Cancer is a group of complex diseases in which cells grow uncontrollably and spread into surrounding tissues and other parts of the body. All types of cancers develop from changes – or mutations – in the genes that affect the pathways involved in controlling the growth of cells.

Different cancers possess unique sets of mutations that affect specific genes, and often, it is difficult to determine which of them play the most important role in a particular type of cancer. For example, pancreatic adenosquamous carcinoma, a rare and aggressive form of pancreatic cancer, is a devastating disease with a poor chance of survival – patients rarely live longer than one year after diagnosis.

While the cells of this particular cancer display distinct features that separate them from other forms of pancreatic cancer, the genetic causes of these features are unclear. Using new technologies, some researchers have reported mutations in a ‘quality control’ gene called ‘UPF1’, which is responsible for destroying faulty forms of genetic material. However, subsequent studies did not find such mutations.

To clarify the role of UPF1 in pancreatic adenosquamous carcinoma, Polaski et al. used mouse and human cancer cells with UPF1 mutations and monitored their effects on tumour growth and the development of features unique to this disease.

Polaski et al. first injected mice with mouse pancreatic cancer cells containing mutations in UPF1 (mutated cells) and cancer cells without. Both groups of mice developed pancreatic tumours but there was no difference in tumour growth between the mutated and non-mutated cells, and neither cell type displayed distinct features. The researchers then generated human mutated cells, which were also found to lack any specific characteristics. Further analysis showed that the mutations did not stop UPF1 from working, in fact, over 40% of these mutations occurred naturally in humans without causing cancer.

This suggests that UPF1 does not seem to be involved in pancreatic adenosquamous carcinoma. Further investigation is needed to illuminate key genetic players in the development of this type of cancer, which will be vital for improving treatments and outcomes for patients suffering from this disease.

Pancreatic adenosquamous carcinoma (PASC) is a rare and aggressive disease that constitutes 1–4% of pancreatic exocrine tumors (Madura et al., 1999). Patient prognosis is extremely poor, with a median survival of 8 months (Simone et al., 2013). Although PASC is clinically and histologically distinct from the more common disease pancreatic adenocarcinoma, the genetic and molecular origins of PASC’s unique features are unknown.

A recent study reported a potential breakthrough in our understanding of PASC etiology. Liu et al., 2014 reported high-frequency mutations affecting UPF1, which encodes a core component of the nonsense-mediated mRNA decay (NMD) pathway, in 78% (18 of 23) of PASC patients. These mutations were absent from patient-matched normal pancreatic tissue (0 of 18) and from non-PASC tumors (0 of 29 non-adenosquamous pancreatic carcinomas and 0 of 21 lung squamous cell carcinomas). The authors used a combination of molecular and histological assays to find that the UPF1 mutations caused UPF1 mis-splicing, loss of UPF1 protein, and impaired NMD, resulting in stable expression of aberrant mRNAs containing premature termination codons that would normally be degraded by NMD. The recurrent, PASC-specific, and focal nature of the reported UPF1 mutations, together with their dramatic effects on NMD activity, suggested that UPF1 mutations are a key feature of PASC biology.

Three subsequent studies of distinct PASC cohorts, however, did not report somatic mutations in UPF1 (Fang et al., 2017; Hayashi et al., 2020; Witkiewicz et al., 2015). This absence of UPF1 mutations is significantly different from the high rate reported by Liu et al. (0 of 34 total PASC samples from three cohorts [Fang et al., 2017; Hayashi et al., 2020; Witkiewicz et al., 2015] vs. 18 of 23 PASC samples from Liu et al; p<10⁻⁸ by the two-sided binomial proportion test). Although these other studies relied on whole-exome and/or genome sequencing instead of targeted UPF1 gene sequencing, those technologies yield good coverage of the relevant UPF1 gene regions because the affected introns are very short. Given this discrepancy, we sought to directly assess the functional contribution of UPF1 mutations to PASC using a combination of biological and molecular assays.

