Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences.

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis.

BACKGROUND: Identifying frequently mutated regions is a key approach to discover DNA elements influencing cancer progression. However, it is challenging to identify these burdened regions due to mutation rate heterogeneity across the genome and across different individuals. Moreover, it is known that this heterogeneity partially stems from genomic confounding factors, such as replication timing and chromatin organization. The increasing availability of cancer whole genome sequences and functional genomics data from the Encyclopedia of DNA Elements (ENCODE) may help address these issues. RESULTS: We developed a negative binomial regression-based Integrative Method for mutation Burden analysiS (NIMBus). Our approach addresses the over-dispersion of mutation count statistics by (1) using a Gamma-Poisson mixture model to capture the mutation-rate heterogeneity across different individuals and (2) estimating regional background mutation rates by regressing the varying local mutation counts against genomic features extracted from ENCODE. We applied NIMBus to whole-genome cancer sequences from the PanCancer Analysis of Whole Genomes project (PCAWG) and other cohorts. It successfully identified well-known coding and noncoding drivers, such as TP53 and the TERT promoter. To further characterize the burdening of non-coding regions, we used NIMBus to screen transcription factor binding sites in promoter regions that intersect DNase I hypersensitive sites (DHSs). This analysis identified mutational hotspots that potentially disrupt gene regulatory networks in cancer. We also compare this method to other mutation burden analysis methods. CONCLUSION: NIMBus is a powerful tool to identify mutational hotspots. The NIMBus software and results are available as an online resource at github.gersteinlab.org/nimbus.

Estimation of the carrier frequency of fumarate hydratase alterations and implications for kidney cancer risk in hereditary leiomyomatosis and renal cancer.

BACKGROUND: Hereditary leiomyomatosis and renal cancer (HLRCC) is a cancer syndrome associated with a germline mutation in fumarate hydratase (FH). The syndrome is associated with cutaneous and uterine leiomyomas, and some patients develop a lethal form of kidney cancer. This study provides estimates for the FH carrier frequency and kidney cancer penetrance. METHODS: Data sets containing sequencing data for the FH gene were used: the 1000 Genomes Project (1000GP) and the Exome Aggregation Consortium (ExAC). Alterations in the FH gene were characterized on the basis of different variant risk tiers: 1) ClinVar annotated variants, 2) loss-of-function alterations, and 3) highly impactful missense alterations. The cumulative incidence of FH alterations overall and by different world populations was evaluated in 1000GP and ExAC. A lifetime penetrance of HLRCC kidney cancer risk was generated with 3 estimates of the annual incidence. RESULTS: The overall allele frequencies of tier 1 to 3 FH alterations in the ExAC and 1000GP data sets were 2.54 × 10-3 (1 in 393) and 1.20 × 10-3 (1 in 835), respectively. There were differences in the allele frequencies of FH alterations between world populations. Based on various estimates of the percentage of kidney cancers with FH alterations, the lifetime kidney cancer penetrance for carrier estimate 3 in ExAC was 1.7% to 5.8%. CONCLUSIONS: FH alterations are common and are carried by approximately 1 in 1000 individuals according to the more conservative estimates. The lifetime kidney cancer penetrance appears lower than previously estimated. Although databases are not population cohorts, they provide a useful quantitative estimate of rare variants with low penetrance.

A comprehensive catalog of predicted functional upstream open reading frames in humans.

Upstream open reading frames (uORFs) latent in mRNA transcripts are thought to modify translation of coding sequences by altering ribosome activity. Not all uORFs are thought to be active in such a process. To estimate the impact of uORFs on the regulation of translation in humans, we first circumscribed the universe of all possible uORFs based on coding gene sequence motifs and identified 1.3 million unique uORFs. To determine which of these are likely to be biologically relevant, we built a simple Bayesian classifier using 89 attributes of uORFs labeled as active in ribosome profiling experiments. This allowed us to extrapolate to a comprehensive catalog of likely functional uORFs. We validated our predictions using in vivo protein levels and ribosome occupancy from 46 individuals. This is a substantially larger catalog of functional uORFs than has previously been reported. Our ranked list of likely active uORFs allows researchers to test their hypotheses regarding the role of uORFs in health and disease. We demonstrate several examples of biological interest through the application of our catalog to somatic mutations in cancer and disease-associated germline variants in humans.

Parallel coding of first- and second-order stimulus attributes by midbrain electrosensory neurons.

Natural stimuli often have time-varying first-order (i.e., mean) and second-order (i.e., variance) attributes that each carry critical information for perception and can vary independently over orders of magnitude. Experiments have shown that sensory systems continuously adapt their responses based on changes in each of these attributes. This adaptation creates ambiguity in the neural code as multiple stimuli may elicit the same neural response. While parallel processing of first- and second-order attributes by separate neural pathways is sufficient to remove this ambiguity, the existence of such pathways and the neural circuits that mediate their emergence have not been uncovered to date. We recorded the responses of midbrain electrosensory neurons in the weakly electric fish Apteronotus leptorhynchus to stimuli with first- and second-order attributes that varied independently in time. We found three distinct groups of midbrain neurons: the first group responded to both first- and second-order attributes, the second group responded selectively to first-order attributes, and the last group responded selectively to second-order attributes. In contrast, all afferent hindbrain neurons responded to both first- and second-order attributes. Using computational analyses, we show how inputs from a heterogeneous population of ON- and OFF-type afferent neurons are combined to give rise to response selectivity to either first- or second-order stimulus attributes in midbrain neurons. Our study thus uncovers, for the first time, generic and widely applicable mechanisms by which parallel processing of first- and second-order stimulus attributes emerges in the brain.

