An open protocol for modeling T Cell Clonotype repertoires using TCRβ CDR3 sequences

T cell receptor repertoires can be profiled using next generation sequencing (NGS) to measure and monitor adaptive dynamical changes in response to disease and other perturbations. Genomic DNA-based bulk sequencing is cost-effective but necessitates multiplex target amplification using multiple primer pairs with highly variable amplification efficiencies. Here, we utilize an equimolar primer mixture and propose a single statistical normalization step that efficiently corrects for amplification bias post sequencing. Using samples analyzed by both our open protocol and a commercial solution, we show high concordance between bulk clonality metrics. This approach is an inexpensive and open-source alternative to commercial solutions.

An open protocol for modeling T Cell Clonotype repertoires using TCRβ CDR3 sequences

T cell receptor repertoires can be profiled using next generation sequencing (NGS) to measure and monitor adaptive dynamical changes in response to disease and other perturbations. Genomic DNA-based bulk sequencing is cost-effective but necessitates multiplex target amplification using multiple primer pairs with highly variable amplification efficiencies. Here, we utilize an equimolar primer mixture and propose a single statistical normalization step that efficiently corrects for amplification bias post sequencing. Using samples analyzed by both our open protocol and a commercial solution, we show high concordance between bulk clonality metrics. This approach is an inexpensive and open-source alternative to commercial solutions.

svpluscnv: analysis and visualization of complex structural variation data

MOTIVATION

Despite widespread prevalence of somatic structural variations (SVs) across most tumor types, understanding of their molecular implications often remains poor. SVs are extremely heterogeneous in size and complexity, hindering the interpretation of their pathogenic role. Tools integrating large SV datasets across platforms are required to fully characterize the cancer's somatic landscape.

RESULTS

svpluscnv R package is a swiss army knife for the integration and interpretation of orthogonal datasets including copy number variant segmentation profiles and sequencing-based structural variant calls. The package implements analysis and visualization tools to evaluate chromosomal instability and ploidy, identify genes harboring recurrent SVs and detects complex rearrangements such as chromothripsis and chromoplexia. Further, it allows systematic identification of hot-spot shattered genomic regions, showing reproducibility across alternative detection methods and datasets.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ccbiolab/svpluscnv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A harmonized meta-knowledgebase of clinical interpretations of somatic genomic variants in cancer

Precision oncology relies on accurate discovery and interpretation of genomic variants, enabling individualized diagnosis, prognosis and therapy selection. We found that six prominent somatic cancer variant knowledgebases were highly disparate in content, structure and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. We developed a framework for harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations. We demonstrated large gains in overlap between resources across variants, diseases and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 57% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide a freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection

BACKGROUND

The phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information.

RESULTS

To facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches.

CONCLUSIONS

The synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon .

Complement C5a Fosters Squamous Carcinogenesis and Limits T Cell Response to Chemotherapy

Complement is a critical component of humoral immunity implicated in cancer development; however, its biological contributions to tumorigenesis remain poorly understood. Using the K14-HPV16 transgenic mouse model of squamous carcinogenesis, we report that urokinase (uPA)⁺ macrophages regulate C3-independent release of C5a during premalignant progression, which in turn regulates protumorigenic properties of C5aR1⁺ mast cells and macrophages, including suppression of CD8⁺ T cell cytotoxicity. Therapeutic inhibition of C5aR1 via the peptide antagonist PMX-53 improved efficacy of paclitaxel chemotherapy associated with increased presence and cytotoxic properties of CXCR3⁺ effector memory CD8⁺ T cells in carcinomas, dependent on both macrophage transcriptional programming and IFNγ. Together, these data identify C5aR1-dependent signaling as an important immunomodulatory program in neoplastic tissue tractable for combinatorial cancer immunotherapy.

Valection: design optimization for validation and verification studies

Background

Platform-specific error profiles necessitate confirmatory studies where predictions made on data generated using one technology are additionally verified by processing the same samples on an orthogonal technology. However, verifying all predictions can be costly and redundant, and testing a subset of findings is often used to estimate the true error profile.

