Heat shock proteins have been implicated in the process of carcinogenesis. HSPA14, a member of the heat shock protein family, remains poorly understood in terms of its significance and pathomechanisms in breast cancer.

We analyzed the expression levels of HSPA14 and its prognostic significance in breast cancer using TCGA data. TCGA data was used to investigate the association between HSPA14 expression and clinicopathological features in breast cancer patients. GSEA analysis was conducted to identify the biological function of HSPA14. Spearman's correlation analysis was performed to examine the correlation between HSPA14 expression and immune cell infiltration, as well as immune checkpoint genes. Single cell transcriptomic data from GSE114727 was utilized to calculate the expression of HSPA14 in different cell subpopulations. The data on HSPA14 levels and drug sensitivity were extracted from the CellMiner dataset. The mRNA expression of HSPA14 was validated through cell experiments.

HSPA14 expression is elevated in breast cancer, which is associated with poor overall survival. It can serve as a diagnostic biomarker for breast cancer patients. Pathway analysis revealed that HSPA14-associated differential genes are involved in cell cycle, apoptosis, cellular response to heat stress, and more. Additionally, HSPA14 expression is significantly correlated with the immune microenvironment. The expression of HSPA14 may also indicate drug sensitivity.

Our study elucidates the involvement of HSPA14 in tumorigenesis, particularly in modulating the immune response, shaping the immune microenvironment, and contributing to drug resistance, which are pivotal for the development of personalized breast cancer therapies.

Identifying breakpoints in signals is crucial for uncovering important features in scientific data. In the biomedical field, the heterogeneity of signals leads to increased complexity in identifying breakpoints. While existing methods and software packages most focus on detecting breakpoints in individual signals, a significant challenge in this field is to detect common breakpoints of multiple signals. To address this challenge, a fast and optimal method has been developed and implemented in the R package MSigSeg as a practical tool.

The proposed method utilizes an optimization approach with ℓ-0 norm penalty to efficiently and accurately detect the locations of common breakpoints in multiple signals. This article provides a detailed description of the mathematical problem, the fast optimization algorithm which is implemented in the package, and the usage of core functions along with example datasets.

To evaluate the performance of the proposed method, a simulation study is conducted, comparing it with other segmentation approaches. Real-world problems such as are also processed to demonstrate the practical value of the package. Substantial efficiency gain can be observed by our results.

Our R package MSigSeg implements an efficient and sensitive method for detecting common breakpoints across multiple signals, serving as a valuable resource for the analysis of intricate biomedical signals. The proposed package is available on the Comprehensive R Archive Network (CRAN) repository https://CRAN.R-project.org/package=MSigSeg.

Colorectal cancer is a common malignant tumor in the gastrointestinal tract, and the mechanisms of its occurrence, development, and metastasis have always been the focus of the medical community's attention. The study of CRC genetic mechanisms began with the identification of oncogenes or tumor suppressor genes and their key pathways. With further research, researchers gradually realized that single genes or pathways alone could not explain the occurrence, development, and metastasis of CRC. The development of bulk sequencing technology has helped us to analyze the occurrence, development, and metastasis mechanisms of CRC from a multi-gene, multi-pathway, and multi-dimensional perspective, but it has not brought significant benefits to the clinical treatment of tumors. The main reason for this is that bulk sequencing technology relies on homogeneous cell grouping and cannot capture the heterogeneity between cells within the tumor and the interactions within the tumor microenvironment. The development of single-cell technology has made it possible to study the mechanisms of heterogeneity between cells within CRC and the interaction within the tumor microenvironment. This review discusses the mechanisms of CRC occurrence and development in three stages: traditional molecular biology level of single gene, bulk sequencing, and single-cell sequencing. These results show that the occurrence of CRC is the result of complex interactions between genetic and non-genetic factors in somatic cell evolution, where the heterogeneity between cells within the tumor and the tumor microenvironment are crucial for CRC progression.

Breast cancer is a highly heterogeneous disease where variations of biomarker expression may exist between individual foci of a cancer (intra-tumoral heterogeneity). The extent of variation of biomarker expression in the cancer cells, distribution of cell types in the local tumor microenvironment and their spatial arrangement could impact on diagnosis, treatment planning and subsequent response to treatment.

Using quantitative multiplex immunofluorescence (MxIF) imaging, we assessed the level of variations in biomarker expression levels among individual cells, density of cell cluster groups and spatial arrangement of immune subsets from regions sampled from 38 multi-focal breast cancers that were processed using whole-mount histopathology techniques. Molecular profiling was conducted to determine the intrinsic molecular subtype of each analysed region.

A subset of cancers (34.2%) showed intra-tumoral regions with more than one molecular subtype classification. High levels of intra-tumoral variations in biomarker expression levels were observed in the majority of cancers studied, particularly in Luminal A cancers. HER2 expression quantified with MxIF did not correlate well with HER2 gene expression, nor with clinical HER2 scores. Unsupervised clustering revealed the presence of various cell clusters with unique IHC4 protein co-expression patterns and the composition of these clusters were mostly similar among intra-tumoral regions. MxIF with immune markers and image patch analysis classified immune niche phenotypes and the prevalence of each phenotype in breast cancer subtypes was illustrated.

