CLINICAL CANCER RESEARCH | PRECISION MEDICINE AND IMAGING
The Proteomic Landscape of Pancreatic DuctalAdenocarcinoma Liver Metastases Identifies MolecularSubtypes and Associations with Clinical Response A C
Henry C.-H. Law1, Dragana Lagund�zin1, Emalie J. Clement1, Fangfang Qiao1, Zachary S. Wagner1,Kimiko L. Krieger1, Diane Costanzo-Garvey2, Thomas C. Caffrey1, Jean L. Grem3, Dominick J. DiMaio2,Paul M. Grandgenett1, Leah M. Cook2, Kurt W. Fisher2, Fang Yu4, Michael A. Hollingsworth1, andNicholas T. Woods1
ABSTRACT◥
Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a highlymetastatic disease that can be separated into distinct subtypes basedon molecular signatures. Identifying PDAC subtype-specific ther-apeutic vulnerabilities is necessary to develop precision medicineapproaches to treat PDAC.
Experimental Design: A total of 56 PDAC liver metastases wereobtained from the UNMC Rapid Autopsy Program and analyzedwith quantitative proteomics. PDAC subtypes were identified byprincipal component analysis based on protein expression profiling.Proteomic subtypes were further characterized by the associatedclinical information, including but not limited to survival analysis,drug treatment response, and smoking and drinking status.
Results: Over 3,960 proteins were identified and usedto delineate four distinct PDAC microenvironment subtypes:
(i) metabolic; (ii) progenitor-like; (iii) proliferative; and (iv)inflammatory. PDAC risk factors of alcohol and tobacco con-sumption correlate with subtype classifications. Enhanced sur-vival is observed in FOLFIRINOX treated metabolic and pro-genitor-like subtypes compared with the proliferative and inflam-matory subtypes. In addition, TYMP, PDCD6IP, ERAP1, andSTMN showed significant association with patient survival in asubtype-specific manner. Gemcitabine-induced alterations in theproteome identify proteins, such as serine hydroxymethyltrans-ferase 1, associated with drug resistance.
Conclusions: These data demonstrate that proteomic analysisof clinical PDAC liver metastases can identify molecular signa-tures unique to disease subtypes and point to opportunities fortherapeutic development to improve the treatment of PDAC.
IntroductionPancreatic ductal adenocarcinoma (PDAC) is among the most
lethal of cancers, with a 5-year survival rate of 8.5% and a cancermortality rate projected to outpace both breast and colon cancer inthe coming years (1). The poor survival of patients with PDAC isassociated with the highly metastatic nature of this disease.Approximately 80% of these patients develop liver metastases, butother common sites include the lung and peritoneum, with mul-tiple organ involvement often observed during the end stages ofthis disease (2). The contribution of disseminated disease tolethality in PDAC is exemplified by the fact that, among patientswith early-stage and resected lesions, 60%–70% will present with
metastatic lesions within 5 years of resection (3, 4). While theanalysis of the primary tumor facilitates our understanding of themolecular etiology of PDAC (5), characterization of metastaticlesions has the potential to improve clinical interventions thataddress the main cause of cancer mortality. Thus, understandingthe underlying molecular features of metastatic PDAC is necessaryto develop effective therapeutic interventions that improve patientsurvival.
Identifying cancer subtypes has the potential to improve patientoutcomes because subtype can be associated with treatment response.This has been most effectively employed for breast cancer (6), wheregene expression profiling using the PAM50 gene expression predictoroutperforms IHC classification methods for its prognostic and pre-dictive ability (7). Several studies have characterized primary PDACsubtypes based on transcriptional profiling (5, 8–11). Using lasercapture microdissection (LCM), Collisson and colleagues classifiedPDAC tumors into three subtypes (exocrine-like, classical, and quasi-mesenchymal; ref. 8). Moffitt and colleagues classified PDAC into twotumor subtypes (classical and basal-like) and two stroma subtypes(normal and activated) using virtual microdissection (9). Using tran-scriptional profiles of intact whole-tumor tissues, Bailey and colleaguesclassified four subtypes of PDAC (squamous, immunogenic, pancre-atic progenitor, and ADEX), in which the squamous subtype isassociated with significantly shorter survival than the other sub-types (10). Puleo and colleagues also evaluated the transcriptome ofPDAC formalin-fixed paraffin-embedded (FFPE) and identified basal-like and classical subtypes (11). The consensus across these studies isthat there are two predominant PDAC tumor cell subtypes: classicaland squamous, following the proposed harmonized nomencla-ture (12). To date, PDAC subtyping is based on RNA expression
1Eppley Institute for Research in Cancer, Fred & Pamela Buffett Cancer Center,University of Nebraska Medical Center, Omaha, Nebraska. 2Department ofPathology and Microbiology, College of Medicine, University of NebraskaMedical Center, Omaha Nebraska. 3Internal Medicine, Division of HematologyOncology, University of Nebraska Medical Center, Omaha Nebraska. 4Depart-ment of Biostatistics, College of Public Health, University of Nebraska MedicalCenter, Omaha Nebraska.
Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).
Corresponding Author: Nicholas T. Woods, University of Nebraska MedicalCenter, BCC9.12.395, 986805 Nebraska Medical Center, Omaha, NE 68198.Phone: 402-559-2248; Fax: 402-559-4651; E-mail: [email protected]
profiles, but orthogonal methodologies, such as proteomics, have thepotential to reveal additional features that could improve the func-tional characterization of PDAC subtypes.
