Validation of the High-Risk Consensus Genomic Staging System in Newly Diagnosed Multiple Myeloma

Abstract

Abstract Introduction: Current identification of high-risk Multiple Myeloma (HRMM) primarily relies on the Revised International Staging System (R-ISS and R2-ISS). However, cytogenetic abnormalities evaluated in these systems may no longer accurately capture prognosis in the era of novel and combination therapies and exclude key prognostic factors such as molecular alterations. Recently, the International Myeloma Society, along with the International Myeloma Working Group (IMS-IMWG) proposed a new Consensus Genomic Staging (CGS) system for MM. We aimed to validate the genomic alterations included in the CGS in a group of newly diagnosed MM. Methods: A total of 1,079 newly diagnosed MM patients were included from four clinical trials: elderly-fit, (n=460, 43%; NCT03742297), transplant-eligible (n=332, 31%; NCT01916252, NCT02406144), and unfit (n=287, 27%; NCT02575144) patients. Cytogenetic alterations were centrally assessed by FISH on sorted CD138+ plasma cells, and TP53 mutations were analyzed by next-generation sequencing (NGS). Modified genomic HRMM by CGS was defined as the presence of ≥1 of the following: del(17p) with >20% clonal fraction and/or TP53 mutation; an IgH translocation, including t(4;14), or t(14;16), along with 1q+ and/or del(1p32); or monoallelic del(1p32) plus 1q+. Traditional HRMM genomic alterations included del(17p), t(4;14), or t(14;16). Results: Median age at diagnosis was 73.8 years (IQR 71.1-77.4). The prevalence of high-risk CGS alterations was as follows: 6.7% for del(17p) with clonal fraction >20% (“high del(17p)”); 5.4% for monoallelic del(1p32) combined with 1q+ (“del1p+1q”); 5.2% for t(4;14) with either monoallelic del(1p32) or 1q+ (“t(4;14)+1p/q”); and 2.4% for t(14;16) with either monoallelic del(1p32) or 1q+ (“t(14;16)+1p/q”). TP53 mutations were identified in only 12 of 287 evaluated patients (4.2%). At a median follow-up of 51.1 months (IQR 35-65-1), the 3-year overall survival (OS) and progression-free survival (PFS) were 76% (95% CI 73-79%) and 67% (95% CI 64-70%), respectively. High del(17p) (p=0.008), del(1p)+1q (p=0.023), and t(4;14)+1p/q (p=0.012) were associated with shorter PFS in univariable models. Only t(4;14)+1p/q remained associated with higher hazard of death (p=0.001). Among 287 patients with available TP53 NGS data, a high-risk CGS genomic alteration was identified in 26% of the cases. Of these, 41% had previously been categorized as standard-risk by traditional criteria. Conversely, 7.1% of patients now classified as standard-risk were formerly considered high-risk. The 3-year OS was lower in the GCS high-risk compared to GCS standard-risk group (73% [95% CI 64-84] vs. 85% [95% CI 80-90], p=0.022). At 3-year PFS was also numerically lower in the GCS high-risk group (57% [95% CI 47-69] vs. 71% [95% CI 65-77], p=0.069). In multivariable models adjusting for clinical trial enrollment, CGS high-risk status remained independently associated with worse OS (HR 1.77 [95% CI 1.08-2.90], p=0.024) and PFS (HR 1.64 [95% CI 1.23-2.18], p=0.045). In contrast, 3-year OS and PFS were similar across the traditional high-risk and traditional standard-risk group (OS: 74% [95% CI 63-86] vs. 83% [95% CI 79-88], p=0.2; PFS: 62% [95% CI 50-76] vs. 69% [95% CI 63-75], p=0.3). Notably, among patients whose risk categorization changed between the CGS and traditional classification systems, the 3-year OS was 100% (95% CI, 100–100) for those reclassified as standard-risk (n = 15), and 85% (95% CI, 71-100%) for those reclassified as high-risk (n = 31). Conclusions: We validated the IMS-IMWG CGS system for HRMM in a large and diverse cohort of newly diagnosed individuals, including fit, unfit, and transplant-eligible MM patients. The CGS independently predicts clinical outcomes and effectively reclassifies a substantial proportion of patients previously misclassified by traditional criteria. These findings support incorporating NGS alongside FISH in the MM diagnostic workflow, and the clinical implementation of CGS to guide risk-adapted therapeutic strategies.