Results

To determine how to create subsets of predictions for validation that maximize accuracy of global error profile inference, we developed Valection, a software program that implements multiple strategies for the selection of verification candidates. We evaluated these selection strategies on one simulated and two experimental datasets.

Conclusions

Valection is implemented in multiple programming languages, available at: http://labs.oicr.on.ca/boutros-lab/software/valection

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2391-z) contains supplementary material, which is available to authorized users.

Functional Precision Medicine Identifies Novel Druggable Targets and Therapeutic Options in Head and Neck Cancer

Purpose: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, with high mortality and a lack of targeted therapies. To identify and prioritize druggable targets, we performed genome analysis together with genome-scale siRNA and oncology drug profiling using low-passage tumor cells derived from a patient with treatment-resistant HPV-negative HNSCC.Experimental Design: A tumor cell culture was established and subjected to whole-exome sequencing, RNA sequencing, comparative genome hybridization, and high-throughput phenotyping with a siRNA library covering the druggable genome and an oncology drug library. Secondary screens of candidate target genes were performed on the primary tumor cells and two nontumorigenic keratinocyte cell cultures for validation and to assess cancer specificity. siRNA screens of the kinome on two isogenic pairs of p53-mutated HNSCC cell lines were used to determine generalizability. Clinical utility was addressed by performing drug screens on two additional HNSCC cell cultures derived from patients enrolled in a clinical trial.Results: Many of the identified copy number aberrations and somatic mutations in the primary tumor were typical of HPV(-) HNSCC, but none pointed to obvious therapeutic choices. In contrast, siRNA profiling identified 391 candidate target genes, 35 of which were preferentially lethal to cancer cells, most of which were not genomically altered. Chemotherapies and targeted agents with strong tumor-specific activities corroborated the siRNA profiling results and included drugs that targeted the mitotic spindle, the proteasome, and G₂-M kinases WEE1 and CHK1 We also show the feasibility of ex vivo drug profiling for patients enrolled in a clinical trial.Conclusions: High-throughput phenotyping with siRNA and drug libraries using patient-derived tumor cells prioritizes mutated driver genes and identifies novel drug targets not revealed by genomic profiling. Functional profiling is a promising adjunct to DNA sequencing for precision oncology. Clin Cancer Res; 24(12); 2828-43. ©2018 AACR.

SHIFT: speedy histopathological-to-immunofluorescent translation of whole slide images using conditional generative adversarial networks

Multiplexed imaging such as multicolor immunofluorescence staining, multiplexed immunohistochemistry (mIHC) or cyclic immunofluorescence (cycIF) enables deep assessment of cellular complexity in situ and, in conjunction with standard histology stains like hematoxylin and eosin (H&E), can help to unravel the complex molecular relationships and spatial interdependencies that undergird disease states. However, these multiplexed imaging methods are costly and can degrade both tissue quality and antigenicity with each successive cycle of staining. In addition, computationally intensive image processing such as image registration across multiple channels is required. We have developed a novel method, speedy histopathological-to-immunofluorescent translation (SHIFT) of whole slide images (WSIs) using conditional generative adversarial networks (cGANs). This approach is rooted in the assumption that specific patterns captured in IF images by stains like DAPI, pan-cytokeratin (panCK), or α-smooth muscle actin ( α-SMA) are encoded in H&E images, such that a SHIFT model can learn useful feature representations or architectural patterns in the H&E stain that help generate relevant IF stain patterns. We demonstrate that the proposed method is capable of generating realistic tumor marker IF WSIs conditioned on corresponding H&E-stained WSIs with up to 94.5% accuracy in a matter of seconds. Thus, this method has the potential to not only improve our understanding of the mapping of histological and morphological profiles into protein expression profiles, but also greatly increase the e ciency of diagnostic and prognostic decision-making.

Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies

Motivation

In recent years, vast advances in biomedical technologies and comprehensive sequencing have revealed the genomic landscape of common forms of human cancer in unprecedented detail. The broad heterogeneity of the disease calls for rapid development of personalized therapies. Translating the readily available genomic data into useful knowledge that can be applied in the clinic remains a challenge. Computational methods are needed to aid these efforts by robustly analyzing genome-scale data from distinct experimental platforms for prioritization of targets and treatments.

Results

We propose a novel, biologically motivated, Bayesian multitask approach, which explicitly models gene-centric dependencies across multiple and distinct genomic platforms. We introduce a gene-wise prior and present a fully Bayesian formulation of a group factor analysis model. In supervised prediction applications, our multitask approach leverages similarities in response profiles of groups of drugs that are more likely to be related to true biological signal, which leads to more robust performance and improved generalization ability. We evaluate the performance of our method on molecularly characterized collections of cell lines profiled against two compound panels, namely the Cancer Cell Line Encyclopedia and the Cancer Therapeutics Response Portal. We demonstrate that accounting for the gene-centric dependencies enables leveraging information from multi-omic input data and improves prediction and feature selection performance. We further demonstrate the applicability of our method in an unsupervised dimensionality reduction application by inferring genes essential to tumorigenesis in the pancreatic ductal adenocarcinoma and lung adenocarcinoma patient cohorts from The Cancer Genome Atlas.

Availability and Implementation

: The code for this work is available at https://github.com/olganikolova/gbgfa.

Contact

: nikolova@ohsu.edu or margolin@ohsu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Germline contamination and leakage in whole genome somatic single nucleotide variant detection

BACKGROUND

The clinical sequencing of cancer genomes to personalize therapy is becoming routine across the world. However, concerns over patient re-identification from these data lead to questions about how tightly access should be controlled. It is not thought to be possible to re-identify patients from somatic variant data. However, somatic variant detection pipelines can mistakenly identify germline variants as somatic ones, a process called "germline leakage". The rate of germline leakage across different somatic variant detection pipelines is not well-understood, and it is uncertain whether or not somatic variant calls should be considered re-identifiable. To fill this gap, we quantified germline leakage across 259 sets of whole-genome somatic single nucleotide variant (SNVs) predictions made by 21 teams as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge.

RESULTS

The median somatic SNV prediction set contained 4325 somatic SNVs and leaked one germline polymorphism. The level of germline leakage was inversely correlated with somatic SNV prediction accuracy and positively correlated with the amount of infiltrating normal cells. The specific germline variants leaked differed by tumour and algorithm. To aid in quantitation and correction of leakage, we created a tool, called GermlineFilter, for use in public-facing somatic SNV databases.

CONCLUSIONS

The potential for patient re-identification from leaked germline variants in somatic SNV predictions has led to divergent open data access policies, based on different assessments of the risks. Indeed, a single, well-publicized re-identification event could reshape public perceptions of the values of genomic data sharing. We find that modern somatic SNV prediction pipelines have low germline-leakage rates, which can be further reduced, especially for cloud-sharing, using pre-filtering software.

Quantitative Multiplex Immunohistochemistry Reveals Myeloid-Inflamed Tumor-Immune Complexity Associated with Poor Prognosis

Here, we describe a multiplexed immunohistochemical platform with computational image processing workflows, including image cytometry, enabling simultaneous evaluation of 12 biomarkers in one formalin-fixed paraffin-embedded tissue section. To validate this platform, we used tissue microarrays containing 38 archival head and neck squamous cell carcinomas and revealed differential immune profiles based on lymphoid and myeloid cell densities, correlating with human papilloma virus status and prognosis. Based on these results, we investigated 24 pancreatic ductal adenocarcinomas from patients who received neoadjuvant GVAX vaccination and revealed that response to therapy correlated with degree of mono-myelocytic cell density and percentages of CD8⁺ T cells expressing T cell exhaustion markers. These data highlight the utility of in situ immune monitoring for patient stratification and provide digital image processing pipelines to the community for examining immune complexity in precious tissue sections, where phenotype and tissue architecture are preserved to improve biomarker discovery and assessment.

A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.

