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Supplementary Methods and Example Analyses. This file provides additional detail on the MultiAssayExperiment data class, methods of preparation of the TCGA objects, methods and additional results for the example analyses. It includes Supplemental Figures 1-2 (Mogsa enrichment scores for Hallmark gene sets), Supplemental Figure 3 (correlation between copy number, mRNA, and protein profiles for the NCI-60 cell lines), Supplemental Table 1 (comparison between the features of MultiAssayExperiment and MultiDataSet), Supplemental Table 2 (summary table of TCGA datasets), and Supplemental Table 3 (elements of the MultiAssayExperiment data class).

Multiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple ‘omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. Cancer Res; 77(21); e39–42. ©2017 AACR.

Supplementary Table 1

Understanding the molecular and phenotypic profile of colorectal cancer (CRC) in West Africa is vital to addressing the regions rising burden of disease. Tissue from unselected Nigerian patients was analyzed with a multigene, next-generation sequencing assay. The rate of microsatellite instability is significantly higher among Nigerian CRC patients (28.1%) than patients from The Cancer Genome Atlas (TCGA, 14.2%) and Memorial Sloan Kettering Cancer Center (MSKCC, 8.5%, P, Understanding the molecular and phenotypic profile of colorectal cancer (CRC) in West Africa is important for early detection and treatment. Here, the authors use a multigene next-generation sequencing panel to identify genomic differences in Nigerian CRCs compared to those from TCGA and MSKCC cohorts.

Purpose: VEGFR2-directed therapy is commonly used to treat metastatic esophagogastric cancer, but disease progresses in most patients within months. Therapeutic resistance is likely mediated in part by co-occurring amplifications of the genes for multiple oncogenic receptor tyrosine kinases (RTK). We therefore tested the efficacy of combined inhibition of VEGFR1-3, PDGFα/β, and FGFR1-3 using nintedanib. Patients and Methods: Patients with metastatic esophagogastric adenocarcinoma and disease progression on first-line chemotherapy were treated with nintedanib 200 mg twice daily. The primary endpoint was progression-free survival (PFS) at 6 months; secondary endpoints included tumor response and safety. Tumor biopsies were profiled by targeted capture next-generation sequencing (NGS) to identify molecular predictors of drug response. Results: The study achieved its primary endpoint; 6 of 32 patients (19%) were progression-free at 6 months. With a median follow-up of 14.5 months among survivors, median overall survival (OS) was 14.2 months [95% confidence interval (CI), 10.8 months–NR]. Nintedanib was well tolerated; grade ≥ 3 toxicities were uncommon and included grade 3 hypertension (15%) and liver enzyme elevation (4%). FGFR2 alterations were identified in 18% of patients but were not predictive of clinical outcome on nintedanib therapy. Alterations in cell-cycle pathway genes were associated with worse median PFS (1.61 months for patients with cell-cycle pathway alterations vs. 2.66 months for patients without, P = 0.019). Conclusions: Nintedanib treatment resulted in modest disease stabilization in patients with metastatic esophagogastric cancer. Alterations in cell-cycle pathway genes and increased global copy-number alteration (CNA) burden warrant further study as prognostic or predictive biomarkers.

