Integrative genomic and functional profiling of the pancreatic cancer genome
© Shain et al.; licensee BioMed Central Ltd. 2013
Received: 19 December 2012
Accepted: 12 September 2013
Published: 16 September 2013
Pancreatic cancer is a deadly disease with a five-year survival of less than 5%. A better understanding of the underlying biology may suggest novel therapeutic targets. Recent surveys of the pancreatic cancer genome have uncovered numerous new alterations; yet systematic functional characterization of candidate cancer genes has lagged behind. To address this challenge, here we have devised a highly-parallel RNA interference-based functional screen to evaluate many genomically-nominated candidate pancreatic cancer genes simultaneously.
For 185 candidate pancreatic cancer genes, selected from recurrently altered genomic loci, we performed a pooled shRNA library screen of cell growth/viability across 10 different cell lines. Knockdown-associated effects on cell growth were assessed by enrichment or depletion of shRNA hairpins, by hybridization to barcode microarrays. A novel analytical approach (COrrelated Phenotypes for On-Target Effects; COPOTE) was used to discern probable on-target knockdown, based on identifying different shRNAs targeting the same gene and displaying concordant phenotypes across cell lines. Knockdown data were integrated with genomic architecture and gene-expression profiles, and selected findings validated using individual shRNAs and/or independent siRNAs. The pooled shRNA library design delivered reproducible data. In all, COPOTE analysis identified 52 probable on-target gene-knockdowns. Knockdown of known oncogenes (KRAS, MYC, SMURF1 and CCNE1) and a tumor suppressor (CDKN2A) showed the expected contrasting effects on cell growth. In addition, the screen corroborated purported roles of PLEKHG2 and MED29 as 19q13 amplicon drivers. Most notably, the analysis also revealed novel possible oncogenic functions of nucleoporin NUP153 (ostensibly by modulating TGFβ signaling) and Kruppel-like transcription factor KLF5 in pancreatic cancer.
By integrating physical and functional genomic data, we were able to simultaneously evaluate many candidate pancreatic cancer genes. Our findings uncover new facets of pancreatic cancer biology, with possible therapeutic implications. More broadly, our study provides a general strategy for the efficient characterization of candidate genes emerging from cancer genome studies.
KeywordsPancreatic cancer Functional genomics RNAi screen shRNA screen NUP153 KLF5
Pancreatic ductal adenocarcinoma (hereafter, pancreatic cancer) is the fourth leading cause of cancer death in the United States [1, 2]. The five-year survival rate is a dismal 5%, as effective treatment regimens are limited . A better understanding of the underlying disease biology is needed to develop new and successful treatment strategies to manage this deadly disease.
Several key molecular genetic alterations in pancreatic cancer have been identified [4, 5]. Activating mutations of KRAS occur in 95% of cases. The CDKN2A locus, encoding p16INK4A and p14ARF, which respectively intersect the Rb and p53 pathways, is homozygously deleted in 80% of tumors. TP53 is itself inactivated, usually through point mutation, in 55% of cases. SMAD4, a central mediator of TGFβ signaling, is deleted in approximately 50% of cases. Furthermore, TGFBR2, its upstream receptor, is deleted in 20% of tumors, underscoring a central importance of this signaling pathway in pancreatic cancer. MYC is amplified in approximately 30% of cases. Recently, deletions and mutations in five different subunits of the SWI/SNF chromatin remodeling complex have been found to occur in about a third of cases . However, despite what is already known, recent surveys of the pancreatic cancer genome have identified scores of additional candidate cancer genes that merit further investigation [7, 8].
With the advent of DNA microarrays and “next-generation” DNA sequencing, the field of genomics has transformed our ability to study diseases like cancer on an “omic” scale. Over the past decade, these technologies have spurred structural studies producing a compendium of cancer alterations, including DNA mutations, deletions, amplifications, and rearrangements. Yet, because of the sheer volume of data, such studies have far outpaced our ability to functionally evaluate candidate cancer genes .
The development of RNA interference (RNAi) techniques has accelerated our capacity to study knockdown phenotypes and infer the function and mechanism of disease genes . While traditionally used to characterize single genes at a time, several groups have adapted the technology to use small interfering RNA (siRNA) or short hairpin RNA (shRNA) libraries for high-throughput screens , including in pancreatic cancer [12–17]. These large-scale, highly parallel efforts provide the potential to functionally annotate genes on an “omic” scale.
