The Challenge of Predicting Chemoimmunotherapy Response Neoadjuvant chemoimmunotherapy (combining platinum-based chemotherapy with PD-1 inhibitors before surgery) is standard care for many resectable NSCLC patients, but pathological response rates vary dramatically. Major pathological response (MPR) rates in trials range from 20% to 60%, and currently no biomarker reliably identifies who will benefit.
Limitations of Existing Biomarkers PD-L1 expression and tumor mutational burden (TMB) - the primary predictors for ICI monotherapy - are poor predictors when chemotherapy is added to immunotherapy. Clinical trials have shown neither PD-L1 nor TMB effectively discriminates responders from non-responders in chemoimmunotherapy settings.
Transcriptomic Approach This study analyzed pretreatment transcriptomic data from NSCLC patients to identify gene expression signatures associated with pathological response to chemoimmunotherapy. By focusing on pretreatment biology rather than post-treatment measurements, the approach identifies predictive (not prognostic) biomarkers.
ATF3 Identified as Key Gene The study identified ATF3 (Activating Transcription Factor 3) as the most critical gene within an interferon signaling signature (NeoIGS) that predicts pathological response. ATF3 is a stress-response transcription factor that links immune signaling to cancer biology.
Discovery Cohort from GEO Transcriptomic data from 24 NSCLC patients treated with neoadjuvant chemoimmunotherapy (GSE207422) were downloaded from the GEO database: 9 with major pathological response (MPR) and 15 without (NMPR). Differential gene expression analysis using DESeq2 identified genes with adjusted p less than 0.05 and absolute log2 fold-change greater than 2.
NeoIGS Signature Construction Gene Set Enrichment Analysis (GSEA) revealed that the interferon signaling pathway was significantly enriched in MPR patients. The subset of interferon pathway genes most differentially expressed between MPR and NMPR patients was compiled into the NeoIGS (Neoadjuvant Interferon Gene Signature) - a multi-gene expression index.
Key Gene Identification by ROC Analysis Among all NeoIGS genes, ATF3 was identified as the single most discriminative gene using ROC curve analysis. ATF3 expression alone showed a high AUC for distinguishing MPR from NMPR patients, warranting its investigation as a standalone clinical biomarker.
Clinical Cohort Validation NeoIGS and ATF3 findings were validated in 53 NSCLC patients receiving neoadjuvant chemoimmunotherapy at Daping Hospital (2017-2024). ATF3 protein expression was measured in pretreatment FFPE biopsy specimens by immunohistochemistry (IHC), making the biomarker measurable with standard pathology tools.
NeoIGS Predicts Response with AUC 0.926 The full NeoIGS interferon gene signature achieved an AUC of 0.926 for predicting pathological response to neoadjuvant chemoimmunotherapy in the GEO discovery cohort - exceptional discriminative performance for a molecular biomarker. NeoIGS also predicted response in an ICI monotherapy cohort, suggesting it captures a general immunotherapy response program.
IPS and TIDE Score Validation Immunophenoscore (IPS) and TIDE (Tumor Immune Dysfunction and Exclusion) scores in the TCGA-NSCLC dataset confirmed NeoIGS's association with immunotherapy benefit. High NeoIGS scores correlated with favorable immune phenotypes: more CD8+ T cell infiltration and higher expression of ICI target molecules.
ATF3 Outperforms PD-L1 Clinically In the 53-patient clinical cohort, ATF3 IHC staining was compared to PD-L1 for predicting MPR. ATF3-high patients achieved a 90.0% MPR rate - remarkably high - while ATF3-low patients had substantially lower response rates. ATF3 outperformed PD-L1 in this direct comparison, establishing it as a potentially superior predictive biomarker.
CD8+ T Cell Correlation Interferon signaling pathway expression and CD8+ T cell infiltration were both significantly higher in the MPR group, confirming that effective neoadjuvant response is associated with a pre-existing inflamed tumor immune microenvironment. ATF3 may serve as a marker of this 'hot' immune state.
Standard Pathology Platform Compatibility ATF3 is measurable by immunohistochemistry (IHC) on FFPE biopsy specimens - the standard diagnostic pathology workflow. Unlike RNA sequencing or complex genomic tests, IHC is universally available, inexpensive, and results can be obtained within hours. This makes ATF3 immediately translatable to clinical practice if validated.
Guiding Neoadjuvant Treatment Selection Patients with ATF3-high pretreatment biopsies could be confidently started on chemoimmunotherapy with high expectation of pathological response. ATF3-low patients might benefit from different approaches - alternative chemotherapy combinations, clinical trial enrollment, or primary surgery without neoadjuvant therapy.
Addressing the PD-L1 Limitation The finding that ATF3 outperforms PD-L1 in the chemoimmunotherapy setting addresses a critical unmet clinical need. PD-L1 testing is currently required for some drugs but has weak predictive value in combination regimens. ATF3 could replace or complement PD-L1 as the companion diagnostic for chemoimmunotherapy.
Biological Mechanism Insight ATF3 functions as a stress-response transcription factor activated by interferons and inflammatory signals. Its role in mediating the interferon signaling response that drives anti-tumor T cell function provides a biological rationale for its predictive value beyond simple correlation.
Small Discovery Cohort The NeoIGS signature was derived from only 24 patients in the GEO dataset (9 MPR, 15 NMPR). This small cohort is prone to overfitting and may not capture the full diversity of NSCLC molecular subtypes and chemoimmunotherapy regimens. Independent discovery cohorts would strengthen the finding.
Heterogeneous Treatment Regimens The 53-patient clinical cohort received various PD-1 antibodies (nivolumab, pembrolizumab, tislelizumab) with different chemotherapy backbones. Whether ATF3 predicts response equivalently across all these regimens, or is specific to certain combinations, requires analysis with larger, regimen-stratified cohorts.
IHC Scoring Standardization ATF3 IHC scoring requires standardized thresholds (what constitutes 'high' vs. 'low' expression) and validated antibody/protocol specifications. Before clinical deployment, a commercially validated ATF3 IHC kit with standardized scoring algorithms would need regulatory approval.
Future Directions Prospective biomarker-stratified clinical trials using ATF3 to select patients for different neoadjuvant strategies represent the highest-priority next step. Research should also explore whether ATF3 can be measured in liquid biopsy, develop an automated IHC scoring algorithm, and investigate whether ATF3 expression changes dynamically during treatment as an early response indicator.