Abstract 3495: Multi-omic landscape of squamous cell lung cancer
Authors: Isis Y. Narvaez-Bandera, Ashley Lui, Eric Welsh, Dalia Ercan, Vanessa Rubio, Hayley Ackerman, Guohui Li, Lancia Darville, Min Liu, Bin Fang, Steven Eschrich, Brooke Fridley, John Koomen, Eric Haura, Gina M. DeNicola, Elsa Flores, Paul Stewart
Journal: Cancer Research (2024)

Abstract Patients with lung squamous cell carcinoma (LSCC) require new drug targets and improved biomarkers due to a lack of targetable genomic drivers and low response rates to immune checkpoint blockade. In a previous study, we analyzed a cohort of 108 LSCC patients by integrating DNA copy number variation, somatic mutations, RNA-sequencing, and expression proteomics. The principal discovery was the identification of three proteomic subtypes, with the majority (87%) of tumors comprising two of these subtypes. The "Inflamed" subtype showed enrichment for B-cell-rich tertiary lymphoid structures, while the "Redox" subtype exhibited enrichment for redox pathways and NFE2L2/KEAP1 alterations but had notably lower immune infiltration. We hypothesized that these subtypes would result in distinct metabolic signatures. Using ultra-high-performance liquid chromatographic separation on a HILIC column, followed by analysis on a Q Exactive HF high resolution mass spectrometer, we performed untargeted metabolomics on 87 tumors from the same LSCC proteogenomics cohort. A total of 7,392 features were obtained from this analysis, leading to the identification of 446 metabolites through m/z and retention time matching against an internal reference library. To understand if metabolomics could recapitulate our proteomic subtypes, we applied consensus clustering and non-negative matrix factorization (NMF) and assessed the resulting clusters using a Random Forest (RF) supervised classifier. Area Under the Curve (AUC) values for consensus clustering (5 clusters, AUC = 0.72), NMF (4 clusters, AUC = 0.73), and proteomics subtypes (Stewart et al. Nature communications. 2019:10:3578) (3 clusters, AUC = 0.74) suggest that metabolite abundances do indeed recapitulate the proteomic subtypes. Differential expression between Redox and Inflamed yielded 29 differentially expressed metabolites (p-value &amp;lt; 0.05 and 1.5 fold change). Glutathione, a key redox metabolite, was modestly elevated in the Redox proteomic subtype (0.58 log2 ratio, p = 1.14E-05). Notably, we identified glutathione metabolism (p = 1.29-5) and arginine biosynthesis (p = 5.22-4) as among the most significant pathways among differentially expressed metabolites. Glutathione, a key redox metabolite, was modestly elevated in the Redox proteomic subtype (0.58 log2 ratio, p = 1.14E-05). In conclusion, metabolomics recapitulates the proteomic subtypes, and there are distinct differences between these subtypes at the metabolite level. Ongoing work is developing a novel, network-based analysis framework to integrate these data quantitatively. Citation Format: Isis Y. Narvaez-Bandera, Ashley Lui, Eric Welsh, Dalia Ercan, Vanessa Rubio, Hayley Ackerman, Guohui Li, Lancia Darville, Min Liu, Bin Fang, Steven Eschrich, Brooke Fridley, John Koomen, Eric Haura, Gina M. DeNicola, Elsa Flores, Paul Stewart. Multi-omic landscape of squamous cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3495.