Multi-omics enrichment analysis of genomic cancer data
Data Management, Data-Intensive Systems, Computer Science Methods in Business Informatics
Epidemiology and Medical Biometry/Statistics
Human Genetics
Public Health, Healthcare Research, Social and Occupational Medicine
Final Report Abstract
Novel experimental high-throughput techniques for multi-omics profiling are increasingly applied in clinical settings for studying the onset, progression, and treatment of cancers. As a consequence, there is an increasing need of computational interpretation of various genomic highthroughput screens for a rapid and cost-effective elucidation of the molecular mechanisms underlying the etiology and progression of cancer. In this project, we developed specific methods for the analysis and interpretation of complex multi-omic cancer data, focusing on methods analyzing the interplay of defined groups of genes or microbes to identify molecular signatures driving onset, progression, and chemotherapy resistance of cancer. Using gene expression data of 1,800 ovarian cancer tumors, we found that the molecular diagnosis of ovarian cancer patients is frequently ambiguous. We published this work in the renowned journal Clinical Cancer Research, and The National Cancer Insitute’s Epidemiology and Genomics Research Program (EGRP) included our paper in their 2018 research highlights. To investigate the sources of the observed ambiguity, we conducted a multi-omic analysis of subtype evolution and heterogeneity, which involved DNA copy number analysis of several hundred ovarian tumors and high-resolution transcriptome profiling of several thousand individual tumor epithelial, immune, and stromal cells at a time. This revealed heterogeneity in tumor cell type composition that drove bulk transcriptome subtypes but demonstrated a lack of intrinsic subtypes among tumor epithelial cells. We published the results of this multi-omics analysis in the renowned journal Cancer Research, and the work has been featured in a press release of the School of Public Health of the City University of New York. This work is further augmented by two companion publications in JCO Clinical Cancer Informatics that detail the DNA copy number analysis from whole-exome sequencing data and further integration of the results with public oncology databases. To improve the biological interpretation of tumor transcriptome profiles, we assessed methods for gene set enrichment analysis (GSEA) on a curated compendium of 75 gene expression datasets investigating 34 cancer types. Based on that we made practical recommendations on how to interpret results depending on the type of gene set test conducted and which methods are best suited to effectively prioritize gene sets with high disease relevance. We published the results of the assessment. In an extension of this project to microbiome data, we translated concepts from GSEA and developed microbial signature resources to improve the interpretability of cancer-linked microbiome profiles. We therefore developed a comprehensive database of microbial signatures to enable systematic interpretation of microbiome studies in terms of microbial physiology and similarity to previously published results. Initial results published in Annals of Epidemiology and Nature Medicine demonstrated for the first time that an application of an GSEA equivalent to microbiome data is feasible and can provide novel etiological insights for existing and future microbiome studies.
Publications
- Multi-omic analysis of subtype evolution and heterogeneity in high-grade serous ovarian carcinoma. Cancer Research, 80(20):4335-45, 2020
Geistlinger L, Oh S, Ramos M, Schiffer L, LaRue R, Henzler C, Munro S, Daughters C, Nelson A, Winter- hoff B, Chang Z, Talukdar S, Shetty M, Mullaney S, Morgan M, Parmigiani G, Birrer M, Qin LX, Riester M, Starr T, Waldron L
(See online at https://doi.org/10.1158/0008-5472.CAN-20-0521) - Orchestrating single-cell analysis with Bioconductor. Nature Methods, 17(2):137-45, 2020
Amezquita RA, Lun ATL, Becht E, Carey VJ, Carpp LN, Geistlinger L, Marini F, Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pages H, Smith ML, Huber W, Morgan M, Gottardo R, Hicks SC
(See online at https://doi.org/10.1038/s41592-019-0654-x) - Microbiome connections with host metabolism and habitual diet from the PREDICT 1 metagenomic study. Nature Medicine, 27(2):321-32, 2021
Asnicar F, Berry S, [... 25 more authors ...], Geistlinger L, Waldron L, Davies R, Hadjigeorgiou G, Wolf J, Ordovas J, Gardner C, Franks P, Chan A, Huttenhower C, Spector T, Segata N
(See online at https://doi.org/10.1038/s41591-020-01183-8) - Toward a gold standard for benchmarking gene set enrichment analysis. Briefings in Bioinformatics, 22(1):545-56, 2021
Geistlinger L, Csaba G, Santarelli M, Ramos M, Schiffer L, Law C, Turaga N, Davis S, Carey V, Morgan M, Zimmer R, Waldron L
(See online at https://doi.org/10.1093/bib/bbz158)
