No abstract

T cell expansion.In a preclinical mouse model of breast cancer, neoadjuvant checkpoint inhibitor combination therapy with anti-PD-1 and anti-CD137 induces a stronger early expansion of tumorspecific CD8 + T cells than the same combination applied in the adjuvant setting and is directly associated with long-term survival 19 . Furthermore, in a trial of 20 patients with macroscopic stage III melanoma, known as OpACIN (Study to Identify the Optimal Adjuvant Combination Scheme of Ipilimumab and Nivolumab in Melanoma Patients), patients treated with checkpoint inhibitors in the neoadjuvant setting showed expansion of a larger number of tumor-resident T cell clones in the peripheral blood than patients who received the same therapy in the adjuvant setting 20 . Interestingly, if a patient did not experience expansion of T cell clones that were undetectable at baseline, and thus was not capable of broadening his or her

PURPOSE Currently, there are no robust biomarkers that predict immunotherapy outcomes in metastatic melanoma. We sought to build multivariable predictive models for response and survival to anti-programmed cell death protein 1 (anti–PD-1) monotherapy or in combination with anticytotoxic T-cell lymphocyte-4 (ipilimumab [IPI]; anti–PD-1 ± IPI) by including routine clinical data available at the point of treatment initiation. METHODS One thousand six hundred forty-four patients with metastatic melanoma treated with anti–PD-1 ± IPI at 16 centers from Australia, the United States, and Europe were included. Demographics, disease characteristics, and baseline blood parameters were analyzed. The end points of this study were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). The final predictive models for ORR, PFS, and OS were determined through penalized regression methodology (least absolute shrinkage and selection operator method) to select the most significant predictors for all three outcomes (discovery cohort, N = 633). Each model was validated internally and externally in two independent cohorts (validation-1 [N = 419] and validation-2 [N = 592]) and nomograms were created. RESULTS The final model for predicting ORR (area under the curve [AUC] = 0.71) in immunotherapy-treated patients included the following clinical parameters: Eastern Cooperative Oncology Group Performance Status, presence/absence of liver and lung metastases, serum lactate dehydrogenase, blood neutrophil-lymphocyte ratio, therapy (monotherapy/combination), and line of treatment. The final predictive models for PFS (AUC = 0.68) and OS (AUC = 0.77) included the same variables as those in the ORR model (except for presence/absence of lung metastases), and included presence/absence of brain metastases and blood hemoglobin. Nomogram calculators were developed from the clinical models to predict outcomes for patients with metastatic melanoma treated with anti–PD-1 ± IPI. CONCLUSION Newly developed combinations of routinely collected baseline clinical factors predict the response and survival outcomes of patients with metastatic melanoma treated with immunotherapy and may serve as valuable tools for clinical decision making.

No abstract