Abstract A038: High-dimensional, multi-omics analyses of proteins, metabolites, transcripts, and genes enable biomarker discovery in early- and late-stage pancreatic cancer

Abstract

Abstract Pancreatic cancer is the third leading cause of cancer-related deaths in the United States. Disease biomarkers quantified from blood-based assays may help reduce mortality by enabling early detection, treatment selection, or response and resistance assessment. PrognomiQ has developed a multi-omics assay and analysis platform that comprehensively profiles blood samples to detect proteins, metabolites, lipids, mRNA, miRNA, cfDNA fragments, and methylation at CpG sites. This platform can provide deep insights into the biology of pancreatic cancer and could enable the development of high sensitivity and specificity tests for the early detection of pancreatic cancer. We conducted a case-control study comprising 196 subjects: 92 with untreated pancreatic cancer and 104 matched controls without pancreatic cancer. For each subject, blood was collected in assay-specific tubes and processed to provide 7 different `omics readouts. cfDNA and mRNA were isolated from samples and assayed following standard NGS protocols. cfDNA fragments were processed to estimate fragment-length disorder and copy-number variation along with CpG site methylation. In addition, targeted and untargeted LCMS were used to detect and quantify proteins, metabolites, and lipids. After normalization, non-parametric univariate analyses of cases versus controls were performed to identify differentially abundant features on all available samples for each assay. Unsupervised learning was used to investigate the separation of subjects into groups based on disease status for the subset of 157 subjects for which complete data on all 7 readouts were available. We detected 2,812 proteins, 811 lipids, 373 metabolites, and 110,864 mRNA transcripts in all samples where data for each assay was available. Of these, 275 proteins, 232 lipids, 49 metabolites, and 3385 mRNA transcripts were significantly different (FWER < 0.05) in cases versus controls. From cfDNA data, we identified 35 non-contiguous genomic regions associated with fragment-length disorder, 557 with copy-number variation, and 5 with multiple, differentially methylated CpGs (FWER < 0.05) that aggregately span 307 protein-coding genes; of these, the overlap with the differentially expressed proteins included E-cadherin (tumor suppressor) and N-cadherin (involved in epithelial-to-mesenchymal transition). Statistically significant genes and proteins were found to be associated with processes including Wnt signaling, regulation of focal adhesion assembly, and actin cytoskeleton organization. Multi-omics, unsupervised learning showed separation of early- and late-stage cases and controls. High-dimensional bioinformatics analyses systematically decomposed each `omics data type into joint and orthogonal components associated with pancreatic cancer. Ongoing multivariate analyses, including supervised machine learning, will further elucidate the biology of pancreatic cancer development, and serve as the foundation for high-sensitivity blood tests for the early detection and monitoring of pancreatic cancer. Citation Format: Ehdieh Khaledian, Preethi Prasad, John Blume, Ghristine Bundalian, Connie Chao-Shern, Jinlyung Choi, Rea Cuaresma, Jared Deyarmin, Jun Heok Jang, Manoj Khadka, Thidar Khin, Yuya Kodama, Ajinkya Kokate, Joon-Yong Lee, Manway Liu, Nithya Mudaliar, Madhuvanthi Ramaiah, Saividya Ramaswamy, Peter Spiro, Kavya Swaminathan, Preston Williams, Mi Yang, James Yee, Brian Young, Robert Zawada, Susan Zhang, Chinmay Belthangady, Bruce Wilcox, Philip Ma. High-dimensional, multi-omics analyses of proteins, metabolites, transcripts, and genes enable biomarker discovery in early- and late-stage pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A038.