Predictive Nomogram for Hyperprogressive Disease During Anti-PD-1/PD-L1 Treatment in Patients with Advanced Non-Small...

Area of Science:

• Oncology
• Immunotherapy
• Cancer Research

Background:

• Anti-PD-1/PD-L1 therapy can paradoxically accelerate tumor growth, leading to hyperprogressive disease (HPD).
• Predictive models for HPD in non-small cell lung cancer (NSCLC) patients are crucial for optimizing treatment outcomes.

Purpose of the Study:

• To develop and validate a predictive nomogram for hyperprogressive disease (HPD) in advanced NSCLC patients treated with PD-1/PD-L1 inhibitors.
• To identify clinical and biological factors associated with HPD development.

Main Methods:

• Retrospective cohort study of 176 patients for model development and 85 for validation.
• HPD defined by tumor growth rate (TGR), tumor growth kinetics (TGK), or time to treatment failure (TTF).
• Univariate and multivariate logistic regression analysis to identify predictive factors; nomogram construction and validation.

Main Results:

• The incidence of HPD was 9.66% in advanced NSCLC patients treated with anti-PD-1/PD-L1 therapy.
• Patients with HPD exhibited significantly shorter overall survival (OS) and progression-free survival (PFS).
• Key predictors for HPD included activated partial thromboplastin time (APTT), regulatory T cells (Treg cells), liver metastasis, and multiple metastatic sites.

Conclusions:

• A validated nomogram incorporating APTT, Treg cells, liver metastasis, and number of metastatic sites can predict HPD risk in NSCLC patients.
• The nomogram demonstrated high accuracy (AUC 0.830 in training, 0.960 in validation) in predicting HPD.
• This predictive tool can assist clinicians in managing immunotherapy for NSCLC patients at risk of HPD.