We first tested the role of the reported UPF1 mutations during tumorigenesis in vivo. Liu et al. reported that the majority of UPF1 mutations caused skipping of UPF1 exons 10 and 11, disrupting UPF1’s RNA helicase domain that is essential for its NMD activity (Lee et al., 2015). We therefore modeled UPF1 mutation-induced exon skipping by designing paired guide RNAs flanking Upf1 exons 10 and 11, such that these exons would be deleted upon Cas9 expression (Figure 1A). We chose mouse pancreatic cancer cells (KPC cells: Kras^G12D; Trp53^R172H/null; Pdx1-Cre) as a model system. KPC cells are defined by mutations affecting KRAS and p53 (encoded by Kras and Trp53 in mouse) that also occur in the vast majority of PASC cases (Borazanci et al., 2015; Fang et al., 2017; Hayashi et al., 2020), making them a genetically appropriate system. We delivered Upf1-targeting paired guide RNAs to KPC cells using recombinant adenoviral vectors and confirmed that guide delivery resulted in the production of UPF1 mRNA lacking exons 10 and 11 and a corresponding reduction in full-length UPF1 protein levels (Figure 1—figure supplement 1A–G). We injected subcloned control and Upf1-targeted KPC cells into the tails of the pancreata of B6 albino mice (n = 10 mice per treatment) and monitored tumor growth and animal survival. We detected no significant differences in tumor volume or survival in mice implanted with control or Upf1-targeted KPC cells (Figure 1B–D and Figure 1—source datas 1 and 2). Tumors derived from control as well as Upf1-targeted cells displayed similar histopathological features characteristic of moderately to poorly differentiated pancreatic ductal adenocarcinomas (Figure 1E–G). Moderately differentiated areas were composed of medium to small duct-like structures or tubules with lower mucin production, while poorly differentiated components were characterized by solid sheets or nests of tumor cells with large eosinophilic cytoplasms and large pleomorphic nuclei. No squamous differentiation was identified by histomorphologic evaluation and no expression of the squamous marker p40 (ΔNp63) was detected (Figure 1H and Supplementary file 1a). We concluded that inducing the reported Upf1 exon skipping in vivo had no detectable effects on pancreatic cancer growth or acquisition of adenosquamous features in the KPC model. However, there are several important caveats to our data. First, we cannot rule out the possibility that inducing Upf1 exon skipping in a different model system or cell type could influence tumorigenesis. Second, as our assays were performed in the complex setting of in vivo tumorigenesis, we cannot infer how inducing the reported Upf1 exon skipping might affect tumor cell proliferation in the controlled setting of in vitro growth. Third, as accurate measurement of Upf1 spicing and UPF1 protein isoforms was only possible for KPC cells prior to orthotopic injection, we cannot infer how the relative frequencies of mis-spliced UPF1 mRNA and the resulting truncated proteins may have changed during tumorigenesis.

Figure 1 with 1 supplement see all

Download asset Open asset

Figure 1—source data 1

Source data for mouse tumor volume (Figure 1C).

https://cdn.elifesciences.org/articles/62209/elife-62209-fig1-data1-v2.xlsx

Download elife-62209-fig1-data1-v2.xlsx

Figure 1—source data 2

Source data for mouse survival (Figure 1D).

https://cdn.elifesciences.org/articles/62209/elife-62209-fig1-data2-v2.xlsx

Download elife-62209-fig1-data2-v2.xlsx

We next assessed molecular phenotypes induced with UPF1 mutations. Liu et al. measured the effects of each mutation on UPF1 splicing using a minigene assay, in which each mutation was introduced into a plasmid containing a small fragment of the UPF1 gene that was subsequently transfected into 293 T cells. Liu et al. concluded that all reported UPF1 mutations caused dramatic UPF1 mis-splicing that disrupted key protein domains that are essential for UPF1 function in NMD. Minigenes are common tools for studying splicing, but they are frequently spliced less efficiently than endogenous genes, presumably because they are gene fragments that lack potentially important sequence features that promote splicing and incompletely capture the close relationship between chromatin and splicing (Luco et al., 2011; Naftelberg et al., 2015).

We modeled UPF1 mutations in 293 T cells in order to mimic Liu et al.’s experimental strategy, but introduced mutations into their endogenous genomic contexts rather than using minigenes. We selected two distinct UPF1 mutations in intron 10 for these studies. We selected IVS10+31G>A (patient 1; P1) because it was reportedly recurrent across three different patients (making it equally or more common than any other mutation) and induced strong mis-splicing on its own (36% mis-spliced mRNA, versus 0% for wild-type UPF1); we selected IVS10-17G>A (patient 9; P9) because it had one of the strongest effects on splicing (90% mis-spliced mRNA). IVS10+31G>A was present in a homozygous state in two of the three patients carrying it, while IVS10-17G>A was present in a heterozygous state.

We introduced each mutation into its endogenous context by transiently transfecting a plasmid expressing Cas9 and a single guide RNA (sgRNA) targeting UPF1 intron 10 as well as appropriate donor DNA for homology-directed repair, screened the resulting cells for the desired genotypes, and established clonal lines. The resulting cell lines contained the desired mutations in the correct copy numbers as well as a point mutation disrupting the protospacer adjacent motif (PAM) site (Figure 2—figure supplement 1A–C). As neither the PAM site itself nor nearby positions were reported as mutated in Liu et al., we additionally established a cell line in which only the PAM site was mutated as a wild-type control.

