Artem Sokolov

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.

PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis

Motivation: A current challenge in understanding cancer processes is to pinpoint which mutations influence the onset and progression of disease. Toward this goal, we describe a method called PARADIGM-SHIFT that can predict whether a mutational event is neutral, gain-or loss-of-function in a tumor sample. The method uses a belief-propagation algorithm to infer gene activity from gene expression and copy number data in the context of a set of pathway interactions. Results: The method was found to be both sensitive and specific on a set of positive and negative controls for multiple cancers for which pathway information was available. Application to the Cancer Genome Atlas glioblastoma, ovarian and lung squamous cancer datasets revealed several novel mutations with predicted high impact including several genes mutated at low frequency suggesting the approach will be complementary to current approaches that rely on the prevalence of events to reach statistical significance. Availability: All source code is available at the github repository http:github.org/paradigmshift. Contact: jstuart@soe.ucsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

N-Myc Drives Neuroendocrine Prostate Cancer Initiated from Human Prostate Epithelial Cells

MYCN amplification and overexpression are common in neuroendocrine prostate cancer (NEPC). However, the impact of aberrant N-Myc expression in prostate tumorigenesis and the cellular origin of NEPC have not been established. We define N-Myc and activated AKT1 as oncogenic components sufficient to transform human prostate epithelial cells to prostate adenocarcinoma and NEPC with phenotypic and molecular features of aggressive, late-stage human disease. We directly show that prostate adenocarcinoma and NEPC can arise from a common epithelial clone. Further, N-Myc is required for tumor maintenance, and destabilization of N-Myc through Aurora A kinase inhibition reduces tumor burden. Our findings establish N-Myc as a driver of NEPC and a target for therapeutic intervention.

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

A basal stem cell signature identifies aggressive prostate cancer phenotypes

Significance Aggressive cancers often possess functional and molecular traits characteristic of normal stem cells. It is unclear if aggressive phenotypes of prostate cancer molecularly resemble normal stem cells residing within the human prostate. Here, we transcriptionally profiled epithelial populations from the human prostate and show that aggressive prostate cancer is enriched for a prostate basal stem cell signature. Within prostate cancer metastases, histological subtypes had varying enrichment of the stem cell signature, with small cell neuroendocrine carcinoma being the most stem cell-like. We further found that small cell neuroendocrine carcinoma and the prostate basal stem cell share a common transcriptional program. Targeting normal stem cell transcriptional programs may provide a new strategy for treating advanced prostate cancer. Evidence from numerous cancers suggests that increased aggressiveness is accompanied by up-regulation of signaling pathways and acquisition of properties common to stem cells. It is unclear if different subtypes of late-stage cancer vary in stemness properties and whether or not these subtypes are transcriptionally similar to normal tissue stem cells. We report a gene signature specific for human prostate basal cells that is differentially enriched in various phenotypes of late-stage metastatic prostate cancer. We FACS-purified and transcriptionally profiled basal and luminal epithelial populations from the benign and cancerous regions of primary human prostates. High-throughput RNA sequencing showed the basal population to be defined by genes associated with stem cell signaling programs and invasiveness. Application of a 91-gene basal signature to gene expression datasets from patients with organ-confined or hormone-refractory metastatic prostate cancer revealed that metastatic small cell neuroendocrine carcinoma was molecularly more stem-like than either metastatic adenocarcinoma or organ-confined adenocarcinoma. Bioinformatic analysis of the basal cell and two human small cell gene signatures identified a set of E2F target genes common between prostate small cell neuroendocrine carcinoma and primary prostate basal cells. Taken together, our data suggest that aggressive prostate cancer shares a conserved transcriptional program with normal adult prostate basal stem cells.

Hierarchical classification of gene ontology terms using the GOstruct method.

Protein function prediction is an active area of research in bioinformatics. Yet, the transfer of annotation on the basis of sequence or structural similarity remains widely used as an annotation method. Most of todays machine learning approaches reduce the problem to a collection of binary classification problems: whether a protein performs a particular function, sometimes with a post-processing step to combine the binary outputs. We propose a method that directly predicts a full functional annotation of a protein by modeling the structure of the Gene Ontology hierarchy in the framework of kernel methods for structured-output spaces. Our empirical results show improved performance over a BLAST nearest-neighbor method, and over algorithms that employ a collection of binary classifiers as measured on the Mousefunc benchmark dataset.

Unbiased tournament selection

Tournament selection is a popular form of selection which is commonly used with genetic algorithms, genetic programming and evolutionary programming. However, tournament selection introduces a sampling bias into the selection process. We review analytic results and present empirical evidence that shows this bias has a significant impact on search performance. We introduce two new forms of unbiased tournament selection that remove or reduce sampling bias in tournament selection.

Geometric subspace methods and time-delay embedding for EEG artifact removal and classification

Generalized singular-value decomposition is used to separate multichannel electroencephalogram (EEG) into components found by optimizing a signal-to-noise quotient. These components are used to filter out artifacts. Short-time principal components analysis of time-delay embedded EEG is used to represent windowed EEG data to classify EEG according to which mental task is being performed. Examples are presented of the filtering of various artifacts and results are shown of classification of EEG from five mental tasks using committees of decision trees.

Combining heterogeneous data sources for accurate functional annotation of proteins

Combining heterogeneous sources of data is essential for accurate prediction of protein function. The task is complicated by the fact that while sequence-based features can be readily compared across species, most other data are species-specific. In this paper, we present a multi-view extension to GOstruct, a structured-output framework for function annotation of proteins. The extended framework can learn from disparate data sources, with each data source provided to the framework in the form of a kernel. Our empirical results demonstrate that the multi-view framework is able to utilize all available information, yielding better performance than sequence-based models trained across species and models trained from collections of data within a given species. This version of GOstruct participated in the recent Critical Assessment of Functional Annotations (CAFA) challenge; since then we have significantly improved the natural language processing component of the method, which now provides performance that is on par with that provided by sequence information. The GOstruct framework is available for download at http://strut.sourceforge.net.

Pathway-Based Genomics Prediction using Generalized Elastic Net

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach.