Prediction model for hyperprogressive disease in patients with advanced solid tumors received immune-checkpoint inhibitors: a pan-cancer study

Abstract

Abstract Background Hyper progressive disease (HPD) describes the phenomenon that patients can’t benefit from immunotherapy but cause rapid tumor progression. HPD is a particular phenomenon in immunotherapy but lacks prediction methods. Our study aims to screen the factors that may forecast HPD and provide a predictive model for risky stratifying. Methods We retrospectively reviewed advanced-stage tumor patients who received immune checkpoint inhibitors (ICI) in the General PLA Hospital. Subsequently, we calculated the tumor growth kinetics ratio (TGKr) and identified typical HPD patients. Differences analysis of clinical characteristics was performed, and a predictive binary classification model was constructed. Results 867 patients with complete image information were screened from more than 3000 patients who received ICI between January 2015 and January 2020. Among them, 36 patients were identified as HPD for TGKr > 2. After the propensity score matched, confounding factors were limited. Survival analysis revealed that the clinical outcome of HPD patients was significantly worse than non-HPD patients. Besides, we found that Body Mass Index (BMI), anemia, lymph node metastasis in non-draining areas, pancreatic metastasis, and whether combined with anti-angiogenesis or chemotherapy therapy were closely connected with the HPD incidence. Based on these risk factors, we constructed a visualised predicted nomogram model, and the Area Under Curve (AUC) is 0.850 in the train dataset, whereas 0.812 in the test dataset. Conclusion We carried out a retrospective study for HPD based on real-world patients and constructed a clinically feasible and practical model for predicting HPD incidence, which could help oncologists to stratify risky patients and select treatment strategies.