Abstract 2433: Multiomic plasma profiling identifies potential signatures of disease progression in early-stage NSCLC

Abstract

Abstract Blood-based markers can be used to non-invasively predict cancer progression after treatment. Here, cell-free DNA (cfDNA) and plasma proteins were evaluated to explore biological signatures of progression in non-small cell lung cancer (NSCLC). Baseline plasma samples (n=24; 16 progressors, 8 non-progressors) were from patients diagnosed in 2004 with stage I-III NSCLC, collected prior to surgical resection, and retrospectively analyzed. Six patients were treated with neoadjuvant therapies, one with adjuvant therapy, and 17 with surgery alone. Progression was defined as a relapse event or death by any cause. Whole-genome sequencing was performed to characterize cfDNA fragments, which reflect nucleosome protection and chromatin state. Transcriptional activation for protein-coding genes was inferred by modeling fragment distribution around each transcription start site. Univariate comparisons of gene activation between progressors and non-progressors and Cox proportional hazard ratios (HRs) were calculated by grouping patients above or below the median of the marker of interest. This analysis revealed IL-1RN, the gene encoding for the IL-1RA antagonist to the IL-1 receptor complex, as the gene most negatively correlated with progression-free survival (PFS) (r = -0.76, p < 0.0001; Cox HR = 13.77, p < 0.001). This gene was also significantly more active in progressors than in non-progressors (p < 0.005). The binding activity of ~500 transcription factors was also inferred by measuring chromatin accessibility across the genome, revealing SOX-9 to be significantly associated with progression (p < 0.0001, FDR = 1.1%) and the factor most negatively correlated with PFS (r = -0.72, p < 0.001, FDR = 16.9%). Both IL-1RN and SOX-9 have been previously reported to affect survival in NSCLC patients.In addition, the abundances of ~450 proteins including cytokines, receptors, and enzymes were measured. Six proteins were identified as differentially abundant between progression groups. Among these, IL-1α was more abundant in progressors vs. non-progressors (effect size = 0.92, p < 0.05). Notably, IL-1RA abundance did not differ between these groups. Both IL-1α (r = -0.61, p < 0.01; Cox HR = 3.78, p < 0.05) and IL-1RA (r = -0.75, p < 0.001; Cox HR = 1.13, p = 0.78) were negatively correlated with PFS in progressors. Finally, all features and analytes were integrated to identify biological signatures that may be shared among proteins and cfDNA. These signatures differed significantly (p < 0.05) between progressors and non-progressors, suggesting differences in cytokine signaling. The multiomics platform described here integrates biological signals with computational featurization to reveal clinically relevant signatures. Specifically, findings from a small cohort of early-stage NSCLC patients demonstrated the potential of this platform to reveal signatures of progression in NSCLC. Citation Format: Francesco Vallania, Hayley Warsinske, Peter Ulz, Tzu-Yu Liu, Karen Assayag, Krishnan K. Palaniappan, Mitch Bailey, Irving Wang, David E. Weinberg, Riley Ennis, C Jimmy Lin, Anne-Marie Martin, Nancy Krunic. Multiomic plasma profiling identifies potential signatures of disease progression in early-stage NSCLC [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2433.