Our work illustrates the extent of spatial heterogeneity in biomarker expression and immune phenotypes, and highlights the importance of a comprehensive spatial assessment of the disease for prognosis and treatment planning.

Estrogen receptor (ER) expression is critical in breast cancer treatment. While low ER (1-9%) resembles triple-negative cancer with chemotherapy efficacy, the significance of "intermediate expression" (≥ 10%) and the therapeutic efficacy remain unclear. This study explores the differences in staining patterns and molecular characteristics of ER-low to intermediate expression to guide treatment.

A total of 104 breast cancer patients treated between January 2008 and July 2024 with an Allred Proportion Score (PS) of 2-4 were included. PS2 (n = 21) was classified as ER-low, while PS3 (n = 26) and PS4 (n = 57) as ER-intermediate (ER-int). ER-int was further divided by ER staining pattern: "Island" (heterogeneous) and "Scatter," (uniform) subgroups. The prognosis, clinical factors, and gene expression profiles (n = 11) were analyzed.

The Island subgroup was associated with poorest prognosis (p = 0.0116), particularly among the patients treated with endocrine-only treatment patients (p < 0.0001). Elevated tumor-infiltrating lymphocyte (TIL) levels correlated with worse prognosis in endocrine-only treatment patients (p < 0.0043), with TIL levels highest in ER-low, followed by Island and Scatter subgroups. Island tumors were enriched in CD36, GZMB, and type I interferon genes; additionally, 23 "ISLAND" genes showed significant prognostic differences in the TCGA BRCA ER-int (10-69%) cohort.

This study emphasizes the importance of recognizing heterogeneity within the ER-int subtype. Identifying distinct ER staining patterns and prognostic significance of TILs and transcriptome in ER-int tumors suggests the need for individualized treatment strategies for Island subtype.

Clonal dispersal, resulting from the intermingling of tumor cell subpopulations, is thought to be a key driver of tumor heterogeneity. Despite advances in spatial modeling of cancer biology, quantification of clonal dispersal has been challenging. This study introduces a straightforward method, relying on fluorescent cell barcoding, to quantify clonal dispersal in various in vitro and in vivo models of colorectal cancer (CRC). Our approach allows for precise localization of clones and uncovering the degree of clonal mixing across different CRC models. Our findings suggest that clonal dispersal is correlated with the expression of genes involved in epithelial-mesenchymal transition and CMS4-related signaling pathways. We further identify a dispersal gene signature, associated with intratumor heterogeneity, which is a robust clinical predictor of poor prognosis and recurrence in CRC, highlighting its potential as a prognostic marker and a putative direction for therapeutic targeting.

Treatment decisions in metastatic castration-resistant prostate cancer are mostly guided by clinical variables, but efforts to molecularly monitor the disease remain hampered by challenges in acquiring tumor tissue repeatedly. In this study, we simultaneously profiled the genome copy number and exome in longitudinal plasma circulating tumor DNA (ctDNA) acquired before, during, and upon progression to serial treatments with androgen signaling inhibitors and taxane chemotherapy from 60 patients with metastatic castration-resistant prostate cancer (2-10 samples per patient). The genomic data were used to delineate the clonal substructure and evolutionary dynamics of each patient, and an evolutionary dynamic index was developed to measure the longitudinal changes of the tumor subclones. Treatment with androgen signaling inhibitors resulted in greater subclonal selection and population structure changes than taxane treatment. The subclones that emerged in association with serial therapy resistance harbored recurrent aberrations in previously identified and new candidate genes, with particular enrichment in genes related to PI3K-AKT signaling. These findings indicate that the integration of detailed clinical and genomic data can provide a framework for future unbiased genomic applications for ctDNA in the clinic to enable precision medicine. Significance: Profiling of the genomic copy number changes and mutations in circulating tumor DNA collected longitudinally from prostate cancer patients receiving serial life-prolonging therapies elucidates evolutionary dynamics and identifies emerging resistant subclones.

DNA sequencing from bulk or clonal human tissues has shown that genetic mosaicism is common and contributes to both cancer and non-cancerous disorders. However, single-cell resolution is required to understand the full genetic heterogeneity that exists within a tissue and the mechanisms that lead to somatic mosaicism. Single-cell DNA-sequencing technologies have traditionally trailed behind those of single-cell transcriptomics and epigenomics, largely because most applications require whole-genome amplification before costly whole-genome sequencing. Now, recent technological and computational advances are enabling the use of single-cell DNA sequencing to tackle previously intractable problems, such as delineating the genetic landscape of tissues with complex clonal patterns, of samples where cellular material is scarce and of non-cycling, postmitotic cells. Single-cell genomes are also revealing the mutational patterns that arise from biological processes or disease states, and have made it possible to track cell lineage in human tissues. These advances in our understanding of tissue biology and our ability to identify disease mechanisms will ultimately transform how disease is diagnosed and monitored.

