Impact of prognostic nutritional index as a predictor of immunotherapy response in patients with gynecologic cancers: Development of a clinical calculator (106)

Abstract

Objectives: Malnutrition is known to impair the immune system. The purpose of this study was to determine if nutritional status (NS) was associated with response to immunotherapy (IT) in patients with gynecologic cancers. Methods: Women with gynecologic cancers treated with IT between 2015 and 2021 in a single institution were eligible for this retrospective observational study. Patients could be eligible more than once if they had multiple ITs. NS was determined by the prognostic nutritional index (PNI), which was calculated by 10 × albumin (g/dL) + 0.005×total lymphocyte count per microliter. The relationship of PNI to disease control response (DCR), defined as complete and partial response (CR, PR), or stable disease (SD), was assessed by t-test and Chi-square test. Survival analyses were performed by Cox proportional hazards. The cut-off value for the PNI was determined by the point of maximum sensitivity and specificity on the receiver operator curve. The clinical calculator was constructed by regression modeling. Conclusions: PNI alone and in combination with prior lines of treatment were shown to impact the DCR to ITs in gynecologic cancer patients. Our clinical calculator may help to predict IT response and personalize decision-making. Prospective trials are needed to validate this. Objectives: Malnutrition is known to impair the immune system. The purpose of this study was to determine if nutritional status (NS) was associated with response to immunotherapy (IT) in patients with gynecologic cancers. Methods: Women with gynecologic cancers treated with IT between 2015 and 2021 in a single institution were eligible for this retrospective observational study. Patients could be eligible more than once if they had multiple ITs. NS was determined by the prognostic nutritional index (PNI), which was calculated by 10 × albumin (g/dL) + 0.005×total lymphocyte count per microliter. The relationship of PNI to disease control response (DCR), defined as complete and partial response (CR, PR), or stable disease (SD), was assessed by t-test and Chi-square test. Survival analyses were performed by Cox proportional hazards. The cut-off value for the PNI was determined by the point of maximum sensitivity and specificity on the receiver operator curve. The clinical calculator was constructed by regression modeling. Conclusions: PNI alone and in combination with prior lines of treatment were shown to impact the DCR to ITs in gynecologic cancer patients. Our clinical calculator may help to predict IT response and personalize decision-making. Prospective trials are needed to validate this.