Expressed Barcoding Enables High-Resolution Tracking of the Evolution of Drug Tolerance.

For a majority of patients with non-small cell lung cancer with EGFR mutations, treatment with EGFR inhibitors (EGFRi) induces a clinical response. Despite this initial reduction in tumor size, residual disease persists that leads to disease relapse. Elucidating the preexisting biological differences between sensitive cells and surviving drug-tolerant persister cells and deciphering how drug-tolerant cells evolve in response to treatment could help identify strategies to improve the efficacy of EGFRi. In this study, we tracked the origins and clonal evolution of drug-tolerant cells at a high resolution by using an expressed barcoding system coupled with single-cell RNA sequencing. This platform enabled longitudinal profiling of gene expression and drug sensitivity in response to EGFRi across a large number of clones. Drug-tolerant cells had higher expression of key survival pathways such as YAP and EMT at baseline and could also differentially adapt their gene expression following EGFRi treatment compared with sensitive cells. In addition, drug combinations targeting common downstream components (MAPK) or orthogonal factors (chemotherapy) showed greater efficacy than EGFRi alone, which is attributable to broader targeting of the heterogeneous EGFRi-tolerance mechanisms present in tumors. Overall, this approach facilitates thorough examination of clonal evolution in response to therapy that could inform the development of improved diagnostic approaches and treatment strategies for targeting drug-tolerant cells. SIGNIFICANCE: The evolution and heterogeneity of EGFR inhibitor tolerance are identified in a large number of clones at enhanced cellular and temporal resolution using an expressed barcode technology coupled with single-cell RNA sequencing.

A first-in-human phase 1/2 study of FGF401 and combination of FGF401 with spartalizumab in patients with hepatocellular carcinoma or biomarker-selected solid tumors.

BACKGROUND: Deregulation of FGF19-FGFR4 signaling is found in several cancers, including hepatocellular carcinoma (HCC), nominating it for therapeutic targeting. FGF401 is a potent, selective FGFR4 inhibitor with antitumor activity in preclinical models. This study was designed to determine the recommended phase 2 dose (RP2D), characterize PK/PD, and evaluate the safety and efficacy of FGF401 alone and combined with the anti-PD-1 antibody, spartalizumab. METHODS: Patients with HCC or other FGFR4/KLB expressing tumors were enrolled. Dose-escalation was guided by a Bayesian model. Phase 2 dose-expansion enrolled patients with HCC from Asian countries (group1), non-Asian countries (group2), and patients with other solid tumors expressing FGFR4 and KLB (group3). FGF401 and spartalizumab combination was evaluated in patients with HCC. RESULTS: Seventy-four patients were treated in the phase I with single-agent FGF401 at 50 to 150 mg. FGF401 displayed favorable PK characteristics and no food effect when dosed with low-fat meals. The RP2D was established as 120 mg qd. Six of 70 patients experienced grade 3 dose-limiting toxicities: increase in transaminases (n = 4) or blood bilirubin (n = 2). In phase 2, 30 patients in group 1, 36 in group 2, and 20 in group 3 received FGF401. In total, 8 patients experienced objective responses (1 CR, 7 PR; 4 each in phase I and phase II, respectively). Frequent adverse events (AEs) were diarrhea (73.8%), increased AST (47.5%), and ALT (43.8%). Increase in levels of C4, total bile acid, and circulating FGF19, confirmed effective FGFR4 inhibition. Twelve patients received FGF401 plus spartalizumab. RP2D was established as FGF401 120 mg qd and spartalizumab 300 mg Q3W; 2 patients reported PR. CONCLUSIONS: At biologically active doses, FGF401 alone or combined with spartalizumab was safe in patients with FGFR4/KLB-positive tumors including HCC. Preliminary clinical efficacy was observed. Further clinical evaluation of FGF401 using a refined biomarker strategy is warranted. TRIAL REGISTRATION: NCT02325739 .

TGFß-blockade uncovers stromal plasticity in tumors by revealing the existence of a subset of interferon-licensed fibroblasts.

