James C. Costello

Wisdom of crowds for robust gene network inference

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ∼1,700 transcriptional interactions at a precision of ∼50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.

Oncogenic NRAS signaling differentially regulates survival and proliferation in melanoma

The discovery of potent inhibitors of the BRAF proto-oncogene has revolutionized therapy for melanoma harboring mutations in BRAF, yet NRAS-mutant melanoma remains without an effective therapy. Because direct pharmacological inhibition of the RAS proto-oncogene has thus far been unsuccessful, we explored systems biology approaches to identify synergistic drug combination(s) that can mimic RAS inhibition. Here, leveraging an inducible mouse model of NRAS-mutant melanoma, we show that pharmacological inhibition of mitogen-activated protein kinase kinase (MEK) activates apoptosis but not cell-cycle arrest, which is in contrast to complete genetic neuroblastoma RAS homolog (NRAS) extinction, which triggers both of these effects. Network modeling pinpointed cyclin-dependent kinase 4 (CDK4) as a key driver of this differential phenotype. Accordingly, combined pharmacological inhibition of MEK and CDK4 in vivo led to substantial synergy in therapeutic efficacy. We suggest a gradient model of oncogenic NRAS signaling in which the output is gated, resulting in the decoupling of discrete downstream biological phenotypes as a result of incomplete inhibition. Such a gated signaling model offers a new framework to identify nonobvious coextinction target(s) for combined pharmacological inhibition in NRAS-mutant melanomas.

A community effort to assess and improve drug sensitivity prediction algorithms

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

TERT promoter mutations and telomerase reactivation in urothelial cancer

The downstream effects of false promotion Special DNA sequences at the ends of chromosomes, called telomeres, are replenished by a dedicated enzyme called telomerase. A subset of human tumors harbors mutations in the promoter region of the TERT gene, which codes for a subunit of telomerase. Borah et al. explored the downstream effects of TERT promoter mutations in cells derived from urothelial (urinary tract) cancers. The mutations were associated with aberrantly high levels of TERT mRNA, TERT protein and telomerase activity, and longer telomeres. A small study of clinical samples suggested that high levels of TERT mRNA may be a marker of more aggressive urothelial cancers. Science, this issue p. 1006 Telomerase reverse transcriptase promoter mutations can cause high-level reactivation of the telomerase enzyme in bladder cancer. Reactivation of telomerase, the chromosome end–replicating enzyme, drives human cell immortality and cancer. Point mutations in the telomerase reverse transcriptase (TERT) gene promoter occur at high frequency in multiple cancers, including urothelial cancer (UC), but their effect on telomerase function has been unclear. In a study of 23 human UC cell lines, we show that these promoter mutations correlate with higher levels of TERT messenger RNA (mRNA), TERT protein, telomerase enzymatic activity, and telomere length. Although previous studies found no relation between TERT promoter mutations and UC patient outcome, we find that elevated TERT mRNA expression strongly correlates with reduced disease-specific survival in two independent UC patient cohorts (n = 35; n = 87). These results suggest that high telomerase activity may be a better marker of aggressive UC tumors than TERT promoter mutations alone.

A community computational challenge to predict the activity of pairs of compounds

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

REVERSE ECOLOGY AND THE POWER OF POPULATION GENOMICS

Abstract Rapid and inexpensive sequencing technologies are making it possible to collect whole genome sequence data on multiple individuals from a population. This type of data can be used to quickly identify genes that control important ecological and evolutionary phenotypes by finding the targets of adaptive natural selection, and we therefore refer to such approaches as “reverse ecology.” To quantify the power gained in detecting positive selection using population genomic data, we compare three statistical methods for identifying targets of selection: the McDonald–Kreitman test, the mkprf method, and a likelihood implementation for detecting dN/dS > 1. Because the first two methods use polymorphism data we expect them to have more power to detect selection. However, when applied to population genomic datasets from human, fly, and yeast, the tests using polymorphism data were actually weaker in two of the three datasets. We explore reasons why the simpler comparative method has identified more genes under selection, and suggest that the different methods may really be detecting different signals from the same sequence data. Finally, we find several statistical anomalies associated with the mkprf method, including an almost linear dependence between the number of positively selected genes identified and the prior distributions used. We conclude that interpreting the results produced by this method should be done with some caution.

