|
1
|
Essogmo FE, Zhilenkova AV, Tchawe YSN, Owoicho AM, Rusanov AS, Boroda A, Pirogova YN, Sangadzhieva ZD, Sanikovich VD, Bagmet NN, Sekacheva MI. Cytokine Profile in Lung Cancer Patients: Anti-Tumor and Oncogenic Cytokines. Cancers (Basel) 2023; 15:5383. [PMID: 38001643 PMCID: PMC10670546 DOI: 10.3390/cancers15225383] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 10/08/2023] [Accepted: 10/09/2023] [Indexed: 11/26/2023] Open
Abstract
Lung cancer is currently the second leading cause of cancer death worldwide. In recent years, checkpoint inhibitor immunotherapy (ICI) has emerged as a new treatment. A better understanding of the tumor microenvironment (TMJ) or the immune system surrounding the tumor is needed. Cytokines are small proteins that carry messages between cells and are known to play an important role in the body's response to inflammation and infection. Cytokines are important for immunity in lung cancer. They promote tumor growth (oncogenic cytokines) or inhibit tumor growth (anti-tumour cytokines) by controlling signaling pathways for growth, proliferation, metastasis, and apoptosis. The immune system relies heavily on cytokines. They can also be produced in the laboratory for therapeutic use. Cytokine therapy helps the immune system to stop the growth or kill cancer cells. Interleukins and interferons are the two types of cytokines used to treat cancer. This article begins by addressing the role of the TMJ and its components in lung cancer. This review also highlights the functions of various cytokines such as interleukins (IL), transforming growth factor (TGF), and tumor necrosis factor (TNF).
Collapse
Affiliation(s)
- Freddy Elad Essogmo
- Institute for Personalized Oncology, Center for Digital Biodesign and Personalized Healthcare, First Moscow State Medical University of the Ministry of Health of Russia (Sechenov University), Moscow 119991, Russia; (F.E.E.); (A.V.Z.); (Y.S.N.T.); (A.M.O.); (A.S.R.); (A.B.); (Y.N.P.); (Z.D.S.); (V.D.S.)
- Cameroon Oncology Center (COC), Douala P.O. Box 1864, Cameroon
| | - Angelina V. Zhilenkova
- Institute for Personalized Oncology, Center for Digital Biodesign and Personalized Healthcare, First Moscow State Medical University of the Ministry of Health of Russia (Sechenov University), Moscow 119991, Russia; (F.E.E.); (A.V.Z.); (Y.S.N.T.); (A.M.O.); (A.S.R.); (A.B.); (Y.N.P.); (Z.D.S.); (V.D.S.)
| | - Yvan Sinclair Ngaha Tchawe
- Institute for Personalized Oncology, Center for Digital Biodesign and Personalized Healthcare, First Moscow State Medical University of the Ministry of Health of Russia (Sechenov University), Moscow 119991, Russia; (F.E.E.); (A.V.Z.); (Y.S.N.T.); (A.M.O.); (A.S.R.); (A.B.); (Y.N.P.); (Z.D.S.); (V.D.S.)
| | - Abah Moses Owoicho
- Institute for Personalized Oncology, Center for Digital Biodesign and Personalized Healthcare, First Moscow State Medical University of the Ministry of Health of Russia (Sechenov University), Moscow 119991, Russia; (F.E.E.); (A.V.Z.); (Y.S.N.T.); (A.M.O.); (A.S.R.); (A.B.); (Y.N.P.); (Z.D.S.); (V.D.S.)
| | - Alexander S. Rusanov
- Institute for Personalized Oncology, Center for Digital Biodesign and Personalized Healthcare, First Moscow State Medical University of the Ministry of Health of Russia (Sechenov University), Moscow 119991, Russia; (F.E.E.); (A.V.Z.); (Y.S.N.T.); (A.M.O.); (A.S.R.); (A.B.); (Y.N.P.); (Z.D.S.); (V.D.S.)