Abstract 5548: Genomic signatures for tumor-to-treatment stratification in pancreatic cancer

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer deaths. PDAC is a broadly heterogeneous disease making it difficult to treat; each patient’s tumor is unique, but it can be classified into different subtypes, and identifying the right therapeutic strategy for each subtype is key to improving patient outcomes. Three sets of transcriptional signatures for PDAC subtype classification have been proposed in the literature; however, preliminary data from in vitro cell line models indicates that they are associated with sensitivity for only a handful of compounds. Computational methods can identify effective individual therapies by robustly analyzing genome-scale data from multiple experimental platforms to derive molecular classifiers predictive of drug response (drug signatures).

Methods: We developed a novel Bayesian framework approach called Gene-wise prior Bayesian Group Factor Analysis (GBGFA) for predicting drug sensitivity in a given sample from genomic data. Our approach integrates data from different omic platforms by modeling the statistical dependencies of measurements made for the same gene via a model parameter called “gene prior”. We identify gene signatures for the clinically relevant to PDAC compounds olaparib and palbociclib, leveraging data from 44 pancreatic cell lines. Using hybrid capture mutation, gene expression, and copy number data, we applied our model to score genes for their ability to predict drug response in pancreatic cell lines and compiled the top predictors into drug signatures.

Results: Our results across a pan-cancer cell line collection and >100 compounds show improved ability to recapitulate known biomarkers and drug targets compared to other state of the art methods. For the MEK1/2 inhibitor selumetinib, we successfully recapitulated BRAF mutation as the top-scored biomarker of sensitivity. In addition, we identified four genes that carry additional information that can be used to stratify samples into sensitive or resistant groups: all were wild type in the sensitive cell lines and mutated in some of the resistant lines. We hypothesize that these genes could be used in conjunction with BRAF mutation status to refine the stratification to selumetinib therapy. We also identified several highly expressed genes in the resistant cell lines, and we hypothesize that these genes may mediate drug resistance. Similar analysis are currently ongoing for olaparib and palbociclib, comparing our findings to the subtypes currently proposed in the literature.

Conclusions: Our integrated approach identifies drug-specific gene signatures in PDAC cell lines. We derived genomic signatures for two clinically relevant to PDAC compounds in the context of the established in the literature PDAC subtypes, and hypothesize that these signatures could be used to identify patients most likely to respond to these therapies.

Citation Format: Olga H. Nikolova, Laura Heiser, Adam A. Margolin. Genomic signatures for tumor-to-treatment stratification in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5548. doi:10.1158/1538-7445.AM2017-5548

Functional kinomics identifies candidate therapeutic targets in head and neck cancer

PURPOSE

To identify novel therapeutic drug targets for p53-mutant head and neck squamous cell carcinoma (HNSCC).

EXPERIMENTAL DESIGN

RNAi kinome viability screens were performed on HNSCC cells, including autologous pairs from primary tumor and recurrent/metastatic lesions, and in parallel on murine squamous cell carcinoma (MSCC) cells derived from tumors of inbred mice bearing germline mutations in Trp53, and p53 regulatory genes: Atm, Prkdc, and p19(Arf). Cross-species analysis of cell lines stratified by p53 mutational status and metastatic phenotype was used to select 38 kinase targets. Both primary and secondary RNAi validation assays were performed on additional HNSCC cell lines to credential these kinase targets using multiple phenotypic endpoints. Kinase targets were also examined via chemical inhibition using a panel of kinase inhibitors. A preclinical study was conducted on the WEE1 kinase inhibitor, MK-1775.

RESULTS

Our functional kinomics approach identified novel survival kinases in HNSCC involved in G2-M cell-cycle checkpoint, SFK, PI3K, and FAK pathways. RNAi-mediated knockdown and chemical inhibition of the WEE1 kinase with a specific inhibitor, MK-1775, had a significant effect on both viability and apoptosis. Sensitivity to the MK-1775 kinase inhibitor is in part determined by p53 mutational status, and due to unscheduled mitotic entry. MK-1775 displays single-agent activity and potentiates the efficacy of cisplatin in a p53-mutant HNSCC xenograft model.

