Development and Validation of an Individualized Immune Prognostic Signature in Early-Stage Nonsquamous Non–Small Cell Lung Cancer
Open accessAbstract
IMPORTANCE: The prevalence of early-stage non-small cell lung cancer (NSCLC) is expected to increase with recent implementation of annual screening programs. Reliable prognostic biomarkers are needed to identify patients at a high risk for recurrence to guide adjuvant therapy. OBJECTIVE: To develop a robust, individualized immune signature that can estimate prognosis in patients with early-stage nonsquamous NSCLC. DESIGN, SETTING, AND PARTICIPANTS: This retrospective study analyzed the gene expression profiles of frozen tumor tissue samples from 19 public NSCLC cohorts, including 18 microarray data sets and 1 RNA-Seq data set for The Cancer Genome Atlas (TCGA) lung adenocarcinoma cohort. Only patients with nonsquamous NSCLC with clinical annotation were included. Samples were from 2414 patients with nonsquamous NSCLC, divided into a meta-training cohort (729 patients), meta-testing cohort (716 patients), and 3 independent validation cohorts (439, 323, and 207 patients). All patients underwent surgery with a negative surgical margin, received no adjuvant or neoadjuvant therapy, and had publicly available gene expression data and survival information. Data were collected from July 22 through September 8, 2016. MAIN OUTCOMES AND MEASURES: Overall survival. RESULTS: Of 2414 patients (1205 men [50%], 1111 women [46%], and 98 of unknown sex [4%]; median age [range], 64 [15-90] years), a prognostic immune signature of 25 gene pairs consisting of 40 unique genes was constructed using the meta-training data set. In the meta-testing and validation cohorts, the immune signature significantly stratified patients into high- vs low-risk groups in terms of overall survival across and within subpopulations with stage I, IA, IB, or II disease and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 1.72 [95% CI, 1.26-2.33; P < .001] to 2.36 [95% CI, 1.47-3.79; P < .001]) after adjusting for clinical and pathologic factors. Several biological processes, including chemotaxis, were enriched among genes in the immune signature. The percentage of neutrophil infiltration (5.6% vs 1.8%) and necrosis (4.6% vs 1.5%) was significantly higher in the high-risk immune group compared with the low-risk groups in TCGA data set (P < .003). The immune signature achieved a higher accuracy (mean concordance index [C-index], 0.64) than 2 commercialized multigene signatures (mean C-index, 0.53 and 0.61) for estimation of survival in comparable validation cohorts. When integrated with clinical characteristics such as age and stage, the composite clinical and immune signature showed improved prognostic accuracy in all validation data sets relative to molecular signatures alone (mean C-index, 0.70 vs 0.63) and another commercialized clinical-molecular signature (mean C-index, 0.68 vs 0.65). CONCLUSIONS AND RELEVANCE: The proposed clinical-immune signature is a promising biomarker for estimating overall survival in nonsquamous NSCLC, including early-stage disease. Prospective studies are needed to test the clinical utility of the biomarker in individualized management of nonsquamous NSCLC.
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The use of only one dataset for discovery does not account for biological heterogeneity, technical biases across different datasets, the large number of genes in a signature or impractical linear scoring modeling methods.
“Unfortunately, none of these existing gene signatures have been widely adopted in clinical practice, which may be due, in part, to the use of only one dataset for discovery that does not account for biological heterogeneity, technical biases across different datasets, the large number of genes in a signature or impractical linear scoring modeling methods17,18.”
Existing gene signatures have not been widely adopted in clinical practice.
“Unfortunately, none of these existing gene signatures have been widely adopted in clinical practice, which may be due, in part, to the use of only one dataset for discovery that does not account for biological heterogeneity, technical biases across different datasets, the large number of genes in a signature or impractical linear scoring modeling methods17,18.”
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References 78 · Cited by 514
- Authors
- Li, B., Cui, Y., Diehn, M., & Li, R.
- Affiliations
- Stanford Medicine (US)
- Hokkaido University (JP)
- Corresponding author
- Li, Ruijiang
- Venue
- JAMA Oncology
- Publisher
- American Medical Association (AMA)
- Published
- 2017-11-01
- Volume
- 3
- Issue
- 11
- Pages
- 1529
- ISSN
- 2374-2437 (ISSN-L)
- PMC
- PMC5710196
- OpenAlex
- W2733991343
- Cited by
- 514
- References
- 78
- Citations by year
- 2026: 3 · 2025: 16 · 2024: 31 · 2023: 49 · 2022: 89 · 2021: 139 · 2020: 136 · 2019: 35 · 2018: 13 · 2017: 3
- FWCI
- 23.03
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- JAMA Oncology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Oncology › Lung Cancer Research Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Keywords
- non-squamous non-small cell lung cancer, gene expression signatures, overall survival, recurrence risk, adjuvant therapy decision, lung adenocarcinoma, chemotaxis, tumor immune microenvironment, early-stage lung cancer, prognostic factors, risk stratification, TIDE score, tumor-infiltrating immune cells, immune checkpoint genes, tumor suppressor
- MeSH terms
- Adenocarcinoma of Lung, Adenocarcinoma, Adolescent, Adult, Aged, Aged, 80 and over, Carcinoma, Non-Small-Cell Lung, Female, Humans, Lung Neoplasms, Male, Middle Aged, Neoplasm Staging, Phenotype, Pneumonectomy, Predictive Value of Tests, Retrospective Studies, Risk Factors, Biomarkers, Tumor, Reproducibility of Results, Proportional Hazards Models, Treatment Outcome, Genetic Predisposition to Disease, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Databases, Genetic, Young Adult, Tumor Microenvironment, Transcriptome
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