We systematically tested the functional consequences of UPF1 mutations for NMD efficiency, UPF1 protein levels, and UPF1 splicing. We measured NMD efficiency in our engineered cells using the well-established beta-globin reporter system, which permits controlled measurement of the relative levels of mRNAs that do or do not contain an NMD-inducing premature termination codon, but which are otherwise identical (Zhang et al., 1998). We did not observe decreased NMD efficiency in UPF1-mutant versus wild-type cells; instead, UPF1-mutant cells exhibited evidence of modestly more efficient NMD, although these differences were not statistically significant (Figure 2A and Figure 2—source data 1). To confirm these results from reporter experiments, we then queried levels of endogenous NMD substrates across the transcriptome. We performed high-coverage RNA-seq on each of the three 293 T cell lines that we engineered to lack or contain defined UPF1 mutations in biological triplicate, quantified transcript expression, and identified differentially expressed transcripts. We focused on NMD substrates arising from alternative splicing, as these are abundant and sensitive biomarkers of NMD efficiency that are internally controlled for gene expression variation (Feng et al., 2015). These analyses revealed that neither UPF1-mutant cell line exhibited global increases in the expression of NMD substrates relative to wild-type cells. Instead, both UPF1-mutant cell lines exhibited modestly lower global levels of endogenous NMD substrates than did wild-type cells, mimicking the trend observed with our NMD reporter experiments. Together, these data confirm that the tested mutations in UPF1 intron 10 do not affect NMD activity (Figure 2B–D and Supplementary file 1b and c).

Figure 2 with 1 supplement see all

Download asset Open asset

Figure 2—source data 1

Source data for qPCR in HEK 293 T cell lines (Figure 2A).

https://cdn.elifesciences.org/articles/62209/elife-62209-fig2-data1-v2.xlsx

Download elife-62209-fig2-data1-v2.xlsx

Figure 2—source data 2

Source data for western blot in HEK 293 T cell lines (Figure 2E).

https://cdn.elifesciences.org/articles/62209/elife-62209-fig2-data2-v2.xlsx

Download elife-62209-fig2-data2-v2.xlsx

Figure 2—source data 3

Source data for RT-PCR in HEK 293 T cell lines (Figure 2F).

https://cdn.elifesciences.org/articles/62209/elife-62209-fig2-data3-v2.xlsx

Download elife-62209-fig2-data3-v2.xlsx

Consistent with similar NMD activity independent of UPF1 mutational status, UPF1 mutations did not cause loss of full-length UPF1 protein (Figure 2E, Figure 2—figure supplement 1D, and Figure 2—source data 2). Although UPF1 protein levels varied between the individual cell lines, this variation in UPF1 protein levels was not associated with variation in NMD efficiency and did not segregate with UPF1 mutational status. We therefore measured the levels of normally spliced and mis-spliced UPF1 mRNA. We readily detected normally spliced UPF1 mRNA in all samples by RT-PCR, but found no evidence of mis-spliced UPF1 mRNA, except in positive control samples in which we spiked in synthesized DNA corresponding to the exon skipping isoform reported in Liu et al. (Figure 2F, Figure 2—figure supplement 1E, and Figure 2—source data 3). We confirmed these results with our RNA-seq data by mapping all reads against all possible splice junctions connecting exons 9, 10, 11, and 12. These analyses revealed no evidence of splice junctions consistent with the reported exon 10 and 11 skipping or other abnormal exon skipping isoforms (Figure 2G).

Given the differences between Liu et al.’s findings of common UPF1 mutations and their absence from subsequent studies of PASC, we wondered whether some of the UPF1 mutations reported by Liu et al. might correspond to inherited genetic variation rather than somatically acquired mutations. We searched for each mutation reported by Liu et al. within databases compiled by the 1000 Genomes Project, NHLBI Exome Sequencing Project, Exome Aggregation Consortium (ExAC), and the genome aggregation database (gnomAD) (Auton et al., 2015; Karczewski et al., 2020; Exome Aggregation Consortium et al., 2016; Server EV, 2016). These databases were constructed from a mix of whole-genome and whole-exome sequencing, both of which are effective for discovering variants within the relevant regions of UPF1 (because UPF1 introns 10, 21, and 22 are very short, they are well covered by exon-capture technologies). We found genetic variants identical to 45% (18 of 40) of the reported UPF1 mutations, one of which is present in the reference human genome. Eighty-nine percent (16 of 18) of UPF1-mutant patients had one or more reported mutations that corresponded to standing genetic variation (Figure 3A–F and Supplementary file 1d). The distribution of overlaps between reported UPF1 mutations and standing genetic variation depended strongly upon genic context. A large fraction of reported intronic UPF1 mutations were identical to standing genetic variation, while the majority of reported exonic UPF1 mutations were not (Supplementary file 1d).

Figure 3

Download asset Open asset

Our discovery that a large fraction of the reported UPF1 mutations are present in databases of germline genetic variation was surprising for two reasons. First, when strongly cancer-linked mutations occur as germline variants, they frequently manifest as cancer predisposition syndromes. However, no such relationship is known for UPF1 genetic variants, despite their reportedly high prevalence as identical somatic mutations in PASC. Second, UPF1 is essential for embryonic viability and development in mammals (Medghalchi et al., 2001), zebrafish (Wittkopp et al., 2009), and Drosophila (Avery et al., 2011). As Liu et al. reported that all UPF1 mutations caused mis-splicing that is expected to disable UPF1 protein function (Liu et al., 2014), then those mutations should be incompatible with life when present as inherited genetic variants. Our finding that two reported mutations had no effect on UPF1 splicing when introduced into their endogenous genomic contexts offers a way to explain this incongruity, at least for the two reported lesions that we studied.