Clubroot, caused by the obligate Chromist pathogen Plasmodiophora brassicae, is an important disease of brassica crops but little is known about its reproductive biology. We enzymatically removed cell walls from dormant spores to generate protoplasts, enabling the first single-cell sequencing of P. brassicae with DNA free from host and soil microbial contamination. Analysis of 4,000 protoplasts from a single root showed moderate genetic diversity, with 2-5 distinct genotypes. A more detailed analysis of the 500 cells indicated the presence of seven distinct genotypes, accounting for rare haplotypes. This level of genetic diversity in a single root supports other indications that there is a high genetic diversity in field populations of P. brassicae. These results support the hypothesis that balancing selection maintains multiple genotypes within the pathogen population. This level of diversity complicates the use of single-gene resistance sources for clubroot management and explains the short durability of clubroot resistance. The predominance of distinct genotypes in a single root is a strong indication that reproduction of P. brassicae is predominantly clonal. This is the first whole genome DNA sequencing of a single-cell of a plant pathogen.

Rationale: Prostate cancer (PCa) growth is facilitated by the androgen receptor (AR) and its downstream signaling pathways, making AR-targeted therapy crucial for treating advanced stages. Despite this, the response to AR-targeted therapies is inconsistent, with a significant proportion of patients even exhibiting unresponsiveness to therapy from the outset, known as primary resistance. Therefore, a refined categorization framework is imperative for the timely detection of resistant phenotypes and the exploration of novel therapeutic avenues. Methods: Tissue microarrays and clinical cohorts were employed to delineate the impact of APOE on the prognostic outcomes and therapeutic resistance in PCa patients. Employing flow cytometry, immunoprecipitation, and mass spectrometry, we dissected the molecular underpinnings of APOE's role in conferring resistance to AR-targeted interventions. Single-cell RNA sequencing elucidated the intricate transcriptomic profiles of PCa with elevated APOE expression. Additionally, the therapeutic potential of anti-PD-L1 agents in treating PCa with APOE induction was rigorously assessed. Results: In this study, we elucidated the pivotal role of APOE in mediating primary resistance to AR-targeted therapy in PCa through the suppression of AR signaling pathways. Mechanistically, APOE was found to enhance the ubiquitination and subsequent degradation of AR by mediating the interaction between the E3-ligase TRIM25 and AR, concurrently dampening the transcriptional activity of AR. Additionally, elevated APOE expression was correlated with an augmented response to anti-PD-L1 treatment, hinting at the therapeutic advantage of immunotherapy in APOE-high PCa contexts. Conclusions: APOE expression could serve as a prognostic biomarker, pivotal for forecasting responses to both AR-targeted therapy and immunotherapy, thereby offering an innovative strategy for the personalized selection of treatment modalities in PCa.

Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spurious CNAs inconsistent with realistic evolutionary constraints. We introduce evolution-aware copy number calling via deep reinforcement learning (CNRein). Our simulations demonstrate CNRein infers more accurate copy-number profiles and better recapitulates ground truth clonal structure than existing methods. On sequencing data of breast and ovarian cancer, CNRein produces more parsimonious solutions than existing methods while maintaining agreement with single-nucleotide variants. Additionally, CNRein shows consistency on a breast cancer patient sequenced with distinct low-pass technologies.

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

Chromosomal instability (CIN) and clonal heterogeneity (CH) are fundamental hallmarks of breast cancer that drive tumor evolution, disease progression, and therapeutic resistance. Understanding the mechanisms underlying these phenomena is essential for improving cancer diagnosis, prognosis, and treatment strategies.

In this review, we provide a comprehensive overview of the biological processes contributing to CIN and CH, highlighting their molecular determinants and clinical relevance.

We discuss the latest advances in detection methods, including single-cell sequencing and other high-resolution techniques, which have enhanced our ability to characterize intratumoral heterogeneity. Additionally, we explore how CIN and CH influence treatment responses, their potential as therapeutic targets, and their role in shaping the tumor immune microenvironment, which has implications for immunotherapy effectiveness.

By integrating recent findings, this review underscores the impact of CIN and CH on breast cancer progression and their translational implications for precision medicine.

Microfluidic devices (MDs) present a novel method for detecting circulating tumor cells (CTCs), enhancing the process through targeted techniques and visual inspection. However, current approaches often yield heterogeneous CTC populations, necessitating additional processing for comprehensive analysis and phenotype identification. These procedures are often expensive, time-consuming, and need to be performed by skilled technicians. In this study, we investigate the potential of a cost-effective and efficient hyperuniform micropost MD approach for CTC classification. Our approach combines mathematical modeling of fluid-structure interactions in a simulated microfluidic channel with machine learning techniques. Specifically, we developed a cell-based modeling framework to assess CTC dynamics in erythrocyte-laden plasma flow, generating a large dataset of CTC trajectories that account for two distinct CTC phenotypes. Convolutional neural network (CNN) and recurrent neural network (RNN) were then employed to analyze the dataset and classify these phenotypes. The results demonstrate the potential effectiveness of the hyperuniform micropost MD design and analysis approach in distinguishing between different CTC phenotypes based on cell trajectory, offering a promising avenue for early cancer detection.

