Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related death among solid malignancies. Unfortunately, PDAC lethality has not substantially decreased over the past 20 years. This aggressiveness is related to the genomic complexity and heterogeneity of PDAC, but also to the absence of an effective screening for the detection of early-stage tumors and a lack of efficient therapeutic options. Therefore, there is an urgent need to improve the arsenal of anti-PDAC drugs for an effective treatment of these patients. Patient-derived xenograft (PDX) mouse models represent a promising strategy to personalize PDAC treatment, offering a bench testing of candidate treatments and helping to select empirical treatments in PDAC patients with no therapeutic targets. Moreover, bioinformatics-based approaches have the potential to offer systematic insights into PDAC etiology predicting putatively actionable tumor-specific genomic alterations, identifying novel biomarkers and generating disease-associated gene expression signatures. This review focuses on recent efforts to individualize PDAC treatments using PDX models. Additionally, we discuss the current understanding of the PDAC genomic landscape and the putative druggable targets derived from mutational studies. PDAC molecular subclassifications and gene expression profiling studies are reviewed as well. Finally, latest bioinformatics methodologies based on somatic variant detection and prioritization, in silico drug response prediction, and drug repositioning to improve the treatment of advanced PDAC tumors are also covered.

Keywords:
Bioinformatics, Drug repositioning, Pancreatic cancer, Personalized medicine, Xenografts
1.
Ryan DP, Hong TS, Bardeesy N: Pancreatic adenocarcinoma. N Engl J Med 2014;371:1039-1049.
2.
Garrido-Laguna, I, Hidalgo M: Pancreatic cancer: from state-of-the-art treatments to promising novel therapies. Nat Rev Clin Oncol 2015;12:319-334.
3.
Von Hoff DD, Ervin T, Arena FP, Chiorean EG, Infante J, Moore M, Seay T, Tjulandin SA, Ma WW, Saleh MN, Harris M, Reni M, Dowden S, Laheru D, Bahary N, Ramanathan RK, Tabernero J, Hidalgo M, Goldstein D, Van Cutsem E, Wei X, Iglesias J, Renschler MF: Increased survival in pancreatic cancer with nab-paclitaxel plus gemcitabine. N Engl J Med 2013;369:1691-1703.
4.
Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P, et al: Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 2008;321:1801-1806.
5.
Biankin AV, Waddell N, Kassahn KS, Gingras MC, Muthuswamy LB, Johns AL, et al: Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 2012;491:399-405.
6.
Waddell N, Pajic M, Patch AM, Chang DK, Kassahn KS, Bailey P, et al: Whole genomes redefine the mutational landscape of pancreatic cancer. Nature 2015;518:495-501.
7.
Xiong HQ, Varadhachary GR, Blais JC, Hess KR, Abbruzzese JL, Wolff RA: Phase 2 trial of oxaliplatin plus capecitabine (XELOX) as second-line therapy for patients with advanced pancreatic cancer. Cancer 2008;113:2046-2052.
8.
Pishvaian MJ, Brody JR: Therapeutic implications of molecular subtyping for pancreatic cancer. Oncology (Williston Park) 2017;31:159-166, 168.
9.
Badea L, Herlea V, Dima SO, Dumitrascu T, Popescu I: Combined gene expression analysis of whole-tissue and microdissected pancreatic ductal adenocarcinoma identifies genes specifically overexpressed in tumor epithelia. Hepatogastroenterology 2008;55:2016-2027.
10.
Collisson EA, Sadanandam A, Olson P, Gibb WJ, Truitt M, Gu S, Cooc J, Weinkle J, Kim GE, Jakkula L, Feiler HS, Ko AH, Olshen AB, Danenberg KL, Tempero MA, Spellman PT, Hanahan D, Gray JW: Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med 2011;17:500-503.
11.
