47TiP - A real-world study of multiparametric prediction of the efficacy of immune checkpoint inhibitors in combination with chemotherapy for advanced non-small cell lung cancer

Background

Immune checkpoint inhibitors (ICIs) combined with chemotherapy has become the first-line treatment option for non-small cell lung cancer (NSCLC) patients without driver mutation genes. However, a large amount of clinical data has confirmed that only part of NSCLC patients can achieve durable clinical benefits, while the incidence of adverse reactions is high. So, there is still an urgent need for reliable biomarkers to construct models for accurate prediction and assessment of treatment outcomes. Recently, ctDNA and tumor mutation burden (TMB) have been proposed as biomarkers for predicting the outcome of immunotherapy, but no consensus has been reached on the cutoff value of TMB, ctDNA clearance threshold and optimal sampling time point. Therefore, this study aims to develop a prediction model for the efficacy of chemo-immunotherapy based on the clinical multiparameter data, and explore the ctDNA change threshold and the optimal time of collection with superior predictive value.

Trial Design

This study was a single-center, real-world study to collect an estimated 56 patients from the Department of Respiratory and Critical Care Medicine, Southeast University Zhongda Hospital, who were diagnosed with inoperable stage IIIB to IV NSCLC and treated with standard chemo-immunotherapy from August 2022 to January 2024. Blood samples at baseline, within 2 days before medication in the 2nd and 3rd treatment cycles, and the time of progression were collected respectively for genetic testing. At the same time, clinical data such as blood counts, blood biochemical indexes, lymphocyte subpopulations, etc. were recorded, and patients were subsequently followed up to record anti-tumor efficacy such as objective responds rate (ORR), progression-free survival (PFS), overall survival (OS), and the occurrence of adverse events. Patients were categorized into durable clinical benefit group (DCB group) and non-durable benefit group (NDB group) according to whether the PFS was more than 8 months, and the predictive model was proposed to be constructed using the support vector mechanism.

Clinical trial identification

NCT05725915.

Legal entity responsible for the study

The authors.

Funding

Beijing Red Clove Public Welfare Development Center.

Disclosure

All authors have declared no conflicts of interest.