Given these discrepancies, we next sought to verify the somatic nature of the UPF1 mutations described in Liu et al., which was reportedly determined by sequencing both tumors and patient-matched controls. The GenBank accession codes reported in Liu et al. corresponded to short nucleotide sequences containing UPF1 mutations, without corresponding data for patient-matched controls. We contacted the senior author (Dr. YanJun Lu) to request primary sequencing data from patient-matched tumor and normal samples, but neither primary sequencing data from matched samples nor the samples themselves were available.

To further explore whether UPF1 is recurrently mutated in PASC, we reanalyzed sequencing data from Fang et al., 2017 to manually search for UPF1 mutations (Supplementary file 1e-f). We focused on the two loci that contained all mutations reported by Liu et al. (UPF1 exons 10-11 and exons 21–23). Because the relevant introns are very short, they were well covered by both the whole-exome and whole-genome sequencing used by Fang et al. Using relaxed mutation-calling criteria to maximize sensitivity (details in Materials and methods), we identified somatic UPF1 mutations in samples from 6 of 17 PASC patients. However, those mutations exhibited genetic characteristics expected of passenger, not driver, mutations. None of those UPF1 mutations matched the UPF1 mutations reported by Liu et al., and only one was present at an allelic frequency equal to the allelic frequency of mutant KRAS, which is a known driver and which we detected in samples from all PASC patients (median allelic frequencies of 12% versus 34% for UPF1 versus KRAS mutations). Furthermore, we also identified UPF1 mutations in samples from patients with non-adenosquamous tumors (3 of 34 pancreatic ductal adenocarcinomas), whereas Liu et al. reported finding no UPF1 mutations in non-adenosquamous pancreatic cancers (0 of 29). In concert with the reports of Witkiewicz et al., 2015 and Hayashi et al., 2020 of finding no UPF1 mutations in their PASC samples, these analyses suggest that UPF1 is not a frequent or adenosquamous-specific mutational target in most PASC cohorts.

UPF1’s role in the pathogenesis of PASC has been unclear and controversial given the seeming discrepancies between its mutational spectrum in different PASC cohorts. Although it is difficult to conclusively prove that a specific genetic change does not promote cancer, we were unable to detect biological or molecular changes arising from two mutations reported by Liu et al. UPF1’s status as an essential gene and our discovery that many reported UPF1 mutations occur as germline genetic variants of no known pathogenicity together suggest that other UPF1 mutations reported by Liu et al. could similarly represent genetic differences that do not functionally contribute to PASC. Our study highlights the need for continued study of the PASC mutational spectrum in order to understand the molecular basis of this disease.

RNA-seq data generated as part of this study have been deposited in the Gene Expression Omnibus (accession number GSE163517). All original gel images are provided.

1. 1. Polaski JT
   2. Udy DB
   3. Escobar-Hoyos LF
   4. Askan G
   5. Leach SD
   6. Ventura A
   7. Kannan R
   8. Bradley RK

   (2020) NCBI Gene Expression Omnibus

   ID GSE163517. The origins and consequences of UPF1 variants in pancreatic adenosquamous carcinoma.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE163517

1. 1. Fang Y
   2. Su Z
   3. Xie J
   4. Xue R
   5. Qi Ma
   6. Li Y
   7. Zhao Y
   8. Song Z
   9. Lu X
   10. Li H
   11. Peng C
   12. Bai F
   13. Shen B

   (2017) NCBI Sequence Read Archive

   ID SRP107982. pancreatic ductal adenocarcinoma and panreatic adenosquamous carcinoma sequencing.

   https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP107982

1. 1. Auton A
   2. Brooks LD
   3. Durbin RM
   4. Garrison EP
   5. Kang HM
   6. Korbel JO
   7. Marchini JL
   8. McCarthy S
   9. McVean GA
   10. Abecasis GR
   11. 1000 Genomes Project Consortium

   (2015) A global reference for human genetic variation

   Nature 526:68–74.

   https://doi.org/10.1038/nature15393

   • PubMed
   • Google Scholar
2. 1. Avery P
   2. Vicente-Crespo M
   3. Francis D
   4. Nashchekina O
   5. Alonso CR
   6. Palacios IM

   (2011) Drosophila Upf1 and Upf2 loss of function inhibits cell growth and causes animal death in a Upf3-independent manner

   RNA 17:624–638.

   https://doi.org/10.1261/rna.2404211

   • PubMed
   • Google Scholar
3. 1. Borazanci E
   2. Millis SZ
   3. Korn R
   4. Han H
   5. Whatcott CJ
   6. Gatalica Z
   7. Barrett MT
   8. Cridebring D
   9. Von Hoff DD

   (2015) Adenosquamous carcinoma of the pancreas: molecular characterization of 23 patients along with a literature review

   World Journal of Gastrointestinal Oncology 7:132–140.

   https://doi.org/10.4251/wjgo.v7.i9.132

   • PubMed
   • Google Scholar
4. 1. Dvinge H
   2. Ries RE
   3. Ilagan JO
   4. Stirewalt DL
   5. Meshinchi S
   6. Bradley RK

