1. Cancer Biology
  2. Stem Cells and Regenerative Medicine
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HNF1A is a novel oncogene that regulates human pancreatic cancer stem cell properties

  1. Ethan V Abel
  2. Masashi Goto
  3. Brian Magnuson
  4. Saji Abraham
  5. Nikita Ramanathan
  6. Emily Hotaling
  7. Anthony A Alaniz
  8. Chandan Kumar-Sinha
  9. Michele L Dziubinski
  10. Sumithra Urs
  11. Lidong Wang
  12. Jiaqi Shi
  13. Meghna Waghray
  14. Mats Ljungman
  15. Howard C Crawford
  16. Diane M Simeone  Is a corresponding author
  1. University of Michigan Health System, United States
  2. New York University Langone Health, United States
  3. New York University Langone Health, United states
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Cite this article as: eLife 2018;7:e33947 doi: 10.7554/eLife.33947

Abstract

The biological properties of pancreatic cancer stem cells (PCSCs) remain incompletely defined and the central regulators are unknown. By bioinformatic analysis of a human PCSC-enriched gene signature, we identified the transcription factor HNF1A as a putative central regulator of PCSC function. Levels of HNF1A and its target genes were found to be elevated in PCSCs and tumorspheres, and depletion of HNF1A resulted in growth inhibition, apoptosis, impaired tumorsphere formation, decreased PCSC marker expression, and downregulation of POU5F1/OCT4 expression. Conversely, HNF1A overexpression increased PCSC marker expression and tumorsphere formation in pancreatic cancer cells and drove pancreatic ductal adenocarcinoma (PDA) cell growth. Importantly, depletion of HNF1A in xenografts impaired tumor growth and depleted PCSC marker-positive cells in vivo. Finally, we established an HNF1A-dependent gene signature in PDA cells that significantly correlated with reduced survivability in patients. These findings identify HNF1A as a central transcriptional regulator of PCSC properties and novel oncogene in PDA.

https://doi.org/10.7554/eLife.33947.001

eLife digest

Pancreatic ductal adenocarcinoma is the most common form of pancreatic cancer. It is also one of the deadliest types of cancer: fewer than one in ten patients live for five years after being diagnosed with the disease. Several reasons can explain this poor outcome including that the cancer is often diagnosed late, when tumor cells have already spread, and that there are not many effective treatments for it.

Pancreatic tumors contain different types of cancer cells with different properties. Among these are the so-called pancreatic cancer stem cells. These aggressive cells produce copies of themselves, contributing to tumor growth and spread. They can also help tumors to resist chemotherapy and radiotherapy. New treatments that specifically target cancer stem cells could therefore prove important for treating pancreatic cancer.

It is still not clear what makes pancreatic cancer stem cells so aggressive, or how they differ from the rest of the cells in a tumor. Abel et al. therefore looked for proteins that were more abundant in human pancreatic cancer stem cells than in other, less aggressive cancer cells with the idea that these proteins are likely to be important for the behavior of the pancreatic cancer stem cells.

Abel et al. found that a protein called HNF1A is enriched in pancreatic cancer stem cells. Experimentally reducing the levels of HNF1A in cells taken from human pancreatic cancers caused the cells to grow less well and form smaller tumors when injected into the pancreases of mice. These tumors contained few cancer stem cells, suggesting that HNF1A is important for maintaining the stem cell state. Further experiments showed that HNF1A increases the amount of many other proteins inside cells, including one that controls the activity of normal stem cells.

Given the importance of HNF1A to pancreatic cancer stem cells, finding ways to prevent this protein from working could lead to new treatments for pancreatic cancer. At the moment there are no drugs that target HNF1A. Further research is therefore needed to develop new drugs that work against HNF1A or one of the other proteins that it affects.

https://doi.org/10.7554/eLife.33947.002

Introduction

Pancreatic ductal adenocarcinoma (PDA) is projected to be the second leading cause of cancer deaths in the U.S. by 2020 (Rahib et al., 2014). The exceeding lethality of PDA is attributed to a complex of qualities frequent to the disease including early and aggressive metastasis and limited responsiveness to current standards of care. While both aspects are in-and-of-themselves multifaceted and can be partially attributed to factors such as the tumor microenvironment (Olive et al., 2009; Provenzano et al., 2012; Waghray et al., 2016) and the mutational profile of the tumor cells (Yachida et al., 2012), cancer stem cells (CSCs) have also been identified to contribute to promoting early metastasis and resistance to therapeutics (Hermann et al., 2007; Li et al., 2011).

CSCs, which were originally identified in leukemias (Bonnet and Dick, 1997; Graham et al., 2002), have been identified in a number of solid tumors including glioblastoma (Singh et al., 2003), pancreas (Li et al., 2007; Hermann et al., 2007) and colon (O'Brien et al., 2007). In these cases, CSCs have been characterized by the ability to establish disease in immunocompromised mice, to resist chemotherapeutics, the capability of both self-renewal and differentiation into the full complement of heterogeneous neoplastic cells that comprise the tumor, and the propensity to metastasize. In each case, CSCs are distinguished from other tumor cell types by the expression of various, sometimes divergent cell surface markers. Our lab was the first to identify pancreatic cancer stem cells (PCSCs), which were found to express the markers EPCAM (ESA), CD44, and CD24 (Li et al., 2007). In addition to these markers, CD133 (Hermann et al., 2007), CXCR4 (Hermann et al., 2007), c-MET (Li et al., 2011), aldehyde dehydrogenase 1 (ALDH1) (Kim et al., 2011), and autofluorescence (Miranda-Lorenzo et al., 2014) have all been proposed markers of PCSCs. In all cases, the identified cells are characterized by being able to form spheres of cells (tumorspheres) under non-adherent, serum-free conditions, as well as an increased ability to form tumors in mice compared to bulk tumor cells. While a number of markers have been identified for PCSCs, relatively little is known about the transcriptional platforms that govern their function and set them apart from the majority of bulk PDA cells. Transcriptional regulators such as NOTCH (Wang et al., 2009; Abel et al., 2014), BMI1 (Proctor et al., 2013), and SOX2 (Herreros-Villanueva et al., 2013) have been demonstrated to play roles in PCSCs, although these proteins are also critical for normal stem cell function in many tissues.

In this study, we sought to better understand the biological heterogeneity of PCSCs and their bulk cell counterparts in an effort to identify novel regulators of PCSCs in the context of low-passage, primary patient-derived PDA cells. Using microarray analysis and comparing primary PDA cell subpopulations with different levels tumorigenic potential and stem-cell-like function, we identified hepatocyte nuclear factor 1-alpha (HNF1A), an endoderm-restricted transcription factor, as a key regulator of the PCSC state. Supporting this hypothesis, depletion of HNF1A resulted in a loss of PCSC marker expression and functionality both in vitro and in vivo. Additionally, ectopic expression of HNF1A augmented PCSC properties in PDA cells and enhanced growth and anchorage-independence in normal pancreatic cell lines. Mechanistically, we found that HNF1A directly regulates transcription of the stem cell transcription factor POU5F1/OCT4, which is necessary for stemness in PCSCs. Based on these data, we postulate a novel pro-oncogenic function for HNF1A through its maintenance of the pancreatic cancer stem cell properties.

Results

An HNF1A gene signature dominates a PCSC gene signature

A transcriptional profile of PCSCs has yet to be established, and we hypothesized that such a profile would contain key regulators of the PCSC state. To pursue this hypothesis, we utilized a series of low-passage, patient-derived PDA cell lines to isolate PCSC-enriching and non-enriching subpopulations for comparative analysis. Using two of our previously described PCSC surface markers, CD44 and EPCAM (Li et al., 2007), we found that low-passage PDA cells generally formed three subpopulations (abbreviated P herein) based on surface staining: CD44High/EPCAMLow (P1), CD44High/EPCAMHigh (P2), or CD44Low/EPCAMHigh (P3) (Figure 1A). Similar expression patterns were observed in 10 primary tumor samples (data not shown). Additionally, a CD44Low/EPCAMLow subpopulation was observed in five samples (data not shown), consistent with our previous data (Li et al., 2007). Using previously described measures of PCSC function (Li et al., 2007; Li et al., 2011), including co-expression of the PCSC marker CD24 (Figure 1—figure supplement 1A), the abilities for isolated subpopulations to reestablish heterogeneous CD44 and EPCAM surface expression (Figure 1B), to form tumorspheres under non-adherent/serum-free culture conditions (Figure 1C,D), and to initiate tumors in immune-deficient mice (Supplementary file 1), we found that P2 cells showed greater enrichment for cells with PCSC properties than their P1 and P3 counterparts.

Figure 1 with 2 supplements see all
HNF1A-signature dominates pancreatic CSCs.

(A) Flow cytometry analysis of CD44 and EPCAM surface expression of three primary PDA samples. (B) CD44High/EPCAMLow (P1), CD44High/EPCAMHigh (P2) and CD44Low/EPCAMHigh (P3) NY8 cells were isolated by FACS and grown in culture for 17 days, followed by flow cytometry for analysis for CD44 and EPCAM expression. (C, D) Isolated subpopulations were grown in tumorsphere media on non-adherent plates (500 cells/well) for 6 days. Representative images of resultant tumorspheres (100X magnification) are shown in (C), while quantitation of spheres (n = 3) is shown in (D). Statistical difference was determined by one-way ANOVA with Tukey’s multiple comparisons test. (E) Heat map representing relative fold differences in qRT-PCR expression of 50 cancer stem-cell-enriched genes in NY8 and NY15 cells. Per-gene values are relative to P1 or P3, whichever is higher. Gene names in red text indicate predicted HNF1A targets and asterisks (*) indicate known HNF1A targets. P1: CD44High/EPCAMLow, P2: CD44High/EPCAMHigh, P3: CD44Low/EPCAMHigh. For all genes, expression levels were normalized to an ACTB mRNA control, n = 3. Only genes with a significant (p<0.05) increase in P2 over both P1 and P3 subpopulations are shown, with statistical difference determined by one-way ANOVA with Tukey’s multiple comparisons test. (F) qRT-PCR analysis of HNF1A mRNA expression, normalized to an ACTB mRNA control, from different primary PDA subpopulations (n = 3). Statistical difference was determined by one-way ANOVA with Tukey’s multiple comparisons test. Related data can be found in Figure 1—figure supplements 1 and 2.

https://doi.org/10.7554/eLife.33947.003

Using two primary PDA lines (NY8 and NY15), P1, P2, and P3 PDA cells were sorted by flow cytometry, prepped immediately for mRNA, and analyzed by Affymetrix GeneChip microarray and validated by quantitative RT-PCR. We found that P2 cells from both lines exhibited a signature of 50 genes that was upregulated (>1.5 fold) relative to both P1 and P3 cell counterparts (Figure 1E). To further refine this gene cohort, we utilized oPOSSUM (Kwon et al., 2012), a web-based system to detect overrepresented transcription-factor-binding sites in gene sets. Interestingly, HNF1A, a P2 cohort gene itself (Figure 1E,F), had predicted binding sites in the ±5000 regions (from start of transcription) of 17/50 of the enriched genes, and due to its stringent consensus sequence (DGTTAATNATTAAC) was the most highly ranked common transcription factor by Z-score (17.895). Of these 50 genes, HNF1A is known to positively regulate cohort genes HNF4A (Boj et al., 2001), NR5A2 (Molero et al., 2012), CDH17 (Zhu et al., 2010), IGFBP1 (Babajko et al., 1993; Powell and Suwanichkul, 1993), and DPP4 (Gu et al., 2008). Interestingly, genome-wide association (GWA) studies have recently identified certain single nucleotide polymorphisms (SNPs) in the HNF1A locus as risk factors for developing PDA (Pierce and Ahsan, 2011; Li et al., 2012; Wei et al., 2012), although the mechanism by which these SNPs exert their influence is currently unknown. Similarly, SNPs in the HNF1A target NR5A2 are also associated with the development of PDA (Petersen et al., 2010; Rizzato et al., 2011), further implicating a role for the HNF1A-transcriptional network in PDA. To further support the enrichment of HNF1A in PCSCs, sorted cells were western blotted for HNF1A and HNF1A-target proteins, CDH17 and DPP4. These proteins were found to be elevated in P2 cell lysates compared to other subpopulations (Figure 1—figure supplement 1B), in agreement with their transcript levels. CSCs are enriched in cancer cell populations grown under low-attachment tumorsphere (S) conditions compared to cells grown in adherent (A) conditions. In keeping with this observation, we found protein levels of HNF1A and CDH17 elevated in multiple PDA lines cultured under tumorsphere conditions (Figure 1—figure supplement 2A and C). Using a GFP-based reporter driven by eight tandem copies of the HNF1A consensus sequence GGTTAATGATTAACC (Figure 1—figure supplement 2B), we found GFP expression was elevated in NY5, NY8, and NY15 cells grown under tumorsphere (S) compared to adherent conditions (A) (Figure 1—figure supplement 2C). This construct showed excellent dependence on HNF1A expression as targeting HNF1A with an HNF1A-specific siRNA ablated expression of both the ectopic GFP and endogenous CDH17 (Figure 1—figure supplement 2D). Lastly, we found the frequency of GFP-positive cells increased in cells grown in suspension (Figure 1—figure supplement 2E), with GFP expression being highest in the P2 subpopulation of NY15 cells (Figure 1—figure supplement 2F). Based on our gene expression and tumorsphere data, we hypothesized that HNF1A is a central regulator of CSC function.

HNF1A is a critical regulator of CSC properties in PDA cells

Consistent with our hypothesis that HNF1A may be an integral component of PDA biology we observed higher levels of HNF1A protein and transcripts in PDA cells compared to non-transformed immortalized pancreatic cell lines HPNE (N) and HPDE (D) (Figure 2A; Figure 2—figure supplement 1A). Immunostaining of a PDA tissue microarray showed HNF1A expression to be significantly elevated (p<0.0001) in PDA neoplastic ducts (n = 41) compared to normal pancreatic ducts (n = 18) (Figure 2—figure supplement 1B,C). To examine the role of HNF1A in PDA cells, we depleted the protein with two distinct siRNAs (Figure 2B). Knockdown of HNF1A resulted in reduced cell numbers in multiple primary PDA lines (Figure 2C). To determine whether the apparent loss in cell number was due to apoptotic cell death, we performed annexin V/DAPI staining on control and HNF1A-depleted NY5, NY8, and NY15 cells. In all cases, knockdown of HNF1A resulted in a significant (p<0.05) increase in apoptotic cells, while not affecting necrotic cell numbers (Figure 2D, data not shown). Furthermore, increased cleavage of caspases 3, 6, 7, and 9 was observed in cells depleted of HNF1A (Figure 2E), indicating apoptotic cell death. These data indicate that HNF1A is important for PDA cell growth and survival.

