Relapse prediction in multiple myeloma patients treated with isatuximab, carfilzomib, and dexamethasone

Multiple myeloma (MM) patients experience repeated cycles of treatment response and relapse, yet despite close monitoring of disease status through M protein measurements, no standard model exists for relapse prediction in MM. We investigate the feasibility of predicting relapse using a hierarchical Bayesian model of subpopulation dynamics by training and testing the model on 229 patients from the IKEMA trial. After observing between 11 and 18 treatment cycles, the model predicted relapse within six cycles with an average sensitivity between 60 and 80 %, and an average specificity between 60 and 90 %. A model of linear extrapolation is preferable when patients have been observed for less than 6 cycles, but for longer observation windows the hierarchical Bayesian model is preferred. Including available baseline and longitudinal covariate information did not improve predictive accuracy. A survival analysis showed that two model parameters separated patients into groups with significantly different PFS (p<0.001). ### Competing Interest Statement F.S. received honorarium from Sanofi, Janssen, BMS, Oncopeptides, Abbvie, GSK, and Pfizer. ### Funding Statement E. M. Myklebust, A. Köhn-Luque, and A. Frigessi were supported by the Center for research-based-innovation BigInsight under grant 237718 by the Research Council of Norway. J. Foo and K. Leder were supported by the Fulbright US-Norway Foundation. J. Foo and K. Leder were supported by the University of Oslo-University of Minnesota Norwegian Centennial Chair Grant. J. Foo was supported by the US National Science Foundation under grant number DMS-2052465. K. Leder was supported by the US National Science Foundation under grant number CMMI-2228034. We acknowledge funding from the Research Council of Norway through projects DL: Pipeline for individually tailoring new treatments in hematological cancers (PINpOINT) under project number 294916, and INTPART-International Partnerships for Excellent Education and Research under project number 309273. The authors also acknowledge the Centre for Digital Life Norway for supporting the partner project PINpOINT. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study protocol of the IKEMA trial ([NCT03275285][1]) was approved by the Institutional Ethics Committee or independent review board for each center. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The code for this project is available at https://github.com/evenmm/mm-predict-ikema. Data from the IKEMA trial ([NCT03275285][1]) can be requested through the data-sharing platform Vivli. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT03275285&atom=%2Fmedrxiv%2Fearly%2F2024%2F05%2F06%2F2024.05.02.24306607.atom