Rajkumar, S. Vincent

e19564 Background: Risk stratification in multiple myeloma (MM) relies on baseline staging systems and cytogenetic abnormalities, yet early relapse occurs in a subset of patients despite favorable risk features. Prior studies showed that clonal plasma cell S-phase assessed by flow cytometry using the plasma cell proliferation (PCPRO) assay at diagnosis and at autologous stem cell transplantation (ASCT) identifies biologically high-risk disease. The prognostic significance of clonal plasma cell S-phase following ASCT has not been well defined. Methods: We retrospectively analyzed MM patients who underwent ASCT within one year of diagnosis between January 1, 2013, and August 31, 2024. Patients without available post-ASCT clonal plasma cell S-phase assessment were excluded, yielding 150 evaluable patients. The proportion of clonal plasma cells in S-phase was determined by DNA content between G0/G1 and G2/M peaks and reported as a percentage. S-phase was assessed at the first post-ASCT marrow evaluation at day +60 (D45–75) and/or day +100 (D76–130), using the earlier assessment when both were available. Patients were classified as low (<2%) or high (≥2%) post-ASCT S-phase. Landmark progression-free survival (PFS) analyses were performed from the post-transplant assessment. Multivariable Cox models adjusted for cytogenetic risk, maintenance therapy, disease response at transplantation, International Staging System (ISS), and age. Results: Among 150 evaluable patients, 36 (24%) had high post-ASCT S-phase (≥2%) and 114 (76%) had low post-ASCT S-phase (<2%). Median age at ASCT was higher in the high S-phase group (64.5 vs 61.0 years; p=0.063), and advanced ISS stage (II/III) was more frequent (78% vs 57%; p=0.02). Cytogenetic risk, induction regimen, depth of response at transplantation, and maintenance therapy were similar between groups. Patients with high post-ASCT S-phase had significantly inferior PFS compared with those with low S-phase (median 17.0 vs 39.7 months; p=0.0003). On multivariable analysis, post-ASCT S-phase ≥2% remained independently associated with inferior PFS (HR 1.76, 95% CI 1.00–3.09; p=0.049). In an exploratory analysis among patients with paired S-phase assessments at the time of ASCT and post-ASCT, dynamic S-phase trajectories further stratified outcomes. Median PFS was longest in patients with persistently low S-phase (low→low; 39.7 months), intermediate in those converting from high to low S-phase (high→low; 27.2 months), and shortest in patients with newly emergent or persistently high post-ASCT S-phase (low→high: 15.0 months; high→high: 11.0 months) p=0.0019. Conclusions: Elevated proliferative activity of residual clonal plasma cells following ASCT identifies a biologically high-risk subset of MM patients with early relapse. Dynamic changes in post-ASCT S-phase further refine post-transplant risk stratification.

7514 Background: Iberdomide (Iber) is a CELMoD that binds with greater specificity & affinity to the cereblon protein of the E3 ligase complex than a traditional IMiD. Both transplant-eligible (TE) & ineligible (TIE) patients (pts) with newly diagnosed multiple myeloma (NDMM) are currently treated with quadruplet induction, consisting of an anti-CD38 monoclonal antibody, a proteasome inhibitor, an IMiD & dexamethasone (dex). We conducted a clinical trial (NCT05392946, IDEAL) to examine the efficacy of a novel finite-duration approach, using Iber, daratumumab, bortezomib & dex (Iber-DVd) quadruplet induction followed by Iber monotherapy maintenance. Methods: Both TE and TIE pts with NDMM were enrolled in this Phase 1 (3+3 design)/Phase 2 (single stage) trial. The primary goal was to assess the ≥complete response (CR) rate — utilizing the more sensitive serum MASS-FIX assay in lieu of serum immunofixation — during induction. Treatment involved twelve 28-day cycles of induction with Iber, given PO at the recommended phase 2 dose (RP2D), days 1-21, daratumumab 1800mg SQ, weekly for 2 cycles, every other week during Cycles 3-6, & every 4 weeks thereafter, bortezomib 1.3 mg/m 2 SQ, weekly & dex 40 mg, weekly, followed by Iber monotherapy (24 cycles). Growth factor was not allowed during the dose-limiting toxicity (DLT) assessment period (Cycle 1). Results: Among 47 pts enrolled, 9 pts were in Phase 1 portion which assessed the maximum tolerated dose (MTD), with Iber starting at 1 mg/day dose, days 1-21 [Dose Level (DL) 1]. DLT (all grade 4 neutropenia) was noted in 3 pts, 2 at DL 1 & 1 at DL -1. This analysis includes 44 pts who received Iber at the RP2D (0.75 mg/day, days 1-21), including 6 pts from the Dose Confirmation Cohort (Phase 1, DL -1), dosed at MTD, & 38 pts in the Dose Expansion Cohort. The median age at registration was 65 years (range 36-86 years), & 23 pts (52.3%) were deemed high-risk. At data cut-off, pts had received a median of 15 cycles (range 3-36) of therapy. Overall response rate was 100%; ≥CR rate, 36.4% (95% CI: 22.4, 52.2) during induction & 52.3% (95% CI: 36.7, 67.5), overall, including the maintenance phase. The marrow flow-based (10 -5 ) MRD negative rate was 29.5% during induction & 47.7% overall. At a median follow-up (FU) 18.3 mos, 2 pts have died (one during FU due to progressive disease & the other on-treatment due to COVID-19). 12- (& 18-) month progression-free survival & overall survival rates were 91% (88%) & 97% (94%), respectively. Most common toxicities at RP2D were lymphopenia, neutropenia (leading 27% of pts to require pegfilgrastim support), rash, peripheral neuropathy, diarrhea, & infections. Cases of high-grade fatigue were low (<5%). Updated data will be presented at the meeting. Conclusions: Iber-DVd is an active and safe regimen for both TE and TIE pts with NDMM, leading to high response rates which improved over time, with nearly one-half of the pts achieving MRD negative state. Clinical trial information: NCT05392946 .