Moffitt RA, Marayati R, Flate EL, Volmar KE, Loeza SG, Hoadley KA, Rashid NU, Williams LA, Eaton SC, Chung AH, Smyla JK, Anderson JM, Kim HJ, Bentrem DJ, Talamonti MS, Iacobuzio-Donahue CA, Hollingsworth MA, Yeh JJ: Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat Genet 2015;47:1168-1178.
12.
Bailey P, Chang DK, Nones K, Johns AL, Patch AM, Gingras MC, et al: Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 2016;531:47-52.
13.
Campbell PJ, Yachida S, Mudie LJ, Stephens PJ, Pleasance ED, Stebbings LA, et al: The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 2010;467:1109-1113.
14.
Yachida S, Jones S, Bozic I, Antal T, Leary R, Fu B, Kamiyama M, Hruban RH, Eshleman JR, Nowak MA, Velculescu VE, Kinzler KW, Vogelstein B, Iacobuzio-Donahue CA: Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 2010;467:1114-1117.
15.
Hidalgo M, Amant F, Biankin AV, Budinská E, Byrne AT, Caldas C, Clarke RB, de Jong S, Jonkers J, Mælandsmo GM, Roman-Roman S, Seoane J, Trusolino L, Villanueva A: Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov 2014;4:998-1013.
16.
Tentler JJ, Tan AC, Weekes CD, Jimeno A, Leong S, Pitts TM, Arcaroli JJ, Messersmith WA, Eckhardt SG: Patient-derived tumour xenografts as models for oncology drug development. Nat Rev Clin Oncol 2012;9:338-350.
17.
Byrne AT, Alférez DG, Amant F, Annibali D, Arribas J, Biankin AV, et al: Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nat Rev Cancer 2017;17:254-268.
18.
Garrido-Laguna I, Uson M, Rajeshkumar NV, Tan AC, de Oliveira E, Karikari C, Villaroel MC, Salomon A, Taylor G, Sharma R, Hruban RH, Maitra A, Laheru D, Rubio-Viqueira B, Jimeno A, Hidalgo M: Tumor engraftment in nude mice and enrichment in stroma-related gene pathways predict poor survival and resistance to gemcitabine in patients with pancreatic cancer. Clin Cancer Res 2011;17:5793-5800.
19.
Rubio-Viqueira B, Jimeno A, Cusatis G, Zhang X, Iacobuzio-Donahue C, Karikari C, Shi C, Danenberg K, Danenberg PV, Kuramochi H, Tanaka K, Singh S, Salimi-Moosavi H, Bouraoud N, Amador ML, Altiok S, Kulesza P, Yeo C, Messersmith W, Eshleman J, Hruban RH, Maitra A, Hidalgo M: An in vivo platform for translational drug development in pancreatic cancer. Clin Cancer Res 2006;12:4652-4661.
20.
Kim MP, Evans DB, Wang H, Abbruzzese JL, Fleming JB, Gallick GE: Generation of orthotopic and heterotopic human pancreatic cancer xenografts in immunodeficient mice. Nat Protoc 2009;4:1670-1680.
21.
Witkiewicz AK, Balaji U, Eslinger C, McMillan E, Conway W, Posner B, Mills GB, O'Reilly EM, Knudsen ES: Integrated patient-derived models delineate individualized therapeutic vulnerabilities of pancreatic cancer. Cell Rep 2016;16:2017-2031.
22.
Bruna A, Rueda OM, Greenwood W, Batra AS, Callari M, Batra RN, et al: A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 2016;167:260-274.e22.
23.
Dong X, Guan J, English JC, Flint J, Yee J, Evans K, Murray N, Macaulay C, Ng RT, Gout PW, Lam WL, Laskin J, Ling V, Lam S, Wang Y: Patient-derived first generation xenografts of non-small cell lung cancers: promising tools for predicting drug responses for personalized chemotherapy. Clin Cancer Res 2010;16:1442-1451.
24.
Hidalgo M, Bruckheimer E, Rajeshkumar NV, Garrido-Laguna I, De Oliveira E, Rubio-Viqueira B, Strawn S, Wick MJ, Martell J, Sidransky D: A pilot clinical study of treatment guided by personalized tumorgrafts in patients with advanced cancer. Mol Cancer Ther 2011;10:1311-1316.