Upfront combination chemoradiotherapy (CRT) represents the standard of care for patients affected by stage III squamous cell carcinoma (SCC) of the anal canal, achieving satisfactory results both in terms of overall survival and local disease control. However, a non-negligible fraction of patients obtain incomplete responses, highlighting the need for innovative prognostic tools. We report the preliminary results of a customized radiomic algorithm designed to predict tumor response to CRT in patients affected by SCC of the anal canal.

We manually annotated pretreatment T2-weighted turbo spin-echo images of 26 consecutive patients with stage III SCC of the anal canal treated with CRT at our institution from 2012 to 2022. Each patient was classified as complete response (CR, 17 patients), or non-complete response (non-CR, 9 patients) based on the absence or presence of residual disease at imaging and endoscopy after treatment. A total of 132 three-dimensional radiomic features were extracted for each patient and fed to a dedicated machine-learning classifier.

Models trained with gray-level co-occurrence matrix features achieved the best performances (accuracy 0.846 ± 0.064, sensitivity 0.900 ± 0.122, specificity 0.833 ± 0.175, area under receiver operating characteristics curve 0.867 ± 0.055), highlighting a more homogeneous distribution of voxel intensities and lower spatial complexity in non-CR patients.

Our radiomic tool accurately predicted tumor response to CRT in patients with stage III SCC of the anal canal, highlighting a more homogeneous tissue composition in poor responders.

The more homogeneous radiomic texture observed in non-CR patients may be imputable to a dominant neoplastic clone with a relatively low mitotic index (therefore, limited tissue necrosis), intrinsically more resistant to CRT than faster-proliferating tumors.

A non-negligible fraction of patients with anal SCC respond unsatisfactorily to CRT. Our radiomic model predicted response to CRT based on pretreatment MRI. We observed a more homogeneous tissue composition in poor responders. The slow proliferation of a dominant clone may explain non-CR to CRT.

Single-cell multi-omics methods enable the study of cell state diversity, which is largely determined by the interplay of the genome, epigenome, and transcriptome. Here, we describe Gtag&T-seq, a genome-and-transcriptome sequencing (G&T-seq) protocol of the same single cells that omits whole-genome amplification (WGA) by using direct genomic tagmentation (Gtag). Gtag drastically decreases the cost and improves coverage uniformity at single-cell and pseudo-bulk levels compared to WGA-based G&T-seq. We also show that transcriptome-based DNA copy number inference has limited resolution and accuracy, underlining the importance of affordable multi-omic approaches. Applying Gtag&T-seq to a melanoma xenograft model before treatment and at minimal residual disease revealed differential cell state plasticity and treatment response between cancer subclones. In summary, Gtag&T-seq is a low-cost and accurate single-cell multi-omics method that explores genetic alterations and their functional consequences in single cells at scale.

Clonal lineage inference ("tumor phylogenetics") has become a crucial tool for making sense of somatic evolution processes that underlie cancer development and are increasingly recognized as part of normal tissue growth and aging. The inference of clonal lineage trees from single-cell sequence data offers particular promise for revealing processes of somatic evolution in unprecedented detail. However, most such tools are based on fairly restrictive models of the types of mutation events observed in somatic evolution and of the processes by which they develop. The present work seeks to enhance the power and versatility of tools for single-cell lineage reconstruction by making more comprehensive use of the range of molecular variant types by which tumors evolve. We introduce Sc-TUSV-ext, an integer linear programming-based tumor phylogeny reconstruction method that, for the first time, integrates single nucleotide variants, copy number alterations, and structural variations into clonal lineage reconstruction from single-cell DNA sequencing data. We show on synthetic data that accounting for these variant types collectively leads to improved accuracy in clonal lineage reconstruction relative to prior methods that consider only subsets of the variant types. We further demonstrate the effectiveness of real data in resolving clonal evolution in the presence of multiple variant types, providing a path toward more comprehensive insight into how various forms of somatic mutability collectively shape tissue development.

Single-cell RNA sequencing (scRNA-seq) has enabled the dissection of complex tumor ecosystems. Recognition of malignant cells as an essential step has a profound impact on downstream interpretation. However, most existing computational strategies are based on prior knowledge of canonical cell-type markers. We have developed a marker-free approach, the Seed-Cluster based Approach for NEoplastic cells Recognition (SCANER), to identify malignant cells based on significant gene expression variations caused by genomic instability. Upon analyzing different cancer types, SCANER achieved superior accuracy and robustness in identifying malignant cells, effectively addressing dropout events and tumor purity variations. Besides, SCANER can significantly detect copy number variations (CNVs) in malignant cells compared to nonmalignant cells, which is further confirmed through the paired whole exome sequencing data. In conclusion, SCANER has the potential to facilitate the biological exploration of the tumor ecosystem by accurately identifying malignant cells and it is applicable across various solid cancer types regardless of prior knowledge. SCANER is available at https://github.com/woolingxiang/SCANER.

Copy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations.

We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to two breast cancer samples from different sequencing protocols.

SCICoNE is available at https://github.com/cbg-ethz/SCICoNE.

