Cancer Genomics: Large-Scale Projects Translate into Therapeutic Advances

Summary Objective: To summarize excellent current research and propose a selection of best papers published in 2016 in the field of Bioinformatics and Translational Informatics with applications in the health domain and clinical care. Methods: We provide a synopsis of the articles selected for the IMIA Yearbook 2017, from which we attempt to derive a synthetic overview of current and future activities in the field. As in 2016, a first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section coverage. Each section editor evaluated separately the set of 951 articles returned and evaluation results were merged for retaining 15 candidate best papers for peer-review. Results: The selection and evaluation process of papers published in the Bioinformatics and Translational Informatics field yielded four excellent articles focusing this year on the secondary use and massive integration of multi-omics data for cancer genomics and non-cancer complex diseases. Papers present methods to study the functional impact of genetic variations, either at the level of the transcription or at the levels of pathway and network. Conclusions: Current research activities in Bioinformatics and Translational Informatics with applications in the health domain continue to explore new algorithms and statistical models to manage, integrate, and interpret large-scale genomic datasets. As addressed by some of the selected papers, future trends would include the question of the international collaborative sharing of clinical and omics data, and the implementation of intelligent systems to enhance routine medical genomics.

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OncoThreads: Visualization of Large Scale Longitudinal Cancer Molecular Data

10.31219/osf.io/b3n4u ◽

2021 ◽

Author(s):

Theresa A Harbig ◽

Sabrina Nusrat ◽

Tali Mazor ◽

Qianwen Wang ◽

Alexander Thomson ◽

...

Keyword(s):

Longitudinal Data ◽

Large Scale ◽

Cancer Genomics ◽

Temporal Patterns ◽

Molecular Data ◽

Liquid Biopsies ◽

Sequencing Technologies ◽

Molecular Features ◽

Wide Range ◽

The Impact

Molecular profiling of patient tumors and liquid biopsies over time with next-generation sequencing technologies and new immuno-profile assays are becoming part of standard research and clinical practice. With the wealth of new longitudinal data, there is a critical need for visualizations for cancer researchers to explore and interpret temporal patterns not just in a single patient but across cohorts. To address this need we developed OncoThreads, a tool for the visualization of longitudinal clinical and cancer genomics and other molecular data in patient cohorts. The tool visualizes patient cohorts as temporal heatmaps and Sankey diagrams that support the interactive exploration and ranking of a wide range of clinical and molecular features. This allows analysts to discover temporal patterns in longitudinal data, such as the impact of mutations on response to a treatment, e.g. emergence of resistant clones. We demonstrate the functionality of OncoThreads using a cohort of 23 glioma patients sampled at 2-4 timepoints. OncoThreads is freely available at http://oncothreads.gehlenborglab.org and implemented in Javascript using the cBioPortal web API as a backend.

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Keynote comment: Are large-scale cancer-genomics projects ready to use?

The Lancet Oncology ◽

10.1016/s1470-2045(06)70588-5 ◽

2006 ◽

Vol 7 (3) ◽

pp. 190-191 ◽

Cited By ~ 2

Author(s):

Jose Antonio Rodriguez ◽

Giuseppe Giaccone

Keyword(s):

Large Scale ◽

Cancer Genomics

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HeartBioPortal: an internet-of-omics for human cardiovascular disease data

10.1101/487744 ◽

2018 ◽

Author(s):

Bohdan Khomtchouk ◽

Kasra A Vand ◽

William C Koehler ◽

Diem-Trang Tran ◽

Kai Middlebrook ◽

...

Keyword(s):

Cardiovascular Disease ◽

Web Application ◽

Large Scale ◽

Data Science ◽

Cancer Genomics ◽

Clinical Information ◽

Visual Access ◽

Public Resources ◽

User Friendly ◽

Computational Platform

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17M deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, the state-of-the-art in computational infrastructure to study datasets associated with CVD has lagged far behind public resources widely available in the oncology field, where improved data science and visualization methods have led to the development of large-scale cancer genomics resources like MSKCC's cBioPortal or NCI's Genomic Data Commons (GDC) Portal. Developing a similar user-friendly computational platform could significantly lower the barriers between complex CVD data and researchers who want rapid, intuitive, and high-quality visual access to molecular profiles and clinical attributes from existing CVD projects. Here we present HeartBioPortal: a publicly available web application that provides intuitive visualization, analysis, and downloads of large-scale CVD data currently focused on gene expression, genetic association, and ancestry information. By democratizing access to anonymized CVD data, HeartBioPortal's aim is to integrate relevant omics and clinical information across the biological dataverse to support CVD clinicians and researchers.

