Multi-omic analysis of esophageal adenocarcinoma uncovers candidate therapeutic targets and cancer-selective posttranscriptional regulation
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J Robert O'Neill Marcos Yébenes Mayordomo Goran Mitulović Sofian Al Shboul Georges Bedran Jakub Faktor Lenka Hernychova Lukas Uhrik Maria Gómez-Herranz Mikołaj Kocikowski Vicki Save Bořivoj Vojtěšek Mark J Arends Rebecca C Fitzgerald Paul AW Edwards Nicola Grehan Barbara Nutzinger Christine Loreno Aisling M Redmond Sujath Abbas Adam Freeman Elizabeth C Smyth Maria O'Donovan Ahmad Miremadi Shalini Malhotra Monika Tripathi Calvin Cheah Hannah Coles Curtis Millington Ginny Devonshire Matthew Eldridge Maria Secrier Sriganesh Jammula Jim Davies Charles Crichton Nick Carroll Richard H Hardwick Peter Safranek Andrew Hindmarsh Vijayendran Sujendran Stephen J Hayes Yeng Ang Andrew Sharrocks Shaun R Preston Izhar Bagwan Vicki Save Richard JE Skipworth Ted R Hupp J Robert O'Neill Olga Tucker Andrew Beggs Philippe Taniere Sonia Puig Gianmarco Contino Timothy J Underwood Robert C Walker Ben L Grace Jesper Lagergren James Gossage Andrew Davies Fuju Chang Ula Mahadeva Vicky Goh Francesca Ciccarelli Grant Sanders Richard Berrisford David Chan Ed Cheong Bhaskar Kumar L Sreedharan Simon L Parsons Irshad Soomro Philip Kaye John Saunders Laurence Lovat Rehan Haidry Michael Scott Sharmila Sothi Suzy Lishman George B Hanna Christopher J Peters Krishna Moorthy Anna Grabowska Richard Turkington Damian McManus Helen Coleman Russell D Petty Freddie Bartlet Ted Hupp Javier Antonio Alfaro
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Abstract
Efforts to address the poor prognosis associated with esophageal adenocarcinoma (EAC) have been hampered by a lack of biomarkers to identify early disease and therapeutic targets. Despite extensive efforts to understand the somatic mutations associated with EAC over the past decade, a gap remains in understanding how the atlas of genomic aberrations in this cancer impacts the proteome and which somatic variants are of importance for the disease phenotype. We performed a quantitative proteomic analysis of 23 EACs and matched adjacent normal esophageal and gastric tissues. We explored the correlation of transcript and protein abundance using tissue-matched RNA-seq and proteomic data from seven patients and further integrated these data with a cohort of EAC RNA-seq data (n = 264 patients), EAC whole-genome sequencing (n = 454 patients), and external published datasets. We quantified protein expression from 5879 genes in EAC and patient-matched normal tissues. Several biomarker candidates with EAC-selective expression were identified, including the transmembrane protein GPA33. We further verified the EAC-enriched expression of GPA33 in an external cohort of 115 patients and confirm this as an attractive diagnostic and therapeutic target. To further extend the insights gained from our proteomic data, an integrated analysis of protein and RNA expression in EAC and normal tissues revealed several genes with poorly correlated protein and RNA abundance, suggesting posttranscriptional regulation of protein expression. These outlier genes, including SLC25A30, TAOK2, and AGMAT, only rarely demonstrated somatic mutation, suggesting post-transcriptional drivers for this EAC-specific phenotype. AGMAT was demonstrated to be overexpressed at the protein level in EAC compared to adjacent normal tissues with an EAC-selective, post-transcriptional mechanism of regulation of protein abundance proposed. Integrated analysis of proteome, transcriptome, and genome in EAC has revealed several genes with tumor-selective, posttranscriptional regulation of protein expression, which may be an exploitable vulnerability.
Journal details
Volume
23
Issue number
6
Pages
100764
Available online
Publication date
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10.1016/j.mcpro.2024.100764
Europe PubMed Central
38604503
Pubmed
38604503
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