Bradley M. Broom

The consensus molecular subtypes of colorectal cancer

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression–based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor–β activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC—with clear biological interpretability—and the basis for future clinical stratification and subtype-based targeted interventions.

A pan-cancer proteomic perspective on The Cancer Genome Atlas

Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumors. Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyze 3,467 patient samples from 11 TCGA “Pan-Cancer” diseases, using 181 high-quality antibodies that target 128 total proteins and 53 post-translationally modified proteins. The resultant proteomic data is integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumor lineages. In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumor lineages. This integrative analysis, with an emphasis on pathways and potentially actionable proteins, provides a framework for determining the prognostic, predictive and therapeutic relevance of the functional proteome.

TCPA: a resource for cancer functional proteomics data

To the Editor: Functional proteomics represents a powerful approach to understand the pathophysiology and therapy of cancer. However, comprehensive cancer proteomic data have been relatively limited. As a part of The Cancer Genome Atlas (TCGA) Project and other efforts, we have generated protein expression data over a large number of tumor and cell line samples using reverse-phase protein arrays (RPPAs). RPPA is a quantitative, antibody-based technology that can assess multiple protein markers in many samples in a cost-effective, sensitive and highthroughput manner1,2. This technology has been extensively validated for both cell line and patient samples3–5, and its applications range from building reproducible prognostic models6 to generating experimentally verified mechanistic insights7. Our RPPA profiling platform includes extensively validated antibodies to nearly 200 proteins and phosphoproteins (Supplementary Methods and Supplementary Table 1). We are in the process of extending it to 500 independent proteins, covering all major signaling pathways, including PI3K, MAPK, mTOR, TGF-b, WNT, cell cycle, apoptosis, DNA damage, Hippo and Notch pathways. The current data release covers 4,379 tumor samples and consists of three parts (Supplementary Table 2). These are (i) TCGA tumor tissue sample sets: 3,467 samples from 11 cancer types, to be extended to 25 cancer types; (ii) independent tumor tissue sample sets: one endometrial tumor set (244 samples)7 and two ovarian tumor sets (99 and 130 samples, respectively)6, with other independent sets to be added soon; and (iii) tumor cell lines: 439 samples in four cell line sets, including both baseline and drug-treated cell lines. To our knowledge, this represents the largest publicly available collection of cancer functional proteomics data with parallel DNA and RNA data. To facilitate broad access to these RPPA data sets, we developed a user-friendly data portal, The Cancer Proteome Atlas (TCPA; http://bioinformatics.mdanderson.org/main/ TCPA:Overview). TCPA provides six modules: Summary, My Protein, Download, Visualization, Analysis and Cell Line (Fig. 1, i). The Summary module provides an overview of the RPPA data with detailed descriptions of each set (Fig. 1, ii). The Download module allows users to obtain any RPPA data set for analysis through a tree-view interface (Fig. 1, iii). The My Protein module provides detailed information about each RPPA protein: protein name, corresponding gene symbol, antibody status and source for the antibody. Users can examine the expression pattern of a protein of interest across different tumor types (for example, HER2 expression shown in Fig. 1, iv). The Visualization module provides two ways to examine global protein expression patterns in a specific RPPA data set . One is through a “next-generation clustered heat map” (Fig. 1, v), which allows users to zoom, navigate and scrutinize clustering patterns of samples or proteins and link those patterns to relevant biological information sources. The other is through a network view (Fig. 1, vi), which overlays the correlation between any two interacting partners in the protein interaction network (curated in the Human Protein Reference Database8). The Analysis module provides three analysis methods. (i) For correlation analysis, given a user-specified data set, correlations between any pair of proteins are presented in a table (Fig. 1, vii). Users can search the results by protein name, rank correlations or visualize the scatter plot of a correlation of interest (for example, there is a strong correlation between PKC-a and its phosphorylated form PKC-a_pS657 in endometrial cancer, as shown in Fig. 1, vii). (ii) For differential analysis, differentially expressed protein markers between two tumor types or subtypes can be identified. Given user-defined comparison groups, the Krzywinski and Cairo reply: We are in full agreement with the core of Katz’s argument that “distortion,” “embellishment,” “concealment” and “unrepresentative displays” have no place in principled communication of scientific information1. There is no controversy here—Katz extrapolates our storytelling metaphor beyond the intended scope of our column and argues against a position we did not take. The Points of View series offers effective strategies for visual presentation of complex data. The scope of the Storytelling column2 was limited to the construction of multipanel figures, which summarize as much as they support detailed exposition of the text. The column did not address how this text should be composed or the broad subject of motivation and design of scientific experiments. We described an approach to structure the flow of concepts and data across panels in a figure as a way to achieve a narrative, not confabulation. The design of visual communication requires a distinct approach because we organize and interpret images very differently than words (Gestalt principles of perception3). Whereas text is a natural place for nuance and alternative interpretations, multiple lines of argument in a figure can easily interfere with our perception of all its parts. Our suggestion to “leave out detail that does not advance the plot” speaks to controlling the amount of information to avoid an incomprehensible image and deferring it to the text, where it can be more suitably framed. To interpret it as “inconvenient truths are [to be] swept away” is a misrepresentation. Readers often look to the abstract and then the figures to provide them with an initial impression and overview of the findings. These are not the only elements that are reported, merely the first elements to be read. At each step, from abstract to figure to text, the level of detail is expanded to accommodate the preparedness of the reader to assimilate new information. It is often impossible to “do justice to experimental complexities and their myriad of interpretations” with a figure. We support Katz’s position that authors should include all the details necessary to appreciate, understand and reproduce the science through the use of visual and written communication that is clear, concise and thoughtful.

