| Literature DB >> 30181174 |
Michael A Chapman1,2, Jonathan Sive3, John Ambrose4, Claire Roddie5, Nicholas Counsell6, Anna Lach7, Mahnaz Abbasian7, Rakesh Popat5, Jamie D Cavenagh3, Heather Oakervee3, Matthew J Streetly8, Stephen Schey9, Mickey Koh10, Fenella Willis10, Andres E Virchis11, Josephine Crowe12, Michael F Quinn13, Gordon Cook14, Charles R Crawley2, Guy Pratt15, Mark Cook15, Nivette Braganza6, Toyin Adedayo6, Paul Smith6, Laura Clifton-Hadley6, Roger G Owen16, Pieter Sonneveld17, Jonathan J Keats18, Javier Herrero4, Kwee Yong7.
Abstract
Improving outcomes in multiple myeloma will involve not only development of new therapies but also better use of existing treatments. We performed RNA sequencing on samples from newly diagnosed patients enrolled in the phase 2 PADIMAC (Bortezomib, Adriamycin, and Dexamethasone Therapy for Previously Untreated Patients with Multiple Myeloma: Impact of Minimal Residual Disease in Patients with Deferred ASCT) study. Using synthetic annealing and the large margin nearest neighbor algorithm, we developed and trained a 7-gene signature to predict treatment outcome. We tested the signature in independent cohorts treated with bortezomib- and lenalidomide-based therapies. The signature was capable of distinguishing which patients would respond better to which regimen. In the CoMMpass data set, patients who were treated correctly according to the signature had a better progression-free survival (median, 20.1 months vs not reached; hazard ratio [HR], 0.40; confidence interval [CI], 0.23-0.72; P = .0012) and overall survival (median, 30.7 months vs not reached; HR, 0.41; CI, 0.21-0.80; P = .0049) than those who were not. Indeed, the outcome for these correctly treated patients was noninferior to that for those treated with combined bortezomib, lenalidomide, and dexamethasone, arguably the standard of care in the United States but not widely available elsewhere. The small size of the signature will facilitate clinical translation, thus enabling more targeted drug regimens to be delivered in myeloma.Entities:
Mesh:
Substances:
Year: 2018 PMID: 30181174 PMCID: PMC6310235 DOI: 10.1182/blood-2018-05-849893
Source DB: PubMed Journal: Blood ISSN: 0006-4971 Impact factor: 25.476