Tissue and molecular subtype distribution among the samples in the entire cohort (A-B), and in the pan-cancer cluster (C-D). The pie charts represent the number of samples from each tissue of origin in the entire cohort (A) and the integrated pan-cancer cluster (C). Black and white matrices illustrate the presence of molecular features of each platform (x-axis) across samples (y-axis), in the entire cohort (B) or in the integrated pan-cancer cluster (D). Data available for this sample for a given platform is marked black, otherwise the entry is white.

Video showing basic functionality of the TumorMap for the first-time user.

Supplemental Figure 5: (A) Overview of the method to compute differential expression for samples in the integrated pan-cancer group vs. other samples in the TCGA cohort. Tissue composition imbalance was corrected for by performing t-tests within each tissue. For each gene, t-statistics were computed within each tumor type separately and then summarized per-gene t-statistics were calculated as an arithmetic mean, weighted by the inverse variance of the all the tissue-specific t-statistics values. (B) The distribution of log-transformed TPM values estimated from the mRNA-Seq data showing a normal-like distribution. (C) Comparing the log-transformed TPM values of the mRNA-Seq data to a normal distribution shows a reasonable agreement with the tumor data having slightly heavier tails. (D) Venn diagram representing the innate and adaptive immune systems and different levels of evidence supporting higher activity of each of the components of those systems in the pan-cancer cluster when compared to the rest of the TCGA cohort. We found evidence of both innate and adaptive immune system signaling with a number of different analyses. (E) Detailed version of the immune-related pathway characterizing the integrated pan-cancer cluster. This version of the pathway shows additional genes and their connections that are not shown in the summarized version of the immune signaling pathway of Figure 1D of the main text.

Supplemental Table 1A: Differential expression scores used in the pan-cancer cluster GSEA analysis. These scores represent integrated t-statistic scores from the differential analysis within each tissue (see Supplemental Methods). Supplemental Table 1B: Full results of the GSEA analysis of the pan-cancer cluster, based on differential expression scores. Supplemental Table 1C: Per-sample ESTIMATE scores used in analysis of the pan-cancer cluster. Supplemental Table 1D: Per-sample surrogate purity scores used in analysis of the pan-cancer cluster. Supplemental Table 1E: List of genes located on sex chromosomes that were excluded from the methylation dataset in order to analyze only autosomal gene features. Supplemental Table 1F: Attributes available in the TumorMap that annotate metadata of the samples, along with descriptions of those attributes. Supplemental Table 1G: Statistical tests computed by different attribute enrichment analysis (AEA) tools available in the TumorMap. Supplemental Table 1H: List of 82 samples in the pan-cancer cluster in the integrated map as well as tissue composition, along with the number of samples, in the integrated pan-cancer cluster. Supplemental Table 1I: Input data for the LAML survival analysis.

Background Metastatic castration resistant prostate cancer (mCRPC) is incurable and progression after drugs that target the androgen receptor-signaling axis is inevitable. Thus, there is an urgent need to develop more effective treatments beyond hormonal manipulation. We sought to identify activated kinases in mCRPC as therapeutic targets for existing, approved agents, with the goal of identifying candidate drugs for rapid translation into proof of concept Phase II trials in mCRPC. Methods To identify evidence of activation of druggable kinases in these patients, we compared mRNA expression from metastatic biopsies of patients with mCRPC (n = 101) to mRNA expression in localized prostate from TCGA and used this analysis to infer differential kinase activity. In addition, we assessed the differential phosphorylation levels for key MAPK pathway kinases between mCRPC and localized prostate cancers. Results Transcriptomic profiling of 101 patients with mCRPC as compared to patients with localized prostate cancer identified evidence of hyperactive ERK1, and whole genome sequencing revealed frequent amplifications of members of the MAPK pathway in 32% of this cohort. Next, we confirmed elevated levels of phosphorylated ERK1/2 in castration resistant prostate cancer as compared to untreated primary prostate cancer. We observed that the presence of detectable phosphorylated ERK1/2 in the primary tumor is associated with biochemical failure after radical prostatectomy independent of clinicopathologic features. ERK1 is the immediate downstream target of MEK1/2, which is druggable with trametinib, an approved therapeutic for melanoma. Trametinib elicited a profound biochemical and clinical response in a patient who had failed multiple prior treatments for mCRPC. Conclusions We conclude that pharmacologic targeting of the MEK/ERK pathway may be a viable treatment strategy for patients with refractory metastatic prostate cancer. An ongoing Phase II trial tests this hypothesis.

