Characterizing CD137 upregulation on NK cells in patients receiving monoclonal antibody therapy.
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 < 0.0001]. For breast cancer and NHL, FcR polymorphism and baseline CD137 expression were significant predictors of increased CD137 expression; for head/neck cancer, FcR polymorphism and age were significant predictors of increased expression.



Our preliminary results suggest that FcR polymorphism, pre-treatment CD137 expression and age are significant predictors of CD137 upregulation in patients. This study demonstrates that the development of a nomogram for therapy response is feasible. Further work validating our models in an independent cohort will provide the next steps in developing a nomogram that may be used to individualize this therapeutic approach for patients (NCT01114256).
Department of Medicine, Division of Oncology, Stanford University , Stanford, CA, USA.
A
A
Makkouk
Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, CA, USA.
V
V
Sundaram
Department of Medicine, Division of Oncology, Stanford University , Stanford, CA, USA.|Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA.
C
C
Chester
Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA.
S
S
Chang
Department of Medicine, Division of Oncology, Stanford University , Stanford, CA, USA.
A D
AD
Colevas
Department of Otorhinolaryngology, Stanford University, Stanford, USA.
J B
JB
Sunwoo
Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA.
H
H
Maecker
Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, CA, USA.
M
M
Desai
Department of Medicine, Division of Oncology, Stanford University , Stanford, CA, USA.
H E
HE
Kohrt
D000368:Aged; D000074322:Antineoplastic Agents, Immunological / Q000494:pharmacology* / Q000627:therapeutic use; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000188:drug therapy / Q000276:immunology; D005260:Female; D006258:Head and Neck Neoplasms / Q000188:drug therapy / Q000276:immunology; D006801:Humans; D007694:Killer Cells, Natural / Q000187:drug effects / Q000378:metabolism*; D008228:Lymphoma, Non-Hodgkin / Q000188:drug therapy / Q000276:immunology; D008297:Male; D053261:Tumor Necrosis Factor Receptor Superfamily, Member 9 / Q000378:metabolism*; D015854:Up-Regulation
D000074322:Antineoplastic Agents, Immunological; D014408:Biomarkers, Tumor; D053261:Tumor Necrosis Factor Receptor Superfamily, Member 9
CD137; biomarker; cancer; monoclonal antibody; natural killer cells; regression tree