Here, we describe a high-throughput functional interrogation of the pancreatic cancer genome using an shRNA-based screen. We simultaneously evaluate 185 candidate pancreatic cancer genes, nominated from genomic profiles, across 10 genetically diverse cell lines. After integrating the functional and genomic data, we further characterize nine top candidates, both uncovering new pancreatic cancer biology and validating an integrative approach for the functional annotation of cancer genomes.
Cancer cell lines were obtained directly from the American Type Culture Collection, and grown in RPMI-1640 high-glucose media (Invitrogen) supplemented with 10% fetal bovine serum (Hyclone). HPDE cells  were obtained from Dr. Ming Tsao (University of Toronto), and grown in keratinocyte serum-free media (supplemented as directed with EGF and bovine pituitary extract; Invitrogen).
Pooled shRNA lentiviral library screen
Cell lines included in shRNA screen
Number of replicate infections
Multiplicity of infection
Immortalized, non-tumorigenic pancreatic ductal epithelial cells
The lentiviral library was then used to infect target cell lines at low multiplicity of infection (average 0.3 integrants/cell; determined by flow cytometry of GFP expression from the GIPZ vector), so that most cells contained a single shRNA knocking down the expression of a single gene. Additionally, enough cells were infected to provide an average representation of approximately 1,000 lentiviral integrations (range 150–2000) for each of the 558 shRNAs in the library, mitigating potential artifacts from specific integration sites or from multiple integrations [20, 21]. To infect target cell lines, lentivirus was diluted in serum/antibiotic-free media containing 10 μg/ml polybrene (determined to optimally enhance infection with minimal cell toxicity). Cells were spun at 30°C for 1 hr at 2,400 rpm, allowed to recover for 4 hrs (37°C, 5% CO2), spun again for an additional 1 hr, and then the media replaced with complete RPMI-1640 growth media. All target cell line infections were carried out minimally in triplicate (range 3–7 replicates).
Two days post infection, a fraction of the infected cells was harvested for an initial time point (T = 0), and puromycin selection (using cell line-specific levels previously determined by killing curves) was then initiated for the remaining cells. Cells were cultured for an additional 4 weeks in the presence of selective media. Care was taken not to allow the cells to become too confluent or to split too thinly. Cells were periodically harvested, including the last time point (T = 4 weeks), which was the standard comparison point for the screen data presented.
Genomic DNA was isolated from harvested cells, sheared ten times through a 25 gauge needle, and subsequently used as template for PCR amplification of library shRNA hairpins. Sufficient genomic DNA template was included in the PCR reaction to ensure an approximate 1,000-fold average representation of each library shRNA hairpin (assuming 5 pg of DNA/cell), thereby maintaining the initial ~1,000-fold representation of integrations. PCR primers common to the shRNAmir backbone vector and bounding the half hairpin target sequence were as follows: Forward 5′-TAGTGAAGCCACAGATGTA-3′; Reverse 5′-ATGTATCAAAGAGATAGCAAGGTATTCAG-3′.
To deconvolute shRNA representation in the T = 4 weeks vs. T = 0 cell pools, gel-purified PCR products were differentially labeled and co-hybridized (using manufacturer protocols) to a custom Agilent microarray designed (using Agilent eArray software) to include probes complementary to shRNA hairpin sequences. Microarrays were imaged and fluorescence intensities extracted. For each microarray, log10 background-subtracted fluorescence ratios were globally normalized (which assumes overall equal library shRNA depletion and enrichment). Unless otherwise specified, screen data reported represent the average ratios from replicate cell line infections.
Screen data analysis and validation
High-confidence on-target shRNAs/phenotypes were identified using a new analytical approach (COrrelated Phenotypes for On-Target Effects; COPOTE), based on finding two or more different shRNAs targeting the same gene and displaying concordant enrichment/depletion profiles across the cell lines (Pearson correlation). Custom Perl scripts were used to calculate the Pearson correlation coefficient between shRNAs targeting the same genes, and also to permute cell line identities to generate the randomized null distribution of correlations. To define meaningful correlations, we determined False Discovery Rates (FDRs) as the ratio of false positives (from the null distribution) to observed positives at or above any given correlation cutoff.