   (2014) Sample processing obscures cancer-specific alterations in leukemic transcriptomes

   PNAS 111:16802–16807.

   https://doi.org/10.1073/pnas.1413374111

   • PubMed
   • Google Scholar
5. 1. Escobar-Hoyos LF
   2. Penson A
   3. Kannan R
   4. Cho H
   5. Pan CH
   6. Singh RK
   7. Apken LH
   8. Hobbs GA
   9. Luo R
   10. Lecomte N
   11. Babu S
   12. Pan FC
   13. Alonso-Curbelo D
   14. Morris JP
   15. Askan G
   16. Grbovic-Huezo O
   17. Ogrodowski P
   18. Bermeo J
   19. Saglimbeni J
   20. Cruz CD
   21. Ho YJ
   22. Lawrence SA
   23. Melchor JP
   24. Goda GA
   25. Bai K
   26. Pastore A
   27. Hogg SJ
   28. Raghavan S
   29. Bailey P
   30. Chang DK
   31. Biankin A
   32. Shroyer KR
   33. Wolpin BM
   34. Aguirre AJ
   35. Ventura A
   36. Taylor B
   37. Der CJ
   38. Dominguez D
   39. Kümmel D
   40. Oeckinghaus A
   41. Lowe SW
   42. Bradley RK
   43. Abdel-Wahab O
   44. Leach SD

   (2020) Altered RNA splicing by mutant p53 activates oncogenic RAS signaling in pancreatic Cancer

   Cancer Cell 38:198–211.

   https://doi.org/10.1016/j.ccell.2020.05.010

   • PubMed
   • Google Scholar
6. 1. Exome Aggregation Consortium
   2. Lek M
   3. Karczewski KJ
   4. Minikel EV
   5. Samocha KE
   6. Banks E
   7. Fennell T
   8. O'Donnell-Luria AH
   9. Ware JS
   10. Hill AJ
   11. Cummings BB
   12. Tukiainen T
   13. Birnbaum DP
   14. Kosmicki JA
   15. Duncan LE
   16. Estrada K
   17. Zhao F
   18. Zou J
   19. Pierce-Hoffman E
   20. Berghout J
   21. Cooper DN
   22. Deflaux N
   23. DePristo M
   24. Do R
   25. Flannick J
   26. Fromer M
   27. Gauthier L
   28. Goldstein J
   29. Gupta N
   30. Howrigan D
   31. Kiezun A
   32. Kurki MI
   33. Moonshine AL
   34. Natarajan P
   35. Orozco L
   36. Peloso GM
   37. Poplin R
   38. Rivas MA
   39. Ruano-Rubio V
   40. Rose SA
   41. Ruderfer DM
   42. Shakir K
   43. Stenson PD
   44. Stevens C
   45. Thomas BP
   46. Tiao G
   47. Tusie-Luna MT
   48. Weisburd B
   49. Won HH
   50. Yu D
   51. Altshuler DM
   52. Ardissino D
   53. Boehnke M
   54. Danesh J
   55. Donnelly S
   56. Elosua R
   57. Florez JC
   58. Gabriel SB
   59. Getz G
   60. Glatt SJ
   61. Hultman CM
   62. Kathiresan S
   63. Laakso M
   64. McCarroll S
   65. McCarthy MI
   66. McGovern D
   67. McPherson R
   68. Neale BM
   69. Palotie A
   70. Purcell SM
   71. Saleheen D
   72. Scharf JM
   73. Sklar P
   74. Sullivan PF
   75. Tuomilehto J
   76. Tsuang MT
   77. Watkins HC
   78. Wilson JG
   79. Daly MJ
   80. MacArthur DG

   (2016) Analysis of protein-coding genetic variation in 60,706 humans

   Nature 536:285–291.

   https://doi.org/10.1038/nature19057

   • PubMed
   • Google Scholar
7. 1. Fang Y
   2. Su Z
   3. Xie J
   4. Xue R
   5. Ma Q
   6. Li Y
   7. Zhao Y
   8. Song Z
   9. Lu X
   10. Li H
   11. Peng C
   12. Bai F
   13. Shen B

   (2017) Genomic signatures of pancreatic adenosquamous carcinoma (PASC)

   The Journal of Pathology 243:155–159.

   https://doi.org/10.1002/path.4943

   • PubMed
   • Google Scholar
8. 1. Feng Q
   2. Snider L
   3. Jagannathan S
   4. Tawil R
   5. van der Maarel SM
   6. Tapscott SJ
   7. Bradley RK

   (2015) A feedback loop between nonsense-mediated decay and the retrogene DUX4 in facioscapulohumeral muscular dystrophy

   eLife 4:e04996.

