Cytokine analysis.
tumor
REGRESSION, TUMOR
Pretreatment serum cytokine levels were detected using the V-PLEX Proinflammatory Panel 1 Human Kit (Meso Scale Discovery) and the V-PLEX Cytokine Panel 1 Human Kit (Meso Scale Discovery) per manufacturer instructions. Samples were read on MSD Quickplex SQ120 instrument and analyzed using the MSD Discovery Workbench Data Analysis Software. Concentrations for each cytokine were logRF classification models were used to estimate the variable importance of demographic, molecular, and relevant clinical features for prediction of response group. Response group was defined as Overall RECIST Binary ORR (meaning at least 30% tumor shrinkage of target lesions was R, otherwise NR). A total of 75 patients had both cytokine and clinical data. After removing 1 patient with missing response and 2 patients with missing RSs, 72 patients (14R, 58NR) with complete data were included. Considering the limited number of responding patients available for model optimization, we chose not to split the data set into training and test sets. Rather, we leveraged data from all patients to build as robust a model as possible using models trained with 5-fold cross-validation. 9 features were included: (a) demographic: age (in years), sex (female/male); (b) clinical: liver treatment (yes/no), prior systemic treatment (yes/no), ECOG performance status (0[fully active, denoted as good]/1[not fully active, denoted as bad]), tumor size (MTD[maximum transverse diameter] in mm); (c) molecular: NLR, IL-8 cytokine level, RSs. Categorical variables were converted to dummy variables using R function Cox PH regression models were used to estimate the association between OS and demographic, molecular, and relevant clinical features. Patients who were alive or lost contact before end of last follow up date were censored (right censoring). The same 9 features listed above were included in the model. Seventy-five patients were used for analysis, as there was no need to remove patients with missing data for Cox models. Negative and positive association between probability of survival and each feature was indicated by coefficients of the model, respectively. Those coefficients (coef) can be directly converted to HR by 