The Influence of Cancer Stem Cells on the Risk of Relapse in Adenocarcinoma and Squamous Cell Carcinoma of the Lung: A Prospective Cohort Study.

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

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification

Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes.

While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research

The OncoLifeS data-biobank for oncology:a comprehensive repository of clinical data, biological samples, and the patient's perspective

BACKGROUND: Understanding cancer heterogeneity, its temporal evolution over time, and the outcomes of guided treatment depend on accurate data collection in a context of routine clinical care. We have developed a hospital-based data-biobank for oncology, entitled OncoLifeS (Oncological Life Study: Living well as a cancer survivor), that links routine clinical data with preserved biological specimens and quality of life assessments. The aim of this study is to describe the organization and development of a data-biobank for cancer research. RESULTS: We have enrolled 3704 patients aged ≥ 18 years diagnosed with cancer, of which 45 with hereditary breast-ovarian cancer (70% participation rate) as of October 24th, 2019. The average age is 63.6 ± 14.2 years and 1892 (51.1%) are female. The following data are collected: clinical and treatment details, comorbidities, lifestyle, radiological and pathological findings, and long-term outcomes. We also collect and store various biomaterials of patients as well as information from quality of life assessments. CONCLUSION: Embedding a data-biobank in clinical care can ensure the collection of high-quality data. Moreover, the inclusion of longitudinal quality of life data allows us to incorporate patients' perspectives and inclusion of imaging data provides an opportunity for analyzing raw imaging data using artificial intelligence (AI) methods, thus adding new dimensions to the collected data

Methods, tools, and computational environment for network-based analysis of biological data

Cancer currently affects more than 18 million persons world-wide annually. It is a leading cause of death and so far, only 60% cure rate can be reached within the most developed health care systems. The nature of cancer has been a mystery for centuries, until discoveries during recent decades shed light on the underlying molecular events. This depended on the progress in understanding cell and tissue biology, developments of molecular technologies and of -omics technologies. Cancer has then emerged as a highly heterogeneous disease, however with some very basic mechanistic features common to all cancers. To deal with the complexity of causes and consequences of pathological changes in the molecular machinery, methods and tools of network analysis can be helpful. Complexity of this task requires easy-to-use tools, which allow researchers and clinicians with no background in computer science to perform network analysis. Paper I describes a web-based framework for network enrichment analysis (NEA), using previously developed algorithm and code. The developed platform introduces functionality for a researcher to use data pre-downloaded from various popular databases as well as own data, perform NEA and obtain statistical estimations, export results in different formats for publications or further use in research pipeline. Paper II presents development of another web server, which provided vast opportunities for exploration and integrated analysis of multiple public cancer datasets that describe in vitro and in vivo sample collections. The web server linked molecular data at the single gene level, phenotype and pharmacological response variables, as well as pathway level variables calculated with NEA and connected to the framework presented in Paper I. Researchers can use the platform for creating multivariate models based on raw or pre-processed data from various sources, visualize created models, estimate their performance and compare them, export models for further usage in own research environments. Paper III demonstrates NEAdriver, a practical application of NEA to probabilistic evaluation of driver roles of mutations reported in ten cancer cohorts. NEAdriver results are compared with cancer gene sets produced by other, both network analysis and network-free methods. The paper demonstrated ability of NEA to be used directly for discovering novel driver genes as well as being used in combination with other methods. In order to demonstrate benefits of using NEA, some rare cancer types and types with low mutation burden were used. Paper IV is a manuscript evaluating performance of most representative methods of network analysis across methods’ parameters, functional ontologies and network versions. This study emphasizes discovery of novel functional associations for known genes, as opposed to previous tests dominated by a few “gold standard” genes which were well characterized previously. We performed the analysis in the context of various topological properties of networks, pathways of interest, and genes. It employed both existing, widely used topological metrics and a number of novel ones developed for this analysis

