2022 World Conference on Lung Cancer (ePosters)-EP08.01-038. Clinical Predictors of Treatment Efficacy in Patients with Lung Adenocarcinoma Receiving Immune Checkpoint Inhibitors

EP08.01-038. Clinical Predictors of Treatment Efficacy in Patients with Lung Adenocarcinoma Receiving Immune Checkpoint Inhibitors

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This study aimed to identify clinical predictors of treatment efficacy in patients with lung adenocarcinoma receiving immune checkpoint inhibitors (ICIs) and develop a predictive model for immunotherapy efficacy. The researchers collected and analyzed data from 201 lung adenocarcinoma patients who were treated with ICIs. They found that several factors were significantly associated with better progression-free survival (PFS) in patients receiving immunotherapy, including male gender, smoking, EGFR wild type, KRAS mutation, positive PD-L1 expression, early TNM stage, no liver metastasis, ICIs combined with chemotherapy, and immune-related adverse effects. Multivariate analysis revealed that sex, TP53 mutation, KRAS mutation, PD-L1 expression, immunotherapy modalities, immune-related adverse effects, and tumor location were independent prognostic factors affecting PFS in these patients. The researchers developed a clinical prediction model that effectively predicted PFS after immunotherapy in lung adenocarcinoma patients. The model was validated through calibration curves and showed good predictive ability. The study found that male patients with TP53 and KRAS mutations, positive PD-L1 expression, immune combination chemotherapy, immune-related adverse reactions, and central tumor location were more likely to have a survival benefit after ICIs treatment. This research provides useful insights into predicting the efficacy of ICIs in lung adenocarcinoma patients and has the potential to guide treatment decisions for these patients.

Asset Subtitle

Fang Hu

Meta Tag

Speaker Fang Hu

Topic Metastatic Non-small Cell Lung Cancer - Immunotherapy

Keywords

lung adenocarcinoma

immune checkpoint inhibitors

treatment efficacy

predictive model

progression-free survival

PD-L1 expression

immune-related adverse effects

multivariate analysis

clinical prediction model

treatment decisions