetal cell type signatures indicative of two dominant cellular programs in OSCC-GB-pEMT and fetal cellular reprogramming. Malignant cells exhibiting fetal cellular and pEMT programs were enriched with the expression of immune-related pathway genes known to be involved in antitumor immune response. In the tumor microenvironment, higher infiltration of immune cells than the stromal cells was observed. The T cell population was large in tumors and diverse subtypes of T cells with varying levels of infiltration were found. We also detected double-negative PLCG2+ T cells and cells with intermediate M1-M2 macrophage polarization. Our findings shed light on unique aspects of cellular heterogeneity and cell states in OSCC-GB.
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Affiliation(s)
| | - Sumitava Roy
- National Institute of Biomedical GenomicsKalyaniIndia
- Regional Centre for BiotechnologyFaridabadIndia
| | - Arunima Acharya
- National Institute of Biomedical GenomicsKalyaniIndia
- Regional Centre for BiotechnologyFaridabadIndia
| | - Paramita Mazumder
- Department of Oral PathologyDr. R. Ahmed Dental College and HospitalKolkataIndia
| | | | - Subrata Patra
- National Institute of Biomedical GenomicsKalyaniIndia
| | - Shekhar Ghosh
- National Institute of Biomedical GenomicsKalyaniIndia
| | | | - Sudip Kundu
- National Institute of Biomedical GenomicsKalyaniIndia
| | | | - Sandip Ghose
- Department of Oral PathologyDr. R. Ahmed Dental College and HospitalKolkataIndia
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12
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Baglamis S, Sheraton VM, Meijer D, Qian H, Hoebe RA, Lenos KJ, Betjes MA, Betjes MA, Tans S, van Zon J, Vermeulen L, Krawczyk PM. Using picoliter droplet deposition to track clonal competition in adherent and organoid cancer cell cultures. Sci Rep 2023; 13:18832. [PMID: 37914743 PMCID: PMC10620187 DOI: 10.1038/s41598-023-42849-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 09/15/2023] [Indexed: 11/03/2023] Open
Abstract
Clonal growth and competition underlie processes of key relevance in etiology, progression and therapy response across all cancers. Here, we demonstrate a novel experimental approach, based on multi-color, fluorescent tagging of cell nuclei, in combination with picoliter droplet deposition, to study the clonal dynamics in two- and three-dimensional cell cultures. The method allows for the simultaneous visualization and analysis of multiple clones in individual multi-clonal colonies, providing a powerful tool for studying clonal dynamics and identifying clonal populations with distinct characteristics. Results of our experiments validate the utility of the method in studying clonal dynamics in vitro, and reveal differences in key aspects of clonal behavior of different cancer cell lines in monoculture conditions, as well as in co-cultures with stromal fibroblasts.
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Affiliation(s)
- Selami Baglamis
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Cancer Center Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Oncode Institute, 3521 AL, Utrecht, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, 1105 AZ, Amsterdam, The Netherlands
| | - Vivek M Sheraton
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Cancer Center Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Oncode Institute, 3521 AL, Utrecht, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, 1105 AZ, Amsterdam, The Netherlands
- Institute for Advanced Study, University of Amsterdam, 1012 WX, Amsterdam, The Netherlands
| | - Debora Meijer
- Cancer Center Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Department of Medical Biology, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Haibin Qian
- Cancer Center Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Department of Medical Biology, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Ron A Hoebe
- Cancer Center Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Department of Medical Biology, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Kristiaan J Lenos
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Cancer Center Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Oncode Institute, 3521 AL, Utrecht, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, 1105 AZ, Amsterdam, The Netherlands
| | - Max A Betjes
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Cancer Center Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Oncode Institute, 3521 AL, Utrecht, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism, 1105 AZ, Amsterdam, The Netherlands
| | | | | | | | - Louis Vermeulen
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Amsterdam UMC Location University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, 1081 HV, Amsterdam, The Netherlands.
- Oncode Institute, 3521 AL, Utrecht, The Netherlands.
- Amsterdam Gastroenterology Endocrinology Metabolism, 1105 AZ, Amsterdam, The Netherlands.
| | - Przemek M Krawczyk
- Cancer Center Amsterdam, 1081 HV, Amsterdam, The Netherlands.
- Department of Medical Biology, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
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13
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Romero-Arias JR, González-Castro CA, Ramírez-Santiago G. A multiscale model of the role of microenvironmental factors in cell segregation and heterogeneity in breast cancer development. PLoS Comput Biol 2023; 19:e1011673. [PMID: 37992135 DOI: 10.1371/journal.pcbi.1011673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 12/06/2023] [Accepted: 11/08/2023] [Indexed: 11/24/2023] Open
Abstract
We analyzed a quantitative multiscale model that describes the epigenetic dynamics during the growth and evolution of an avascular tumor. A gene regulatory network (GRN) formed by a set of ten genes that are believed to play an important role in breast cancer development was kinetically coupled to the microenvironmental agents: glucose, estrogens, and oxygen. The dynamics of spontaneous mutations was described by a Yule-Furry master equation whose solution represents the probability that a given cell in the tissue undergoes a certain number of mutations at a given time. We assumed that the mutation rate is modified by a spatial gradient of nutrients. The tumor mass was simulated by means of cellular automata supplemented with a set of reaction diffusion equations that described the transport of microenvironmental agents. By analyzing the epigenetic state space described by the GRN dynamics, we found three attractors that were identified with cellular epigenetic states: normal, precancer and cancer. For two-dimensional (2D) and three-dimensional (3D) tumors we calculated the spatial distribution of the following quantities: (i) number of mutations, (ii) mutation of each gene and, (iii) phenotypes. Using estrogen as the principal microenvironmental agent that regulates cell proliferation process, we obtained tumor shapes for different values of estrogen consumption and supply rates. It was found that he majority of mutations occurred in cells that were located close to the 2D tumor perimeter or close to the 3D tumor surface. Also, it was found that the occurrence of different phenotypes in the tumor are controlled by estrogen concentration levels since they can change the individual cell threshold and gene expression levels. All results were consistently observed for 2D and 3D tumors.
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Affiliation(s)
- J Roberto Romero-Arias
- Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
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14
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Vu T, Seal S, Ghosh T, Ahmadian M, Wrobel J, Ghosh D. FunSpace: A functional and spatial analytic approach to cell imaging data using entropy measures. PLoS Comput Biol 2023; 19:e1011490. [PMID: 37756338 PMCID: PMC10561868 DOI: 10.1371/journal.pcbi.1011490] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 10/09/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Spatial heterogeneity in the tumor microenvironment (TME) plays a critical role in gaining insights into tumor development and progression. Conventional metrics typically capture the spatial differential between TME cellular patterns by either exploring the cell distributions in a pairwise fashion or aggregating the heterogeneity across multiple cell distributions without considering the spatial contribution. As such, none of the existing approaches has fully accounted for the simultaneous heterogeneity caused by both cellular diversity and spatial configurations of multiple cell categories. In this article, we propose an approach to leverage spatial entropy measures at multiple distance ranges to account for the spatial heterogeneity across different cellular organizations. Functional principal component analysis (FPCA) is applied to estimate FPC scores which are then served as predictors in a Cox regression model to investigate the impact of spatial heterogeneity in the TME on survival outcome, potentially adjusting for other confounders. Using a non-small cell lung cancer dataset (n = 153) as a case study, we found that the spatial heterogeneity in the TME cellular composition of CD14+ cells, CD19+ B cells, CD4+ and CD8+ T cells, and CK+ tumor cells, had a significant non-zero effect on the overall survival (p = 0.027). Furthermore, using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset (n = 33), our proposed method identified a significant impact of cellular interactions between tumor and immune cells on the overall survival (p = 0.046). In simulation studies under different spatial configurations, the proposed method demonstrated a high predictive power by accounting for both clinical effect and the impact of spatial heterogeneity.
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Affiliation(s)
- Thao Vu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Souvik Seal
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Tusharkanti Ghosh
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Mansooreh Ahmadian
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Julia Wrobel
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
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15
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Kluska M, Piastowska-Ciesielska AW, Tokarz P. Cell Cycle Status Influences Resistance to Apoptosis Induced by Oxidative Stress in Human Breast Cancer Cells, Which Is Accompanied by Modulation of Autophagy. Curr Issues Mol Biol 2023; 45:6325-6338. [PMID: 37623218 PMCID: PMC10453102 DOI: 10.3390/cimb45080399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/21/2023] [Accepted: 07/28/2023] [Indexed: 08/26/2023] Open
Abstract
Cancer cells are characterised by uncontrolled cell proliferation; however, some of them can temporarily arrest their cell cycle at the G0 or G1 phase, which could contribute to tumour heterogeneity and drug resistance. The cell cycle status plays a critical role in chemosensitivity; however, the influence of G0- and G1-arrest has not been elucidated. To study the cell cycle arrest-mediated resistance, we used MCF-7 cells and generated three populations of cells: (1) cells arrested in the G0-like phase, (2) cells that resumed the cell cycle after the G0-like phase and (3) cells arrested in early G1 with a history of G0-like arrest. We observed that both the G0-like- and the G1-arrested cells acquired resistance to apoptosis induced by oxidative stress, accompanied by a decreased intracellular reactive oxygen species and DNA damage. This effect was associated with increased autophagy, likely facilitating their survival at DNA damage insult. The cell cycle reinitiation restored a sensitivity to oxidative stress typical for cells with a non-modulated cell cycle, with a concomitant decrease in autophagy. Our results support the need for further research on the resistance of G0- and G1-arrested cancer cells to DNA-damaging agents and present autophagy as a candidate for targeting in anticancer treatment.
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Affiliation(s)
- Magdalena Kluska
- Department of Molecular Genetics, University of Lodz, Pomorska 141/143, 90-236 Lodz, Poland
| | | | - Paulina Tokarz
- Department of Molecular Genetics, University of Lodz, Pomorska 141/143, 90-236 Lodz, Poland
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16
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Maeser N, Khan A, Sun R. Somatic variant detection from multi-sampled genomic sequencing data of tumor specimens using the ith.Variant pipeline. STAR Protoc 2023; 4:101927. [PMID: 36586123 PMCID: PMC9816983 DOI: 10.1016/j.xpro.2022.101927] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 09/23/2022] [Accepted: 11/21/2022] [Indexed: 12/30/2022] Open
Abstract
A common technique for uncovering intra-tumor genomic heterogeneity (ITH) is variant detection. However, it can be challenging to reliably characterize ITH given uneven sample quality (e.g., depth of coverage, tumor purity, and subclonality). We describe a protocol for calling point mutations and copy number alterations using sequencing of multiple related clinical patient samples across diverse tissue, optimizing for sensitivity with specificity. The ith.Variant pipeline can be run on single- or multi-region whole-genome and whole-exome sequencing. For complete details on the use and execution of this protocol, please refer to Sun et al. (2017).1.