Gene-set analysis (GSA) summarizes information from molecule to gene-set level to facilitate biological interpretation of molecular profiling experiments. We present a statistical framework for single sample GSA of multiple 'omic data. MOGSA learns the most variant biomolecules and integrates data sets to generate gene set scores (GSS) for each sample. It extracts the contribution of individual data sets and biomolecules to GSS. MOGSA is rigorously benchmarked with simulated and real biological data and is implemented in the Bioconductor package “mogsa.”, Graphical Abstract Highlights MOGSA is a new integrative multi 'omics single sample gene set analysis method. Integrating diverse biomolecular data increases the power to discover gene sets. MOGSA evaluates the contribution of biomolecules, samples and data sets to gene sets. The method and tutorial are available in the R/Bioconductor package mogsa., Gene-set analysis (GSA) summarizes individual molecular measurements to more interpretable pathways or gene-sets and has become an indispensable step in the interpretation of large-scale omics data. However, GSA methods are limited to the analysis of single omics data. Here, we introduce a new computation method termed multi-omics gene-set analysis (MOGSA), a multivariate single sample gene-set analysis method that integrates multiple experimental and molecular data types measured over the same set of samples. The method learns a low dimensional representation of most variant correlated features (genes, proteins, etc.) across multiple omics data sets, transforms the features onto the same scale and calculates an integrated gene-set score from the most informative features in each data type. MOGSA does not require filtering data to the intersection of features (gene IDs), therefore, all molecular features, including those that lack annotation may be included in the analysis. Using simulated data, we demonstrate that integrating multiple diverse sources of molecular data increases the power to discover subtle changes in gene-sets and may reduce the impact of unreliable information in any single data type. Using real experimental data, we demonstrate three use-cases of MOGSA. First, we show how to remove a source of noise (technical or biological) in integrative MOGSA of NCI60 transcriptome and proteome data. Second, we apply MOGSA to discover similarities and differences in mRNA, protein and phosphorylation profiles of a small study of stem cell lines and assess the influence of each data type or feature on the total gene-set score. Finally, we apply MOGSA to cluster analysis and show that three molecular subtypes are robustly discovered when copy number variation and mRNA data of 308 bladder cancers from The Cancer Genome Atlas are integrated using MOGSA. MOGSA is available in the Bioconductor R package “mogsa.”

Background Angiogenesis is critical to gastroesophageal adenocarcinoma growth and metastasis. Regorafenib is a multikinase inhibitor targeting angiogenic and stromal receptor tyrosine kinases. We evaluated whether regorafenib augments the antitumor effect of first-line chemotherapy in metastatic esophagogastric cancer. Materials and methods Patients with previously untreated metastatic gastroesophageal adenocarcinoma received 5-fluorouracil, leucovorin, and oxaliplatin (mFOLFOX6) every 14 days and regorafenib 160 mg daily on days 4 to 10 of each 14-day cycle. The primary endpoint was 6-month progression-free survival (PFS). To identify predictive biomarkers of outcome, we examined correlations between genomic characteristics of sequenced pretreatment tumors and PFS. Results Between August 2013 and November 2014, 36 patients with metastatic esophagogastric cancer were accrued to this single-center phase II study (NCT01913639). The most common grade 3-4 treatment-related adverse events were neutropenia (36%), leucopenia (11%) and hypertension (8%). The 6-month PFS was 53% (95% confidence interval [CI], 38%-71%), the objective response rate was 54% (95% CI, 37%-70%), and the disease control rate was 77% (95% CI, 67%-94%). Next-generation sequencing did not identify any genomic alterations significantly correlated with response, and there was no association between homologous recombination deficiency and PFS with platinum-based chemotherapy. Conclusion Regorafenib (one week on-one week off schedule) is well tolerated in combination with first-line FOLFOX but does not improve 6-month PFS relative to historical control. Implications for practice Prognosis for metastatic esophagogastric cancer remains poor despite modern systemic therapy regimens. This phase II trial indicates that the combination of regorafenib and FOLFOX is well tolerated but does not add to the efficacy of first-line chemotherapy in metastatic esophagogastric cancer. Notably, recently reported data suggest potential synergy between regorafenib and the PD-1 inhibitor nivolumab. As this study demonstrates that regorafenib plus FOLFOX is safe, and combined chemotherapy and immunotherapy show favorable toxicity profiles, future studies combining immunotherapy with regorafenib and chemotherapy may be feasible.