Figure 2 with 1 supplement see all
Knockdown of HNF1A in primary PDA cells inhibits growth in vitro.

(A) Western blot analysis of HNF1A expression in a panel of primary PDA lines compared to immortalized pancreatic ductal cell line HPNE and HPDE. Quantitation of HNF1A protein is indicated below the respective blots. (B) Western blot of NY5, NY8, and NY15 cells transfected with non-targeting (Ctl) or HNF1A-targeting siRNA for 3 days, showing effective depletion of HNF1A protein by RNAi. Quantitation of HNF1A protein is indicated below the respective blots. (C) 1.5 × 105 PDA cells were transfected with control (Ctl) or HNF1A-targeting siRNA (Day 1). Cells were collected and manually counted 3 and 6 days after transfection (n = 3). Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test. Red and green p values indicate Ctl vs. HNF1A#1 or #2, respectively. (D) Annexin V staining was performed on NY5, NY8, and NY15 cells transfected with control (Ctl) or HNF1A-targeting siRNA (H1, H2) for 3 days. The amount of apoptotic (annexin V+) cells are quantitated (n = 4). Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test, with p values relative to the control siRNA group indicated. (E) Western blot analysis of cleaved caspases in NY8 and NY15 cells following HNF1A-knockdown (3 days). Actin serves as a loading control. Quantitation of proteins is indicated below the respective blots. Related data can be found in Figure 2—figure supplement 1.

https://doi.org/10.7554/eLife.33947.008

Next, we pursued whether depletion of HNF1A impacted PDA subpopulation distribution. Consistent with a central role in maintaining heterogeneous EPCAM and CD44 expression, we observed a change in P2 in all cell lines (Figure 3A, Figure 3—figure supplement 1A) with a concomitant increase in the P3 population (Figure 3—figure supplement 1A,C). NY8 cells showed a loss in the P1 population as well (Figure 3—figure supplement 1A,B). Collectively, these results support a role for HNF1A in maintaining cellular heterogeneity, with the most dramatic change being the consistent loss of the PCSC population. In addition to changes in CD44 and EPCAM surface expression, we also observed a marked decrease in CD24 surface expression (Figure 3B, Figure 3—figure supplement 1D) and mRNA levels (data not shown) in multiple PDA lines; suggesting that loss of HNF1A depletes the CSC compartment. To assess functional consequences of HNF1A-depletion on the PCSC compartment, cells (NY5, NY8, NY15) expressing HNF1A shRNAs were grown under tumorsphere-promoting conditions. These shRNAs effectively depleted HNF1A as well as CDH17 (Figure 3C), indicating downstream signaling inhibition. Consistent with a role in PCSC function, HNF1A knockdown showed a marked reduction in tumorsphere formation (p<0.05) (Figure 3D,E; Figure 3—figure supplement 1E).

Figure 3 with 1 supplement see all
Knockdown of HNF1A depletes CSC numbers.

(A) Multiple PDA cells were transfected with HNF1A-targeting siRNA or non-targeting control siRNA for 6 days. Surface expression of CD44 and EPCAM was measured by flow cytometry, and the percentage of CD44High/EPCAMHigh (P2) cells are represented (mean ± SEM, n = 3). Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test. (B) Quantitation of CD24 +cells in multiple primary PDA cells following HNF1A knockdown for 6 days, n = 4. Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test. (C) NY5, NY8, and NY15 cells expressing LacZ2.1 (L) or two distinct HNF1A-targeting shRNAs (H1 and H2) were lysed and western blotted for HNF1A, CDH17, and Actin, showing effective knockdown of HNF1A and downstream signaling (CDH17). Quantitation of proteins is indicated below the respective blots. (D, E) NY5, NY8, and NY15 cells expressing LacZ2.1 or HNF1A-targeting shRNAs were grown in tumorsphere media on non-adherent plates (1500 cells/well). The number of tumorspheres formed after 6 days were counted (n = 3). Representative images of spheres (100X magnification) are shown in (F) and in Figure 3—figure supplement 1, with quantitation in (E). Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test. Related data can be found in Figure 3—figure supplement 1.

https://doi.org/10.7554/eLife.33947.012

HNF1A exhibits oncogenic properties in pancreatic cells

We next sought to determine whether CSC properties could be augmented by ectopic expression of HNF1A in PDA cells. For these studies, we selected PDA lines with high (NY15), medium (NY8), and low (NY53) expression of HNF1A (Figure 2A) to determine if additional HNF1A expression could bolster PCSC properties under different cellular contexts. Using doxycycline-inducible expression of HNF1A (Figure 4A,B), we noted increased expression of CD24, CD44, and EPCAM in multiple primary PDA lines (Figure 4B–D, data not shown), indicating that ectopic HNF1A can increase PCSC marker expression in PDA cells. Additionally, we found that HNF1A-expressing cells formed ~2.5 fold more tumorspheres than their counterparts (Figure 4E) in all PDA cells tested. Taken together, these data indicate that ectopic HNF1A can promote PCSC properties, even in the presence of higher endogenous expression (i.e. NY15).

Figure 4 with 1 supplement see all
Overexpression of HNF1A promotes CSC properties in PDA cells and normal pancreatic cell lines.

(A) NY15 and NY53 cells Western blotted for HNF1A and control gene induction following 48 hr ± doxycycline (Dox). Quantitation of proteins is indicated below the respective blots. (B) NY8 cells were treated 48 hr ± Dox to induce ectopic HNF1A. Lysates were western blotted for HNF1A, Actin, and PCSC markers EPCAM and CD44. Quantitation of proteins is indicated below the respective blots. (C) Representative surface expression of CD24 and EPCAM on NY15 cells expressing GFP or HNF1A. (D) Quantitation of CD24 +NY15 GFP and HNF1A cells and NY8 and NY53 LacZ and HNF1A cells by flow cytometry (n = 3). Statistical difference was determined by one-way ANOVA with Tukey’s multiple comparisons test. (E) NY15 GFP and HNF1A, NY8 and NY53 LacZ and HNF1A cells were grown under sphere-forming conditions ± Dox. The number of tumorspheres formed after 7 days were quantitated (n = 4). Statistical difference was determined by one-way ANOVA with Tukey’s multiple comparisons test. Representative images (100X magnification) of spheres are shown in the upper panels. (F, G) HPNE LacZ and HNF1A cells were plated at 200 cells/6 cm dish and treated ±Dox for 2 weeks, fixed, and stained with crystal violet (F). (G) Resultant colonies were quantitated (n = 3). Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test. (H, I) HPDE cells expressing inducible LacZ, LacZ with KRASG12D, HNF1A, or HNF1A with KRASG12D were embedded in soft agar + Dox and monitored for signs of anchorage-independent growth for 21 days. (H) Representative images of resultant colonies (100X magnification) and (I) quantitation of colonies after 21 days (n = 3). Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test. Related data can be found in Figure 4—figure supplement 1.

https://doi.org/10.7554/eLife.33947.015

We next examined the effects of ectopic HNF1A expression in the non-tumorigenic pancreatic ductal cell lines HPDE and HPNE, which were devoid of endogenous HNF1A expression (Figure 2A). Doxycycline-inducible ectopic expression of HNF1A alone or in concert with ectopic KRASG12D was readily achieved in HPDE cells (Figure 4—figure supplement 1A). Consistent with previous reports, KRASG12D-induced phosphorylation of both ERK1/2 and AKT in HPDE cells. Similar effects were seen in HPNE cells constitutively expressing HNF1A and KRASG12D alone or in combination (Figure 4—figure supplement 1A). We then tested the impact the of HNF1A and/or KRASG12D expression, either alone or in combination, on HPDE cell growth. Under normal growth conditions with serum, (LacZ) HPDE cells grew to confluency but did not form colonies, presumably due to contact-inhibition (Figure 4—figure supplement 1B). Expression of KRASG12D, however, resulted in colony formation, indicating a bypass of contact inhibition. HNF1A alone resulted in significantly increased colony formation, which was further enhanced by the additional expression of KRASG12D. Similar effects were seen in HPNE cells (data not shown). In clonogenicity assays, HNF1A-expressing HPNE cells formed similar numbers of colonies to control and KRASG12D-expressing cells (Figure 4F,G); however, HNF1A alone promoted enhanced colony size. HPDE cells failed to form colonies at clonal densities in the presence of serum. In addition to foci formation, anchorage-independent growth can indicate cellular transformation in vitro. When suspended in soft agar, control HPDE cells failed to grow over a 21-day period (Figure 4H,I). The addition of KRASG12D alone did not significantly promote colony formation, consistent with its relatively weak transforming ability in HPDE cells. Interestingly, HNF1A alone resulted in numerous small colonies which in turn synergized with the expression of KRASG12D in the form of numerous large colonies. Neither HNF1A nor KRASG12D alone resulted in anchorage-independent growth in HPNE cells (data not shown). Lastly, we examined the effects of both transgenes on PCSC marker expression. Expression of HNF1A increased expression of EPCAM, CD44, and CD24 in HPDE cells (Figure 4—figure supplement 1A,C). Control HPNE cells lacked expression of both EPCAM and CD24, but expressed high levels of CD44. Expression of HNF1A was able to increase CD44 surface expression, while not changing EPCAM status (Figure 4—figure supplement 1C, data not shown). Remarkably, CD24 was potently induced upon HNF1A expression, with nearly 83% of HPNE cells expressing CD24 compared to 0.5% of LacZ-expressing control cells. These data would suggest that HNF1A possesses properties of an oncogene capable of cooperation with oncogenic KRAS.

HNF1A is required for tumor growth and cancer stem cells properties in vivo

To determine whether HNF1A was necessary for tumorigenesis, we implanted two HNF1A-high primary lines (NY5 and NY15) expressing control or two HNF1A-targeting shRNAs orthotopically in the pancreas of NOD/SCID mice. HNF1A-depleted cells showed significantly reduced tumor growth compared to their control cohorts (p<0.05), (Figure 5A,B). Similar results were observed with HNF1A knockdown in subcutaneous xenografts of NY5 and NY15 cells (Figure 5C, Figure 5—figure supplement 1A). To determine whether inhibition of tumor growth was due to effects on the PCSC compartment, NY5 tumors were dissociated and analyzed by flow cytometry. Consistent with our in vitro findings, the EPCAM+/CD44+/CD24 +cell population was significantly reduced in HNF1A-depleted tumors (p<0.05) (Figure 5D,E). Importantly, western blot analysis of resultant tumor lysates confirmed that shRNAs remained effective at depleting HNF1A during the course of the experiment (Figure 5—figure supplement 1B).

Figure 5 with 1 supplement see all
Knockdown of HNF1A impairs tumor growth and depletes CSCs in vivo.

(A, B) 10,000 firefly luciferase-labeled NY5 and NY15 cells expressing control or HNF1A shRNAs were implanted orthotopically into the pancreata of NOD/SCID mice and monitored by IVIS imaging for 6 weeks (10 mice per group). Representative luminescence image of tumors prior to sacrifice is shown (A). Final tumor volumes determined during necropsy are quantitated in (B). Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test. (C) 103 control or HNF1A-depleted NY5 cells were implanted subcutaneously in NOD/SCID mice (10 mice per shRNA/bilateral injections) for 11 weeks. Tumors were measured by caliper to determine tumor growth (C, upper panel). Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test. Red and orange p values indicate LacZ2.1 vs. HNF1A#1 or #2, respectively. ‘n.d.’ indicates that tumors were not detected. Representative tumors excised at sacrifice are shown (C, lower panel). (D, E) NY5 tumors from (A) were dissociated and stained for EPCAM, CD44, and CD24. Representative flow cytometry plots for recovered tumor cells are shown in (D), where the R22 gate denotes EPCAMHigh/CD44High cells, and CD24 +cells are donated in red. Quantitation of EPCAM+/CD44+/CD24 +cells is shown in (E), n = 6 tumors each for shRNA. Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test. Related data can be found in Figure 5—figure supplement 1.

https://doi.org/10.7554/eLife.33947.019

HNF1A regulates stemness through POU5F1/OCT4 expression

As a direct relationship between HNF1A and stem cell function has not been reported, we examined mRNA expression of central stemness regulators MYC, SOX2, KLF4, NANOG, and POU5F1/OCT4 in HNF1A-depleted cells. Of these transcription factors, only POU5F1/OCT4 mRNA showed consistent downregulation in multiple PDA cell lines in response to HNF1A knockdown (Figure 6A, data not shown). Similarly, we found that POU5F1/OCT4 mRNA was upregulated in response to overexpression of HNF1A in both PDA cells and HPDE cells (Figure 6B), indicating regulation of POU5F1/OCT4 expression by HNF1A in pancreatic-lineage cells. To determine whether POU5F1/OCT4 mRNA was correlated with HNF1A expression, qRT-PCR was performed in 22 primary PDA lines as well as HPNE and HPDE cells. The Pearson correlation coefficient of POU5F1/OCT4 mRNA was found to be significantly correlated (p=0.0094) with HNF1A mRNA levels (Figure 6C). Additionally, POU5F1/OCT4 and HNF1A mRNA levels were correlated (Pearson’s r = 0.406, p=8.9×10−8) in patient tumors samples from The Cancer Genome Atlas (TCGA) dataset for PDA (PAAD cohort, data not shown), further supporting relationship between the two genes. Despite a strong association between POU5F1/OCT4 and HNF1A mRNA levels, we did not observe a significant association between POU5F1/OCT4 mRNA and any of the PDA subpopulations, indicating that factors other than HNF1A modulate the levels of POU5F1/OCT4 mRNA in different PDA subpopulations (data not shown).

Figure 6 with 2 supplements see all
HNF1A regulates stemness through POU5F1/OCT4 regulation.