There have been comparative proteomics studies on PDAC thathave identified proteins from tissue, plasma, pancreatic juice, cystfluid, and urine associated with this disease. These efforts illustrate thepotential of applying proteomics approaches to improve early detec-tion and treatment of PDAC based on single proteins (13, 14). IHCevaluating expressions of only KRT81 and HNF1A proteins has beenused to stratify PDAC tumors as either classical, quasi-mesenchymal,or exocrine-like (15). However, prior to this study, proteomics-basedapproaches have not been performed at a scale that supports PDACsubtype classification based on proteome quantification. Furtherexploration of the PDAC tumor proteome along with detailed clinicalrecords could improve the diagnosis and treatment of this cancer.
The overarching goal of this project was to evaluate the proteomeof PDAC liver metastases to distinguish unique subtypes fromclinical samples. As a proof-of-principle that PDAC microenviron-ment subtypes can be delineated using proteomics, we have devel-oped and validated a classification system using quantitative pro-teomics data from 68 tissue samples in total from the rapid autopsyprogram (RAP) managed by the UNMC Pancreas SPORE. Thisproteomics analysis identified over 3,960 proteins and quantitativeprofiling of the 916 of these proteins was used to delineate fourdistinct subtypes of PDAC liver metastases, which share manymolecular signatures with transcriptomic-derived subtypes. Theproposed proteomic-based subtyping system showed a significantassociation with patients’ alcohol and tobacco exposure. In addi-tion, a survival advantage was observed in the metabolic andprogenitor-like subtypes treated with FOLFIRINOX (5-fluoroura-cil, leucovorin, irinotecan, and oxaliplatin) and gemcitabine com-pared with gemcitabine, but this survival benefit was not observedin the inflammatory and proliferative subtypes. The serine hydro-xymethyltransferase (SHMT1), a metabolic enzyme involved insingle carbon metabolism, was identified as a mediator of gemci-tabine resistance. In addition, 52 protein expression profileswere found to correlate with patients’ survival in a subtype-specific manner. These data demonstrate the clinical relevance ofthis proteomics classification model and illustrate its potential forthe development of therapeutic strategies to target PDAC livermetastases.
Materials and MethodsEthics statement
Investigators obtained informed consent for each patient enrolled inthe UNMC Rapid Autopsy Program (IRB #091-01). This study wasconducted in accordance with the ethical guidelines established by theDeclaration of Helsinki.
Sample preparationThe frozen PDAC tissues and the corresponding tumor-adjacent
tissueswere available from theRAP inUNMC. For each of the samples,5 mg of the frozen tissue was ground into a fine powder with a liquidnitrogen-cooled mortar and pestle. The ground tissue was then lysedwith 1 mL of RIPA buffer (25 mmol/L Tris-HCl pH 7.6, 150 mmol/LNaCl, 1% NP-40, 1% sodium deoxycholate, 0.1% SDS) and was frozenin �80�C until further used. The albumin and IgG contents in theprotein lysates were first depleted with the Pierce Top 2 AbundantProtein Depletion Spin Columns and labeled with TMT reagents perthe manufacturer's instructions. Detailed procedures were listed in theSupporting Information.
LC/MS-MS and bioinformatics analysisThe mass spectrometry data was acquired on a Dionex Nano
Ultimate 3000 coupled with an Orbitrap Fusion Lumos. The fractionscollected from the high-pH separation were resuspended in 20 mLof 0.1% formic acid. Two microliters of each fraction was injectedinto the system for tandem mass spectrometry analysis. The MS andMSn spectra collected from the experiment were searched against thehomo sapiens protein sequence database (downloaded in 10/2017,42252 entries) and the respective decoy database with Sequest HT inthe Proteome Discoverer 2.2 pipeline. The reporter ion ratios of theseproteins were exported from the ProteomeDiscoverer and the P valueswere calculated with the Wilcoxon-signed rank test using R. Thesoftware packages used in the postdatabase search analysis are listed inthe Supporting Information. Mass spectrometry data files have beendeposited to the ProteomeXchange Consortium via the PRIDE (16)partner repository with the project accessions: PXD012173 andPXD015492.
Gemcitabine treatment of MIA PaCa-2 cellsThe human PDAC cell lines MIA PaCa-2 and Panc 10.05 was
obtained from the ATCC and were cultured per the manufacturer'sinstructions. Briefly, the cells were grown in DMEM supplementedwith 10% FBS, 2.5% horse serum, amphotericin B, and penicillin–streptomycin (Corning) in 5%CO2 atmosphere at 37 �C.Gemcitabine-conditioned MIA PaCa-2 cells were generated by incubation with10 nmol/L gemcitabine (Selleckchem) freshly diluted in DMSO for6 days. The cell lysates were collected with RIPA buffer (25 mmol/LTris-HCl pH 7.6, 150 mmol/L NaCl, 1% NP-40, 1% sodium deox-ycholate, 0.1% SDS) and probed forMTHFD1 and SHMT1byWesternblotting (detailed conditions in Supporting Information).
