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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Nat Med. Author manuscript; available in PMC 2013 August 28.
Published in final edited form as:
Published online 2011 April 3. doi:  10.1038/nm.2344
PMCID: PMC3755490

Subtypes of Pancreatic Ductal Adenocarcinoma and Their Differing Responses to Therapy

Pancreatic ductal adenocarcinoma (PDA) is a lethal disease. Overall survival is typically six months from diagnosis1. Numerous phase III trials of agents effective in other malignancies have failed to benefit unselected PDA populations, although patients do occasionally respond. Studies in other solid tumors have shown that heterogeneity in response is determined, in part, by molecular differences between tumors. Further, treatment outcomes are improved by targeting drugs to tumor subtypes in which they are selectively effective, with breast2 and lung3 cancers providing recent examples. Identification of PDA molecular subtypes has been frustrated by a paucity of tumor specimens available for study. We have overcome this problem by combined analysis of transcriptional profiles of primary PDA samples from several studies along with human and mouse PDA cell lines. We define three PDA subtypes: classical, quasi-mesenchymal, and exocrine-like, and present evidence for clinical outcome and therapeutic response differences between them. We further define gene signatures for these subtypes that may have utility in stratifying patients for treatment and present preclinical model systems that may be used to identify new subtype specific therapies.

Global gene expression analysis has proved useful for subtype identification in many human tumor types4. We approached PDA subtype identification by first identifying intrinsically variable (standard deviation > 0.8) genes in two gene expression microarray datasets from resected PDA. We generated one dataset using microdissected PDA material (UCSF tumors, n=27) from clinical samples for which information on survival duration was available and the second was previously published (Badea, et al.)5. To increase power, we merged these two datasets using the distance weighted discrimination (DWD) method6,7 and included intrinsically variable genes common to both studies. We then performed nonnegative matrix factorization (NMF) analysis with consensus clustering8 to identify subtypes of the disease. This analysis supported up to three subtypes (cophenetic coefficient >0.99; Supplementary Figs. 1, 2a and Supplementary Tables 13). We then developed a gene signature by using subtypes defined in NMF analysis of the merged clinical datasets to supervise significance analysis of microarrays (SAM) analysis9 with false discovery rate (FDR) less than 0.001. This resulted in a 62 gene signature, designated PDAssigner. The three PDA subtypes in the merged clinical dataset and their expression of PDAssigner genes are shown in Fig. 1a. We designated these subtypes as classical, quasi-mesenchymal (QM-PDA) and exocrine-like, based on our interpretation of subtype specific gene expression. The classical subtype had high expression of adhesion-associated and epithelial genes, the QM-PDA subtype showed high expression of mesenchyme associated genes. The exocrine-like subtype showed relatively high expression of tumor cell derived digestive enzyme genes, with immunohistochemical staining supporting this observation (Supplementary Fig. 3). Analysis of PDAssigner gene expression in three additional published PDA expression datasets of unique origin, platform or processing1012 also supported these three subtypes (Supplementary Fig. 4) demonstrating the robust nature of the subtype classification in early stage PDA.

Figure 1
Subtypes of PDA in tumors and cell lines and their prognostic significance

Survival after PDA resection has been associated with many factors including stage (tumor size and nodal involvement) and grade (degree of differentiation)13, but no one factor has been consistently prognostic14,15. We found that stratification by PDA transcriptional subtype provided useful prognostic information in one PDA dataset (UCSF) for which clinical annotation was available. Specifically, patients with classical subtype tumors fared better than patients with QM-PDA subtype tumors after resection (p=0.038, log rank, Fig. 1b). In this same data set, stage and grade were not significantly related (p>0.99), stage was not significantly associated with subtype (p=0.40), while grade was (p=0.041) (univariate analyses). In a multivariate Cox regression model including stage and subtype, subtype was an independent predictor of overall survival (p=0.024) indicating that PDA subtype independently contributed prognostic information to pathological staging in PDA. Associations of PDA subtype with other clinical variables are summarized in Supplementary Table 4. This analysis supports the use of subtypes (as defined using PDAssigner) as an independent prognostic indicator in resected PDA.

Further validation of PDA subtypes and preclinical identification of subtype specific therapeutic agents would be facilitated by the availability of laboratory models of the subtypes. Therefore, we asked if the PDA subtypes were represented in a collection of 19 human and 15 mouse PDA cell lines. The 19 human PDA cell lines were selected from publicly available PDA lines while the 15 mouse lines were derived by us from genetically engineered Tp53−/− and INK4A−/−16 models of PDA. The analyses of the human and mouse PDA cell lines using the 62 PDAssigner genes are shown in Fig. 1c,d, as well as in Supplementary Figs. 2b–e. Supplementary Tables 5 and 6. These cell line collections contain representatives of the classical and QM-PDA subtypes, but not the exocrine-like subtype. The absence of the exocrine-like subtype in the cell line collection raises the possibility that this subtype is an artifact of contaminating normal pancreas tissue adjacent to tumor. However, the UCSF samples were prepared by microdissection to enrich for PDA cancer cells thereby minimizing contaminating tumor-associated stroma and/or adjacent normal exocrine pancreas. This dataset includes the exocrine-like subtype, which argues that it is a bona fide PDA subtype. Thus, we conclude that the cell line collections model two of the PDA subtypes thereby enabling exploration of biological differences between these two PDA subtypes and may facilitate screening for classical and QM-PDA subtype-specific therapeutic agents and biological properties.

