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Functional impact of genomic complexity on the transcriptome of Multiple Myeloma


  • Ziccheddu B.,
  • Da Via M. C.,
  • Lionetti M.,
  • Maeda A.,
  • Morlupi S.,
  • Dugo M.,
  • Todoerti K.,
  • Oliva S.,
  • D'Agostino M.,
  • Corradini P.,
  • Landgren O.,
  • Iorio F.,
  • Pettine L.,
  • Pompa A.,
  • Manzoni M.,
  • Baldini L.,
  • Neri A.,
  • Maura F.,
  • Bolli N.


Purpose: Multiple Myeloma (MM) is a biologically heterogenous plasma-cell disorder. In this study we aimed at dissecting the functional impact on transcriptome of gene mutations, copy-number abnormalities (CNAs), and chromosomal rearrangements (CRs). Moreover, we applied a geno-transcriptomic approach to identify specific biomarkers for personalized treatments. Methods: We analyzed 514 newly-diagnosed patients from the IA12 release of the CoMMpass study, accounting for mutations in MM driver genes, structural variants, copy-number segments and raw-transcript counts. We performed an in-silico drug sensitivity screen (DSS), interrogating the DepMap dataset after anchoring cell lines to primary tumor samples using the Celligner algorithm. Results: Immunoglobulin translocations, hyperdiploidy and Chr(1q)gain/amps were associated with the highest number of deregulated genes. Other CNAs and specific gene mutations had a lower but very distinct impact affecting specific pathways. Many recurrent genes showed a hotspot(HS)-specific effect. The clinical relevance of double-hit MM found strong biological bases in our analysis. Bi-allelic deletions of tumor suppressors and chr(1q)-amplifications showed the greatest impact on gene expression, deregulating pathways related to cell-cycle, proliferation and expression of immunotherapy targets. Moreover, our in-silico DSS showed that not only t(11;14) but also chr(1q)gain/amps and CYLD inactivation predicted differential expression of transcripts of the BCL2-axis and response to venetoclax. Conclusions: The MM genomic architecture and transcriptome have a strict connection, led by CNAs and CRs. Gene mutations impacted especially with HS-mutations of oncogenes and bi-allelic tumor suppressor gene inactivation. Finally, a comprehensive geno-transcriptomic analysis allows the identification of specific deregulated pathways and candidate biomarkers for personalized treatments in MM.

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