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129th General Meeting of Korean Chemical Society & Exposition MRM-based prediction of disease subtypes: A case of pancreatic ductal adenocarcinoma

Submission Date :
2 / 28 / 2022 , 14 : 42 : 20
Abstract Number :
129022827188
Presenting Type:
Poster Presentation
Presenting Area :
Analytical Chemistry
Authors :
Jiwon Hong, Seunghoon Back, Dowoon Nam, Jingi Bae, Hokeun Kim, Su-Jin Kim, Chaewon Kang, Kwon Hee Bok, Hye-Kyeong Kwon, Sang-Won Lee*
Department of Chemistry, Korea University, Korea
Assigned Code :
ANAL.P-332 Assigend Code Guideline
Presenting Time :
April 14 (THU) 11:00~13:00
Identification of disease subtypes can facilitate tailoring therapeutic strategies. At a glance, progression of a disease may seem similiar across all patients, yet the underlying mechanism at molecular level can differ greatly. Applying appropriate therapies to target the right progression pathway would enable patients to have optimal therapeutic benefits with minimized side effects. In order to optimize the therapeutic option, it is crucial to be able to identify disease subtypes promptly with high certainty. Here, a MRM-based subtype prediction method is introduced with its application to pancreatic ductal adenocarcinoma (PDAC), one of the diseases with lowest average 5-year survival rate. More than 90% of the patients do not show response to surgery or chemotherapy, which necessitates a way to tailor appropriate therapeutic options. An extensive proteogenomic characterization identified 6 subtypes of PDAC and subtype specific signature peptides. Based on pathway enrichment and network analyses, as well as the adequacy for MRM, a set of subtype specific peptides were chosen for MRM-based subtype identification. These peptides were stable isotope labeled (SIL), purified, quantified respectively then mixed together to create a PDAC subtype identification SIL peptide mixture for spiking in MRM validation experiments. From the MRM-quantified endogenous peptide amounts, key subtype signature peptides were extracted and taken to build a PLS-DA model with an average 88.9% accuracy and AUC of 0.905 in all 6 subtypes. We plan to examine the correlation between survival rates and the deduced subtypes from the prediction model to assess the value of the PDAC subtype identification technology (PDAC-SIT) for clinical trials of drug candidates as a predictive enrichment strategy.