Entropy-Based Analysis of DNA Sequences and IGHV Mutational Status in Chronic Lymphocytic Leukemia: Predicting Patient Survival

This article discusses the use of a combination of mutation status of the immunoglobulin heavy chain (IGHV) gene and entropies of DNA sequence for the analysis of leukemia patients’ survival. Using a chronic lymphocytic leukemia (CLL) patient database, the study calculates different entropy measures for each patient’s DNA sequence. An entropy maximization algorithm is developed to estimate the statistical DNA information of each patient, allowing for the classification of patients into two groups without relying on population properties. Survival analysis of leukemia patients is conducted by combining IGHV subtype analysis and entropy measurements. Statistical significance is found when comparing groups with high and low entropy, as well as different IGHV subtypes. The analysis indicates that the combination of a mutated IGHV subtype and high entropy of DNA sequence can significantly impact the life expectancy of leukemia patients. The results are validated using Kaplan-Meier survival analysis, Cox regressions, and a Generalized Linear Mixed Model (GLMM). Overall, the study demonstrates the value of combining IGHV gene mutation status and entropy analysis of DNA sequences for leukemia patient survival prediction. The findings highlight the potential for leveraging multiple prognostic factors to improve the identification of patients with varying lifespan prognoses.