Somatic copy number alterations (sCNAs) are valuable phylogenetic markers for inferring evolutionary relationships among tumor cell subpopulations. Advances in single-cell DNA sequencing technologies are making it possible to obtain such sCNAs datasets at ever-larger scales. However, existing methods for reconstructing phylogenies from sCNAs are often too slow for large datasets. We propose two new distance-based methods, DICE-bar and DICE-star, for reconstructing single-cell tumor phylogenies from sCNA data. Using carefully simulated datasets, we find that DICE-bar matches or exceeds the accuracies of all other methods on noise-free datasets and that DICE-star shows exceptional robustness to noise and outperforms all other methods on noisy datasets. Both methods are also orders of magnitude faster than many existing methods. Our experimental analysis also reveals how noise/error in copy number inference, as expected for real datasets, can drastically impact the accuracies of most methods. We apply DICE-star, the most accurate method on error-prone datasets, to several real single-cell breast and ovarian cancer datasets and find that it rapidly produces phylogenies of equivalent or greater reliability compared with existing methods.

Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.

Hepatoblastoma (HB) is a common paediatric liver malignancy characterised by significant intratumoral heterogeneity and a complex tumour microenvironment (TME). Using single-cell RNA sequencing (scRNA-seq), we analysed 43,592 cells from three tumour regions and adjacent normal tissue of an HB patient. Our study revealed distinct cellular compositions and varying degrees of malignancy across different tumour regions, with the T1 region showing the highest malignancy and overexpression of HMGB2 and TOP2A. Survival analysis demonstrated that high HMGB2 expression is associated with poor prognosis and increased recurrence, suggesting its potential as a prognostic marker. Additionally, we identified a diverse immune microenvironment enriched with regulatory T cells (Tregs) and CD8+ effector memory T cells (Tem), indicating potential immune evasion mechanisms. Notably, CTLA-4 and PD-1 were highly expressed in Tregs and Tem cells, highlighting their potential as immunotherapy targets. Myeloid cells, including Kupffer cells and dendritic cells, also exhibited distinct functional roles in different tumour regions. This study provides the first comprehensive single-cell atlas of HB, revealing critical insights into its intratumoral heterogeneity and immune microenvironment. Our findings not only advance the understanding of HB biology but also offer new directions for precision medicine, including the development of targeted therapies and immunotherapeutic strategies to improve patient outcomes.

The response to neoadjuvant chemoradiotherapy (NACRT) for locally advanced rectal cancer (LARC) varies from achieving a complete pathological response to encountering resistance to treatment. Therefore, biomarkers for predicting the NACRT responses should be identified. This prospective study aimed to identify key genomic biomarkers as the predictors of the NACRT response with LARC.

Overall, 67 patients with LARC treated with NACRT and proctectomy were divided into two groups based on the tumor regression grade (TRG) for identifying key biomarkers. Patients with a TRG of 0 or 1 were assigned to the sensitive response group, and patients with a TRG of 2 or 3 were the resistant response group. Twenty-nine postsurgical tumor samples were collected for whole exome sequencing (WES) to identify genomic variation biomarkers. The other 38 pairs of tumor specimens from pretreatment and postsurgery samples were evaluated by immunohistochemistry (IHC) to examine the biomarker features.

In the WES subcohort, 11 genes showed copy number variation, including FNKBIA, ARID1A, CCND2, CDK4, LYN, MDM2, RAD51B, RARA, SPEN, STAT3, and Daxx, which has the highest copy number variation. For the IHC subcohort, Daxx was initially highly expressed in the nuclei of tumor cells, particularly in the sensitive response group, while varying its expression after NACRT, demonstrating that Daxx levels were related to treatment responses and the survival benefit, especially a better disease-free survival (DFS).

We identified multiple genomic variations between sensitive and resistant responders and verified that Daxx is a potential predictive biomarker of the response to NACRT in LARC.

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding of cancer and tumor heterogeneity, clonal evolution, and the complex interplay between cancer cells and tumor microenvironment. SCS provides high-resolution profiling of individual cells in genomic, transcriptomic, and epigenomic landscapes, facilitating the detection of rare mutations, the characterization of cellular diversity, and the integration of molecular data with phenotypic traits. The integration of SCS with multi-omics has provided a multidimensional view of cellular states and regulatory mechanisms in cancer, uncovering novel regulatory mechanisms and therapeutic targets. Advances in computational tools, artificial intelligence (AI), and machine learning have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and the development of predictive models for patient stratification. Furthermore, there have been emerging technologies such as spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor microenvironment organization and cellular interactions. As SCS and its related technologies continue to advance, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

Liver cancer, particularly hepatocellular carcinoma (HCC), remains a significant global health challenge due to its high mortality rate and late-stage diagnosis. The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes. This review provides a comprehensive overview of current and emerging biomarkers for HCC, including alpha-fetoprotein, des-gamma-carboxy prothrombin, glypican-3, Golgi protein 73, osteopontin, and microRNAs. Despite advancements, the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages. The review emphasizes the importance of integrating multi-omics approaches, combining genomics, proteomics, and metabolomics, to develop more robust biomarker panels. Such integrative methods have the potential to capture the complex molecular landscape of HCC, offering insights into disease mechanisms and identifying targets for personalized therapies. The significance of large-scale validation studies, collaboration between research institutions and clinical settings, and consideration of regulatory pathways for clinical implementation is also discussed. In conclusion, while substantial progress has been made in biomarker discovery, continued research and innovation are essential to address the remaining challenges. The successful translation of these discoveries into clinical practice will require rigorous validation, standardization of protocols, and cross-disciplinary collaboration. By advancing the development and application of novel biomarkers, we can improve the early detection and management of HCC, ultimately enhancing patient survival and quality of life.

