A mutation-level covariate model for mutational signatures.

Mutational processes and their exposures in particular genomes are key to our understanding of how these genomes are shaped. However, current analyses assume that these processes are uniformly active across the genome without accounting for potential covariates such as strand or genomic region that could impact such activities. Here we suggest the first mutation-covariate models that explicitly model the effect of different covariates on the exposures of mutational processes. We apply these models to test the impact of replication strand on these processes and compare them to strand-oblivious models across a range of data sets. Our models capture replication strand specificity, point to signatures affected by it, and score better on held-out data compared to standard models that do not account for mutation-level covariate information.

A data-driven approach for constructing mutation categories for mutational signature analysis.

Mutational processes shape the genomes of cancer patients and their understanding has important applications in diagnosis and treatment. Current modeling of mutational processes by identifying their characteristic signatures views each base substitution in a limited context of a single flanking base on each side. This context definition gives rise to 96 categories of mutations that have become the standard in the field, even though wider contexts have been shown to be informative in specific cases. Here we propose a data-driven approach for constructing a mutation categorization for mutational signature analysis. Our approach is based on the assumption that tumor cells that are exposed to similar mutational processes, show similar expression levels of DNA damage repair genes that are involved in these processes. We attempt to find a categorization that maximizes the agreement between mutation and gene expression data, and show that it outperforms the standard categorization over multiple quality measures. Moreover, we show that the categorization we identify generalizes to unseen data from different cancer types, suggesting that mutation context patterns extend beyond the immediate flanking bases.

Matrix (factorization) reloaded: flexible methods for imputing genetic interactions with cross-species and side information.

MOTIVATION: Mapping genetic interactions (GIs) can reveal important insights into cellular function and has potential translational applications. There has been great progress in developing high-throughput experimental systems for measuring GIs (e.g. with double knockouts) as well as in defining computational methods for inferring (imputing) unknown interactions. However, existing computational methods for imputation have largely been developed for and applied in baker's yeast, even as experimental systems have begun to allow measurements in other contexts. Importantly, existing methods face a number of limitations in requiring specific side information and with respect to computational cost. Further, few have addressed how GIs can be imputed when data are scarce. RESULTS: In this article, we address these limitations by presenting a new imputation framework, called Extensible Matrix Factorization (EMF). EMF is a framework of composable models that flexibly exploit cross-species information in the form of GI data across multiple species, and arbitrary side information in the form of kernels (e.g. from protein-protein interaction networks). We perform a rigorous set of experiments on these models in matched GI datasets from baker's and fission yeast. These include the first such experiments on genome-scale GI datasets in multiple species in the same study. We find that EMF models that exploit side and cross-species information improve imputation, especially in data-scarce settings. Further, we show that EMF outperforms the state-of-the-art deep learning method, even when using strictly less data, and incurs orders of magnitude less computational cost. AVAILABILITY: Implementations of models and experiments are available at: https://github.com/lrgr/EMF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Functional protein representations from biological networks enable diverse cross-species inference.

Transferring knowledge between species is key for many biological applications, but is complicated by divergent and convergent evolution. Many current approaches for this problem leverage sequence and interaction network data to transfer knowledge across species, exemplified by network alignment methods. While these techniques do well, they are limited in scope, creating metrics to address one specific problem or task. We take a different approach by creating an environment where multiple knowledge transfer tasks can be performed using the same protein representations. Specifically, our kernel-based method, MUNK, integrates sequence and network structure to create functional protein representations, embedding proteins from different species in the same vector space. First we show proteins in different species that are close in MUNK-space are functionally similar. Next, we use these representations to share knowledge of synthetic lethal interactions between species. Importantly, we find that the results using MUNK-representations are at least as accurate as existing algorithms for these tasks. Finally, we generalize the notion of a phenolog ('orthologous phenotype') to use functionally similar proteins (i.e. those with similar representations). We demonstrate the utility of this broadened notion by using it to identify known phenologs and novel non-obvious ones supported by current research.

Criticality in tumor evolution and clinical outcome.

