Large-scale cancer sequencing data enable discovery of rare germline cancer susceptibility variants. Here we systematically analyse 4,034 cases from The Cancer Genome Atlas cancer cases representing 12 cancer types. We find that the frequency of rare germline truncations in 114 cancer-susceptibility-associated genes varies widely, from 4% (acute myeloid leukaemia (AML)) to 19% (ovarian cancer), with a notably high frequency of 11% in stomach cancer. Burden testing identifies 13 cancer genes with significant enrichment of rare truncations, some associated with specific cancers (for example, RAD51C, PALB2 and MSH6 in AML, stomach and endometrial cancers, respectively). Significant, tumour-specific loss of heterozygosity occurs in nine genes (ATM, BAP1, BRCA1/2, BRIP1, FANCM, PALB2 and RAD51C/D). Moreover, our homology-directed repair assay of 68 BRCA1 rare missense variants supports the utility of allelic enrichment analysis for characterizing variants of unknown significance. The scale of this analysis and the somatic-germline integration enable the detection of rare variants that may affect individual susceptibility to tumour development, a critical step toward precision medicine.

Local concentrations of mutations are well-known in human cancers. However, their 3-dimensional (3D) spatial relationships have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and inter-molecular clusters, some of which showed tumor/tissue specificity. In addition, we identified 369 rare mutations from genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium recurrence mutations from genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all residing within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high throughput phosphorylation data and cell-line based experimental evaluation. Finally, drug-mutation cluster/network analysis predicted over 800 promising candidates of druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

Complex indels are formed by simultaneously deleting and inserting DNA fragments of different sizes at a common genomic location. Here, we present a systematic analysis of somatic complex indels in the coding sequences of over 8,000 cancer cases using Pindel-C. We discovered 285 complex indels in cancer genes (e.g., PIK3R1, TP53, ARID1A, GATA3, and KMT2D) in approximately 3.5% of cases analyzed; nearly all instances of complex indels were overlooked (81.1%) or mis-annotated (17.6%) in 2,199 samples previously reported. In-frame complex indels are enriched in PIK3R1 and EGFR while frameshifts are prevalent in VHL, GATA3, TP53, ARID1A, PTEN, and ATRX. Further, complex indels display strong tissue specificity (e.g., VHL from kidney cancer and GATA3 from breast cancer). Finally, structural analyses support findings of previously missed, but potentially druggable mutations in EGFR, MET, and KIT oncogenes. This study indicates the critical importance of improving complex indel discovery and interpretation in medical research.

The BARD1 protein, which heterodimerizes with BRCA1, is encoded by a known breast cancer susceptibility gene. While several BARD1 variants have been identified as pathogenic, many more missense variants exist that do not occur frequently enough to assign a clinical risk. In this paper, whole exome sequencing of over 10,000 cancer samples from 33 cancer types identified from somatic mutations and loss of heterozygosity in tumors 76 potentially cancer-associated BARD1 missense and truncation variants. These variants were tested in a functional assay for homology-directed repair (HDR), as HDR deficiencies have been shown to correlate with clinical pathogenicity for BRCA1 variants. From these 76 variants, 4 in the ankyrin repeat domain and 5 in the BRCT domain were found to be non-functional in HDR. Two known benign variants were found to be functional in HDR, and three known pathogenic variants were non-functional, supporting the notion that the HDR assay can be used to predict the clinical risk of BARD1 variants. The identification of HDR-deficient variants in the ankyrin repeat domain indicates there are DNA repair functions associated with this domain that have not been closely examined. In order to examine whether BARD1-associated loss of HDR function results in DNA damage sensitivity, cells expressing non-functional BARD1 variants were treated with ionizing radiation or cisplatin. These cells were found to be more sensitive to DNA damage, and variations in the residual HDR function of non-functional variants did not correlate with variations in sensitivity. These findings improve the understanding of BARD1 functional domains in DNA repair and support that this functional assay is useful for predicting the cancer association of BARD1 variants.