(TRCN000034766 and TRCN000034767; shSHMT1 #1 and #2) anda nontargeting scrambled control (shScr) in pLKO.1 were used in thisstudy (Sigma). The lentiviral supernatant was produced by calciumphosphate transfection into 293FT cells, as described previously (17),and used to transduce MIA PaCa-2 and PANC 10.05 cells. Thetransduced cells were selected with puromycin for 5 days before usein cytotoxicity assays. Knockdown of SHMT1 was confirmed by
Translational Relevance
Pancreatic ductal adenocarcinoma is a deadly disease with apropensity to metastasize even at the earliest detectable stage.Effective treatment strategies must address metastatic disease,which requires a better understanding of the underlying molecularfeatures of this disease. Stratifying PDAC into distinct microen-vironment subtypes based on proteomic signatures has the poten-tial to identify subtype-specific treatment vulnerabilities that couldimprove patient outcomes. Utilizing a quantitative mass spectrom-etry approach, we identify four unique metastatic PDAC micro-environment subtypes and demonstrate subtype-specific vulner-abilities using patient treatment data, and identify 52 proteins thatexhibit subtype-specific correlations with patient survival. Theclassification system and the protein expression signaturesdescribed here provide a basis to facilitate the design and imple-mentation of subtype-specific PDAC treatment strategies.
Western blotting. For the gemcitabine cytotoxicity assay, 1,000 cellswere aliquoted into each well in a 96-well plate. The cells wereincubated with gemcitabine at different concentrations for 3 days.Cell viability was determined by CellTiter-Glo (Promega), andthe luminescent signal was measured by FLUOstar Optima (BMGLabtech). EC50(s) were estimated with GraphPad Prism 7 from theexported data (GraphPad).
ResultsAcquisition of the PDAC liver metastases proteome
This project aimed to explore proteomic variance in PDAC livermetastases from patient samples collected by the UNMC PancreasSPORERapid Autopsy Program. This Program ensures all samples are
collected at the same stage of disease under a standardized proce-dure (18). The metastatic tissue proteome was determined from acohort of 59 patients [56 PDAC, 3 pancreatic neuroendocrine tumors(PanNET)] that were annotated for tumor stage at diagnosis, gender,age, overall survival (OS) calculated from the day of diagnosis,metastatic involvement, and PDAC risk factors of alcohol and tobaccoconsumption (Table 1; Supplementary Table S1). These samples wererandomly divided into seven batches and differentially labeled withisotopic tags using the 10-plex TMT kit (Fig. 1A; SupplementaryTable S2). The reference mix of all 59 samples was tagged with theTMT126 label, which serves as a common reference for quantitationacross all seven batches (Supplementary Table S2). The overall analysisidentified 30,811 peptides mapping to 3,960 proteins in which 1,842were quantified and 916 were quantified with at least 5 peptides across
Extractproteins
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Figure 1.
A, Proteomicsworkflow for the analysis of the PDAC liver metastases proteome. B,Overview of the proteomics data (FDR).C, Score plot of themultivariate analysisof PDAC and PanNET proteomes. D, Score plot of the multivariate analysis of the liver metastases and the tumor-adjacent tissue proteome. E, Score plot of themultivariate analysis of the liver metastases proteome.
Proteomics of Pancreatic Ductal Adenocarcinoma Metastases
80% of the samples (Fig. 1B; Supplementary Tables S3 and S4). The setof 916 proteins were used in the multivariate analysis (Fig. 1B;Supplementary Table S5). These 916 proteins represent a broad arrayof cellular functions across the proteome, including extracellularmatrix organization, protein processing and transport, translation,glycolytic processes, NADPH metabolism, cell migration, immuneresponse, fibronectin binding, and cell homeostasis determined bySpatial Analysis of Functional Enrichment (SAFE) (SupplementaryFig. S1; Supplementary Table S6; ref. 19).
Partial least squares-discriminant analysis (PLS-DA) effectivelydistinguished PDAC from PanNET liver metastases with high confi-dence (Fisher probability¼ 3.1� 10�5; Fig. 1C).We further tested themethodology by comparing the proteomic signatures of 9 PDAC livermetastases against matched tumor-adjacent uninvolved liver. Princi-pal component analysis (PCA) effectively separated the liver metas-tases and the uninvolved liver tissue into two distinct categories(Fig. 1D). All pairs of liver metastases and tumor-adjacent tissueproteomes were well separated on the score plot (SupplementaryFig. S2A). In the corresponding PLS-DA model, the ROC curvesdifferentiated both PDAC and PanNET tumors from the correspond-ing tumor-adjacent liver tissues with high sensitivity and specificity[area under the curve, AUC(PDAC) ¼ 1; AUC(PanNET) ¼ 1;Supplementary Fig. S2B and C]. These analyses demonstrate that theunderlying quantitative proteomics data provide sufficient sensitivityand specificity to differentiate two distinct types of pancreatic cancermetastases to the liver as well as distinguish tumor tissue from theadjacent uninvolved liver.
To explore the variance in liver metastases proteome, we con-structed a PCA model with hierarchical clustering using the 916quantified proteins (Fig. 1E; Supplementary Table S5). The sum ofsquared error analysis revealed that the intragroup variance was bestexplainedwhen the samples were divided into fourmajor subtypes andthree protein clusters (Supplementary Fig. S3A and S3B). The fourprotein subtypes identified were as follows: (i) metabolic (n ¼ 7); (ii)progenitor-like (n ¼ 21); (iii) proliferative (n ¼ 11); and (iv) inflam-matory (n ¼ 17; Fig. 1E). Subtype nomenclature is based on proteinenrichments for each subtype or their relation to previously describedtranscriptional subtypes. The robustness of themodel was evaluated bythe 7-fold cross-validation operation built-in in SIMCA 15. Thegoodness of fit (R2X) and the predictability (Q2) of this unsupervisedPCAmodel were 0.63 and 0.42, whichwas comparable with other PCAmodels in the literature (20, 21). The coefficients for each of theproteins in the corresponding supervised PLS-DAmodel were listed inSupplementary Table S7. The x2 analysis showed that batch effects didnot impact subtype classifications (Supplementary Fig. S3C).