(A) qRT-PCR analysis of POU5F1/OCT4 mRNA in NY5, NY8 and NY15 cells expressing control (LacZ2.1) or HNF1A shRNAs. ACTB was used as an internal control, n = 3. Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test. (B) LacZ or HNF1A was induced in NY8, NY15 or HPDE cells with doxycycline for 6 days. Levels of POU5F1/OCT4 mRNA were measured by qRT-PCR with ACTB as an internal control, n = 3. Statistical difference was determined by unpaired t test with Welch’s correction. (C) Pearson correlation coefficient of POU5F1/OCT4 and HNF1A mRNA levels from NY PDA cells (n = 22, red) relative to HPNE and HPDE (blue) cells. (D) Western blot of OCT4A and HNF1A protein in NY5 and NY8 cells transfected with POU5F1/OCT4 (labeled OCT4) or HNF1A SMARTpool siRNA for 3 days. Quantitation of proteins is indicated below the respective blots. (E) NY5 and NY8 cells were transfected with HNF1A or POU5F1/OCT4 SMARTpool siRNA for 3 days and then grown in tumorsphere media on non-adherent plates (1500 cells/well). Spheres were quantitated 7 days later, n = 3. Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test. (F–H) NY15 cells were transfected with POU5F1/OCT4 SMARTpool (SP) siRNA or individual sequences for 3 days and either harvested to assess OCT4A knockdown by Western blot (F) or grown in tumorsphere media on non-adherent plates (1500 cells/well) (G). Spheres were quantitated 7 days later, n = 3. Statistical difference was determined by one-way ANOVA with Dunnett’s multiple comparisons test. Representative spheres are shown in (H). (I) NY8 and NY15 cells transduced with OCT4A (labeled OCT4) or empty vector control (EV) were transiently transfected with control (Ctl) or HNF1A-targeting siRNA for 72 hr, and then grown in tumorsphere media on non-adherent plates (1500 cells/well). Spheres were quantitated 7 days later, n = 3. Statistical difference was determined by one-way ANOVA with Tukey’s multiple comparisons test. Related data can be found in Figure 6—figure supplements 1 and 2.

https://doi.org/10.7554/eLife.33947.023

Previously published HNF1A chromatin immunoprecipitation sequencing (ChIP-seq) data performed in HepG2 cells by The Encyclopedia of DNA Elements (ENCODE) project (Consortium and ENCODE Project Consortium, 2012) identified a region of enrichment of HNF1A upstream of the POU5F1/PSORS1C3 gene loci proximal to recently identified retrotransposon long terminal repeat (LTR)-rich region that can serve as a promoter for both genes (Malakootian et al., 2017). Additionally, enrichment of this LTR region by TATA-binding protein (TBP) and acetylated lysine 27 histone H3 supports the involvement of this region in the transcription of POU5F1/OCT4. Interestingly, this LTR promoter region contains three consensus half-sites for HNF1A (Figure 6—figure supplement 1A). To test whether HNF1A binds directly to these half-sites, ChIP-PCR was performed in NY5, NY8, and NY15 cells. Consistent with the ENCODE data we observed significant enrichment of two half-sites in NY5 and all three half-sites in NY8 and NY15 by HNF1A. By contrast, the canonical distal enhancer of POU5F1/OCT4 (Yeom et al., 1996), located 14-kbp downstream of the LTR promoter, and HNF1A non-target gene MYOD showed no significant enrichment by HNF1A (Figure 6—figure supplement 1B), demonstrating the specificity of enrichment observed. To validate the LTR promoter region as an HNF1A-responsive promoter region, a reporter construct was generated encompassing the three putative HNF1A half-sites (Figure 6—figure supplement 1C). Co-transfection of 293FT cells (which lack endogenous HNF1A) with the LTR reporter and an HNF1A-expression plasmid resulted in a 4.5-fold induction of Cypridina luciferase expression over LacZ-expression plasmid co-transfected cells (Figure 6—figure supplement 1D). Additionally, neither the cloning vector nor the canonical downstream promoter region of POU5F1/OCT4 showed responsiveness to HNF1A expression, supporting the POU5F1/OCT4 LTR promoter as the HNF1A-responsive promoter for the gene.

POU5F1/OCT4 has previously been shown to be elevated in PCSCs (Miranda-Lorenzo et al., 2014; Luo et al., 2017), although a functional role for the protein has not been demonstrated in this context. To determine if POU5F1/OCT4 regulation was a key event in HNF1A-dependent stemness, we targeted POU5F1/OCT4 with multiple siRNAs, either in combination or as single sequences. Depletion of POU5F1/OCT4 resulted in a pronounced inhibition of tumorsphere formation, comparable to HNF1A knockdown (Figure 6D–H). To determine whether changes in apoptosis or cell cycle were responsible for the loss of tumorsphere formation in response to POU5F1/OCT4 knockdown, we performed annexin V/DAPI staining and propidium iodide staining in NY8 cells following transfection with POU5F1/OCT4 siRNA. Consistent with its role as a regulator of stemness in normal and cancer stem cells (Okita et al., 2007; Takahashi and Yamanaka, 2006; Lu et al., 2013; Kumar et al., 2012; Nishi et al., 2014), we did not observe changes in either apoptosis or cell cycle in response to POU5F1/OCT4 knockdown (Figure 6—figure supplement 2A,B). Importantly, knockdown of either HNF1A or POU5F1/OCT4 had comparable effects on the protein levels of OCT4A (Figure 6D), the isoform responsible for imparting stemness (Lee et al., 2006). To determine whether expression of OCT4A was sufficient to rescue stemness of PDA cells depleted of HNF1A, NY8, and NY15 cells were transduced with OCT4A-expressing lentiviruses or vector controls and transfected with HNF1A siRNA. Consistent with our previous results, loss of HNF1A impaired tumorsphere formation in both lines expressing the vector control, however, this effect was rescued by the expression of OCT4A (Figure 6I, Figure 6—figure supplement 2C,D). These data indicate that HNF1A mediates stemness of PCSCs through direct transcriptional regulation of POU5F1/OCT4.

HNF1A targets associated with poor survival in PDA patients

Lastly, we sought to gain insight into the transcriptional activity and genomic binding of HNF1A in PDA and determine whether its targets held prognostic information similar to other signatures in PDA (Bailey et al., 2016; Collisson et al., 2011). In order to identify transcriptional targets of HNF1A, we performed Bru-seq with control and HNF1A-depleted NY8 and NY15 cells (two replicates each of control shRNA and 2 HNF1A-targeting shRNAs per cell line). Bru-seq is a variation of RNA-seq which measures changes in nascent RNA levels (bona fide transcription rate) as opposed to steady-state mRNA changes measured by conventional RNA-seq and microarray (Paulsen et al., 2013). Differentially expressed genes were defined by adjusted p value<0.1 for at least one HNF1A-targeting shRNA and a mean expression level across samples (in RPKM) greater than 0.25. Of these differentially expressed genes, 243 HNF1A upregulated and 46 HNF1A downregulated were found to be in common between NY8 and NY15 (Figure 7A).

Figure 7 with 1 supplement see all
HNF1A regulates a transcriptional program associated with poor survival in PDA.

(A) Venn diagrams illustrating overlapping genes with altered transcription (Bru-seq) following HNF1A knockdown in NY8 and NY15 cells. ‘HNF1A upregulated’ genes denote genes that were downregulated by HNF1A shRNAs, while ‘HNF1A downregulated’ genes were upregulated by HNF1A shRNAs. Cells expressed shRNAs constitutively for >14 days prior to Bru-seq analysis. (B) Proportion of HNF1A shRNA-downregulated genes identified in both NY8 and NY15 with HNF1A ChIP-seq peaks. Proximal peaks are ±5 kbp of the transcription start site (TSS) of a given gene and distal peaks are ±100 kbp of a TSS. Peaks are recognized only if they are closer to the TSS of a given gene than to other expressed genes. Peaks overlapping putative enhancer regions (ENCODE) are indicated in dark grey. (C) HNF1A ChIP-seq and HNF1A shRNA Bru-seq traces for the genes CDH17 and EPCAM in NY8 and NY15 cells. Traces represent normalized read coverage (in RPKM) across the indicated genomic ranges. MACS-identified ChIP peaks are represented by bars under the corresponding trace. (D) Transcription factor (TF) motif over-representation analysis of HNF1A upregulated and downregulated genes (±5 kbp of TSS). The top 10 over-represented TF motifs, ranked by z-score, are listed. (E) HNF1A upregulated and bound genes were ranked according to model significance and the direction of survival association using TCGA PDA patient data. Magnitude indicates significance (log10-transformed FDR-adjusted Wald p values for Cox PH models) and sign represents survival direction (determined by hazard ratio). Red bars indicate FDR < 0.1, orange bars indicate FDR < 0.25, and gray bars are not significant. The FDR thresholds are also indicated by dotted horizontal lines. Insets: each histogram represents the null distribution of a permutation test (N = 10,000) for fraction of genes significantly associated with reduced survival (the tests use FDR thresholds of 0.1 or 0.25, as indicated). Vertical lines represent values for the set of HNF1A target genes; red: FDR < 0.1 test; orange: FDR < 0.25 test. * - indicates significant p value estimates for the permutation tests. Related data found in Figure 7—figure supplement 1.

https://doi.org/10.7554/eLife.33947.028

To assess genomic binding of HNF1A, we performed ChIP-seq using an HNF1A-specific antibody from NY8 and NY15 (two replicates each). ChIP-seq peaks were called using MACS (Feng et al., 2012) with the a priori assumption of narrow, transcription factor-like peaks. Input DNA was used to discern peaks from the background. Peaks were assigned to genes based on proximity and minimum mean expression level (0.25 RPKM) obtained from the Bru-seq data. Common peaks between NY8 and NY15 cells were defined as those peaks with overlap in at least one replicate of both cell lines. Genes were then classified as proximal, distal or neither, given the distance of the closest common peak to the transcription start site (proximal:±5 kb, distal:±100 kb, neither:>100 kb or no peak). The closest peak to a gene must also identify that gene as its closest gene, to discern among genes with nearby TSSs. 139/239 (57.2%) and 11/46 (23.9%) HNF1A upregulated/downregulated genes had HNF1A binding based on this criteria (Figure 7B), and supports the role of HNF1A as a transcriptional activator.

To further understand the regulatory role of HNF1A, we asked whether the HNF1A peaks overlapped with enhancer regions. The ENCODE combined segmentation model (a model for regulatory regions based on the ChromHMM and Segway models) was selected for this purpose (Hoffman et al., 2013; Ernst and Kellis, 2012; Hoffman et al., 2012). Of the six cell lines represented in this data set, it is not clear if any one best represents our PDA cell lines. We therefore extracted regions designated ‘strong enhancer’ (E) from all the cell lines and merged them into one set of enhancer regions. 72.7% of HNF1A-bound genes had peaks overlapping in at least one of these putative enhancer regions (Consortium and ENCODE Project Consortium, 2012) (Figure 7B), suggesting that HNF1A has significant interaction with regulatory regions.

A number of known HNF1A target genes exhibited HNF1A promoter-proximal binding and transcriptional responsiveness via Bru-seq/ChIP-seq, including CDH17 (Figure 7C). Additionally, the PCSC marker EPCAM also showed HNF1A distal binding and transcriptional responsiveness, implicating HNF1A as a direct regulator of this gene. CD24, which showed decreased transcription in response to HNF1A loss, did not show direct binding, indicating an indirect mechanism of regulation (data not shown). POU5F1/OCT4 transcription was found to decrease in both NY8 (34.3%) and NY15 (41.5%) cells, with weak enrichment of the LTR promoter region (data not shown), further supporting direct regulation of POU5F1/OCT4 transcription by HNF1A. To determine whether POU5F1/OCT4 contributes to the deregulation of genes by HNF1A knockdown, we tested for overrepresentation of TF-binding motifs in the proximal promoter regions (±5 kbp from TSS) of HNF1A upregulated and downregulated genes using oPOSSUM. The POU5F1/OCT4 motif was the most significantly over-represented transcription factor motif in HNF1A downregulated genes (z-score = 18.381; 13/45 genes). The POU5F1/OCT4 motif was enriched in the HNF1A upregulated genes, though less highly ranked (rank #60; z-score = 2.104; 47/231 genes; Supplementary file 2). Of the predicted POU5F1/OCT4 targets, four have previously been identified (CACNA2D1, GATA2, SNAI1, and ZEB2) (Marsboom et al., Li et al., 2010; Ben-Porath et al., 2008). Additionally, other reported POU5F1/OCT4 targets (Ben-Porath et al., 2008) were identified among non-predicted targets, including the HNF1A upregulated genes KLF5 and ZHX2 and the HNF1A downregulated gene GJA1. These data demonstrate an overlap between HNF1A and POU5F1/OCT4 transcriptional networks.

Because CSC and oncogene gene signatures have been linked to prognosis in a variety of cancer types (Bartholdy et al., 2014; Eppert et al., 2011; Glinsky et al., 2005; Merlos-Suárez et al., 2011; Rosenwald et al., 2003), we asked if expression of HNF1-regulated genes was related to survival as a clinical outcome. The TCGA dataset for PDA (PAAD) consists of 178 tumor samples from different patients where both gene expression (RNA-seq) and clinical survival data was collected. Of these, we selected those tumors (n = 169) not identified as histologically neuroendocrine. For each gene, we generated a Cox proportional hazards survival model. We asked what fraction of genes in the HNF1A-responsive genes exhibited significance via Cox regression and whether they were associated with increased or reduced survival (hazard ratio <1 or>1, respectively). p Values were FDR-adjusted for multiple testing and two thresholds were explored. 13/237 (5.5%) of HNF1A upregulated genes were associated with reduced survival at FDR < 0.1 and 57/237 (24.1%) at FDR < 0.25, with only one gene associated with increased survival passing the FDR < 0.25 threshold (Figure 7—figure supplement 1A). For HNF1A upregulated and bound, we found a similar pattern; 11/137 (8.0%) genes associated with reduced survival and 37/137 (27.0%) genes at FDR < 0.25 and 0 genes passing the FDR < 0.25 threshold (Figure 7E). For HNF1A downregulated genes, 1/45 (2.2%) genes were significant at FDR < 0.25 only (Figure 7—figure supplement 1B). A background set of genes, defined as those genes expressed above a minimal threshold in the Bru-seq data and mappable to gene identifiers in the TCGA data (see Materials and methods, was selected for permutations testing). The permutation tests showed that HNF1A upregulated genes were significantly associated with poorer outcomes versus randomly selected genes (insets, Figure 7E and Figure 7—figure supplement 1A; see Materials and methods for details). These findings further support the oncogenic role for HNF1A in PDA as a direct regulator of a set of genes associated with poor patient survival.

Discussion

In this study, we identified the transcription factor HNF1A as putative regulator of a PCSC gene signature. Functional studies revealed that HNF1A was not only central to the regulation of this gene signature, but also PCSC function. Depletion of HNF1A effectively inhibited PDA cell growth, tumorsphere formation, and tumor growth, with a loss of PCSC marker expression observed both in vitro and in vivo. Mechanistically, HNF1A appears to promote stemness through positive regulation of pluripotency factor POU5F1/OCT4. Finally, we found that expression of HNF1A upregulated genes significantly predicted poor survival outcomes in patients with PDA. These data point to a novel oncogenic role for HNF1A in pancreatic cancer, particularly in promoting PCSC properties.

