Dataset Information

Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance.

ABSTRACT: A rapid blood-based diagnostic modality to detect pancreatic ductal adenocarcinoma (PDAC) with high accuracy is an unmet medical need. The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and Machine Learning to the diagnosis of PDAC. Peripheral blood samples were collected from a total of 322 consecutive PDAC patients and 265 controls with a family history of PDAC. Five µl of serum samples were analyzed using PESI-MS system. The mass spectra from each specimen were then fed into machine learning algorithms to discriminate between control and cancer cases. A total of 587 serum samples were analyzed. The sensitivity of the machine learning algorithm using PESI-MS profiles to identify PDAC is 90.8% with specificity of 91.7% (95% CI 83.9%-97.4% and 82.8%-97.7% respectively). Combined PESI-MS profiles with age and CA19-9 as predictors, the accuracy for stage 1 or 2 of PDAC is 92.9% and for stage 3 or 4 is 93% (95% CI 86.3-98.2; 87.9-97.4 respectively). The accuracy and simplicity of the PESI-MS profiles combined with machine learning provide an opportunity to detect PDAC at an early stage and must be applicable to the examination of at-risk populations.

SUBMITTER: Chung WY

PROVIDER: S-EPMC7013221 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance.

Chung Wen Y WY Correa Elon E Yoshimura Kentaro K Chang Ming-Chu MC Dennison Ashley A Takeda Sen S Chang Yu-Ting YT

American journal of translational research 20200115 1

A rapid blood-based diagnostic modality to detect pancreatic ductal adenocarcinoma (PDAC) with high accuracy is an unmet medical need. The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and Machine Learning to the diagnosis of PDAC. Peripheral blood samples were collected from a total of 322 consecutive PDAC patients and 265 controls with a family history of PDAC. Five µl of serum samples were analyzed using PESI-MS system. The m ...[more]

PMID: 32051746

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Project description:We present the first comprehensive capillary electrophoresis electrospray ionization mass spectrometry (CESI-MS) analysis of post-translational modifications derived from H1 and core histones. Using a capillary electrophoresis system equipped with a sheathless high-sensitivity porous sprayer and nano-liquid chromatography electrospray ionization mass spectrometry (nano-LC-ESI-MS) as two complementary techniques, we characterized H1 histones isolated from rat testis. Without any pre-separation of the perchloric acid extraction, a total of 70 different modified peptides, including 50 phosphopeptides, were identified in the rat linker histones H1.0, H1a-H1e, and H1t. Out of the 70 modified H1 histone peptides, 27 peptides could be identified with CESI-MS only, and 11 solely with LC-ESI-MS. Immobilized metal-affinity chromatography enrichment prior to MS analysis yielded a total of 55 phosphopeptides; 22 of these peptides could be identified only by CESI-MS, and 19 only by LC-ESI-MS, showing the complementarity of the two techniques. We mapped 42 H1 modification sites, including 31 phosphorylation sites, of which 8 were novel sites. For the analysis of core histones, we chose a different strategy. In a first step, the sulfuric-acid-extracted core histones were pre-separated using reverse-phase high-performance liquid chromatography. Individual rat testis core histone fractions obtained in this way were digested and analyzed via bottom-up CESI-MS. This approach yielded the identification of 42 different modification sites including acetylation (lysine and N(?)-terminal); mono-, di-, and trimethylation; and phosphorylation. When we applied CESI-MS for the analysis of intact core histone subtypes from butyrate-treated mouse tumor cells, we were able to rapidly detect their degree of modification, and we found this method very useful for the separation of isobaric trimethyl and acetyl modifications. Taken together, our results highlight the need for additional techniques for the comprehensive analysis of post-translational modifications. CESI-MS is a promising new proteomics tool as demonstrated by this, the first comprehensive analysis of histone modifications, using rat testis as an example.

Dataset Information

Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance.

Publications

Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance.

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