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Affiliation(s)
- Nicole Maeser
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA
| | - Aziz Khan
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ruping Sun
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN 55455, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455, USA.
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17
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Parikh AS, Yu VX, Flashner S, Okolo OB, Lu C, Henick BS, Momen-Heravi F, Puram SV, Teknos T, Pan Q, Nakagawa H. Patient-derived three-dimensional culture techniques model tumor heterogeneity in head and neck cancer. Oral Oncol 2023; 138:106330. [PMID: 36773387 PMCID: PMC10126876 DOI: 10.1016/j.oraloncology.2023.106330] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/08/2022] [Accepted: 01/25/2023] [Indexed: 02/11/2023]
Abstract
Head and neck squamous cell carcinoma (HNSCC) outcomes remain stagnant, in part due to a poor understanding of HNSCC biology. The importance of tumor heterogeneity as an independent predictor of outcomes and treatment failure in HNSCC has recently come to light. With this understanding, 3D culture systems, including patient derived organoids (PDO) and organotypic culture (OTC), that capture this heterogeneity may allow for modeling and manipulation of critical subpopulations, such as p-EMT, as well as interactions between cancer cells and immune and stromal cells in the microenvironment. Here, we review work that has been done using PDO and OTC models of HNSCC, which demonstrates that these 3D culture models capture in vivo tumor heterogeneity and can be used to model tumor biology and treatment response in a way that faithfully recapitulates in vivo characteristics. As such, in vitro 3D culture models represent an important bridge between 2D monolayer culture and in vivo models such as patient derived xenografts.
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Affiliation(s)
- Anuraag S Parikh
- Department of Otolaryngology-Head and Neck Surgery, Columbia University, New York, NY, United States; Columbia University Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Victoria X Yu
- Department of Otolaryngology-Head and Neck Surgery, Columbia University, New York, NY, United States
| | - Samuel Flashner
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University, New York, NY, United States
| | - Ogoegbunam B Okolo
- Columbia University Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States
| | - Chao Lu
- Department of Genetics and Development, Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, United States
| | - Brian S Henick
- Division of Hematology/Oncology, Department of Medicine, Columbia Unversity, New York, NY, United States
| | - Fatemeh Momen-Heravi
- Columbia University College of Dental Medicine, Columbia University, New York, NY, United States
| | - Sidharth V Puram
- Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine, St. Louis, MO, United States; Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
| | - Theodoros Teknos
- Department of Otolaryngology, Case Western Reserve University, Cleveland, OH, United States
| | - Quintin Pan
- Department of Otolaryngology, Case Western Reserve University, Cleveland, OH, United States
| | - Hiroshi Nakagawa
- Division of Digestive and Liver Diseases, Department of Medicine, Columbia University, New York, NY, United States.
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18
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Derbal Y. Cell Adaptive Fitness and Cancer Evolutionary Dynamics. Cancer Inform 2023; 22:11769351231154679. [PMID: 36860424 PMCID: PMC9969436 DOI: 10.1177/11769351231154679] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 01/17/2023] [Indexed: 02/26/2023] Open
Abstract
Genome instability of cancer cells translates into increased entropy and lower information processing capacity, leading to metabolic reprograming toward higher energy states, presumed to be aligned with a cancer growth imperative. Dubbed as the cell adaptive fitness, the proposition postulates that the coupling between cell signaling and metabolism constrains cancer evolutionary dynamics along trajectories privileged by the maintenance of metabolic sufficiency for survival. In particular, the conjecture postulates that clonal expansion becomes restricted when genetic alterations induce a sufficiently high level of disorder, that is, high entropy, in the regulatory signaling network, abrogating as a result the ability of cancer cells to successfully replicate, leading to a stage of clonal stagnation. The proposition is analyzed in the context of an in-silico model of tumor evolutionary dynamics to illustrate how cell-inherent adaptive fitness may predictably constrain clonal evolution of tumors, which would have significant implications for the design of adaptive cancer therapies.
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Affiliation(s)
- Youcef Derbal
- Youcef Derbal, Ted Rogers School of
Information Technology Management, Toronto Metropolitan University, 350 Victoria
Street, Toronto, ON M5B 2K3, Canada.
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19
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van den Bosch T, Derks S, Miedema DM. Chromosomal Instability, Selection and Competition: Factors That Shape the Level of Karyotype Intra-Tumor Heterogeneity. Cancers (Basel) 2022; 14:4986. [PMID: 36291770 PMCID: PMC9600040 DOI: 10.3390/cancers14204986] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/07/2022] [Accepted: 10/09/2022] [Indexed: 12/03/2022] Open
Abstract
Intra-tumor heterogeneity (ITH) is a pan-cancer predictor of survival, with high ITH being correlated to a dismal prognosis. The level of ITH is, hence, a clinically relevant characteristic of a malignancy. ITH of karyotypes is driven by chromosomal instability (CIN). However, not all new karyotypes generated by CIN are viable or competitive, which limits the amount of ITH. Here, we review the cellular processes and ecological properties that determine karyotype ITH. We propose a framework to understand karyotype ITH, in which cells with new karyotypes emerge through CIN, are selected by cell intrinsic and cell extrinsic selective pressures, and propagate through a cancer in competition with other malignant cells. We further discuss how CIN modulates the cell phenotype and immune microenvironment, and the implications this has for the subsequent selection of karyotypes. Together, we aim to provide a comprehensive overview of the biological processes that shape the level of karyotype heterogeneity.
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Affiliation(s)
- Tom van den Bosch
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers—Location AMC, 1105 AZ Amsterdam, The Netherlands
- Oncode Institute, 1105 AZ Amsterdam, The Netherlands
| | - Sarah Derks
- Oncode Institute, 1105 AZ Amsterdam, The Netherlands
- Department of Medical Oncology, Amsterdam University Medical Centers—Location VUmc, 1081 HV Amsterdam, The Netherlands
| | - Daniël M. Miedema
- Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Amsterdam Gastroenterology Endocrinology Metabolism, Amsterdam University Medical Centers—Location AMC, 1105 AZ Amsterdam, The Netherlands
- Oncode Institute, 1105 AZ Amsterdam, The Netherlands
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20
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Qin R, Zhao H, He Q, Li F, Li Y, Zhao H. Advances in single-cell sequencing technology in the field of hepatocellular carcinoma. Front Genet 2022; 13:996890. [PMID: 36303541 PMCID: PMC9592975 DOI: 10.3389/fgene.2022.996890] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/28/2022] [Indexed: 11/13/2022] Open
Abstract
Tumors are a class of diseases characterized by altered genetic information and uncontrolled growth. Sequencing technology provide researchers with a better way to explore specific tumor pathogenesis. In recent years, single-cell sequencing technology has shone in tumor research, especially in the study of liver cancer, revealing phenomena that were unexplored by previous studies. Single-cell sequencing (SCS) is a technique for sequencing the cellular genome, transcriptome, epigenome, proteomics, or metabolomics after dissociation of tissues into single cells. Compared with traditional bulk sequencing, single-cell sequencing can dissect human tumors at single-cell resolution, finely delineate different cell types, and reveal the heterogeneity of tumor cells. In view of the diverse pathological types and complex pathogenesis of hepatocellular carcinoma (HCC), the study of the heterogeneity among tumor cells can help improve its clinical diagnosis, treatment and prognostic judgment. On this basis, SCS has revolutionized our understanding of tumor heterogeneity, tumor immune microenvironment, and clonal evolution of tumor cells. This review summarizes the basic process and development of single-cell sequencing technology and its increasing role in the field of hepatocellular carcinoma.
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Affiliation(s)
- Rongyi Qin
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
| | - Haichao Zhao
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Qizu He
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
| | - Feng Li
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
| | - Yanjun Li
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- *Correspondence: Yanjun Li, ; Haoliang Zhao,
| | - Haoliang Zhao
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- *Correspondence: Yanjun Li, ; Haoliang Zhao,
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21
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Khatib SA, Ma L, Dang H, Forgues M, Chung JY, Ylaya K, Hewitt SM, Chaisaingmongkol J, Rucchirawat M, Wang XW. Single-cell biology uncovers apoptotic cell death and its spatial organization as a potential modifier of tumor diversity in HCC. Hepatology 2022; 76:599-611. [PMID: 35034369 DOI: 10.1002/hep.32345] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/05/2022] [Accepted: 01/06/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND AIMS HCC is a highly aggressive and heterogeneous cancer type with limited treatment options. Identifying drivers of tumor heterogeneity may lead to better therapeutic options and favorable patient outcomes. We investigated whether apoptotic cell death and its spatial architecture is linked to tumor molecular heterogeneity using single-cell in situ hybridization analysis. APPROACH AND RESULTS We analyzed 254 tumor samples from two HCC cohorts using tissue microarrays. We developed a mathematical model to quantify cellular diversity among HCC samples using two tumor markers, cyclin-dependent kinase inhibitor 3 and protein regulator of cytokinesis 1 as surrogates for heterogeneity and caspase 3 (CASP3) as an apoptotic cell death marker. We further explored the impact of potential dying-cell hubs on tumor cell diversity and patient outcome by density contour mapping and spatial proximity analysis. We also developed a selectively controlled in vitro model of cell death using CRISPR/CRISPR-associated 9 to determine therapy response and growth under hypoxic conditions. We found that increasing levels of CASP3+ tumor cells are associated with higher tumor diversity. Interestingly, we discovered regions of densely populated CASP3+ , which we refer to as CASP3+ cell islands, in which the nearby cellular heterogeneity was found to be the greatest compared to cells farther away from these islands and that this phenomenon was associated with survival. Additionally, cell culture experiments revealed that higher levels of cell death, accompanied by increased CASP3 expression, led to greater therapy resistance and growth under hypoxia. CONCLUSIONS These results are consistent with the hypothesis that increased apoptotic cell death may lead to greater tumor heterogeneity and thus worse patient outcomes.