A clear role for HNF1A in PDA has not previously been established. An early study of the putative oncogene FGFR4, frequently expressed in PDA (Ohta et al., 1995), is directly regulated by HNF1A through intronic binding sites (Shah et al., 2002). More recently, 73% of PDA samples were found to stain positive for HNF1A (Kong et al., 2015). A more direct role for HNF1A in PDA has been suggested by multiple GWA studies implicating certain SNPs in HNF1A as risk factors for the development of PDA (Pierce and Ahsan, 2011; Wei et al., 2012; Li et al., 2012). Nearly all the identified HNF1A SNPs are non-coding and relatively common (minor allele frequencies between 30 and 40%), suggesting these SNPs may serve as potential contributing rather than driving factors in pancreatic tumorigenesis. Interestingly, PDA-associated HNF1A SNPs rs7310409, rs1169300, and rs2464196 are also associated with both an elevated risk (1.5–2 fold) of developing lung cancer and elevated circulating C-reactive protein (CRP). A well-established direct target of HNF1A (Toniatti et al., 1990), CRP is downregulated in patients with inactivating mutations in HNF1A (Thanabalasingham et al., 2011). As several PDA-associated SNPs are associated with elevated CRP, it is therefore possible that these SNPs augment the activity/expression of HNF1A rather than diminish it, as in the case of maturity-onset diabetes of the young 3 (MODY3) variants which reduce or abolish HNF1A expression or function. Still, a tumor suppressive role for HNF1A in PDA has also been proposed (Hoskins et al., 2014; Luo et al., 2015). In these studies, HNF1A was found to possess pro-apoptotic/anti-proliferative properties contrary to the data in this study. Differences in these results may be technical in nature (control cells in Luo et al. exhibited unusually high baseline apoptosis approaching 50%); however, it is also possible that the role of HNF1A may differ between different molecular subtypes of PDA (Bailey et al., 2016) or in a dynamic manner like fellow transcription factor PDX1 (Roy et al., 2016). Supporting the former, HNF1A expression has been proposed as a biomarker to distinguish between the exocrine/ADEX subtype (HNF1A high/KRT81 low) and the quasi-mesenchymal/squamous/basal-like subtype (HNF1A low/KRT81 high) (Muckenhuber et al., 2018; Noll et al., 2016), and supports previous observations that the quasi-mesenchymal/squamous/basal-like subtype is associated with poorer prognosis and drug resistance (Bailey et al., 2016; Collisson et al., 2011; Moffitt et al., 2015). Although these studies suggest that HNF1A expression may be highest in the exocrine/ADEX subtype of PDA, HNF1A function was not specifically examined. It is possible that like other pancreas-lineage transcription factors, such as PDX1 (Roy et al., 2016) and FOXA1 (Roe et al., 2017), HNF1A is associated with subtypes of PDA that retain elements of pancreatic identity (classical and exocrine/ADEX), but are nonetheless important maintenance of the disease. Interestingly, Noll et al. demonstrated that high expression of CYP3A5 in the exocrine/ADEX subtype mediates resistance to tyrosine kinase inhibitors and paclitaxel. Our work identifies CYP3A5 as a direct target of HNF1A, suggesting that HNF1A may play a direct role in drug resistance in PDA, and future studies should explore this possibility.

While we found an association between HNF1A upregulated genes and poor patient survival, we did not observe a significant association between HNF1A mRNA expression and survival (p=0.7017). As the promoters of HNF1A upregulated genes were enriched for transcription factor known to play roles in PDA including GATA (likely GATA5 or GATA6) (Roe et al., 2017; Martinelli et al., 2017; Zhong et al., 2011), PDX1 (Roy et al., 2016), and SOX9 (Camaj et al., 2014; Kopp et al., 2012; Tsuda et al., 2018), it is possible that HNF1A may work in concert with other transcription factors to elicit its full oncogenic function in PDA. A similar interaction between the transcription factors Foxa1 and Gata5 was recently described in driving metastasis in murine models of PDA (Roe et al., 2017).

Our data on HPDE and HPNE cells support a partially transforming capacity for HNF1A, wherein it overcomes contact-inhibition and anchorage-dependent growth. As cooperation with oncogenic KRAS was observed in these cells, it is feasible that HNF1A provides additional oncogenic input, possibly by altering the differentiation state of KRAS-mutant, precancerous pancreatic cells or by expanding the resident stem cell/cancer stem cell population. Indeed, expression of HNF1A alone was sufficient to increase CD24 expression/positivity in both HPDE and HPNE cells.

Typically a marker of endodermal differentiation, HNF1A has not previously been reported as necessary for normal or cancer stem cells. HNF1A plays a critical role in the normal functionality of the endocrine pancreas, with hereditary inactivating mutations in the gene and promoter region resulting in MODY3, an autosomal dominant form of diabetes resulting from β cell insufficiency. Additionally, murine knockout models recapitulate the diabetic phenotype seen in humans (Lee et al., 1998), with elegant transcriptomic work demonstrating a requirement for murine Hnf1a in β cell proliferation (Servitja et al., 2009). The role for HNF1A in the exocrine pancreas is less clear, and compared to islet and liver cells in the latter study, we only identified 11 overlapping HNF1A upregulated genes (ANXA4, CEACAM1, CHKA, DPP4, HNF4A, HSD17B2, LGALS3, MTMR11, NR0B2, SLC16A5, TM4SF4), suggesting distinct activity for HNF1A in PDA compared to either β cells or the liver. Regulation of POU5F1/OCT4 transcription by HNF1A is an especially exciting finding, connecting HNF1A with a previously unidentified role in regulating stemness. Our study identifies a recently described LTR promoter region (Malakootian et al., 2017), upstream from the canonical POU5F1/OCT4 promoter, as a likely region of direct transcriptional regulation of POU5F1/OCT4 by HNF1A, supported by both ChIP and reporter assays (Figure 6—figure supplement 1B,D). As this promoter region, is not conserved between humans and rodents, it is possible the interaction between HNF1A and POU5F1/OCT4 is an acquisition of human evolution and may explain why POU5F1/OCT4 has not previously been identified as an HNF1A target. Interestingly, a recent study SPINK1-positive castrate resistant prostate cancer identified POU5F1/OCT4 as part of a gastrointestinal gene signature present in SPINK1-positive prostate cancer and regulated by HNF1A and its target gene HNF4G (Shukla et al., 2017). Consistent with our findings, this study showed downregulation/upregulation of POU5F1/OCT4 mRNA in response to HNF1A knockdown/overexpression, respectively. While direct regulation of POU5F1/OCT4 and HNF1A was not explored in this study, it does support an association between these two transcription factors, not only in gastrointestinal cells, but other cancers as well. This could indicate a more general role for HNF1A in regulating stem cell properties in human cells in which it is normally expressed.

Given that HNF1A upregulated genes were found to be significantly associated with poor survival in patients with PDA, it is likely that multiple target genes contribute to HNF1A’s oncogenic influence, and future studies should be done to assess the functions of these genes in PDA to ascertain their value as either potential biomarkers or therapeutic targets. Further studies are also needed in regards to HNF1A’s role in the exocrine pancreas and whether its function is redirected during the development of PDA, particularly under the influence of oncogenic KRAS. Overall, this study further validates the importance of HNF1A to PDA while providing a novel and critical role for HNF1A in driving pancreatic cancer stem cell properties.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
Gene (Human)HNF1AThis paperCloned from NY5 cDNA
Gene
(Escherichia coli)
LacZInvitrogenOriginally from Catalog
number: K499000
Subcloned into
pLentipuro3/TO/V5-DEST
Gene
(Aequorea victoria)
PatGFPThis paperVariant of EGFP containing
the following mutations:
S31R, Y40N, S73A, F100S,
N106T, Y146F, N150K, M154T,
V164A, I168T, I172V, A207V
Gene (Human)KRAS G12DThis paperCloned from NY5 cDNA
Gene (Human)POU5F1 (OCT4A)Transomic
Technologies
Catalog number:
BC117435
Subcloned into pLenti6.3
/UbC/V5-DEST
Gene
(Escherichia coli)
LacZ2.1 shRNAThis paperSequence: CACCAAATCGCTGATTT
GTGTAGTCGTTCAAGAGACGACT
ACACAAATCAGCGA
Gene (Human)HNF1A shRNA#1This paperSequence: CACCGCTAGTGGAGGA
GTGCAATTTCAAGAGAATTGCACTC
CTCCACTAGC
Gene (Human)HNF1A shRNA#2This paperSequence: CACCGTCCCTTAGTGA
CAGTGTCTATTCAAGAGATAGA
CACTGTCACTAAGGGAC
Gene
(Escherichia coli)
IVS-TetR-P2A-BsdThis paperIVS-TetR and Bsd were
subcloned from pLenti6/TR
(Invitrogen) with a P2A
peptide linker added by PCR
and Gibson Assembly
Gene
(Aequorea victoria)
PatGFP-Luc2This paperPatGFP and Luc2 (Promega)
were amplified by PCR and
fused by Gibson Assembly
Strain, strain
background
(Mouse)
NOD.CB17-Prkdcscid/JThe Jackson
Laboratory
Catalog number:
001303; RRID:
IMSR_JAX:001303
Cell line (Human)HPDECraig Logsdon,
MD Anderson
Cell line (Human)HPNEATCCCatalog number:
ATCC CRL-4023;
RRID:CVCL_C466
Cell line (Human)Capan-2ATCCCatalog number:
ATCC HTB-80;
RRID:CVCL_0026
Cell line (Human)HPAF-IIATCCCatalog number:
ATCC CRL-1997;
RRID:CVCL_0313
Cell line (Human)BxPC-3ATCCCatalog number:
ATCC CRL-1687;
RRID:CVCL_0186
Cell line (Human)AsPC-1ATCCCatalog number:
ATCC CRL-1682;
RRID:CVCL_0152
Cell line (Human)MiaPaCa-2ATCCCatalog number:
ATCC CRL-1420;
RRID:CVCL_0428
Cell line (Human)Panc-1ATCCCatalog number:
ATCC CRL-1469;
RRID:CVCL_0480
Cell line (Human)NY1This paperLow passage pancreatic
adenocarcinoma patient
primary cell line
established from xenograft
Cell line (Human)NY2This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY3This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY5This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY6This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY8This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY9This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY12This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY15This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY16This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY17This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY19This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY28This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY32This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)NY53This paperLow passage pancreatic
adenocarcinoma patient
primary cell line established
from xenograft
Cell line (Human)293FTInvitrogenCatalog number:
R70007
Transfected
construct (Gaussia)
pTK-GDLucThis paperThe Gaussia coding region
of pTK-Gluc (New England
Biolabs) was replaced with
the Gaussia Dura coding
region (Millipore)
Transfected
construct (Cypridina)
pCLuc-Basic2New England
Biolabs
Catalog number:
N0317S
Transfected
construct (Cypridina)
pCLuc-Basic2/OCT4
LTR promoter
This paper1.7 kbp OCT4 LTR
promoter region from NY5
was subcloned into
pCLuc-Basic2
Transfected
construct (Cypridina)
pCLuc-Basic2/OCT4
canonical promoter
This paper/AddgeneOriginally from
Catalog number:
38776
OCT4 promoter from
phOct4-EGFP (Addgene)
was subcloned into
pCLuc-Basic2
AntibodyCD326 (EpCAM)-FITCMiltenyi BiotecCatalog number:
130-113-263;
RRID:AB_2726064
Application:
flow cytometry
AntibodyBD Pharmingen APC
Mouse Anti-Human
CD44
BD BiosciencesCatalog number:
559942;
RRID:AB_398683
Application:
flow cytometry
AntibodyBD Pharmingen
PE Mouse Anti-Human
CD24
BD BiosciencesCatalog number:
555428;
RRID:AB_395822
Application:
flow cytometry
AntibodyH-2Kd/H-2Dd
clone 34-1-2S
SouthernBiotechCatalog number:
1911–08;
RRID:AB_1085008
Application:
flow cytometry
AntibodyAnti-HNF1 antibody
[GT4110]
AbcamCatalog number:
ab184194;
RRID:AB_2538735
Application:
IHC, Western blot
AntibodyHNF-1 alpha Antibody
(C-19)
Santa Cruz
Biotechnology
Catalog number:
sc-6547;
RRID:AB_648295
ChIP
AntibodyNormal Rabbit IgGCell Signaling
Technology
Catalog number:
2729S;
RRID:AB_1031062
ChIP
AntibodyHNF1α (D7Z2Q)Cell Signaling
Technology
Catalog number:
89670S;
RRID:AB_2728751
Application:
Western blot
Antibodyβ-Actin (clone AC-74)Sigma AldrichCatalog number:
A2228-200UL;
RRID:AB_476697
Application:
Western blot
AntibodyCDH17 antibodyProteintechCatalog number:
50-608-369;
RRID:AB_2728752
Application:
Western blot
AntibodyDPP4/CD26 (D6D8K)Cell Signaling
Technology
Catalog number:
67138S;
RRID:AB_2728750
Application:
Western blot
AntibodyCD44 (156–3 C11)Cell Signaling
Technology
Catalog number:
3570S;
RRID:AB_10693293
Application:
Western blot
AntibodyEpCAM (D1B3)Cell Signaling
Technology
Catalog number:
2626S;
RRID:AB_2728749
Application:
Western blot
AntibodyCleaved Caspase-3
(Asp175) (5A1E)
Cell Signaling
Technology
Catalog number:
9664S;
RRID:AB_2070042
Application:
Western blot
AntibodyCleaved Caspase-6
(Asp162)
Cell Signaling
Technology
Catalog number:
9761S;
RRID:AB_2290879
Application:
Western blot
AntibodyCleaved Caspase-7
(Asp198) (D6H1)
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AntibodyCleaved Caspase-9
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AntibodyGFP (D5.1) XPCell Signaling
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14429S;
RRID:AB_2728748
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AntibodyPhospho-p44/42 MAPK
(Erk1/2)
(Thr202/Tyr204)
(D13.14.4E) XP
Cell Signaling
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Catalog number:
4370S;
RRID:AB_2315112
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(D9E) XP
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Catalog number:
4060S;
RRID:AB_2315049
Application:
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AntibodyOct-4A (C52G3)Cell Signaling
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Catalog number:
2890S;
RRID:AB_2167725
Application:
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AntibodyAnti-KRAS + HRAS +
NRAS antibody
AbcamCatalog number:
ab55391;
RRID:AB_941040
Application:
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AntibodyAnti-β-GalactosidasePromegaCatalog number:
Z3781;
RRID:AB_430877
Application:
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AntibodyIRDye 800CW Goat
anti-Mouse IgG
LicorCatalog number:
926–32210;
RRID:AB_621842
Application:
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AntibodyIRDye 800CW Goat
anti-Rabbit
LicorCatalog number:
926–32211;
RRID:AB_621843
Application:
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AntibodyIRDye 680LT goat
anti-mouse
LicorCatalog number:
926–68020;
RRID:AB_10706161
Application:
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AntibodyIRDye 680LT Goat
anti-Rabbit IgG
LicorCatalog number:
926–68021;
RRID:AB_10706309
Application:
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Recombinant
DNA reagent
pLentipuro3/TO/
V5-DEST
Andrew E. Aplin,
Thomas Jefferson
University
Recombinant
DNA reagent
pLentineo3/TO/
V5-DEST
Andrew E. Aplin,
Thomas Jefferson
University
Recombinant
DNA reagent
pLentihygro3/TO/
V5-DEST
Andrew E. Aplin,
Thomas Jefferson
University
Recombinant
DNA reagent
pLenti0.3/EF/
V5-DEST
This paperHuman EF1-alpha
promoter was substituted
for the CMV promoter of
pLenti6.3/UbC/V5-DEST
and the SV40 promoter/Bsd
cassette was removed
Recombinant
DNA reagent
pLenti6.3/UbC/
V5-DEST
Andrew E. Aplin,
Thomas Jefferson
University
Recombinant
DNA reagent
pLenti6.3/UbC
empty vector
This paperEcoRV digest/re-ligation
to remove Gateway element
Recombinant
DNA reagent
pLentipuro3/
Block-iT-DEST
Andrew E. Aplin,
Thomas Jefferson
University
Recombinant
DNA reagent
pLenti0.3/EF/GW/IVS
-Kozak-TetR-P2A-Bsd
This paperLR recombination of
IVS-TetR-P2A-Bsd cassette
into pLenti0.3/EF/V5-DEST
Recombinant
DNA reagent
pLenti0.3/EF/GW/
PatGFP-Luc2
This paperLR recombination of
PatGFP-Luc2 cassette
into pLenti0.3/EF/V5-DEST
Recombinant
DNA reagent
pLP1Andrew E. Aplin,
Thomas Jefferson
University
Lentivirus packaging
plasmid originally from
Invitrogen
Recombinant
DNA reagent
pLP2Andrew E. Aplin,
Thomas Jefferson
University
Lentivirus packaging
plasmid originally from
Invitrogen
Recombinant
DNA reagent
pLP/VSVGAndrew E. Aplin,
Thomas Jefferson
University
Lentivirus packaging
plasmid originally from
Invitrogen
Sequence-based
reagent
Non-targeting
control siRNA
DharmaconCatalog number:
D-001810-01-20
Sequence-based
reagent
HNF1A siRNA #1DharmaconCatalog number:
D-008215-01-0002
Sequence:
GGAGGAACCGTTTCAAGTG
Sequence-based
reagent
HNF1A siRNA #2DharmaconCatalog number:
D-008215-02-0002
Sequence:
GCAAAGAGGCACTGATCCA
Sequence-based
reagent
POU5F1/OCT4
siRNA #5
DharmaconCatalog number:
D-019591-05-0002
Sequence:
CATCAAAGCTCTGCAGAAA
Sequence-based
reagent
POU5F1/OCT4
siRNA #6
DharmaconCatalog number:
D-019591-06-0002
Sequence:
GATATACACAGGCCGATGT
Sequence-based
reagent
POU5F1/OCT4
siRNA #9
DharmaconCatalog number:
D-019591-09-0002
Sequence:
GCGATCAAGCAGCGACTAT
Sequence-based
reagent
POU5F1/OCT4
siRNA #10
DharmaconCatalog number:
D-019591-10-0002
Sequence:
TCCCATGCATTCAAACTGA
Peptide,
recombinant protein
Recombinant
human EGF
InvitrogenCatalog number:
PHG0311L
Peptide,
recombinant protein
FGF-basic
Recombinant
Human
InvitrogenCatalog number:
PHG0264
Peptide,
recombinant protein
Leukemia
Inhibitory Factor
human
Sigma AldrichCatalog number:
L5283
Peptide,
recombinant protein
Bone Morphogenetic
Protein four human
PeprotechCatalog number:
120–05
Commercial
assay or kit
SimpleChIP Enzymatic
Chromatin IP Kit
(Magnetic Beads)
Cell Signaling
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Catalog number:
9003
Commercial
assay or kit
BioLux Gaussia
Luciferase Assay Kit
New England
Biolabs
Catalog number:
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assay or kit
BioLux
Cypridina Luciferase
Assay Kit
New England
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Catalog number:
E3309S
Commercial
assay or kit
RNeasy Plus Mini
Kit coupled with
RNase-free DNase set
QiagenCatalog number:
74136 and 79254
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assay or kit
High Capacity
RNA-to-cDNA Master
Mix
Applied
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Catalog number:
4387406
Commercial
assay or kit
Power SYBR Green
PCR Master Mix
Applied
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Catalog number:
4367659
Chemical
compound, drug
APC-Cy7 StreptavidinBD BiosciencesCatalog number:
554063
Chemical
compound, drug
DAPI
(4',6-Diamidino-2-Phenylindole,
Dilactate)
InvitrogenCatalog number:
3571
Chemical
compound, drug
APC Annexin VBD BiosciencesCatalog number:
550474
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compound, drug
Annexin V Binding
Buffer, 10x concentrate
BD BiosciencesCatalog number:
556454
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compound, drug
RNase AInvitrogenCatalog number:
12091021
Chemical
compound, drug
Lipofectamine 2000
Reagent
InvitrogenCatalog number:
11668019
Chemical
compound, drug
Lipofectamine RNAiMAX
Reagent
InvitrogenCatalog number:
13778150
Chemical
compound, drug
Propidium iodideInvitrogenCatalog number:
P1304MP
Chemical
compound, drug
GentamicinInvitrogenCatalog number:
15710072
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Antibiotic-Antimycotic (100X)InvitrogenCatalog number:
15240062
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N-2 Supplement (100X)InvitrogenCatalog number:
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B-27 Serum-Free
Supplement (50X)
InvitrogenCatalog number:
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Chemical
compound, drug
DoxycyclineSigma AldrichD9891-100G
Software,
algorithm
GraphPad Prism 6GraphPad Software;
http://www.graphpad.com
RRID:SCR_002798
Software,
algorithm
oPOSSUM 3.0http://opossum.cisreg.ca/oPOSSUM3/;
PMID: 22973536
RRID:SCR_010884
Software,
algorithm
Bowtie v1.1.1PMID: 19261174RRID:SCR_005476
Software,
algorithm
Bowtie v0.12.8PMID: 19261174RRID:SCR_005476
Software,
algorithm
MACS v1.4.2PMID: 18798982RRID:SCR_013291
Software,
algorithm
TopHat v1.4.1PMID: 19289445RRID:SCR_013035
Software,
algorithm
DESeq v1.24.0PMID: 20979621RRID:SCR_000154
Software,
algorithm
bedtools v.2.26.0PMID: 20110278RRID:SCR_006646
Software,
algorithm
survival v2.40–1DOI: 10.1007/978-1-4757-3294-8

