Patient similarity network of newly diagnosed multiple myeloma identifies patient subgroups with distinct genetic features and clinical implications

Sherry Bhalla, David T. Melnekoff, Adolfo Aleman, Violetta Leshchenko, Paula Restrepo, Jonathan Keats, Kenan Onel, Jeffrey R. Sawyer, Deepu Madduri, Joshua Richter, Shambavi Richard, Ajai Chari, Hearn Jay Cho, Joel T. Dudley, Sundar Jagannath, Alessandro Laganà, Samir Parekh

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

The remarkable genetic heterogeneity of multiple myeloma poses a substantial challenge for proper prognostication and clinical management of patients. Here, we introduce MM-PSN, the first multiomics patient similarity network of myeloma. MM-PSN enabled accurate dissection of the genetic and molecular landscape of the disease and determined 12 distinct subgroups defined by five data types generated from genomic and transcriptomic profiling of 655 patients. MM-PSN identified patient subgroups not previously described defined by specific patterns of alterations, enriched for specific gene vulnerabilities, and associated with potential therapeutic options. Our analysis revealed that co-occurrence of t(4;14) and 1q gain identified patients at significantly higher risk of relapse and shorter survival as compared to t(4;14) as a single lesion. Furthermore, our results show that 1q gain is the most important single lesion conferring high risk of relapse and that it can improve on the current International Staging Systems (ISS and R-ISS).

Original languageEnglish
Article numbereabg9551
JournalScience advances
Volume7
Issue number47
DOIs
StatePublished - Nov 2021

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