Abstract Introduction: Relapsed Multiple Myeloma (MM) is a genetically heterogeneous malignancy characterized by variable treatment responses in later lines of therapy. Even with highly effective therapies, most patients will eventually have relapses and require additional treatment options. In this context, precision medicine is playing an increasingly important role in the treatment of relapsed MM. Although numerous drugs have been approved in recent years, the ability to tailor individual therapies remains limited due to the absence of robust predictive biomarkers. We investigated a novel high-throughput drug sensitivity assay (clinicaltrials.gov ID: NCT03389347) to predict treatment responses in MM. Methods: Eligible patients had a diagnosis of MM or plasma cell leukemia, with relapsed/refractory disease, with either 3 prior lines of therapy (LOT) including an IMID and a PI, VGPR to initial therapy, or early relapse (12 months) after autologous stem cell transplant or after 1st line of therapy. The primary objective was to successfully test myeloma cells in a high-throughput drug sensitivity assay and obtain an actionable result. Plasma cells were collected from either bone marrow aspirate or biopsy or both, blood if plasma cells were circulating , or plasmacytoma biopsy samples. CD138+ plasma cells were isolated by magnetic bead separation and used for the drug sensitivity assay at the Quellos HTS Core at the University of Washington, CLIA approved as the test site since 2014. Tumor cells were tested against a panel of 170 drugs (Oncopanel2 v1, n=30 patients), and then against a second iteration with 46 drugs (Oncopanel2 v2, n=10 patients), culled from those most active during the assays using the initial version of the panel. FDA approved and investigational agents for treatment of MM and other malignancies were included. Drug response was determined using the concentration of experimental compound required to achieve 50% in vitro response inhibition (IC50) and the area under the dose response curve (AUC). Breeze 2.0 software, a validated platform for analysis of drug sensitivity assay results, was utilized to calculate drug sensitivity scores (DSS). Treatment recommendations for next LOT were based off assay results. For drugs with sufficient patient numbers, receiver operator analysis was used to evaluate performance of DSS for predicting treatment response. Optimal DSS thresholds were determined using Youden's J index. Results: We enrolled 40 patients between March 2018 and December 2024, with a mean of 5 prior LOT (range: 2–15). All patients (100%) had prior exposure to lenalidomide, 97.5% to bortezomib, 85% to carfilzomib, 82% to daratumumab, 77.5% to pomalidomide, and 12.5% BCMA CAR-T therapy. 30 patients (75%) enrolled on Oncopanel2 v1, and 10 patients (25%) OncoPanel2 v2. From Oncopanel2 v1, the top drugs by DSS included bortezomib (median DSS 47.7), carfilzomib (median DSS 47.3), panobinostat (median DSS 47), and romidepsin (median DSS 45.4). From Oncopanel2 v2, the top drugs by DSS were marizomib (an investigational PI, with median DSS 46.1), carfilzomib (median DSS 40.2), ixazomib (median DSS 37.2), and oprozomib (an investigational PI, with median DSS 31.6). Of enrolled patients, 38/40 received a subsequent LOT, with an ORR of 51.4%, including 29.7% partial response (PR), 13.5% very good partial response (VGPR), and 5.4% complete response (CR). Median PFS for all patients was 5.7 months (95% CI 2.5 – 13 months). We evaluated DSS thresholds for the most administered drugs (Bortezomib n=12 and Selinexor n=8). ROC analysis revealed AUCs of 0.471 and 0.462, respectively. Among 8 patients who received selinexor, responders showed a tendency toward higher DSS scores (range: 27-35) compared to most non-responders (range: 20-30), though this was not statistically significant (p=0.1) Conclusions: The use of a high throughput drug sensitivity assay was feasible among patients with relapsed/refractory MM. In our analysis of drug-specific DSS thresholds for bortezomib and Selinexor, DSS performance varied by agent, highlighting the need for drug-specific threshold optimization. In the patients who received Selinexor, there was a non-significant tendency toward higher DSS scores in responders, though sample size limited statistical evaluation. Larger validation cohorts with a limited set of drugs, are needed to determine the validity of DSS-guided treatment selection.
Donnelly et al. (Mon,) studied this question.
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