Abstract Vast amounts of molecular data are being collected on tumor samples, which provide unique opportunities for discovering trends within and between cancer subtypes. Such cross-cancer analyses require computational methods that enable intuitive and interactive browsing of thousands of samples based on their molecular similarity. We created a portal called TumorMap to assist in exploration and statistical interrogation of high-dimensional complex “omics” data in an interactive and easily interpretable way. In the TumorMap, samples are arranged on a hexagonal grid based on their similarity to one another in the original genomic space and are rendered with Google's Map technology. While the important feature of this public portal is the ability for the users to build maps from their own data, we pre-built genomic maps from several previously published projects. We demonstrate the utility of this portal by presenting results obtained from The Cancer Genome Atlas project data. Cancer Res; 77(21); e111–4. ©2017 AACR.

Rapid species radiation due to adaptive changes or occupation of new ecospaces challenges our understanding of ancestral speciation and the relationships of modern species. At the molecular level, rapid radiation with successive speciations over short time periods—too short to fix polymorphic alleles—is described as incomplete lineage sorting. Incomplete lineage sorting leads to random fixation of genetic markers and hence, random signals of relationships in phylogenetic reconstructions. The situation is further complicated when you consider that the genome is a mosaic of ancestral and modern incompletely sorted sequence blocks that leads to reconstructed affiliations to one or the other relative, depending on the fixation of their shared ancestral polymorphic alleles. The laurasiatherian relationships among Chiroptera, Perissodactyla, Cetartiodactyla, and Carnivora present a prime example for such enigmatic affiliations. We performed whole-genome screenings for phylogenetically diagnostic retrotransposon insertions involving the representatives bat (Chiroptera), horse (Perissodactyla), cow (Cetartiodactyla), and dog (Carnivora), and extracted among 162,000 preselected cases 102 virtually homoplasy-free, phylogenetically informative retroelements to draw a complete picture of the highly complex evolutionary relations within Laurasiatheria. All possible evolutionary scenarios received considerable retrotransposon support, leaving us with a network of affiliations. However, the Cetartiodactyla–Carnivora relationship as well as the basal position of Chiroptera and an ancestral laurasiatherian hybridization process did exhibit some very clear, distinct signals. The significant accordance of retrotransposon presence/absence patterns and flanking nucleotide changes suggest an important influence of mosaic genome structures in the reconstruction of species histories.

Tarsiers are phylogenetically located between the most basal strepsirrhines and the most derived anthropoid primates. While they share morphological features with both groups, they also possess uncommon primate characteristics, rendering their evolutionary history somewhat obscure. To investigate the molecular basis of such attributes, we present here a new genome assembly of the Philippine tarsier ( Tarsius syrichta ), and provide extended analyses of the genome and detailed history of transposable element insertion events. We describe the silencing of Alu monomers on the lineage leading to anthropoids, and recognize an unexpected abundance of long terminal repeat-derived and LINE1-mobilized transposed elements ( Tarsius interspersed elements; TINEs). For the first time in mammals, we identify a complete mitochondrial genome insertion within the nuclear genome, then reveal tarsier-specific, positive gene selection and posit population size changes over time. The genomic resources and analyses presented here will aid efforts to more fully understand the ancient characteristics of primate genomes.