IBAG: Integrative Bayesian analysis of high-dimensional multiplatform genomics data

Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current data integration approaches are limited in that they do not consider the fundamental biological relationships that exist among the data obtained from different platforms. Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses hierarchical modeling to combine the data obtained from multiple platforms into one model. Results: We assess the performance of our methods using several synthetic and real examples. Simulations show our integrative methods to have higher power to detect disease-related genes than non-integrative methods. Using the Cancer Genome Atlas glioblastoma dataset, we apply the iBAG model to integrate gene expression and methylation data to study their associations with patient survival. Our proposed method discovers multiple methylation-regulated genes that are related to patient survival, most of which have important biological functions in other diseases but have not been previously studied in glioblastoma. Availability: http://odin.mdacc.tmc.edu/∼vbaladan/. Contact: veera@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online.

Inhibition of mTORC1/2 Overcomes Resistance to MAPK Pathway Inhibitors Mediated by PGC1α and Oxidative Phosphorylation in Melanoma

Metabolic heterogeneity is a key factor in cancer pathogenesis. We found that a subset of BRAF- and NRAS-mutant human melanomas resistant to the MEK inhibitor selumetinib displayed increased oxidative phosphorylation (OxPhos) mediated by the transcriptional coactivator PGC1α. Notably, all selumetinib-resistant cells with elevated OxPhos could be resensitized by cotreatment with the mTORC1/2 inhibitor AZD8055, whereas this combination was ineffective in resistant cell lines with low OxPhos. In both BRAF- and NRAS-mutant melanoma cells, MEK inhibition increased MITF expression, which in turn elevated levels of PGC1α. In contrast, mTORC1/2 inhibition triggered cytoplasmic localization of MITF, decreasing PGC1α expression and inhibiting OxPhos. Analysis of tumor biopsies from patients with BRAF-mutant melanoma progressing on BRAF inhibitor ± MEK inhibitor revealed that PGC1α levels were elevated in approximately half of the resistant tumors. Overall, our findings highlight the significance of OxPhos in melanoma and suggest that combined targeting of the MAPK and mTORC pathways may offer an effective therapeutic strategy to treat melanomas with this metabolic phenotype.