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Affiliation(s)
- Subreen A Khatib
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA.,Department of Tumor Biology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Lichun Ma
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Hien Dang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA.,Division of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Marshonna Forgues
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Joon-Yong Chung
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Kris Ylaya
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Stephen M Hewitt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
| | - Jittporn Chaisaingmongkol
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, Thailand.,Center of Excellence on Environmental Health and Toxicology, Office of the Higher Education Commission, Ministry of Education, Bangkok, Thailand
| | - Mathuros Rucchirawat
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, Thailand.,Center of Excellence on Environmental Health and Toxicology, Office of the Higher Education Commission, Ministry of Education, Bangkok, Thailand
| | - Xin Wei Wang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA.,Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland, USA
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22
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Wu HJ, Temko D, Maliga Z, Moreira AL, Sei E, Minussi DC, Dean J, Lee C, Xu Q, Hochart G, Jacobson CA, Yapp C, Schapiro D, Sorger PK, Seeley EH, Navin N, Downey RJ, Michor F. Spatial intra-tumor heterogeneity is associated with survival of lung adenocarcinoma patients. CELL GENOMICS 2022; 2:100165. [PMID: 36419822 PMCID: PMC9681138 DOI: 10.1016/j.xgen.2022.100165] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Intra-tumor heterogeneity (ITH) of human tumors is important for tumor progression, treatment response, and drug resistance. However, the spatial distribution of ITH remains incompletely understood. Here, we present spatial analysis of ITH in lung adenocarcinomas from 147 patients using multi-region mass spectrometry of >5,000 regions, single-cell copy number sequencing of ~2,000 single cells, and cyclic immunofluorescence of >10 million cells. We identified two distinct spatial patterns among tumors, termed clustered and random geographic diversification (GD). These patterns were observed in the same samples using both proteomic and genomic data. The random proteomic GD pattern, which is characterized by decreased cell adhesion and lower levels of tumor-interacting endothelial cells, was significantly associated with increased risk of recurrence or death in two independent patient cohorts. Our study presents comprehensive spatial mapping of ITH in lung adenocarcinoma and provides insights into the mechanisms and clinical consequences of GD.
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Affiliation(s)
- Hua-Jun Wu
- Center for Precision Medicine Multi-Omics Research, School of Basic Medical Sciences, Peking University Health Science Center and Peking University Cancer Hospital and Institute, Beijing, China,These authors contributed equally
| | - Daniel Temko
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA,These authors contributed equally
| | - Zoltan Maliga
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA 02215, USA,These authors contributed equally
| | - Andre L. Moreira
- Department of Pathology, New York University Langone Health, New York, NY 10016, USA
| | - Emi Sei
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Darlan Conterno Minussi
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jamie Dean
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Charlotte Lee
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA,Harvard-MIT Program in Health Sciences and Technology, Harvard Medical School, Boston, MA 02215, USA
| | - Qiong Xu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | | | - Connor A. Jacobson
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Clarence Yapp
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Denis Schapiro
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA 02215, USA,Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School, Boston, MA 02215, USA,Ludwig Center at Harvard, Boston, MA 02215, USA
| | - Erin H. Seeley
- Department of Chemistry, University of Texas at Austin, Austin, TX, USA
| | - Nicholas Navin
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA,Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Robert J. Downey
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA,Correspondence: (R.J.D.), (F.M.)
| | - Franziska Michor
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA,Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA,Ludwig Center at Harvard, Boston, MA 02215, USA,Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02215, USA,Lead contact,Correspondence: (R.J.D.), (F.M.)
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23
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Tokura M, Nakayama J, Prieto-Vila M, Shiino S, Yoshida M, Yamamoto T, Watanabe N, Takayama S, Suzuki Y, Okamoto K, Ochiya T, Kohno T, Yatabe Y, Suto A, Yamamoto Y. Single-Cell Transcriptome Profiling Reveals Intratumoral Heterogeneity and Molecular Features of Ductal Carcinoma In Situ. Cancer Res 2022; 82:3236-3248. [DOI: 10.1158/0008-5472.can-22-0090] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 04/25/2022] [Accepted: 07/11/2022] [Indexed: 11/16/2022]
Abstract
Abstract
Ductal carcinoma in situ (DCIS) is a precursor to invasive breast cancer. The frequency of DCIS is increasing because of routine mammography; however, the biological features and intratumoral heterogeneity of DCIS remain obscure. To address this deficiency, we performed single-cell transcriptomic profiling of DCIS and invasive ductal carcinoma (IDC). DCIS was found to be composed of several transcriptionally distinct subpopulations of cancer cells with specific functions. Several transcripts, including long noncoding RNAs, were highly expressed in IDC compared to DCIS and might be related to the invasive phenotype. Closeness centrality analysis revealed extensive heterogeneity in DCIS, and the prediction model for cell-to-cell interactions implied that the interaction network among luminal cells and immune cells in DCIS was comparable to that in IDC. Additionally, transcriptomic profiling of HER2+ luminal DCIS indicated HER2 genomic amplification at the DCIS stage. These data provide novel insight into the intratumoral heterogeneity and molecular features of DCIS, which exhibit properties similar to IDC.
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Affiliation(s)
- Momoko Tokura
- National Cancer Center Research Institute, Tokyo, Japan
| | - Jun Nakayama
- National Cancer Center Research Institute, Tokyo, Japan
| | - Marta Prieto-Vila
- Institute of Medical Science, Tokyo Medical University, Tokyo, Japan
| | - Sho Shiino
- National Cancer Center Hospital, Keio University School of Medicine, Tokyo, Japan
| | | | | | | | | | | | - Koji Okamoto
- National Cancer Center Research Institute, Tokyo, Japan
| | | | - Takashi Kohno
- National Cancer Center Research Institute, Tokyo, Japan
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24
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Wu X, Song P, Guo L, Ying J, Li W. Mutant-Allele Tumor Heterogeneity, a Favorable Biomarker to Assess Intra-Tumor Heterogeneity, in Advanced Lung Adenocarcinoma. Front Oncol 2022; 12:888951. [PMID: 35847947 PMCID: PMC9286753 DOI: 10.3389/fonc.2022.888951] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/29/2022] [Indexed: 11/30/2022] Open
Abstract
Background Intra-tumor heterogeneity (ITH) plays a vital role in drug resistance and recurrence of lung cancer. We used a mutant-allele tumor heterogeneity (MATH) algorithm to assess ITH and investigated its association with clinical and molecular features in advanced lung adenocarcinoma. Methods Tissues from 63 patients with advanced lung adenocarcinoma were analyzed by next-generation sequencing (NGS) using a panel targeting 520 cancer-relevant genes. We calculated the MATH values from NGS data and further investigated their correlation with clinical and molecular characteristics. Results Among the 63 patients with advanced lung adenocarcinoma, the median value of MATH was 33.06. Patients with EGFR mutation had higher level of MATH score than those with wild-type EGFR status (P = 0.008). Patients with stage IV disease showed a trend to have a higher MATH score than those with stage III (P = 0.052). MATH was higher in patients with disruptive TP53 mutations than in those with non-disruptive mutations (P = 0.036) or wild-type sequence (P = 0.023), but did not differ between tumors with non-disruptive mutations and wild-type TP53 (P = 0.867). High MATH is associated with mutations in mismatch repair (MMR) pathway (P = 0.026) and base excision repair (BER) pathway (P = 0.008). In addition, MATH was found to have a positive correlation with tumor mutational burden (TMB) (Spearman ρ = 0.354; P = 0.004). In 26 patients harboring EGFR mutation treated with first generation EGFR TKI as single-agent therapy, the objective response rate was higher in the Low-MATH group than in the High-MATH group (75% vs. 21%; P = 0.016) and Low-MATH group showed a significantly longer progression-free survival than High-MATH group (median PFS: 13.7 months vs. 10.1 months; P = 0.024). Conclusions For patients with advanced lung adenocarcinoma, MATH may serve as a clinically practical biomarker to assess intratumor heterogeneity.
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Affiliation(s)
- Xiaoxuan Wu
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Peng Song
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lei Guo
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianming Ying
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Wenbin Li, ; Jianming Ying,
| | - Wenbin Li
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Wenbin Li, ; Jianming Ying,
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25
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Danenberg E, Bardwell H, Zanotelli VRT, Provenzano E, Chin SF, Rueda OM, Green A, Rakha E, Aparicio S, Ellis IO, Bodenmiller B, Caldas C, Ali HR. Breast tumor microenvironment structures are associated with genomic features and clinical outcome. Nat Genet 2022; 54:660-669. [PMID: 35437329 PMCID: PMC7612730 DOI: 10.1038/s41588-022-01041-y] [Citation(s) in RCA: 128] [Impact Index Per Article: 42.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/03/2022] [Indexed: 12/12/2022]
Abstract
The functions of the tumor microenvironment (TME) are orchestrated by precise spatial organization of specialized cells, yet little is known about the multicellular structures that form within the TME. Here we systematically mapped TME structures in situ using imaging mass cytometry and multitiered spatial analysis of 693 breast tumors linked to genomic and clinical data. We identified ten recurrent TME structures that varied by vascular content, stromal quiescence versus activation, and leukocyte composition. These TME structures had distinct enrichment patterns among breast cancer subtypes, and some were associated with genomic profiles indicative of immune escape. Regulatory and dysfunctional T cells co-occurred in large 'suppressed expansion' structures. These structures were characterized by high cellular diversity, proliferating cells and enrichment for BRCA1 and CASP8 mutations and predicted poor outcome in estrogen-receptor-positive disease. The multicellular structures revealed here link conserved spatial organization to local TME function and could improve patient stratification.
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Affiliation(s)
- Esther Danenberg
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Helen Bardwell
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Vito R T Zanotelli
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Elena Provenzano
- Department of Histopathology, Addenbrookes Hospital, Cambridge, UK
| | - Suet-Feung Chin
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Oscar M Rueda
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Andrew Green
- Department of Pathology, University of Nottingham, Nottingham, UK
| | - Emad Rakha
- Department of Pathology, University of Nottingham, Nottingham, UK
| | - Samuel Aparicio
- British Columbia Cancer Agency, University of British Columbia, Vancouver, British Columbia, Canada
| | - Ian O Ellis
- Department of Pathology, University of Nottingham, Nottingham, UK
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
| | - Carlos Caldas
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK.
| | - H Raza Ali
- CRUK Cambridge Institute, University of Cambridge, Cambridge, UK.
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Department of Histopathology, Addenbrookes Hospital, Cambridge, UK.
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26
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Applications of Single-Cell Sequencing Technology to the Enteric Nervous System. Biomolecules 2022; 12:biom12030452. [PMID: 35327644 PMCID: PMC8946246 DOI: 10.3390/biom12030452] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/12/2022] [Accepted: 03/13/2022] [Indexed: 02/05/2023] Open
Abstract
With recent technical advances and diminishing sequencing costs, single-cell sequencing modalities have become commonplace. These tools permit analysis of RNA expression, DNA sequence, chromatin structure, and cell surface antigens at single-cell resolution. Simultaneous measurement of numerous parameters can resolve populations including rare cells, thus revealing cellular diversity within organs and permitting lineage reconstruction in developing tissues. Application of these methods to the enteric nervous system has yielded a wealth of data and biological insights. We review recent papers applying single-cell sequencing tools to the nascent neural crest and to the developing and mature enteric nervous system. These studies have shown significant diversity of enteric neurons and glia, suggested paradigms for neuronal specification, and revealed signaling pathways active during development. As technology evolves and multiome techniques combining two or more of transcriptomic, genomic, epigenetic, and proteomic data become prominent, we anticipate these modalities will become commonplace in ENS research and may find a role in diagnostic testing and personalized therapeutics.