25.
Rubio-Camarillo M, Gómez-López G, Fernández JM, Valencia A, Pisano DG: RUbioSeq: a suite of parallelized pipelines to automate exome variation and bisulfite-seq analyses. Bioinformatics 2013;29:1687-1689.
26.
Conway T, Wazny J, Bromage A, Tymms M, Sooraj D, Williams ED, Beresford-Smith B: Xenome - a tool for classifying reads from xenograft samples. Bioinformatics 2012;28:i172-i178.
27.
Tso KY, Lee SD, Lo KW, Yip KY: Are special read alignment strategies necessary and cost-effective when handling sequencing reads from patient-derived tumor xenografts? BMC Genomics 2014;15:1172.
28.
Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, et al: Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 2012;483:570-575.
29.
Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012;483:603-607.
30.
Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, et al: An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 2013;154:1151-1161.
31.
Cowley GS, Weir BA, Vazquez F, Tamayo P, Scott JA, Rusin S, et al: Parallel genome-scale loss of function screens in 216 cancer cell lines. Sci Data 2014;1:140035.
32.
Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, et al: A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. bioRxiv. 2017. http://biorxiv.org/content/early/2017/05/10/136168.
33.
Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander ES, Golub TR: The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006;313:1929-1935.
34.
Wagner AH, Coffman AC, Ainscough BJ, Spies NC, Skidmore ZL, Campbell KM, Krysiak K, Pan D, McMichael JF, Eldred JM, Walker JR, Wilson RK, Mardis ER, Griffith M, Griffith OL: DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res 2016;44:D1036-D1044.
35.
Gohlke BO, Nickel J, Otto R, Dunkel M, Preissner R: CancerResource - updated database of cancer-relevant proteins, mutations and interacting drugs. Nucleic Acids Res 2016;44:D932-D937.
37.
Piñeiro-Yañez E, Reboiro-Jato M, Perales-Patón J, Troulé K, Rodriguez JM, Tejero H, Shimamura T, Carretero J, Valencia A, Gómez-López G, Hidalgo M, Glez-Peña D, Al-Shahrour F: PanDrugs: prioritizing drug treatment in cancer patients according to individual genomic data, submitted.
38.
Dhir M, Choudry HA, Holtzman MP, Pingpank JF, Ahrendt SA, Zureikat AH, Hogg ME, Bartlett DL, Zeh HJ, Singhi AD, Bahary N: Impact of genomic profiling on the treatment and outcomes of patients with advanced gastrointestinal malignancies. Cancer Med 2017;6:195-206.
39.
Garralda E, Paz K, López-Casas PP, Jones S, Katz A, Kann LM, López-Rios F, Sarno F, Al-Shahrour F, Vasquez D, Bruckheimer E, Angiuoli SV, Calles A, Diaz LA, Velculescu VE, Valencia A, Sidransky D, Hidalgo M: Integrated next-generation sequencing and avatar mouse models for personalized cancer treatment. Clin Cancer Res 2014;20:2476-2484.
40.
Xiong HQ, Varadhachary GR, Blais JC, Hess KR, Abbruzzese JL, Wolff RA: Phase 2 trial of oxaliplatin plus capecitabine (XELOX) as second-line therapy for patients with advanced pancreatic cancer. Cancer 2008;113:2046-2052.
41.
Chong CR, Sullivan DJ Jr: New uses for old drugs. Nature 2007;448:645-646.
42.
Würth R, Thellung S, Bajetto A, Mazzanti M, Florio T, Barbieri F: Drug-repositioning opportunities for cancer therapy: novel molecular targets for known compounds. Drug Discov Today 2016;21:190-199.
43.
Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ: Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med 2011;3:96ra77.
44.
Moffat JG, Rudolph J, Bailey D: Phenotypic screening in cancer drug discovery - past, present and future. Nat Rev Drug Discov 2014;13:588-602.