Breast cancer is the most prevalent neoplasm in women. ER+ (Luminal subtype), representing over 70% of breast tumors, is a genetically diverse group. Structural and Numerical-Chromosomal instability initiates tumor development and is recognized as the primary driver of genetic alteration in luminal breast tumors. Genomic instability refers to the increased tendency of cancer cells to accumulate genomic alterations during cell proliferation. The cell cycle check-point response to constant and stable genomic alterations in tumor cells drives this process. The impact of CNV patterns and aneuploidies in cell cycle and proliferation perturbation has recently been highlighted by scientists in Luminal breast tumors. The impact of chromosomal instability on cancer therapy and prognosis is not a new concept. Still, the degree of emerging genomic instability leads to prognosis alteration following cell cycle deregulation by chromosomal instability could be predicted by CNVs-based reclassification of breast tumors. In this review, we try to explain the effect of CIN in the cell cycle that ended with genomic instability and altered prognosis and the impact of CIN in decision-making for a therapy strategy for patients with luminal breast cancer.

Genomic heterogeneity has largely been overlooked in single-cell replication timing (scRT) studies. Here, we develop MnM, an efficient machine learning-based tool that allows disentangling scRT profiles from heterogenous samples. We use single-cell copy number data to accurately perform missing value imputation, identify cell replication states, and detect genomic heterogeneity. This allows us to separate somatic copy number alterations from copy number changes resulting from DNA replication. Our methodology brings critical insights into chromosomal aberrations and highlights the ubiquitous aneuploidy process during tumorigenesis. The copy number and scRT profiles obtained by analysing >119,000 high-quality human single cells from different cell lines, patient tumours and patient-derived xenograft samples leads to a multi-sample heterogeneity-resolved scRT atlas. This atlas is an important resource for cancer research and demonstrates that scRT profiles can be used to study replication timing heterogeneity in cancer. Our findings also highlight the importance of studying cancer tissue samples to comprehensively grasp the complexities of DNA replication because cell lines, although convenient, lack dynamic environmental factors. These results facilitate future research at the interface of genomic instability and replication stress during cancer progression.

Intra-tumor heterogeneity (ITH) is a compounding factor for cancer prognosis and treatment. Single-cell DNA sequencing (scDNA-seq) provides cellular resolution of the variations in a cell and has been widely used to study cancer progression and responses to drug and treatment. While the low coverage scDNA-seq technologies typically provides a large number of cells, accurate cell clustering is essential for effectively characterizing ITH. Existing cell clustering methods typically are based on either single nucleotide variations (SNV) or copy number alterations (CNA), without leveraging both signals together. Since both SNVs and CNAs are indicative of the cell subclonality, in this paper, we designed a robust cell clustering tool that integrates both signals using a graph autoencoder. Our model co-trains the graph autoencoder and a graph convolutional network (GCN) to guanrantee meaningful clustering results and to prevent all cells from collapsing into a single cluster. Given the low dimensional embedding generated by the autoencoder, we adopted a Gaussian Mixture Model to further cluster cells. We evaluated our method on eight simulated datasets and a real cancer sample. Our results demonstrate that our method consistently achieves higher V-measure scores compared to SBMClone, a SNV-based method, and a K-means method, which relies solely on CNA signals. These findings highlight the advantage of integrating both SNV and CNA signals within a graph autoencoder framework for accurate cell clustering. SCGclust is publicly available at https://github.com/compbio-mallory/cellClustering_GNN.

Multiple myeloma (MM), the second most common hematologic malignancy, is characterized by the clonal expansion of myeloma cells and accumulation of genetic lesions. MM progression is accompanied by increased aggressiveness and drug resistance. Even the goal of "cure" remains hard to reach for most patients, advances in diagnosis and treatment have allowed some to achieve durable remissions and transition to plateau phase. Single-cell sequencing, with its powerful ability to analyze cellular heterogeneity and molecular patterns at ground-breaking resolution, is informative for deciphering tumors and their microenvironment. In this review, we summarize the new insights of studies facilitated by emerging single-cell sequencing into clonal evolution, myeloma-supported microenvironment transformation, epigenetic changes, and novel prognostic and therapeutic strategies for MM, revealing the key mechanisms underlying MM progression and the direction of future efforts. With the continuous expansion of the research scope and optimization of related technologies, single-cell sequencing is expected to revolutionize our understanding of the biology and evolutionary dynamics of MM and contribute to the radical and precise improvement of treatment.