179 Background: Aberrant androgen receptor (AR) phenotypes (e.g. splice variants, amplification) are strongly correlated with abiraterone (Abi) and enzalutamide (Enz) resistance. Low serum dehydroepiandrosterone (DHEA) is also associated with poor outcomes to AR-targeted therapy. Here, we investigate the relationship between serum DHEA, AR phenotype, and treatment efficacy in the WCDT. Methods: Patients (pts) with progressive mCRPC enrolled to the WCDT from UCSF, OHSU, UCLA, UBC, and UCD were included in this analysis. Serum DHEA was analyzed via high-pressure liquid chromatography and tandem mass spectrometry. Limit of quantitation (LQ) of DHEA was 0.2ng/mL. Full-length AR (AR-FL) and AR-v7 expression, obtained via RNAseq of metastatic tumor biopsies, was expressed as total reads mapped to gene. PSA response (PSAr) was defined as ≥ 50% PSA decline. Results: 35 pts were included in this analysis: 15 had treatment-naïve mCRPC, and 20 had prior AR-targeted therapy (14 Abi, 6 Enz). All pts were docetaxel-naïve. 11 pts had DHEA < LQ; of these, 10 had received prior AR-targeted therapy. 12 pts received subsequent chemotherapy, and 23 received subsequent Abi/Enz (7 Abi, 16 Enz). In pts with DHEA < LQ, 4/5 (80%) chemotherapy-treated pts had PSAr, while 1/6 (17%) Abi/Enz-treated pts had PSAr. In pts with DHEA ≥ LQ, 2/7 (27%) chemotherapy-treated pts had PSAr, while 9/16 (56%) Abi/Enz-treated pts had PSAr. The relationship between DHEA and PSAr was significantly different between the treatment groups (p = 0.0285). DHEA was higher in patients with PSAr to Abi/Enz versus those without PSAr (median, 0.871 versus 0.275ng/mL, p = 0.006). In an analysis of 27 pts with RNAseq data, the AR-v7/AR-FL ratio was significantly higher in those with DHEA < LQ (median ratio 8.91, versus 3.38 in DHEA ≥ LQ, p = 0.032). Conclusions: In this exploratory analysis, there is a significant difference in the relationship between DHEA and PSAr in chemotherapy- versus Abi/Enz-treated patients. DHEA < LQ was also associated with a higher AR-v7/AR-FL ratio, a potential avenue for further exploration of tumor biology. These results support a larger study to evaluate DHEA as a potential biomarker in mCRPC. Clinical trial information: NCT02432001.

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.

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.

Background: Clinical datasets typically have small sample size limiting the ability to generate inference with strong statistical significance. By combining: 1) existing datasets; 2)integrated analysis of different data types 3) curated pathways and 4) state of the art machine learning classifiers into one analysis pipeline, clinicians and researchers may be able to combine multiple observations to spot a pattern in an individual patient sample to base a critical decision about therapy. However, few tools exist to enable a diverse set of analytical results to be coherently pooled and disseminated to a research team. New web-based modalities are urgently needed to meet the demands of new genomics-based oncology use cases. Methods: Building on the success of MSKCC cBioPortal and UCSC Cancer Genomics Browser, an easy to use workbench allows users to generate new datasets by integrating these types of data: Import public datasets from NCBI,Wrangle data into standard format,Import clinical from OnCore,RNA Seq analysis and mutation analysis,Unsupervised clustering analysis,Sharing Data with collaborators in a secure manner,Differential gene expression analysis,Pathway enrichment analysis,Training classifiers to recognize events on existing datasets,Applying classifiers to new datasets to infer molecular events. We have created a new Medical Information System called MedBook to give a context for integrating multiple observations about patients and their biopsies into a unified social network. Data can be shared with clinicians and collaborators in a secure manner. Borrowing terminology from systems such as FaceBook and Google Plus, we describe an organization of collaborative analyses as evidence “streams” that allow sharing, annotation, and nucleation points for further analyses. Streams are composed of “evidence cards” that encapsulate figures and/or tables. Discussion threads allow interpretation and commenting on findings associated with each evidence card. Results: We demonstrate the utility of the stream concept using a gene expression based signature that predicts small cell disease in castration resistant prostate cancer. Importantly, the stream concept allows 1) redefinition of the signature to incorporate additional patient samples and clinical definitions on-the-fly, 2) the application of the signature to query samples, 3) viewing the predictions in cBioPortal to assist clinicians judgment of samples in the context of other relevant genomics events, 3) extend the results by leveraging additional bioinformatics apps like the Medbook Workbench and observation Deck 4) the recording of the provenance, and 5) creates a focal point for deliberation about patients, signatures, genes, mutations, pathways, and clinical inferences. Conclusions: Prior to MedBook, there was a cognitive gap between individual doctor observations and large research paper driven cohort meta-analysis. MedBook is able to fill the gap by facilitating the online integration necessary for data analysis in a dynamic clinical environment. Citation Format: Robert Baertsch, Chris Wong, Jack Youngren, Josh Stuart, Eric Small, Ted Goldstein. Using Medbook Workbench to create evidence streams to guide medical decisions. [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 A1-46.