For validation, Q-RT-PCR was performed using Assay-on-Demand TaqMan probes and reagents (Applied Biosystems). Catalog numbers for probes include: NUP153 (Hs01018919_m1) and KLF5 (Hs00156145_m1). Western blots were done on whole cell lysates, using the following primary antibodies: Myc (Santa Cruz sc-40), pan-Ras (Millipore 05–516), NUP153 (Acris BM5527), GAPDH (Santa Cruz sc-25778), SMURF1 (Santa Cruz H-60). Densitometry calculations were carried out using publicly available ImageJ software. For siRNA knockdown, ON-TARGETplus SMARTpool siRNAs were obtained from Thermo Scientific: KRAS (L-005069-00-0005), SMURF1 (L-007191-00-0005), NUP153 (L-005283-00-0005), KLF5 (L-013571-00-0005), non-targeting control (D-001810-10-20). Cell growth/viability assays were done using a modified WST-1 protocol as previously described . In each assay, a full time course was performed, though usually only day 5 is shown for brevity.
Array-based comparative genomic hybridization (aCGH) data
Findings from the screen were interpreted in the context of previously published aCGH data  (GSE26089). Briefly, that data set comprises Agilent 244 K CGH array profiles from 70 pancreatic cancers (48 primary tumor xenografts and 22 cancer cell lines). Tumor/normal fluorescence ratios were normalized and mapped onto the genome (build 18) using Agilent software.
Results and discussion
Targeted shRNA screen
In pancreatic (as with other) cancer genomes, loci that are recurrently amplified or deleted are likely enriched for known or novel cancer genes . Typically, each such locus is studied individually to discover the driver gene(s). In an effort to accelerate this process, we developed a pooled shRNA screening strategy to simultaneously evaluate 185 candidate pancreatic cancer genes that together represent 104 different loci of recurrent DNA amplification or deletion; thus the genes selected included both candidate oncogenes and tumor suppressors (see Additional file 1). Most of the 185 candidates were focally amplified or deleted in a subset of cancers, and some were also reported to carry mutations (Additional file 1) [6, 7]. A few known cancer genes (e.g. KRAS, MYC, and CDKN2A) were also included in the screen as positive controls.
The pooled shRNA screen was carried out as a competitive growth/viability assay. The general workflow, adapted from Schlabach et al., is depicted in Figure 1 and described in more detail in Methods. In brief, pancreatic cancer cell lines were infected with a pooled shRNA lentiviral library comprising 558 shRNAs targeting the 185 genes (on average 3 hairpins/gene) (Additional file 1). Infections were done at low multiplicity of infection, ensuring that most cells harbored a single shRNA knocking down the expression of a single gene. The pooled, infected cells were then cultured for four weeks, after which depleted or enriched shRNAs (i.e., those targeting genes conferring positive or negative growth advantage, respectively) were identified by PCR amplification of shRNA hairpins and comparative hybridization (T = 4 weeks vs. T = 0) to a custom hairpin microarray.
The shRNA library screen was carried out on ten different cell lines (Table 1). These included seven genetically-diverse pancreatic cancer cell lines that together harbor the vast majority of copy number alterations from which the 185 genes were selected. We also included a single immortalized, non-tumorigenic human pancreatic ductal epithelial (HPDE) cell line  to facilitate discovery of tumor suppressor genes (as enriched shRNAs targeting growth suppressive genes). Lastly, we also screened two breast cancer cell lines to help distinguish genome-specific from generic essential genes (i.e., an “out-group”). The raw and cell line-averaged screening data are available in Additional files 2 and 3.
Screen analysis and validation
When analyzing hundreds of shRNAs, it is possible that two different shRNAs targeting the same gene have a similar growth phenotype (across the panel of cell lines) just by random chance. Therefore, to correct for multiple hypothesis testing, we compared the distribution of observed Pearson correlations to that from randomly permuted data (Figure 2D). Notably, we observed a significant rightward shift in the observed distribution of correlations, indicating an enrichment of shRNAs targeting the same gene (and showing similar phenotype) above that expected by chance; false discovery rates (FDR; q values) are reported in Additional file 4. Of the 185 genes included in the screen (and of the 157 genes (85%) represented by two or more shRNAs and therefore evaluable), we identified 52 genes with “high confidence” knockdown phenotypes, defined by having at least two different shRNAs exhibited significantly correlated depletion/enrichment profiles (R > 0.7; q < 0.135) across the ten cell lines.