7518 Background: Monoclonal gammopathy of undetermined significance (MGUS) is common, yet only a minority progress to malignant plasma-cell disorders. Mayo risk stratification uses baseline factors and is commonly implemented/validated in a Cox proportional hazards (CoxPH) framework, which may miss prognostic longitudinal patterns. Methods: We assembled 15,158 MGUS patients with longitudinal laboratory data and progression outcomes (smoldering multiple myeloma, multiple myeloma, AL amyloidosis, or Waldenström macroglobulinemia). We compared a Mayo-factor Cox proportional hazards (CoxPH) baseline versus STRaTS, a transformer-based survival model, to predict progression risk at 3-, 5-, and 10-year horizons. STRaTS leveraged longitudinal trajectories of routine labs including hemoglobin, platelets, differential counts (neutrophils/lymphocytes/monocytes), immunoglobulins (IgG/IgA/IgM), free light chains (kappa/lambda), M-spike, and creatinine. To minimize bias, we used laboratory results collected from 1–3 years after diagnosis and started follow-up at year 3; patients progressing before year 3 or lacking follow-up beyond year 3 were excluded. A total of 7,305 patients met eligibility and were split into train/validation/test cohorts of 4,383/1,461/1,461. Discrimination was assessed by concordance index (C-index). Clinical operating performance was evaluated on a held-out test cohort by comparing sensitivity and specificity at the same proportion of patients selected for intensified surveillance (same workload). Results: Among MGUS patients with complete baseline Mayo factors(N=3,388), Kaplan–Meier–estimated 3-/5-/10-year cumulative progression risk was 0.1/0.4/3.4% (Low), 3.0/4.3/7.9% (Low-Intermediate), 4.4/6.4/14.1% (High-Intermediate), and 8.9/12.5/16.9% (High). On a held-out test cohort, STRaTS demonstrated superior discrimination versus the Mayo-factor CoxPH (C-index 0.75 vs 0.538; p<0.0001). Both models were trained and evaluated on identical train/test splits. At the same surveillance workload, STRaTS achieved higher sensitivity with comparable specificity; when 40.3% of patients were selected for closer surveillance, 3-year sensitivity was 81.0% for STRaTS versus 47.6% for CoxPH (Table). Conclusions: Among MGUS patients who remain progression-free at 3 years, transformer-based survival modeling of longitudinal laboratory trajectories provides more accurate individualized conditional progression risk estimates and improved workload-matched identification of progressors compared with the conventional Mayo/Cox approach. Sensitivity/specificity (%). Operating Point Flagged % STRaTS Sens / Spec (%) CoxPH Sens / Spec (%) Int+High, 3y 40.3 81.0 / 58.5 47.6 / 58.9 Int+High, 5y 40.3 81.2 / 60.4 46.9 / 58.8 Int+High, 10y 40.3 25.0 / 56.3 23.8 / 58.7 Two-Group High (≥2 factors), 5y 5.6 37.4 / 93.4 35.4 / 93.1

11059 Background: Payments from the drug industry to oncologists are common and influence physicians’ prescribing decisions. However, whether payments affect the likelihood that patients receive optimal cancer treatment has not been assessed. Methods: This was a population-based cohort study. We used fee-for-service Medicare claims to identify patients with incident cancer diagnosis (new cancer diagnosis code after >= 1 year with no cancer diagnosis codes) from 2014-2020. We used National Comprehensive Cancer Network (NCCN) Guidelines to identify treatment scenarios (e.g., metastatic melanoma, stage III colon adjuvant therapy) with variation in the clinical benefit among recommended treatment options. The patient cohort corresponding to each scenario was identified using claims. We used NCCN Guidelines to identify the “optimal” treatment for each scenario (defined as the designated “preferred” treatment, with NCCN Evidence Blocks scores used for tiebreaks) at each time point across the study period. The primary outcome was whether a patient received the treatment that was, as of their diagnosis date, the optimal treatment for their cancer (vs. any non-optimal treatment). The primary exposure[s] were whether a patient’s medical oncologist received personal (“general”) payments (source: linked Open Payments data) related to 1) optimal treatment or 2) any non-optimal treatment, during the year prior to the patient’s cancer diagnosis. We fit Poisson-family generalized estimating equations, with physician-level clustering and adjustment for scenario, year, and patient characteristics, to estimate the relative risk (RR) of optimal treatment. Results: There were 14 clinical scenarios comprising 15,835 patients. There were 3,429 (21.7%) patients whose oncologist received payment for the optimal treatment, 4,891 (30.9%) for non-optimal payments, and 1,993 (12.6%) to both payment types. Oncologist receipt of payment for optimal treatment was associated with a greater likelihood (RR=1.07, 95%CI:1.01-1.13), and payment for non-optimal treatment was associated with lower likelihood (RR= 0.94, 95%CI:0.89-0.98) of patients receiving optimal treatment. Associations became stronger as payment dollar value increased; for optimal payments ≥$10,000, RR=1.26 (95%CI:1.02-1.57), and for non-optimal payments ≥$10,000, RR=0.70 (95%CI: 0.49-1.00). An estimated 41.6% of unexposed patients received optimal treatment; versus 52.6%, 29.3%, and 33.8% among patients exposed to ≥$10,000 of payments for optimal treatment only, ≥$10,000 non-optimal payments only, and ≥$10,000 of both payment types, respectively. Conclusions: Industry payments related to the optimal cancer treatment were associated with increased use of the optimal treatment. However, payments related to non-optimal treatments were more common and were associated with decreased use of optimal treatment.