Mutational burden of resectable pancreatic cancer, as determined by whole transcriptome and whole exome sequencing, predicts a poor prognosis

Grassi,Elisa; Durante,Sandra; Astolfi,Annalisa; Tarantino,Giuseppe; Indio,Valentina; Freier,Eva; Vecchiarelli,Silvia; Ricci,Claudio; Casadei,Riccardo; Formica,Francesca; Filippini,Daria; Comito,Francesca; Serra,Carla; Santini,Donatella; D' Errico,Antonietta; Minni,Francesco; Biasco,Guido; Di Marco,Mariacristina

doi:10.3892/ijo.2018.4344

Mutational burden of resectable pancreatic cancer, as determined by whole transcriptome and whole exome sequencing, predicts a poor prognosis

Authors:
- Elisa Grassi
- Sandra Durante
- Annalisa Astolfi
- Giuseppe Tarantino
- Valentina Indio
- Eva Freier
- Silvia Vecchiarelli
- Claudio Ricci
- Riccardo Casadei
- Francesca Formica
- Daria Filippini
- Francesca Comito
- Carla Serra
- Donatella Santini
- Antonietta D' Errico
- Francesco Minni
- Guido Biasco
- Mariacristina Di Marco
View Affiliations

Affiliations: Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Sant' Orsola-Malpighi Hospital, I-40138 Bologna, Italy, Interdepartmental Center of Cancer Research University of Bologna, Sant' Orsola-Malpighi Hospital, I-40138 Bologna, Italy, Department of Medical and Surgical Sciences, University of Bologna, Sant' Orsola-Malpighi Hospital, I-40138 Bologna, Italy, Department of Internal Medicine, University of Bologna, Sant' Orsola-Malpighi Hospital, I-40138 Bologna, Italy, Department of Pathology, University of Bologna, Sant' Orsola-Malpighi Hospital, I-40138 Bologna, Italy
Published online on: March 29, 2018 https://doi.org/10.3892/ijo.2018.4344
Pages: 1972-1980

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Despite the genomic characterization of pancreatic cancer (PC), marked advances in the development of prognosis classification and novel therapeutic strategies have yet to come. The present study aimed to better understand the genomic alterations associated with the invasive phenotype of PC, in order to improve patient selection for treatment options. A total of 30 PC samples were analysed by either whole transcriptome (9 samples) or exome sequencing (21 samples) on an Illumina platform (75X2 or 100X2 bp), and the results were matched with normal DNA to identify somatic events. Single nucleotide variants and insertions and deletions were annotated using public databases, and the pathogenicity of the identified variants was defined according to prior knowledge and mutation-prediction tools. A total of 43 recurrently altered genes were identified, which were involved in numerous pathways, including chromatin remodelling and DNA damage repair. In addition, an analysis limited to a subgroup of early stage patients (50% of samples) demonstrated that poor prognosis was significantly associated with a higher number of known PC mutations (P=0.047). Samples from patients with a better overall survival (>25 months) harboured an average of 24 events, whereas samples from patients with an overall survival of <25 months presented an average of 40 mutations. These findings indicated that a complex genetic profile in the early stage of disease may be associated with increased aggressiveness, thus suggesting an urgent requirement for an innovative approach to classify this disease.