Background: Recent efforts to improve outcomes for high-grade serous ovarian cancer, a leading cause of cancer death in women, have focused on identifying molecular subtypes and prognostic gene signatures, but existing subtypes have poor cross-study robustness. We tested the contribution of cell admixture in published ovarian cancer molecular subtypes and prognostic gene signatures. Methods: Gene signatures of tumor and stroma were developed using paired microdissected tissue from two independent studies. Stromal genes were investigated in two molecular subtype classifications and 61 published gene signatures. Prognostic performance of gene signatures of stromal admixture was evaluated in 2,527 ovarian tumors (16 studies). Computational simulations of increasing stromal cell proportion were performed by mixing gene-expression profiles of paired microdissected ovarian tumor and stroma. Results: Recently described ovarian cancer molecular subtypes are strongly associated with the cell admixture. Tumors were classified as different molecular subtypes in simulations where the percentage of stromal cells increased. Stromal gene expression in bulk tumors was associated with overall survival (hazard ratio, 1.17; 95% confidence interval, 1.11–1.23), and in one data set, increased stroma was associated with anatomic sampling location. Five published prognostic gene signatures were no longer prognostic in a multivariate model that adjusted for stromal content. Conclusions: Cell admixture affects the interpretation and reproduction of ovarian cancer molecular subtypes and gene signatures derived from bulk tissue. Elucidating the role of stroma in the tumor microenvironment and in prognosis is important. Impact: Single-cell analyses may be required to refine the molecular subtypes of high-grade serous ovarian cancer.

PurposeRecent efforts to improve outcomes for high-grade serous ovarian cancer, a leading cause of cancer death in women, have focused on identifying molecular subtypes and prognostic gene signatures, but existing subtypes have poor cross-study robustness. We tested the contribution of cell admixture in published ovarian cancer molecular subtypes and prognostic gene signatures.Experimental DesignPublic gene expression data, two molecular subtype classifications, and 61 published gene signatures of ovarian cancer were examined. Using microdissected data, we developed gene signatures of ovarian tumor and stroma. Computational simulations of increasing stromal cell proportion were performed by mixing gene expression profiles of paired microdissected ovarian tumor and stroma.ResultsEstablished ovarian cancer molecular subtypes are strongly associated with the cell admixture. Tumors were classified as different molecular subtypes in simulations, when the percentage of stromal cells increased. Stromal gene expression in bulk tumor was weakly prognostic, and in one dataset, increased stroma was associated with anatomic sampling location. Five published prognostic gene signatures were no longer prognostic in a multivariate model that adjusted for stromal content alone.ConclusionsThe discovery that molecular subtypes of high grade serous ovarian cancer is influenced by cell admixture, and stromal cell gene expression is crucial for interpretation and reproduction of ovarian cancer molecular subtypes and gene signatures derived from bulk tissue. Single cell analysis may be required to refine the molecular subtypes of high grade serous ovarian cancer. Because stroma proportion was weakly prognostic, elucidating the role of the tumor microenvironment’s components will be important.Translational relevanceOvarian cancer is a leading cause of cancer death in women in the United States. Although the tumor responds to standard therapy for the majority of patients, it frequently recurs and becomes drug-resistant. Recent efforts have focused on identifying molecular subtypes and prognostic gene signatures of ovarian cancer in order to tailor therapy and improve outcomes. This study demonstrates that molecular subtype identification depends on the ratio of tumor to stroma within the specimen. We show that the specific anatomic location of the biopsy may influence the proportion of stromal involvement and potentially the resulting gene expression pattern. It will be crucial for these factors to be taken into consideration when interpreting and reproducing ovarian cancer molecular subtypes and gene signatures derived using bulk tissue and single cells. Furthermore, it will be important to define the relative proportions of stromal cells and model their prognostic importance in the tumor microenvironment.

Multiomics experiments are increasingly commonplace in biomedical research and add layers of complexity to experimental design, data integration, and analysis. R and Bioconductor provide a generic framework for statistical analysis and visualization, as well as specialized data classes for a variety of high-throughput data types, but methods are lacking for integrative analysis of multiomics experiments. The MultiAssayExperiment software package, implemented in R and leveraging Bioconductor software and design principles, provides for the coordinated representation of, storage of, and operation on multiple diverse genomics data. We provide the unrestricted multiple ‘omics data for each cancer tissue in The Cancer Genome Atlas as ready-to-analyze MultiAssayExperiment objects and demonstrate in these and other datasets how the software simplifies data representation, statistical analysis, and visualization. The MultiAssayExperiment Bioconductor package reduces major obstacles to efficient, scalable, and reproducible statistical analysis of multiomics data and enhances data science applications of multiple omics datasets. Cancer Res; 77(21); e39–42. ©2017 AACR.