Despite the increasing interest in targeting stromal elements of the tumor microenvironment, we still face tremendous challenges in developing adequate therapeutics to modify the tumor stromal landscape. A major obstacle to this is our poor understanding of the phenotypic and functional heterogeneity of stromal cells in tumors. Herein, we perform an unbiased interrogation of tumor mesenchymal cells, delineating the co-existence of distinct subsets of cancer-associated fibroblasts (CAFs) in the microenvironment of murine carcinomas, each endowed with unique phenotypic features and functions. Furthermore, our study shows that neutralization of TGFß in vivo leads to remodeling of CAF dynamics, greatly reducing the frequency and activity of the myofibroblast subset, while promoting the formation of a fibroblast population characterized by strong response to interferon and heightened immunomodulatory properties. These changes correlate with the development of productive anti-tumor immunity and greater efficacy of PD1 immunotherapy. Along with providing the scientific rationale for the evaluation of TGFß and PD1 co-blockade in the clinical setting, this study also supports the concept of plasticity of the stromal cell landscape in tumors, laying the foundation for future investigations aimed at defining pathways and molecules to program CAF composition for cancer therapy.

Multiomic Analysis of Subtype Evolution and Heterogeneity in High-Grade Serous Ovarian Carcinoma.

Multiple studies have identified transcriptome subtypes of high-grade serous ovarian carcinoma (HGSOC), but their interpretation and translation are complicated by tumor evolution and polyclonality accompanied by extensive accumulation of somatic aberrations, varying cell type admixtures, and different tissues of origin. In this study, we examined the chronology of HGSOC subtype evolution in the context of these factors using a novel integrative analysis of absolute copy-number analysis and gene expression in The Cancer Genome Atlas complemented by single-cell analysis of six independent tumors. Tumor purity, ploidy, and subclonality were reliably inferred from different genomic platforms, and these characteristics displayed marked differences between subtypes. Genomic lesions associated with HGSOC subtypes tended to be subclonal, implying subtype divergence at later stages of tumor evolution. Subclonality of recurrent HGSOC alterations was evident for proliferative tumors, characterized by extreme genomic instability, absence of immune infiltration, and greater patient age. In contrast, differentiated tumors were characterized by largely intact genome integrity, high immune infiltration, and younger patient age. Single-cell sequencing of 42,000 tumor cells revealed widespread heterogeneity in tumor cell type composition that drove bulk subtypes but demonstrated a lack of intrinsic subtypes among tumor epithelial cells. Our findings prompt the dismissal of discrete transcriptome subtypes for HGSOC and replacement by a more realistic model of continuous tumor development that includes mixtures of subclones, accumulation of somatic aberrations, infiltration of immune and stromal cells in proportions correlated with tumor stage and tissue of origin, and evolution between properties previously associated with discrete subtypes. SIGNIFICANCE: This study infers whether transcriptome-based groupings of tumors differentiate early in carcinogenesis and are, therefore, appropriate targets for therapy and demonstrates that this is not the case for HGSOC.

Reliable Analysis of Clinical Tumor-Only Whole-Exome Sequencing Data.

PURPOSE: Allele-specific copy number alteration (CNA) analysis is essential to study the functional impact of single-nucleotide variants (SNVs) and the process of tumorigenesis. However, controversy over whether it can be performed with sufficient accuracy in data without matched normal profiles and a lack of open-source implementations have limited its application in clinical research and diagnosis. METHODS: We benchmark allele-specific CNA analysis performance of whole-exome sequencing (WES) data against gold standard whole-genome SNP6 microarray data and against WES data sets with matched normal samples. We provide a workflow based on the open-source PureCN R/Bioconductor package in conjunction with widely used variant-calling and copy number segmentation algorithms for allele-specific CNA analysis from WES without matched normals. This workflow further classifies SNVs by somatic status and then uses this information to infer somatic mutational signatures and tumor mutational burden (TMB). RESULTS: Application of our workflow to tumor-only WES data produces tumor purity and ploidy estimates that are highly concordant with estimates from SNP6 microarray data and matched normal WES data. The presence of cancer type-specific somatic mutational signatures was inferred with high accuracy. We also demonstrate high concordance of TMB between our tumor-only workflow and matched normal pipelines. CONCLUSION: The proposed workflow provides, to our knowledge, the only open-source option with demonstrated high accuracy for comprehensive allele-specific CNA analysis and SNV classification of tumor-only WES. An implementation of the workflow is available on the Terra Cloud platform of the Broad Institute (Cambridge, MA).