| | - Alexander Boroda
- Institute for Personalized Oncology, Center for Digital Biodesign and Personalized Healthcare, First Moscow State Medical University of the Ministry of Health of Russia (Sechenov University), Moscow 119991, Russia; (F.E.E.); (A.V.Z.); (Y.S.N.T.); (A.M.O.); (A.S.R.); (A.B.); (Y.N.P.); (Z.D.S.); (V.D.S.)
| | - Yuliya N. Pirogova
- Institute for Personalized Oncology, Center for Digital Biodesign and Personalized Healthcare, First Moscow State Medical University of the Ministry of Health of Russia (Sechenov University), Moscow 119991, Russia; (F.E.E.); (A.V.Z.); (Y.S.N.T.); (A.M.O.); (A.S.R.); (A.B.); (Y.N.P.); (Z.D.S.); (V.D.S.)
| | - Zaiana D. Sangadzhieva
- Institute for Personalized Oncology, Center for Digital Biodesign and Personalized Healthcare, First Moscow State Medical University of the Ministry of Health of Russia (Sechenov University), Moscow 119991, Russia; (F.E.E.); (A.V.Z.); (Y.S.N.T.); (A.M.O.); (A.S.R.); (A.B.); (Y.N.P.); (Z.D.S.); (V.D.S.)
| | - Varvara D. Sanikovich
- Institute for Personalized Oncology, Center for Digital Biodesign and Personalized Healthcare, First Moscow State Medical University of the Ministry of Health of Russia (Sechenov University), Moscow 119991, Russia; (F.E.E.); (A.V.Z.); (Y.S.N.T.); (A.M.O.); (A.S.R.); (A.B.); (Y.N.P.); (Z.D.S.); (V.D.S.)
| | - Nikolay N. Bagmet
- Petrovsky National Research Centre of Surgery, Moscow 117418, Russia;
| | - Marina I. Sekacheva
- Institute for Personalized Oncology, Center for Digital Biodesign and Personalized Healthcare, First Moscow State Medical University of the Ministry of Health of Russia (Sechenov University), Moscow 119991, Russia; (F.E.E.); (A.V.Z.); (Y.S.N.T.); (A.M.O.); (A.S.R.); (A.B.); (Y.N.P.); (Z.D.S.); (V.D.S.)
| |
Collapse
|
|
2
|
Li J, Guan Y, Zhu R, Wang Y, Zhu H, Wang X. Identification of metabolic genes for the prediction of prognosis and tumor microenvironment infiltration in early-stage non-small cell lung cancer. Open Life Sci 2022; 17:881-892. [PMID: 36045718 PMCID: PMC9372707 DOI: 10.1515/biol-2022-0091] [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: 02/11/2022] [Revised: 05/03/2022] [Accepted: 05/03/2022] [Indexed: 11/15/2022] Open
Abstract
Early-stage non-small cell lung cancer (NSCLC) patients are at substantial risk of poor prognosis. We attempted to develop a reliable metabolic gene-set-based signature that can predict prognosis accurately for early-stage patients. Least absolute shrinkage and selection operator method Cox regression models were performed to filter the most useful prognostic genes, and a metabolic gene-set-based signature was constructed. Forty-two metabolism-related genes were finally identified, and with specific risk score formula, patients were classified into high-risk and low-risk groups. Overall survival was significantly different between the two groups in discovery (HR: 5.050, 95% CI: 3.368-7.574, P < 0.001), internal validation series (HR: 6.044, 95% CI: 3.918-9.322, P < 0.001), GSE30219 (HR: 2.059, 95% CI: 1.510-2.808, P < 0.001), and GSE68456 (HR: 2.448, 95% CI: 1.723-3.477, P < 0.001). Survival receiver operating characteristic curve at the 5 years suggested that the metabolic signature (area under the curve [AUC] = 0.805) had better prognostic accuracy than any other clinicopathological factors. Further analysis revealed the distinct differences in immune cell infiltration and tumor purity reflected by an immune and stromal score between high- and low-risk patients. In conclusion, the novel metabolic signature developed in our study shows robust prognostic accuracy in predicting prognosis for early-stage NSCLC patients and may function as a reliable marker for guiding more effective immunotherapy strategies.