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Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

PLoS ONE ◽

10.1371/journal.pone.0242780 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0242780

Author(s):

Houriiyah Tegally ◽

Kevin H. Kensler ◽

Zahra Mungloo-Dilmohamud ◽

Anisah W. Ghoorah ◽

Timothy R. Rebbeck ◽

...

Keyword(s):

Gene Expression ◽

Large Scale ◽

Cancer Genomics ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Driver Mutations ◽

Literature Mining ◽

Cancer Genes ◽

Multiple Cancer ◽

Driver Genes

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

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Supervised dimension reduction for large-scale “omics” data with censored survival outcomes under possible non-proportional hazards

10.1101/586529 ◽

2019 ◽

Author(s):

Lauren Spirko-Burns ◽

Karthik Devarajan

Keyword(s):

Large Scale ◽

Linear Models ◽

Proportional Hazards ◽

Failure Time ◽

Cancer Genomics ◽

Large Fraction ◽

Prognostic Index ◽

Specific Area ◽

Survival Prediction ◽

Area Of Interest

AbstractThe past two decades have witnessed significant advances in high-throughput “omics” technologies such as genomics, proteomics, metabolomics, transcriptomics and radiomics. These technologies have enabled simultaneous measurement of the expression levels of tens of thousands of features from individual patient samples and have generated enormous amounts of data that require analysis and interpretation. One specific area of interest has been in studying the relationship between these features and patient outcomes, such as overall and recurrence-free survival, with the goal of developing a predictive “omics” profile. Large-scale studies often suffer from the presence of a large fraction of censored observations and potential time-varying effects of features, and methods for handling them have been lacking. In this paper, we propose supervised methods for feature selection and survival prediction that simultaneously deal with both issues. Our approach utilizes continuum power regression (CPR) - a framework that includes a variety of regression methods - in conjunction with the parametric or semi-parametric accelerated failure time (AFT) model. Both CPR and AFT fall within the linear models framework and, unlike black-box models, the proposed prognostic index has a simple yet useful interpretation. We demonstrate the utility of our methods using simulated and publicly available cancer genomics data.

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Cytogenetics and Cytogenomics Evaluation in Cancer

International Journal of Molecular Sciences ◽

10.3390/ijms20194711 ◽

2019 ◽

Vol 20 (19) ◽

pp. 4711 ◽

Cited By ~ 2

Author(s):

Ilda Patrícia Ribeiro ◽

Joana Barbosa Melo ◽

Isabel Marques Carreira

Keyword(s):

High Throughput ◽

Cancer Biology ◽

Large Scale ◽

Cancer Genomics ◽

Clonal Evolution ◽

Chromosomal Rearrangements ◽

Therapy Resistance ◽

Cellular Heterogeneity ◽

Comparative Genomic ◽

Driver Genes

The availability of cytogenetics and cytogenomics technologies improved the detection and identification of tumor molecular signatures as well as the understanding of cancer initiation and progression. The use of large-scale and high-throughput cytogenomics technologies has led to a fast identification of several cancer candidate biomarkers associated with diagnosis, prognosis, and therapeutics. The advent of array comparative genomic hybridization and next-generation sequencing technologies has significantly improved the knowledge about cancer biology, underlining driver genes to guide targeted therapy development, drug-resistance prediction, and pharmacogenetics. However, few of these candidate biomarkers have made the transition to the clinic with a clear benefit for the patients. Technological progress helped to demonstrate that cellular heterogeneity plays a significant role in tumor progression and resistance/sensitivity to cancer therapies, representing the major challenge of precision cancer therapy. A paradigm shift has been introduced in cancer genomics with the recent advent of single-cell sequencing, since it presents a lot of applications with a clear benefit to oncological patients, namely, detection of intra-tumoral heterogeneity, mapping clonal evolution, monitoring the development of therapy resistance, and detection of rare tumor cell populations. It seems now evident that no single biomarker could provide the whole information necessary to early detect and predict the behavior and prognosis of tumors. The promise of precision medicine is based on the molecular profiling of tumors being vital the continuous progress of high-throughput technologies and the multidisciplinary efforts to catalogue chromosomal rearrangements and genomic alterations of human cancers and to do a good interpretation of the relation genotype—phenotype.

