Predicting progression‐free survival from measurable residual disease in chronic lymphocytic leukemia

• Published in: Clinical and translational science 2024-08, Vol.17 (8), p.e13905-n/a
• Main Authors: Tettamanti, Florencia A., Kimko, Holly, Sharma, Shringi, Di Veroli, Giovanni
• Format: Article
• Language: English
• Subjects: Approximation; Binomial distribution; Chemotherapy; Chronic lymphocytic leukemia; Clinical trials; Clinical Trials as Topic; Disease; Humans; Leukemia; Leukemia, Lymphocytic, Chronic, B-Cell - blood; Leukemia, Lymphocytic, Chronic, B-Cell - diagnosis; Leukemia, Lymphocytic, Chronic, B-Cell - drug therapy; Leukemia, Lymphocytic, Chronic, B-Cell - mortality; Leukemia, Lymphocytic, Chronic, B-Cell - pathology; Leukocytes; Medical prognosis; Neoplasm, Residual; Patients; Predictions; Prognosis; Progression-Free Survival; Survival
• ISSN: 1752-8054; 1752-8062; 1752-8062
• DOI: 10.1111/cts.13905

Association between measurable residual disease (MRD) and survival outcomes in chronic lymphocytic leukemia (CLL) has often been reported. However, limited quantitative analyses over large datasets have been undertaken to establish the predictive power of MRD. Here, we provide a comprehensive assessment of published MRD data to explore the utility of MRD in the prediction of progression‐free survival (PFS). We undertook two independent analyses, which leveraged available published data to address two complimentary questions. In the first, data from eight clinical trials was modeled via a meta‐regression approach, showing that median PFS can be predicted from undetectable MRD rates at 3–6 months of post‐treatment. The resulting model can be used to predict the probability of technical success of a planned clinical trial in chemotherapy. In the second, we investigated the evidence for predicting PFS from competing MRD metrics, for example baseline value and instantaneous MRD value, via a joint modeling approach. Using data from four small studies, we found strong evidence that including MRD metrics in joint models improves predictions of PFS compared with not including them. This analysis suggests that incorporating MRD is likely to better inform individual progression predictions. It is therefore proposed that systematic MRD collection should be accompanied by modeling to generate algorithms that inform patients' progression.