Tumor growth assays

Eight- to 10-week-old, evenly sex-mixed NOD/SCID mice were used for all experiments. Orthotopic implantation of PDA cells to the pancreas has previously been described (Abel et al., 2014). Briefly, mice were anesthetized with an intraperitoneal injection of 100 mg/kg ketamine/5 mg/kg xylazine, and a small left subcostal incision was performed. 10,000 PatGFP-Luc2-labeled tumor cells in a volume of 50 µl (1:1 vol of cell suspension in growth media and Matrigel) were injected into the tail of the pancreas using a 30-gauge needle. Weekly bioluminescent imaging of implanted orthotopic tumors in mice was performed using a Xenogen IVIS 200 Imaging System (Xenogen Biosciences, Cranbury, NJ). For subcutaneous implantation of tumor cells, 10,000 tumor cells in a volume of 50 µl (1:1 vol of cell suspension in growth media and Matrigel) was injected subcutaneously into both the left and right midflank regions of mice. Tumor growth was monitored weekly by digital caliper and tumor volumes calculated by the (length x width2)/2 method. All mice were sacrificed once any tumors reached 20 mm3 in volume.

Immunofluorescence and immunohistochemistry

Formalin-fixed, paraffin-embedded tumor samples were sectioned and processed for immunofluorescent staining by the University of Michigan ULAM Pathology Cores for Animal Research. Immunohistochemistry was performed using a Ventana BenchMark Ultra autostainer. HNF1A antibody (GT4110) was used for immunohistochemistry at a 1:100 dilution. A PDA/normal pancreas tissue microarray was generated by the University of Michigan Department of Pathology.

Microscopy

All microscopies were performed on an Olympus IX83 motorized inverted microscope with cellSens Dimension software (Olympus Corporation, Waltham, MA).

Lentiviral constructs

Lentiviral destination vectors were generously provided by Dr. Andrew Aplin (Thomas Jefferson University). For construction of HNF1A, KRASG12D, GFP and LacZ cDNA lentiviruses, pLentipuro3/TO/V5-DEST, pLentineo3/TO/V5-DEST, pLentihygro3/TO/V5-DEST were used. For OCT4A, pLenti6.3/UbC/V5-DEST was used. An EcoRV digested/re-ligated pLenti6.3/UbC/V5-DEST (removing the Gateway cloning element) was used as an empty vector control. For construction of shRNA lentiviruses, pLentipuro3/BLOCK-iT-DEST was used. Human HNF1A and KRASG12D were cloned from primary PDA cDNA into pENTR/D-TOPO (Invitrogen). Human OCT4A was cloned from pCR4-TOPO clone BC117435 (Transomic Technologies) into pENTR/D-TOPO. LacZ and PatGFP (a variant of EGFP containing the following mutations: S31R, Y40N, S73A, F100S, N106T, Y146F, N150K, M154T, V164A, I168T, I172V, A207V) were also cloned into pENTR/D-TOPO as control proteins. For labeling cells with firefly luciferase, PatGFP was fused to the N-terminus of firefly luciferase Luc2 (subcloned from pGL4.10) and cloned into pENTR/D-TOPO using Gibson Assembly (New England Biolabs). PatGFP-Luc2 was recombined into pLenti0.3/EF/V5-DEST, a modified version of pLenti6.3/UbC/V5-DEST with the human EF-1α promoter instead of the human UbC promoter and no downstream promoter/selective marker cassette, to generate pLenti0.3/EF/GW/PatGFP-Luc2. To generate doxycycline-inducible cell lines, a cassette containing the IVS-TetR region from pLenti6/TR (Invitrogen) was subcloned into pLenti0.3/EF/V5-DEST, along with a C-terminal P2A peptide-blasticidin resistance gene (Bsd) reading frame to generate pLenti0.3/EF/GW/IVS-Kozak-TetR-P2A-Bsd. The resultant lentiviruses were used to transduce NY8, NY15, NY53, and HPDE to generate doxycycline-inducible ‘TR’ lines. To generate the HNF1A-responsive reporter, the multiple cloning site and minimal promoter from pTA-Luc (Takara, Mountain View, CA) was subcloned upstream of PatGFP. Eight tandem repeats of the HNF1A-binding site with spacer nucleotides (CTTGGTTAATGATTAACCAGA) was cloned between the MluI and BglII sites of the multiple cloning site. LacZ2.1 (CACCAAATCGCTGATTTGTGTAGTCGTTCAAGAGACGACTACACAAATCAGCGA), HNF1A shRNA#1 (CACCGCTAGTGGAGGAGTGCAATTTCAAGAGAATTGCACTCCTCCACTAGC), and HNF1A shRNA#2 (CACCGTCCCTTAGTGACAGTGTCTATTCAAGAGATAGACACTGTCACTAAGGGAC) were cloned into pENTR/H1/TO (Invitrogen). cDNA and shRNA constructs were recombined into their respective lentiviral plasmids using LR Clonase II (Invitrogen). The resulting constructs were packaged in 293FT cells as previously described.

siRNA sequences

Non-targeting control (Cat#D-001810–01)

HNF1A-targeting siRNA#1 (GGAGGAACCGTTTCAAGTG)

HNF1A-targeting siRNA#2 (GCAAAGAGGCACTGATCCA)

POU5F1/OCT4-targeting siRNA#5 (CATCAAAGCTCTGCAGAAA)

POU5F1/OCT4-targeting siRNA#6 (GATATACACAGGCCGATGT)

POU5F1/OCT4-targeting siRNA#9 (GCGATCAAGCAGCGACTAT)

POU5F1/OCT4-targeting siRNA#10 (TCCCATGCATTCAAACTGA)

Cell lines

HPDE cells were a generous gift from Dr. Craig Logsdon (MD Anderson). HPNE, Capan-2, HPAF-II, BxPC-3, AsPC-1, Panc-1, and MiaPaCa-2 cells were purchased from ATCC (Manassas, VA). For all low-passage human primary PDA cells, primary PDA xenograft tumors were cut into small pieces with scissors and then minced completely using sterile scalpel blades. Single cells were obtained described previously (Li et al., 2007). The cells used in this article are passaged less than 10 times in vitro. All cells were authenticated by STR profiling (University of Michigan DNA Sequencing Core). Cells were routinely tested for mycoplasma contamination using the MycoScope PCR Detection kit (Genlantis, San Diego, CA) and only mycoplasma-free cells were used for experimentation. ATCC and primary PDA cells were cultured in RPMI-1640 with GlutaMAX-I supplemented with 10% FBS (Gibco), 1% antibiotic-antimycotic (Gibco), and 100 µg/ml gentamicin (Gibco). HPDE cells were maintained in keratinocyte SFM supplemented (Invitrogen) with included EGF and bovine pituitary extract as well as 1% antibiotic-antimycotic and 100 µg/ml gentamicin.