HGNChelper: identification and correction of invalid gene symbols for human and mouse.

Gene symbols are recognizable identifiers for gene names but are unstable and error-prone due to aliasing, manual entry, and unintentional conversion by spreadsheets to date format. Official gene symbol resources such as HUGO Gene Nomenclature Committee (HGNC) for human genes and the Mouse Genome Informatics project (MGI) for mouse genes provide authoritative sources of valid, aliased, and outdated symbols, but lack a programmatic interface and correction of symbols converted by spreadsheets. We present HGNChelper, an R package that identifies known aliases and outdated gene symbols based on the HGNC human and MGI mouse gene symbol databases, in addition to common mislabeling introduced by spreadsheets, and provides corrections where possible. HGNChelper identified invalid gene symbols in the most recent Molecular Signatures Database (MSigDB 7.0) and in platform annotation files of the Gene Expression Omnibus, with prevalence ranging from ~3% in recent platforms to 30-40% in the earliest platforms from 2002-03. HGNChelper is installable from CRAN.

Genomic profiling of NETs: a comprehensive analysis of the RADIANT trials.

Neuroendocrine tumors (NETs) have historically been subcategorized according to histologic features and the site of anatomic origin. Here, we characterize the genomic alterations in patients enrolled in 3 phase 3 clinical trials of NET of different anatomic origins and assessed the potential correlation with clinical outcomes. Whole-exome and targeted panel sequencing was used to characterize 225 NET samples collected in the RADIANT series of clinical trials. Genomic profiling of NET was analyzed along with nongenomic biomarker data on tumor grade and circulating chromogranin A (CgA) and neuron specific enolase (NSE) levels from these patients enrolled in clinical trials. Our results highlight recurrent large-scale chromosomal alterations as a common theme among NET. Although the specific pattern of chromosomal alterations differed between tumor subtypes, the evidence for generalized chromosomal instability (CIN) was observed across all primary sites of NET. In pancreatic NET, although the P-value was not significant, higher CIN suggests a trend towards longer survival (HR, 0.55, P=0.077); whereas in the gastrointestinal NET, lower CIN was associated with longer survival (HR, 0.44, P=0.0006). Our multivariate analyses demonstrated that when combined with other clinical data among patients with progressive advanced NETs, chromosomal level alteration adds important prognostic information. Large-scale CIN is a common feature of NET, and specific patterns of chromosomal gain and loss appeared to have independent prognostic value in NET subtypes. However, whether CIN in general has clinical significance in NET requires validation in larger patient cohort and warrants further mechanistic studies.

Author Correction: Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features.

The original version of this Article contained an error in the author affiliations. The affiliation of Kevin P. White with Tempus Labs, Inc. Chicago, IL, USA was inadvertently omitted. This has now been corrected in both the PDF and HTML versions of the Article.

Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features.

Racial/ethnic disparities in breast cancer mortality continue to widen but genomic studies rarely interrogate breast cancer in diverse populations. Through genome, exome, and RNA sequencing, we examined the molecular features of breast cancers using 194 patients from Nigeria and 1037 patients from The Cancer Genome Atlas (TCGA). Relative to Black and White cohorts in TCGA, Nigerian HR + /HER2 - tumors are characterized by increased homologous recombination deficiency signature, pervasive TP53 mutations, and greater structural variation-indicating aggressive biology. GATA3 mutations are also more frequent in Nigerians regardless of subtype. Higher proportions of APOBEC-mediated substitutions strongly associate with PIK3CA and CDH1 mutations, which are underrepresented in Nigerians and Blacks. PLK2, KDM6A, and B2M are also identified as previously unreported significantly mutated genes in breast cancer. This dataset provides novel insights into potential molecular mechanisms underlying outcome disparities and lay a foundation for deployment of precision therapeutics in underserved populations.

Genetic mechanisms of target antigen loss in CAR19 therapy of acute lymphoblastic leukemia.

We identified genetic mutations in CD19 and loss of heterozygosity at the time of CD19- relapse to chimeric antigen receptor (CAR) therapy. The mutations are present in the vast majority of resistant tumor cells and are predicted to lead to a truncated protein with a nonfunctional or absent transmembrane domain and consequently to a loss of surface antigen. This irreversible loss of CD19 advocates for an alternative targeting or combination CAR approach.