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The Cancer Genomics Cloud: Collaborative, Reproducible, and Democratized—A New Paradigm in Large-Scale Computational Research

Cancer Research ◽

10.1158/0008-5472.can-17-0387 ◽

2017 ◽

Vol 77 (21) ◽

pp. e3-e6 ◽

Cited By ~ 53

Author(s):

Jessica W. Lau ◽

Erik Lehnert ◽

Anurag Sethi ◽

Raunaq Malhotra ◽

Gaurav Kaushik ◽

...

Keyword(s):

Large Scale ◽

Cancer Genomics ◽

New Paradigm ◽

Computational Research

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Translating Genomics to the Clinic: Implications of Cancer Heterogeneity

Clinical Chemistry ◽

10.1373/clinchem.2012.184580 ◽

2013 ◽

Vol 59 (1) ◽

pp. 127-137 ◽

Cited By ~ 13

Author(s):

Nardin Samuel ◽

Thomas J Hudson

Keyword(s):

Genome Sequencing ◽

Large Scale ◽

Clinical Decision Making ◽

Cancer Genomics ◽

Clinical Decision ◽

Sequencing Analysis ◽

Breast Cancers ◽

Sequencing Data ◽

Cancer Heterogeneity ◽

Cancer Genomes

BACKGROUND Sequencing of cancer genomes has become a pivotal method for uncovering and understanding the deregulated cellular processes driving tumor initiation and progression. Whole-genome sequencing is evolving toward becoming less costly and more feasible on a large scale; consequently, thousands of tumors are being analyzed with these technologies. Interpreting these data in the context of tumor complexity poses a challenge for cancer genomics. CONTENT The sequencing of large numbers of tumors has revealed novel insights into oncogenic mechanisms. In particular, we highlight the remarkable insight into the pathogenesis of breast cancers that has been gained through comprehensive and integrated sequencing analysis. The analysis and interpretation of sequencing data, however, must be considered in the context of heterogeneity within and among tumor samples. Only by adequately accounting for the underlying complexity of cancer genomes will the potential of genome sequencing be understood and subsequently translated into improved management of patients. SUMMARY The paradigm of personalized medicine holds promise if patient tumors are thoroughly studied as unique and heterogeneous entities and clinical decisions are made accordingly. Associated challenges will be ameliorated by continued collaborative efforts among research centers that coordinate the sharing of mutation, intervention, and outcomes data to assist in the interpretation of genomic data and to support clinical decision-making.

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How Cancer Genomics Drives Cancer Biology: Does Synthetic Lethality Explain Mutually Exclusive Oncogenic Mutations?

10.1101/091173 ◽

2016 ◽

Author(s):

Harold Varmus ◽

Arun M. Unni ◽

William W. Lockwood

Keyword(s):

Cancer Biology ◽

Large Scale ◽

Cancer Genomics ◽

Synthetic Lethality ◽

Mutual Exclusivity ◽

Physiological Basis ◽

Oncogenic Mutations ◽

Mutational Pattern ◽

Cancer Multiple ◽

Lung Adenocarcinomas

AbstractLarge-scale analyses of cancer genomes are revealing patterns of mutations that suggest biologically significant ideas about many aspects of cancer, including carcinogenesis, classification, and preventive and therapeutic strategies. Among those patterns is “mutual exclusivity”, a phenomenon observed when two or more mutations that are commonly observed in samples of a type of cancer are not found combined in individual tumors. We have been studying a striking example of mutual exclusivity: the absence of co-existing mutations in theKRASandEGFRproto-oncogenes in human lung adenocarcinomas, despite the high individual frequencies of such mutations in this common type of cancer. Multiple lines of evidence suggest that toxic effects of the joint expression ofKRASandEGFRmutant oncogenes, rather than loss of any selective advantages conferred by a second oncogene that operates through the same signaling pathway, are responsible for the observed mutational pattern. We discuss the potential for understanding the physiological basis of such toxicity, for exploiting it therapeutically, and for extending the studies to other examples of mutual exclusivity.

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