bioRxivCancer Biology
Disentangling heterogeneity of Malignant Pleural Mesothelioma through deep integrative omics analyses
L. Mangiante, N. Alcala, A. Di Genova, A. Sexton-Oates, A. Gonzalez-Perez, A. Khandekar, E. Bergstrom, J. Kim, C. Giacobi, N. Le-Stang, S. Boyault, C. Cuenin, S. Tabone-Eglinger, F. Damiola, C. Voegele, M. Ardin, M.-C. Michallet, L. Soudade, T. Delhomme, A. Poret, M. Brevet, M.-C. Copin, S. Giusiano-Courcambeck, D. Damotte, C. Girard, , Meyer
Abstract
Malignant Pleural Mesothelioma (MPM) is an aggressive cancer with rising incidence and challenging clinical management. Using the largest series of whole-genome sequencing data integrated with transcriptomic and epigenomic data using multi-omic factor analysis, we demonstrate that MPM heterogeneity arises from four sources of variation: tumor cell morphology, ploidy, adaptive immune response, and CpG island methylator phenotype. Previous genomic studies focused on describing only the tumor cell morphology factor, although we robustly find the three other sources in all publicly available cohorts. We prove how these sources of variation explain the biological functions performed by the cancer cells, and how genomic events shape MPM molecular profiles. We show how these new sources of variation help understand the heterogeneity of the clinical behavior of MPM and drug responses measured in cell lines. These findings unearth the interplay between MPM functional biology and its genomic history, and ultimately, inform classification, prognostication and treatment. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/461908v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@b6c43corg.highwire.dtl.DTLVardef@10d45a9org.highwire.dtl.DTLVardef@58746eorg.highwire.dtl.DTLVardef@143f880_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: whole-genome sequencing
- assay: bulk RNA-seq
- assay: DNA methylation
- research focus: clustering
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
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