여름정기학술대회
2022여름초록
발표자 및 발표 내용
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Brief Oral Presentation 발표신청 | |
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국가 |
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공동저자
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접수자
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Even though a
disease may seem histologically similar, their underlying progression pathway
at molecular level can vary vastly. To offer optimal treatment options, the
identification of the patient’s disease subtype would be a critical step. Here,
we developed a MRM-based subtype identification technology, with its
application to pancreatic ductal adenocarcinoma (PDAC) patients. PDAC is a
disease with one of the lowest 5-year survival rate where more than 90% of the
patients show cold response to surgery or chemotherapy emphasizing the need for
tailoring appropriate therapeutic options.
LC-MRM-MS/MS experiments targeting 153 targets were conducted
across three replicates of 129 PDAC patient tissue samples using 6495C Agilent
triple quadruple mass spectrometer combined with a dual-nanoflow LC system for
subtype prediction. Subtypes of these PDAC patients were previously
characterized into 6 distinct subtypes through an extensive proteogeonomic analysis.
Based on these characterized subtypes, subtype-specific signature peptide sets
were selected from each of these subtype which sequences were stable isotope
labeled (SIL). These peptides were purified, AAA (amino acid analysis)-MS
quantified and mixed together to generate a PDAC subtype identification mixture
consisting of 153 signature peptides. With optimized MRM conditions and amounts
for each of these peptides, LC-MRM-MS/MS experiments were performed., Area
ratios of the SIL and endogenous peptide were taken to quantify endogenous
peptide amount of each subtype, then taken to further select key subtype
signature peptides. With these key peptides, a PLS-DA (Partial least
squares-discriminant analysis) model was built resulting an average of 88.9%
prediction accuracy and AUC of 0.905 across all 6 subtypes.
Correlation between survival rates and the predicted subtypes is planned to be examined to assess the value of PDAC subtype identification technology (PDAC-SIT) in clinical trials in selecting drug candidates as a method of predictive enrichment strategy.
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