How mutation and selection determine the fitness landscape of tumors and hence clinical outcome is an open fundamental question in cancer biology, crucial for the assessment of therapeutic strategies and resistance to treatment. Here we explore the mutation-selection phase diagram of 6,721 tumors representing 23 cancer types by quantifying the overall somatic point mutation load (ML) and selection (dN/dS) in the entire proteome of each tumor. We show that ML strongly correlates with patient survival, revealing two opposing regimes around a critical point. In low-ML cancers, a high number of mutations indicates poor prognosis, whereas high-ML cancers show the opposite trend, presumably due to mutational meltdown. Although the majority of cancers evolve near neutrality, deviations are observed at extreme MLs. Melanoma, with the highest ML, evolves under purifying selection, whereas in low-ML cancers, signatures of positive selection are observed, demonstrating how selection affects tumor fitness. Moreover, different cancers occupy specific positions on the ML-dN/dS plane, revealing a diversity of evolutionary trajectories. These results support and expand the theory of tumor evolution and its nonlinear effects on survival.

Modeling clinical and molecular covariates of mutational process activity in cancer.

MOTIVATION: Somatic mutations result from processes related to DNA replication or environmental/lifestyle exposures. Knowing the activity of mutational processes in a tumor can inform personalized therapies, early detection, and understanding of tumorigenesis. Computational methods have revealed 30 validated signatures of mutational processes active in human cancers, where each signature is a pattern of single base substitutions. However, half of these signatures have no known etiology, and some similar signatures have distinct etiologies, making patterns of mutation signature activity hard to interpret. Existing mutation signature detection methods do not consider tumor-level clinical/demographic (e.g. smoking history) or molecular features (e.g. inactivations to DNA damage repair genes). RESULTS: To begin to address these challenges, we present the Tumor Covariate Signature Model (TCSM), the first method to directly model the effect of observed tumor-level covariates on mutation signatures. To this end, our model uses methods from Bayesian topic modeling to change the prior distribution on signature exposure conditioned on a tumor's observed covariates. We also introduce methods for imputing covariates in held-out data and for evaluating the statistical significance of signature-covariate associations. On simulated and real data, we find that TCSM outperforms both non-negative matrix factorization and topic modeling-based approaches, particularly in recovering the ground truth exposure to similar signatures. We then use TCSM to discover five mutation signatures in breast cancer and predict homologous recombination repair deficiency in held-out tumors. We also discover four signatures in a combined melanoma and lung cancer cohort-using cancer type as a covariate-and provide statistical evidence to support earlier claims that three lung cancers from The Cancer Genome Atlas are misdiagnosed metastatic melanomas. AVAILABILITY AND IMPLEMENTATION: TCSM is implemented in Python 3 and available at https://github.com/lrgr/tcsm, along with a data workflow for reproducing the experiments in the paper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma.

BACKGROUND: Papillary renal-cell carcinoma, which accounts for 15 to 20% of renal-cell carcinomas, is a heterogeneous disease that consists of various types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. METHODS: We performed comprehensive molecular characterization of 161 primary papillary renal-cell carcinomas, using whole-exome sequencing, copy-number analysis, messenger RNA and microRNA sequencing, DNA-methylation analysis, and proteomic analysis. RESULTS: Type 1 and type 2 papillary renal-cell carcinomas were shown to be different types of renal cancer characterized by specific genetic alterations, with type 2 further classified into three individual subgroups on the basis of molecular differences associated with patient survival. Type 1 tumors were associated with MET alterations, whereas type 2 tumors were characterized by CDKN2A silencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2-antioxidant response element (ARE) pathway. A CpG island methylator phenotype (CIMP) was observed in a distinct subgroup of type 2 papillary renal-cell carcinomas that was characterized by poor survival and mutation of the gene encoding fumarate hydratase (FH). CONCLUSIONS: Type 1 and type 2 papillary renal-cell carcinomas were shown to be clinically and biologically distinct. Alterations in the MET pathway were associated with type 1, and activation of the NRF2-ARE pathway was associated with type 2; CDKN2A loss and CIMP in type 2 conveyed a poor prognosis. Furthermore, type 2 papillary renal-cell carcinoma consisted of at least three subtypes based on molecular and phenotypic features. (Funded by the National Institutes of Health.).