Tracking the Evolution of Resistance to ALK Tyrosine Kinase Inhibitors through Longitudinal Analysis of Circulating Tumor DNA.

PURPOSE: ALK rearrangements predict for sensitivity to ALK tyrosine kinase inhibitors (TKIs). However, responses to ALK TKIs are generally short-lived. Serial molecular analysis is an informative strategy for identifying genetic mediators of resistance. Although multiple studies support the clinical benefits of repeat tissue sampling, the clinical utility of longitudinal circulating tumor DNA analysis has not been established in ALK-positive lung cancer. METHODS: Using a 566-gene hybrid-capture next-generation sequencing (NGS) assay, we performed longitudinal analysis of plasma specimens from 22 ALK-positive patients with acquired resistance to ALK TKIs to track the evolution of resistance during treatment. To determine tissue-plasma concordance, we compared plasma findings to results of repeat biopsies. RESULTS: At progression, we detected an ALK fusion in plasma from 19 (86%) of 22 patients, and identified ALK resistance mutations in plasma specimens from 11 (50%) patients. There was 100% agreement between tissue- and plasma-detected ALK fusions. Among 16 cases where contemporaneous plasma and tissue specimens were available, we observed 100% concordance between ALK mutation calls. ALK mutations emerged and disappeared during treatment with sequential ALK TKIs, suggesting that plasma mutation profiles were dependent on the specific TKI administered. ALK G1202R, the most frequent plasma mutation detected after progression on a second-generation TKI, was consistently suppressed during treatment with lorlatinib. CONCLUSIONS: Plasma genotyping by NGS is an effective method for detecting ALK fusions and ALK mutations in patients progressing on ALK TKIs. The correlation between plasma ALK mutations and response to distinct ALK TKIs highlights the potential for plasma analysis to guide selection of ALK-directed therapies.

Software for the Integration of Multiomics Experiments in Bioconductor.

Multiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple 'omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. Cancer Res; 77(21); e39-42. ©2017 AACR.

Distance in cancer gene expression from stem cells predicts patient survival.

The degree of histologic cellular differentiation of a cancer has been associated with prognosis but is subjectively assessed. We hypothesized that information about tumor differentiation of individual cancers could be derived objectively from cancer gene expression data, and would allow creation of a cancer phylogenetic framework that would correlate with clinical, histologic and molecular characteristics of the cancers, as well as predict prognosis. Here we utilized mRNA expression data from 4,413 patient samples with 7 diverse cancer histologies to explore the utility of ordering samples by their distance in gene expression from that of stem cells. A differentiation baseline was obtained by including expression data of human embryonic stem cells (hESC) and human mesenchymal stem cells (hMSC) for solid tumors, and of hESC and CD34+ cells for liquid tumors. We found that the correlation distance (the degree of similarity) between the gene expression profile of a tumor sample and that of stem cells orients cancers in a clinically coherent fashion. For all histologies analyzed (including carcinomas, sarcomas, and hematologic malignancies), patients with cancers with gene expression patterns most similar to that of stem cells had poorer overall survival. We also found that the genes in all undifferentiated cancers of diverse histologies that were most differentially expressed were associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes. Thus, a stem cell-oriented phylogeny of cancers allows for the derivation of a novel cancer gene expression signature found in all undifferentiated forms of diverse cancer histologies, that is competitive in predicting overall survival in cancer patients compared to previously published prediction models, and is coherent in that gene expression was associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes associated with regulation of the multicellular state.

Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells.

Conventional DNA bisulfite sequencing has been extended to single cell level, but the coverage consistency is insufficient for parallel comparison. Here we report a novel method for genome-wide CpG island (CGI) methylation sequencing for single cells (scCGI-seq), combining methylation-sensitive restriction enzyme digestion and multiple displacement amplification for selective detection of methylated CGIs. We applied this method to analyzing single cells from two types of hematopoietic cells, K562 and GM12878 and small populations of fibroblasts and induced pluripotent stem cells. The method detected 21 798 CGIs (76% of all CGIs) per cell, and the number of CGIs consistently detected from all 16 profiled single cells was 20 864 (72.7%), with 12 961 promoters covered. This coverage represents a substantial improvement over results obtained using single cell reduced representation bisulfite sequencing, with a 66-fold increase in the fraction of consistently profiled CGIs across individual cells. Single cells of the same type were more similar to each other than to other types, but also displayed epigenetic heterogeneity. The method was further validated by comparing the CpG methylation pattern, methylation profile of CGIs/promoters and repeat regions and 41 classes of known regulatory markers to the ENCODE data. Although not every minor methylation differences between cells are detectable, scCGI-seq provides a solid tool for unsupervised stratification of a heterogeneous cell population.