View Figures

View References

1	Vogelzang NJ, Benowitz SI, Adams S, Aghajanian C, Chang SM, Dreyer ZE, Janne PA, Ko AH, Masters GA, Odenike O, et al: Clinical cancer advances 2011: Annual Report on Progress Against Cancer from the American Society of Clinical Oncology. J Clin Oncol. 30:88–109. 2012. View Article : Google Scholar
2	Katz MH, Wang H, Fleming JB, Sun CC, Hwang RF, Wolff RA, Varadhachary G, Abbruzzese JL, Crane CH, Krishnan S, et al: Long-term survival after multidisciplinary management of resected pancreatic adenocarcinoma. Ann Surg Oncol. 16:836–847. 2009. View Article : Google Scholar
3	Conroy T, Desseigne F, Ychou M, Bouché O, Guimbaud R, Bécouarn Y, Adenis A, Raoul JL, Gourgou-Bourgade S, de la Fouchardière C, et al Groupe Tumeurs Digestives of Unicancer; PRODIGE Intergroup: FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. N Engl J Med. 364:1817–1825. 2011. View Article : Google Scholar
4	Von Hoff DD, Ervin T, Arena FP, Chiorean EG, Infante J, Moore M, Seay T, Tjulandin SA, Ma WW, Saleh MN, et al: Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. N Engl J Med. 369:1691–1703. 2013. View Article : Google Scholar
5	Meyerson M, Gabriel S and Getz G: Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet. 11:685–696. 2010. View Article : Google Scholar
6	Cancer Genome Atlas Research Network: Electronic address: andrew_aguirre@dfci.harvard.eduCancer Genome Atlas Research Network: Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma. Cancer Cell. 32:185–203.e13. 2017. View Article : Google Scholar
7	Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P, Mankoo P, Carter H, Kamiyama H, Jimeno A, et al: Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 321:1801–1806. 2008. View Article : Google Scholar
8	Schubert M, Lindgreen S and Orlando L: AdapterRemoval v2: Rapid adapter trimming, identification, and read merging. BMC Res Notes. 9:882016. View Article : Google Scholar
9	Lang B, Trapnell C, Pop M and Salzberg L: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10:R252009. View Article : Google Scholar
10	Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al: 1000 Genome roject Data Processing Subgroup. The SequenceAlignment/Map format and SAMtools. Bioinformatics. 25:2078–2079. 2009. View Article : Google Scholar
11	McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, et al: The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20:1297–1303. 2010. View Article : Google Scholar
12	Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES and Getz G: Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 31:213–219. 2013. View Article : Google Scholar
13	Goya R, Sun MGF, Morin RD, Leung G, Ha G, Wiegand KC, Senz J, Crisan A, Marra MA, Hirst M, et al: SNVMix: Predicting single nucleotide variants from next-generation sequencing of tumors. Bioinformatics. 26:730–736. 2010. View Article : Google Scholar
14	Wang K, Li M and Hakonarson H: ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38:e1642010. View Article : Google Scholar
15	Ramos AH, Lichtenstein L, Gupta M, Lawrence MS, Pugh TJ, Saksena G, Meyerson M and Getz G: Oncotator: Cancer variant annotation tool. Hum Mutat. 36:E2423–E2429. 2015. View Article : Google Scholar
16	Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al: Catalog of Somatic Mutations in Cancer and bioinformatics mutation-prediction tools PolyPhen2. Nat Methods. 7:248–249. 2010. View Article : Google Scholar
17	Choi Y and Chan AP: PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics. 31:2745–2747. 2015. View Article : Google Scholar
18	Ng PC and Henikoff S: SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 31:3812–3814. 2003. View Article : Google Scholar
19	Boeva V, Popova T, Bleakley K, Chiche P, Cappo J, Schleiermacher G, Janoueix-Lerosey I, Delattre O and Barillot E: Control-FREEC: A tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics. 28:423–425. 2012. View Article : Google Scholar
20	Amarasinghe KC, Li J, Hunter SM, Ryland GL, Cowin PA, Campbell IG and Halgamuge SK: Inferring copy number and genotype in tumour exome data. BMC Genomics. 15:7322014. View Article : Google Scholar
21	Collisson EA, Sadanandam A, Olson P, Gibb WJ, Truitt M, Gu S, Cooc J, Weinkle J, Kim GE, Jakkula L, et al: Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med. 17:500–503. 2011. View Article : Google Scholar
22	Bailey P, Chang DK, Nones K, Johns AL, Patch AM, Gingras MC, Miller DK, Christ AN, Bruxner TJ, Quinn MC, et al Australian Pancreatic Cancer Genome Initiative: Genomic analyses identify molecular subtypes of pancreatic cancer. Nature. 531:47–52. 2016. View Article : Google Scholar
23	Waddell N, Pajic M, Patch AM, Chang DK, Kassahn KS, Bailey P, Johns AL, Miller D, Nones K, Quek K, et al Australian Pancreatic Cancer Genome Initiative: Whole genomes redefine the mutational landscape of pancreatic cancer. Nature. 518:495–501. 2015. View Article : Google Scholar
24	Di Marco M, Astolfi A, Grassi E, Vecchiarelli S, Macchini M, Indio V, Casadei R, Ricci C, D’Ambra M, Taffurelli G, et al: Characterization of pancreatic ductal adenocarcinoma using whole transcriptome sequencing and copy number analysis by single-nucleotide polymorphism array. Mol Med Rep. 12:7479–7484. 2015. View Article : Google Scholar
25	Samuel N and Hudson TJ: The molecular and cellular heterogeneity of pancreatic ductal adenocarcinoma. Nat Rev Gastroenterol Hepatol. 9:77–87. 2011. View Article : Google Scholar
26	Collins MA and Pasca di Magliano M: Kras as a key oncogene and therapeutic target in pancreatic cancer. Front Physiol. 4:4072014. View Article : Google Scholar
27	Biankin AV, Waddell N, Kassahn KS, Gingras MC, Muthuswamy LB, Johns AL, Miller DK, Wilson PJ, Patch AM, Wu J, et al Australian Pancreatic Cancer Genome Initiative: Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature. 491:399–405. 2012. View Article : Google Scholar
28	Blackford A, Serrano OK, Wolfgang CL, Parmigiani G, Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Eshleman JR, et al: SMAD4 gene mutations are associated with poor prognosis in pancreatic cancer. Clin Cancer Res. 15:4674–4679. 2009. View Article : Google Scholar
29	Shin SH, Kim HJ, Hwang DW, Lee JH, Song KB, Jun E, Shim IK, Hong SM, Kim HJ, Park KM, et al: The DPC4/SMAD4 genetic status determines recurrence patterns and treatment outcomes in resected pancreatic ductal adenocarcinoma: A prospective cohort study. Oncotarget. 8:17945–17959. 2017. View Article : Google Scholar
30	Urey C, Andersson B, Ansari D, Sasor A, Said-Hilmersson K, Nilsson J and Andersson R: Low MUC4 expression is associated with survival benefit in patients with resectable pancreatic cancer receiving adjuvant gemcitabine. Scand J Gastroenterol. 52:595–600. 2017. View Article : Google Scholar
31	Jonckheere N, Lahdaoui F and Van Seuningen I: Targeting MUC4 in pancreatic cancer: miRNAs. Oncoscience. 2:799–800. 2015.
32	Koh YX, Chok AY, Zheng HL, Tan CS and Goh BK: Systematic review and meta-analysis comparing the surgical outcomes of invasive intraductal papillary mucinous neoplasms and conventional pancreatic ductal adenocarcinoma. Ann Surg Oncol. 21:2782–2800. 2014. View Article : Google Scholar
33	Jiang X, Hao HX, Growney JD, Woolfenden S, Bottiglio C, Ng N, Lu B, Hsieh MH, Bagdasarian L, Meyer R, et al: Inactivating mutations of RNF43 confer Wnt dependency in pancreatic ductal adenocarcinoma. Proc Natl Acad Sci USA. 110:12649–12654. 2013. View Article : Google Scholar
34	Brierley JD, Gospodarowicz MK and Wittekind C: UICC TNM Classification of Malignant Tumours. 8th edition. Wiley-Blackwell; Oxford: 2016