Two nomograms constructed for predicting the efficacy and prognosis of advanced non‑small cell lung cancer patients treated with anti‑PD‑1 inhibitors based on the absolute counts of lymphocyte subsets

Aqing Liu ^1,2,3, Guan Zhang ^1,3, Yanjie Yang ^1,3

⁰Department of Oncology, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China.

Cancer Immunology, Immunotherapy : Cii +

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June 4, 2024

View abstract on PubMed

Summary

This study developed nomograms using absolute lymphocyte counts (ALCs) to predict treatment efficacy and progression-free survival (PFS) in advanced non-small cell lung cancer (aNSCLC) patients receiving anti-PD-1 inhibitors. These tools aid in identifying patients likely to benefit from immunotherapy.

Area Of Science

Oncology
Immunotherapy
Biostatistics

Background

Immune checkpoint inhibitors (ICIs) offer benefits for advanced non-small cell lung cancer (aNSCLC) but carry risks of adverse events.
Identifying patients who will benefit from ICIs remains a challenge.
Lymphocyte subsets are crucial in antitumor responses.

Purpose Of The Study

To develop predictive models for treatment efficacy and progression-free survival (PFS) in aNSCLC patients treated with anti-PD-1 inhibitors.
To combine absolute lymphocyte counts (ALCs) with clinicopathological parameters for prognostic nomograms.
To aid clinicians in patient selection and personalized treatment strategies.

Main Methods

Retrospective analysis of aNSCLC patients (training: n=200, validation: n=100) treated with anti-PD-1 inhibitors.
Logistic and Cox regression identified factors for efficacy and PFS.
Nomograms were constructed and validated using concordance index (C-index), calibration curves, and ROC curves.

Main Results

Lower baseline CD3+, CD4+ counts correlated with poorer efficacy.
Hepatic metastases and lower CD3+, CD4+, CD8+, and B cell counts were associated with shorter PFS.
Nomograms demonstrated excellent predictive accuracy (AUC-ROC for response: 0.908-0.984; C-index for PFS: 0.825-0.832) and calibration.

Conclusions

Two nomograms integrating ALCs were developed to predict efficacy and PFS in advanced NSCLC patients treated with anti-PD-1 inhibitors.
These nomograms can assist clinicians in screening patients for immunotherapy benefit.
The tools support individualized treatment decisions for advanced NSCLC.

Keywords:

Absolute count Anti-PD-1inhibitor Lymphocyte subsets Nomogram Non-small cell lung cancer