Mutational Landscape Of Cancer Research Articles

The Mutational Landscape of Cancer's Vulnerability to Ionizing Radiation.

Large-scale sequencing efforts have established that cancer-associated genetic alterations are highly diverse, posing a challenge to the identification of variants that regulate complex phenotypes like radiation sensitivity. The impact of the vast majority of rare or common genetic variants on the sensitivity of cancers to radiotherapy remains largely unknown. We developed a scalable gene editing and irradiation platform to assess the role of categories of variants in cells. Variants were prioritized on the basis of genotype-phenotype associations from a previously completed large-scale cancer cell line radiation profiling study. Altogether, 488 alleles (396 unique single-nucleotide variants) from 92 genes were generated and profiled in an immortalized lung cell line, BEAS-2B. We validated our results in other cell lines (TRT-HU1 and NCI-H520), in vivo via the use of both cell line and patient-derived murine xenografts, and in clinical cohorts. We show that resistance to radiation is characterized by substantial inter- and intra-gene allelic variation. Some genes (e.g., KEAP1) demonstrated significant intragenic allelic variation in the magnitude of conferred resistance and other genes (e.g., CTNNB1) displayed both resistance and sensitivity in a protein domain-dependent manner. We combined results from our platform with gene expression and metabolite features and identified the upregulation of amino acid transporters that facilitate oxidative reductive capacity and cell-cycle deregulation as key regulators of radiation sensitivity. Our results reveal new insights into the genetic determinants of tumor sensitivity to radiotherapy and nominate a multitude of cancer mutations that are predicted to impact treatment efficacy.

OC-0400 The mutational landscape of cancer’s sensitivity to ionizing radiation

OC-0400 The mutational landscape of cancer’s sensitivity to ionizing radiation

Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples.

Mutual exclusivity of cancer driving mutations is a frequently observed phenomenon in the mutational landscape of cancer. The long tail of rare mutations complicates the discovery of mutually exclusive driver modules. The existing methods usually suffer from the problem that only few genes in some identified modules cover most of the cancer samples. To overcome this hurdle, an efficient method UniCovEx is presented via identifying mutually exclusive driver modules of balanced exclusive coverages. UniCovEx first searches for candidate driver modules with a strong topological relationship in signaling networks using a greedy strategy. It then evaluates the candidate modules by considering their coverage, exclusivity, and balance of coverage, using a novel metric termed exclusive entropy of modules, which measures how balanced the modules are. Finally, UniCovEx predicts sample‐specific driver modules by solving a minimum set cover problem using a greedy strategy. When tested on 12 The Cancer Genome Atlas datasets of different cancer types, UniCovEx shows a significant superiority over the previous methods. The software is available at: https://sourceforge.net/projects/cancer‐pathway/files/.

Breast Cancer Neoantigens Can Induce CD8+ T-Cell Responses and Antitumor Immunity.

Next-generation sequencing technologies have provided insights into the biology and mutational landscape of cancer. Here, we evaluate the relevance of cancer neoantigens in human breast cancers. Using patient-derived xenografts from three patients with advanced breast cancer (xenografts were designated as WHIM30, WHIM35, and WHIM37), we sequenced exomes of tumor and patient-matched normal cells. We identified 2,091 (WHIM30), 354 (WHIM35), and 235 (WHIM37) nonsynonymous somatic mutations. A computational analysis identified and prioritized HLA class I-restricted candidate neoantigens expressed in the dominant tumor clone. Each candidate neoantigen was evaluated using peptide-binding assays, T-cell cultures that measure the ability of CD8+ T cells to recognize candidate neoantigens, and preclinical models in which we measured antitumor immunity. Our results demonstrate that breast cancer neoantigens can be recognized by the immune system, and that human CD8+ T cells enriched for prioritized breast cancer neoantigens were able to protect mice from tumor challenge with autologous patient-derived xenografts. We conclude that next-generation sequencing and epitope-prediction strategies can identify and prioritize candidate neoantigens for immune targeting in breast cancer. Cancer Immunol Res; 5(7); 516-23. ©2017 AACR.

Reflections on the Founding of the International Cancer Genome Consortium

Five years have passed since the strategy meeting that launched the International Cancer Genome Consortium (ICGC)4 in October 2007. The intervening 5 years have been a time of remarkable progress in cancer genome research, with great strides having been made toward understanding the mutational landscape of cancer and the early adoption of genome analyses in the clinical management of patients. Although progress in the field of cancer genomics has received wide coverage in the scientific literature and lay media, not much has been written about the events that led to the founding of the international consortium. Given our involvement in these events, we were asked to look back at the circumstances that attracted many of the world's leading cancer agencies, genome and cancer scientists, ethicists, computer scientists, and other experts to work together. The common purposes were to accelerate the discovery of many new cancer biomarkers and potential cancer targets and to spur the development of new clinical tests and therapeutic interventions that would benefit cancer patients worldwide. Fig. 1 is a slide from the opening talk at the International Cancer Genomics Strategy Meeting held in Toronto, Canada, on October 1 and 2, 2007. This meeting was convened by 6 organizations—the European Commission, Genome Canada, the National Cancer Institute, the National Human Genome Research Institute, the Ontario Institute for Cancer Research, and the Wellcome Trust. The meeting drew 122 participants from 22 countries, including world leaders in cancer genomics research, ethics, statistics, informatics, and pathology, as well as directors of funding agencies. International researchers and funding agencies used the forum to exchange knowledge and discuss the range of opportunities for a consortium initiative that would generate an atlas of genomic abnormalities in cancer. Fig. 1. Challenges and opportunities supporting the formation of the International Cancer Genome Consortium. TCGA, The Cancer Genome Atlas; WTSI CGP, Wellcome Trust Sanger Institute–Cancer Genome Project; TSP, Tumor Sequencing …

Genetic progression and the waiting time to cancer.

Cancer results from genetic alterations that disturb the normal cooperative behavior of cells. Recent high-throughput genomic studies of cancer cells have shown that the mutational landscape of cancer is complex and that individual cancers may evolve through mutations in as many as 20 different cancer-associated genes. We use data published by Sjöblom et al. (2006) to develop a new mathematical model for the somatic evolution of colorectal cancers. We employ the Wright-Fisher process for exploring the basic parameters of this evolutionary process and derive an analytical approximation for the expected waiting time to the cancer phenotype. Our results highlight the relative importance of selection over both the size of the cell population at risk and the mutation rate. The model predicts that the observed genetic diversity of cancer genomes can arise under a normal mutation rate if the average selective advantage per mutation is on the order of 1%. Increased mutation rates due to genetic instability would allow even smaller selective advantages during tumorigenesis. The complexity of cancer progression can be understood as the result of multiple sequential mutations, each of which has a relatively small but positive effect on net cell growth.