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27
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Özkan H, Öztürk DG, Korkmaz G. Transcriptional Factor Repertoire of Breast Cancer in 3D Cell Culture Models. Cancers (Basel) 2022; 14:cancers14041023. [PMID: 35205770 PMCID: PMC8870600 DOI: 10.3390/cancers14041023] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 02/13/2022] [Accepted: 02/14/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Knowledge of the transcriptional regulation of breast cancer tumorigenesis is largely based on studies performed in two-dimensional (2D) monolayer culture models, which lack tissue architecture and therefore fail to represent tumor heterogeneity. However, three-dimensional (3D) cell culture models are better at mimicking in vivo tumor microenvironment, which is critical in regulating cellular behavior. Hence, 3D cell culture models hold great promise for translational breast cancer research. Abstract Intratumor heterogeneity of breast cancer is driven by extrinsic factors from the tumor microenvironment (TME) as well as tumor cell–intrinsic parameters including genetic, epigenetic, and transcriptomic traits. The extracellular matrix (ECM), a major structural component of the TME, impacts every stage of tumorigenesis by providing necessary biochemical and biomechanical cues that are major regulators of cell shape/architecture, stiffness, cell proliferation, survival, invasion, and migration. Moreover, ECM and tissue architecture have a profound impact on chromatin structure, thereby altering gene expression. Considering the significant contribution of ECM to cellular behavior, a large body of work underlined that traditional two-dimensional (2D) cultures depriving cell–cell and cell–ECM interactions as well as spatial cellular distribution and organization of solid tumors fail to recapitulate in vivo properties of tumor cells residing in the complex TME. Thus, three-dimensional (3D) culture models are increasingly employed in cancer research, as these culture systems better mimic the physiological microenvironment and shape the cellular responses according to the microenvironmental cues that will regulate critical cell functions such as cell shape/architecture, survival, proliferation, differentiation, and drug response as well as gene expression. Therefore, 3D cell culture models that better resemble the patient transcriptome are critical in defining physiologically relevant transcriptional changes. This review will present the transcriptional factor (TF) repertoire of breast cancer in 3D culture models in the context of mammary tissue architecture, epithelial-to-mesenchymal transition and metastasis, cell death mechanisms, cancer therapy resistance and differential drug response, and stemness and will discuss the impact of culture dimensionality on breast cancer research.
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Affiliation(s)
- Hande Özkan
- School of Medicine, Koç University, Istanbul 34450, Turkey;
- Research Centre for Translational Medicine (KUTTAM), Koç University, Istanbul 34450, Turkey
| | - Deniz Gülfem Öztürk
- School of Medicine, Koç University, Istanbul 34450, Turkey;
- Research Centre for Translational Medicine (KUTTAM), Koç University, Istanbul 34450, Turkey
- Correspondence: (D.G.Ö.); (G.K.)
| | - Gozde Korkmaz
- School of Medicine, Koç University, Istanbul 34450, Turkey;
- Research Centre for Translational Medicine (KUTTAM), Koç University, Istanbul 34450, Turkey
- Correspondence: (D.G.Ö.); (G.K.)
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28
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Hong Y, Limback D, Elsarraj HS, Harper H, Haines H, Hansford H, Ricci M, Kaufman C, Wedlock E, Xu M, Zhang J, May L, Cusick T, Inciardi M, Redick M, Gatewood J, Winblad O, Aripoli A, Huppe A, Balanoff C, Wagner JL, Amin AL, Larson KE, Ricci L, Tawfik O, Razek H, Meierotto RO, Madan R, Godwin AK, Thompson J, Hilsenbeck SG, Futreal A, Thompson A, Hwang ES, Fan F, Behbod F, the Grand Challenge PRECISION Consortium. Mouse-INtraDuctal (MIND): an in vivo model for studying the underlying mechanisms of DCIS malignancy. J Pathol 2022; 256:186-201. [PMID: 34714554 PMCID: PMC8738143 DOI: 10.1002/path.5820] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/05/2021] [Accepted: 10/25/2021] [Indexed: 11/24/2022]
Abstract
Due to widespread adoption of screening mammography, there has been a significant increase in new diagnoses of ductal carcinoma in situ (DCIS). However, DCIS prognosis remains unclear. To address this gap, we developed an in vivo model, Mouse-INtraDuctal (MIND), in which patient-derived DCIS epithelial cells are injected intraductally and allowed to progress naturally in mice. Similar to human DCIS, the cancer cells formed in situ lesions inside the mouse mammary ducts and mimicked all histologic subtypes including micropapillary, papillary, cribriform, solid, and comedo. Among 37 patient samples injected into 202 xenografts, at median duration of 9 months, 20 samples (54%) injected into 95 xenografts showed in vivo invasive progression, while 17 (46%) samples injected into 107 xenografts remained non-invasive. Among the 20 samples that showed invasive progression, nine samples injected into 54 xenografts exhibited a mixed pattern in which some xenografts showed invasive progression while others remained non-invasive. Among the clinically relevant biomarkers, only elevated progesterone receptor expression in patient DCIS and the extent of in vivo growth in xenografts predicted an invasive outcome. The Tempus XT assay was used on 16 patient DCIS formalin-fixed, paraffin-embedded sections including eight DCISs that showed invasive progression, five DCISs that remained non-invasive, and three DCISs that showed a mixed pattern in the xenografts. Analysis of the frequency of cancer-related pathogenic mutations among the groups showed no significant differences (KW: p > 0.05). There were also no differences in the frequency of high, moderate, or low severity mutations (KW; p > 0.05). These results suggest that genetic changes in the DCIS are not the primary driver for the development of invasive disease. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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MESH Headings
- Animals
- Biomarkers, Tumor/genetics
- Biomarkers, Tumor/metabolism
- Breast Neoplasms/genetics
- Breast Neoplasms/metabolism
- Breast Neoplasms/pathology
- Carcinoma, Intraductal, Noninfiltrating/genetics
- Carcinoma, Intraductal, Noninfiltrating/metabolism
- Carcinoma, Intraductal, Noninfiltrating/pathology
- Cell Movement
- Cell Proliferation
- Disease Progression
- Epithelial Cells/metabolism
- Epithelial Cells/pathology
- Epithelial Cells/transplantation
- Female
- Heterografts
- Humans
- Mice, Inbred NOD
- Mice, SCID
- Mutation
- Neoplasm Invasiveness
- Neoplasm Transplantation
- Receptors, Progesterone/metabolism
- Time Factors
- Mice
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Affiliation(s)
- Yan Hong
- Department of Pathology and Laboratory MedicineThe University of Kansas Medical CenterKansas CityKSUSA
| | - Darlene Limback
- Department of Pathology and Laboratory MedicineThe University of Kansas Medical CenterKansas CityKSUSA
| | - Hanan S Elsarraj
- Department of Pathology and Laboratory MedicineThe University of Kansas Medical CenterKansas CityKSUSA
| | - Haleigh Harper
- University of Kansas School of MedicineThe University of Kansas Medical CenterKansas CityKSUSA
| | - Haley Haines
- Department of Pathology and Laboratory MedicineThe University of Kansas Medical CenterKansas CityKSUSA
| | - Hayley Hansford
- Department of Pathology and Laboratory MedicineThe University of Kansas Medical CenterKansas CityKSUSA
| | - Michael Ricci
- Department of Pathology and Laboratory MedicineThe University of Kansas Medical CenterKansas CityKSUSA
| | - Carolyn Kaufman
- University of Kansas School of MedicineThe University of Kansas Medical CenterKansas CityKSUSA
| | - Emily Wedlock
- Department of Pathology and Laboratory MedicineThe University of Kansas Medical CenterKansas CityKSUSA
| | - Mingchu Xu
- Department of Genomic MedicineThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Jianhua Zhang
- Department of Genomic MedicineThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Lisa May
- Department of RadiologyThe University of Kansas School of Medicine‐WichitaWichitaKSUSA
| | - Therese Cusick
- Department of SurgeryThe University of Kansas School of Medicine‐WichitaWichitaKSUSA
| | - Marc Inciardi
- Department of RadiologyThe University of Kansas Medical CenterKansas CityKSUSA
| | - Mark Redick
- Department of RadiologyThe University of Kansas Medical CenterKansas CityKSUSA
| | - Jason Gatewood
- Department of RadiologyThe University of Kansas Medical CenterKansas CityKSUSA
| | - Onalisa Winblad
- Department of RadiologyThe University of Kansas Medical CenterKansas CityKSUSA
| | - Allison Aripoli
- Department of RadiologyThe University of Kansas Medical CenterKansas CityKSUSA
| | - Ashley Huppe
- Department of RadiologyThe University of Kansas Medical CenterKansas CityKSUSA
| | - Christa Balanoff
- Department of General Surgery, Breast Surgical Oncology DivisionThe University of Kansas Medical CenterKansas CityKSUSA
| | - Jamie L Wagner
- Department of General Surgery, Breast Surgical Oncology DivisionThe University of Kansas Medical CenterKansas CityKSUSA
| | - Amanda L Amin
- Department of General Surgery, Breast Surgical Oncology DivisionThe University of Kansas Medical CenterKansas CityKSUSA
| | - Kelsey E Larson
- Department of General Surgery, Breast Surgical Oncology DivisionThe University of Kansas Medical CenterKansas CityKSUSA
| | - Lawrence Ricci
- Department of RadiologyTruman Medical CenterKansas CityMOUSA
| | - Ossama Tawfik
- Department of Pathology, St Luke's Health System of Kansas CityMAWD Pathology GroupKansas CityMOUSA
| | | | - Ruby O Meierotto
- Breast RadiologySaint Luke's Cancer Institute, Saint Luke's Health SystemKansas CityMOUSA
| | - Rashna Madan
- Department of Pathology and Laboratory MedicineThe University of Kansas Medical CenterKansas CityKSUSA
| | - Andrew K Godwin
- Department of Pathology and Laboratory MedicineThe University of Kansas Medical CenterKansas CityKSUSA
| | - Jeffrey Thompson
- Department of BiostatisticsThe University of Kansas Medical CenterKansas CityKSUSA
| | - Susan G Hilsenbeck
- Lester and Sue Smith Breast Center, Biostatistics and Informatics Shared Resources, Duncan Cancer CenterBaylor College of MedicineHoustonTXUSA
| | - Andy Futreal
- Department of Genomic Medicine, Division of Cancer MedicineThe University of Texas MD Anderson Cancer CenterHoustonTXUSA
| | - Alastair Thompson
- Section of Breast SurgeryBaylor College of Medicine, Lester and Sue Smith Breast Center, Dan L Duncan Comprehensive Cancer CenterHoustonTXUSA
| | | | - Fang Fan
- Department of PathologyCity of Hope Medical CenterDuarteCAUSA
| | - Fariba Behbod
- Department of Pathology and Laboratory MedicineThe University of Kansas Medical CenterKansas CityKSUSA
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29
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Heng J, Heng HH. Genome Chaos, Information Creation, and Cancer Emergence: Searching for New Frameworks on the 50th Anniversary of the "War on Cancer". Genes (Basel) 2021; 13:genes13010101. [PMID: 35052441 PMCID: PMC8774498 DOI: 10.3390/genes13010101] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 12/22/2021] [Accepted: 12/29/2021] [Indexed: 12/26/2022] Open
Abstract
The year 2021 marks the 50th anniversary of the National Cancer Act, signed by President Nixon, which declared a national “war on cancer.” Powered by enormous financial support, this past half-century has witnessed remarkable progress in understanding the individual molecular mechanisms of cancer, primarily through the characterization of cancer genes and the phenotypes associated with their pathways. Despite millions of publications and the overwhelming volume data generated from the Cancer Genome Project, clinical benefits are still lacking. In fact, the massive, diverse data also unexpectedly challenge the current somatic gene mutation theory of cancer, as well as the initial rationales behind sequencing so many cancer samples. Therefore, what should we do next? Should we continue to sequence more samples and push for further molecular characterizations, or should we take a moment to pause and think about the biological meaning of the data we have, integrating new ideas in cancer biology? On this special anniversary, we implore that it is time for the latter. We review the Genome Architecture Theory, an alternative conceptual framework that departs from gene-based theories. Specifically, we discuss the relationship between genes, genomes, and information-based platforms for future cancer research. This discussion will reinforce some newly proposed concepts that are essential for advancing cancer research, including two-phased cancer evolution (which reconciles evolutionary contributions from karyotypes and genes), stress-induced genome chaos (which creates new system information essential for macroevolution), the evolutionary mechanism of cancer (which unifies diverse molecular mechanisms to create new karyotype coding during evolution), and cellular adaptation and cancer emergence (which explains why cancer exists in the first place). We hope that these ideas will usher in new genomic and evolutionary conceptual frameworks and strategies for the next 50 years of cancer research.
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Affiliation(s)
- Julie Heng
- Harvard College, 16 Divinity Ave, Cambridge, MA 02138, USA;
| | - Henry H. Heng
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201, USA
- Department of Pathology, Wayne State University School of Medicine, Detroit, MI 48201, USA
- Correspondence:
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30
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Kashyap A, Rapsomaniki MA, Barros V, Fomitcheva-Khartchenko A, Martinelli AL, Rodriguez AF, Gabrani M, Rosen-Zvi M, Kaigala G. Quantification of tumor heterogeneity: from data acquisition to metric generation. Trends Biotechnol 2021; 40:647-676. [PMID: 34972597 DOI: 10.1016/j.tibtech.2021.11.006] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 01/18/2023]
Abstract
Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.
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Affiliation(s)
- Aditya Kashyap
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland
| | | | - Vesna Barros
- Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa, 3498825, Israel; The Hebrew University, The Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
| | - Anna Fomitcheva-Khartchenko
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland; Eidgenössische Technische Hochschule (ETH-Zurich), Vladimir-Prelog-Weg 1-5/10, 8099 Zurich, Switzerland
| | | | | | - Maria Gabrani
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland
| | - Michal Rosen-Zvi
- Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa, 3498825, Israel; The Hebrew University, The Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
| | - Govind Kaigala
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland.
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31
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Radziuviene G, Rasmusson A, Augulis R, Grineviciute RB, Zilenaite D, Laurinaviciene A, Ostapenko V, Laurinavicius A. Intratumoral Heterogeneity and Immune Response Indicators to Predict Overall Survival in a Retrospective Study of HER2-Borderline (IHC 2+) Breast Cancer Patients. Front Oncol 2021; 11:774088. [PMID: 34858854 PMCID: PMC8631965 DOI: 10.3389/fonc.2021.774088] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/22/2021] [Indexed: 11/13/2022] Open
Abstract
Breast cancer (BC) categorized as human epidermal growth factor receptor 2 (HER2) borderline [2+ by immunohistochemistry (IHC 2+)] presents challenges for the testing, frequently obscured by intratumoral heterogeneity (ITH). This leads to difficulties in therapy decisions. We aimed to establish prognostic models of overall survival (OS) of these patients, which take into account spatial aspects of ITH and tumor microenvironment by using hexagonal tiling analytics of digital image analysis (DIA). In particular, we assessed the prognostic value of Immunogradient indicators at the tumor–stroma interface zone (IZ) as a feature of antitumor immune response. Surgical excision samples stained for estrogen receptor (ER), progesterone receptor (PR), Ki67, HER2, and CD8 from 275 patients with HER2 IHC 2+ invasive ductal BC were used in the study. DIA outputs were subsampled by HexT for ITH quantification and tumor microenvironment extraction for Immunogradient indicators. Multiple Cox regression revealed HER2 membrane completeness (HER2 MC) (HR: 0.18, p = 0.0007), its spatial entropy (HR: 0.37, p = 0.0341), and ER contrast (HR: 0.21, p = 0.0449) as independent predictors of better OS, with worse OS predicted by pT status (HR: 6.04, p = 0.0014) in the HER2 non-amplified patients. In the HER2-amplified patients, HER2 MC contrast (HR: 0.35, p = 0.0367) and CEP17 copy number (HR: 0.19, p = 0.0035) were independent predictors of better OS along with worse OS predicted by pN status (HR: 4.75, p = 0.0018). In the non-amplified tumors, three Immunogradient indicators provided the independent prognostic value: CD8 density in the tumor aspect of the IZ and CD8 center of mass were associated with better OS (HR: 0.23, p = 0.0079 and 0.14, p = 0.0014, respectively), and CD8 density variance along the tumor edge predicted worse OS (HR: 9.45, p = 0.0002). Combining these three computational indicators of the CD8 cell spatial distribution within the tumor microenvironment augmented prognostic stratification of the patients. In the HER2-amplified group, CD8 cell density in the tumor aspect of the IZ was the only independent immune response feature to predict better OS (HR: 0.22, p = 0.0047). In conclusion, we present novel prognostic models, based on computational ITH and Immunogradient indicators of the IHC biomarkers, in HER2 IHC 2+ BC patients.
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Affiliation(s)
- Gedmante Radziuviene
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania.,Institute of Biosciences, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Allan Rasmusson
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania.,Faculty of Medicine, Institute of Biomedical Sciences, Vilnius University, Vilnius, Lithuania
| | - Renaldas Augulis
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania.,Faculty of Medicine, Institute of Biomedical Sciences, Vilnius University, Vilnius, Lithuania
| | - Ruta Barbora Grineviciute
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania
| | - Dovile Zilenaite
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania.,Faculty of Medicine, Institute of Biomedical Sciences, Vilnius University, Vilnius, Lithuania
| | - Aida Laurinaviciene
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania.,Faculty of Medicine, Institute of Biomedical Sciences, Vilnius University, Vilnius, Lithuania
| | - Valerijus Ostapenko
- Department of Breast Surgery and Oncology, National Cancer Institute, Vilnius, Lithuania
| | - Arvydas Laurinavicius
- National Center of Pathology, Affiliate of Vilnius University Hospital Santaros Clinics, Vilnius, Lithuania.,Faculty of Medicine, Institute of Biomedical Sciences, Vilnius University, Vilnius, Lithuania
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Piemonte KM, Anstine LJ, Keri RA. Centrosome Aberrations as Drivers of Chromosomal Instability in Breast Cancer. Endocrinology 2021; 162:6381103. [PMID: 34606589 PMCID: PMC8557634 DOI: 10.1210/endocr/bqab208] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Indexed: 12/12/2022]
Abstract
Chromosomal instability (CIN), or the dynamic change in chromosome number and composition, has been observed in cancer for decades. Recently, this phenomenon has been implicated as facilitating the acquisition of cancer hallmarks and enabling the formation of aggressive disease. Hence, CIN has the potential to serve as a therapeutic target for a wide range of cancers. CIN in cancer often occurs as a result of disrupting key regulators of mitotic fidelity and faithful chromosome segregation. As a consequence of their essential roles in mitosis, dysfunctional centrosomes can induce and maintain CIN. Centrosome defects are common in breast cancer, a heterogeneous disease characterized by high CIN. These defects include amplification, structural defects, and loss of primary cilium nucleation. Recent studies have begun to illuminate the ability of centrosome aberrations to instigate genomic flux in breast cancer cells and the tumor evolution associated with aggressive disease and poor patient outcomes. Here, we review the role of CIN in breast cancer, the processes by which centrosome defects contribute to CIN in this disease, and the emerging therapeutic approaches that are being developed to capitalize upon such aberrations.
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Affiliation(s)
- Katrina M Piemonte
- Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
- Department of Cancer Biology, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44195, USA
| | - Lindsey J Anstine
- Department of Pharmacology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
- Department of Cancer Biology, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44195, USA
- Cleveland Clinic Lerner College of Medicine at Case Western Reserve University, Cleveland, OH 44195, USA
| | - Ruth A Keri
- Department of Cancer Biology, Cleveland Clinic Lerner Research Institute, Cleveland, OH 44195, USA
- Cleveland Clinic Lerner College of Medicine at Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
- Correspondence: Ruth A. Keri, PhD, Department of Cancer Biology, Cleveland Clinic Lerner Research Institute, 9500 Euclid Avenue, Cleveland, OH 44195, USA.
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Fatfat Z, Fatfat M, Gali-Muhtasib H. Therapeutic potential of thymoquinone in combination therapy against cancer and cancer stem cells. World J Clin Oncol 2021; 12:522-543. [PMID: 34367926 PMCID: PMC8317652 DOI: 10.5306/wjco.v12.i7.522] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/11/2021] [Accepted: 06/18/2021] [Indexed: 02/06/2023] Open
Abstract
The long-term success of standard anticancer monotherapeutic strategies has been hampered by intolerable side effects, resistance to treatment and cancer relapse. These monotherapeutic strategies shrink the tumor bulk but do not effectively eliminate the population of self-renewing cancer stem cells (CSCs) that are normally present within the tumor. These surviving CSCs develop mechanisms of resistance to treatment and refuel the tumor, thus causing cancer relapse. To ensure durable tumor control, research has moved away from adopting the monotreatment paradigm towards developing and using combination therapy. Combining different therapeutic modalities has demonstrated significant therapeutic outcomes by strengthening the anti-tumor potential of monotreatment against cancer and cancer stem cells, mitigating their toxic adverse effects, and ultimately overcoming resistance. Recently, there has been growing interest in combining natural products from different sources or with clinically used chemotherapeutics to further improve treatment efficacy and tolerability. Thymoquinone (TQ), the main bioactive constituent of Nigella sativa, has gained great attention in combination therapy research after demonstrating its low toxicity to normal cells and remarkable anticancer efficacy in extensive preclinical studies in addition to its ability to target chemoresistant CSCs. Here, we provide an overview of the therapeutic responses resulting from combining TQ with conventional therapeutic agents such as alkylating agents, antimetabolites and antimicrotubules as well as with topoisomerase inhibitors and non-coding RNA. We also review data on anticancer effects of TQ when combined with ionizing radiation and several natural products such as vitamin D3, melatonin and other compounds derived from Chinese medicinal plants. The focus of this review is on two outcomes of TQ combination therapy, namely eradicating CSCs and treating various types of cancers. In conclusion, the ability of TQ to potentiate the anticancer activity of many chemotherapeutic agents and sensitize cancer cells to radiotherapy makes it a promising molecule that could be used in combination therapy to overcome resistance to standard chemotherapeutic agents and reduce their associated toxicities.