We note that other approaches have been used to leverage information from multiple shRNAs targeting the same gene, including redundant siRNA activity (RSA) analysis , RNAi gene enrichment ranking (RIGER) , and the Gene Activity Ranking Profile (GARP) score . All of these methods consider either the top-most depleted shRNA(s) or all shRNAs for a given gene. Our approach is fundamentally different in that we consider all shRNA pairs, therefore focusing primarily on the evidence for correlated (implying on-target) phenotype rather than on the strength of shRNA depletion.
Summary of validation for target knockdown and growth phenotype
validated by RT-PCR
validated by cancer
Observations on known cancer genes
The shRNAs targeting the MYC oncogene also exhibited variable depletion across the panel of cell lines. However, unlike with KRAS, the cell lines with lower MYC transcript levels were associated with substantially higher depletion levels (Figure 3B). The basis for this observation is unclear, and we caution that the RNAi knockdown and phenotype requires validation. Nonetheless, we speculate that there might exist a threshold below which MYC levels are insufficient to support cell growth, and that in cell lines with high MYC levels incomplete shRNA-mediated knockdown is insufficient to pass that threshold.
We recently reported SMURF1, encoding an E3 ubiquitin ligase, to be amplified in a subset of pancreatic cancers, where it drives cell invasion but not growth in AsPC1 cells . Here, in the context of the pooled shRNA screen, we reproduced that result. The single shRNA targeting SMURF1 was neither depleted nor enriched in SMURF1-amplified AsPC1 cells (Figure 3C), consistent with its role in cell invasion but not growth. Interestingly, in non-amplified cell lines the SMURF1 shRNA was generally depleted, suggesting a possible additional role in cell growth specific to a non-amplified context, a knockdown threshold effect as speculated above for MYC (endogenous SMURF1 expression levels are substantially elevated with amplification ), or an off-target effect. The findings for SMURF1 also suggest an added benefit of screening additional phenotypes, e.g. cell invasion.
We also observed the expected pattern of shRNA enrichment for shRNAs targeting known tumor suppressor genes, best exemplified by CDKN2A. A single shRNA targeting CDKN2A was enriched in a subset of the screened cell lines (Figure 3D). Notably, the cell lines that did not show substantial enrichment were in fact those with homozygous deletion of CDKN2A (where knockdown of a non-existent gene would not be expected to promote growth). These data suggest a utility of the shRNA knockdown screen approach in identifying not only oncogenes (by reduced growth fitness), but also potentially novel tumor suppressor genes (by enhanced growth fitness).
While the focus of our screen was pancreatic cancer, the inclusion of a breast cancer out-group also provided an opportunity to identify breast cancer selective dependencies. Among the high-confidence genes/phenotype, most notably shRNAs targeting the cell-cycle regulator CCNE1 showed selective depletion in the two breast cancer cell lines (Figure 3E). CCNE1 has been reported to be the driver of 19q12 amplification in breast cancer [34, 35]. Indeed, Natajaran et al. reported MDA157 breast cancer cells to be sensitive to CCNE1 knockdown , which we replicated here (Figure 3E). Our findings underscore a likely selective dependency and possible point of therapeutic attack in breast cancer.
Cytoband 19q13 amplicon
From our screen, three shRNAs targeting genes residing at 19q13 were preferentially depleted in those cell lines harboring 19q13 amplification (Figure 4B). Among these, two high-confidence (correlated) shRNAs targeted PLEKHG2, encoding a plekstrin homology domain protein, and a single shRNA (and the only one included in the screen) targeted MED29, a mediator of RNA polymerase II transcription complex subunit. These data agree well with findings described by Kuuselo et al.. Those investigators performed a focused siRNA-based analysis of genes within the 19q13 amplicon, reporting that knockdown of PLEKHG2 and MED29 reduced cell viability in Panc1 (19q-amplified) but not Miapaca2 (not amplified) cells. Thus, our data corroborate and extend (using independent assays and additional cell lines) prior work and highlight a still under-appreciated role of PLEKHG2 and MED29 (rather than AKT2) as likely oncogenes driving 19q13 amplification in pancreatic cancer.
NUP153/FAST1 TGFβ signaling axis
In our screen, two different high-confidence shRNAs targeting NUP153 showed varying levels of depletion across the cell line panel. Notably, cell lines with an intact upstream TGFβ signaling pathway (determined by absence of SMAD4 and TGFBR2 mutation/deletion, and/or TGFβ-induced growth suppression) generally exhibited higher levels of NUP153-shRNA depletion, compared to cell lines with a compromised TGFβ signaling pathway (Figure 5C) (Additional file 5). Our findings therefore suggest that altered NUP153-FAST1 shuttling of SMADs may provide an alternate means by which pancreatic cancers disrupt TGFβ signaling. The role of an altered NUP153/FAST1 TGFβ shuttling axis in pancreatic carcinogenesis warrants further investigation.