45.
Jahchan NS, Dudley JT, Mazur PK, Flores N, Yang D, Palmerton A, Zmoos AF, Vaka D, Tran KQ, Zhou M, Krasinska K, Riess JW, Neal JW, Khatri P, Park KS, Butte AJ, Sage J: A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small cell lung cancer and other neuroendocrine tumors. Cancer Discov 2013;3:1364-1377.
46.
Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z: A survey of current trends in computational drug repositioning. Brief Bioinform 2016;17:2-12.
47.
Iorio F, Rittman T, Ge H, Menden M, Saez-Rodriguez J: Transcriptional data: a new gateway to drug repositioning? Drug Discov Today 2013;18:350-357.
48.
Dudley JT, Sirota M, Shenoy M, Pai RK, Roedder S, Chiang AP, Morgan AA, Sarwal MM, Pasricha PJ, Butte AJ: Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med 2011;3:96ra76.
49.
Iorio F, Saez-Rodriguez J, di Bernardo D: Network based elucidation of drug response: from modulators to targets. BMC Syst Biol 2013;7:139.
50.
Sorrells TR, Johnson AD: Making sense of transcription networks. Cell 2015;161:714-723.
51.
Wang RS, Saadatpour A, Albert R: Boolean modeling in systems biology: an overview of methodology and applications. Phys Biol 2012;9:055001.
52.
O'Brien EJ, Monk JM, Palsson BO: Using genome-scale models to predict biological capabilities. Cell 2015;161:971-987.
53.
Califano A, Alvarez MJ: The recurrent architecture of tumour initiation, progression and drug sensitivity. Nat Rev Cancer 2017;17:116-113.
54.
Giulietti M, Occhipinti G, Principato G, Piva F: Weighted gene co-expression network analysis reveals key genes involved in pancreatic ductal adenocarcinoma development. Cell Oncol (Dordr) 2016;39:379-388.
55.
Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, Califano A: Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet 2016;48:838-847.
56.
Ma Y, Hu J, Zhang N, Dong X, Li Y, Yang B, Tian W, Wang X: Prediction of candidate drugs for treating pancreatic cancer by using a combined approach. PLoS One 2016;11:e0149896.
57.
Rajeshkumar NV, Yabuuchi S, Pai S, De Oliveira E, Kamphorst JJ, Rabinowitz JD, Tejero H, Al-Shahrour F, Hidalgo M, Maitra A, Dang CV: Treatment of pancreatic cancer patient-derived xenograft panel with metabolic inhibitors reveals efficacy of phenformin. Clin Cancer Res 2017, Epub ahead of print.
58.
Kordes S, Pollak MN, Zwinderman AH, Mathot RA, Weterman MJ, Beeker A, et al: Metformin in patients with advanced pancreatic cancer: a double-blind, randomised, placebo-controlled phase 2 trial. Lancet Oncol 2015;16:839-847.
59.
Knudsen E, Vail P, Balaji U, Ngo H, Botros IW, Makarov V, Riaz N, Balachandran VP, Leach SD, Thompson DM, Chan TA, Witkiewicz AK: Stratification of pancreatic ductal adenocarcinoma: combinatorial genetic, stromal, and immunological markers. Clin Cancer Res 2017;23:4429-4440.
60.
Zhu Y, Knolhoff BL, Meyer MA, Nywening TM, West BL, Luo J, Wang-Gillam A, Goedegebuure SP, Linehan DC, DeNardo DG: CSF1/CSF1R blockade reprograms tumor-infiltrating macrophages and improves response to T-cell checkpoint immunotherapy in pancreatic cancer models. Cancer Res 2014;74:5057-5069.
61.
Kidd BA, Wroblewska A, Boland MR, Agudo J, Merad M, Tatonetti NP, Brown BD, Dudley JT: Mapping the effects of drugs on the immune system. Nat Biotechnol 2016;34:47-54.
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