   https://doi.org/10.7554/eLife.04996

   • Google Scholar
9. 1. Flicek P
   2. Ahmed I
   3. Amode MR
   4. Barrell D
   5. Beal K
   6. Brent S
   7. Carvalho-Silva D
   8. Clapham P
   9. Coates G
   10. Fairley S
   11. Fitzgerald S
   12. Gil L
   13. García-Girón C
   14. Gordon L
   15. Hourlier T
   16. Hunt S
   17. Juettemann T
   18. Kähäri AK
   19. Keenan S
   20. Komorowska M
   21. Kulesha E
   22. Longden I
   23. Maurel T
   24. McLaren WM
   25. Muffato M
   26. Nag R
   27. Overduin B
   28. Pignatelli M
   29. Pritchard B
   30. Pritchard E
   31. Riat HS
   32. Ritchie GR
   33. Ruffier M
   34. Schuster M
   35. Sheppard D
   36. Sobral D
   37. Taylor K
   38. Thormann A
   39. Trevanion S
   40. White S
   41. Wilder SP
   42. Aken BL
   43. Birney E
   44. Cunningham F
   45. Dunham I
   46. Harrow J
   47. Herrero J
   48. Hubbard TJ
   49. Johnson N
   50. Kinsella R
   51. Parker A
   52. Spudich G
   53. Yates A
   54. Zadissa A
   55. Searle SM

   (2013) Ensembl 2013

   Nucleic Acids Research 41:D48–D55.

   https://doi.org/10.1093/nar/gks1236

   • PubMed
   • Google Scholar
10. 1. Gibson DG
    2. Young L
    3. Chuang RY
    4. Venter JC
    5. Hutchison CA
    6. Smith HO

    (2009) Enzymatic assembly of DNA molecules up to several hundred kilobases

    Nature Methods 6:343–345.

    https://doi.org/10.1038/nmeth.1318

    • PubMed
    • Google Scholar
11. 1. Hayashi A
    2. Fan J
    3. Chen R
    4. Ho Y-J
    5. Makohon-Moore AP
    6. Lecomte N
    7. Zhong Y
    8. Hong J
    9. Huang J
    10. Sakamoto H
    11. Attiyeh MA
    12. Kohutek ZA
    13. Zhang L
    14. Boumiza A
    15. Kappagantula R
    16. Baez P
    17. Bai J
    18. Lisi M
    19. Chadalavada K
    20. Melchor JP
    21. Wong W
    22. Nanjangud GJ
    23. Basturk O
    24. O’Reilly EM
    25. Klimstra DS
    26. Hruban RH
    27. Wood LD
    28. Overholtzer M
    29. Iacobuzio-Donahue CA

    (2020) A unifying paradigm for transcriptional heterogeneity and squamous features in pancreatic ductal adenocarcinoma

    Nature Cancer 1:59–74.

    https://doi.org/10.1038/s43018-019-0010-1

    • Google Scholar
12. 1. Hingorani SR
    2. Wang L
    3. Multani AS
    4. Combs C
    5. Deramaudt TB
    6. Hruban RH
    7. Rustgi AK
    8. Chang S
    9. Tuveson DA

    (2005) Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice

    Cancer Cell 7:469–483.

    https://doi.org/10.1016/j.ccr.2005.04.023

    • PubMed
    • Google Scholar
13. 1. Karczewski KJ
    2. Francioli LC
    3. Tiao G
    4. Cummings BB
    5. Alföldi J
    6. Wang Q
    7. Collins RL
    8. Laricchia KM
    9. Ganna A
    10. Birnbaum DP
    11. Gauthier LD
    12. Brand H
    13. Solomonson M
    14. Watts NA
    15. Rhodes D
    16. Singer-Berk M
    17. England EM
    18. Seaby EG
    19. Kosmicki JA
    20. Walters RK
    21. Tashman K
    22. Farjoun Y
    23. Banks E
    24. Poterba T
    25. Wang A
    26. Seed C
    27. Whiffin N
    28. Chong JX
    29. Samocha KE
    30. Pierce-Hoffman E
    31. Zappala Z
    32. O'Donnell-Luria AH
    33. Minikel EV
    34. Weisburd B
    35. Lek M
    36. Ware JS
    37. Vittal C
    38. Armean IM
    39. Bergelson L
    40. Cibulskis K
    41. Connolly KM
    42. Covarrubias M
    43. Donnelly S
    44. Ferriera S
    45. Gabriel S
    46. Gentry J
    47. Gupta N
    48. Jeandet T
    49. Kaplan D
    50. Llanwarne C
    51. Munshi R
    52. Novod S
    53. Petrillo N
    54. Roazen D
    55. Ruano-Rubio V
    56. Saltzman A
    57. Schleicher M
    58. Soto J
    59. Tibbetts K
    60. Tolonen C
    61. Wade G
    62. Talkowski ME
    63. Neale BM
    64. Daly MJ
    65. MacArthur DG
    66. Genome Aggregation Database Consortium

    (2020) The mutational constraint spectrum quantified from variation in 141,456 humans

    Nature 581:434–443.

    https://doi.org/10.1038/s41586-020-2308-7

    • PubMed
    • Google Scholar
14. 1. Katz Y
    2. Wang ET
    3. Airoldi EM
    4. Burge CB

    (2010) Analysis and design of RNA sequencing experiments for identifying isoform regulation

