Resolution of Novel Pancreatic Ductal Adenocarcinoma Subtypes by Global Phosphotyrosine Profiling

Abstract

Comprehensive characterization of signaling in pancreatic ductal adenocarcinoma (PDAC) promises to enhance our understanding of the molecular aberrations driving this devastating disease, and may identify novel therapeutic targets as well as biomarkers that enable stratification of patients for optimal therapy. Here, we use immunoaffinity-coupled high-resolution mass spectrometry to characterize global tyrosine phosphorylation patterns across two large panels of human PDAC cell lines: the ATCC series (19 cell lines) and TKCC series (17 cell lines). This resulted in the identification and quantification of over 1800 class 1 tyrosine phosphorylation sites and the consistent segregation of both PDAC cell line series into three subtypes with distinct tyrosine phosphorylation profiles. Subtype-selective signaling networks were characterized by identification of subtype-enriched phosphosites together with pathway and network analyses. This revealed that the three subtypes characteristic of the ATCC series were associated with perturbations in signaling networks associated with cell-cell adhesion and epithelial-mesenchyme transition, mRNA metabolism, and receptor tyrosine kinase (RTK) signaling, respectively. Specifically, the third subtype exhibited enhanced tyrosine phosphorylation of multiple RTKs including the EGFR, ERBB3 and MET. Interestingly, a similar RTK-enriched subtype was identified in the TKCC series, and 'classifier' sites for each series identified using Random Forest models were able to predict the subtypes of the alternate series with high accuracy, highlighting the conservation of the three subtypes across the two series. Finally, RTK-enriched cell lines from both series exhibited enhanced sensitivity to the small molecule EGFR inhibitor erlotinib, indicating that their phosphosignature may provide a predictive biomarker for response to this targeted therapy. These studies highlight how resolution of subtype-selective signaling networks can provide a novel taxonomy for particular cancers, and provide insights into PDAC biology that can be exploited for improved patient management. Author contributions: ESH undertook MS-based phosphotyrosine profiling across the ATCC cohort as well as Western blot analyses and erlotinib sensitivity assays for this panel. AMN and MP generated cell lines and subsequent cell lysates for the TKCC cohort, and AMN characterized erlotinib sensitivity across this panel. S-PS undertook phosphotyrosine profiling for the TKCC cohort. GML facilitated tissue culture processes. ESH, FH, LGH, DKC, AVB and RJD contributed to experimental design and project planning. Bioinformatic analyses were primarily undertaken by JW, with bioinformatics contributions and figure generation performed by ESH. ESH, JW and RJD prepared the manuscript, with input from RGP and AY. Overall project coordination was undertaken by RJD. 1 The abbreviations used are: PDAC, pancreatic ductal adenocarci-

INKA , an integrative data analysis pipeline for phosphoproteomic inference of active kinases

“…To assess the global correlation of INKA with cell line drug efficacy data, we analyzed our cell line use cases (Fig ) along with two cancer cell panels, with publicly available label‐free phosphoproteomics data (Piersma et al , ; van der Mijn et al , ; Humphrey et al , ) and associated drug IC50 data with drug–kinase relationships (Karaman et al , ; Davis et al , ; Iorio et al , ; Klaeger et al , ). To this end, we developed an algorithm that takes into account drug IC50 values for different cell lines and provides a global metric for association of drug efficacy and kinase activity ranking.…”

“…We asked which kinase ranking tool, INKA, its four components, or KARP, correlates kinase activities best with drug efficacy data. Both TiO 2 capture data (CRC, PXD001550; Piersma et al , ) and pTyr antibody capture data (pancreatic ductal adenocarcinoma, PDAC; PXD003198; Humphrey et al , ) were included. First, for each method, kinase scores were divided by their maximum value, resulting in a normalized kinase activity score (KAS) for each of the ranked kinases.…”

“…To this end, we compiled suitable and publicly available cell line phosphoproteomics data. First, a pTyr IP cell line dataset derived from PDAC cell lines (Humphrey et al , ), available from proteomeXchange (PXD003198), was composed and supplemented with pTyr data from HCC827, SK‐Mel28, K‐562, and H2228 cell lines used in this manuscript, with additional pTyr data of a U87 cell line obtained from proteomeXchange (PXD001565; van der Mijn et al , ). Second, a TiO 2 enrichment‐based global phosphoproteomics dataset was assembled from data published by Piersma et al () (proteomeXchange PXD001550) and supplemented with TiO 2 data for HCC827, SK‐Mel28, K‐562, and H2228 cell lines from the current study.…”

Phosphoproteomic Profiling Reveals ALK and MET as Novel Actionable Targets across Synovial Sarcoma Subtypes

“…To identify signaling networks that exhibited differential activity across novel subgroups A-C, an ANOVA-based approach was applied to identify subgroup-enriched phosphosites, followed by bioinformatic determination of protein-protein interaction networks or pathways associated with these sites (27). Subgroup A was characterized by hyperphosphorylation of 48 phosphosites, with significant enrichment for the pathways associated with Fc gamma receptor (FCGR)-dependent phagocytosis, EPH-Ephrin signaling, adherens junctions, and cytoskeletal organization (Supplementary Table S5).…”

Opportunities and Resistance to Signal-targeted Therapies in Pancreatic Cancer: What Can We Learn from Proteomic Studies?

“…The identification of peptide signatures specific to each type of precancerous lesions would thus make it possible to discriminate them from normal pancreatic tissue. Similarly, other studies proposes classifications of pancreatic cancer cell lines based on the levels of tyrosine phosphorylation of RTKs, potentially identifying three groups of cell lines as low pTyr, enriched in RTK and mixed [52]. These data indicate that a combination of RTK is usually activated in pancreatic cancer, suggesting that single agent strategies towards RTKs are likely to be inefficient.…”