CONCLUSIONS

WEE1 kinase is a potential therapeutic drug target for HNSCC. This study supports the application of a functional kinomics strategy to identify novel therapeutic targets for cancer.

The NIH BD2K center for big data in translational genomics

The world's genomics data will never be stored in a single repository - rather, it will be distributed among many sites in many countries. No one site will have enough data to explain genotype to phenotype relationships in rare diseases; therefore, sites must share data. To accomplish this, the genetics community must forge common standards and protocols to make sharing and computing data among many sites a seamless activity. Through the Global Alliance for Genomics and Health, we are pioneering the development of shared application programming interfaces (APIs) to connect the world's genome repositories. In parallel, we are developing an open source software stack (ADAM) that uses these APIs. This combination will create a cohesive genome informatics ecosystem. Using containers, we are facilitating the deployment of this software in a diverse array of environments. Through benchmarking efforts and big data driver projects, we are ensuring ADAM's performance and utility.

Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning

Motivation: Human immunodeficiency virus (HIV) and cancer require personalized therapies owing to their inherent heterogeneous nature. For both diseases, large-scale pharmacogenomic screens of molecularly characterized samples have been generated with the hope of identifying genetic predictors of drug susceptibility. Thus, computational algorithms capable of inferring robust predictors of drug responses from genomic information are of great practical importance. Most of the existing computational studies that consider drug susceptibility prediction against a panel of drugs formulate a separate learning problem for each drug, which cannot make use of commonalities between subsets of drugs.

Results: In this study, we propose to solve the problem of drug susceptibility prediction against a panel of drugs in a multitask learning framework by formulating a novel Bayesian algorithm that combines kernel-based non-linear dimensionality reduction and binary classification (or regression). The main novelty of our method is the joint Bayesian formulation of projecting data points into a shared subspace and learning predictive models for all drugs in this subspace, which helps us to eliminate off-target effects and drug-specific experimental noise. Another novelty of our method is the ability of handling missing phenotype values owing to experimental conditions and quality control reasons. We demonstrate the performance of our algorithm via cross-validation experiments on two benchmark drug susceptibility datasets of HIV and cancer. Our method obtains statistically significantly better predictive performance on most of the drugs compared with baseline single-task algorithms that learn drug-specific models. These results show that predicting drug susceptibility against a panel of drugs simultaneously within a multitask learning framework improves overall predictive performance over single-task learning approaches.

Availability and implementation: Our Matlab implementations for binary classification and regression are available at https://github.com/mehmetgonen/kbmtl.

Contact:mehmet.gonen@sagebase.org

Supplementary Information:Supplementary data are available at Bioinformatics online.

Toward better benchmarking: challenge-based methods assessment in cancer genomics

Rapid technological development has created an urgent need for improved evaluation of algorithms for the analysis of cancer genomics data. We outline how challenge-based assessment may help fill this gap by leveraging crowd-sourcing to distribute effort and reduce bias.

Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin

Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies.

Assessing the clinical utility of cancer genomic and proteomic data across tumor types

Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, microRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We find that incorporating molecular data with clinical variables yields statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2-23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data.

To Catch a Pre-Leukemia

DNMT3A mutations occur in pre-leukemic hematopoietic stem cells and define early-stage events in the progression to acute myeloid leukemia.

Moving from Unknown Unknown to Known Unknown

Genomic analysis of nearly 5000 tumors demonstrates that we are far from finding all the cancer genes.

The Stream algorithm: computationally efficient ridge-regression via Bayesian model averaging, and applications to pharmacogenomic prediction of cancer cell line sensitivity