Reconstructing clonal lineage trees ("tumor phylogenetics") has become a core tool of cancer genomics. Earlier approaches based on bulk DNA sequencing (DNA-seq) have largely given way to single-cell DNA-seq (scDNA-seq), which offers far greater resolution for clonal substructure. Available data has lagged behind computational theory, though. While single-cell RNA-seq (scRNA-seq) has become widely available, scDNA-seq is still sufficiently costly and technically challenging to preclude routine use on large cohorts. This forces difficult tradeoffs between the limited genome coverage of scRNA-seq, limited availability of scDNA-seq, and limited clonal resolution of bulk DNA-seq. These limitations are especially problematic for studying structural variations and focal copy number variations that are crucial to cancer progression but difficult to observe in RNA-seq.

We develop a method, TUSV-int, combining advantages of these various genomic technologies by integrating bulk DNA-seq and scRNA-seq data into a single deconvolution and phylogenetic inference computation while allowing for single nucleotide variant (SNV), copy number alteration (CNA) and structural variant (SV) data. We accomplish this by using integer linear programming (ILP) to deconvolve heterogeneous variant types and resolve them into a clonal lineage tree. We demonstrate improved deconvolution performance over comparative methods lacking scRNA-seq data or using more limited variant types. We further demonstrate the power of the method to better resolve clonal structure and mutational histories through application to a previously published DNA-seq/scRNA-seq breast cancer data set.

The source code for TUSV-int is available at https://github.com/CMUSchwartzLab/TUSV-INT.git.

Intratumoral heterogeneity emerges from accumulating genetic and epigenetic changes during tumorigenesis, which may contribute to therapeutic failure and drug resistance. However, the lack of a quick and convenient approach to determine the intratumoral epigenetic heterogeneity (eITH) limit the application of eITH in clinical settings. Here, we aimed to develop a tool that can evaluate the eITH using the DNA methylation profiles from bulk tumors.

Genomic DNA of three laser micro-dissected tumor regions, including digestive tract surface, central bulk, and invasive front, was extracted from formalin-fixed paraffin-embedded sections of colorectal cancer patients. The genome-wide methylation profiles were generated with methylation array. The most variable methylated probes were selected to construct a DNA methylation-based heterogeneity (MeHEG) estimation tool that can deconvolve the proportion of each reference tumor region with the support vector machine model-based method. A PCR-based assay for quantitative analysis of DNA methylation (QASM) was developed to specifically determine the methylation status of each CpG in MeHEG assay at single-base resolution to realize fast evaluation of epigenetic heterogeneity.

In the discovery set with 79 patients, the differentially methylated CpGs among the three tumor regions were found. The 7 most representative CpGs were identified and subsequently selected to develop the MeHEG algorithm. We validated its performance of deconvolution of tumor regions in an independent cohort. In addition, we showed the significant association of MeHEG-based epigenetic heterogeneity with the genomic heterogeneity in mutation and copy number variation in our in-house and TCGA cohorts. Besides, we found that the patients with higher MeHEG score had worse disease-free and overall survival outcomes. Finally, we found dynamic change of epigenetic heterogeneity based on MeHEG score in cancer cells under the treatment of therapeutic drugs.

By developing a 7-loci panel using a machine learning approach combined with the QASM assay for PCR-based application, we present a valuable method for evaluating intratumoral heterogeneity. The MeHEG algorithm offers novel insights into tumor heterogeneity from an epigenetic perspective, potentially enriching current knowledge of tumor complexity and providing a new tool for clinical and research applications in cancer biology.

The existence of cancer stem cells (CSCs) in various tumors has become increasingly clear in addition to their prominent role in therapy resistance, metastasis, and recurrence. For early diagnosis, disease progression monitoring, and targeting, there is a high demand for clinical-grade methods for quantitative measurement of CSCs from patient samples. Despite years of active research, standard measurement of CSCs has not yet reached clinical settings, especially in the case of solid tumors. This is because detecting this plastic heterogeneous population of cells is not straightforward. This review summarizes various techniques, highlighting their benefits and limitations in detecting CSCs from patient samples. In addition, methods designed to detect CSCs based on secreted and niche-associated signaling factors are reviewed. Spatial and single-cell methods for analyzing patient tumor tissues and noninvasive techniques such as liquid biopsy and in vivo imaging are discussed. Additionally, methods recently established in laboratories, preclinical studies, and clinical assays are covered. Finally, we discuss the characteristics of an ideal method as we look toward the future.

Single-cell technology (SCT), which enables the examination of the fundamental units comprising biological organs, tissues, and cells, has emerged as a powerful tool, particularly in the field of biology, with a profound impact on stem cell research. This innovative technology opens new pathways for acquiring cell-specific data and gaining insights into the molecular pathways governing organ function and biology. SCT is not only frequently used to explore rare and diverse cell types, including stem cells, but it also unveils the intricacies of cellular diversity and dynamics. This perspective, crucial for advancing stem cell research, facilitates non-invasive analyses of molecular dynamics and cellular functions over time. Despite numerous investigations into potential stem cell therapies for genetic disorders, degenerative conditions, and severe injuries, the number of approved stem cell-based treatments remains limited. This limitation is attributed to the various heterogeneities present among stem cell sources, hindering their widespread clinical utilization. Furthermore, stem cell research is intimately connected with cutting-edge technologies, such as microfluidic organoids, CRISPR technology, and cell/tissue engineering. Each strategy developed to overcome the constraints of stem cell research has the potential to significantly impact advanced stem cell therapies. Drawing on the advantages and progress achieved through SCT-based approaches, this study aims to provide an overview of the advancements and concepts associated with the utilization of SCT in stem cell research and its related fields.

