Next-generation prognostic assessment for diffuse large B-cell lymphoma.
Current standard of care therapy for diffuse large B-cell lymphoma (DLBCL) cures a majority of patients with additional benefit in salvage therapy and autologous stem cell transplant for patients who relapse. The next generation of prognostic models for DLBCL aims to more accurately stratify patients for novel therapies and risk-adapted treatment strategies. This review discusses the significance of host genetic and tumor genomic alterations seen in DLBCL, clinical and epidemiologic factors, and how each can be integrated into risk stratification algorithms. In the future, treatment prediction and prognostic model development and subsequent validation will require data from a large number of DLBCL patients to establish sufficient statistical power to correctly predict outcome. Novel modeling approaches can augment these efforts.
Department of Hematology & Medical Oncology, Emory University, Atlanta, GA 30322, USA.
Ashley D
AD
Staton
Department of Hematology & Medical Oncology, Emory University, Atlanta, GA 30322, USA.
Jean L
JL
Koff
H Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30318, USA.
Qiushi
Q
Chen
H Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30318, USA.
Turgay
T
Ayer
Department of Hematology & Medical Oncology, Emory University, Atlanta, GA 30322, USA.
Christopher R
CR
Flowers
D015981:Epidemiologic Factors; D020022:Genetic Predisposition to Disease; D023281:Genomics / Q000379:methods; D006801:Humans; D016403:Lymphoma, Large B-Cell, Diffuse / Q000175:diagnosis* / Q000209:etiology / Q000401:mortality* / Q000628:therapy; D008962:Models, Theoretical; D011159:Population Surveillance / Q000379:methods; D011379:Prognosis; D015203:Reproducibility of Results; D059039:Standard of Care; D016896:Treatment Outcome
Cox regression models; activated B cells; diffuse large B-cell lymphoma; gene-expression profiling; logistic regression; molecular subtyping; risk prediction models