Collapse
Affiliation(s)
- Jing Li
- Department of CyberKnife Center, Huashan Hospital, Fudan University, No. 525, Hongfeng Road, Pudong District, Shanghai 200040, China
| | - Yun Guan
- Department of CyberKnife Center, Huashan Hospital, Fudan University, No. 525, Hongfeng Road, Pudong District, Shanghai 200040, China
| | - Rongrong Zhu
- Department of Rehabilitation, Northern Jiangsu People's Hospital, Yangzhou, 225001, China
| | - Yang Wang
- Department of CyberKnife Center, Huashan Hospital, Fudan University, No. 525, Hongfeng Road, Pudong District, Shanghai 200040, China
| | - Huaguang Zhu
- Department of CyberKnife Center, Huashan Hospital, Fudan University, No. 525, Hongfeng Road, Pudong District, Shanghai 200040, China
| | - Xin Wang
- Department of CyberKnife Center, Huashan Hospital, Fudan University, No. 525, Hongfeng Road, Pudong District, Shanghai 200040, China
| |
Collapse
|
|
3
|
Masciale V, Banchelli F, Grisendi G, D’Amico R, Maiorana A, Stefani A, Morandi U, Stella F, Dominici M, Aramini B. OUP accepted manuscript. Stem Cells Transl Med 2022; 11:239-247. [PMID: 35356974 PMCID: PMC8968653 DOI: 10.1093/stcltm/szab029] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 11/26/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose Lung cancer relapse may be associated with the presence of a small population of cancer stem cells (CSCs) with unlimited proliferative potential. Our study assessed the relationship between CSCs and the relapse rate in patients harboring adenocarcinoma (ADL) and squamous cell carcinoma of the lung (SCCL). Experimental design This is an observational prospective cohort study (NCT04634630) assessing the influence of CSC frequency on relapse rate after major lung resection in 35 patients harboring early (I-II) (n = 21) and locally advanced (IIIA) (n = 14) ADL and SCCL. There was a 2-year enrollment period followed by a 1-year follow-up period. Surgical tumor specimens were processed, and CSCs were quantified by cytofluorimetric analysis. Results Cancer stem cells were expressed in all patients with a median of 3.1% of the primary cell culture. Primary analysis showed no influence of CSC frequency on the risk of relapse (hazard ratio [HR] = 1.05, 95% confidence interval [CI] = 0.85-1.30). At secondary analysis, patients with locally advanced disease with higher CSC frequency had an increased risk of relapse (HR = 1.26, 95% CI = 1.14-1.39), whereas this was not observed in early-stage patients (HR = 0.90, 95% CI = 0.65-1.25). Conclusion No association was found between CSC and relapse rates after major lung resection in patients harboring ACL and SCCL. However, in locally advanced-stage patients, a positive correlation was observed between CSC frequency and risk of relapse. These results indicate a need for further molecular investigations into the prognostic role of CSCs at different lung cancer stages. Clinical Trial Registration NCT04634630.
Collapse
Affiliation(s)
- Valentina Masciale
- Division of Thoracic Surgery, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Division of Oncology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Federico Banchelli
- Center of Medical Statistic, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Giulia Grisendi
- Division of Oncology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Roberto D’Amico
- Center of Medical Statistic, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Antonino Maiorana
- Institute of Pathology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Alessandro Stefani
- Division of Thoracic Surgery, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Uliano Morandi
- Division of Thoracic Surgery, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Franco Stella
- Division of Thoracic Surgery, Department of Experimental, Diagnostic and Specialty Medicine—DIMES of the Alma Mater Studiorum, University of Bologna, G.B. Morgagni—L. Pierantoni Hospital, Forlì, Italy
| | - Massimo Dominici
- Division of Oncology, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Beatrice Aramini
- Division of Thoracic Surgery, Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Division of Thoracic Surgery, Department of Experimental, Diagnostic and Specialty Medicine—DIMES of the Alma Mater Studiorum, University of Bologna, G.B. Morgagni—L. Pierantoni Hospital, Forlì, Italy
- Corresponding author: Beatrice Aramini, Division of Thoracic Surgery, Department of Experimental, Diagnostic and Specialty Medicine - DIMES of the Alma Mater Studiorum, University of Bologna, G.B. Morgagni - L. Pierantoni Hospital, 34 Carlo Forlanini Street, 47121 Forlì, Italy Forlì, Italy.
