Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles

• Published in: Frontiers in oncology Vol. 11; p. 652063
• Main Authors: Borisov, Nicolas, Sergeeva, Anna, Suntsova, Maria, Raevskiy, Mikhail, Gaifullin, Nurshat, Mendeleeva, Larisa, Gudkov, Alexander, Nareiko, Maria, Garazha, Andrew, Tkachev, Victor, Li, Xinmin, Sorokin, Maxim, Surin, Vadim, Buzdin, Anton
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 15.04.2021
• Subjects: bortezomib; fibroblast growth factor receptor 3; gene expression; machine learning; multiple myeloma; Oncology; treatment response; PAD; multiple myeloma; VCD; treatment response; fibroblast growth factor receptor 3; bortezomib; machine learning; gene expression
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2021.652063

Multiple myeloma (MM) affects ~500,000 people and results in ~100,000 deaths annually, being currently considered treatable but incurable. There are several MM chemotherapy treatment regimens, among which eleven include bortezomib, a proteasome-targeted drug. MM patients respond differently to bortezomib, and new prognostic biomarkers are needed to personalize treatments. However, there is a shortage of clinically annotated MM molecular data that could be used to establish novel molecular diagnostics. We report new RNA sequencing profiles for 53 MM patients annotated with responses on two similar chemotherapy regimens: bortezomib, doxorubicin, dexamethasone (PAD), and bortezomib, cyclophosphamide, dexamethasone (VCD), or with responses to their combinations. Fourteen patients received both PAD and VCD; six received only PAD, and 33 received only VCD. We compared profiles for the good and poor responders and found five genes commonly regulated here and in the previous datasets for other bortezomib regimens (all upregulated in the good responders): , , , , and . Four of these genes are linked with known immunoglobulin locus rearrangements. We then used five machine learning (ML) methods to build a classifier distinguishing good and poor responders for two cohorts: PAD + VCD (53 patients), and separately VCD (47 patients). We showed that the application of FloWPS dynamic data trimming was beneficial for all ML methods tested in both cohorts, and also in the previous MM bortezomib datasets. However, the ML models build for the different datasets did not allow cross-transferring, which can be due to different treatment regimens, experimental profiling methods, and MM heterogeneity.