Hierarchical HotNet: identifying hierarchies of altered subnetworks.

Motivation: The analysis of high-dimensional 'omics data is often informed by the use of biological interaction networks. For example, protein-protein interaction networks have been used to analyze gene expression data, to prioritize germline variants, and to identify somatic driver mutations in cancer. In these and other applications, the underlying computational problem is to identify altered subnetworks containing genes that are both highly altered in an 'omics dataset and are topologically close (e.g. connected) on an interaction network. Results: We introduce Hierarchical HotNet, an algorithm that finds a hierarchy of altered subnetworks. Hierarchical HotNet assesses the statistical significance of the resulting subnetworks over a range of biological scales and explicitly controls for ascertainment bias in the network. We evaluate the performance of Hierarchical HotNet and several other algorithms that identify altered subnetworks on the problem of predicting cancer genes and significantly mutated subnetworks. On somatic mutation data from The Cancer Genome Atlas, Hierarchical HotNet outperforms other methods and identifies significantly mutated subnetworks containing both well-known cancer genes and candidate cancer genes that are rarely mutated in the cohort. Hierarchical HotNet is a robust algorithm for identifying altered subnetworks across different 'omics datasets. Availability and implementation: http://github.com/raphael-group/hierarchical-hotnet. Supplementary information: Supplementary material are available at Bioinformatics online.

Mutational landscape and significance across 12 major cancer types.

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known (for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase, Wnt/ß-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.

A weighted exact test for mutually exclusive mutations in cancer.

MOTIVATION: The somatic mutations in the pathways that drive cancer development tend to be mutually exclusive across tumors, providing a signal for distinguishing driver mutations from a larger number of random passenger mutations. This mutual exclusivity signal can be confounded by high and highly variable mutation rates across a cohort of samples. Current statistical tests for exclusivity that incorporate both per-gene and per-sample mutational frequencies are computationally expensive and have limited precision. RESULTS: We formulate a weighted exact test for assessing the significance of mutual exclusivity in an arbitrary number of mutational events. Our test conditions on the number of samples with a mutation as well as per-event, per-sample mutation probabilities. We provide a recursive formula to compute P-values for the weighted test exactly as well as a highly accurate and efficient saddlepoint approximation of the test. We use our test to approximate a commonly used permutation test for exclusivity that conditions on per-event, per-sample mutation frequencies. However, our test is more efficient and it recovers more significant results than the permutation test. We use our Weighted Exclusivity Test (WExT) software to analyze hundreds of colorectal and endometrial samples from The Cancer Genome Atlas, which are two cancer types that often have extremely high mutation rates. On both cancer types, the weighted test identifies sets of mutually exclusive mutations in cancer genes with fewer false positives than earlier approaches. AVAILABILITY AND IMPLEMENTATION: See http://compbio.cs.brown.edu/projects/wext for software. CONTACT: braphael@cs.brown.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Simultaneous identification of multiple driver pathways in cancer.

Distinguishing the somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a key challenge in cancer genomics. Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple genes. This heterogeneity complicates the identification of driver mutations by their recurrence across samples, as different combinations of mutations in driver pathways are observed in different samples. We introduce the Multi-Dendrix algorithm for the simultaneous identification of multiple driver pathways de novo in somatic mutation data from a cohort of cancer samples. The algorithm relies on two combinatorial properties of mutations in a driver pathway: high coverage and mutual exclusivity. We derive an integer linear program that finds set of mutations exhibiting these properties. We apply Multi-Dendrix to somatic mutations from glioblastoma, breast cancer, and lung cancer samples. Multi-Dendrix identifies sets of mutations in genes that overlap with known pathways - including Rb, p53, PI(3)K, and cell cycle pathways - and also novel sets of mutually exclusive mutations, including mutations in several transcription factors or other genes involved in transcriptional regulation. These sets are discovered directly from mutation data with no prior knowledge of pathways or gene interactions. We show that Multi-Dendrix outperforms other algorithms for identifying combinations of mutations and is also orders of magnitude faster on genome-scale data. Software available at: http://compbio.cs.brown.edu/software.