Overview of the classification schemeWe identified significant correlations between the subtypes iden-
tified by proteomics and those identified by Moffitt and colleagues[x2 test, P (tumor) ¼ 8.92 � 10�6, P (stroma) ¼ 2.06 � 10�3],Collisson and colleagues (P ¼ 2.10 � 10�5), and Bailey andcolleagues (P ¼ 8.85 � 10�8; Fig. 2A; Supplementary Fig. S4A–S4D). In comparison with the Moffitt and colleagues classificationsystem, three representative proteins with the highest weight factor(ALDH2, IDH1, and TST) associated with the classical tumorsignature exhibit higher expression in the metabolic and the pro-genitor-like subtypes than the other two proteomic subtypes, whilethe inflammatory subtype showed a high expression of basal-liketumor signature genes (ANXA1, ANXA3, and ITGA2), resulting inhigher signature scores (Fig. 2B and C). There were significantdifferences between the expression of extracellular proteins like
gelsolin (GSN) and lumican (LUM) between the proliferative andthe inflammatory subtypes that only resulted in a slight differencebetween the signature scores of the normal and the activated stromasubtype between these two proteomic subtypes (Fig. 2B and D).This demonstrates the proteomic PDAC subtyping method incor-porates extracellular protein expression, which may not be capturedby transcriptomic approaches.
There exists a debate whether the exocrine-like and ADEX classi-fications represent unique subtypes or contamination from acinar cellspresent in the tumor microenvironment (5, 12). However, the exis-tence of patient-derived cell lines and propagated xenografts that areclassified as exocrine-like suggest they may represent unique sub-types (15, 22). Furthermore, different cellular components of thePDAC tumor microenvironment affect a range of cancer phenotypesincludingmetastatic potential and treatment efficacy and contribute tothe overall tumor microenvironment (23, 24). Like our study, Baileyand colleagues also used the whole tumor for their analysis withoutvirtual or physical microdissection. Therefore, we chose to includethe cross-comparison to the Bailey and colleagues classificationsystem. The proliferative and inflammatory subtypes share signa-tures associated with the squamous/quasi-mesenchymal subtype(Fig. 2A, E, and F). However, our proteomic classification systemcan further subcategorize the squamous subtype into two distinctsubtypes (inflammatory and proliferative). The progenitor-likesubtype nomenclature was used because of the similarity to theBailey and colleagues progenitor subtype. There is also an associ-ation between the metabolic and the ADEX/exocrine-like subtypes,which would not be attributed to acinar cell contamination becausethis analysis used liver metastases. The metabolic association withthe ADEX subtype (Bailey and colleagues) may provide furthersupport to an exocrine-like subtype of PDAC, but we cannot ruleout the possibility that it is a byproduct of signals from normal livercells. However, liver tissue adjacent to metabolic tumors exhibitsunique protein expression signatures that can differentiate thesetissues (Figs. 1D; Supplementary Fig. S2).
The consistencies between our proteomics-based and the transcrip-tomic-based classification systems suggest that many of the sametranscriptional signatures found in primary tumors can be found byproteomics in the liver metastases. This is supported by a recent studythat determined RNA signatures frommetastatic tissue obtained froma variety of anatomic sites can be used to identify PDAC subtypes (25).To confirm this, we performed quantitative proteomics on ninematched primary tumor samples to evaluate PDAC subtype conser-vation with metastases. The PLS-DA model had R2X, R2Y, and Q2 of0.695, 0.99, and 0.401, respectively (Supplementary Fig. S5). Thisindicates PDACmolecular subtypes were generally conserved betweenthe primary tumor and the liver metastases in matched samples.
The protein expression profiles observed in PDAC liver metastasesdistinguish three groups of similarly expressed proteins (proteincluster 1–3) associated with a range of biological processes (Supple-mentary Fig. S6A and S6B). The metabolic and progenitor-like sub-types are characterized by metabolism-related proteins in proteincluster 1, enriched with proteins in the ethanol oxidation pathways,mitochondrial fatty acid b-oxidation, and retinoic acid signalingpathways (Supplementary Fig. S6C; Supplementary Fig. S7; Supple-mentary Table S8; Supplementary Information). Even though both themetabolic and the progenitor-like subtypes were characterized byprotein cluster 1, the metabolic subtype exhibits higher expression ofthese proteins, such as those associated with signaling by retinoic acid(Supplementary Fig. S6D). The proliferative subtype proteome isenriched with ribonucleoproteins and Cajal body proteins in protein
A, Mapping of the proteomic subtypes to the Moffitt et al., Collisson et al., and Bailey et al. classification schemes for each of the samples. The missing data in theribbon above the heatmap indicate the signatures scores for these samples did not reach the threshold to accurately assign a corresponding transcriptomic subtype.Thedetails of thex2 test are shown in Supplementary Fig. S4. Heatmap showing the association betweenprotein expression and the proteomic subtypes. The red andblue colors in each pixel indicate protein up- anddownregulation, respectively.B,Representative signature gene expressions in theMoffitt et al. classification schemeacross the four proteomic subtypes. C–F, The signature scores of the four proteomics subtypes in the Moffitt et al., Collisson et al., and Bailey et al. classificationsystems.
Proteomics of Pancreatic Ductal Adenocarcinoma Metastases
cluster 2 that are associated with translation, cell proliferation,and telomere maintenance in cancer cells (SupplementaryFig. S6C and S6E; Supplementary Tables S8 and S9; SupplementaryInformation; ref. 26). The inflammatory subtype is characterized byprotein cluster 3 and is enriched for proteins related to pentosephosphate pathway, adaptive immune response, complement acti-vation, IL8 production, and extracellular fibril organization (Sup-plementary Figs. S6C, S6F, and S6G; Supplementary Tables S9 andS10; Supplementary Information). These pathways and processesare known to create an immunosuppressive and chemoresistantenvironment that supports tumor growth (27–29). The gene setenrichment analysis on the average protein expression of eachsubtype in the Reactome pathways also agreed with the biologydescribed above (Supplementary Table S11). Together, these datademonstrate that coexpressed proteins participate in cancer-associated pathways that are differentially represented across PDACsubtypes identified by proteomics.
Proteomic signatures of PDAC liver metastases associate withclinical features
Health and lifestyle data are collected for each RAP donor,including diabetes, alcohol use, and tobacco use, which are riskfactors for the development of PDAC (30). There are no significantdifferences in the neoplastic cellularity across the four proteomicssubtypes in the tumors analyzed by pathologic review (Fig. 3A),suggesting this variable did not affect subtype classification. Inaddition, the incidence of diabetes is distributed at expected ratesacross each of the four proteomics-defined subtypes and does notappear to influence subtype classification (Fig. 3B). However, thereare significantly more patients than expected with the proliferativesubtype that reported a history of alcohol use (hypergeometric test,P¼ 0.025), and there are no patients with the inflammatory subtypethat reported alcohol use (P ¼ 0.002; Fig. 3C). With regard totobacco, the metabolic subtype includes more patients who reporttobacco use than expected (P ¼ 0.009), while the inflammatorysubtype included significantly fewer than expected tobacco users (P¼ 0.014; Fig. 3D).
We grouped the proliferative and the inflammatory subtypesbecause combined they correspond to the squamous subtype in theBailey and colleagues classification system (Fig. 2A), which wasdemonstrated to be associated with shorter survival than the othertranscriptionally defined subtypes in that study (10). Because thepatients in the RAP cohort analyzed in this study were not treatedwith a standard set of chemotherapeutics, we focused our evaluationof patient survival on three treatment groups: (i) untreated: didnot receive either gemcitabine or FOLFIRINOX; (ii) gemcitabine:received at least gemcitabine; and (iii) FOLFIRINOXþGem: receivedFOLFIRINOX followed by at least gemcitabine. OS is calculated fromthe day of diagnosis. Across all subtypes, OS for gemcitabine-treatedpatients was 271.5 days and 336 days for FOLFIRINOX þ Gem (HR,2.18; 95% confidence interval (CI), 1.57–3.01; P¼ 0.02; Fig. 3E and F).The OS for patients classified with proliferative and inflammatorysubtypes treated with gemcitabine was 258 days and 288 days forFOLFIRINOXþGem (HR, 1.57; 95% CI, 1.02–2.41; P¼ 0.29; Fig. 3Gand H). However, OS for patients classified with the metabolicand progenitor-like subtypes treated with gemcitabine was 286.5 daysand 401.5 days for FOLFIRINOXþGem (HR, 3.37; 95%CI, 1.02–5.90;P ¼ 0.03; Fig. 3I and J). These data indicate that the metabolic andprogenitor-like subtypes, but not the proliferative and inflammatorysubtypes, display a decreased risk of deathwhen FOLFIRINOX is givenin addition to gemcitabine as part of the treatment course. These
results support the concept of PDAC subtype–specific response totherapy.
Patient survival based on treatment(s) with gemcitabine, FOL-FIRINOX/FOLFOX, abraxane/paclitaxel, tarceva/erlotinib, andradiation in each PDAC subtype was also evaluated (SupplementaryFig. S10). Patients could be grouped in multiple treatments becausetheir inclusion was dictated by whether they received the indicatedtherapy or not, and patient treatments were variable. This analysisindicated that patients with the progenitor-like subtype treated withgemcitabine have a significant increase in survival compared withcases that do not receive this treatment (P ¼ 0.001). Notably, asignificant increase in survival probability was not observed in theprogenitor-like subtype for any of the other treatments evaluated. Inaddition, the trends observed in the metabolic subtype followingabraxane/paclitaxel treatment suggest a negative correlationbetween receiving this treatment and survival probability (P ¼0.008; Supplementary Fig. S10). It will be important to evaluatepatient outcomes in response to individual therapies stratified bysubtype in a much larger dataset to identify significant trends thatwould support the implementation of personalized treatmentsbased on PDAC subtypes.
Association between individual protein expression and survivalis subtype-dependent
Protein expression patterns in PDAC liver metastases also havethe potential to reveal new associations with clinical outcomes oridentify novel therapeutic targets (14, 31). With the understandingthat PDAC is not a singular disease, we hypothesized that associa-tions between protein expression and patient survival might displaysubtype-specific characteristics. Partial least squares (PLS) analysiswas first used to evaluate the association between the number ofsurvival days after diagnosis with protein expression from thenentire 56-patient cohort used in this study. This identified 52proteins associated with either increased or decreased survivalprobability (Fig. 4A). The Kaplan–Meier survival curves wereplotted using the upper and lower tertiles (SupplementaryTable S12), and the log-rank test was used to determine significantdifferences in survival probability between the two groups. Proteinswith P � 0.05 were cross-referenced with the PLS survival model asinternal validation. A total of 32 proteins with elevated expressiondemonstrated a significant association with increased survival(Supplementary Fig. S11). An additional 20 proteins demonstratedan inverse correlation between expression and survival probability(Supplementary Fig. S12). Some of these proteins include thymidinephosphorylase (TYMP; Fig. 4B), programmed cell death 6-inter-acting protein (PDCD6IP; Fig. 4C), stathmin 1 (STMN1; Fig. 4D),and endoplasmic reticulum aminopeptidase 1 (ERAP1; Fig. 4E),which are known to be associated with cancer phenotypes (detailsdiscussed in Supplementary Information; Supplementary Fig. S14;refs. 32–39).