Soft agar assays

Low-melting agarose (Invitrogen) was dissolved in serum-free RPMI-1640 with GlutaMAX-I to a final concentration of 2% at 60°C and cooled to 42°C. 200 µL per well 2% agarose was evenly spread at the bottom of a 24-well dish, followed by 250 µL of 0.6% agarose (diluted with complete keratinocyte SFM and supplemented with FBS to 2.5%), a 250 µL of 0.4% agarose/cell suspension, and a 250 µL of acellular 0.4% agarose. Each layer was allowed to solidify a 4°C for 10 min and then heated to 37°C prior to adding the next layer. 500 µl of complete keratinocyte SFM and supplemented with 2.5% FBS was added atop each gel and replenished every 3 days.

Flow cytometry

Flow cytometry was performed as described previously (Li et al., 2007). Cells were dissociated with 2.5% trypsin/EDTA solution, counted and transferred to 5 mL tubes, washed with HBSS supplemented with FBS twice and resuspended in HBSS/2% FBS at a concentration of 1 million cells/100 µL. Primary antibodies were diluted 1:40 in cell suspensions and incubated for 30 min on ice with occasional vortexing. Cells were washed twice with HBSS/2% FBS and incubated for 20 min on ice with APC-Cy7 Streptavidin diluted 1:200. Cells were washed twice with HBSS/2% FBS and resuspended in HBSS/2%FBS containing 3 µM 4',6-diamidino-2-phenylindole (DAPI) (Invitrogen, Carlsbad, CA). Flow cytometry and sorting was done using a FACSAria (BD Biosciences, Franklin Lakes, NJ). Side scatter and forward scatter profiles were used to eliminate cell doublets, APC-Cy7 was used to exclude mouse cells. For PatGFP-Luc2 labeling, GFP+/DAPI- cells were isolated by sorting and expanded for one passage prior to implantation. For analysis of apoptosis, APC-conjugated Annexin V and Annexin V binding buffer (BD Biosciences) was used following manufacturer’s recommendations with 3 µM DAPI added immediately before analysis to stain permeable cells/necrotic debris.

Propidium iodide staining

Cells were trypsinized, washed in PBS and fixed in 70% ethanol for 4 hr. Cells were then permeabilized with PBS containing 0.1% Triton X100 and 200 µg/ml RNase A for 2 hr at 37°C and stained with 167 µg/ml propidium iodide for 30 min. DNA content was measured by flow cytometry on a CytoFLEX flow cytometer (Beckman Coulter) and analyzed Summit v6.2 software (Beckman Coulter).

Microarray analysis

Flow sorted NY8 and NY15 P1, P2, and P3 cells were immediately used for RNA isolation using the RNeasy Plus Mini Kit coupled with RNase-free DNase set (Qiagen). Microarrays and analyses were performed by the University of Michigan DNA Sequencing Core. RNA labeling and hybridization was conducted using the Human Genome U133 Plus 2.0 microarray (Affymetrix, Santa Clara, CA). Probe signals were normalized and corrected according to background signal. Adjusted signal strength was used to generate quantitative raw values, which were log-transformed for all subsequent analyses.

Transcription-factor-binding site analysis

For both the PCSC-enriched genes (related to Figure 1) and the HNF1A target genes (related to Figure 7), oPOSSUM 3.0 (http://opossum.cisreg.ca/oPOSSUM3/) (Kwon et al., 2012) was used to detect over-represented conserved transcription factor binding sites. The program was run using the following options: conservation cutoff of 0.4, matrix score threshold of 85%, and search region of 5 kbp, upstream and downstream of the transcription start site. The query was entered against a background of 24,752 genes in the oPOSSUM database.

Quantitative reverse transcription-PCR (qRT-PCR)

Total RNA was extracted using RNeasy Plus Mini Kit coupled with RNase-free DNase set (Qiagen) and reverse transcribed with High Capacity RNA-to-cDNA Master Mix

(Applied Biosystem). The resulting cDNAs were used for PCR using Power SYBR Green PCR Master Mix (Applied Biosystem) in triplicates. qPCR and data collection were performed on a ViiA7 Real-Time PCR system (Invitrogen). Conditions used for qPCR were 95°C hold for 10 min, 40 cycles of 95°C for 10 s, 60°C for 15 s, and 72°C for 20 s. All quantitations were normalized to an endogenous control ACTB. The relative quantitation value for each target gene compared to the calibrator for that target is expressed as 2-(Ct-Cc) (Ct and Cc are the mean threshold cycle differences after normalizing to ACTB).

Tumorsphere cultures

Single cells were suspended in tumorsphere culture media containing 1% N2 supplement, 2% B27 supplement, 1% antibiotic-antimycotic, 20 ng/mL epidermal growth factor (Gibco, Carlsbad, CA), 20 ng/mL human bFGF-2 (Invitrogen), 10 ng/mL BMP4 (Sigma-Aldrich, St. Louis, MO), 10 ng/mL LIF (Sigma-Aldrich) and plated in six-well Ultra-Low Attachment Plates (Corning, Corning, NY).

siRNA transfection siRNA were purchased from Dharmacon (Lafayette, CO) and were transfected at 25 nM using Lipofectamine RNAiMAX Reagent (Invitrogen). siRNA sequences can be found in the Supplementary Material and methods.

Western blotting

All lysates were boiled in 1x Laemmli sample buffer with β-mercaptoethanol for 5 min followed by electrophoresis on 4–20% Mini-PROTEAN TGX precast Tris-Glycine-SDS gels (Bio-Rad, Hercules, CA). Proteins were transferred to low-fluorescent PVDF (Bio-Rad) and incubated overnight in primary antibody at 1:1000 dilution. Blots were incubated in IRDye-conjugated secondary antibodies at room temperature for 1 hr and imaged/quantitated by an Odyssey CLx imaging system (Li-Cor, Lincoln, NE). For western blotting, HNF1A (clone GT4110) and KRAS (ab55391) from Abcam (Cambridge, MA), β-Actin (clone AC-74) from Sigma-Aldrich, Cadherin-17 (CDH17) from Proteintech (Rosemont, IL), β-Galactosidase from Promega (Madison, WI) and RASG12D, CD44, EPCAM, DPP4, Cleaved Caspase-3 (D175), Cleaved Caspase-6 (D162), Cleaved Caspase-7 (D198), Cleaved Caspase-9 (D315), Cleaved Caspase-9 (D330), phospho-ERK1/2, phospho-AKT S473, OCT4A and GFP from Cell Signaling Technology (Danvers, MA). For flow cytometry, mouse anti-human EPCAM (CD326) clone HEA-125 was purchased from Miltenyi Biotec (San Diego, CA). Mouse anti-human CD44 clone G44-26, CD24 clone ML5 and APC-Cy7 Streptavidin were purchased from BD Biosciences (San Jose, CA). Biotinylated mouse anti-mouse H-2Kd/H-2Dd clone 34-1-2S was purchased from SouthernBiotech (Birmingham, AL).

Reporter assays

For the Cypridina luciferase construct containing the full-length canonical OCT4 promoter, a 3.9-kbp insert was excised from phOct4-EGFP (Gerrard et al., 2005) by XhoI and BamHI digestion, followed by ligation into pCLuc-Basic2 (New England Biolabs). phOct4-EGFP was a gift from Wei Cui (Addgene plasmid # 38776). For the POU5F1/OCT4 LTR construct, a 1.7-kbp insert was amplified from NY5 genomic DNA with the following primers: 5’-ATCTTGGAATTCTGGGCACTCAGTTTATTGTTAGG-3’ and 5’-GGTGGCGGATCCTGTGTTAATCCTCCTCGGGG-3’. The insert was digested with EcoRI and BamHI and cloned into pCLuc-Basic2. Cypridina luciferase constructs were co-transfected with Lipofectamine 2000 (Invitrogen) into 293FT cells with either LacZ or HNF1A lentiviral expression plasmids and the internal control plasmid pTK-GDLuc, a variant of pTK-GLuc (New England Biolabs) in which the Gaussia luciferase coding region was replaced with the coding region for Gaussia-Dura (Millipore) in order to provide a more stable luciferase signal. Cypridina and Gaussia-Dura luciferase activities were measured in conditioned media 48 hr post-transfection with the BioLux Cypridina Luciferase and BioLux Gaussia Luciferase Assay Kits (New England Biolabs), respectively.

Chromatin immunoprecipitation sequencing (ChIP-seq)

A confluent 15 cm culture plate of cells was used per immunoprecipitation. Cells were fixed with 1% formaldehyde for 10 min. Nuclei were collected and chromatin sheared to 1–10 nucleosomes using the SimpleChIP Plus Enzymatic Chromatin IP kit and protocol (Cell Signaling). HNF1A was immunoprecipitated with goat polyclonal antibody C-19 (Santa Cruz). Libraries from HNF1A-immunoprecipitated chromatin and input chromatin was prepared by the University of Michigan Sequencing Core and sequenced on the Illumina HiSeq 4000.

Chromatin Immunoprecipitation-PCR

Chromatin was prepared as indicated for ChIP-seq and immunoprecitated with either normal goat IgG (R and D Systems) or anti-HNF1A (C-19, Santa Cruz Biotechnology) overnight using the SimpleChIP Plus Enzymatic Chromatin IP kit and protocol. Quantitative PCR was performed using immunoprecipitated DNA and 2% chromatin input DNA as described earlier for qRT-PCR using modified thermocycling conditions: 95°C hold for 10 min, 45 cycles of 95°C for 15 s and 60°C for 60 s. Percent Input for immunoprecipitated DNA was calculated using the formula 2% x 2(Ct 2% Input Sample - Ct IP Sample). Primers for POU5F1/OCT4 regulatory regions were as follows: half-site #1 (HS1) (5’-GTGAAATCTTTAGTGTTGTGAG-3’ and 5’-CCAAGAAATGTAGCAGGACGAGCCCC-3’), half-site #2 (HS2) (5’-AACCTTTTACATGAGCAGGTTTG-3’ and 5’-AATGGTGGAAAGAATTACATGG-3’), half-site #3 (HS3) (5’-GGGCACTCAGTTTATTGTTAGG-3’ and 5’-TTTCCTGTCACAGGGGTTTAGTG-3’), and distal enhancer (DE) (5’-GAGAGGCCGTCTTCTTGGCAGAC-3’ and 5’-GTTCACTTCTCGGCCTTTAACTGCCC-3’). MYOD (primers 5’-AGACTGCCAGCACTTTGCTATC-3’ and 5’-ATAGAAGTCGTCCGTTGTGGC-3’) was used as a non-HNF1A target gene control.

Bromouridine labeling and sequencing (Bru-seq)

Nascent RNA labeling and sequencing (Bru-seq) was performed as previously described (Paulsen et al., 2013). For each shRNA (LacZ2.1, HNF1A shRNA#1, and #2), two replicates were performed in each cell line (NY8 and NY15). Cells were incubated in media containing 2 mM bromouridine (Bru) (Aldrich) for 30 min at 37°C. Total RNA was isolated after lysis in Trizol and Bru-RNA was isolated using anti-BrdU antibodies conjugated to magnetic beads. Strand-specific libraries were made using the Illumina TruSeq kit and sequenced on the Illumina HiSeq 4000 platform at the University of Michigan Sequencing Core (Ann Arbor, MI). Genes were recognized as differentially expressed in both cell lines if the fold change after knockdown was greater than 1.5 (and FDR < 0.1 in NY15) and the mean RPKM for a given comparison was greater than 0.25 in either HNF1A shRNA#1 or shRNA#2 per cell line.

ChIP-seq analysis

The HNF1A ChIP-seq experiment consisted of 2 replicates each of input and ChIP libraries from both NY8 and NY15 cells (eight libraries altogether). 52-base, single end reads were aligned to the human reference genome (hg19) using Bowtie v1.1.1 (with options: -n 3 k 1 m 1). Peaks were called using MACS v1.4.2 using the default options and input samples as controls. MACS peaks overlapping ENCODE blacklist regions (https://www.encodeproject.org/annotations/ENCSR636HFF) were removed. Peak counts were 5057 (NY15 rep1), 8616 *NY15 rep2), 64603(NY8 rep1), and 13169 (NY8 rep2). Each peak was assigned to the closest expressed gene’s transcription start site (TSS). Then, for each TSS, the distance to the nearest peak was measured. If the nearest associated peak was within ±5 kb of the TSS, it was considered proximal. In the absence of a proximal peak, the nearest associated peak within ±100 kb of the TSS was considered distal. A gene was recognized as having a proximal or distal peak if at least one replicate in both cell lines identified a proximal or distal peak. If a gene was found to have both proximal and distal peaks (usually due to differences between replicates), the gene was identified as distal if it had distal peaks in both replicates of both cell lines, otherwise it was identified as neither. Manipulation of genomic regions was performed using bedtools2 (v2.26.0).

Bru-seq analysis

The HNF1A knockdown experiment used for Bru-seq consisted of a control shRNA and two different HNF1A-targeting shRNAs for each of NY8 and NY15 cells, and 2 replicates of each (12 samples altogether). 52-base, stranded, single end reads were aligned first to ribosomal DNA (U13369.1) using Bowtie v0.12.8 and the remaining reads aligned to the human reference genome (hg19) using TopHat v1.4.1. Differential gene expression analysis was performed using DESeq v1.24.0 (R v3.3.1). Gene annotation and counting was performed as previously described (Paulsen et al., 2014). Differentially expressed genes were selected based on the following criteria: mean RPKM >0.25 across samples, minimum gene length 300, absolute value of log2 fold-change >0.58 (1.5 fold-change), adjusted p value<0.1, and these requirements met for at least one HNF1A shRNA in both cell lines.

Data access

All ChIP-seq and Bru-seq data from this study are available at the NCBI Gene Expression Omnibus (GEO; accession # GSE108151).

Enhancer-related analysis

Enhancer regions used in this study were taken from the ENCODE Combined Segmentation annotation (http://hgdownload.soe.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeAwgSegmentation/) (Hoffman et al., 2013; Ernst and Kellis, 2012; Hoffman et al., 2012). Regions labeled ‘E’ (strong enhancers) were extracted from all six cell lines used in the Combined Segmentation analysis, then merged to create a set of general putative enhancer regions. The enhancer regions were then queried against peak coordinated from each list of ChIP-seq peaks (see Supplemental file 2). All genomic region manipulations were performed using bedtools2 (v.2.26.0).