    Nature Methods 7:1009–1015.

    https://doi.org/10.1038/nmeth.1528

    • Google Scholar
15. 1. Katz Y
    2. Wang ET
    3. Silterra J
    4. Schwartz S
    5. Wong B
    6. Thorvaldsdóttir H
    7. Robinson JT
    8. Mesirov JP
    9. Airoldi EM
    10. Burge CB

    (2015) Quantitative visualization of alternative exon expression from RNA-seq data

    Bioinformatics 31:2400–2402.

    https://doi.org/10.1093/bioinformatics/btv034

    • PubMed
    • Google Scholar
16. 1. Langmead B
    2. Trapnell C
    3. Pop M
    4. Salzberg SL

    (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

    Genome Biology 10:R25.

    https://doi.org/10.1186/gb-2009-10-3-r25

    • Google Scholar
17. 1. Lee SR
    2. Pratt GA
    3. Martinez FJ
    4. Yeo GW
    5. Lykke-Andersen J

    (2015) Target discrimination in Nonsense-Mediated mRNA decay requires Upf1 ATPase activity

    Molecular Cell 59:413–425.

    https://doi.org/10.1016/j.molcel.2015.06.036

    • PubMed
    • Google Scholar
18. 1. Li B
    2. Dewey CN

    (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

    BMC Bioinformatics 12:323.

    https://doi.org/10.1186/1471-2105-12-323

    • Google Scholar
19. 1. Liu C
    2. Karam R
    3. Zhou Y
    4. Su F
    5. Ji Y
    6. Li G
    7. Xu G
    8. Lu L
    9. Wang C
    10. Song M
    11. Zhu J
    12. Wang Y
    13. Zhao Y
    14. Foo WC
    15. Zuo M
    16. Valasek MA
    17. Javle M
    18. Wilkinson MF
    19. Lu Y

    (2014) The UPF1 RNA surveillance gene is commonly mutated in pancreatic adenosquamous carcinoma

    Nature Medicine 20:596–598.

    https://doi.org/10.1038/nm.3548

    • Google Scholar
20. 1. Luco RF
    2. Allo M
    3. Schor IE
    4. Kornblihtt AR
    5. Misteli T

    (2011) Epigenetics in alternative pre-mRNA splicing

    Cell 144:16–26.

    https://doi.org/10.1016/j.cell.2010.11.056

    • PubMed
    • Google Scholar
21. 1. Maddalo D
    2. Manchado E
    3. Concepcion CP
    4. Bonetti C
    5. Vidigal JA
    6. Han YC
    7. Ogrodowski P
    8. Crippa A
    9. Rekhtman N
    10. de Stanchina E
    11. Lowe SW
    12. Ventura A

    (2014) In vivo engineering of oncogenic chromosomal rearrangements with the CRISPR/Cas9 system

    Nature 516:423–427.

    https://doi.org/10.1038/nature13902

    • PubMed
    • Google Scholar
22. 1. Madura JA
    2. Jarman BT
    3. Doherty MG
    4. Yum MN
    5. Howard TJ

    (1999) Adenosquamous carcinoma of the pancreas

    Archives of Surgery 134:599–603.

    https://doi.org/10.1001/archsurg.134.6.599

    • PubMed
    • Google Scholar
23. 1. Medghalchi SM
    2. Frischmeyer PA
    3. Mendell JT
    4. Kelly AG
    5. Lawler AM
    6. Dietz HC

    (2001) Rent1, a trans-effector of nonsense-mediated mRNA decay, is essential for mammalian embryonic viability

    Human Molecular Genetics 10:99–105.

    https://doi.org/10.1093/hmg/10.2.99

    • PubMed
    • Google Scholar
24. 1. Meyer LR
    2. Zweig AS
    3. Hinrichs AS
    4. Karolchik D
    5. Kuhn RM
    6. Wong M
    7. Sloan CA
    8. Rosenbloom KR
    9. Roe G
    10. Rhead B
    11. Raney BJ
    12. Pohl A
    13. Malladi VS
    14. Li CH
    15. Lee BT
    16. Learned K
    17. Kirkup V
    18. Hsu F
    19. Heitner S
    20. Harte RA
    21. Haeussler M
    22. Guruvadoo L
    23. Goldman M
    24. Giardine BM
    25. Fujita PA
    26. Dreszer TR
    27. Diekhans M
    28. Cline MS
    29. Clawson H
    30. Barber GP
    31. Haussler D
    32. Kent WJ

    (2013) The UCSC genome browser database: extensions and updates 2013

    Nucleic Acids Research 41:D64–D69.

    https://doi.org/10.1093/nar/gks1048

    • PubMed
    • Google Scholar
25. 1. Naftelberg S
    2. Schor IE
    3. Ast G
    4. Kornblihtt AR

    (2015) Regulation of alternative splicing through coupling with transcription and chromatin structure

    Annual Review of Biochemistry 84:165–198.

