Adrian Bivol

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.

Abstract A12: Identification of pathways associated with abiraterone resistance in metastatic castration resistant prostate cancer: Preliminary results from the SU2C/AACR West Coast Prostate Cancer Dream Team

Background: The efficacy of androgen signaling inhibitors such as Abiraterone (Abi) or Enzalutamide (Enz) has changed the standard of care in mCRPC. However, adaptive resistance to these agents is a consistent outcome with this therapy that undermines their benefit. The mechanisms underlying acquired resistance to Abi or Enz are poorly understood. The goals of the WCDT project are to identify the molecular pathways underlying the adaptive response to these targeted therapies through expression and mutational analysis of metastatic biopsies. Methods: Following central radiologic review, eligible mCRPC pts underwent biopsy at one of 5 WCDT clinical sites, using a uniform biopsy protocol. Tissue was both frozen, and formalin fixed/paraffin embedded (FFPE). Frozen samples were subject to laser capture microdissection for isolation of RNA and DNA enriched for mCRPC. FFPE tissue underwent a CLIA-certified assessment of a mutational panel, IHC for PTEN, and fluorescence in situ hybridization (FISH) for AR+. Pathway assessment is performed using RNA-seq and mutation data from mCRPC biopsies mapped onto a comprehensive pathway database connecting a tumor sample with genetic regulatory logic. Results: 70 of 300 planned mCRPC pts have undergone a metastasis biopsy. To date, biopsies have been obtained prior to treatment and following progression from one patient receiving Abi and one receiving Enz. Data collection from biopsies has been possible in 52 of 72 samples (72% success rate), and clinically actionable results have been returned to the care providers for 50 samples. The most commonly mutated gene assessed by the mutational panel was p53. Importantly, acquired mutation did not appear to be a mechanism for drug resistance in mCRPC, as the prevalence of tumors positive for mutations in genes contained in the panel was lower in patients who had progressed on Abi or Enz (9 of 16, 56%) than it was in treatment naive patients (14 of 17, 82%). Gene expression-based signatures uncovered several pathways enriched in Abiraterone naive compared to resistant samples. Conclusions: Genomic sequencing and expression analysis can be accomplished in small bone and soft tissue mCRPC biopsies. Pathway-based gene expression analysis appears to be a promising strategy to identify adaptive processes and targeting opportunities in Abi resistant mCRPC. Citation Format: Jack F. Youngren, Adam Foye, George Thomas, Joshua M. Stuart, Ted Goldstein, Baertsch Robert, Adrian Bivol, Artem Sokolov, Charles J. Ryan, Nader Pourmand, Tomasz M. Beer, Christopher P. Evans, Christopher P. Evans, Primo Lara, Jr., Martin E. Gleave, Kim N. Chi, Robert E. Reiter, Matthew Rettig, Owen Witte, Eric J. Small. Identification of pathways associated with abiraterone resistance in metastatic castration resistant prostate cancer: Preliminary results from the SU2C/AACR West Coast Prostate Cancer Dream Team. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Drug Sensitivity and Resistance: Improving Cancer Therapy; Jun 18-21, 2014; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(4 Suppl): Abstract nr A12.

Abstract PR02: Multiple Pathway Learning accurately predicts gene essentiality in the Cancer Cell Line Encyclopedia

We applied biologically-motivated feature transformations coupled with established machine learning methods to predict gene essentiality in CCLE cell line models. By leveraging additional large datasets, such as The Cancer Genome Atlas PanCancer12 data and MSigDB pathway definitions, we improved the robustness and biological interpretability of our models. We developed a multi-pathway learning (MPL) approach that associates a genetic pathway from MSigDB with a distinct kernel for use in a multiple kernel learning setting. We evaluated the performance of MPL compared to several other regression methods including random forests, kernel ridge regression, and elastic net linear models, We combined multiple approaches using an ensemble technique on the diverse set of predictors. We found that the best performing method was an ensemble combining MPL and random forest predictions. Both models utilized features derived from both gene expression and copy number data, the latter of which were filtered to those predicted as driver events in prior pan-cancer studies. The ensemble method was a joint winner in the recent DREAM 9 gene essentiality prediction challenge. MPL also demonstrated merit as a feature selector when used with other downstream methods. The ensemble performed best at predicting the essentiality of genes involved in cell cycle control (cyclins and cyclin-dependent kinases), protein degradation (proteasome complex), cell proliferation signaling (sonic hedgehog, Aurora-B, RAC1), apoptosis (RB1,TP53) and hypoxia response (VEGF, VHL). Many of the key genes in those pathways are known to be drivers of cancer progression, suggesting our method9s utility as a biomarker for detecting key tumorigenic events. The advantage of MPL is that mechanistically coherent gene sets are automatically selected as high scoring pathway kernels (HSPKs). We investigated whether the HSPKs identify cellular processes relevant to the loss of key genes. To do this, we inspected the HSPKs for a few of the most abundantly mutated genes in cancer. The MPL predictor for TP53 included the targets of this transcription factor as well as HSPKs involved in apoptosis, a cellular process regulated by TP53. The retinoblastoma gene (RB1) MPL predictor included RB1 targets as well as HSPKs involved in the regulation of histone deacetylase (HDAC) that interacts with RB1 to suppress DNA synthesis. These findings suggest trends in the MPL results could reveal a pathway-level view of the synthetic lethal architecture of cells. Such a map, that links patterns of pathway expression to potential genetic vulnerabilities, could provide an invaluable tool for exploring new avenues to target cancer cells. Citation Format: Vladislav Uzunangelov, Evan Paull, Sahil Chopra, Daniel Carlin, Adrian Bivol, Kyle Ellrott, Kiley Graim, Yulia Newton, Sam Ng, Artem Sokolov, Joshua Stuart. Multiple Pathway Learning accurately predicts gene essentiality in the Cancer Cell Line Encyclopedia. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR02.