We found that many of these 52 proteins display subtype-specificexpression patterns (Fig. 4F). Because the proteomics subtype model(R2X ¼ 65%) better explained the variance observed in the proteomethan the survival prediction model (R2X ¼ 48%), we hypothesizedthat a better survival regression model could be built based onindividual subtypes. Figure 4G–J shows the loading plot of the same52 proteins in Fig. 4A for the individual subtype survival regressionmodel. The variance captured by the regressed survival time (Q2Y)increased from 24% to 38%–84%. TYMP is highly correlated withsurvival in the progenitor-like subtype (Spearman correlation coeffi-cient, r ¼ 0.62), but this correlation is not as prominent in the other
A,Percentage neoplastic cellularity in each of the four proteomics subtypes determined by pathologic review of H&E-stained slides. The t test P valueswere all>0.05in all combinations of subtypes. The distribution of patients with a history of diabetes (B), alcohol consumption (C), and tobacco use (D) across different proteomicssubtypes. HypergeometricP<0.05 are displayed above the bars. Survival analysis of patient treatment groups based onproteomic subtypes. Kaplan–Meier curves ofall patients (E), combined proliferative and inflammatory subtypes (G), and combined metabolic and progenitor subtypes (I). Forest plots of the Cox proportionalregression adjusted HRs and the corresponding P values of all patients (F), combined proliferative and inflammatory subtypes (H), and combined metabolic andprogenitor subtypes (J). Survival days were calculated from the day of diagnosis.
Proteomics of Pancreatic Ductal Adenocarcinoma Metastases
Metabolic, r = 0.14Progenitor-like, r = 0.62 Proliferative, r = 0.33Inflammatory, r = 0.23
Metabolic, r = 0.82Progenitor-like, r = 0.10 Proliferative, r = 0.17Inflammatory, r = 0.20
Metabolic, r = −0.10Progenitor-like, r = −0.55 Proliferative, r = 0.07Inflammatory, r = −0.01
Metabolic, r = -0.39Progenitor-like, r = −0.33 Proliferative, r = −0.49Inflammatory, r = −0.04
TYMP PDCD6IP
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A, Loading plot of the multivariate analysis in survival days versus the proteome. The size and intensity of the red color in each dot correlates with the variance ofimportance (VIP) in the model. B–E, Kaplan–Meier curves and log-rank test P values of representative survival markers, TYMP, PDCD6IP, STMN1, and ERAP1. F,Heatmap of themedian expression of the 52 potential survival markers in the four proteomic subtypes. Color of protein name indicates gene ontology classification:metabolism, black; signal transduction and cytoskeleton rearrangement, orange; protein synthesis, blue; protein transport and synthesis, pink; peptidase activity,green; redox homeostasis, purple; other, brown. G–J, Loading plots of the multivariate analysis in survival days versus the proteome. K–N, Scatter plots of survivaldaysversus expression of TYMP, PDCD6IP, STMN1, andERAP1. Spearmancorrelation coefficients for the individual subtypes are depictedon the right-hand sideof thegraphs.
subtypes (r ¼ 0.14–0.33, Fig. 4K). PDCD6IP and STMN1 showed asignificant correlation with survival in themetabolic (r¼ 0.82) and theprogenitor-like (r ¼ �0.55) subtypes, respectively (Fig. 4L and M).The expression of ERAP1 inversely correlated with survival in themetabolic, progenitor-like, and proliferative subtypes (r ¼ �0.49 to�0.33), but not in the inflammatory subtype (r ¼ �0.04, Fig. 4N).These results demonstrate that subtype-based regression models arebetter suited for identifying proteins associated with patient survival.
Gemcitabine treatment represses SHMT1 to promote drugresistance
To validate the proteomic differences observed in our datasetare biologically meaningful, we evaluated the changes in the pro-teome associated with gemcitabine treatment. PDAC is a refractorycancer that readily develops resistance to chemotherapy, includinggemcitabine (40, 41). With death as the common endpoint, it isassumed that protein expression changes associated with gemcita-bine resistance could be distinguished in the proteomic data fromthe RAP samples. Among the cohort of 56 PDAC donor samples,there are 6 donors that were treatment-na€�ve and 9 donors treatedonly with gemcitabine. A total of 63 proteins are upregulated and 44proteins are downregulated significantly in the gemcitabine treat-ment group compared with the treatment-na€�ve group (Fig. 5A;Supplementary Fig. S15A; and Supplementary Table S13). Thesedifferentially regulated proteins influenced by gemcitabine treat-ment have the potential to identify PDAC mechanisms of drugresistance.