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Affiliation(s)
- Zaynab Fatfat
- Department of Biology, American University of Beirut, Beirut 1107 2020, Lebanon
| | - Maamoun Fatfat
- Department of Biology, American University of Beirut, Beirut 1107 2020, Lebanon
| | - Hala Gali-Muhtasib
- Department of Biology, American University of Beirut, Beirut 1107 2020, Lebanon
- Center for Drug Discovery, American University of Beirut, Beirut 1107 2020, Lebanon
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A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis. Genes (Basel) 2021; 12:genes12060854. [PMID: 34199440 PMCID: PMC8228406 DOI: 10.3390/genes12060854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 05/27/2021] [Accepted: 05/29/2021] [Indexed: 11/17/2022] Open
Abstract
DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.
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Cell Heterogeneity Analysis in Single-Cell RNA-seq Data Using Mixture Exponential Graph and Markov Random Field Model. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9919080. [PMID: 34095314 PMCID: PMC8164540 DOI: 10.1155/2021/9919080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 04/30/2021] [Indexed: 11/18/2022]
Abstract
Advanced single-cell profiling technologies promote exploration of cell heterogeneity, and clustering of single-cell RNA (scRNA-seq) data enables discovery of coexpression genes and network relationships between genes. In particular, single-cell profiling of circulating tumor cells (CTCs) can provide unique insights into tumor heterogeneity (including in triple-negative breast cancer (TNBC)), while scRNA-seq leads to better understanding of subclonal architecture and biological function. Despite numerous reports suggesting a direct correlation between circulating tumor cells (CTCs) and poor clinical outcomes, few studies have provided a thorough heterogeneity characterization of CTCs. In addition, TNBC is a disease with not only intertumor but also intratumor heterogeneity and represents various biological distinct subgroups that may have relationships with immune functions that are not clearly established yet. In this article, we introduce a new scheme for detecting genotypic characterization of single-cell heterogeneities and apply it to CTC and TNBC single-cell RNA-seq data. First, we use an existing mixture exponential family graph model to partition the cell-cell network; then, with the Markov random field model, we obtain more flexible network rewiring. Finally, we find the cell heterogeneity and network relationships according to different high coexpression gene modules in different cell subsets. Our results demonstrate that this scheme provides a reasonable and effective way to model different cell clusters and different biological enrichment gene clusters. Thus, using different internal coexpression genes of different cell clusters, we can infer the differences in tumor composition and diversity.
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Paul D. Cancer as a form of life: Musings of the cancer and evolution symposium. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 165:120-139. [PMID: 33991584 DOI: 10.1016/j.pbiomolbio.2021.05.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 05/04/2021] [Accepted: 05/07/2021] [Indexed: 12/12/2022]
Abstract
Advanced cancer is one of the major problems in oncology as currently, despite the recent technological and scientific advancements, the mortality of metastatic disease remains very high at 70-90%. The field of oncology is in urgent need of novel ideas in order to improve quality of life and prognostic of cancer patients. The Cancer and Evolution Symposium organized online October 14-16, 2020 brought together a group of specialists from different fields that presented innovative strategies for better understanding, preventing, diagnosing, and treating cancer. Today still, the main reasons behind the high incidence and mortality of advanced cancer are, on one hand, the paucity of funding and effort directed to cancer prevention and early detection, and, on the other hand, the lack of understanding of the cancer process itself. I argue that besides being a disease, cancer is also a form of life, and, this frame of reference may provide a fresh look on this complex process. Here, I provide a different angle to several contemporary cancer theories discussing them from the perspective of "cancer-forms of life" (i.e. bionts) point of view. The perspectives and the several "bionts" introduced here, by no means exclusive or comprehensive, are just a shorthand that will hopefully encourage the readers, to further explore the contemporary oncology theoretical landscape.
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Affiliation(s)
- Doru Paul
- Medical Oncology, Weill Cornell Medicine, 1305 York Avenue 12th Floor, New York, NY, 10021, USA.
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37
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Ebrahimi S, Nonacs P. Genetic diversity through social heterosis can increase virulence in RNA viral infections and cancer progression. ROYAL SOCIETY OPEN SCIENCE 2021; 8:202219. [PMID: 34035948 PMCID: PMC8097216 DOI: 10.1098/rsos.202219] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 04/12/2021] [Indexed: 05/04/2023]
Abstract
In viral infections and cancer tumours, negative health outcomes often correlate with increasing genetic diversity. Possible evolutionary processes for such relationships include mutant lineages escaping host control or diversity, per se, creating too many immune system targets. Another possibility is social heterosis where mutations and replicative errors create clonal lineages varying in intrinsic capability for successful dispersal; improved environmental buffering; resource extraction or effective defence against immune systems. Rather than these capabilities existing in one genome, social heterosis proposes complementary synergies occur across lineages in close proximity. Diverse groups overcome host defences as interacting 'social genomes' with group genetic tool kits exceeding limited individual plasticity. To assess the possibility of social heterosis in viral infections and cancer progression, we conducted extensive literature searches for examples consistent with general and specific predictions from the social heterosis hypothesis. Numerous studies found supportive patterns in cancers across multiple tissues and in several families of RNA viruses. In viruses, social heterosis mechanisms probably result from long coevolutionary histories of competition between pathogen and host. Conversely, in cancers, social heterosis is a by-product of recent mutations. Investigating how social genomes arise and function in viral quasi-species swarms and cancer tumours may lead to new therapeutic approaches.
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Affiliation(s)
- Saba Ebrahimi
- Department of Ecology and Evolutionary Biology, University of California, 621 Young Drive South, Los Angeles, CA 90024, USA
| | - Peter Nonacs
- Department of Ecology and Evolutionary Biology, University of California, 621 Young Drive South, Los Angeles, CA 90024, USA
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Immune infiltrate diversity confers a good prognosis in follicular lymphoma. Cancer Immunol Immunother 2021; 70:3573-3585. [PMID: 33929583 PMCID: PMC8571143 DOI: 10.1007/s00262-021-02945-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 04/14/2021] [Indexed: 11/29/2022]
Abstract
Background Follicular lymphoma (FL) prognosis is influenced by the composition of the tumour microenvironment. We tested an automated approach to quantitatively assess the phenotypic and spatial immune infiltrate diversity as a prognostic biomarker for FL patients. Methods Diagnostic biopsies were collected from 127 FL patients initially treated with rituximab-based therapy (52%), radiotherapy (28%), or active surveillance (20%). Tissue microarrays were constructed and stained using multiplex immunofluorescence (CD4, CD8, FOXP3, CD21, PD-1, CD68, and DAPI). Subsequently, sections underwent automated cell scoring and analysis of spatial interactions, defined as cells co-occurring within 30 μm. Shannon’s entropy, a metric describing species biodiversity in ecological habitats, was applied to quantify immune infiltrate diversity of cell types and spatial interactions. Immune infiltrate diversity indices were tested in multivariable Cox regression and Kaplan–Meier analysis for overall (OS) and progression-free survival (PFS). Results Increased diversity of cell types (HR = 0.19 95% CI 0.06–0.65, p = 0.008) and cell spatial interactions (HR = 0.39, 95% CI 0.20–0.75, p = 0.005) was associated with favourable OS, independent of the Follicular Lymphoma International Prognostic Index. In the rituximab-treated subset, the favourable trend between diversity and PFS did not reach statistical significance. Conclusion Multiplex immunofluorescence and Shannon’s entropy can objectively quantify immune infiltrate diversity and generate prognostic information in FL. This automated approach warrants validation in additional FL cohorts, and its applicability as a pre-treatment biomarker to identify high-risk patients should be further explored. The multiplex image dataset generated by this study is shared publicly to encourage further research on the FL microenvironment. Supplementary Information The online version contains supplementary material available at 10.1007/s00262-021-02945-0.
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Understanding breast cancer heterogeneity through non-genetic heterogeneity. Breast Cancer 2021; 28:777-791. [PMID: 33723745 DOI: 10.1007/s12282-021-01237-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 03/04/2021] [Indexed: 01/01/2023]
Abstract
Intricacy in treatment and diagnosis of breast cancer has been an obstacle due to genotype and phenotype heterogeneity. Understanding of non-genetic heterogeneity mechanisms along with considering role of genetic heterogeneity may fill the gaps in landscape painting of heterogeneity. The main factors contribute to non-genetic heterogeneity including: transcriptional pulsing/bursting or discontinuous transcriptions, stochastic partitioning of components at cell division and various signal transduction from tumor ecosystem. Throughout this review, we desired to provide a conceptual framework focused on non-genetic heterogeneity, which has been intended to offer insight into prediction, diagnosis and treatment of breast cancer.
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40
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Danilenko M, Clifford SC, Schwalbe EC. Inter and intra-tumoral heterogeneity as a platform for personalized therapies in medulloblastoma. Pharmacol Ther 2021; 228:107828. [PMID: 33662447 DOI: 10.1016/j.pharmthera.2021.107828] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/16/2021] [Indexed: 01/01/2023]
Abstract
Medulloblastoma is the most common malignant CNS tumor of childhood, affecting ~350 patients/year in the USA. In 2020, most children are cured of their disease, however, survivors are left with life-long late-effects as a consequence of intensive surgery, and application of chemo- and radio-therapy to the developing brain. A major contributor to improvements in patient survival has been the development of risk-stratified treatments derived from a better understanding of the prognostic value of disease biomarkers. The characterization and validation of these biomarkers has engendered a comprehensive understanding of the extensive heterogeneity that exists within the disease, which can occur both between and within tumors (inter- and intra-tumoral heterogeneity, respectively). In this review, we discuss inter-tumoral heterogeneity, describing the early characterization of clinical and histopathological disease heterogeneity, the more recent elucidation of molecular disease subgroups, and the potential for novel therapies based on specific molecular defects. We reflect on the limitations of current approaches when applied to a rare disease. We then review early investigations of intra-tumoral heterogeneity using FISH and immunohistochemical approaches, and focus on the application of next generation sequencing on bulk tumors to elucidate intra-tumoral heterogeneity. Finally, we critically appraise the applications of single-cell sequencing approaches and discuss their potential to drive next biological insights, and for routine clinical application.
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Affiliation(s)
- Marina Danilenko
- Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Steven C Clifford
- Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Edward C Schwalbe
- Newcastle University Centre for Cancer, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; Department of Applied Sciences, Northumbria University, Newcastle upon Tyne, UK.