Survival analysis

Gene expression and patient survival data for pancreatic adenocarcinoma were obtained through the Broad Institute TCGA Genome Data Analysis Center (PAAD cohort; 2016; Firehose stddata__2016_01_28 run; Broad Institute of MIT and Harvard; doi:10.7908/C11G0KM9). Clinical metadata were obtained from both the Merge Clinical Level one and Clinical Pick Tier 1 Level four data sets. Gene expression values were obtained from the Level 3 RSEM genes (normalized) data set and log10-transformed prior to analysis (a constant of 1 added to preserve zeros). Samples identified as primary solid tumors and of non-neuroendocrine origin were used. Specifically, samples with the following values in the ‘patient.histological_type_other’ field were rejected: ‘82463 neuroendocrine carcinoma nos’, ‘moderately differentiated ductal adenocarcinoma 60% + neuroendocrine 40%‘, ‘neuroendocrine’, ‘neuroendocrine carcinoma’, and ‘neuroendocrine carcinoma nos’. The background set of genes were defined as those with Bru-seq RPKM greater than 0.5 in at least one replicate of both NY8 and NY15 cells and which mapped to either gene symbol or entrez gene ID in the TCGA expression data. Cox proportional hazards survival models were created using the R package survival (v2.40–1). For permutation testing against a particular set of HNF1A-related genes, random sets of genes of the same size were selected from the background set and the percent of genes significantly associated with reduced or increased survival (using FDR thresholds of 0.1 and 0.25) were calculated. In order for the estimated error of the estimated p value to be less than 10% (at significant level α = 0.05), we set the number of permutations (N) to 10,000.

Other statistical analysis

The following methods are specific to analysis of the data represented in Figures 16 and Figure 1—figure supplement 2, Figure 2—figure supplement 1, Figure 3—figure supplement 1, Figure 4—figure supplement 1, Figure 6—figure supplement 1, and Figure 6—figure supplement 2. Data are expressed as the mean ±SEM. Statistically significant differences between two groups was determined by the two-sided Student t-test for continuous data, while ANOVA was used for comparisons among multiple groups. Significance was defined as p<0.05. GraphPad Prism six was used for these analyses.

Study approval

All animal protocols were approved by University Committee for the Use and Care of Animals (UCUCA) at University of Michigan. The animal welfare assurance number for this study is A3114-01. Patient samples were collected under a protocol approved by the IRB at the The University of Michigan. All patients gave informed consent. The human assurance number for this study is FWA00004969.

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Decision letter

  1. Richard M White
    Reviewing Editor; Memorial Sloan Kettering Cancer Center, United States
  2. Kevin Struhl
    Senior Editor; Harvard Medical School, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "HNF1A is a Novel Oncogene and Central Regulator of Pancreatic Cancer Stem Cells" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Kevin Struhl as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Francisco X Real (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this letter to clarify our concerns about this work.

In this manuscript, Abel et al. describe a function for HNF1A as a potential new marker of the pancreatic cancer stem cell, and provide evidence of its oncogenic behavior. They utilize a series of patient derived cell lines and in vitro assays to demonstrate its function. The authors also provide evidence that HNF1A promotes stemness features in PDA through regulation of POU5F1/OCT4. The paper presents interesting new data, providing evidence for the oncogenic role of HNF1A in pancreatic cancer including functions in enhancing tumor initiating cell activity. However, there are some very significant concerns about the inconsistencies of cell lines for given experiments, lack of statistical analyses, and the interpretation of whether HNF1A is truly a CSC marker vs. simply an oncogene. These concerns raise questions as to the robustness of the proposed model.

Major points:

1) Inconsistency in which cell lines were used:

The study uses multiple cell lines (i.e. NY5, NY8, NY15, NY90). While it is useful to have multiple cell lines, in many cases, they seem to be chosen at random and mainly to support the authors hypothesis. The significantly weakens the study. Some examples of this include:

A) In Figure 2A, there looks to be little to no expression of HNF1A in NY8 or NY15 and yet surprisingly in Figure 2B, they then do knockdowns in those cell lines which in the previous Western blot looked to barely have any expression. If NY8 has no HNF1A expression, then how did they statistically quantify the effect of knockdown?

B) Using siRNA against HNF1A (Figure 2C), the authors have shown that HNF1A-depletion strongly attenuates the growth of primary PDA cells NY5, NY8 and NY15, and produces a more modest effect on NY90. The P2 population is also reduced following HNF1A-depletion in these lines (Figure 3A), but present no statistics to support this. The authors suggest that basal HNF1A levels may predict dependency, but it is not clear whether the NY90 line has lower HNF1A protein levels than the other lines. Also, it would seem that correlating HNF1A levels specifically in the P2 population with dependency would be more consistent with the authors proposed model that HNF1A has specific functions in this sub-population of cells. These questions could be clarified by associating response to HNF1A knockdown and expression in the P2 population and by adding additional lines to the analysis.

C) In Figure 4, the authors again use the NY8 line, which shows essentially no HNF1A expression, yet that is the line they previously (in Figure 3E) used to support knockdown experiments and decreased tumorsphere formation. How do they explain this discrepancy?

D) In Figure 1—figure supplement 1, they use NY8 for Figure 1—figure supplement 1C but then NY15 for Figure 1—figure supplement 1D. Similarly, for Figure 1—figure supplement 2, they use NY15 for Figure 1—figure supplement 2D, then unclear what they used for Figure 1—figure supplement 2E.

These inconsistencies make the manuscript difficult to follow. To address this, the authors need to provide consistent data across the cell lines for each assay they have done. If a particular cell line is not appropriate, they need to explain the rationale for choosing that particular line. Moreover, it is essential that they provide statistical analysis of each assay performed.

2) HNF1A as a CSC marker versus an oncogene

It is clear from the data that HNF1A promotes the growth of PDAC cells, and thus is likely an important oncogene. However, what is less clear is whether this is truly a CSC marker versus just an oncogene promoting growth. In Figure 1C, it does not look like there is much enrichment for HNF1A in NY8 cells, and in fact the co-expression of CD44 appears lower in the P2 fraction. In Figure 1D, the DPP fraction, which they argue is a HNF1A target, does not appear especially enriched in the P2 population in the NY5 line. Along the same lines, in Figure 1—figure supplement 1, there does not appear to be any enrichment of HNF1A in the stem cell fraction in NY5 cells. In Figure 3E, couldn't the decreased tumorspheres simply be due to a lower rate of proliferation as seen in Figure 2? This does not seem to support a stem cell function as much as a proliferation function. In Figure 4C, the authors once again switch to NY15 cells for unclear reasons, but the bigger issue with Figure 4, however, is that this is not really arguing for a CSC function – it is mainly arguing that HNF1A acts as a classical oncogene – it leads to more colony formation (Figure 4F and 4G) and cooperates with KRAS (Figure 4H and 4I).

Along the same lines, in Figure 5, the authors use mouse xenografts to argue that HNF1A is a CSC marker with knockdown studies. But the assays they did in Figure 5A-C really just argues it is an oncogene. To show stem cell function, they would need to do serial transplantation and limiting dilution analysis. I am not sure exactly what the data in Figure 5C is supposed to show, since the subcutaneous tumors are not very physiologically relevant yet this is where they show the depletion of the EPCAM/CD44/CD24 population – why wasn't this done in the pancreatic tumors instead? Also, in Figure 5D, there does not appear to be much knockdown in HNF1A#2, so why do they then see this quantification in NY5 cells in Figure 5E? In Figure 6D, the authors present data that knockdown of either HNF1A or OCT4 decreases tumorsphere, but along the same lines as previously noted, are these cells just sicker and/or not proliferating as well? This is especially true for the knockdown of OCT4 – how does this affect growth or apoptosis? The photo shown in Figure 6G would suggest that the OCT4 knockdown cells are not very healthy. One way of addressing this issue is via the use of inducible shRNAs and/or cDNA rescue, to show that you can discern the growth inhibition effects versus just sick cells.

3) HNF1A/OCT4

It is important to show the data on the ChIP-PCR of HNF1A at the OCT4 promoter (mentioned in the Discussion) and provide some more detail about the putative elements involved in regulation. It would be desirable to show promoter-reporter assays at this locus to demonstrate this is really important in its function.

4) Bioinformatics issues

A more detailed description of the ChIP-seq methods (description of experiments, replicates, number of peaks, concordance of replicates, etc.) and analysis of the results of these experiments. In addition, a more detailed description of the merged results of RNA-Seq and ChIP-seq would be valuable, including motif analysis of the promoter of genes whose expression was deregulated upon HNF1A knockdown (is OCT4 motif enriched? Any other candidates? Are OCT4 targets enriched as well among the deregulated genes?). A more detailed study of the possible role of distant regulatory sites through the analysis of ENCODE data for enhancer activity would be also important to support the regulatory role on target genes.

Regarding the analysis of association with prognosis: were these analyses corrected for multiple testing? Were signatures better than individual genes? Could the data be validated in the ICGC series? These are relevant questions that should be readily addressable.

Can the authors clarify whether OCT4 expression correlates with HNF1A expression across PDA cell lines and tumors and whether OCT4 is enriched in the P2 population?

https://doi.org/10.7554/eLife.33947.041

Author response

Major points:

1) Inconsistency in which cell lines were used:

The study uses multiple cell lines (i.e. NY5, NY8, NY15, NY90). While it is useful to have multiple cell lines, in many cases, they seem to be chosen at random and mainly to support the authors hypothesis. The significantly weakens the study. Some examples of this include:

A) In Figure 2A, there looks to be little to no expression of HNF1A in NY8 or NY15 and yet surprisingly in Figure 2B, they then do knockdowns in those cell lines which in the previous Western blot looked to barely have any expression. If NY8 has no HNF1A expression, then how did they statistically quantify the effect of knockdown?

B) Using siRNA against HNF1A (Figure 2C), the authors have shown that HNF1A-depletion strongly attenuates the growth of primary PDA cells NY5, NY8 and NY15, and produces a more modest effect on NY90. The P2 population is also reduced following HNF1A-depletion in these lines (Figure 3A), but present no statistics to support this. The authors suggest that basal HNF1A levels may predict dependency, but it is not clear whether the NY90 line has lower HNF1A protein levels than the other lines. Also, it would seem that correlating HNF1A levels specifically in the P2 population with dependency would be more consistent with the authors proposed model that HNF1A has specific functions in this sub-population of cells. These questions could be clarified by associating response to HNF1A knockdown and expression in the P2 population and by adding additional lines to the analysis.

We agree that the levels of HNF1A in the cell lines used might have been confusing in the original submission, and have re-configured the figures as well as added numerous additional new western blots to demonstrate expression data in a more robust way. We have focused our studies on 3 low-passage primary human PDA lines (NY5, NY8, NY15), and have used them throughout the manuscript to maintain consistency. Additionally, quantitation of Western blots has been added throughout the manuscript to help with interpretation, including that HNF1A protein is elevated in the P2 (EPCAM+/CD44+) subpopulation and that HNF1A expression is tightly linked to PCSC marker expression.

C) In Figure 4, the authors again use the NY8 line, which shows essentially no HNF1A expression, yet that is the line they previously (in Figure 3E) used to support knockdown experiments and decreased tumorsphere formation. How do they explain this discrepancy?

We have included clear data by western blotting (Figure 2A) and mRNA expression (Figure 2—figure supplement 1A) that demonstrates expression of HNF1A in NY8 cells. Further, we have demonstrated that both knockdown (Figure 3C-E), and overexpression of HNF1A (Figure 4B and 4E) is significantly correlated with tumorsphere formation.

D) In Figure 1—figure supplement 1, they use NY8 for Figure 1—figure supplement 1C but then NY15 for Figure 1—figure supplement 1D. Similarly, for Figure 1—figure supplement 2, they use NY15 for Figure 1—figure supplement 2D, then unclear what they used for Figure 1—figure supplement 2E.

These inconsistencies make the manuscript difficult to follow. To address this, the authors need to provide consistent data across the cell lines for each assay they have done. If a particular cell line is not appropriate, they need to explain the rationale for choosing that particular line. Moreover, it is essential that they provide statistical analysis of each assay performed.

As stated above, we have streamlined our use of consistent primary PDA cell lines throughout the manuscript. In addition, we have provided statistical analysis for each assay performed throughout the manuscript.

2) HNF1A as a CSC marker versus an oncogene

It is clear from the data that HNF1A promotes the growth of PDAC cells, and thus is likely an important oncogene. However, what is less clear is whether this is truly a CSC marker versus just an oncogene promoting growth. In Figure 1C, it does not look like there is much enrichment for HNF1A in NY8 cells, and in fact the co-expression of CD44 appears lower in the P2 fraction.

We provide clear data throughout the manuscript that HNF1A is upregulated in NY8 cells in the P2 population and that this population possesses CSC functionality, including self-renewal, expression of cancer stem cell markers, sphere formation, and increased tumor initiation. In addition to the differentially expressed HNF1A mRNA quantitated in Figure 1C, quantitation of HNF1A protein levels from sorted NY5, NY8, and NY15 has been added to clarify the association of HNF1A expression and the P2 subpopulation. Cells in the P2 subpopulation express medium levels of EPCAM and CD44 when compared to the P1 and P3 (Figure 1—figure supplement 1B), and as such we have re-described the P2 cells as “CD44Med/EPCAMMed.”

In Figure 1D, the DPP fraction, which they argue is a HNF1A target, does not appear especially enriched in the P2 population in the NY5 line. Along the same lines, in Figure 1—figure supplement 1, there does not appear to be any enrichment of HNF1A in the stem cell fraction in NY5 cells.

To address the concerns regarding expression of DPP4, an additional known transcriptional target of HNF1A, we have included quantitated Western blots to support that DPP4 is most highly expressed in the P2 subpopulation (Figure 1—figure supplement 1B), similar to HNF1A and CDH17.

In Figure 3E, couldn't the decreased tumorspheres simply be due to a lower rate of proliferation as seen in Figure 2? This does not seem to support a stem cell function as much as a proliferation function.

Tumorsphere formation is a well-established in vitro measure of CSC function. In Figures 1C and 1D we show that the P1 and P3 subpopulations of PDA cells, which have reduced HNF1A expression, are also markedly reduced in their ability to form tumorspheres without any additional perturbation (i.e. HNF1A knockdown). Additionally, HNF1A expression is increased in tumorspheres (Figure 1—figure supplement 2). In Figures 3 and 4, we demonstrate that altering HNF1A expression in PDA cells results in increased or decreased tumorsphere forming activity in a manner that corresponds to HNF1A expression levels. Collectively, these data support a tight association between HNF1A levels and tumorsphere formation. Lastly, Figure 6 and Figure 6—figure supplement 2 demonstrate that OCT4 is the mechanistic link between HNF1A and sphere formation, and importantly that knockdown of OCT4 does not affect cell cycle or apoptosis in PDA cells (Figure 6—figure supplement 2).

