Wei-Yi Cheng

Biomolecular events in cancer revealed by attractor metagenes

Mining gene expression profiles has proven valuable for identifying signatures serving as surrogates of cancer phenotypes. However, the similarities of such signatures across different cancer types have not been strong enough to conclude that they represent a universal biological mechanism shared among multiple cancer types. Here we present a computational method for generating signatures using an iterative process that converges to one of several precise attractors defining signatures representing biomolecular events, such as cell transdifferentiation or the presence of an amplicon. By analyzing rich gene expression datasets from different cancer types, we identified several such biomolecular events, some of which are universally present in all tested cancer types in nearly identical form. Although the method is unsupervised, we show that it often leads to attractors with strong phenotypic associations. We present several such multi-cancer attractors, focusing on three that are prominent and sharply defined in all cases: a mesenchymal transition attractor strongly associated with tumor stage, a mitotic chromosomal instability attractor strongly associated with tumor grade, and a lymphocyte-specific attractor.

Development of a Prognostic Model for Breast Cancer Survival in an Open Challenge Environment

A computational modeling approach that combined several molecular features yielded a robust breast cancer prognostic model that was independently validated in a new patient data set. DREAMing of Biomedicine’s Future Although they no longer live in the lab, scientific editors still enjoy doing experiments. The simultaneous publication of two unusual papers offered Science Translational Medicine’s editors the chance to conduct an investigation into peer-review processes for competition-based crowdsourcing studies designed to address problems in biomedicine. In a Report by Margolin et al. (which was peer-reviewed in the traditional way), organizers of the Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge (BCC) describe the contest’s conception, execution, and insights derived from its outcome. In the companion Research Article, Cheng et al. outline the development of the prognostic computational model that won the Challenge. In this experiment in scientific publishing, the rigor of the Challenge design and scoring process formed the basis for a new style of publication peer review. DREAM—Dialogue for Reverse Engineering Assessments and Methods—conducts a variety of computational Challenges with the goal of catalyzing the “interaction between theory and experiment, specifically in the area of cellular network inference and quantitative model building in systems biology.” Previous Challenges involved, for example, modeling of protein-protein interactions for binding domains and peptides and the specificity of transcription factor binding. In the BCC—which was a step in the translational direction—participants competed to create an algorithm that could predict, more accurately than current benchmarks, the prognosis of breast cancer patients from clinical information (age, tumor size, histological grade), genome-scale tumor mRNA expression data, and DNA copy number data. Participants were given Web access to such data for 1981 women diagnosed with breast cancer and used it to train computational models that were then submitted to a common, open-access computational platform as re-runnable source code. The predictive value of each model was assessed in real-time by calculating a concordance index (CI) of predicted death risks compared to overall survival in a held-out data set, and CIs were posted on a public leaderboard. The winner of the Challenge was ultimately determined when a select group of top models were validated in a new breast cancer data set. The winning model, described by Cheng et al., was based on sets of genes (signatures)—called attractor metagenes—that the same research group had previously shown to be associated, in various ways, with multiple cancer types. Starting with these gene sets and some other clinical and molecular features, the team modeled various feature combinations, selecting ones that improved performance of their prognostic model until they ultimately fashioned the winning algorithm. Before the BCC was initiated, Challenge organizers approached Science Translational Medicine about the possibility of publishing a Research Article that described the winning model. The Challenge prize would be a scholarly publication—a form of “academic currency.” The editors pondered whether winning the Challenge, with its built-in transparency and check on model reproducibility, would be sufficient evidence in support of the model’s validity to substitute for traditional peer review. Because the specific conditions of a Challenge are critical in determining the meaningfulness of the outcome, the editors felt it was not. Thus, they chose to arrange for peer-reviewers, chosen by the editors, to be embedded within the challenge process, as members of the organizing team—a so-called Challenge-assisted review. The editors also helped to develop criteria for determining the winning model, and if the criteria were not met, there would have been no winner—and no publication. Last, the manuscript was subjected to advisory peer review after it was submitted to the journal. So what new knowledge was gained about reviewing an article in which the result is an active piece of software? Reviewing such a model required that referees have access to the data and platform used for the Challenge and have the ability to re-run each participant’s code; in the context of the BCC, this requirement was easily achievable, because Challenge-partner Sage Bionetworks had created a platform (Synapse) with this goal in mind. In fact, both the training and validation datasets for the BCC are available to readers via links into Synapse (for a six month period of time). In general, this requirement should not be an obstacle, as there are code-hosting sites such as GitHub and TopCoder.com that can accommodate data sharing. Mechanisms for confidentiality would need to be built into any computational platform to be used for peer review. Finally, because different conventions are used in divergent scientific fields, communicating the science to an interdisciplinary audience is not a trivial endeavor. The architecture of the Challenge itself is critical in determining the real-world importance of the result. The question to be investigated must be framed so as to capture a significant outcome. In the BCC, participants’ models had to score better than a set of 60 different prognostic models developed by a team of expert programmers during a Challenge precompetition as well as a previously described first-generation 70-gene risk predictor. Thus, the result may or may not be superior to existing gene expression profiling tests used in clinical practice. This remains to be tested. It also remains to be seen whether prize-based crowdsourcing contests can make varied and practical contributions in the clinic. Indeed, DREAM and Sage Bionetworks have immediate plans to collaborate on new clinically relevant Challenges. But there is no doubt that the approach has value in solving big-data problems. For example, in a recent contest, non-immunologists generated a method for annotating the complex genome sequence of the antibody repertoire when the contest organizers translated the problem into generic language. In the BCC, the Challenge winners used a mathematical approach to identify biological modules that might, with continued investigation, teach us something about cancer biology. These examples support the notion that harnessing the expertise of contestants outside of traditional biological disciplines may be a powerful way to accelerate the translation of biomedical science to the clinic. The accuracy with which cancer phenotypes can be predicted by selecting and combining molecular features is compromised by the large number of potential features available. In an effort to design a robust prognostic model to predict breast cancer survival, we hypothesized that signatures consisting of genes that are coexpressed in multiple cancer types should correspond to molecular events that are prognostic in all cancers, including breast cancer. We previously identified several such signatures—called attractor metagenes—in an analysis of multiple tumor types. We then tested our attractor metagene hypothesis as participants in the Sage Bionetworks–DREAM Breast Cancer Prognosis Challenge. Using a rich training data set that included gene expression and clinical features for breast cancer patients, we developed a prognostic model that was independently validated in a newly generated patient data set. We describe our model, which was based on three attractor metagenes associated with mitotic chromosomal instability, mesenchymal transition, or lymphocyte-based immune recruitment.