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Tao Y, Rajaraman A, Cui X, Cui Z, Chen H, Zhao Y, Eaton J, Kim H, Ma J, Schwartz R. Assessing the contribution of tumor mutational phenotypes to cancer progression risk. PLoS Comput Biol 2021; 17:e1008777. [PMID: 33711014 PMCID: PMC7990181 DOI: 10.1371/journal.pcbi.1008777] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 03/24/2021] [Accepted: 02/06/2021] [Indexed: 01/10/2023] Open
Abstract
Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, which act in different combinations or degrees in different cancers. These variations in mutability phenotypes are predictive of progression outcomes independent of the specific mutations they have produced to date. Here we explore the question of how and to what degree these differences in mutational phenotypes act in a cancer to predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational phenotypes, followed by a machine learning framework to identify key features predictive of progression. Analyses of breast invasive carcinoma and lung carcinoma demonstrate that a large fraction of the risk of future clinical outcomes of cancer progression-overall survival and disease-free survival-can be explained solely from mutational phenotype features derived from the phylogenetic analysis. We further show that mutational phenotypes have additional predictive power even after accounting for traditional clinical and driver gene-centric genomic predictors of progression. These results confirm the importance of mutational phenotypes in contributing to cancer progression risk and suggest strategies for enhancing the predictive power of conventional clinical data or driver-centric biomarkers.
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Affiliation(s)
- Yifeng Tao
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Joint Carnegie Mellon-University of Pittsburgh Ph.D. Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America
| | - Ashok Rajaraman
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Xiaoyue Cui
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Joint Carnegie Mellon-University of Pittsburgh Ph.D. Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America
| | - Ziyi Cui
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Haoran Chen
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Joint Carnegie Mellon-University of Pittsburgh Ph.D. Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America
| | - Yuanqi Zhao
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Jesse Eaton
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Hannah Kim
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Jian Ma
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Russell Schwartz
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Biological Sciences, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
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A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors. PLoS Comput Biol 2021; 17:e1008266. [PMID: 33566821 PMCID: PMC7901744 DOI: 10.1371/journal.pcbi.1008266] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 02/23/2021] [Accepted: 01/16/2021] [Indexed: 12/12/2022] Open
Abstract
Increasingly complex in silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting.
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The Intratumoral Heterogeneity of Cancer Metabolism. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1311:149-160. [PMID: 34014541 DOI: 10.1007/978-3-030-65768-0_11] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Cancer is one of the deadliest diseases in the world, causing over half a million deaths a year in the USA alone. Despite recent advances made in the field of cancer biology and the therapies that have been developed [1, 2], it is clear that more advances are necessary for us to classify cancer as curable. The logical question that arises is simple: Why, despite all the technologies and medical innovations of our time, has a complete cure eluded us? This chapter sheds light on one of cancer's most impactful attributes: its heterogeneity and, more specifically, the intratumoral heterogeneity of cancer metabolism. Simply put, what makes cancer one of the deadliest diseases is its ability to change and adapt. Cancer cells' rapid evolution, coupled with their irrepressible ability to divide, gives most of them the advantage over our immune systems. In this chapter, we delve into the complexities of this adaptability and the vital role that metabolism plays in the rise and progression of this heterogeneity.
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Siraj S, Masoodi T, Siraj AK, Azam S, Qadri Z, Ahmed SO, AlBalawy WN, Al-Obaisi KA, Parvathareddy SK, AlManea HM, AlHussaini HF, Abduljabbar A, Alhomoud S, Al-Dayel FH, Alkuraya FS, Al-Kuraya KS. Clonal Evolution and Timing of Metastatic Colorectal Cancer. Cancers (Basel) 2020; 12:cancers12102938. [PMID: 33053768 PMCID: PMC7601934 DOI: 10.3390/cancers12102938] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/03/2020] [Accepted: 10/07/2020] [Indexed: 12/12/2022] Open
Abstract
Colorectal cancer (CRC) is the third most frequently diagnosed cancer worldwide, where ~50% of patients develop metastasis, despite current improved management. Genomic characterisation of metastatic CRC, and elucidating the effects of therapy on the metastatic process, are essential to help guide precision medicine. Multi-region whole-exome sequencing was performed on 191 sampled tumour regions of patient-matched therapy-naïve and treated CRC primary tumours (n = 92 tumour regions) and metastases (n = 99 tumour regions), in 30 patients. Somatic variants were analysed to define the origin, composition, and timing of seeding in the metastatic progression of therapy-naïve and treated metastatic CRC. High concordance, with few genomic differences, was observed between primary CRC and metastases. Most cases supported a late dissemination model, via either monoclonal or polyclonal seeding. Polyclonal seeding appeared more common in therapy-naïve metastases than in treated metastases. Whereby, treatment prompted for the selection of distinct resistant clones, through monoclonal seeding to distant metastatic sites. Overall, this study reinforces the importance of early clinical detection and surgical excision of the CRC tumour, whilst further highlighting the clinical challenges for metastatic CRC with increased intratumour heterogeneity (either due to early dissemination or polyclonal metastatic spread) and the underlying risk of future therapeutic resistance in treated patients.
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Affiliation(s)
- Sarah Siraj
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (S.S.); (T.M.); (A.K.S.); (S.A.); (Z.Q.); (S.O.A.); (W.N.A.); (K.A.A.-O.); (S.K.P.)
| | - Tariq Masoodi
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (S.S.); (T.M.); (A.K.S.); (S.A.); (Z.Q.); (S.O.A.); (W.N.A.); (K.A.A.-O.); (S.K.P.)
| | - Abdul K. Siraj
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (S.S.); (T.M.); (A.K.S.); (S.A.); (Z.Q.); (S.O.A.); (W.N.A.); (K.A.A.-O.); (S.K.P.)
| | - Saud Azam
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (S.S.); (T.M.); (A.K.S.); (S.A.); (Z.Q.); (S.O.A.); (W.N.A.); (K.A.A.-O.); (S.K.P.)
| | - Zeeshan Qadri
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (S.S.); (T.M.); (A.K.S.); (S.A.); (Z.Q.); (S.O.A.); (W.N.A.); (K.A.A.-O.); (S.K.P.)
| | - Saeeda O. Ahmed
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (S.S.); (T.M.); (A.K.S.); (S.A.); (Z.Q.); (S.O.A.); (W.N.A.); (K.A.A.-O.); (S.K.P.)
| | - Wafaa N. AlBalawy
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (S.S.); (T.M.); (A.K.S.); (S.A.); (Z.Q.); (S.O.A.); (W.N.A.); (K.A.A.-O.); (S.K.P.)
| | - Khadija A. Al-Obaisi
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (S.S.); (T.M.); (A.K.S.); (S.A.); (Z.Q.); (S.O.A.); (W.N.A.); (K.A.A.-O.); (S.K.P.)
| | - Sandeep K. Parvathareddy
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (S.S.); (T.M.); (A.K.S.); (S.A.); (Z.Q.); (S.O.A.); (W.N.A.); (K.A.A.-O.); (S.K.P.)
| | - Hadeel M. AlManea
- Department of Pathology and Laboratory Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (H.M.A.); (H.F.A.); (F.H.A.-D.)
| | - Hussah F. AlHussaini
- Department of Pathology and Laboratory Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (H.M.A.); (H.F.A.); (F.H.A.-D.)
| | - Alaa Abduljabbar
- Department of Surgery, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (A.A.); (S.A.)
| | - Samar Alhomoud
- Department of Surgery, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (A.A.); (S.A.)
| | - Fouad H. Al-Dayel
- Department of Pathology and Laboratory Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (H.M.A.); (H.F.A.); (F.H.A.-D.)
| | - Fowzan S. Alkuraya
- Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia;
- Department of Anatomy and Cell Biology, College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia
| | - Khawla S. Al-Kuraya
- Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia; (S.S.); (T.M.); (A.K.S.); (S.A.); (Z.Q.); (S.O.A.); (W.N.A.); (K.A.A.-O.); (S.K.P.)
- Correspondence: ; Tel.: +966-112-055-2167
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45
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Ye CJ, Sharpe Z, Heng HH. Origins and Consequences of Chromosomal Instability: From Cellular Adaptation to Genome Chaos-Mediated System Survival. Genes (Basel) 2020; 11:E1162. [PMID: 33008067 PMCID: PMC7601827 DOI: 10.3390/genes11101162] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 09/22/2020] [Accepted: 09/29/2020] [Indexed: 12/15/2022] Open
Abstract
When discussing chromosomal instability, most of the literature focuses on the characterization of individual molecular mechanisms. These studies search for genomic and environmental causes and consequences of chromosomal instability in cancer, aiming to identify key triggering factors useful to control chromosomal instability and apply this knowledge in the clinic. Since cancer is a phenomenon of new system emergence from normal tissue driven by somatic evolution, such studies should be done in the context of new genome system emergence during evolution. In this perspective, both the origin and key outcome of chromosomal instability are examined using the genome theory of cancer evolution. Specifically, chromosomal instability was linked to a spectrum of genomic and non-genomic variants, from epigenetic alterations to drastic genome chaos. These highly diverse factors were then unified by the evolutionary mechanism of cancer. Following identification of the hidden link between cellular adaptation (positive and essential) and its trade-off (unavoidable and negative) of chromosomal instability, why chromosomal instability is the main player in the macro-cellular evolution of cancer is briefly discussed. Finally, new research directions are suggested, including searching for a common mechanism of evolutionary phase transition, establishing chromosomal instability as an evolutionary biomarker, validating the new two-phase evolutionary model of cancer, and applying such a model to improve clinical outcomes and to understand the genome-defined mechanism of organismal evolution.
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Affiliation(s)
- Christine J. Ye
- The Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Zachary Sharpe
- Center for Molecular Medicine and Genomics, Wayne State University School of Medicine, Detroit, MI 48201, USA;
| | - Henry H. Heng
- Center for Molecular Medicine and Genomics, Wayne State University School of Medicine, Detroit, MI 48201, USA;
- Department of Pathology, Wayne State University School of Medicine, Detroit, MI 48201, USA
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46
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Noble R, Burley JT, Le Sueur C, Hochberg ME. When, why and how tumour clonal diversity predicts survival. Evol Appl 2020; 13:1558-1568. [PMID: 32821272 PMCID: PMC7428820 DOI: 10.1111/eva.13057] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 06/24/2020] [Accepted: 06/25/2020] [Indexed: 12/23/2022] Open
Abstract
The utility of intratumour heterogeneity as a prognostic biomarker is the subject of ongoing clinical investigation. However, the relationship between this marker and its clinical impact is mediated by an evolutionary process that is not well understood. Here, we employ a spatial computational model of tumour evolution to assess when, why and how intratumour heterogeneity can be used to forecast tumour growth rate and progression-free survival. We identify three conditions that can lead to a positive correlation between clonal diversity and subsequent growth rate: diversity is measured early in tumour development; selective sweeps are rare; and/or tumours vary in the rate at which they acquire driver mutations. Opposite conditions typically lead to negative correlation. In cohorts of tumours with diverse evolutionary parameters, we find that clonal diversity is a reliable predictor of both growth rate and progression-free survival. We thus offer explanations-grounded in evolutionary theory-for empirical findings in various cancers, including survival analyses reported in the recent TRACERx Renal study of clear-cell renal cell carcinoma. Our work informs the search for new prognostic biomarkers and contributes to the development of predictive oncology.