Two genes associated with PDA subtypes, GATA binding protein 6 (GATA6) and v-ki-ras2 kirsten rat sarcoma viral oncogene homolog (KRAS), two variable genes in our UCSF PDA dataset, Supplementary Table 1a), have been implicated in both aspects of normal development and cancer pathophysiology in published studies. GATA-family transcription factors are associated with tissue specific differentiation and have been demonstrated to be subtype specific markers in other cancers. For example, GATA binding protein 3 (GATA3) is required for luminal differentiation in the breast17, and high GATA3 characterizes luminal subtype breast cancers18. Likewise, GATA6 is essential for pancreatic development19 and has been implicated in PDA20,21. GATA6 is highly expressed in most classical subtype tumors and cell lines, and comparatively low in the QM-PDA cell lines and tumors. Additionally, a previously published gene signature associated with GATA6 overexpression20 is enriched in the classical subtype (Supplementary Fig. 5). Seeking to establish a possible functional role underlying the observed differences in GATA6 expression, we assessed the impact of GATA6-directed RNAi knockdown on colony formation in soft agar in the classical and QM-PDA cell lines. GATA6 depletion impaired anchorage-independent growth in classical PDA cell lines, but not in QM-PDA cell lines (Fig. 2), consistent with a functional, subtype-specific role for this transcription factor in the classical PDA subtype.

Figure 2
Classical PDA subtype and the GATA6 transcription factor

Recent work in the mouse demonstrates that PDA can arise from a variety of precursor cells by activating KRAS in distinct cellular compartments of the pancreas22. Others have found that cancer cell lines harboring mutant KRAS differ in their dependence on KRAS23. These findings imply plasticity in either reliance on KRAS signaling or a cell-type specific role for mutant KRAS in different cells of origin/lineages in PDA, or both. They further suggested to us that despite KRAS mutation in most PDAs, KRAS dependence might differ by PDA subtype. We found KRAS mRNA levels elevated in classical subtype PDAs relative to the other subtypes (Supplementary Fig. 6, p<0.05 in UCSF samples). We explored the relationship between KRAS dependence and subtype by using RNAi to probe KRAS-mutant human PDA cell lines for dependence on KRAS. Classical PDA lines proved to be relatively more dependent on KRAS than QM-PDA lines (Fig. 3). Further, a previously reported signature of KRAS-addiction23 is enriched in the classical subtype (Supplementary Fig. 7). These results suggest that KRAS-directed therapy, while not yet a clinical reality, might be best deployed in classical PDA. Mouse models capable of sequentially activating and then deleting mutant KRAS would further these observations to genetically define the respective roles mutant KRAS plays in both the initiation and ongoing maintenance of PDA.

Figure 3
Classical subtype cells depend on KRas

We tested the possibility that PDA subtypes with different biological characteristics might have subtype-specific drug responses by measuring responses to gemcitabine and erlotinib (the backbone of current treatment regimens24) in human PDA cell lines of known subtype. QM-PDA subtype lines were, on average, more sensitive to gemcitabine than the classical subtype (Fig. 4). Conversely, erlotinib was more effective in classical subtype cell lines. This suggests that KRAS mutation status is an imperfect predictor of sensitivity to EGFR-targeted therapy in PDA, an observation consistent with findings in nonsmall cell lung25 and colorectal cancers26, and implies that cancer cells dependent on mutant KRAS still employ the EGFR to some extent. These results further establish phenotypic differences between the classical and QM-PDA subtypes, and suggest that these and perhaps additional drugs will show subtype specificity in PDA, a distinction that could be exploited in clinical trial sensitivity enrichment schemes. More immediately, these results indicate that gemcitabine and erlotinib are preferentially active in different PDA subtypes, so that the current practice of combining them may increase toxicity without increasing efficacy for many patients. Combining agents with similar subtype specificity might be considered instead.

Figure 4
Drug Responses Differ by Subtype

In summary, our data support the existence of three intrinsic subtypes of PDA that progress at different rates clinically and may respond differently to selected therapies. The validity of these subtypes is supported by analysis of multiple primary clinical datasets as well as of PDA cell lines both from human tumors and from mouse models engineered with the hallmark genetic lesions of human PDA. Knowledge of these subtypes may motivate investigation of associations between clinico-pathologic variables and these subtypes by collaborative consortia examining the molecular diversity of PDA27. The markers that define these subtypes may have prognostic utility in risk-adapted surgical approaches28 or predictive utility in sensitivity enrichment schemes. The use of subtyped human and mouse PDA preclinical models promises to accelerate identification of subtype specific functional and regulatory processes that can be exploited to therapeutic benefit. In turn, such assay systems could be used to screen therapeutic approaches, empirically or based on mechanism, to identify those that are potent against PDA, either in a specific subtype that would then be used to personalize treatment29, or spanning the subtypes with possible therapeutic generality.