In Figure 4C the authors once again switch to NY15 cells for unclear reasons, but the bigger issue with Figure 4, however, is that this is not really arguing for a CSC function – it is mainly arguing that HNF1A acts as a classical oncogene – it leads to more colony formation (Figure 4F and 4G) and cooperates with KRAS (Figure 4H and 4I).

The updated Figure 4 now contains overexpression of HNF1A in three primary PDA models (NY8, NY15, and NY53) throughout the figure. The purpose of using these cells, which was to test whether overexpression of HNF1A could further promote CSC-marker expression and tumorsphere formation in PDA cells with varying endogenous levels of HNF1A, is now clearly stated in the Results. The results show that HNF1A overexpression increases both CSC-marker expression and tumorsphere formation, consistent with the knockdown data in Figure 3.

We agree with the reviewer that HNF1A is capable of acting as an oncogene and have chosen this suggested designation throughout the revised manuscript, including the title. That said, our data also support that part of its oncogenic activity is through its regulation of PCSC function like other oncogenes we and others have identified, such as NOTCH (Abel et al., 2014), BMI1 (Proctor et al., 2013), c-MET (Li et al., 2011a; Li et al., 2011b), and NRAS (Li et al., 2013).

Along the same lines, in Figure 5, the authors use mouse xenografts to argue that HNF1A is a CSC marker with knockdown studies. But the assays they did in Figure 5A-C really just argues it is an oncogene. To show stem cell function, they would need to do serial transplantation and limiting dilution analysis.

We agree with the reviewer that limiting dilution analysis is a valuable assay to assess CSC function. Due to the time constraints imposed for revision, this assay was not feasible. However, to acknowledge these concerns, we have adopted the language that HNF1A acts as an oncogene that regulates PSCS properties. To support the latter claim, we demonstrate increased expression of HNF1A in a CSC population capable of self-renewal and generation of the diverse progeny observed in the original tumor sample (Figure 1 and Figure 1—figure supplement 1), enhanced expression of HNF1A in tumor-sphere forming conditions (Figure 1—figure supplement 2), the ability of HNF1A to regulation expression of CSC markers (Figures 3 and 4; and related figure supplements) and enhancement of tumor growth (Figure 5 and Figure 5—figure supplement 1). In the revised manuscript, we also provide new data (Figure 6; Figures 6—figure supplements 1 and 2) that HNF1A is linked to the stem cell protein OCT4 as a critical downstream mediator of HNF1A function, through its direct interaction of its distal promoter.

We conclude that HNF1A joins a group of oncoproteins that regulate cancer stem cell activity (e.g. c-MET, NOTCH, BMI1, etc.).

I am not sure exactly what the data in Figure 5C is supposed to show, since the subq tumors are not very physiologically relevant yet this is where they show the depletion of the EPCAM/CD44/CD24 population – why wasn't this done in the pancreatic tumors instead?

We have previously shown that assays assessing PCSC function are similar in head-to-head orthotopic and subcutaneous implantation experiments (Li et al., 2007; Li et al., 2011a), demonstrating that the subcutaneous model is equally effective as the orthotopic model in defining PCSC function. Consistent with our previous findings, both orthotopic and subcutaneous tumor assays had similar results with regards to tumor growth in this study. Additionally, orthotopic tumors were labeled with GFP-luciferase, which prevented use of flow cytometry to quantitate PCSCs. Unlabeled cells were used for the subcutaneous tumor models, which allowed for PCSC staining. The results of these two experiments are complementary.

Also, in Figure 5D there does not appear to be much knockdown in HNF1A#2, so why do they then see this quantification in NY5 cells in Figure 5E?

We have provided a clearer representative image in Figure 5D to demonstrate that the HNF1A shRNA#2 provide significant knockdown effects, and the cumulative results in Figure 5E show the effectiveness of HNF1A knockdown in depleting the CSC population in vivo(p<0.001 for both shRNAs).

In Figure 6D, the authors present data that knockdown of either HNF1A or OCT4 decreases tumorsphere, but along the same lines as previously noted, are these cells just sicker and/or not proliferating as well? This is especially true for the knockdown of OCT4 – how does this affect growth or apoptosis? The photo shown in Figure 6G would suggest that the OCT4 knockdown cells are not very healthy. One way of addressing this issue is via the use of inducible shRNAs and/or cDNA rescue, to show that you can discern the growth inhibition effects versus just sick cells.

We have added new data to show that PDA cells are responsive to OCT4 knockdown in tumorsphere assays (Figure 6E and 6I), and did not show a significant change in cell cycle (propidium iodide staining) or apoptosis (annexin V staining) following knockdown of OCT4 (Figure 6—figure supplements 2A and 2B). These data indicate that knockdown of OCT4 is not preventing tumorsphere formation via cell death or cytostasis in PDA cells. Additionally, OCT4 is a well-established stem cell regulator (Okita et al., 2007; Takahashi and Yamanaka, 2006), and has been shown to play a role in cancer stem cells as well (Lu et al., 2013; Kumar et al., 2012; Nishi et al., 2013). Our data is consistent with the canonical role of OCT4 as a key regulator of stemness, both in normal cells and in cancer.

3) HNF1A/OCT4

It is important to show the data on the ChIP-PCR of HNF1A at the OCT4 promoter (mentioned in the Discussion) and provide some more detail about the putative elements involved in regulation. It would be desirable to show promoter-reporter assays at this locus to demonstrate this is really important in its function.

We appreciate this comment and have in fact done several additional experiments to support that HNF1A binds to the OCT4 promoter. Using ChIP-PCR, we were able to demonstrate enrichment of the OCT4 LTR promoter (Malakootian et al., 2017) (Figure 6—figure supplement 1C), previously mentioned in the Discussion. Additionally, we were able to demonstrate that this region functions as an HNF1A-responsive promoter in promoter-reporter assays (Figure 6—figure supplement 1D). These data support a direct regulatory interaction between HNF1A and the OCT4 LTR promoter.

4) Bioinformatics issues

A more detailed description of the ChIP-seq methods (description of experiments, replicates, number of peaks, concordance of replicates, etc.) and analysis of the results of these experiments.

We have added more detail about the Bru-seq/ChIP-seq in the Results and Materials and methods section.

In addition, a more detailed description of the merged results of RNA-Seq and ChIP-seq would be valuable, including motif analysis of the promoter of genes whose expression was deregulated upon HNF1A knockdown (is OCT4 motif enriched? Any other candidates? Are OCT4 targets enriched as well among the deregulated genes?).

We used an overrepresentation analysis to find TF binding sites in promoters of HNF1A-responsive genes (see Figure 7D and Supplementary file 2 for the complete results). POU5F1 (OCT4) was the most highly ranked predicted TF for the HNF1A-repressed genes. OCT4 targets were indeed found among the HNF1A-repressed genes (now included in the Results).

A more detailed study of the possible role of distant regulatory sites through the analysis of ENCODE data for enhancer activity would be also important to support the regulatory role on target genes.

Using ENCODE enhancer data from 6 merged cell lines we examined ChIP-seq peak overlap with identified enhancer regions. For the HNF1A-bound genes represented in Figure 7B, we indicated the fraction of genes with enhancer-overlapping peaks. We have also added a list of merged enhancer regions and a list of peaks indicating overlap or non-overlap in the Supplementary file 2. 72.7% of HNF1A-bound genes had peaks overlapping in at least one of these putative enhancer regions, suggesting that HNF1A has significant interaction with regulatory regions.

Regarding the analysis of association with prognosis: were these analyses corrected for multiple testing?

The original analysis presented did not apply p value adjustment before selecting significant genes. In the process of revisiting this issue, we decided to use a Cox PH model for survival, since gene expression is a continuous variable and is thus well suited, instead of the median-stratification model we used previously. We present the revised analysis showing both the FDR 0.1 and 0.25 as thresholds for gene selection (Figure 7, Figure 7—figure supplement 1). The permutation tests at the different thresholds agree, suggesting the selection threshold is not critical.

Were signatures better than individual genes?

We have added additional analysis of the TCGA dataset. Permutation tests showed that HNF1A-activated genes were significantly associated with poorer outcomes versus randomly selected genes (insets, Figure 7E and Figure 7—figure supplement 1A; see Materials and methods for details). These findings further support the oncogenic role for HNF1A in PDA as a direct regulator of a set of genes associated with poor patient survival.

Could the data be validated in the ICGC series? These are relevant questions that should be readily addressable.

As suggested, we applied our survival analysis to the ICGC data set (PACA-AU cohort). For HNF1A activated/bound genes, 7 overlapping genes were identified in this manner using both the TCGA and ICGC analyses. The ICGC result contrasts the TCGA result in that genes associated with increased survival were also identified, whereas in the TCGA result, no such genes were found (Author response image 1). A likely source of this discrepancy is a general disagreement among the single-gene survival models. At p < 0.01 (highly significant) very few background genes overlapped between the two datasets (Author response image 2).

It is possible that the underlying difference between the ICGC and TCGA datasets is the relatively high tumor cellularity requirement for ICGC samples (at least 40%) (Waddell et al., 2015; Bailey et al., 2016). By contrast, TCGA also utilized samples with low tumor cellularity (Network, 2017), a common hallmark of PDA. While it is unclear that sample selection criterion accounts for this disparity between datasets, we do however believe that the usage of a more representative collection of samples by the TCGA justifies our reliance on the dataset in our study. We are receptive to alternative solutions should the reviewer disagree.

Author response image 1
Comparison of TCGA and ICGC survival analyses.
https://doi.org/10.7554/eLife.33947.034
Author response image 2
Comparison of TCGA and ICGC background gene concordance.

Overlap of all genes is on the left, “UP” refers to increased survival genes, “DN” to reduced survival genes.

https://doi.org/10.7554/eLife.33947.035

Can the authors clarify whether OCT4 expression correlates with HNF1A expression across PDA cell lines and tumors and whether OCT4 is enriched in the P2 population?

Using qRT-PCR analysis to quantitate both HNF1A and POU5F1 (OCT4) mRNA levels in 22 primary PDA cell lines as well as HPNE and HPDE cells, we found a significant positive correlation (r=0.5189, p=0.0094, Figure 6C) between the mRNA levels of both genes. Additionally, we also found a positive correlation (r=0.4064, p=8.9x10-8, Author response image 3) between HNF1A and POU5F1 mRNA levels in patient data from TCGA. We did not, however, observe a significant association between POU5F1 mRNA and any of the PDA subpopulations (Author response image 3). This would suggest that factors other than HNF1A modulate the levels of POU5F1 mRNA in different PDA subpopulations.

Author response image 3
Correlation of HNF1A and POU5F1 expression.
https://doi.org/10.7554/eLife.33947.036
https://doi.org/10.7554/eLife.33947.042

Article and author information

Author details

  1. Ethan V Abel

    1. Department of Molecular and Integrative Physiology, University of Michigan Health System, Ann Arbor, United States
    2. Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2922-617X
  2. Masashi Goto

    Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Conceptualization, Validation, Investigation, Methodology
    Competing interests
    No competing interests declared
  3. Brian Magnuson

    1. Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    2. Department of Biostatistics, School of Public Health, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Resources, Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5301-3302
  4. Saji Abraham

    Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  5. Nikita Ramanathan

    Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  6. Emily Hotaling

    Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  7. Anthony A Alaniz

    Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  8. Chandan Kumar-Sinha

    Department of Pathology, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Resources, Formal analysis, Validation
    Competing interests
    No competing interests declared
  9. Michele L Dziubinski

    1. Department of Molecular and Integrative Physiology, University of Michigan Health System, Ann Arbor, United States
    2. Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  10. Sumithra Urs

    Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  11. Lidong Wang

    1. Department of Surgery, New York University Langone Health, New York, United States
    2. Perlmutter Cancer Center, New York University Langone Health, New York, United states
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  12. Jiaqi Shi

    1. Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    2. Department of Pathology, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Resources, Investigation
    Competing interests
    No competing interests declared
  13. Meghna Waghray

    Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Supervision
    Competing interests
    No competing interests declared
  14. Mats Ljungman

    1. Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    2. Department of Radiation Oncology, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Resources, Methodology
    Competing interests
    No competing interests declared
  15. Howard C Crawford

    1. Department of Molecular and Integrative Physiology, University of Michigan Health System, Ann Arbor, United States
    2. Translational Oncology Program, University of Michigan Health System, Ann Arbor, United States
    Contribution
    Supervision, Funding acquisition, Investigation, Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
  16. Diane M Simeone

    1. Department of Surgery, New York University Langone Health, New York, United States
    2. Perlmutter Cancer Center, New York University Langone Health, New York, United states
    3. Department of Pathology, New York University Langone Health, New York, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    diane.simeone@nyumc.org
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5142-3087

Funding

American Cancer Society (127662-PF-15-033-01-DDC)

  • Ethan V Abel

Pancreatic Cancer Action Network (16-70-25-ABEL)

  • Ethan V Abel

University of Michigan Comprehensive Cancer Center (Core Grant P30 CA046592)

  • Brian Magnuson

SKY Foundation

  • Howard C Crawford
  • Diane M Simeone

Gershenson Pancreatic Cancer Fund

  • Diane M Simeone

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank the University of Michigan Flow Cytometry Core facility for assistance with performing FACS analysis and sorting, the University of Michigan DNA Sequencing Core facility for assistance ChIP-seq and microarray setup and analysis, Armand Bankhead for bioinformatic consultation, and Michelle Paulsen for preparing samples for Bru-seq. The work was supported by the Pancreatic Cancer Action Network-AACR Pathway to Leadership Grant (16-70-25-ABEL) and the American Cancer Society Postdoctoral Fellowship (127662-PF-15-033-01-DDC) (to EVA), University of Michigan Comprehensive Cancer Center Core Grant (P30 CA046592) (to BM), and the Gershenson Pancreatic Cancer Fund (DMS) and SKY Foundation (HC and DMS).

Ethics

Human subjects: Patient samples were collected under a protocol approved by the IRB at the The University of Michigan. All patients gave informed consent. The human assurance number for this study is FWA00004969.

Animal experimentation: All animal protocols were approved by University Committee for the Use and Care of Animals (UCUCA) at The University of Michigan. The animal welfare assurance number for this study is A3114-01. Every effort was made throughout this study to minimize stress to and suffering of animal subjects.

Senior Editor

  1. Kevin Struhl, Harvard Medical School, United States

Reviewing Editor

  1. Richard M White, Memorial Sloan Kettering Cancer Center, United States

Publication history

  1. Received: November 29, 2017
  2. Accepted: August 1, 2018
  3. Accepted Manuscript published: August 3, 2018 (version 1)
  4. Version of Record published: September 4, 2018 (version 2)

Copyright

© 2018, Abel et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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