Abstract Background: Multiple lines of evidence demonstrate that castration-resistant prostate cancers (CRPCs) remain reliant on androgens that activate the androgen receptor. Treatment with the novel anti-androgen enzalutamide improves progression-free survival and overall survival in CRPC patients; however, nearly 50% of patients never respond, and progression is universal (Beer, 2014, Scher, 2012). Mechanisms of enzalutamide resistance are largely unknown and few treatments exist for enzalutamide-resistant CRPC. Recent work demonstrates that CRPC tumors harbor countless genomic aberrations that control many hallmarks of cancer (Grasso, 2012, Hanahan and Weinberg, 2011). Based on our prior work (Heiser, 2012, Vaske, 2010), we hypothesize that these aberrations operate in concert to drive enzalutamide resistance and influence specific cancer hallmarks. Methods: We performed genomic studies using paired enzalutamide-sensitive and resistant LNCaP cell models. After transcriptional and copy number profiling, we performed an integrative pathway-informed PARADIGM analysis to identify differentially regulated cellular networks (Heiser, 2012, Vaske, 2010). These large-scale networks underwent regression analysis to identify sub-networks associated with acquired resistance. Genes residing within significant sub-networks were nominated for functional validation studies with RNAi or existing therapeutic compounds that impinge upon significant sub-networks in resistant models. Results: We used PARADIGM to compare the genomic alterations between the parental and enzalutamide-resistant cell line models and identified critical deregulated networks that may be targeted therapeutically. Currently, we are applying this same PARADIGM analysis to additional model systems and metastatic patient tumors obtained prior to treatment and at the time of disease progression through a West Coast Dream Team prospective enzalutamide clinical trial. Conclusions: PARADIGM integrative genomic analysis identifies specific sub-networks that contribute to enzalutamide resistance. A predicted outcome of our efforts is the development of rationally designed clinical trials with specific enzalutamide drug combinations in distinct molecular subsets of CRPC patients in the near-term. Citation Format: Josha Woodward, Carly King, Daniel Coleman, Robert Lisac, Jacob Schwartzman, Nicholas Wang, Martin Gleave, Joe Gray, George Thomas, Tomasz M. Beer, Katy Van Hook, Robert Baertsch, Ted Goldstein, Josh Stuart, Lina Gao, Joshua Urrutia, Laura Heiser, Joshi J. Alumkal. Integrative genomic analysis to identify emergent enzalutamide resistance mechanisms in castration-resistant prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 731. doi:10.1158/1538-7445.AM2015-731

Abstract 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 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 B1-37.

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.

Freed from the competition of large raptors, Paleocene carnivores could expand their newly acquired habitats in search of prey. Such changing conditions might have led to their successful distribution and rapid radiation. Today, molecular evolutionary biologists are faced, however, with the consequences of such accelerated adaptive radiations, because they led to sequential speciation more rapidly than phylogenetic markers could be fixed. The repercussions being that current genealogies based on such markers are incongruent with species trees.Our aim was to explore such conflicting phylogenetic zones of evolution during the early arctoid radiation, especially to distinguish diagnostic from misleading phylogenetic signals, and to examine other carnivore-related speciation events. We applied a combination of high-throughput computational strategies to screen carnivore and related genomes in silico for randomly inserted retroposed elements that we then used to identify inconsistent phylogenetic patterns in the Arctoidea group, which is well known for phylogenetic discordances.Our combined retrophylogenomic and in vitro wet lab approach detected hundreds of carnivore-specific insertions, many of them confirming well-established splits or identifying and solving conflicting species distributions. Our systematic genome-wide screens for Long INterspersed Elements detected homoplasy-free markers with insertion-specific truncation points that we used to distinguish phylogenetically informative markers from conflicting signals. The results were independently confirmed by phylogenetic diagnostic Short INterspersed Elements. As statistical analysis ruled out ancestral hybridization, these doubly verified but still conflicting patterns were statistically determined to be genomic remnants from a time of ancestral incomplete lineage sorting that especially accompanied large parts of Arctoidea evolution.