Clinical Samples

After institutional review board approval, we selected archival material from pancreatic ductal adenocarcinoma resections performed at the University of California, San Francisco between 1993 and 2006. G.E.K. reviewed all cases prior to inclusion in the study. Tissue processing is described in Supplementary Methods.

Merging of Microarray Datasets

After processing of microarrays (as described in Supplementary Information), we screened the UCSF and Badea et al.,5 PDA datasets separately by selecting probes with standard deviation (SD) > 0.8. We then merged SD-selected datasets using DWD7 as described6. We column (samples) normalized to N(0,1) and row (probe or gene) normalized each dataset by median centering. We merged the processed datasets using DWD and finally, we median centered the rows.

NMF and SAM Analysis

We analyzed the merged datasets by consensus clustering-based NMF8 to identify stable subtypes using R code from GenePattern30. See supplement for details regarding the interpretation of subtypes derived from consensus-based NMF clustering. We identified PDAssigner genes using three-class SAM analysis based on classes identified from NMF analysis using the Bioconductor31 package, Siggenes, and generated heatmaps of samples by PDAssigner genes using Cluster software32. For cell line classification, we merged the cell line datasets with core PDA clinical datasets after selection of the 62 PDAssigner genes from each dataset followed by DWD based merging. We visualized datasets using the Hierarchical Clustering Viewer (HCV) from GenePattern30.

Clinical Outcome Analysis

We calculated overall survival in days from the time of PDA resection until date of death as defined by the State of California Death Registry and clinical records. We employed the log-rank test for univariate associations with survival or the Cox proportional hazards model for multivariate modeling of survival. We applied Fisher’s exact test to investigate the relationships among subtype, stage and grade. We used the R language for all analyses. We drew the survival curve using web-based GenePattern30.

Drug Sensitivity

We plated 2.5x103 cells per well on day 0, treated with erlotinib or gemcitabine in nine, five-fold serial dilutions on day 1 and estimated cell number using Cell Titre Glow assay (CTG, Promega) on day 4. IC50 was calculated using the Calcusyn program (Biosoft).


We obtained validated pLKO-based shRNA vectors shKRAS#533 from Dr. B.R. Stockwell (NYU) and shGATA6#5, and shLuc34 from Dr. R Adam, (Boston Children’s Hospital). We packaged lentiviruses, transduced cells and then selected in puromycin for 48 hours. For shKRAS proliferation experiments, we plated 2.5 x103 transduced cells per well on day 0 in 96 well plates, then counted one plate on day one and the other plate on day four. We confirmed protein knockdown by western blotting using the Odyssey system, with 10ug per lane of total protein and the c19 KRas antibody (Santa Cruz), normalized to total actin (I-19, Santa Cruz) and compared to pLKOshLuc -KRas levels. For GATA6 knockdown experiments, after puromycin selection cells we trypsinized and plated transduced cells in soft agar as described35. We assessed GATA6 transcript levels on the day of plating in soft agar as described34.

See Supplementary Information for detailed methods.

Supplementary Material


We are grateful to M. Lenburg, the Gray, Hanahan, and Speed labs for discussion. We thank L. Chin (Dana-Farber Cancer Institute), S. Batra (University of Nebraska Medical Center), M. McMahon (University of California San Francisco) and A. Singh (Massachusetts General Hospital) for cell lines, and B. Stockwell (New York University) and R. Adam (Children’s Hospital Boston) for shRNA. E.A.C was supported by a Young Investigator Award from the American Society of Clinical Oncology and US National Cancer Institute (NCI) K08 CA137153. A.S. was supported by Department of Defense (DOD) Postdoctoral Fellowship (BC087768). The research in the laboratory of D.H. was supported by a NCI Program Project Grant PO1 CA 117969; D.H. is an American Cancer Society Research Professor. This work was supported by the Director, Office of Science, Office of Biological & Environmental Research, of the United States Department of Energy under Contract No. DE-AC02-05CH11231, by the NIH/NCI grants P50 CA 58207, P50 CA 83639 and by the U54 CA 112970 to J.W.G.


Author Contributions:

E.A.C. and A.S. designed, conducted and interpreted experiments, and wrote the manuscript. P.O., W.J.G, M.T., S.G., J.C., J.W., L.J., and H.S.F. performed experiments. K.L.D., and P.T.S. provided support and interpreted experiments. G.E.K. and A.H.K. coordinated clinical sample acquisition. A.B.O. provided statistical expertise. M.A.T provided support, interpreted experiments and coordinated clinical sample acquisition. D.H. and J.W.G. designed and interpreted experiments, wrote the manuscript and supervised the project.


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