Molecular and immune characterization of Chinese early-stage non-squamous non-small cell lung cancer: a multi-omics cohort study

Haoxin Peng ^1,2,3, Xiangrong Wu ^2,3,4, Xiaoli Cui ⁵

¹Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China.
²Department of Thoracic Oncology and Surgery, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
³Department of Clinical Medicine, Nanshan School, Guangzhou Medical University, Guangzhou, China.
⁴Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
⁵Shenzhen Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis and Therapy, YuceBio Technology Co., Ltd., Shenzhen, China.
⁶Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China.
⁷Department of Artificial Intelligence Research, Pazhou Lab, Guangzhou, China.
⁸Department of Radiation Oncology, Peking University Cancer Hospital & Institute, Beijing, China.
⁹Department of General Internal Medicine, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Cener for Cancer Medicine, Guangzhou, China.
¹⁰Department of Medical Oncology, The First People's Hospital of Zhaoqing, Zhaoqing, China.

⁰Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China.

Translational Lung Cancer Research +

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May 13, 2024

View abstract on PubMed

Summary

This study analyzed early-stage non-squamous non-small cell lung cancer (Ns-NSCLC) in Chinese patients, revealing key mutations and immune markers. Machine learning models accurately predict disease-free survival, offering new prognostic insights.

Area Of Science

Genomics and Transcriptomics
Cancer Biology
Immunology

Background

Despite better survival rates, approximately 30% of early-stage non-squamous non-small cell lung cancer (Ns-NSCLC) patients experience relapse within five years.
The specific biological characteristics driving relapse in early-stage Ns-NSCLC, particularly within the Chinese population, remain incompletely understood.

Purpose Of The Study

To conduct a multi-omics analysis of early-stage Ns-NSCLC in a Chinese cohort.
To identify genomic, transcriptomic, and T-cell receptor (TCR) repertoire features associated with disease-free survival (DFS).
To develop predictive models for DFS using machine learning (ML).

Main Methods

Whole-exome sequencing (WES), RNA sequencing, and TCR sequencing were performed on 76 paired early-stage Ns-NSCLC tissues and blood samples.
Analysis included mutation profiling, copy number variation (CNV) analysis, and assessment of immune-related features like tumor mutational burden (TMB) and TCR diversity.
Seven machine-learning algorithms were utilized to predict DFS based on integrated clinical, genomic, transcriptomic, and TCR data.

Main Results

Frequent mutations in <i>EGFR</i> (55%), <i>TP53</i> (37%), and <i>TTN</i> (26%) were observed. Specific mutations (<i>MUC17</i>, <i>ABCA2</i>, <i>PDE4DIP</i>, <i>MYO18B</i>) and CNVs (amplifications in 8q24.3, 14q13.1, 14q11.2; deletion in 3p21.1) correlated with unfavorable DFS.
Ever-smokers showed higher human leukocyte antigen loss of heterozygosity (HLA-LOH), TMB, and tumor neoantigen burden (TNB). HLA-LOH was linked to increased TMB, TNB, intratumoral heterogeneity (ITH), and chromosomal instability (wCIN).
Higher ITH predicted better DFS, while increased expression of immune-related genes (<i>CRABP2</i>, <i>ULBP2</i>, <i>IL31RA</i>, <i>IL1A</i>) indicated a poorer prognosis. Enhanced peripheral blood TCR diversity predicted better outcomes.
A random forest (RF) model combining clinical and RNA features achieved high predictive accuracy for DFS (AUC 97.5% training, 83.3% testing).

Conclusions

This study provides a comprehensive multi-omics profile of early-stage Ns-NSCLC in Chinese patients.
Identified genomic alterations, immune signatures, and TCR repertoire features serve as potential biomarkers for prognosis.
The findings offer insights into the unique biology of Ns-NSCLC and support the development of advanced predictive models for patient outcomes.

Keywords:

Early-stage lung cancer (early-stage LC) disease-free survival (DFS) machine-learning (ML) multi-omics