The folic acid cycle proteins cytosolic C-1-tetrahydrofolate synthase(MTHFD1) and SHMT1 were significantly downregulated in PDAClivermetastases frompatients treatedwith gemcitabine comparedwithtreatment-na€�ve samples (Fig. 5B). MTHFD1 catalyzes the hydrolysisof 5,10-methenyltetrahydrofolate into 10-formyltetrahydrofolate,while SHMT1 catalyzes the conversion of tetrahydrofolate (THF) andthe amino acid serine into 5,10-methenyltetrahydrofolate, the sub-strate required by MTHFD1 (Fig. 5C). The regulation of nucleotidepools, such as dCTP, is a mechanism of gemcitabine resistance inPDAC cell lines (40). The folic acid cycle generates and recycles themetabolites required for the conversion of deoxyuridine monopho-sphate (dUMP) to deoxythymidine monophosphate (dTMP), neces-sary to support DNA synthesis. Thymidylate synthase (TYMS) con-verts dUMP to dTMP and is inhibited by both 5-fluorouracil andgemcitabine metabolites leading to defects in DNA replication. Inaddition, TYMS expression is correlated with gemcitabine resis-tance (42). This evidence suggests gemcitabine-mediated regulationof the folic acid pathway is important for the development of drugresistance.
Both MTHFD1 and SHMT1 are downregulated in MIA PaCa-2PDAC cells conditioned with 10 nmol/L gemcitabine for 6 days(Fig. 5D and E). To determine whether inhibition of SHMT1 expres-sion is associated with PDAC response to gemcitabine, we establishedstable MIA PaCa-2 and Panc 10.05 cell lines with targeted knockdownof SHMT1 using two different shRNA constructs (SupplementaryFig. S15B and S15C). In MIA PaCa-2 cells, the EC50 of gemcitabineincreased from 2.7 nmol/L in the control cells to 17.9–19.9 nmol/L inthe SHMT1 knockdown cells (Fig. 5F; Supplementary Fig. S15D).Similarly, Panc 10.05 displayed an increase in the gemcitabine EC50
from 1.6 mmol/L in control to 9.4 mmol/L and 7.3 mmol/L in theSHMT1 knockdown cells (Fig. 5G; Supplementary Fig. S15E), sug-gesting that the reduced expression of this protein observed ingemcitabine-treated patients could act as a mechanism of drug resis-tance. Because the depletion of SHMT1 could prevent DNA synthesis
by restricting dTMP pools, we evaluated the cell-cycle profile andobserved a significant increase in S-phase and a decrease in G2–M inSHMT1 knockdown cells compared with control (Fig. 5H; Supple-mentary Fig. S16). Overall SHMT1 expression, regardless of gemci-tabine treatment, is higher in the metabolic and progenitor-likesubtypes compared with proliferative or inflammatory subtypes(Fig. 5I). This may indicate that individual PDAC subtypes could bemore resistant to gemcitabine based on the inherent expression levelsof the enzymes regulating the folic acid cycle.
DiscussionMolecular subtyping of cancer can improve therapeutic out-
comes by stratifying distinct subtypes of cancer into treatmentgroups based on their predicted response characteristics. Therehave been several different approaches to subtype PDAC usingtranscriptomics that prove this is not a singular disease and thatspecific subtypes may exhibit unique response profiles to therapies.Furthermore, because of the metastatic nature of this disease clinicalsubtyping of PDAC should incorporate metastatic characterizationto address the primary cause of patient mortality. This studyrepresents the first proteomics-based subtype classification systemfor PDAC using liver metastases that could provide the basis forimproving clinical therapy of this disease.
The high-dimensional data obtained in this proteomics studyprovides the ability to discern both complementary and uniquePDAC subtypes. Previous PDAC proteomics studies have not beenamenable in comparison with established PDAC subtypes due tothe small number of tumors analyzed or the limited repertoire ofproteins identified for analysis (43). Therefore, this is the firstproteomics analysis of clinical PDAC samples that overcomes thelimitations of previous studies to support a cross-comparison withtranscriptomic-based approaches for determining PDAC subtypes.The comparison of the proteomics-based subtypes with transcrip-tomic-based subtyping efforts by Moffitt and colleagues, Collissonand colleagues, and Bailey and colleagues identifies significantconcordance between these studies. However, the additional strat-ification of the squamous subtype into both the proliferative andinflammatory subtypes suggests proteomics could be used as acomplementary method to identify additional PDAC subtypes.Because the squamous subtype has been defined as a more aggres-sive PDAC tumor, further stratification based on proteomics has thepotential to identify subtype-specific features that could impactclinical response.
In our analysis, multiple predictive models were built to correlatethe proteomics data with the clinical metadata, including gender,age, stage at diagnosis, number of primary or metastatic sites,history of diabetes, and treatment(s) administered. These modelsshowed no significant correlation with the PDAC subtypes. How-ever, PDAC subtypes exhibit a significant correlation with reportedPDAC modifiable risk factors of alcohol and tobacco usage, sug-gesting these variables may influence the molecular pathogenesis ofPDAC metastasis. Additional experiments are required to deter-mine the influence of alcohol and tobacco use on both the primaryPDAC and liver proteomes to delineate how these factors influencesubtype-specific selection because it could impact patient responseto therapy.
Precision medicine approaches that exploit the unique molecularvulnerabilities of PDAC subtypes could be envisioned to provide amore robust clinical response. Surgical removal and FOLFIRINOXchemotherapy are common treatment strategies for PDAC (44).
