Abstract 3924: Deep, unbiased multi-omics approach for identification of pancreatic cancer biomarkers from blood
Authors: Wilcox Bruce , Blume John , Swaminathan Kavya , Williams Preston , Khadka Manoj , Deyarmin Jared , Ramaswamy Saividya , Kodama Yuya , Young Brian , Belthangady Chinmay , Liu Manway , Yang Mi , Ma Philip 
Published: June 2022

Pancreatic cancer is the seventh leading cause of cancer-related death worldwide and the third leading cause of cancer-related death in the USA. The low survival rate of pancreatic cancer is due to the challenges in early detection of disease, highlighting the need for early diagnostic test development. While cancer signatures are less challenging to identify at the localized pancreatic tumor via biopsy, cancer signals found in the bloodstream due to cellular leakage, metastasis, signaling, innate immune response, remain of key interest due to reduced invasive sampling. The key challenges encountered in liquid biopsy cancer biomarker discovery studies are analyte degradation and dilution in a complex biological matrix, which limits high specificity and sensitivity measurements. To overcome these challenges, PrognomiQ has developed a comprehensive multi-omics platform that facilitates uncovering previously untapped information to gain a more holistic biological perspective at unprecedented depths and integrate molecular signatures across complex levels of biology. Implementation of this approach has led to the discovery of new pancreatic cancer-specific biomarkers and a deeper understanding of the integrated pathways of pancreatic cancer. In this case-control study, the plasma proteome and metabolome data were collected from 193 human plasma samples comprising 92 pancreatic cancer and 104 healthy subjects utilizing liquid chromatography-mass spectrometry. Subject samples were collected post-diagnosis, but pre-treatment for cancer subjects versus non-cancer controls. Sample collection and handling were the same for all samples. In our initial analysis, we detected 3,381 proteins in all samples (minimum of 3 samples per class), and utilizing a Bonferroni correction (FDR = 0.05) we showed 124 proteins to be statistically significant. We also determined ~200 lipids out of 678 total lipids and 49 of 299 metabolites present in all samples (minimum of 3 samples per class) to be statistically significant with a Bonferroni correction (FDR = 0.05). The detected analytes (proteins, lipids, and metabolites) are both known, and unknown, to have an association with pancreatic cancer. Analysis of the data collected from the described cohort will continue to determine Analysis is ongoing to integrate the multi-omics datasets and determine multivariate statistical performance to detect pancreatic cancer.

Conclusion: The intent of this cohort and study was to detect biological signal for pancreatic cancer, and this preliminary analysis suggests there are significant differences between classes in the samples as collected. It remains to be seen if combining features within and across analyte classes improve detection. This is a case-control study, not an intent to test study but shows promise for the detection of pancreatic cancer across a multitude of analyte classes.

Citation Format: Bruce Wilcox, John Blume, Kavya Swaminathan, Preston Williams, Manoj Khadka, Jared Deyarmin, Saividya Ramaswamy, Yuya Kodama, Brian Young, Chinmay Belthangady, Manway Liu, Mi Yang, Philip Ma. Deep, unbiased multi-omics approach for identification of pancreatic cancer biomarkers from blood [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3924.