Abstract A2-64: A signature catalog to classify tumor mixtures: Application to recognition of metastatic disease in prostate cancer

Background: Molecular-based subtypes most certainly play a role in cancer progression and treatment. The recent results from the The Cancer Genome Atlas (TCGA) Pan-Cancer-12 analyses revealed connections between the cell of origin and patient outcomes. For example, bladder cancers were found to relate to three major Pan-Cancer integrative subtypes, with adeno-like and squamous-like bladder cancers associated with poorer prognosis than tumors with bladder-distinct profiles. Furthermore, both adeno-like lung and squamous-like bladder cancers were found to be associated with the most aggressive form of the disease. Methods: We are collecting a catalog of molecular signatures for each subtype found from Pan-Cancer analyses in TCGA and from relevant external datasets. Our goal is to map every tumor sample to one or more signatures in this collection using machine-learning methods. This mapping will allow us to predict the subclonal composition of primary tumor biopsies and to compare them to inferences from the variant allele frequency analysis, shedding light on the gene expression changes associated with key events in tumor evolution. As a pilot study, we compared signatures derived from metastatic prostate samples to subtypes of primary prostate tumors. Our goal is to test whether a molecular profile of metastatic disease can be recognized early on in primary tumors. To do so, we used unsupervised classification of mRNA expression profiles to define clusters of metastatic disease from external datasets as well as separately for primary tumors including the TCGA prostate adenocarcinoma dataset. We then performed an all-against-all comparison of signatures derived from metastatic subtypes to signatures derived from primary tumor subtypes. Results: The majority of metastatic tumors are most closely associated with one out of four primary subtypes, suggesting we have identified a possible primary signature associated with more aggressive disease. The finding is supported by enrichment analysis of clinical variables in the primary subtypes. Specifically, the primary subtype most often associated with the metastatic tumors have higher Gleason scores and higher tumor grade. In addition, several molecular pathways (e.g. BioCarta Vitamin D Receptor and KEGG Integrins in Angiogenesis pathways) and genes (e.g. MMP9, FGA, and LYZ) were found to be associated with the location of metastasis. Conclusions: Training molecular subtype recognizers may hold promise for detecting minor populations of subclones in primary and metastatic tumors. The subclone decomposition can be used to detect the presence of more aggressive disease that may resist standard treatment regimens. We are now expanding our signature catalog to include a more comprehensive collection and applying to additional subtypes of interest. We will make all datasets and signatures available through a mature version of the UCSC TumorMap portal. Citation Format: Kiley Graim, Yulia Newton, Adrian Bivol, Artem Sokolov, Kyle Ellrott, Robert Baertsch, Joshua Stuart. A signature catalog to classify tumor mixtures: Application to recognition of metastatic disease in prostate cancer. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-64.

Abstract 4177: Identification of pathways relevant for metastatic site prediction in prostate cancer

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA Background and Significance: We address the problem of metastatic site prediction in prostate adenocarcinoma (PRAD), with a specific focus on identifying molecular pathways that are activated in association with the homing to a particular metastatic site. The approach can reveal the molecular mechanisms in metastatic cancer while also providing clues about potential drug targets. Further experimental validation of our findings may lead to the discovery of novel therapies for patients who are in the advanced stages of disease. Methods: We downloaded four PRAD datasets that contained met-site information from the Gene Expression Omnibus (GEO) and trained multi-class predictors on this set. The predictors were then evaluated on patient samples collected as part of the Stand Up To Cancer (SU2C) initiative. Standard normalization techniques were used to remove batch effects associated with non-biological factors such as the institution from which the materials were collected and/or assays conducted. We focused our attention on linear models due to their straightforward interpretation: higher weights indicate stronger association of the corresponding genomic features with a particular metastatic site. To identify pathways implicated by the relevant genomic features, we employed model regularization via group LASSO. This technique groups genes according to their pathway membership using the PathwayCommons database. The regularizer (penalty trading accurate classification with model complexity) sets the weights of an entire group to zero if those groups were uninformative for met-site prediction and non-zero otherwise. Results: We trained a multi-class linear predictor to recognize lymphatic node, liver and bone metastatic sites from gene expression data. The resulting model gave rise to two linear signatures: one that distinguished liver mets from the rest, and another that distinguished lymph node mets from the rest. The signatures were enriched for pathways commonly associated with liver development and liver progenitor cells, as well as pathways involved in integrin interactions on the cell surface. Based on the latter, we hypothesize that the up-regulation of particular integrin-signaling pathways may be responsible for driving the tendency of metastatic PRAD cells to prefer one site over another. We are currently in the process of investigating whether there is further evidence of this hypothesis in the SU2C data, as well as comparing group LASSO to other regularization techniques that also incorporate prior pathway information. Conclusion: We used linear methods to identify several pathways that may be responsible for localization of metastatic prostate adenocarcinoma cells to specific tissues. Our empirical results provide evidence that integrin-signaling may play a key role in this process. We are working on robustness evaluation of these findings, as well as experimental validation with our SU2C collaborators. Citation Format: Adrian Bivol, Kiley Graim, Evan Paull, Dan Carlin, Robert Baertsch, Artem Sokolov, Josh Stuart. Identification of pathways relevant for metastatic site prediction in prostate cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4177. doi:10.1158/1538-7445.AM2014-4177