Session: 651. Myeloma: Biology and Pathophysiology, excluding Therapy: Poster III
Hematology Disease Topics & Pathways:
multiple myeloma, Diseases, Technology and Procedures, Plasma Cell Disorders, Lymphoid Malignancies, genetic profiling, Clinically relevant, NGS, RNA sequencing, WGS
In MM-PSN, we integrated 5 data types: gene expression, gene fusions, copy number alterations (CNA), somatic mutations and chromosomal translocations with clinical data from each patient. Spectral clustering of MM-PSN revealed 3 main patient groups and 12 subgroups of highly similar patients, each characterized by specific patterns of genetic and molecular features (Fig. 1A). Group 1 included n=357 patients (54.5%) mainly enriched for hyperdiploidy (HD), MYC translocations and NRAS mutations and was comprised of 4 subgroups. Group 2 included 166 patients (25.3%) and was overall enriched for the MMSET (tMMSET) and MAF (tMAF) translocations. Group 3 included n=132 patients (20.15%), was enriched for the CCND1 translocation (tCCND1) and was comprised of 3 subgroups (Fig. 1B).
Survival analysis revealed several novel prognostic findings that improve on current cytogenetic risk classification systems. For example, while HD patients are considered to have better prognosis, our analysis revealed that concurrent gain(1q) identifies a subgroup of HD patients at higher risk (1c) for relapse (Fig. 1C). Similarly, the MMSET translocation t(4;14) is currently considered to confer poor prognosis and identifies high-risk in R-ISS. Our findings demonstrate significant heterogeneity within this group, where patients in subgroup 2e tMMSET+gain(1q) had the poorest prognosis in terms of both progression-free (PFS) and overall survival (OS) in the entire cohort, while those in subgroup 2a (tMMSET alone) had a significant better prognosis, comparable to that of the HD subgroups (Fig. 1D). This finding has important implications, as it shows that tMMSET does not always confer poor prognosis and that only the fraction of patients with concurrent gain(1q) are at high-risk. Additionally, our model revealed a protective effect conferred by gain(15q), whose presence determined a significantly longer PFS and OS, even after adjusting for gain(1q).
Further analysis of pathways enriched by gene expression in subgroups revealed specific enrichment for interleukin signaling in group 1, growth, proliferation and differentiation pathways in group 2 and inflammation and other immune-related pathways in group 3. We have further identified therapeutic vulnerabilities and treatment options that may be important in a subgroup specific manner, for example CDK6 in the tMAF subgroup 2b, IGF1R in the tMMSET+gain(1q) subgroup 2e and CCND2 across several subgroups of the HD and tMMSET/tMAF groups. Drug repurposing analysis identified novel potential therapeutic options in a subgroup-specific manner, such as XPO1 inhibitors in HD subgroup 1b and MAPK inhibitors in tMMSET+gain(1q) subgroup 2e.
In conclusion, MM-PSN integrates multi-omics data from MM patients and reveals greater molecular and clinical heterogeneity than current cytogenetic classifications. Ongoing research is focused on a deeper investigation of the molecular mechanisms driving each subgroup and further validation of treatment options identified for specific subgroups.
Disclosures: Madduri: Foundation Medicine: Consultancy; Kinevant: Consultancy; GSK: Consultancy; Sanofi: Consultancy; Legend: Consultancy; Takeda: Consultancy; BMS: Consultancy; Janssen: Consultancy. Richter: Janssen: Consultancy, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Astra Zeneca: Consultancy; Secura bio: Consultancy; Adaptive biotechnologies: Consultancy; Oncopeptides: Consultancy; X4 pharmaceuticals: Consultancy; Sanofi: Consultancy; Antengene: Consultancy; BMS: Consultancy; Karyopharm: Consultancy. Chari: Secura Bio: Consultancy; Array BioPharma: Honoraria; The Binding Site: Honoraria; Adaptive Biotechnology: Honoraria; Glaxo Smith Kline: Consultancy; Antengene: Consultancy; Takeda: Consultancy, Research Funding; Oncopeptides: Consultancy; Seattle Genetics: Consultancy, Research Funding; Sanofi Genzyme: Consultancy; Karyopharm: Consultancy; Pharmacyclics: Research Funding; Bristol Myers Squibb: Consultancy; Amgen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Novartis: Honoraria. Dudley: Tempus Labs Inc: Current Employment. Jagannath: Sanofi: Consultancy, Honoraria; Legend Biotech: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Takeda: Consultancy, Honoraria. Parekh: Celgene: Research Funding; Karyopharm: Research Funding; Foundation Medicine: Consultancy.
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