A Machine Learning Model Based on Tumor and Immune Biomarkers to Predict Undetectable MRD and Survival Outcomes in Multiple Myeloma

Abstract

Purpose: Undetectable measurable residual disease (MRD) is a surrogate of prolonged survival in multiple myeloma. Thus, treatment individualization based on the probability of a patient achieving undetectable MRD with a singular regimen could represent a new concept toward personalized treatment, with fast assessment of its success. This has never been investigated; therefore, we sought to define a machine learning model to predict undetectable MRD at the onset of multiple myeloma. Experimental Design: This study included 487 newly diagnosed patients with multiple myeloma. The training (n = 152) and internal validation cohorts (n = 149) consisted of 301 transplant-eligible patients with active multiple myeloma enrolled in the GEM2012MENOS65 trial. Two external validation cohorts were defined by 76 high-risk transplant-eligible patients with smoldering multiple myeloma enrolled in the Grupo Español de Mieloma(GEM)-CESAR trial, and 110 transplant-ineligible elderly patients enrolled in the GEM-CLARIDEX trial. Results: The most effective model to predict MRD status resulted from integrating cytogenetic [t(4;14) and/or del(17p13)], tumor burden (bone marrow plasma cell clonality and circulating tumor cells), and immune-related biomarkers. Accurate predictions of MRD outcomes were achieved in 71% of cases in the GEM2012MENOS65 trial (n = 214/301) and 72% in the external validation cohorts (n = 134/186). The model also predicted sustained MRD negativity from consolidation onto 2 years maintenance (GEM2014MAIN). High-confidence prediction of undetectable MRD at diagnosis identified a subgroup of patients with active multiple myeloma with 80% and 93% progression-free and overall survival rates at 5 years. Conclusions: It is possible to accurately predict MRD outcomes using an integrative, weighted model defined by machine learning algorithms. This is a new concept toward individualized treatment in multiple myeloma. See related commentary by Pawlyn and Davies, p. 2482

The LMR-SSIGN-MAPS model Predicts Disease-free Survival in Patients with Localized Clear Cell Renal Cell Carcinoma

“…Based on the LMR-SSIGN-MAPS model, the K-M survival analysis showed that higher LMR-SSIGN-MAPS scores were signi cantly correlated with poorer DFS in the overall cohort (P < 0.0001). Therefore, the integrated prognostic model LMR-SSIGN-MAPS was in agreement with the view that multiple markers integration can be used to provide higher accuracy and predictive e cacy [30].…”

The LMR-SSIGN-MAPS model predicts disease-free survival in patients with localized clear cell renal cell carcinoma.

“…Based on the LMR-SSIGN-MAPS model, the K-M survival analysis showed that higher LMR-SSIGN-MAPS scores were significantly correlated with poorer DFS in the overall cohort (p < 0.0001). Therefore, the integrated prognostic model LMR-SSIGN-MAPS was in agreement with the view that multiple marker integration can be used to provide higher accuracy and predictive efficacy [31]. However, certain limitations with this model warrant further discussion.…”

The immune system in multiple myeloma and precursor states: Lessons and implications for immunotherapy and interception

“…Recent studies have demonstrated correlations between immune status and outcome following current therapies, including in long-term survivors. 26,[102][103][104][105][106] As these models continue to be refined, it is likely that we may be able to utilize immune status as a tool to select patients for specific immune therapies, as well as optimal sequencing and combinations.…”