Despite recent insights into prostate cancer (PCa)-associated genetic changes, full understanding of prostate tumorigenesis remains elusive due to complexity of interactions among various cell types and soluble factors present in prostate tissue. We found upregulation of Nuclear Factor of Activated T Cells c1 (NFATc1) in human PCa and cultured PCa cells, but not in normal prostates and non-tumorigenic prostate cells. To understand the role of NFATc1 in prostate tumorigenesis in situ, we temporally and spatially controlled the activation of NFATc1 in mouse prostate and showed that such activation resulted in prostatic adenocarcinoma with features similar to those seen in human PCa. Our results indicate that the activation of a single transcription factor, NFATc1 in prostatic luminal epithelium to PCa can affect expression of diverse factors in both cells harboring the genetic changes and in neighboring cells through microenvironmental alterations. In addition to the activation of oncogenes c-MYC and STAT3 in tumor cells, a number of cytokines and growth factors, such as IL1β, IL6, and SPP1 (Osteopontin, a key biomarker for PCa), were upregulated in NFATc1-induced PCa, establishing a tumorigenic microenvironment involving both NFATc1 positive and negative cells for prostate tumorigenesis. To further characterize interactions between genes involved in prostate tumorigenesis, we generated mice with both NFATc1 activation and Pten inactivation in prostate. We showed that NFATc1 activation led to acceleration of Pten-null–driven prostate tumorigenesis by overcoming the PTEN loss–induced cellular senescence through inhibition of p21 activation. This study provides direct in vivo evidence of an oncogenic role of NFATc1 in prostate tumorigenesis and reveals multiple functions of NFATc1 in activating oncogenes, in inducing proinflammatory cytokines, in oncogene addiction, and in overcoming cellular senescence, which suggests calcineurin-NFAT signaling as a potential target in preventing PCa.

BackgroundThe microbiota from different body sites are dominated by different major groups of microbes, but the variations within a body site such as the mouth can be more subtle. Accurate predictive models can serve as useful tools for distinguishing sub-sites and understanding key organisms and their roles and can highlight deviations from expected distributions of microbes. Good classification depends on choosing the right combination of classifier, feature representation, and learning model. Machine-learning procedures have been used in the past for supervised classification, but increased attention to feature representation and selection may produce better models and predictions.ResultsWe focused our attention on the classification of nine oral sites and dental plaque in particular, using data collected from the Human Microbiome Project. A key focus of our representations was the use of phylogenetic information, both as the basis for custom kernels and as a way to represent sets of microbes to the classifier. We also used the PICRUSt software, which draws on phylogenetic relationships to predict molecular functions and to generate additional features for the classifier. Custom kernels based on the UniFrac measure of community dissimilarity did not improve performance. However, feature representation was vital to classification accuracy, with microbial clade and function representations providing useful information to the classifier; combining the two types of features did not yield increased prediction accuracy. Many of the best-performing clades and functions had clear associations with oral microflora.ConclusionsThe classification of oral microbiota remains a challenging problem; our best accuracy on the plaque dataset was approximately 81 %. Perfect accuracy may be unattainable due to the close proximity of the sites and intra-individual variation. However, further exploration of the space of both classifiers and feature representations is likely to increase the accuracy of predictive models.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-015-0114-5) contains supplementary material, which is available to authorized users.

BackgroundAlthough large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers.MethodsTo overcome such challenges, we utilized the Database of Evidence for Precision Oncology (DEPO) to link druggability to genomic, transcriptomic, and proteomic biomarkers. Using a pan-cancer cohort of 6570 tumors, we identified tumors with potentially druggable biomarkers consisting of drug-associated mutations, mRNA expression outliers, and protein/phosphoprotein expression outliers identified by DEPO.ResultsWithin the pan-cancer cohort of 6570 tumors, we found that 3% are druggable based on FDA-approved drug-mutation interactions in specific cancer types. However, mRNA/phosphoprotein/protein expression outliers and drug repurposing across cancer types suggest potential druggability in up to 16% of tumors. The percentage of potential drug-associated tumors can increase to 48% if we consider preclinical evidence. Further, our analyses showed co-occurring potentially druggable multi-omics alterations in 32% of tumors, indicating a role for individualized combinational therapy, with evidence supporting mTOR/PI3K/ESR1 co-inhibition and BRAF/AKT co-inhibition in 1.6 and 0.8% of tumors, respectively. We experimentally validated a subset of putative druggable mutations in BRAF identified by a protein structure-based computational tool. Finally, analysis of a large-scale drug screening dataset lent further evidence supporting repurposing of drugs across cancer types and the use of expression outliers for inferring druggability.ConclusionsOur results suggest that an integrated analysis platform can nominate multi-omics alterations as biomarkers of druggability and aid ongoing efforts to bring precision oncology to patients.Electronic supplementary materialThe online version of this article (10.1186/s13073-018-0564-z) contains supplementary material, which is available to authorized users.

No abstract