Pancreatic ductal adenocarcinoma ubiquitination profiling reveals specific prognostic and theranostic markers

Abstract

Background: Pancreatic ductal adenocarcinoma (PDAC) has been widely studied at multiomics level. However, little is known about its specific ubiquitination, a major post-translational modification (PTM). As PTMs regulate the final function of any gene, we decided to establish the ubiquitination profiles of 60 PDAC.

Methods: We used specific proteomic tools to establish the ubiquitin dependent proteome (ubiquitinome) of frozen PDXs (Patients' derived xenographs). Then, we performed bioinformatics analysis to identify the possible associations of these ubiquitination profiles with tumour phenotype, patient survival and resistance to chemotherapies. Finally, we used proximity ligation assays (PLA) to detect and quantify the ubiquitination level of one identified marker.

Findings: We identified 38 ubiquitination site profiles correlating with the transcriptomic phenotype of tumours and four had notable prognostic capabilities. Seventeen ubiquitination profiles displayed potential theranostic marker for gemcitabine, seven for 5-FU, six for oxaliplatin and thirteen for irinotecan. Using PLA, we confirmed the use of one ubiquitination profile as a drug-response marker, directly on paraffin embedded tissues, supporting the possible application of these biomarkers in the clinical setting.

Interpretation: These findings bring new and important insights on the relationship between ubiquitination levels of proteins and different molecular and clinical features of PDAC patients. Markers identified in this study could have a potential application in clinical settings to help to predict response to chemotherapies thereby allowing the personalization of treatments.

Funding: Fondation ARC (PJA 20181208270 and PGA 12021010002840_3562); INCa; Canceropôle PACA; DGOS; Amidex Foundation; Fondation de France; and INSERM.

Keywords: Pancreatic cancer; Ubiquitin profiling; Ubiquitin prognostic markers; Ubiquitin theranostic markers.

Fig. 1

Ubiquitination profiling landscape in PDACs from 60 patients. (a) Illustration of experimental design used to generate the ubiquitination site profiles of 60 PDACs. Biopsies from patients with PDACs were grown in immunocompromised mice to produce PDXs. Theses PDXs were then processed to extract cellular proteins that were digested with Lys-C and trypsin proteases. DiGly-pepts corresponding to ubiquitinated sites were enriched using specific antibodies and were identified and relatively quantified by tandem mass spectrometry. (b) Distribution of diGly-pepts according to their length. (c) Distribution of the diGly-pepts according to the number ubiquitination sites identified in this study. (d) Distribution of diGly-pepts according to their global intensities. (e) Venn diagram showing the relative proportion of diGly-pepts that are human specific and those that are ambivalent human or mouse.

Fig. 2

Correlation of the ubiquitination sites with transcriptomic phenotype in PDAC. (a) Volcano plot of the correlation results of all ubiquitination sites to the pancreatic adenocarcinoma molecular gradient (PAMG). Only ubiquitination sites significantly correlated to the PAMG are detailed (p-value <0.05, colour intensity). Ubiquitination sites at the right (red) have positive correlation while the one at the left (green) were negatively correlated (size of dots according to correlation coefficient). (b) Scatterplots showing examples of positively and negatively correlated ubiquitination sites with the PAMG (Figure S3) using a linear regression model and Spearman correlation (low values of PAMG are red and high values are blue). (c) Graphical representation of enrichment analysis results based on ubiquitinated proteins significantly correlated with the PAMG. Main enrichment terms are shown of different databases including Gene Ontology Biological Process (GO_PB), KEGG, and Reactome. Circle size corresponds to gene count in each enrichment term, and the colour corresponds to the adjusted p-value (p.adjust). (d) Gene-enrichment network of main enriched terms showing the functional relationship between different terms and involved genes.

Fig. 3

Identification of ubiquitination sites associated with survival of patients with PDAC. (a) Volcano plot showing univariate Cox regression analysis for each ubiquitination site. Only the ubiquitination sites (characterized by protein name and diGly conjugated lysine number) with significant p-value (log rank <0.05) are shown. The ubiquitination sites with a Hazard Ratio (HR) above 1 are associated with short survival while the ones with HR below 1 HR are associated with better survival. (b) Receiver operating characteristic (ROC) curves for the validation of the prognostic significance of the four selected ubiquitination sites. ROC curves were made at different survival time point and the value of the highest area under curve (AUC) is shown. (c) Kaplan-Meier curves of 4 ubiquitination sites (name of proteins and number of the ubiquitinated lysine) selected based on their p-value as well as their ROC curves shown in b. The patient cohort was divided into two groups, one with high (red) and one with low level of ubiquitination (purple).

Fig. 4

Ubiquitination sites associated with chemotherapeutic drug response in PDXs model. (a) Dot-plot showing all ubiquitination sites significantly correlated with the resistance score to four chemotherapeutic drugs including 5-FU, gemcitabine, irinotecan and oxaliplatin. (b) GO_BP and reactome terms enrichments results for all ubiquitination sites correlating with the different PDXs' resistance scores. Triangle and circle size corresponds to gene count in each enrichment term, and the colour corresponds to the adjusted p-value (p.adjust). (c) Networks of gene-enrichments and involved proteins for the four different drugs.

Fig. 5

Experimental validation of the ubiquitinome strategy and potential application to the clinic. (a) Scatterplots showing the positive correlation between levels of PSMD2 K27 ubiquitination determined by mass spectrometry and drug resistance score to 5-FU (n = 7) and irinotecan (n = 8) in the PDX model. (b) Typical images obtained after proximity ligation assay (PLA) detection of PSMD2 ubiquitination. Top: a PDX with low PSMD2 ubiquitination. Bottom: a PDX with high PSMD2 ubiquitination. Red arrows indicate positive PLA signals. (c) Scatterplot showing the correlation between quantified PLA signals of PSMD2 ubiquitination and the resistance score of PDXs to 5-FU (n = 9). (d) As in c with irinotecan resistance score (n = 11). R: Pearson's correlation; p: p-value of statistic test.

Fig. 6

Validation of ALDOA ubiquitination as a prognosis marker in PDAC. (a) HES and PLA ALDOA-Ubiquitin images extracted from a tumour microarray (TMA) of 22 distinct PDAC surgical samples from an independent cohort of patient. HES stained TMA shows the delimitations of the tumour compartment (blue line) which were considered for PLA quantification. PLA pictures show a low and a high level of ALDOA ubiquitination. (b) Kaplan-Meier curves of ALDOA ubiquitination as determined by PLA quantification. The patients' cohort was divided into two groups, one with high (orange) and one with low (blue) level of ubiquitination giving the lowest p-value. (c) Forest plot evaluating the Hazard Ratios (HR) in the model of univariate analysis for ALDOA peptide ubiquitination level (low and high). A group of reference was set to HR = 1, and a HR < 1 indicates a decreased risk of the outcome (death). The mean value and quartiles of the confidence interval (CI) of 95% are shown.