| Autor: |
Praneeth Reddy Sudalagunta, Rafael Canevarolo, Mark Meads, Ariosto Silva, Xiaohong zhao, Christopher Cubitt, Gabriel DeAvila, Raghunandan Reddy Alugubelli, Constantine Logothetis, Alexandre Tungesvik, Qi Zhang, Oliver Hampton, Jamie Teer, Eric Welsh, Sean Yoder, Bijal Shah, Lori Hazlehurst, Robert Gatenby, Dane Van Domelen, Yi Chai, Feng Wang, Andrew DeCastro, Amanda Bloomer, Erin Seigel, Daniel Sullivan, Melissa Alsina, Taiga Nishihori, Jason Brayer, John L. Cleveland, William Dalton, Robert Gillies, Christopher Walker, Yosef Landesman, Rachid Baz, Ariosto Siqueira Silva, Kenneth Shain |
| Rok vydání: |
2022 |
| Popis: |
We present a unique database of tumor samples from 399 multiple myeloma (MM) patients, whose functional genomic landscape was assessed by integrating ex vivo drug sensitivity to 138 drugs, clinical variables, cytogenetics, mutational profiles and transcriptomes. These analyses revealed a novel MM transcriptomic topology that, when associated with ex vivo drug sensitivity, generates “footprints” having both mechanistic and predictive applications. We validated the transcriptomic footprint for the nuclear export inhibitor selinexor in two clinical trials, BOSTON and MCC17814, and showed such a gene signature can accurately classify clinical response and suggest mechanism of resistance. Finally, we propose a novel evolutionary-based therapeutic strategy involving sequential therapy of monoclonal antibody daratumumab and selinexor, based on anti-correlative transcriptomic footprints, which was validated in two clinical trials (STOMP and XPORT-MM-028). Collectively, this database and computational framework can be leveraged to inform underlying biology, and to identify novel therapeutic strategies to improve treatment of MM. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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