The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects.

Summary Somatic mutations in cancer are more frequent in heterochromatic and late-replicating regions of the genome. We report that regional disparities in mutation density are virtually abolished within transcriptionally silent genomic regions of cutaneous squamous cell carcinomas (cSCCs) arising in an XPC−/− background. XPC−/− cells lack global genome nucleotide excision repair (GG-NER), thus establishing differential access of DNA repair machinery within chromatin-rich regions of the genome as the primary cause for the regional disparity. Strikingly, we find that increasing levels of transcription reduce mutation prevalence on both strands of gene bodies embedded within H3K9me3-dense regions, and only to those levels observed in H3K9me3-sparse regions, also in an XPC-dependent manner. Therefore, transcription appears to reduce mutation prevalence specifically by relieving the constraints imposed by chromatin structure on DNA repair. We model this novel relationship between transcription, chromatin state and DNA repair, revealing a new, personalized determinant of cancer risk.

As organisms adaptively evolve to a new environment, selection results in the improvement of certain traits, bringing about an increase in fitness. Trade-offs may result from this process if function in other traits is reduced in alternative environments either by the adaptive mutations themselves or by the accumulation of neutral mutations elsewhere in the genome. Though the cost of adaptation has long been a fundamental premise in evolutionary biology, the existence of and molecular basis for trade-offs in alternative environments are not well-established. Here, we show that yeast evolved under aerobic glucose limitation show surprisingly few trade-offs when cultured in other carbon-limited environments, under either aerobic or anaerobic conditions. However, while adaptive clones consistently outperform their common ancestor under carbon limiting conditions, in some cases they perform less well than their ancestor in aerobic, carbon-rich environments, indicating that trade-offs can appear when resources are non-limiting. To more deeply understand how adaptation to one condition affects performance in others, we determined steady-state transcript abundance of adaptive clones grown under diverse conditions and performed whole-genome sequencing to identify mutations that distinguish them from one another and from their common ancestor. We identified mutations in genes involved in glucose sensing, signaling, and transport, which, when considered in the context of the expression data, help explain their adaptation to carbon poor environments. However, different sets of mutations in each independently evolved clone indicate that multiple mutational paths lead to the adaptive phenotype. We conclude that yeasts that evolve high fitness under one resource-limiting condition also become more fit under other resource-limiting conditions, but may pay a fitness cost when those same resources are abundant.

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Infiltration of T cells in breast tumors correlates with improved survival of patients with breast cancer, despite relatively few mutations in these tumors. To determine if T-cell specificity can be harnessed to augment immunotherapies of breast cancer, we sought to identify the alpha-beta paired T-cell receptors (TCRs) of tumor-infiltrating lymphocytes shared between multiple patients. Because TCRs function as heterodimeric proteins, we used an emulsion-based RT-PCR assay to link and amplify TCR pairs. Using this assay on engineered T-cell hybridomas, we observed ∼85% accurate pairing fidelity, although TCR recovery frequency varied. When we applied this technique to patient samples, we found that for any given TCR pair, the dominant alpha-or beta-binding partner comprised ∼90% of the total binding partners. Analysis of TCR sequences from primary tumors showed about fourfold more overlap in tumor-involved relative to tumor-free sentinel lymph nodes. Additionally, comparison of sequences from both tumors of a patient with bilateral breast cancer showed 10% overlap. Finally, we identified a panel of unique TCRs shared between patients' tumors and peripheral blood that were not found in the peripheral blood of controls. These TCRs encoded a range of V, J, and complementarity determining region 3 (CDR3) sequences on the alpha-chain, and displayed restricted V-beta use. The nucleotides encoding these shared TCR CDR3s varied, suggesting immune selection of this response. Harnessing these T cells may provide practical strategies to improve the shared antigen-specific response to breast cancer.T-cell receptors | breast cancer | emulsion RT-PCR | high-throughput sequencing | T-cell repertoire profiling I nfiltration of numerous tumors by CD8 + alpha-beta T cells is associated with better outcomes and longer survival times for patients with breast cancer (1-5). Targeted immune-based therapies hold great promise toward improving breast cancer treatments (6, 7). Studies have examined the immune phenotype of breast cancer tumor-infiltrating lymphocytes (TILs), suggesting that activated nonsuppressive T cells are of most benefit (8). Further assessment of the TIL repertoire has broad implications for breast cancer therapies in antigen discovery, cancer vaccines, and adoptive cell therapies (7).Unique genetic recombination events are required to produce the T-cell receptor (TCR) (reviewed in 9). The alpha-and beta-chains of the TCR undergo V(D)J recombination and heterodimerize in the thymus, resulting in a diverse T-cell repertoire that specifically recognizes peptide-MHC complexes. Many studies have analyzed the alpha-and beta-chains of TIL TCRs separately using highthroughput sequencing to describe the diversity of TILs (10-13). Pairs are readily identified after expansion of T-cell clones, although culture of T cells can lead to substantial skewing of the repertoire (14), which may select for T cells of varied affinity or avidity (15). Single-cell sequencing identifies alpha-beta pairs, but is often laborious and has rela...