A multi-cancer mesenchymal transition gene expression signature is associated with prolonged time to recurrence in glioblastoma.

A stage-associated gene expression signature of coordinately expressed genes, including the transcription factor Slug (SNAI2) and other epithelial-mesenchymal transition (EMT) markers has been found present in samples from publicly available gene expression datasets in multiple cancer types, including nonepithelial cancers. The expression levels of the co-expressed genes vary in a continuous and coordinate manner across the samples, ranging from absence of expression to strong co-expression of all genes. These data suggest that tumor cells may pass through an EMT-like process of mesenchymal transition to varying degrees. Here we show that, in glioblastoma multiforme (GBM), this signature is associated with time to recurrence following initial treatment. By analyzing data from The Cancer Genome Atlas (TCGA), we found that GBM patients who responded to therapy and had long time to recurrence had low levels of the signature in their tumor samples (P = 3×10−7). We also found that the signature is strongly correlated in gliomas with the putative stem cell marker CD44, and is highly enriched among the differentially expressed genes in glioblastomas vs. lower grade gliomas. Our results suggest that long delay before tumor recurrence is associated with absence of the mesenchymal transition signature, raising the possibility that inhibiting this transition might improve the durability of therapy in glioma patients.

Human cancer cells express Slug-based epithelial-mesenchymal transition gene expression signature obtained in vivo

BackgroundThe biological mechanisms underlying cancer cell motility and invasiveness remain unclear, although it has been hypothesized that they involve some type of epithelial-mesenchymal transition (EMT).MethodsWe used xenograft models of human cancer cells in immunocompromised mice, profiling the harvested tumors separately with species-specific probes and computationally analyzing the results.ResultsHere we show that human cancer cells express in vivo a precise multi-cancer invasion-associated gene expression signature that prominently includes many EMT markers, among them the transcription factor Slug, fibronectin, and α-SMA. We found that human, but not mouse, cells express the signature and Slug is the only upregulated EMT-inducing transcription factor. The signature is also present in samples from many publicly available cancer gene expression datasets, suggesting that it is produced by the cancer cells themselves in multiple cancer types, including nonepithelial cancers such as neuroblastoma. Furthermore, we found that the presence of the signature in human xenografted cells was associated with a downregulation of adipocyte markers in the mouse tissue adjacent to the invasive tumor, suggesting that the signature is triggered by contextual microenvironmental interactions when the cancer cells encounter adipocytes, as previously reported.ConclusionsThe known, precise and consistent gene composition of this cancer mesenchymal transition signature, particularly when combined with simultaneous analysis of the adjacent microenvironment, provides unique opportunities for shedding light on the underlying mechanisms of cancer invasiveness as well as identifying potential diagnostic markers and targets for metastasis-inhibiting therapeutics.

Breast Cancer Prognostic Biomarker Using Attractor Metagenes and the FGD3–SUSD3 Metagene

BACKGROUND The winning model of the Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge made use of several molecular features, called attractor metagenes, as well as another metagene defined by the average expression level of the two genes FGD3 and SUSD3. This is a follow-up study toward developing a breast cancer prognostic test derived from and improving upon that model. METHODS We designed a feature selector facility calculating the prognostic scores of combinations of features, including those that we had used earlier, as well as those used in existing breast cancer biomarker assays, identifying the optimal selection of features for the test. RESULTS The resulting test, called BCAM (Breast Cancer Attractor Metagenes), is universally applicable to all clinical subtypes and stages of breast cancer and does not make any use of breast cancer molecular subtype or hormonal status information, none of which provided additional prognostic value. BCAM is composed of several molecular features: the breast cancer-specific FGD3-SUSD3 metagene, four attractor metagenes present in multiple cancer types (CIN, MES, LYM, and END), three additional individual genes (CD68, DNAJB9, and CXCL12), tumor size, and the number of positive lymph nodes. CONCLUSIONS Our analysis leads to the unexpected and remarkable suggestion that ER, PR, and HER2 status, or molecular subtype classification, do not provide additional prognostic value when the values of the FGD3-SUSD3 and attractor metagenes are taken into consideration. IMPACT Our results suggest that BCAMs prognostic predictions show potential to outperform those resulting from existing breast cancer biomarker assays.

