Multi-omics integration in Esophageal Adenocarcinoma reveals therapeutic targets and EAC-specific regulation of protein abundances

BackgroundEfforts 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. Differences in transcript and the corresponding protein abundances remain under-explored, leaving gaps in our understanding of the mechanisms underlying the disease. MethodsWe 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 used tissue-matched RNAseq and proteomic data from 7 patients and further integrated these data with a cohort of EAC RNA-seq data (n=264 patients), whole-genome sequencing (n=454 patients) and external published datasets. ResultsWe quantified protein expression from 5897 genes in EAC and patient-matched normal tissues. Several biomarker candidates with EAC-specific 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 post-transcriptional 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 over-expressed at the protein level in EAC compared to adjacent normal tissues with an EAC-specific post-transcriptional mechanism of regulation of protein expression proposed. ConclusionsBy quantitative proteomic analysis we have identified GPA33 as an EAC-specific biomarker. Integrated analysis of proteome, transcriptome, and genome in EAC has revealed several genes with tumor-specific post-transcriptional regulation of protein expression which may be an exploitable vulnerability.