| |
Collapse
|
|
4
|
Ma J, Stingo FC, Hobbs BP. Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants. Biom J 2019; 61:902-917. [PMID: 30786040 PMCID: PMC7341533 DOI: 10.1002/bimj.201700323] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 09/28/2018] [Accepted: 12/04/2018] [Indexed: 01/13/2023]
Abstract
The evolution of "informatics" technologies has the potential to generate massive databases, but the extent to which personalized medicine may be effectuated depends on the extent to which these rich databases may be utilized to advance understanding of the disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology for dimension reduction. Yet, statistical methods proposed to address challenges arising with the high-dimensionality of omics-type data predominately rely on linear models and emphasize associations deriving from prognostic biomarkers. Existing methods are often limited for discovering predictive biomarkers that interact with treatment and fail to elucidate the predictive power of their resultant selection rules. In this article, we present a Bayesian predictive method for personalized treatment selection that is devised to integrate both the treatment predictive and disease prognostic characteristics of a particular patient's disease. The method appropriately characterizes the structural constraints inherent to prognostic and predictive biomarkers, and hence properly utilizes these complementary sources of information for treatment selection. The methodology is illustrated through a case study of lower grade glioma. Theoretical considerations are explored to demonstrate the manner in which treatment selection is impacted by prognostic features. Additionally, simulations based on an actual leukemia study are provided to ascertain the method's performance with respect to selection rules derived from competing methods.
Collapse
Affiliation(s)
- Junsheng Ma
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center Houston, Texas 77030
| | - Francesco C. Stingo
- Department of Statistica, Informatica, Applicazioni “G.Parenti”, University of Florence, Florence, 50134, Italy
| | - Brian P. Hobbs
- Quantitative Health Sciences and The Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio 44195
| |
Collapse
|
|
5
|
Zhang D, Rennhack J, Andrechek ER, Rockwell CE, Liby KT. Identification of an Unfavorable Immune Signature in Advanced Lung Tumors from Nrf2-Deficient Mice. Antioxid Redox Signal 2018; 29:1535-1552. [PMID: 29634345 PMCID: PMC6421995 DOI: 10.1089/ars.2017.7201] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 03/09/2018] [Accepted: 03/10/2018] [Indexed: 12/19/2022]
Abstract
AIMS Activation of the nuclear factor (erythroid-derived 2)-like 2 (Nrf2) pathway in normal cells inhibits carcinogenesis, whereas constitutive activation of Nrf2 in cancer cells promotes tumor growth and chemoresistance. However, the effects of Nrf2 activation in immune cells during lung carcinogenesis are poorly defined and could either promote or inhibit cancer growth. Our studies were designed to evaluate tumor burden and identify immune cell populations in the lungs of Nrf2 knockout (KO) versus wild-type (WT) mice challenged with vinyl carbamate. RESULTS Nrf2 KO mice developed lung tumors earlier than the WT mice and exhibited more and larger tumors over time, even at late stages. T cell populations were lower in the lungs of Nrf2 KO mice, whereas tumor-promoting macrophages and myeloid-derived suppressor cells were elevated in the lungs and spleen, respectively, of Nrf2 KO mice relative to WT mice. Moreover, 34 immune response genes were significantly upregulated in tumors from Nrf2 KO mice, especially a series of cytokines (Cxcl1, Csf1, Ccl9, Cxcl12, etc.) and major histocompatibility complex antigens that promote tumor growth. INNOVATION Our studies discovered a novel immune signature, characterized by the infiltration of tumor-promoting immune cells, elevated cytokines, and increased expression of immune response genes in the lungs and tumors of Nrf2 KO mice. A complementary profile was also found in lung cancer patients, supporting the clinical significance of our findings. CONCLUSION Overall, our results confirmed a protective role for Nrf2 in late-stage carcinogenesis and, unexpectedly, suggest that activation of Nrf2 in immune cells may be advantageous for preventing or treating lung cancer. Antioxid. Redox Signal.