Computational efficiency is important for learning algorithms operating in the "large p, small n" setting. In computational biology, the analysis of data sets containing tens of thousands of features ("large p"), but only a few hundred samples ("small n"), is nowadays routine, and regularized regression approaches such as ridge-regression, lasso, and elastic-net are popular choices. In this paper we propose a novel and highly efficient Bayesian inference method for fitting ridge-regression. Our method is fully analytical, and bypasses the need for expensive tuning parameter optimization, via cross-validation, by employing Bayesian model averaging over the grid of tuning parameters. Additional computational efficiency is achieved by adopting the singular value decomposition reparametrization of the ridge-regression model, replacing computationally expensive inversions of large p × p matrices by efficient inversions of small and diagonal n × n matrices. We show in simulation studies and in the analysis of two large cancer cell line data panels that our algorithm achieves slightly better predictive performance than cross-validated ridge-regression while requiring only a fraction of the computation time. Furthermore, in comparisons based on the cell line data sets, our algorithm systematically out-performs the lasso in both predictive performance and computation time, and shows equivalent predictive performance, but considerably smaller computation time, than the elastic-net.

Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data

Large-scale pharmacogenomic screens of cancer cell lines have emerged as an attractive pre-clinical system for identifying tumor genetic subtypes with selective sensitivity to targeted therapeutic strategies. Application of modern machine learning approaches to pharmacogenomic datasets have demonstrated the ability to infer genomic predictors of compound sensitivity. Such modeling approaches entail many analytical design choices; however, a systematic study evaluating the relative performance attributable to each design choice is not yet available. In this work, we evaluated over 110,000 different models, based on a multifactorial experimental design testing systematic combinations of modeling factors within several categories of modeling choices, including: type of algorithm, type of molecular feature data, compound being predicted, method of summarizing compound sensitivity values, and whether predictions are based on discretized or continuous response values. Our results suggest that model input data (type of molecular features and choice of compound) are the primary factors explaining model performance, followed by choice of algorithm. Our results also provide a statistically principled set of recommended modeling guidelines, including: using elastic net or ridge regression with input features from all genomic profiling platforms, most importantly, gene expression features, to predict continuous-valued sensitivity scores summarized using the area under the dose response curve, with pathway targeted compounds most likely to yield the most accurate predictors. In addition, our study provides a publicly available resource of all modeling results, an open source code base, and experimental design for researchers throughout the community to build on our results and assess novel methodologies or applications in related predictive modeling problems.

Oncogenic Driver Mutations: Neither Tissue-Specific nor Independent

Genomic analysis of 12 cancers identifies common somatic mutations across tumor types.

It Takes Two to Make a RAF Signal Right

A new technology allows single molecule–resolution imaging of RAS multimer formation.

Enabling transparent and collaborative computational analysis of 12 tumor types within The Cancer Genome Atlas

The Cancer Genome Atlas Pan-Cancer Analysis Working Group collaborated on the Synapse software platform to share and evolve data, results and methodologies while performing integrative analysis of molecular profiling data from 12 tumor types. The group's work serves as a pilot case study that provides (i) a template for future large collaborative studies; (ii) a system to support collaborative projects; and (iii) a public resource of highly curated data, results and automated systems for the evaluation of community-developed models.

Co-opting a Tumor Predator to Provide Life Support

Tumor cells acquire resistance to anti-VEGF therapy by mobilizing immune cells through an IL-17–mediated paracrine signaling network.

The Bionic Cancer-Resistant Extracellular Matrix of the Naked Mole-Rat

Naked mole-rats resist cancer through a high-molecular-mass version of the extracellular matrix component hyaluronan.

The Enemy of the Enemy of My Enemy Is a Therapeutic Target

A phase 1 clinical trial of the monoclonal antibody to PD-1, lambrolizumab.

Improving breast cancer survival analysis through competition-based multidimensional modeling

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks-DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models.

Genomics of T-ALL Reveals a Weapon of an Evasive Foe

Genomic profiling of T-ALL samples at diagnosis, remission, and relapse identifies NT5C2 as a chemoresistance gene.