Identification of intracellular bacteria from multiple single-cell RNA-seq platforms using CSI-Microbes.

The study of the tumor microbiome has been garnering increased attention. We developed a computational pipeline (CSI-Microbes) for identifying microbial reads from single-cell RNA sequencing (scRNA-seq) data and for analyzing differential abundance of taxa. Using a series of controlled experiments and analyses, we performed the first systematic evaluation of the efficacy of recovering microbial unique molecular identifiers by multiple scRNA-seq technologies, which identified the newer 10x chemistries (3' v3 and 5') as the best suited approach. We analyzed patient esophageal and colorectal carcinomas and found that reads from distinct genera tend to co-occur in the same host cells, testifying to possible intracellular polymicrobial interactions. Microbial reads are disproportionately abundant within myeloid cells that up-regulate proinflammatory cytokines like IL1Β and CXCL8, while infected tumor cells up-regulate antigen processing and presentation pathways. These results show that myeloid cells with bacteria engulfed are a major source of bacterial RNA within the tumor microenvironment (TME) and may inflame the TME and influence immunotherapy response.

Genecentric: a package to uncover graph-theoretic structure in high-throughput epistasis data.

BACKGROUND: New technology has resulted in high-throughput screens for pairwise genetic interactions in yeast and other model organisms. For each pair in a collection of non-essential genes, an epistasis score is obtained, representing how much sicker (or healthier) the double-knockout organism will be compared to what would be expected from the sickness of the component single knockouts. Recent algorithmic work has identified graph-theoretic patterns in this data that can indicate functional modules, and even sets of genes that may occur in compensatory pathways, such as a BPM-type schema first introduced by Kelley and Ideker. However, to date, any algorithms for finding such patterns in the data were implemented internally, with no software being made publically available. RESULTS: Genecentric is a new package that implements a parallelized version of the Leiserson et al. algorithm (J Comput Biol 18:1399-1409, 2011) for generating generalized BPMs from high-throughput genetic interaction data. Given a matrix of weighted epistasis values for a set of double knock-outs, Genecentric returns a list of generalized BPMs that may represent compensatory pathways. Genecentric also has an extension, GenecentricGO, to query FuncAssociate (Bioinformatics 25:3043-3044, 2009) to retrieve GO enrichment statistics on generated BPMs. Python is the only dependency, and our web site provides working examples and documentation. CONCLUSION: We find that Genecentric can be used to find coherent functional and perhaps compensatory gene sets from high throughput genetic interaction data. Genecentric is made freely available for download under the GPLv2 from http://bcb.cs.tufts.edu/genecentric.

A Biterm Topic Model for Sparse Mutation Data.

Mutational signature analysis promises to reveal the processes that shape cancer genomes for applications in diagnosis and therapy. However, most current methods are geared toward rich mutation data that has been extracted from whole-genome or whole-exome sequencing. Methods that process sparse mutation data typically found in practice are only in the earliest stages of development. In particular, we previously developed the Mix model that clusters samples to handle data sparsity. However, the Mix model had two hyper-parameters, including the number of signatures and the number of clusters, that were very costly to learn. Therefore, we devised a new method that was several orders-of-magnitude more efficient for handling sparse data, was based on mutation co-occurrences, and imitated word co-occurrence analyses of Twitter texts. We showed that the model produced significantly improved hyper-parameter estimates that led to higher likelihoods of discovering overlooked data and had better correspondence with known signatures.

ScalpelSig Designs Targeted Genomic Panels from Data to Detect Activity of Mutational Signatures.