Combined Tumor Suppressor Defects Characterize Clinically Defined Aggressive Variant Prostate Cancers

Purpose: Morphologically heterogeneous prostate cancers that behave clinically like small-cell prostate cancers (SCPC) share their chemotherapy responsiveness. We asked whether these clinically defined, morphologically diverse, “aggressive variant prostate cancer (AVPC)” also share molecular features with SCPC. Experimental Design: Fifty-nine prostate cancer samples from 40 clinical trial participants meeting AVPC criteria, and 8 patient-tumor derived xenografts (PDX) from 6 of them, were stained for markers aberrantly expressed in SCPC. DNA from 36 and 8 PDX was analyzed by Oncoscan for copy number gains (CNG) and losses (CNL). We used the AVPC PDX to expand observations and referenced publicly available datasets to arrive at a candidate molecular signature for the AVPC. Results: Irrespective of morphology, Ki67 and Tp53 stained ≥10% cells in 80% and 41% of samples, respectively. RB1 stained <10% cells in 61% of samples and AR in 36%. MYC (surrogate for 8q) CNG and RB1 CNL showed in 54% of 44 samples each and PTEN CNL in 48%. All but 1 of 8 PDX bore Tp53 missense mutations. RB1 CNL was the strongest discriminator between unselected castration-resistant prostate cancer (CRPC) and the AVPC. Combined alterations in RB1, Tp53, and/or PTEN were more frequent in the AVPC than in unselected CRPC and in The Cancer Genome Atlas samples. Conclusions: Clinically defined AVPC share molecular features with SCPC and are characterized by combined alterations in RB1, Tp53, and/or PTEN. Clin Cancer Res; 22(6); 1520–30. ©2015 AACR.

Selective Genomic Copy Number Imbalances and Probability of Recurrence in Early-Stage Breast Cancer

A number of studies of copy number imbalances (CNIs) in breast tumors support associations between individual CNIs and patient outcomes. However, no pattern or signature of CNIs has emerged for clinical use. We determined copy number (CN) gains and losses using high-density molecular inversion probe (MIP) arrays for 971 stage I/II breast tumors and applied a boosting strategy to fit hazards models for CN and recurrence, treating chromosomal segments in a dose-specific fashion (-1 [loss], 0 [no change] and +1 [gain]). The concordance index (C-Index) was used to compare prognostic accuracy between a training (n = 728) and test (n = 243) set and across models. Twelve novel prognostic CNIs were identified: losses at 1p12, 12q13.13, 13q12.3, 22q11, and Xp21, and gains at 2p11.1, 3q13.12, 10p11.21, 10q23.1, 11p15, 14q13.2-q13.3, and 17q21.33. In addition, seven CNIs previously implicated as prognostic markers were selected: losses at 8p22 and 16p11.2 and gains at 10p13, 11q13.5, 12p13, 20q13, and Xq28. For all breast cancers combined, the final full model including 19 CNIs, clinical covariates, and tumor marker-approximated subtypes (estrogen receptor [ER], progesterone receptor, ERBB2 amplification, and Ki67) significantly outperformed a model containing only clinical covariates and tumor subtypes (C-Index full model, train[test]  =  0.72[0.71] ± 0.02 vs. C-Index clinical + subtype model, train[test]  =  0.62[0.62] ± 0.02; p<10−6). In addition, the full model containing 19 CNIs significantly improved prognostication separately for ER–, HER2+, luminal B, and triple negative tumors over clinical variables alone. In summary, we show that a set of 19 CNIs discriminates risk of recurrence among early-stage breast tumors, independent of ER status. Further, our data suggest the presence of specific CNIs that promote and, in some cases, limit tumor spread.

Targeting Poly(ADP-Ribose) Polymerase and the c-Myb–Regulated DNA Damage Response Pathway in Castration-Resistant Prostate Cancer

