S767
Clinical - Lung
ESTRO 2026
Purpose/Objective: Idiopathic Pulmonary Fibrosis (IPF) substantially worsens clinical outcomes in non-small cell lung cancer (NSCLC), in part through increased susceptibility to treatment-related side-effects [1]. Although large Genome-Wide Association Studies (GWAS) have identified robust IPF-associated loci [2][3], the prognostic relevance of these genes at the transcriptomic level in NSCLC has not previously investigated. We aimed to evaluate whether a validated 22-gene IPF-associated gene signature provides prognostic information in NSCLC. Material/Methods: RNA-sequencing and clinical data from TCGA lung adenocarcinoma and squamous carcinoma (LUAD/LUSC; n=1,052) were analysed. An independent TCGA breast cancer cohort (n=1,118) served as an organ-control comparator. Raw counts for all 22 genes were variance-stabilised. The genes were grouped by biological function: telomere maintenance (TERT, TERC, RTEL1, PARN); mTOR regulation (NPRL3, DEPTOR); spindle/mitotic checkpoint (KNL1, MAD1L1, KIF15, STMN3); epithelial/surfactant/adhesion (MUC5B, SFTPA2, SFTPC, DSP, ATP11A); innate immunity/signalling (TOLLIP, AKAP13, DPP9); epithelial repair (FAM13A) and transcription/metabolism (ZKSCAN1, KANSL1, IVD). We performed differential expression analysis between lung and breast tumours to confirm lung-specific transcriptional patterns. A linear predictor was used to derive a composite risk score for the 22 genes. Prognostic performance was evaluated using Kaplan–Meier overall survival (OS) analysis, log-rank testing (Cox regression analysis), Harrell’s C-index, and likelihood-ratio (LR) χ² statistics. The independent prognostic value of Cox-weighted, z- standardized 22-gene IPF risk scores was evaluated using multivariable Cox models adjusted for age, gender, and stage. Results: NSCLC patients demonstrated a distinct IPF-associated transcriptional profile with upregulation of SFTPA2, SFTPC, ATP11A, FAM13A and TERT, and downregulation of DEPTOR and ZKSCAN1. Four genes were independently associated with OS; increased NPRL3 (HR=1.35; p=0.004) and KNL1 (HR=1.33; p=0.002) expression predicted worse OS, whereas higher IVD (HR=0.80; p=0.002) and ZKSCAN1 (HR=0.80; p=0.014) predicted improved OS. Baseline discrimination was modest (C-index=0.605; LR χ² =45.8; p=0.002). The composite gene risk score significantly stratified OS across tertiles ( χ² =24.4; p<0.001): median OS was 72.3 months low-risk, 57.9 months intermediate-risk, and 36.6 months high-risk (Figure 1). After adjusting for clinical covariates, the score remained independently prognostic (HR=1.99 per unit; 95% CI 1.61–2.47; p<0.001), improving model discrimination (C-index=0.653; LR χ² =86.8; p<0.001)
calculated. Statistical analyses (descriptive statistics, Cox regression, survival analyses) were employed to determine the relationship between MLR, dMLR and clinical endpoints (LRC, PFS). Cutoff values for these biomarkers were calculated partially based on published literature in order to divide the whole cohort (n=183) as well as the Durvalumab subgroup (n=112) for log-rank comparisons. Results: Median follow-up was 30.3 months. Cutoff values for MLR and dMLR were 0.665 and 0.945, respectively. Patients with a high MLR demonstrated a significantly worse 2-year LRC than those with a low MLR (2-year LRC 55.7% vs 75.5%; p=0.018). Similarly, in the Durvalumab subgroup a high MLR was significantly associated with worse 2-year LRC (57.5% vs 77.3%, p=0.030). Patients with high MLR showed worse 2-year PFS in the whole cohort: 20.5% vs 56.1% (p<0.001). Likewise, the 2-year PFS was inferior for high MLR in the Durvalumab subgroup (31.2% vs 64.6%, p=0.04). High dMLR (>0.995) showed a significantly worse 2- year PFS: 17.4% vs 56.3% (p<0.001) in the whole cohort, likewise as in the Durvalumab subgroup (2- year PFS 23.1% vs 64.1%, p=0.003). Finally, multivariate analyses for MLR and dMLR revealed that they, together with non-squamous-cell carcinoma histology, were independent risk factors for worse LRC and PFS. Conclusion: This study highlights the potential of biomarkers of systemic inflammation, particularly MLR and dMLR, as predictors for LRC and PFS in unresectable NSCLC stage III. They can be collected easily and repeatedly from routine peripheral blood sampling. Prospective trials are needed to generate and validate standardized cutoff values. Keywords: NSCLC stage III, inflammatory biomarkers, MLR/dMLR Digital Poster Highlight 936 A 22-gene idiopathic pulmonary fibrosis– associated signature predicts overall survival in non-small cell lung cancer Badie Abuzaid 1 , Ahmed Salem 2 , Fatima Farhan 1,3 , Abdelrahman Mohammad Masheh 1 , Mohammad Akash 1 , Lina Al-Zerikat 1 , Azadeh Abravan 4,5 1 Faculty of Medicine, The Hashemite University, Zarqa, Jordan. 2 Department of Anatomy, Physiology and Biochemistry, Faculty of Medicine, The Hashemite University, Zarqa, Jordan. 3 Clinical Bioinformatician, Bionl, MA, USA. 4 Institute of genetics and cancer, University of Edinburgh, Edinburgh, United Kingdom. 5 Division of cancer sciences, The University of Manchester, Manchester, United Kingdom
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