Collapse
Affiliation(s)
- Di Zhang
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan
| | - Jonathan Rennhack
- Department of Physiology, Michigan State University, East Lansing, Michigan
| | - Eran R. Andrechek
- Department of Physiology, Michigan State University, East Lansing, Michigan
| | - Cheryl E. Rockwell
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan
| | - Karen T. Liby
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan
| |
Collapse
|
|
6
|
Multiregion gene expression profiling reveals heterogeneity in molecular subtypes and immunotherapy response signatures in lung cancer. Mod Pathol 2018; 31:947-955. [PMID: 29410488 DOI: 10.1038/s41379-018-0029-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 12/07/2017] [Accepted: 12/10/2017] [Indexed: 12/14/2022]
Abstract
Intra-tumor heterogeneity may be present at all molecular levels. Genomic intra-tumor heterogeneity at the exome level has been reported in many cancer types, but comprehensive gene expression intra-tumor heterogeneity has not been well studied. Here, we delineated the gene expression intra-tumor heterogeneity by exploring gene expression profiles of 35 tumor regions from 10 non-small cell lung cancer tumors (three or four regions/tumor), including adenocarcinoma, squamous cell carcinoma, large-cell carcinoma, and pleomorphic carcinoma of the lung. Using Affymetrix Gene 1.0 ST arrays, we generated the gene expression data for every sample. Inter-tumor heterogeneity was generally higher than intra-tumor heterogeneity, but some tumors showed a substantial level of intra-tumor heterogeneity. The analysis of various clinically relevant gene expression signatures including molecular subtype, epithelial-to-mesenchymal transition, and anti-PD-1 resistance signatures also revealed heterogeneity between different regions of the same tumor. The gene expression intra-tumor heterogeneity we observed was associated with heterogeneous tumor microenvironments represented by stromal and immune cells infiltrated. Our data suggest that RNA-based prognostic or predictive molecular tests should be carefully conducted in consideration of the gene expression intra-tumor heterogeneity.
Collapse
|
|
7
|
Ma J, Hobbs BP, Stingo FC. Integrating genomic signatures for treatment selection with Bayesian predictive failure time models. Stat Methods Med Res 2016; 27:2093-2113. [PMID: 27807177 DOI: 10.1177/0962280216675373] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Over the past decade, a tremendous amount of resources have been dedicated to the pursuit of developing genomic signatures that effectively match patients with targeted therapies. Although dozens of therapies that target DNA mutations have been developed, the practice of studying single candidate genes has limited our understanding of cancer. Moreover, many studies of multiple-gene signatures have been conducted for the purpose of identifying prognostic risk cohorts, and thus are limited for selecting personalized treatments. Existing statistical methods for treatment selection often model treatment-by-covariate interactions that are difficult to specify, and require prohibitively large patient cohorts. In this article, we describe a Bayesian predictive failure time model for treatment selection that integrates multiple-gene signatures. Our approach relies on a heuristic measure of similarity that determines the extent to which historically treated patients contribute to the outcome prediction of new patients. The similarity measure, which can be obtained from existing clustering methods, imparts robustness to the underlying stochastic data structure, which enhances feasibility in the presence of small samples. Performance of the proposed method is evaluated in simulation studies, and its application is demonstrated through a study of lung squamous cell carcinoma. Our Bayesian predictive failure time approach is shown to effectively leverage genomic signatures to match patients to the therapies that are most beneficial for prolonging their survival.