The DNA damage response is an appealing target for androgen inhibitor–resistant prostate cancer. Improving Therapy in Prostate Cancer Blocking androgen receptor (AR) signaling is standard therapy for prostate cancer, but tumor growth often recurs. Li et al. examined the gene expression profile in patient samples of primary and metastatic prostate cancer from patients in which AR signaling was blocked. Metastatic disease, which is associated with androgen inhibitor–resistant relapse, correlated with increased expression of genes encoding proteins in the DNA damage response (DDR) and MYB expression. AR and c-Myb shared a subset of target genes that encode DDR proteins; thus, c-Myb may functionally substitute for AR in the regulation of their common DDR targets. Targeting proteins within the Myb-regulated network in combination with a poly[adenosine 5′-diphosphate (ADP)–ribose] polymerase (PARP) inhibitor, which compromises the DDR, generated synergistic lethality in prostate cancer cells in culture and in mouse xenografts, suggesting potential new options for prostate cancer patients. Androgen deprivation is the standard treatment for advanced prostate cancer (PCa), but most patients ultimately develop resistance and tumor recurrence. We found that MYB is transcriptionally activated by androgen deprivation therapy or genetic silencing of the androgen receptor (AR). MYB silencing inhibited PCa growth in culture and xenografts in mice. Microarray data revealed that c-Myb and AR shared a subset of target genes that encode DNA damage response (DDR) proteins, suggesting that c-Myb may supplant AR as the dominant regulator of their common DDR target genes in AR inhibition–resistant or AR-negative PCa. Gene signatures including AR, MYB, and their common DDR-associated target genes positively correlated with metastasis, castration resistance, tumor recurrence, and decreased survival in PCa patients. In culture and in xenograft-bearing mice, a combination strategy involving the knockdown of MYB, BRCA1, or TOPBP1 or the abrogation of cell cycle checkpoint arrest with AZD7762, an inhibitor of the checkpoint kinase Chk1, increased the cytotoxicity of the poly[adenosine 5′-diphosphate (ADP)–ribose] polymerase (PARP) inhibitor olaparib in PCa cells. Our results reveal new mechanism-based therapeutic approaches for PCa by targeting PARP and the DDR pathway involving c-Myb, TopBP1, ataxia telangiectasia mutated– and Rad3-related (ATR), and Chk1.

A Semantic Web management model for integrative biomedical informatics.

Background Data, data everywhere. The diversity and magnitude of the data generated in the Life Sciences defies automated articulation among complementary efforts. The additional need in this field for managing property and access permissions compounds the difficulty very significantly. This is particularly the case when the integration involves multiple domains and disciplines, even more so when it includes clinical and high throughput molecular data. Methodology/Principal Findings The emergence of Semantic Web technologies brings the promise of meaningful interoperation between data and analysis resources. In this report we identify a core model for biomedical Knowledge Engineering applications and demonstrate how this new technology can be used to weave a management model where multiple intertwined data structures can be hosted and managed by multiple authorities in a distributed management infrastructure. Specifically, the demonstration is performed by linking data sources associated with the Lung Cancer SPORE awarded to The University of Texas MDAnderson Cancer Center at Houston and the Southwestern Medical Center at Dallas. A software prototype, available with open source at www.s3db.org, was developed and its proposed design has been made publicly available as an open source instrument for shared, distributed data management. Conclusions/Significance The Semantic Web technologies have the potential to addresses the need for distributed and evolvable representations that are critical for systems Biology and translational biomedical research. As this technology is incorporated into application development we can expect that both general purpose productivity software and domain specific software installed on our personal computers will become increasingly integrated with the relevant remote resources. In this scenario, the acquisition of a new dataset should automatically trigger the delegation of its analysis.

Modulating microtubule stability enhances the cytotoxic response of cancer cells to Paclitaxel.

The extracellular matrix protein TGFBI enhances the cytotoxic response of cancer cells to paclitaxel by affecting integrin signals that stabilize microtubules. Extending the implications of this knowledge, we tested the more general hypothesis that cancer cell signals which increase microtubule stability before exposure to paclitaxel may increase its ability to stabilize microtubules and thereby enhance its cytotoxicity. Toward this end, we carried out an siRNA screen to evaluate how genetic depletion affected microtubule stabilization, cell viability, and apoptosis. High content microscopic analysis was carried out in the absence or presence of paclitaxel. Kinase knockdowns that stabilized microtubules strongly enhanced the effects of paclitaxel treatment. Conversely, kinase knockdowns that enhanced paclitaxel-mediated cytotoxicity sensitized cells to microtubule stabilization by paclitaxel. The siRNA screen identified several genes that have not been linked previously to microtubule regulation or paclitaxel response. Gene shaving and Bayesian resampling used to classify these genes suggested three pathways of paclitaxel-induced cell death related to apoptosis and microtubule stability, apoptosis alone, or neither process. Our results offer a functional classification of the genetic basis for paclitaxel sensitivity and they support the hypothesis that stabilizing microtubules prior to therapy could enhance antitumor responses to paclitaxel treatment.