Genome Sequencing to Discover Drivers of Clonal Expansion in Smoldering Multiple Myeloma

• Published in: Blood 2023-11, Vol.142 (Supplement 1), p.4143-4143
• Main Authors: Alberge, Jean-Baptiste, Dutta, Ankit K., Lightbody, Elizabeth D., Poletti, Andrea, Wallin, Sofia, Dunford, Andrew, Coorens, Tim, Loinaz, Xavi, Boehner, Cody J., Su, Nang Kham, Hevenor, Laura, Towle, Katherine, Perry, Jacqueline, Kübler, Kirsten, Sklavenitis-Pistofidis, Romanos, Hess, Julian, Stewart, Chip, Getz, Gad, Ghobrial, Irene M.
• Format: Article
• Language: English
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood-2023-184900

Background Better prediction models are required to identify patients with high-risk smoldering multiple myeloma (SMM) who can benefit from early intervention therapy before progression. Several studies have reported heterogeneity of genetic abnormalities found at the SMM stage, but whether any mutations are sufficient to cause, or instead prevent progression, remains an important open question. We leveraged whole-genome sequencing (WGS) in a large cohort of precursor patients, including repeated assessment over time, to discover drivers of disease growth and monitor malignant transformation to multiple myeloma (MM). Methods WGS was performed on tumor and normal cells from 141 untreated patients with SMM or Monoclonal Gammopathy of Undetermined Significance (MGUS) from the PCROWD study. Moreover, this dataset was combined with genomic data from the literature totaling 1,034 patients with WGS and/or exomes sampled across MM stages for comparative analysis and biomarker discovery. MutSig2CV, GISTIC2, and a novel method for structural variants were used to establish the list of candidate drivers across disease stages, with statistical power to detect myeloma drivers from points mutations found in ≥2% of patients. Longitudinal WGS data were analyzed with the PhylogicNDT suite of tools to characterize clonal competition in treated and untreated patients. Results Patients were followed for a median time of 20 months from the sample date (range: 3 to 78 months), during which 11 progressed. Patients evenly distributed across 20/2/20 stages: 44 Low-Risk (37%), 29 Intermediate-Risk (24%), 47 High-Risk (39%) SMM were combined with 20 MGUS, showing our method is reliable to perform WGS in lower disease burden settings. A validation cohort comprising 61 SMM with a median follow-up time of 36 months (range: 5 to 121), with 22 progressions was used to corroborate our findings. As significant drivers could be specifically enriched among indolent disease (i.e., protective alleles), we ran de novo driver mutation discovery across MM stages. In addition to well-characterized MM drivers ( KRAS, NRAS, etc.), 16 new candidate genes were found significantly mutated, including IKFZ3 (Aiolos), a transcription factor and direct target of degradation with lenalidomide therapy, harboring frameshift and stop-gain mutations in the protein dimerization domain which could affect complete differentiation of plasma cells. Mutants in KRAS found primarily at codons Q61, G12, G13, and A146, w