Motif-informed analysis of phenotype heterogeneity in cancer

The landscape of cancer genomics harbors a wealth of DNA motifs, whose thorough analysis and integration provide a pivotal method to decipher the complex molecular interactions underlying cancer. This dissertation delineates novel computational methodologies for robust DNA motif analysis and data integration, aiming to elucidate the implications of DNA motifs on cancer heterogeneity and clinical outcomes. Chapter 1 lays the groundwork by showing the significance of DNA motifs in the genomic framework and delineating the current biomarkers in cancer. It highlights the opportunity that DNA motif analysis presents in unveiling a nuanced understanding of genomic interactions. It also indicates the motivations and specific aims of the study of both DNA motif quantification and co-localization analysis. In Chapter 2, a foundational marker for quantifying the prevalence of DNA repetitive motifs, termed as “Non-B DNA Burden”, is introduced. A user-centric platform is also developed to facilitate the efficient computation and visualization of this metric across various genomic scales. Together, they are offering a novel perspective for analyzing DNA motif heterogeneity. Transitioning to Chapter 3, the focus evolves toward an integrated marker approach. By integrating the prevalence analysis of DNA motifs in conjunction with the frequency of co-localized mutations, novel markers mlTNB (mutation-localized total non-B burden) and nbTMB (non-B informed tumor mutation burden) are proposed. Their potential in predicting cancer prognosis and treatment responses is specifically explored. Chapter 4 broadens the analytical foundation by defining MoCoLo (Motif Co-Localization), a robust statistical framework for testing multi-modal DNA motif co-localization. Through this framework, we are able to explore the complex interplay of genomic features and provide a methodical approach to investigate their co-localization in a multi-modal data integration context. Case studies are employed to showcase the utility of MoCoLo in examining the co-localization of genomic features, thus facilitating the understanding of genomic interactions that are pivotal to cancer biology. Chapter 5 synthesizes the findings from the preceding explorations, outlining the contributions of the developed methodologies to the field of cancer genomics and bioinformatics. It demonstrates the potential impact of DNA motif analysis and data integration on understanding phenotype heterogeneity in cancer and shows the prospective avenues it provides for impactful future research. Overall, this work is structured to contribute to the bioinformatics community by weaving together innovative tools and analyses focused on DNA motif analysis and data integration. It strives to pave a beneficial way forward to a deeper understanding of the cancer genome, thereby enhancing potential diagnostic and therapeutic strategies.Cellular and Molecular Biolog

Genomic Evolution of Chemoresistance in Triple-Negative Breast Cancer Delineated by Single Cell Sequencing

Triple-negative breast cancer (TNBC) is an aggressive subtype that displays extensive intratumor heterogeneity and frequently (46%) develops resistance to neoadjuvant chemotherapy (NAC). Currently, the genomic basis of chemoresistance remains poorly understood. An important question is whether resistance to chemotherapy is driven by the selection of rare pre-existing subclones with genomic mutations and transcriptional programs that confer resistance to chemotherapy (adaptive resistance) or by the spontaneous induction of new mutations and expression changes that confer a resistant phenotype (acquired resistance). To investigate this question we applied single cell DNA and RNA sequencing methods and deep-exome sequencing to longitudinal time-point samples collected from a cohort of 20 TNBC patients. Deep-exome sequencing of the cohort at three time points revealed patterns of both clonal extinction and clonal persistence, with a subset of patients displaying adaptive selection of pre-existing rare mutations. Single-cell copy number profiling of 900 cells from 8 patients also identified an adaptive resistance model, wherein minor subclones from the pre-treatment tumors were selected and expanded in response to NAC. In contrast, single cell RNA sequencing of 6,862 cells from 8 patients identified subclones with chemoresistant phenotypes that were reprogrammed in response to NAC. These data suggest that chemoresistance at the genotypic level evolves through the selection of pre-existing point mutations and copy number changes, while chemoresistance at the phenotypic level evolves through the reprogramming of expression changes in signaling pathways associated with chemoresistance. These characterizations of adaptive and acquired resistance shed light on the evolutionary trajectory of chemoresistance in TNBC patients

Understanding Genotype-Phenotype Effects in Cancer via Network Approaches.

Cancer is now increasingly studied from the perspective of dysregulated pathways, rather than as a disease resulting from mutations of individual genes. A pathway-centric view acknowledges the heterogeneity between genomic profiles from different cancer patients while assuming that the mutated genes are likely to belong to the same pathway and cause similar disease phenotypes. Indeed, network-centric approaches have proven to be helpful for finding genotypic causes of diseases, classifying disease subtypes, and identifying drug targets. In this review, we discuss how networks can be used to help understand patient-to-patient variations and how one can leverage this variability to elucidate interactions between cancer drivers