    https://doi.org/10.1146/annurev-biochem-060614-034242

    • Google Scholar
26. 1. Perez AR
    2. Pritykin Y
    3. Vidigal JA
    4. Chhangawala S
    5. Zamparo L
    6. Leslie CS
    7. Ventura A

    (2017) GuideScan software for improved single and paired CRISPR guide RNA design

    Nature Biotechnology 35:347–349.

    https://doi.org/10.1038/nbt.3804

    • PubMed
    • Google Scholar
27. 1. Ran FA
    2. Hsu PD
    3. Wright J
    4. Agarwala V
    5. Scott DA
    6. Zhang F

    (2013) Genome engineering using the CRISPR-Cas9 system

    Nature Protocols 8:2281–2308.

    https://doi.org/10.1038/nprot.2013.143

    • PubMed
    • Google Scholar
28. 1. Richardson CD
    2. Ray GJ
    3. DeWitt MA
    4. Curie GL
    5. Corn JE

    (2016) Enhancing homology-directed genome editing by catalytically active and inactive CRISPR-Cas9 using asymmetric donor DNA

    Nature Biotechnology 34:339–344.

    https://doi.org/10.1038/nbt.3481

    • PubMed
    • Google Scholar
29. 1. Robinson JT
    2. Thorvaldsdóttir H
    3. Winckler W
    4. Guttman M
    5. Lander ES
    6. Getz G
    7. Mesirov JP

    (2011) Integrative genomics viewer

    Nature Biotechnology 29:24–26.

    https://doi.org/10.1038/nbt.1754

    • PubMed
    • Google Scholar
30. Software

    1. Server EV

    (2016) NHLBI GO exome sequencing project (ESP), version 0.0.25

    BIOGPS.

    http://biogps.org/plugin/1138/nhlbi-exome-sequencing-project-esp-exome-variant-server/
31. 1. Simone CG
    2. Zuluaga Toro T
    3. Chan E
    4. Feely MM
    5. Trevino JG
    6. George TJ

    (2013) Characteristics and outcomes of adenosquamous carcinoma of the pancreas

    Journal of Clinical Oncology 6:311–379.

    https://doi.org/10.1200/jco.2013.31.4_suppl.311

    • PubMed
    • Google Scholar
32. 1. Trapnell C
    2. Pachter L
    3. Salzberg SL

    (2009) TopHat: discovering splice junctions with RNA-Seq

    Bioinformatics 25:1105–1111.

    https://doi.org/10.1093/bioinformatics/btp120

    • PubMed
    • Google Scholar
33. 1. Witkiewicz AK
    2. McMillan EA
    3. Balaji U
    4. Baek G
    5. Lin WC
    6. Mansour J
    7. Mollaee M
    8. Wagner KU
    9. Koduru P
    10. Yopp A
    11. Choti MA
    12. Yeo CJ
    13. McCue P
    14. White MA
    15. Knudsen ES

    (2015) Whole-exome sequencing of pancreatic Cancer defines genetic diversity and therapeutic targets

    Nature Communications 6:6744.

    https://doi.org/10.1038/ncomms7744

    • PubMed
    • Google Scholar
34. 1. Wittkopp N
    2. Huntzinger E
    3. Weiler C
    4. Saulière J
    5. Schmidt S
    6. Sonawane M
    7. Izaurralde E

    (2009) Nonsense-mediated mRNA decay effectors are essential for zebrafish embryonic development and survival

    Molecular and Cellular Biology 29:3517–3528.

    https://doi.org/10.1128/MCB.00177-09

    • PubMed
    • Google Scholar
35. 1. Zhang J
    2. Sun X
    3. Qian Y
    4. Maquat LE

    (1998) Intron function in the nonsense-mediated decay of beta-globin mRNA: indications that pre-mRNA splicing in the nucleus can influence mRNA translation in the cytoplasm

    RNA 4:801–815.

    https://doi.org/10.1017/S1355838298971849

    • PubMed
    • Google Scholar

Author details

1. Jacob T Polaski

   1. Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
   2. Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States

   Contribution

   Conceptualization, Investigation, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6570-1789

2. Dylan B Udy

   1. Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
   2. Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
   3. Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

3. Luisa F Escobar-Hoyos

   1. David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, United States
   2. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
   3. Department of Pathology, Stony Brook University, New York, United States
   4. Yale University School of Medicine, New Haven, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Gokce Askan

   David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Steven D Leach

   1. David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, United States
   2. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, United States
   3. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, United States
   4. Dartmouth Norris Cotton Cancer Center, Lebanon, United States

   Contribution

   Supervision

   Competing interests

   No competing interests declared

6. Andrea Ventura

   Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Conceptualization, Supervision

   Competing interests

   No competing interests declared

7. Ram Kannan

   Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, United States

   Contribution

   Conceptualization, Investigation, Writing - original draft

   For correspondence

   ramk.1019@gmail.com

   Competing interests

   No competing interests declared

8. Robert K Bradley

   1. Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States
   2. Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, United States

   Contribution

   Conceptualization, Supervision, Writing - original draft

   For correspondence

   rbradley@fredhutch.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8046-1063

• 1,477

  views

• 187

  downloads

• 7

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.