SORA

Advancing, promoting and sharing knowledge of health through excellence in teaching, clinical practice and research into the prevention and treatment of illness

RNA-seq of newly diagnosed patients in the PADIMAC study leads to a bortezomib/lenalidomide decision signature.

Chapman, MA; Sive, J; Ambrose, J; Roddie, C; Counsell, N; Lach, A; Abbasian, M; Popat, R; Cavenagh, JD; Oakervee, H; et al. Chapman, MA; Sive, J; Ambrose, J; Roddie, C; Counsell, N; Lach, A; Abbasian, M; Popat, R; Cavenagh, JD; Oakervee, H; Streetly, MJ; Schey, S; Koh, M; Willis, F; Virchis, AE; Crowe, J; Quinn, MF; Cook, G; Crawley, CR; Pratt, G; Cook, M; Braganza, N; Adedayo, T; Smith, P; Clifton-Hadley, L; Owen, RG; Sonneveld, P; Keats, JJ; Herrero, J; Yong, K (2018) RNA-seq of newly diagnosed patients in the PADIMAC study leads to a bortezomib/lenalidomide decision signature. Blood, 132 (20). pp. 2154-2165. ISSN 1528-0020 https://doi.org/10.1182/blood-2018-05-849893
SGUL Authors: Koh, Mickey

[img]
Preview
PDF Accepted Version
Available under License ["licenses_description_publisher" not defined].

Download (3MB) | Preview
[img] Microsoft Word (.docx) (Supplementary materials) Accepted Version
Available under License ["licenses_description_publisher" not defined].

Download (3MB)

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.

Item Type: Article
Additional Information: © 2018 by The American Society of Hematology
Keywords: Antineoplastic Agents, Antineoplastic Combined Chemotherapy Protocols, Bortezomib, Dexamethasone, Doxorubicin, Humans, Kaplan-Meier Estimate, Lenalidomide, Machine Learning, Multiple Myeloma, Mutation, Proportional Hazards Models, Sequence Analysis, RNA, Transcriptome, Treatment Outcome, United States, Humans, Multiple Myeloma, Doxorubicin, Dexamethasone, Antineoplastic Agents, Antineoplastic Combined Chemotherapy Protocols, Treatment Outcome, Proportional Hazards Models, Sequence Analysis, RNA, Mutation, United States, Kaplan-Meier Estimate, Transcriptome, Machine Learning, Bortezomib, Lenalidomide, 1102 Cardiovascular Medicine And Haematology, 1103 Clinical Sciences, 1114 Paediatrics And Reproductive Medicine, Immunology
SGUL Research Institute / Research Centre: Academic Structure > Institute of Medical & Biomedical Education (IMBE)
Academic Structure > Institute of Medical & Biomedical Education (IMBE) > Centre for Clinical Education (INMECE )
Journal or Publication Title: Blood
ISSN: 1528-0020
Language: eng
Dates:
DateEvent
15 November 2018Published
4 September 2018Published Online
28 July 2018Accepted
Publisher License: Publisher's own licence
Projects:
Project IDFunderFunder ID
UNSPECIFIEDWellcome Trusthttp://dx.doi.org/10.13039/100004440
C416/A18088Cancer Research UKhttp://dx.doi.org/10.13039/501100000289
C416/A25145Cancer Research UKhttp://dx.doi.org/10.13039/501100000289
LRF/10018BloodwiseUNSPECIFIED
PubMed ID: 30181174
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/111173
Publisher's version: https://doi.org/10.1182/blood-2018-05-849893

Actions (login required)

Edit Item Edit Item