Collapse
Affiliation(s)
- Junsheng Ma
- 1 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, USA
| | - Brian P Hobbs
- 1 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, USA
| | - Francesco C Stingo
- 2 Dipartimento Di Statistica, informatica applicazionio, University of Florence, Italy
| |
Collapse
|
|
8
|
Preclinical Study of a Combination of Erlotinib and Bevacizumab in Early Stages of Unselected Non-Small Cell Lung Cancer Patient-Derived Xenografts. Target Oncol 2016; 11:507-14. [DOI: 10.1007/s11523-015-0415-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
|
|
9
|
Li W, Freudenberg J, Oswald M. Principles for the organization of gene-sets. Comput Biol Chem 2015; 59 Pt B:139-49. [PMID: 26188561 DOI: 10.1016/j.compbiolchem.2015.04.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2015] [Accepted: 04/08/2015] [Indexed: 12/23/2022]
Abstract
A gene-set, an important concept in microarray expression analysis and systems biology, is a collection of genes and/or their products (i.e. proteins) that have some features in common. There are many different ways to construct gene-sets, but a systematic organization of these ways is lacking. Gene-sets are mainly organized ad hoc in current public-domain databases, with group header names often determined by practical reasons (such as the types of technology in obtaining the gene-sets or a balanced number of gene-sets under a header). Here we aim at providing a gene-set organization principle according to the level at which genes are connected: homology, physical map proximity, chemical interaction, biological, and phenotypic-medical levels. We also distinguish two types of connections between genes: actual connection versus sharing of a label. Actual connections denote direct biological interactions, whereas shared label connection denotes shared membership in a group. Some extensions of the framework are also addressed such as overlapping of gene-sets, modules, and the incorporation of other non-protein-coding entities such as microRNAs.
Collapse
Affiliation(s)
- Wentian Li
- The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY, USA.
| | - Jan Freudenberg
- The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY, USA
| | - Michaela Oswald
- The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institute for Medical Research, North Shore LIJ Health System, Manhasset, NY, USA
| |
Collapse
|
|
10
|
The Robustness of Pathway Analysis in Identifying Potential Drug Targets in Non-Small Cell Lung Carcinoma. MICROARRAYS 2014; 3:212-25. [PMID: 27600345 PMCID: PMC4979055 DOI: 10.3390/microarrays3040212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Revised: 10/04/2014] [Accepted: 10/13/2014] [Indexed: 11/18/2022]
Abstract
The identification of genes responsible for causing cancers from gene expression data has had varied success. Often the genes identified depend on the methods used for detecting expression patterns, or on the ways that the data had been normalized and filtered. The use of gene set enrichment analysis is one way to introduce biological information in order to improve the detection of differentially expressed genes and pathways. In this paper we show that the use of network models while still subject to the problems of normalization is a more robust method for detecting pathways that are differentially overrepresented in lung cancer data. Such differences may provide opportunities for novel therapeutics. In addition, we present evidence that non-small cell lung carcinoma is not a series of homogeneous diseases; rather that there is a heterogeny within the genotype which defies phenotype classification. This diversity helps to explain the lack of progress in developing therapies against non-small cell carcinoma and suggests that drug development may consider multiple pathways as treatment targets.
Collapse
|
|
11
|
Leong D, Rai R, Nguyen B, Lee A, Yip D. Advances in adjuvant systemic therapy for non-small-cell lung cancer. World J Clin Oncol 2014; 5:633-645. [PMID: 25302167 PMCID: PMC4129528 DOI: 10.5306/wjco.v5.i4.633] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2013] [Revised: 02/17/2014] [Accepted: 05/14/2014] [Indexed: 02/06/2023] Open
Abstract
Non-small-cell lung cancer remains a leading cause of death around the world. For most cases, the only chance of cure comes from resection for localised disease, however relapse rates remain high following surgery. Data has emerged over recent years regarding the utility of adjuvant chemotherapy for improving disease-free and overall survival of patients following curative resection. This paper reviews the clinical trials that have been conducted in this area along with the studies integrating radiation therapy in the adjuvant setting. The role of prognostic gene signatures are reviewed as well as ongoing clinical trials including those incorporating biological or targeted therapies.
Collapse
|