Distinguishing Benign Renal Tumors with an Oncocytic Gene Expression (ONEX) Classifier.

Renal oncocytoma (RO) accounts for 5% of renal cancers and generally behaves as a benign tumor with favorable long-term prognosis. It is difficult to confidently distinguish between benign RO and other renal malignancies, particularly chromophobe renal cell carcinoma (chRCC). Therefore, RO is often managed aggressively with surgery. We sought to identify molecular biomarkers to distinguish RO from chRCC and other malignant renal cancer mimics. In a 44-patient discovery cohort, we identified a significant differential abundance of nine genes in RO relative to chRCC. These genes were used to train a classifier to distinguish RO from chRCC in an independent 57-patient cohort. The trained classifier was then validated in five independent cohorts comprising 89 total patients. This nine-gene classifier trained on the basis of differential gene expression showed 93% sensitivity and 98% specificity for distinguishing RO from chRCC across the pooled validation cohorts, with a c-statistic of 0.978. This tool may be a useful adjunct to other diagnostic modalities to decrease the diagnostic and management uncertainty associated with small renal masses and to enable clinicians to recommend more confidently less aggressive management for some tumors. PATIENT SUMMARY: Renal oncocytoma is generally a benign form of kidney cancer that does not necessarily require surgical removal. However, it is difficult to distinguish renal oncocytoma from other more aggressive forms of kidney cancer, so it is treated most commonly with surgery. We built a classification tool based on the RNA levels of nine genes that may help avoid these surgeries by reliably distinguishing renal oncocytoma from other forms of kidney cancer.

An integrative ENCODE resource for cancer genomics.

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

Genomic Heterogeneity and the Small Renal Mass.

Purpose: Tumor heterogeneity may represent a barrier to preoperative genomic characterization by needle biopsy in clear cell renal cell carcinoma (ccRCC). The extent of heterogeneity in small renal tumors remains unknown. Therefore, we set out to evaluate heterogeneity in resected large and small renal tumors.Experimental Design: We conducted a study from 2013 to 2016 that evaluated 47 consecutive ccRCC tumors resected during radical or partial nephrectomy. Cases were designated as small (<4 cm) and large (>7 cm) tumors. Each tumor had three regions sampled. Copy-number variation (CNV) was assessed and gene expression analysis was performed to characterize the clear-cell A and B (ccA/ccB) profile and the cell-cycle progression (CCP) score. Genomic heterogeneity between three regions was evaluated using CNV subclonal events, regional expression profiles, and correlation between gene expression.Results: Twenty-three small and 24 large tumors were analyzed. Total CNVs and subclonal CNVs events were less frequent in small tumors (P < 0.001). Significant gene expression heterogeneity was observed for both CCP scores and ccA/ccB classifications. Larger tumors had more variance in CCP scores (P = 0.026). The distribution of ccA/ccB differed between small and large tumors with mixed ccA/ccB tumors occurring more frequently in the larger tumors (P = 0.024). Analysis of five mixed tumors (with both ccA/ccB regions) demonstrated the more aggressive ccB phenotype had greater CNV events (P = 0.014).Conclusions: Small renal tumors have much less genomic complexity and fewer subclonal events. Pretreatment genomic characterization with single-needle biopsy in small tumors may be useful to assess biologic potential and may influence therapy. Clin Cancer Res; 24(17); 4137-44. ©2018 AACR.

Using ALoFT to determine the impact of putative loss-of-function variants in protein-coding genes.

Variants predicted to result in the loss of function of human genes have attracted interest because of their clinical impact and surprising prevalence in healthy individuals. Here, we present ALoFT (annotation of loss-of-function transcripts), a method to annotate and predict the disease-causing potential of loss-of-function variants. Using data from Mendelian disease-gene discovery projects, we show that ALoFT can distinguish between loss-of-function variants that are deleterious as heterozygotes and those causing disease only in the homozygous state. Investigation of variants discovered in healthy populations suggests that each individual carries at least two heterozygous premature stop alleles that could potentially lead to disease if present as homozygotes. When applied to de novo putative loss-of-function variants in autism-affected families, ALoFT distinguishes between deleterious variants in patients and benign variants in unaffected siblings. Finally, analysis of somatic variants in >6500 cancer exomes shows that putative loss-of-function variants predicted to be deleterious by ALoFT are enriched in known driver genes.Variants causing loss of function (LoF) of human genes have clinical implications. Here, the authors present a method to predict disease-causing potential of LoF variants, ALoFT (annotation of Loss-of-Function Transcripts) and show its application to interpreting LoF variants in different contexts.