Abstract

In the era of personalized cancer medicine, identifying techniques for effectively matching patients to efficacious treatments is a critical step in the treatment process. The advent of anti-cancer immunotherapies necessitates novel approaches to biomarker identification beyond traditional genomic profiling. One promising approach is incorporation of nomograms into treatment decisions. Nomograms are prediction tools, based on statistical modeling, designed to predict treatment outcomes. As a first step toward developing a nomogram, we conducted analyses to predict CD137 expression of natural killer cells after monoclonal antibody (mAb) treatment.Patient, tumor and immune characteristics were collected from 199 patients with breast cancer (N?=?62), head/neck cancers (N?=?46) and non-Hodgkin's lymphoma (NHL) (N?=?91), who were receiving mAb therapy as part of clinical trials. The difference in CD137 expression before and after mAb therapy was assessed by flow cytometry. To evaluate those who respond to mAb therapy via increased CD137 expression, we applied classification and regression trees (CART), multivariable lasso regression tools and Random Forest.The CD137 expression was significantly different for each cancer type [mean (SD): Breast: 6.6 (6.5); Head/Neck: 11.0 (7.0); NHL: 7.5 (7.1), P?

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