SATMM: A systems genetic network analysis–based risk stratification tool validated across the disease trajectory of relapsed/refractory multiple myeloma.

Abstract

7559 Background: Precision medicine in multiple myeloma (MM) requires risk stratification tools that account for the patient-specific impact of cytogenetic abnormalities on disease progression. We developed SATMM (SYGNAL Analytics Tests for MM) based on each patient’s chromosomal abnormality subtype using the previously developed mmSYGNAL network. While originally validated in newly diagnosed (ND) cohorts, this study provides updated validation of SATMM in the relapsed/refractory (RR) setting. Methods: SATMM performance was evaluated in a new RR cohort from the Moffitt Cancer Center, stratified into Early RR (ERR; 1–3 prior events, n=258) and Late RR (LRR; > 3 prior events, n=251). Predictive accuracy was assessed via ROC analysis (AUC) and Kaplan-Meier (KM) survival curves to determine Median Progression-Free Survival (mPFS) and Cox Hazard Ratios (HR). SATMM was benchmarked against the established GEP70 and SKY92 gene panels. Results: As detailed in the accompanying table, SATMM demonstrated robust and superior predictive performance compared to existing panels throughout the RR disease trajectory: Prognostic Accuracy: SATMM achieved superior overall accuracy in both ERR (0.61) and LRR (0.55) groups compared to GEP70 (0.52 and 0.37) and SKY92 (0.54 and 0.43). Early-Stage Performance: In the ERR group, SATMM provided the most significant clinical separation with a Δ mPFS of 10.7 months (KM p =1.60e-05), outperforming GEP70 (8.9 months, KM p =0.00053) and SKY92 (4.4 months, non-significant). Late-Stage Performance: In the heavily pre-treated LRR group, SATMM maintained significant stratification (KM p =0.0068, HR p =0.0078) with an AUC of 0.62, GEP70 showed lesser performance (KM p =0.021, HR p =0.021) while SKY92 failed to provide accurate risk prognosis (AUC=0.43). High-Risk Patient Detection: SATMM identified a larger proportion of high-risk patients (ERR n=93 sen = 0.47; LRR n=117, sen = 0.51) with higher sensitivity (sen) compared to GEP70 (ERR n=25, sen =0.13; LRR n=46, sen = 0.20) and SKY92 (ERR n=59, sen = 0.27; LRR n=111, sen = 0.41). Conclusions: SATMM is a patient-specific prognostic test that effectively stratifies risk based on the unique patient genomic profile compared to current gene panels. These data confirm that SATMM not only accurately predicts progression in ND patients but maintains its high performance across the entire disease trajectory, offering superior accuracy and sensitivity for high-risk detection for RR patients. Method Disease status AUC Δ Median PFS KM pvalue High risk n Cox HR Cox HR pvalue Accuracy Sensitivity SATMM ERR 0.65 10.7 1.6E-05 93 0.512 1.3E-04 0.609 0.47 GEP70 ERR 0.64 8.9 0.00053 25 0.412 7.9E-04 0.523 0.129 SKY92 ERR 0.58 4.4 0.37 59 0.825 0.324 0.539 0.273 SATMM LRR 0.62 3 0.0068 117 0.679 0.0078 0.546 0.508 GEP70 LRR 0.6 3.8 0.021 46 0.661 0.0212 0.367 0.204 SKY92 LRR 0.43 0.74 0.6 111 1.11 0.494 0.426 0.414