PureCN: copy number calling and SNV classification using targeted short read sequencing.

BACKGROUND: Matched sequencing of both tumor and normal tissue is routinely used to classify variants of uncertain significance (VUS) into somatic vs. germline. However, assays used in molecular diagnostics focus on known somatic alterations in cancer genes and often only sequence tumors. Therefore, an algorithm that reliably classifies variants would be helpful for retrospective exploratory analyses. Contamination of tumor samples with normal cells results in differences in expected allelic fractions of germline and somatic variants, which can be exploited to accurately infer genotypes after adjusting for local copy number. However, existing algorithms for determining tumor purity, ploidy and copy number are not designed for unmatched short read sequencing data. RESULTS: We describe a methodology and corresponding open source software for estimating tumor purity, copy number, loss of heterozygosity (LOH), and contamination, and for classification of single nucleotide variants (SNVs) by somatic status and clonality. This R package, PureCN, is optimized for targeted short read sequencing data, integrates well with standard somatic variant detection pipelines, and has support for matched and unmatched tumor samples. Accuracy is demonstrated on simulated data and on real whole exome sequencing data. CONCLUSIONS: Our algorithm provides accurate estimates of tumor purity and ploidy, even if matched normal samples are not available. This in turn allows accurate classification of SNVs. The software is provided as open source (Artistic License 2.0) R/Bioconductor package PureCN (http://bioconductor.org/packages/PureCN/).

The Doppelgänger Effect: Hidden Duplicates in Databases of Transcriptome Profiles.

Whole-genome analysis of cancer specimens is commonplace, and investigators frequently share or re-use specimens in later studies. Duplicate expression profiles in public databases will impact re-analysis if left undetected, a so-called "doppelgänger" effect. We propose a method that should be routine practice to accurately match duplicate cancer transcriptomes when nucleotide-level sequence data are unavailable, even for samples profiled by different microarray technologies or by both microarray and RNA sequencing. We demonstrate the effectiveness of the method in databases containing dozens of datasets and thousands of ovarian, breast, bladder, and colorectal cancer microarray profiles and of matching microarray and RNA sequencing expression profiles from The Cancer Genome Atlas (TCGA). We identified probable duplicates among more than 50% of studies, originating in different continents, using different technologies, published years apart, and even within the TCGA itself. Finally, we provide the doppelgangR Bioconductor package for screening transcriptome databases for duplicates. Given the potential for unrecognized duplication to falsely inflate prediction accuracy and confidence in differential expression, doppelgänger-checking should be a part of standard procedure for combining multiple genomic datasets.

Molecular analysis of a male breast cancer patient with prolonged stable disease under mTOR/PI3K inhibitors BEZ235/everolimus.

The mTORC1 inhibitor everolimus (Afinitor/RAD001) has been approved for multiple cancer indications, including ER(+)/HER2(-) metastatic breast cancer. However, the combination of everolimus with the dual PI3K/mTOR inhibitor BEZ235 was shown to be more efficacious than either everolimus or BEZ235 alone in preclinical models. Herein, we describe a male breast cancer (MBC) patient who was diagnosed with hormone receptor-positive (HR(+))/HER2(-) stage IIIA invasive ductal carcinoma and sequentially treated with chemoradiotherapy and hormonal therapy. Upon the development of metastases, the patient began a 200 mg twice-daily BEZ235 and 2.5 mg weekly everolimus combination regimen. The patient sustained a prolonged stable disease of 18 mo while undergoing the therapy, before his tumor progressed again. Therefore, we sought to both better understand MBC and investigate the underlying molecular mechanisms of the patient's sensitivity and subsequent resistance to the BEZ235/everolimus combination therapy. Genomic and immunohistochemical analyses were performed on samples collected from the initial invasive ductal carcinoma pretreatment and a metastasis postprogression on the BEZ235/everolimus combination treatment. Both tumors were relatively quiet genomically with no overlap to recurrent MBC alterations in the literature. Markers of PI3K/mTOR pathway hyperactivation were not identified in the pretreatment sample, which complements previous reports of HR(+) female breast cancers being responsive to mTOR inhibition without this activation. The postprogression sample, however, demonstrated greater than fivefold increased estrogen receptor and pathogenesis-related protein expression, which could have constrained the PI3K/mTOR pathway inhibition by BEZ235/everolimus. Overall, these analyses have augmented the limited episteme on MBC genetics and treatment.