Over the past decade, a promising line of cancer research has utilized machine learning to mine statistical patterns of mutations in cancer genomes for information. Recent work shows that these statistical patterns, commonly referred to as "mutational signatures," have diverse therapeutic potential as biomarkers for cancer therapies. However, translating this potential into reality is hindered by limited access to sequencing in the clinic. Almost all methods for mutational signature analysis (MSA) rely on whole genome or whole exome sequencing data, while sequencing in the clinic is typically limited to small gene panels. To improve clinical access to MSA, we considered the question of whether targeted panels could be designed for the purpose of mutational signature detection. Here we present ScalpelSig, to our knowledge the first algorithm that automatically designs genomic panels optimized for detection of a given mutational signature. The algorithm learns from data to identify genome regions that are particularly indicative of signature activity. Using a cohort of breast cancer genomes as training data, we show that ScalpelSig panels substantially improve accuracy of signature detection compared to baselines. We find that some ScalpelSig panels even approach the performance of whole exome sequencing, which observes over 10 × as much genomic material. We test our algorithm under a variety of conditions, showing that its performance generalizes to another dataset of breast cancers, to smaller panel sizes, and to lesser amounts of training data.

SuperDendrix algorithm integrates genetic dependencies and genomic alterations across pathways and cancer types.

Recent genome-wide CRISPR-Cas9 loss-of-function screens have identified genetic dependencies across many cancer cell lines. Associations between these dependencies and genomic alterations in the same cell lines reveal phenomena such as oncogene addiction and synthetic lethality. However, comprehensive identification of such associations is complicated by complex interactions between genes across genetically heterogeneous cancer types. We introduce and apply the algorithm SuperDendrix to CRISPR-Cas9 loss-of-function screens from 769 cancer cell lines, to identify differential dependencies across cell lines and to find associations between differential dependencies and combinations of genomic alterations and cell-type-specific markers. These associations respect the position and type of interactions within pathways: for example, we observe increased dependencies on downstream activators of pathways, such as NFE2L2, and decreased dependencies on upstream activators of pathways, such as CDK6. SuperDendrix also reveals dozens of dependencies on lineage-specific transcription factors, identifies cancer-type-specific correlations between dependencies, and enables annotation of individual mutated residues.

A mixture model for signature discovery from sparse mutation data.

Mutational signatures are key to understanding the processes that shape cancer genomes, yet their analysis requires relatively rich whole-genome or whole-exome mutation data. Recently, orders-of-magnitude sparser gene-panel-sequencing data have become increasingly available in the clinic. To deal with such sparse data, we suggest a novel mixture model, Mix. In application to simulated and real gene-panel sequences, Mix is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature. We further demonstrate its utility in several clinical settings, successfully predicting therapy benefit and patient groupings from MSK-IMPACT pan-cancer data. Availability: https://github.com/itaysason/Mix-MMM .

Mutational Signatures: From Methods to Mechanisms.

Mutations are the driving force of evolution, yet they underlie many diseases, in particular, cancer. They are thought to arise from a combination of stochastic errors in DNA processing, naturally occurring DNA damage (e.g., the spontaneous deamination of methylated CpG sites), replication errors, and dysregulation of DNA repair mechanisms. High-throughput sequencing has made it possible to generate large datasets to study mutational processes in health and disease. Since the emergence of the first mutational process studies in 2012, this field is gaining increasing attention and has already accumulated a host of computational approaches and biomedical applications.

A systematic genome-wide mapping of oncogenic mutation selection during CRISPR-Cas9 genome editing.

Recent studies have reported that genome editing by CRISPR-Cas9 induces a DNA damage response mediated by p53 in primary cells hampering their growth. This could lead to a selection of cells with pre-existing p53 mutations. In this study, employing an integrated computational and experimental framework, we systematically investigated the possibility of selection of additional cancer driver mutations during CRISPR-Cas9 gene editing. We first confirm the previous findings of the selection for pre-existing p53 mutations by CRISPR-Cas9. We next demonstrate that similar to p53, wildtype KRAS may also hamper the growth of Cas9-edited cells, potentially conferring a selective advantage to pre-existing KRAS-mutant cells. These selective effects are widespread, extending across cell-types and methods of CRISPR-Cas9 delivery and the strength of selection depends on the sgRNA sequence and the gene being edited. The selection for pre-existing p53 or KRAS mutations may confound CRISPR-Cas9 screens in cancer cells and more importantly, calls for monitoring patients undergoing CRISPR-Cas9-based editing for clinical therapeutics for pre-existing p53 and KRAS mutations.