Proteomics of Pancreatic Ductal Adenocarcinoma Metastases
A, Heatmap showing the expression of the 107 protein markers altered in gemcitabine-treated patients. The ribbon below the heatmap showed the proteomicsubtype of the patients. Green, blue, red, and yellow represent the metabolic, progenitor-like, proliferative, and inflammatory subtypes, respectively. B, Bar chartshowing the expression ofMTHFD1 and SHMT1 in samples fromnontreated and gemcitabine-treated patients. The t testP values are displayed at the top of the figure.C,MTHFD1 and SHMT1 involvement in the folic acid cycle. D, The experimental setup for generating gemcitabine-conditioned MIA PaCa-2 cells. E,Western blot andcorrespondingquantitative analysis ofMTHFD1 andSHMT1 expression inMIAPaCa-2 cellswithout andwith gemcitabine treatment. The t testP values are displayedatthe topof thefigure. TheEC50 and the corresponding F testP values for control shScr and two independent shRNAs targetingSHMT1 inMIAPaCa-2 (F) andPanc 10.05(G) cells treated with gemcitabine. H, Cell-cycle analysis of shScr and shSHMT1 MIA PaCa-2 cell. I, Bar chart showing the expression of SHMT1 across different PDACsubtypes. t test P values are displayed in the figure.
Law et al.
Clin Cancer Res; 2020 CLINICAL CANCER RESEARCHOF10
Surgical removal is typically available to only 10%–15% of patientswith PDAC (45). FOLFIRINOX can improve patient survival, butbecause of its toxicity FOLFIRINOX cannot be universally applied toall patients with PDAC (44, 46). While gemcitabine is still widely usedin the clinic, FOLFIRINOX treatment as an adjuvant therapy followingresection increases disease-free survival to 21.6months comparedwith12.8 months for gemcitabine at the cost of increased adverseeffects (47). It is possible that the benefits of FOLFIRINOX orgemcitabine are restricted to certain PDAC subtypes. Recently, theCOMPASS trial also determined that patients with the basal subtypeare less likely to respond to first-line chemotherapy (48). Similarly, ouranalysis indicates the metabolic and progenitor-like subtypes displayan increase in survival time in response to FOLFIRINOX þ Gemcompared with gemcitabine, but this is not observed in the prolifer-ative and the inflammatory subtypes. In addition, the progenitor-likesubtype showed a significant benefit when the patients were treatedwith gemcitabine and/or capecitabine. Although this analysis wasbased on a small sample size, it is a proof-of-concept for the person-alized treatment of PDAC based on proteomic signatures usingtraditional chemotherapeutics that could be readily implemented inthe clinic.
Ultimately, PDAC subtyping must be accomplished on clinicallyobtainable tissues to inform first-line cancer treatment. Two recentstudies have demonstrated the transcriptomics-based PDAC subtyp-ing can be performed on percutaneous core biopsies (25, 48). However,in both studies, the average time to return results based on RNAsequencing was approximately 35–39 days, which could be used toinform second, but not first-line therapy. For RNA-based subtyping,NanoString is a platform that could be used on a subset of transcriptswithout the time-intensive steps required for RNA-seq library prepand data analysis (49). Proteomics could also provide a complemen-tary rapid assay platform that could be completed in several days (50).Moving forward, it will be important to establish protocols to obtainand subtype PDAC samples in a clinically meaningful timeframe toinform first-line therapeutic decisions.
This study provides further evidence that PDAC is not a singledisease and that quantitative proteomics can be used to delineate
unique subtypes. The subtype-specific associations with response tochemotherapy observed in this study support the notion that theunique features of each PDAC subtype should be incorporated at alllevels of therapeutic development.
Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.
Authors’ ContributionsConception and design: H.C.-H. Law, M.A. Hollingsworth, N.T. WoodsDevelopment of methodology: H.C.-H. Law, D. Lagund�zin, N.T. WoodsAcquisition of data (provided animals, acquired and managed patients, providedfacilities, etc.): H.C.-H. Law, D. Lagund�zin, F. Qiao, Z.S. Wagner, D. Costanzo-Garvey, T.C. Caffrey, J.L. Grem, D.J. DiMaio, P.M. Grandgenett, L.M. Cook,M.A. Hollingsworth, N.T. WoodsAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): H.C.-H. Law, D. Lagund�zin, K.W. Fisher, F. Yu,M.A. Hollingsworth, N.T. WoodsWriting, review, and/or revision of the manuscript: H.C.-H. Law, E.J. Clement,K.L. Krieger, T.C. Caffrey, K.W. Fisher, F. Yu, M.A. Hollingsworth, N.T. WoodsAdministrative, technical, or material support (i.e., reporting or organizing data,constructing databases): H.C.-H. Law, F. Qiao, Z.S. Wagner, T.C. Caffrey,K.W. Fisher, N.T. WoodsStudy supervision: H.C.-H. Law, D. Lagund�zin, N.T. Woods
AcknowledgmentsThe authors thank the patients and their families for their participation in the
UNMC Pancreatic SPORE Rapid Autopsy Program. The authors thank the UNMCMass Spectrometry and Proteomics core facility for project support, the UNMC FlowCytometry core facility, and Dr. Jennifer Black and Dr. Amar Natarajan for theirhelpful suggestions. This work was supported by the NIH grant numbersP20GM121316, P30CA036727, and 1P50CA127297.
The costs of publication of this article were defrayed in part by the payment of pagecharges. This article must therefore be hereby marked advertisement in accordancewith 18 U.S.C. Section 1734 solely to indicate this fact.
Received May 7, 2019; revised October 19, 2019; accepted December 11, 2019;published first December 17, 2019.
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Published OnlineFirst December 17, 2019.Clin Cancer Res Henry C.-H. Law, Dragana Lagundzin, Emalie J. Clement, et al. Subtypes and Associations with Clinical ResponseAdenocarcinoma Liver Metastases Identifies Molecular The Proteomic Landscape of Pancreatic Ductal
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