Molecular Alterations and Everolimus Efficacy in Human Epidermal Growth Factor Receptor 2-Overexpressing Metastatic Breast Cancers: Combined Exploratory Biomarker Analysis From BOLERO-1 and BOLERO-3.

PURPOSE: Two recent phase III trials, BOLERO-1 and BOLERO-3 (Breast Cancer Trials of Oral Everolimus), evaluated the addition of everolimus to trastuzumab and chemotherapy in human epidermal growth factor receptor 2-overexpressing advanced breast cancer. The current analysis aimed to identify biomarkers to predict the clinical efficacy of everolimus treatment. METHODS: Archival tumor samples from patients in BOLERO-1 and BOLERO-3 were analyzed using next-generation sequencing, immunohistochemistry, and Sanger sequencing. RESULTS: Biomarker data were available for 549 patients. PIK3CA activating mutations and PTEN loss were reported in 30% and 16% of BOLERO-1 samples and in 32% and 12% of BOLERO-3 samples, respectively. PI3K pathway was hyperactive (PIK3CA mutations and/or PTEN loss and/or AKT1 mutation) in 47% of BOLERO-1 and 41% of BOLERO-3 samples. In both studies, differential progression-free survival (PFS) benefits of everolimus were consistently observed in patient subgroups defined by their PI3K pathway status. When analyzing combined data sets of both studies, everolimus was associated with a decreased hazard of progression in patients with PIK3CA mutations (hazard ratio [HR], 0.67; 95% CI, 0.45 to 1.00), PTEN loss (HR, 0.54; 95% CI, 0.31 to 0.96), or hyperactive PI3K pathway (HR, 0.67; 95% CI, 0.48 to 0.93). Patients with wild-type PIK3CA (HR, 1.10; 95% CI, 0.83 to 1.46), normal PTEN (HR, 1.00; 95% CI, 0.80 to 1.26), or normal PI3K pathway activity (HR, 1.19; 95% CI, 0.87 to 1.62) did not derive PFS benefit from everolimus. CONCLUSION: This analysis, although exploratory, suggests that patients with human epidermal growth factor receptor 2-positive advanced breast cancer having tumors with PIK3CA mutations, PTEN loss, or hyperactive PI3K pathway could derive PFS benefit from everolimus.

Meta-Analysis in Gene Expression Studies.

This chapter introduces methods to synthesize experimental results from independent high-throughput genomic experiments, with a focus on adaptation of traditional methods from systematic review of clinical trials and epidemiological studies. First, it reviews methods for identifying, acquiring, and preparing individual patient data for meta-analysis. It then reviews methodology for synthesizing results across studies and assessing heterogeneity, first through outlining of methods and then through a step-by-step case study in identifying genes associated with survival in high-grade serous ovarian cancer.

An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer.

The traditional view of cancer as a genetic disease that can successfully be treated with drugs targeting mutant onco-proteins has motivated whole-genome sequencing efforts in many human cancer types. However, only a subset of mutations found within the genomic landscape of cancer is likely to provide a fitness advantage to the cell. Distinguishing such "driver" mutations from innocuous "passenger" events is critical for prioritizing the validation of candidate mutations in disease-relevant models. We design a novel statistical index, called the Hitchhiking Index, which reflects the probability that any observed candidate gene is a passenger alteration, given the frequency of alterations in a cross-sectional cancer sample set, and apply it to a mutational data set in colorectal cancer. Our methodology is based upon a population dynamics model of mutation accumulation and selection in colorectal tissue prior to cancer initiation as well as during tumorigenesis. This methodology can be used to aid in the prioritization of candidate mutations for functional validation and contributes to the process of drug discovery.