ATARiS: computational quantification of gene suppression phenotypes from multisample RNAi screens

Genome-scale RNAi libraries enable the systematic interrogation of gene function. However, the interpretation of RNAi screens is complicated by the observation that RNAi reagents designed to suppress the mRNA transcripts of the same gene often produce a spectrum of phenotypic outcomes due to differential on-target gene suppression or perturbation of off-target transcripts. Here we present a computational method, Analytic Technique for Assessment of RNAi by Similarity (ATARiS), that takes advantage of patterns in RNAi data across multiple samples in order to enrich for RNAi reagents whose phenotypic effects relate to suppression of their intended targets. By summarizing only such reagent effects for each gene, ATARiS produces quantitative, gene-level phenotype values, which provide an intuitive measure of the effect of gene suppression in each sample. This method is robust for data sets that contain as few as 10 samples and can be used to analyze screens of any number of targeted genes. We used this analytic approach to interrogate RNAi data derived from screening more than 100 human cancer cell lines and identified HNF1B as a transforming oncogene required for the survival of cancer cells that harbor HNF1B amplifications. ATARiS is publicly available at http://broadinstitute.org/ataris.

Chemical genomics identifies small-molecule MCL1 repressors and BCL-xL as a predictor of MCL1 dependency

MCL1, which encodes the antiapoptotic protein MCL1, is among the most frequently amplified genes in human cancer. A chemical genomic screen identified compounds, including anthracyclines, that decreased MCL1 expression. Genomic profiling indicated that these compounds were global transcriptional repressors that preferentially affect MCL1 due to its short mRNA half-life. Transcriptional repressors and MCL1 shRNAs induced apoptosis in the same cancer cell lines and could be rescued by physiological levels of ectopic MCL1 expression. Repression of MCL1 released the proapoptotic protein BAK from MCL1, and Bak deficiency conferred resistance to transcriptional repressors. A computational model, validated in vivo, indicated that high BCL-xL expression confers resistance to MCL1 repression, thereby identifying a patient-selection strategy for the clinical development of MCL1 inhibitors.

Abstract 5114: Integrative analysis of the Cancer Cell Line Encyclopedia reveals genetic and transcriptional predictors of compound sensitivity

The Cancer Cell Line Encyclopedia (CCLE) represents a collaborative effort to assemble a comprehensive resource of human cancer models for basic and translational research. It contains a detailed genetic profiling of approximately 1,000 human cancer cell lines spanning many tumor types. Thus far, high-density SNP array data, gene expression microarray data and mutation data from hybrid capture sequencing of 1,650 cancer genes has been obtained. Additionally, we have assessed the sensitivity of these same cell lines using a series of pharmacological compounds that represent both conventional cytotoxic and targeted agents. On major goal of the CCLE effort involves systematic integration of the genomic and pharmacologic datasets in order to identify putative targets of prevalent genetic alterations as well as predictors and modifiers of pharmacologic sensitivity and resistance. The availability of high-quality data generated by uniform criteria across hundreds of cell lines markedly enhances the statistical power to discover genetic alterations involved in carcinogenesis and molecular predictors of pharmacologic vulnerability. We developed a framework based on an elastic net machine-learning regression algorithm, and combined with a bootstrapping procedure, to derive predictive models of the sensitivity to each compound, using all genetic features of the cell lines in the collection. Through this computational prediction approach, we have both rediscovered molecular features predicting response to most drugs in our set but also uncovered novel potential biomarkers of sensitivity and resistance to targeted agents and chemotherapy drugs. For instance, we have found that response to topoisomerase 1 inhibitors seems to be linked to the expression of a single gene. We have also observed that tissue lineage is a key predictor for sensitivity to certain compounds, providing rationale for clinical trials of these drugs in particular cancer types and we identified potential stratifiers for existing EGFR targeted therapies. Finally, we have found an additional target for a certain chemotype of MEK inhibitors, and shown that this interaction was responsible for growth suppression, which might be a new indication for this kind of drug. Our cell line-based platform provides a valuable tool for the development of personalized cancer medicine, revealing critical tumor dependencies and helping to stratify patients for clinical trials.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5114. doi:1538-7445.AM2012-5114

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

Empirical Bayes analysis of quantitative proteomics experiments

Background

Advances in mass spectrometry-based proteomics have enabled the incorporation of proteomic data into systems approaches to biology. However, development of analytical methods has lagged behind. Here we describe an empirical Bayes framework for quantitative proteomics data analysis. The method provides a statistical description of each experiment, including the number of proteins that differ in abundance between 2 samples, the experiment's statistical power to detect them, and the false-positive probability of each protein.