Tumor-derived Cell Lines as Molecular Models of Cancer Pharmacogenomics

Compared with normal cells, tumor cells have undergone an array of genetic and epigenetic alterations. Often, these changes underlie cancer development, progression, and drug resistance, so the utility of model systems rests on their ability to recapitulate the genomic aberrations observed in primary tumors. Tumor-derived cell lines have long been used to study the underlying biologic processes in cancer, as well as screening platforms for discovering and evaluating the efficacy of anticancer therapeutics. Multiple -omic measurements across more than a thousand cancer cell lines have been produced following advances in high-throughput technologies and multigroup collaborative projects. These data complement the large, international cancer genomic sequencing efforts to characterize patient tumors, such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Given the scope and scale of data that have been generated, researchers are now in a position to evaluate the similarities and differences that exist in genomic features between cell lines and patient samples. As pharmacogenomics models, cell lines offer the advantages of being easily grown, relatively inexpensive, and amenable to high-throughput testing of therapeutic agents. Data generated from cell lines can then be used to link cellular drug response to genomic features, where the ultimate goal is to build predictive signatures of patient outcome. This review highlights the recent work that has compared -omic profiles of cell lines with primary tumors, and discusses the advantages and disadvantages of cancer cell lines as pharmacogenomic models of anticancer therapies. Mol Cancer Res; 14(1); 3–13. ©2015 AACR.

Transcriptome Analysis Identifies Regulators of Hematopoietic Stem and Progenitor Cells

Summary Hematopoietic stem cells (HSCs) maintain blood homeostasis and are the functional units of bone marrow transplantation. To improve the molecular understanding of HSCs and their proximal progenitors, we performed transcriptome analysis within the context of the ImmGen Consortium data set. Gene sets that define steady-state and mobilized HSCs, as well as hematopoietic stem and progenitor cells (HSPCs), were determined. Genes involved in transcriptional regulation, including a group of putative transcriptional repressors, were identified in multipotent progenitors and HSCs. Proximal promoter analyses combined with ImmGen module analysis identified candidate regulators of HSCs. Enforced expression of one predicted regulator, Hlf, in diverse HSPC subsets led to extensive self-renewal activity ex vivo. These analyses reveal unique insights into the mechanisms that control the core properties of HSPCs.

Concurrent Alterations in TERT, KDM6A, and the BRCA Pathway in Bladder Cancer

Purpose: Genetic analysis of bladder cancer has revealed a number of frequently altered genes, including frequent alterations of the telomerase (TERT) gene promoter, although few altered genes have been functionally evaluated. Our objective is to characterize alterations observed by exome sequencing and sequencing of the TERT promoter, and to examine the functional relevance of histone lysine (K)–specific demethylase 6A (KDM6A/UTX), a frequently mutated histone demethylase, in bladder cancer. Experimental Design: We analyzed bladder cancer samples from 54 U.S. patients by exome and targeted sequencing and confirmed somatic variants using normal tissue from the same patient. We examined the biologic function of KDM6A using in vivo and in vitro assays. Results: We observed frequent somatic alterations in BRCA1 associated protein-1 (BAP1) in 15% of tumors, including deleterious alterations to the deubiquitinase active site and the nuclear localization signal. BAP1 mutations contribute to a high frequency of tumors with breast cancer (BRCA) DNA repair pathway alterations and were significantly associated with papillary histologic features in tumors. BAP1 and KDM6A mutations significantly co-occurred in tumors. Somatic variants altering the TERT promoter were found in 69% of tumors but were not correlated with alterations in other bladder cancer genes. We examined the function of KDM6A, altered in 24% of tumors, and show depletion in human bladder cancer cells, enhanced in vitro proliferation, in vivo tumor growth, and cell migration. Conclusions: This study is the first to identify frequent BAP1 and BRCA pathway alterations in bladder cancer, show TERT promoter alterations are independent of other bladder cancer gene alterations, and show KDM6A loss is a driver of the bladder cancer phenotype. Clin Cancer Res; 20(18); 4935–48. ©2014 AACR.

Crowdsourcing biomedical research: leveraging communities as innovation engines

The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.