Pharmacogenetics–based preliminary algorithm to predict the incidence of infection in patients receiving cytotoxic chemotherapy for hematological malignancies: A discovery cohort

Abstract

Introduction: Infections in hematological cancer patients are common and usually lifethreatening; avoiding them could decrease morbidity, mortality, and cost. Genes associated with antineoplastics’ pharmacokinetics or with the immune/inflammatory response could explain variability in infection occurrence. Objective: To build a pharmacogenetic-based algorithm to predict the incidence of infections in patients undergoing cytotoxic chemotherapy. Methods: Prospective cohort study in adult patients receiving cytotoxic chemotherapy to treat leukemia, lymphoma, or myeloma in two hospitals in Santiago, Chile.We constructed the predictive model using logistic regression. We assessed thirteen genetic polymorphisms (including nine pharmacokinetic—related genes and four inflammatory response-related genes) and sociodemographic/clinical variables to be incorporated into the model. The model’s calibration and discrimination were used to compare models; they were assessed by the Hosmer-Lemeshow goodness-of-fit test and area under the ROC curve, respectively, in association with Pseudo-R2. Results: We analyzed 203 chemotherapy cycles in 50 patients (47.8 ± 16.1 years; 56% women), including 13 (26%) with acute lymphoblastic and 12 (24%) with myeloblastic leukemia. Pharmacokinetics-related polymorphisms incorporated into the model were CYP3A4 rs2242480C>T and OAT4 rs11231809T>A. Immune/inflammatory response-related polymorphisms were TLR2 rs4696480T>A and IL-6 rs1800796C>G. Clinical/ demographic variables incorporated into the model were chemotherapy type and cycle, diagnosis, days in neutropenia, age, and sex. The Pseudo-R2 was 0.56, the p-value of the Hosmer-Lemeshow test was 0.98, showing good goodness-of-fit, and the area under the ROC curve was 0.93, showing good diagnostic accuracy. Conclusions: Genetics can help to predict infections in patients undergoing chemotherapy. This algorithm should be validated and could be used to save lives, decrease economic costs, and optimize limited health resources.