Remaining challenges in predicting patient outcomes for diffuse large B-cell lymphoma. Issue 11 (2nd November 2019) - Harkins, R. Andrew; Chang, Andres; Patel, Sharvil P.; Lee, Michelle J.; Goldstein, Jordan S.; Merdan, Selin; Flowers, Christopher R.; Koff, Jean L.
ABSTRACT: Introduction: Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma and is an aggressive malignancy with heterogeneous outcomes. Diverse methods for DLBCL outcomes assessment ranging from clinical to genomic have been developed with variable predictive and prognostic success. Areas covered: The authors provide an overview of the various methods currently used to estimate prognosis in DLBCL patients. Models incorporating cell of origin, genomic features, sociodemographic factors, treatment effectiveness measures, and machine learning are described. Expert opinion: The clinical and genetic heterogeneity of DLBCL presents distinct challenges in predicting response to therapy and overall prognosis. Successful integration of predictive and prognostic tools in clinical trials and in a standard clinical workflow for DLBCL will likely require a combination of methods incorporating clinical, sociodemographic, and molecular factors with the aid of machine learning and high-dimensional data analysis.