In precision cancer therapy, addressing intra-tumor heterogeneity poses a significant obstacle. Due to the heterogeneity of each cell subtype and between cells within the tumor, the sensitivity and resistance of different patients to targeted drugs, chemotherapy, etc., are inconsistent. Concerning a specific tumor type, many feasible treatments or combinations can be used by specifically targeting the tumor microenvironment. To solve this problem, it is necessary to further study the tumor microenvironment. Single-cell sequencing techniques can dissect distinct tumor cell populations by isolating cells and using statistical computational methods. This technology may assist in the selection of targeted combination therapy, and the obtained cell subset information is crucial for the rational application of targeted therapy. In this review, we summarized the research and application advances of single-cell sequencing technology in the tumor microenvironment, including the most commonly used single-cell genomic and transcriptomic sequencing, and their future development direction was proposed. The application of single-cell sequencing technology has been expanded to include epigenomics, proteomics, metabolomics, and microbiome analysis. The integration of these different omics approaches has significantly advanced the development of single-cell multiomics sequencing technology. This innovative approach holds immense potential for various fields, such as biological research and medical investigations. Finally, we discussed the advantages and disadvantages of using single-cell sequencing to explore the tumor microenvironment.

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Lineage tracing methods have extensively advanced our understanding of physiological cell behaviour in vivo and in situ and have vastly contributed to decipher the phylogeny and cellular hierarchies during normal and tumour development. In recent years, increasingly complex systems have been developed to track thousands of cells within a given tissue or even entire organisms. Cellular barcoding comprises all techniques designed to genetically label single cells with unique DNA sequences or with a combination of fluorescent proteins, in order to trace their history and lineage production in space and time. We distinguish these two types of cellular barcoding as genetic or optical barcodes. Furthermore, transcribed cellular barcodes can integrate the lineage information with single-cell profiling of each barcoded cell. This enables the potential identification of specific markers or signalling pathways defining distinct stem cell states during development, but also signals promoting tumour growth and metastasis or conferring therapy resistance.In this chapter, we describe recent advances in cellular barcoding technologies and outline experimental and computational challenges. We discuss the biological questions that can be addressed using single-cell dynamic lineage tracing, with a focus on the study of cellular hierarchies in the mammary epithelium and in breast cancer.

Although recent advancements have shed light on the crucial role of coordinated evolution among cell subpopulations in influencing disease progression, the full potential of these insights has not yet been fully harnessed in the clinical application of personalized precision medicine for prostate cancer (PCa). In this study, we utilized single-cell sequencing to identify the evolutionary characteristics of tumoral cell states and employed comprehensive bulk RNA sequencing to evaluate their potential as prognostic indicators and therapeutic targets. Leveraging advancements in artificial intelligence, we integrated machine learning with multi-omics to develop and validate the tumor evolutionary characteristic predictive indicator (TECPI). TECPI not only demonstrated superior prognostic performance compared to traditional clinical predictors and 81 previously published models but also improved patient outcomes by accurately identifying individuals who would benefit from immunotherapy and targeted therapies. Furthermore, we experimentally validated the critical role of AMOTL1 in PCa pharmacodynamics through its interaction with AR, pivotal for modulating the sensitivity to AR antagonist. Additionally, we demonstrated the generalizability and applicability of TECPI across pan-cancers. In summary, this study emphasizes the importance of understanding cellular diversity and dynamics within the tumor microenvironment to predict PCa progression and to guide targeted therapy effectively.

Cancer is an evolutionary process involving the accumulation of diverse somatic mutations and clonal evolution over time. Phylogenetic inference from samples obtained from an individual patient offers a powerful approach to unraveling the intricate evolutionary history of cancer and provides insights that can inform cancer treatment. Somatic copy number alterations (CNAs) are important in cancer evolution and are often used as markers, alone or with other somatic mutations, for phylogenetic inferences, particularly in low-coverage DNA sequencing data. Many phylogenetic inference methods using CNAs detected from bulk or single-cell DNA sequencing data have been developed over the years. However, there have been no systematic reviews on these methods. To summarize the state-of-the-art of the field and inform future development, this review presents a comprehensive survey on the major challenges in inference, different types of methods, and applications of these methods. The challenges are discussed from the aspects of input data, models of evolution, and inference algorithms. The different methods are grouped according to the markers used for inference and the types of the reconstructed trees. The applications include using phylogenetic inference to understand intra-tumor heterogeneity, metastasis, treatment resistance, and early cancer development. This review also sheds light on future directions of cancer phylogenetic inference using CNAs, including the improvement of scalability, the utilization of new types of data, and the development of more realistic models of evolution.