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Genetic program activity delineates risk, relapse, and therapy responsiveness in multiple myeloma.
Wall, Matthew A; Turkarslan, Serdar; Wu, Wei-Ju; Danziger, Samuel A; Reiss, David J; Mason, Mike J; Dervan, Andrew P; Trotter, Matthew W B; Bassett, Douglas; Hershberg, Robert M; Lomana, Adrián López García de; Ratushny, Alexander V; Baliga, Nitin S.
Afiliação
  • Wall MA; Institute for Systems Biology, Seattle, WA, USA.
  • Turkarslan S; Institute for Systems Biology, Seattle, WA, USA.
  • Wu WJ; Institute for Systems Biology, Seattle, WA, USA.
  • Danziger SA; Bristol-Myers Squibb, Summit, NJ, USA.
  • Reiss DJ; Bristol-Myers Squibb, Summit, NJ, USA.
  • Mason MJ; Sage Bionetworks, Seattle, WA, USA.
  • Dervan AP; Bristol-Myers Squibb, Summit, NJ, USA.
  • Trotter MWB; Celgene Institute for Translational Research Europe (CITRE), a Bristol-Myers Squibb Company, Summit, NJ, USA.
  • Bassett D; Bristol-Myers Squibb, Summit, NJ, USA.
  • Hershberg RM; Celgene Corporation, Seattle, WA, USA.
  • Lomana ALG; Institute for Systems Biology, Seattle, WA, USA. adrian.lopezgarciadelomana@isbscience.org.
  • Ratushny AV; Bristol-Myers Squibb, Summit, NJ, USA. alexander.ratushnyy@bms.com.
  • Baliga NS; Institute for Systems Biology, Seattle, WA, USA. nitin.baliga@isbscience.org.
NPJ Precis Oncol ; 5(1): 60, 2021 Jun 28.
Article em En | MEDLINE | ID: mdl-34183722
Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to multiple lines of therapies. Therefore, we not only need the ability to predict which patients are at high risk for disease progression but also a means to understand the mechanisms underlying their risk. Here, we report a transcriptional regulatory network (TRN) for MM inferred from cross-sectional multi-omics data from 881 patients that predicts how 124 chromosomal abnormalities and somatic mutations causally perturb 392 transcription regulators of 8549 genes to manifest in distinct clinical phenotypes and outcomes. We identified 141 genetic programs whose activity profiles stratify patients into 25 distinct transcriptional states and proved to be more predictive of outcomes than did mutations. The coherence of these programs and accuracy of our network-based risk prediction was validated in two independent datasets. We observed subtype-specific vulnerabilities to interventions with existing drugs and revealed plausible mechanisms for relapse, including the establishment of an immunosuppressive microenvironment. Investigation of the t(4;14) clinical subtype using the TRN revealed that 16% of these patients exhibit an extreme-risk combination of genetic programs (median progression-free survival of 5 months) that create a distinct phenotype with targetable genes and pathways.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article