Methodology/Principal Findings

We analyzed 2 types of mass spectrometric experiments. First, we showed that the method identified the protein targets of small-molecules in affinity purification experiments with high precision. Second, we re-analyzed a mass spectrometric data set designed to identify proteins regulated by microRNAs. Our results were supported by sequence analysis of the 3′ UTR regions of predicted target genes, and we found that the previously reported conclusion that a large fraction of the proteome is regulated by microRNAs was not supported by our statistical analysis of the data.

Conclusions/Significance

Our results highlight the importance of rigorous statistical analysis of proteomic data, and the method described here provides a statistical framework to robustly and reliably interpret such data.

Genome-wide identification of post-translational modulators of transcription factor activity in human B cells

The ability of a transcription factor (TF) to regulate its targets is modulated by a variety of genetic and epigenetic mechanisms, resulting in highly context-dependent regulatory networks. However, high-throughput methods for the identification of proteins that affect TF activity are still largely unavailable. Here we introduce an algorithm, modulator inference by network dynamics (MINDy), for the genome-wide identification of post-translational modulators of TF activity within a specific cellular context. When used to dissect the regulation of MYC activity in human B lymphocytes, the approach inferred novel modulators of MYC function, which act by distinct mechanisms, including protein turnover, transcription complex formation and selective enzyme recruitment. MINDy is generally applicable to study the post-translational modulation of mammalian TFs in any cellular context. As such it can be used to dissect context-specific signaling pathways and combinatorial transcriptional regulation.

Theory and limitations of genetic network inference from microarray data

Since the advent of gene expression microarray technology more than 10 years ago, many computational approaches have been developed aimed at using statistical associations between mRNA abundance profiles to predict transcriptional regulatory interactions. The ultimate goal is to develop causal network models describing the transcriptional influences that genes exert on each other (via their protein products), which can be used to predict network disruptions (e.g., mutations) leading to a disease phenotype, as well as the appropriate therapeutic intervention. However, microarray data measure only a small component of the interacting variables in a genetic regulatory network, as cells are known to regulate gene expression via many diverse mechanisms. Although many researchers have acknowledged the questionable interpretation of statistical dependencies between mRNA profiles, very little work has been done on theoretically characterizing the nature of inferred dependencies using models that account for unobserved interacting variables. In this work, we review the theory behind reverse engineering algorithms derived from three separate disciplines-system control theory, graphical models, and information theory-and highlight several mathematical relationships between the various methods. We then apply recent theoretical work on constructing graphical models with latent variables to the context of reverse engineering genetic networks. We demonstrate that even the addition of simple latent variables induces statistical dependencies between non-directly interacting (e.g., co-regulated) genes that cannot be eliminated by conditioning on any observed variables.

Reverse engineering cellular networks

We describe a computational protocol for the ARACNE algorithm, an information-theoretic method for identifying transcriptional interactions between gene products using microarray expression profile data. Similar to other algorithms, ARACNE predicts potential functional associations among genes, or novel functions for uncharacterized genes, by identifying statistical dependencies between gene products. However, based on biochemical validation, literature searches and DNA binding site enrichment analysis, ARACNE has also proven effective in identifying bona fide transcriptional targets, even in complex mammalian networks. Thus we envision that predictions made by ARACNE, especially when supplemented with prior knowledge or additional data sources, can provide appropriate hypotheses for the further investigation of cellular networks. While the examples in this protocol use only gene expression profile data, the algorithm's theoretical basis readily extends to a variety of other high-throughput measurements, such as pathway-specific or genome-wide proteomics, microRNA and metabolomics data. As these data become readily available, we expect that ARACNE might prove increasingly useful in elucidating the underlying interaction models. For a microarray data set containing approximately 10,000 probes, reconstructing the network around a single probe completes in several minutes using a desktop computer with a Pentium 4 processor. Reconstructing a genome-wide network generally requires a computational cluster, especially if the recommended bootstrapping procedure is used.