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Affiliation(s)
- Robert Noble
- Department of Biosystems Science and EngineeringETH ZurichBaselSwitzerland
- SIB Swiss Institute of BioinformaticsBaselSwitzerland
- Department of Evolutionary Biology and Environmental StudiesUniversity of ZurichZurichSwitzerland
- Present address:
Department of MathematicsCity, University of LondonLondonUK
| | - John T. Burley
- Department of Ecology and Evolutionary BiologyBrown UniversityProvidenceRIUSA
- Institute at Brown for Environment and SocietyBrown UniversityProvidenceRIUSA
| | - Cécile Le Sueur
- Department of Biosystems Science and EngineeringETH ZurichBaselSwitzerland
| | - Michael E. Hochberg
- Institut des Sciences de l’EvolutionUniversity of MontpellierMontpellierFrance
- Santa Fe InstituteNMUSA
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Merlo LMF, Sprouffske K, Howard TC, Gardiner KL, Caulin AF, Blum SM, Evans P, Bedalov A, Sniegowski PD, Maley CC. Application of simultaneous selective pressures slows adaptation. Evol Appl 2020; 13:1615-1625. [PMID: 32952608 PMCID: PMC7484835 DOI: 10.1111/eva.13062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 02/22/2020] [Accepted: 03/05/2020] [Indexed: 12/01/2022] Open
Abstract
Beneficial mutations that arise in an evolving asexual population may compete or interact in ways that alter the overall rate of adaptation through mechanisms such as clonal or functional interference. The application of multiple selective pressures simultaneously may allow for a greater number of adaptive mutations, increasing the opportunities for competition between selectively advantageous alterations, and thereby reducing the rate of adaptation. We evolved a strain of Saccharomyces cerevisiae that could not produce its own histidine or uracil for ~500 generations under one or three selective pressures: limitation of the concentration of glucose, histidine, and/or uracil in the media. The rate of adaptation was obtained by measuring evolved relative fitness using competition assays. Populations evolved under a single selective pressure showed a statistically significant increase in fitness on those pressures relative to the ancestral strain, but the populations evolved on all three pressures did not show a statistically significant increase in fitness over the ancestral strain on any single pressure. Simultaneously limiting three essential nutrients for a population of S. cerevisiae effectively slows the rate of evolution on any one of the three selective pressures applied, relative to the single selective pressure cases. We identify possible mechanisms for fitness changes seen between populations evolved on one or three limiting nutrient pressures by high-throughput sequencing. Adding multiple selective pressures to evolving disease like cancer and infectious diseases could reduce the rate of adaptation and thereby may slow disease progression, prolong drug efficacy and prevent deaths.
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Affiliation(s)
| | - Kathleen Sprouffske
- Disease Area OncologyNovartis Institutes for BioMedical ResearchBaselSwitzerland
| | - Taylor C. Howard
- Department of Pathology and Laboratory MedicineUC Davis HealthSacramentoCaliforniaUSA
| | - Kristin L. Gardiner
- School of Veterinary MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Steven M. Blum
- Department of Medical OncologyDana‐Farber Cancer InstituteBroad Institute at MIT and HarvardHarvard Medical School, and Massachusetts General Hospital Cancer CenterBostonMassachusettsUSA
| | - Perry Evans
- Department of Biomedical and Health InformaticsChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Antonio Bedalov
- Clinical Research DivisionFred Hutchinson Cancer Research CenterSeattleWashingtonUSA
| | - Paul D. Sniegowski
- Department of BiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Carlo C. Maley
- Arizona State UniversitySchool of Life SciencesBiodesign InstituteTempeArizonaUSA
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48
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Westcott JM, Camacho S, Nasir A, Huysman ME, Rahhal R, Dang TT, Riegel AT, Brekken RA, Pearson GW. ΔNp63-Regulated Epithelial-to-Mesenchymal Transition State Heterogeneity Confers a Leader-Follower Relationship That Drives Collective Invasion. Cancer Res 2020; 80:3933-3944. [PMID: 32661136 DOI: 10.1158/0008-5472.can-20-0014] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 06/04/2020] [Accepted: 07/08/2020] [Indexed: 01/05/2023]
Abstract
Defining how interactions between tumor subpopulations contribute to invasion is essential for understanding how tumors metastasize. Here, we find that the heterogeneous expression of the transcription factor ΔNp63 confers distinct proliferative and invasive epithelial-to-mesenchymal transition (EMT) states in subpopulations that establish a leader-follower relationship to collectively invade. A ΔNp63-high EMT program coupled the ability to proliferate with an IL1α- and miR-205-dependent suppression of cellular protrusions that are required to initiate collective invasion. An alternative ΔNp63-low EMT program conferred cells with the ability to initiate and lead collective invasion. However, this ΔNp63-low EMT state triggered a collateral loss of fitness. Importantly, rare growth-suppressed ΔNp63-low EMT cells influenced tumor progression by leading the invasion of proliferative ΔNp63-high EMT cells in heterogeneous primary tumors. Thus, heterogeneous activation of distinct EMT programs promotes a mode of collective invasion that overcomes cell intrinsic phenotypic deficiencies to induce the dissemination of proliferative tumor cells. SIGNIFICANCE: These findings reveal how an interaction between cells in different EMT states confers properties that are not induced by either EMT program alone.
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Affiliation(s)
- Jill M Westcott
- Hamon Center for Therapeutic Oncology, University of Texas, Southwestern Medical Center, Dallas, Texas
| | - Sharon Camacho
- Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC
| | - Apsra Nasir
- Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC
| | - Molly E Huysman
- Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC
| | - Raneen Rahhal
- Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC
| | - Tuyen T Dang
- Department of Neurosurgery, University of Oklahoma Health Science Center, Oklahoma City, Oklahoma
| | - Anna T Riegel
- Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC
| | - Rolf A Brekken
- Hamon Center for Therapeutic Oncology, University of Texas, Southwestern Medical Center, Dallas, Texas.,Department of Surgery, University of Texas, Southwestern Medical Center, Dallas, Texas
| | - Gray W Pearson
- Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University, Washington, DC.
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49
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Swanson DM, Lien T, Bergholtz H, Sørlie T, Frigessi A. A Bayesian two-way latent structure model for genomic data integration reveals few pan-genomic cluster subtypes in a breast cancer cohort. Bioinformatics 2020; 35:4886-4897. [PMID: 31077301 DOI: 10.1093/bioinformatics/btz381] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 04/05/2019] [Accepted: 05/01/2019] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION Unsupervised clustering is important in disease subtyping, among having other genomic applications. As genomic data has become more multifaceted, how to cluster across data sources for more precise subtyping is an ever more important area of research. Many of the methods proposed so far, including iCluster and Cluster of Cluster Assignments (COCAs), make an unreasonable assumption of a common clustering across all data sources, and those that do not are fewer and tend to be computationally intensive. RESULTS We propose a Bayesian parametric model for integrative, unsupervised clustering across data sources. In our two-way latent structure model, samples are clustered in relation to each specific data source, distinguishing it from methods like COCAs and iCluster, but cluster labels have across-dataset meaning, allowing cluster information to be shared between data sources. A common scaling across data sources is not required, and inference is obtained by a Gibbs Sampler, which we improve with a warm start strategy and modified density functions to robustify and speed convergence. Posterior interpretation allows for inference on common clusterings occurring among subsets of data sources. An interesting statistical formulation of the model results in sampling from closed-form posteriors despite incorporation of a complex latent structure. We fit the model with Gaussian and more general densities, which influences the degree of across-dataset cluster label sharing. Uniquely among integrative clustering models, our formulation makes no nestedness assumptions of samples across data sources so that a sample missing data from one genomic source can be clustered according to its existing data sources. We apply our model to a Norwegian breast cancer cohort of ductal carcinoma in situ and invasive tumors, comprised of somatic copy-number alteration, methylation and expression datasets. We find enrichment in the Her2 subtype and ductal carcinoma among those observations exhibiting greater cluster correspondence across expression and CNA data. In general, there are few pan-genomic clusterings, suggesting that models assuming a common clustering across genomic data sources might yield misleading results. AVAILABILITY AND IMPLEMENTATION The model is implemented in an R package called twl ('two-way latent'), available on CRAN. Data for analysis are available within the R package. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- David M Swanson
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Tonje Lien
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Helga Bergholtz
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Therese Sørlie
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
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50
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Song MJ, Lee SH, Kim EY, Chang YS. Increased number of subclones in lung squamous cell carcinoma elicits overexpression of immune related genes. Transl Lung Cancer Res 2020; 9:659-669. [PMID: 32676328 PMCID: PMC7354124 DOI: 10.21037/tlcr-19-589] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background Intratumoral heterogeneity is a cause of drug resistance that leads to treatment failure. We investigated the clinical implication of intratumoral heterogeneity inferred from the number of subclones that constituted a tumor and reasoned the etiology of subclonal expansion using RNA sequencing data. Methods Simple nucleotide variation, clinical data, copy number variation, and RNA-sequencing data from 481 The Cancer Genome Atlas-Lung Squamous Cell Carcinoma (TCGA-LUSC) cases were obtained from the Genomic Data Commons data portal. Clonal status was estimated from the allele frequency of the mutated genes using the SciClone package. Results The number of subclones that comprised a tumor had a positive correlation with the total mutations in a tumor (σ=0.477, P-value <0.001) and tumor stage (σ=0.111, P-value <0.015). Male LUSC tumors had a higher probability of having more subclones than female tumors (2.28 vs. 1.89, P-value =0.002, Welch Two Sample t-test). On comparing the gene expression in the tumors that were comprised of five subclones with those of a single clone, 291 genes were found to be upregulated and 102 genes were found to be downregulated in the five subclone tumors. The upregulated genes included UGT1A10, SRY, FDCSP, MRLM, and EREG, in order of magnitude of upregulation, and the biologic function of the upregulated genes was strongly enriched for the positive regulation of immune processes and inflammatory responses. Conclusions Male LUSC tumors were composed of a greater number of subclones than female tumors. The tumors with large numbers of subclones had overexpressed genes that positively regulated the immune processes and inflammatory responses more than tumors that consisted of a single clone.
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Affiliation(s)
- Myung Jin Song
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sang Hoon Lee
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Eun Young Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yoon Soo Chang
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
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