Abstract 2878: BCAM (breast cancer attractor metagenes): A new tool for assessing breast cancer prognosis

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA The winning model of the Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge (Sci Transl Med, 17 April 2013: Vol. 5, Issue 181, p. 181ra50) made use of several novel molecular features, called attractor metagenes, as well as another metagene involving the expression levels of two genes, FGD3 and SUSD3, which are genomically adjacent to each other. Here we present the results of our follow-up work developing a breast cancer prognostic test, called BCAM (Breast Cancer Attractor Metagenes). BCAM was derived from the Challenge winning models by excluding unusable features and optimizing performance in predicting breast cancer specific survival. BCAM incorporates underlying tumor biology by including five molecular features: the FGD3-SUSD3 metagene and four attractor metagenes (CIN, MES, LYM, and HER2, which are associated with mitotic chromosomal instability, mesenchymal transition, lymphocyte infiltration, and expression of the HER2 amplicon, respectively); as well as incorporating the extent of disease: tumor size and the number of positive lymph nodes. Based on analysis of three breast cancer data sets with appropriate whole transcriptome and clinical outcomes data (allowing for time to recurrence as a phenotype), our results suggest that the combination of features used in BCAM outperforms the combination of features used in existing breast cancer biomarker products: Oncotype DX, Mammaprint and PAM50. The molecular features in BCAM were also shown to have improved performance against the Oncotype DX and PAM50 features in the subset of ER positive tumors treated with hormonal therapy, and against MammaPrint in the subset of lymph node negative tumors with size less than 50 mm. In addition, performance was significantly improved when the “ER group” of the Oncotype DX panel was replaced by the FGD3-SUSD3 metagene. All evaluations of prognostic performance were shown to be statistically significant by multiple rounds of random splitting. We also present a web-based version of BCAM (http://128.59.65.24:8080/brcabiomarker), in which uploaded gene expression data from a patients tumor are analyzed and integrated with tumor size and number of positive nodes, and a report is generated containing a percentile prognostic score against the 2,000 patient METABRIC data set (Nature, 21 June 2012: Vol. 486, Issue 7403, p. 346-52), the corresponding ten-year breast cancer specific survival rate, and additional scores representing individual molecular features. We currently envision this facility evolving into an open crowd-sourced biomarker tool providing prognosis and therapy response prediction. This facility will also allow these scores to be directly obtained and used in any breast cancer cohort study. Citation Format: Wei-Yi Cheng, Tai-Hsien Ou Yang, Matthew Maurer, Dimitris Anastassiou. BCAM (breast cancer attractor metagenes): A new tool for assessing breast cancer prognosis. [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 2878. doi:10.1158/1538-7445.AM2014-2878

Human Cancer Cells Express Slug-based Epithelial-Mesenchymal Transition Gene Signature Obtained in Vivo

The biological mechanisms underlying cancer cell motility and invasiveness remain unclear, although it has been hypothesized that they involve some type of epithelial-mesenchymal transition (EMT). Here we show that human cancer cells express in vivo a precise multi-cancer invasion-associated gene expression signature characterized by the prominent presence of collagen COL11A1 and thrombospondin THBS2. The signature is present in the expression of all solid tumor datasets that we analyzed and includes numerous EMT markers, among them the transcription factor Slug, fibronectin, and α SMA. Using xenograft models of human cancer cells in immunocompromised mice and profiling the harvested tumors separately with species specific probes, we found that human, but not mouse, cells express most of the genes of the signature and Slug is the only upregulated EMT-inducing transcription factor. Taken together with the presence of the signature in many publicly available datasets, our results suggest that this Slug-based EMT signature is produced by the cancer cells themselves in multiple cancer types, including even nonepithelial cancers such as neuroblastoma. Furthermore, we found that the presence of the signature in human xenografted cells was associated with a downregulation of adipocyte markers in the mouse tissue adjacent to the invasive tumor, suggesting contextual microenvironmental interactions when the cancer cells encounter adipocytes, as previously reported. The known and consistent gene composition of this cancer EMT signature, particularly when combined with simultaneous analysis of the adjacent microenvironment, provides unique opportunities for shedding light on the underlying mechanisms of cancer invasiveness as well as identifying potential diagnostic markers and targets for metastasis-inhibiting therapeutics. N at ur e P re ce di ng s : h dl :1 01 01 /n pr e. 20 11 .6 24 3. 1 : P os te d 13 A ug 2 01 1