Biology subjects

Kon, O. L.

Publications and source records attributed to Kon, O. L..

3 recordsLinked to original sources

bioRxiv2021

A comprehensive mass spectral library for human thyroid tissues

Thyroid nodules occur in about 60% of the population. Current diagnostic strategies, however, often fail at distinguishing malignant nodules before surgery, thus leading to unnecessary, invasive treatments. As proteins are involved in all physio/pathological processes, a proteome investigation of biopsied nodules may help correctly classify and identify malignant nodules and discover therapeutic targets. Quantitative mass spectrometry data-independent acquisition (DIA) enables highly reproducible and rapid throughput investigation of proteomes. An exhaustive spectral library of thyroid nodules is essential for DIA yet still unavailable. This study presents a comprehensive thyroid spectral library covering five types of thyroid tissue: multinodular goiter, follicular adenoma, follicular and papillary thyroid carcinoma, and normal thyroid tissue. Our library includes 925,330 transition groups, 157,548 peptide precursors, 121,960 peptides, 9941 protein groups, and 9826 proteins from proteotypic peptides. This library resource was evaluated using three papillary thyroid carcinoma samples and their corresponding adjacent normal thyroid tissue, leading to effective quantification of up to 7863 proteins from biopsy-level thyroid tissues.

systems biology↗

bioRxiv2020

Phenotype Prediction using a Tensor Representation and Deep Learning from Data Independent Acquisition Mass Spectrometry

A novel approach for phenotype prediction is developed for mass spectrometric data. First, the data-independent acquisition (DIA) mass spectrometric data is converted into a novel file format called "DIA tensor" (DIAT) which contains all the peptide precursors and fragments information and can be used for convenient DIA visualization. The DIAT format is fed directly into a deep neural network to predict phenotypes without the need to identify peptides or proteins. We applied this strategy to a collection of 102 hepatocellular carcinoma samples and achieved an accuracy of 96.8% in classifying malignant from benign samples. We further applied refined model to 492 samples of thyroid nodules to predict thyroid cancer; and achieved a predictive accuracy of 91.7% in an independent cohort of 216 test samples. In conclusion, DIA tensor enables facile 2D visualization of DIA proteomics data as well as being a new approach for phenotype prediction directly from DIA-MS data.

bioinformatics↗

bioRxiv2019

ERO1α promotes hypoxic tumour progression and is associated with poor prognosis in pancreatic cancer

Pancreatic cancer is a leading cause of mortality worldwide due to difficulty detecting early-stage disease and our poor understanding of the mediators that drive the progression of hypoxic solid tumours. We, therefore, used a heavy isotope pulse/trace proteomic approach to determine how hypoxia alters pancreatic tumour expression of proteins that confer treatment resistance, promote metastasis, and suppress host immunity. Using this method, we identified that hypoxia stress stimulates pancreatic cancer cells to rapidly translate proteins that enhance metastasis (NOTCH2, NCS1, CD151, NUSAP1), treatment resistant (ABCB6), immune suppression (NFIL3,WDR4), angiogenesis (ANGPT4, ERO1, FOS), alter cell metabolic activity (HK2, ENO2), and mediate growth-promoting cytokine responses (CLK3, ANGPTL4). Database mining confirmed that elevated gene expression of these hypoxia-induced mediators is significantly associated with poor patient survival in various stages of pancreatic cancer. Among these proteins, the oxidoreductase enzyme ERO1 was highly sensitive to induction by hypoxia stress across a range of different pancreatic cancer cell lines and was associated with particularly poor prognosis in human patients. Consistent with these data, genetic deletion of ERO1 substantially reduced growth rates and colony formation in pancreatic cancer cells when assessed in a series of functional assays in vitro. Accordingly, when transferred into a mouse xenograft model, ERO1-deficient tumour cells exhibited severe growth restriction and negligible disease progression in vivo. Together, these data indicate that ERO1 is potential prognostic biomarker and novel drug target for pancreatic cancer therapy.

cancer biology↗