In our screen, three different high-confidence shRNAs targeting KLF5 showed depletion in most cell lines (Figure 6B). Though interestingly, a single cell line, Panc1, with extremely low levels of KLF5 transcript showed substantial KLF5-shRNA enrichment (Figure 6B-D). We verified on-target KLF5 knockdown and the contrasting knockdown growth-phenotypes (in Panc1 compared to two other lines) by siRNA transfection (Figure 6B, C). The basis for shRNA enrichment in the sole Panc1 line is unclear. We speculate that KLF5 might be epigenetically-silenced in Panc1 (based on barely-detectable expression) because in that cell line it plays a growth suppressive role; thus further knockdown would promote cell growth. In contrast, in every other cell line in the panel, KLF5 transcript levels are appreciable, and knockdown reduces cell growth (Figure 6D, E). Taken together, our findings support a context-dependency of KLF5 function, whilst the preponderance of data (focal amplification and growth dependency) supports a predominantly oncogenic role in pancreatic cancer.
Of note, a recent exome sequencing study  reported a single heterozygous mutation (H389N) in the DNA-binding domain of KLF5 in the pancreatic cell line Panc5.04. The authors interpreted the mutation to be functionally inactivating, suggesting a likely tumor suppressive role. Although Panc5.04 was not included in our screening panel, we sought to further characterize KLF5 in that line. By analysis of microarray data , KLF5 showed relatively high transcript levels in Panc5.04 compared to other pancreatic cancer cell lines (Figure 6D). Further, from our prior transcriptome sequencing (RNA-seq) data , 64% of KLF5 reads in Panc5.04 mapped to the wildtype allele (78 WT reads vs. 45 mutant reads), thus excluding epigenetic silencing of the wildtype allele. Notably, siRNA mediated knockdown of KLF5 in Panc5.04 cells resulted in marked growth inhibition (Figure 6F). This finding suggests that the single KLF5 mutation in Panc5.04 is most likely a passenger mutation, and provides additional support for KLF5 being predominantly oncogenic in pancreatic cancer. Future studies should define the transcriptional targets and mechanisms underlying KLF5 dependency in pancreatic cancer.
In summary, we have detailed a proof-of-principle approach for the highly-parallel functional evaluation of candidate cancer genes, here for pancreatic cancer. We have simultaneously evaluated 185 candidate pancreatic cancer genes, selected as those recurrently and focally amplified or deleted, by a pooled shRNA library screen on 10 genetically-diverse cell lines. We have also described a novel approach, COPOTE, to enrich for on-target shRNAs and knockdown phenotypes, based on identifying shRNAs targeting the same gene that exhibit correlated phenotype. Our screen has uncovered novel pancreatic cancer genes and pathways, most notably highlighting potential roles of a putative NUP153-FAST1 SMAD shuttling axis controlling TGFβ signaling and an oncogenic function of the KLF5 transcription factor, both meriting further study. Future screens might include more shRNAs per gene (plausibly decreasing the likelihood of false negatives), more cell lines, and additional phenotypic assays. Nonetheless, our study here supports the general feasibility of a highly-parallel functional analysis of candidate cancer genes, addressing a fundamental bottleneck in the annotation of cancer genomes.
Note added in proof
During review of our manuscript, Shao et al. described an RNAi-screen analysis approach (ATARiS) that is conceptually similar to ours (COPOTE).
Array-based comparative genomic hybridization
COrrelated phenotypes for on-target effects
False discovery rate
Polymerase chain reaction
Reverse transcription PCR
Short hairpin RNA
Short interfering RNA.
We wish to thank members of the Pollack lab for helpful discussions. This study was funded in part by grants from the NCI: R01CA112016 (J.R.P), and from the Stanford Digestive Disease Center (J.R.P). A.H.S. was supported by fellowships from the NSF, Stanford Graduate Fellowship program, and Cancer Biology program. K.S. was supported by fellowships from the Stanford Medical Scientist Training Program and the Paul & Daisy Soros Foundation. C.P.G. was supported by a fellowship from the Stanford Genome Training Program.
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