Christian Pilarsky and Robert Grützmann

Dresden, Germany

Genomics of pancreatic ductal adenocarcinoma

Christian Pilarsky and Robert Grützmann

Dresden, Germany

Pancreatic cancer is one of the worst prognostic cancers because of the late diagnosis and the absence of effective treatment. Within all subtypes of this disease, ductal adenocarcinoma has the shortest survival time. In recent years, global genomics profiling allowed the identification of hundreds of genes that are perturbed in pancreatic cancer. The integration of different omics sources in the study of pancreatic cancer has revealed several molecular mechanisms, indicating the complex history of its development. However, validation of these genes as biomarkers for early diagnosis, prognosis or treatment efficacy is still incomplete but should lead to new approaches for the treatment of the disease in the future.

pancreatic ductal adenocarcinoma;

genomes;

DNA;

RNA;

next-generation sequencing;

precision medicine

Introduction

Pancreatic cancer is still one of the most malignant and aggressive types of cancer in humans with a very dismal prognosis. About 40 000 new cases are diagnosed in the United States each year making pancreatic cancer the fourth male and the fifth female leading cause of cancer related death.[1]The most abundant form of exocrine pancreatic cancer is ductal adenocarcinoma (PDAC).[2]In the last decades only small improvements were made in the therapy of this disease. The worse prognosis is due to the delayed diagnosis. At time of diagnosis many patients have already irresectable or/and metastasized cancer and only a small portion of patients have an opportunity of potential curative surgical treatment. The actual 5-year survival rate after surgical resection is about 20%, and 5% for all patients.[1]

Pancreatic cancer has several subtypes. Subtyping is done either by localization of the tumor, e.g. tumors of the ampulla Vateri,[3]by histopathological definition, e.g. intraductal papillary mucinous neoplasms[4,5]or by type of the originating cells, e.g. pancreatic neuroendocrine tumors.[6]This delineation can be done with conventional technologies and it is routine. Some patients live much longer than the observed median survival time. Therefore the interesting question of the classification of PDAC patients into clinical subgroups should be answered in future. In recent years omics technologies have generated a wealth of information regarding the changes in primary tumors, metastasis and cancer models. Those technologies are an integral part of modern cancer research.

Challenges in the analysis of primary tumors

Many efforts to understand the cancer genome have evolved from the model system and especially cell lines. However, cell lines are poor models of cancer since solid tumor samples consist of a variety of tissues, of which the tumor cells might comprise only a small portion.[7]The adjacent other tissues contain surrounding stromal and endothelial cells. Microdissection might be needed to identify the true changes between the tumor cells and their normal counterpart. In pancreatic cancer this is the case, since the tumor contains a high content of stromal tissue.[8]Microdissection can be performed manually or with a laser-based system. Manual microdissection leads to an enrichment of the tumor tissue to about 90%, whereas with the laser-based system pure tumor cells can be obtained. Manual microdissection gives the researcher the opportunity to produce samples for analysis much faster.[9]If laser assisted microdissection is used the selection of instruments can be important for the results.[10]Microdissection is also the technique of choice if other tissues in a tumor need to beanalyzed. With microdissection usually only low amounts of nucleic acids are obtained. This is a major drawback for any omics technology, since sometimes some micrograms of nucleic acid are needed. Often microdissection is omitted for fast analysis leading to a skewed picture of the changes in the tissue analyzed and impeding the follow up analysis. Furthermore, the combination of microdissection and modern molecular analysis identifies a large number of changes. This leads to a question which changes in a cancer are associated with which type of cells, making it worthwhile to analyze not whole tumors anymore but parts of the cancer, indicating that whole tumor analysis may be a part of molecular biology heritage.[11]

Different ome's and next generation sequencing

Researchers in the past were partly defined by the technology they used to assess the properties of a given sample. Based on the selected technology they were able to use different omics technologies to analyze different parts of the cellular machinery focusing on their technology field instead on the complete compendium of crucial changes involved in the progress of a tumor. The field of omics research changed radically with the introduction of next generation sequencing (NGS) platforms. NGS is a converging technology like the smart phone. By the ability to integrate the different technologies relevant to cancer research (mutation detection, copy number assignment, gene expression profiling and methylation analysis), NGS frees the researcher from technical challenges of each technological platform leading to new scientific questions. Today's challenges are therefore twofold. First, defining the right tissue to use. Clinical characterization of tissue by classic histopathology in combination with microdissection to use purified nucleic acids is a good starting point. Second, to work with the amounts of data, which drop from NGS machines. At the moment this can only be done by trained biostatisticians, which should work faithfully with other scientists to identify the relevant changes in the tumor based on the questions developed beforehand. Analyzing tumors with NGS has to incorporate guidelines developed by clinical studies, with the definition of the samples to be tested and stop criteria.

Transcriptome

Transcriptome analysis is a common practice in modern molecular biology. It roots in the application of DNA microarrays to analyze the gene expression of different samples. Within PDAC large studies to understand the differential expression between tumor and normal tissue have been performed successfully.[12-15]Gene expression profiling has several disadvantages, which can be overcome using a different technology for expression profiling like NGS. The first approach combining transcriptomics and sequencing employed the use of cDNA libraries to understand which part of the genome is transcribed; the so called expressed sequence tags.[16]As a result from these efforts databases were created allowing for expression profiling in the computer for the identification of candidate genes in PDAC.[17]With the advent of NGS instruments large scale sequencing of RNA (RNA-Seq) regained focus of the scientists. RNA-Seq can be used to master major shortcomings of other transcriptomics techniques and is complementary to other methods of NGS like exome sequencing. It can detect somatic mutations and is important in the discovery of recurrent mutations in cancer.[18]RNA-Seq can be used for gene expression profiling with a high sensitivity, since the detection rate of transcripts is only limited by the number of reads produced. This has to be balanced against intrinsic problems of RNA-Seq. Since every available RNA molecule is sequenced and the gene expression profiling is biased to highly expressed genes, if low amounts of reads are produced. In contrast to other techniques such as microarrays, RNA-Seq tends to limit the signal strength of expressed genes. The generated expression profiles with such methods over-represent genes with intermediately expression.[19,20]

RNA-Seq is a one-stop shop for the determination of mutations, allele frequency and gene expression (mRNA, but also non-coding RNAs). It is also able to detect chimeric RNA molecule, differential splicing events and identification of unknown classes of RNA molecules. The detection of chimeric RNA molecules enables researchers to identify the translocation candidates for further analysis. Translocations are notoriously hard to identify, since the partners are unknown and conventional cytogenetic analysis lacks the resolution. However, the current limits of RNA-Seq limit the usable samples with large amounts of high quality RNA typically found in cell lines and tumor xenografts.[21]

Methylome

Hypermethylation of DNA in the coding areas of the human genome is a hallmark of cancer development.[22,23]It can be assumed that hypermethylation of genes is an early event in tumorigenesis.[24]Methylation marker might be identified in different ways, direct by analysis of the methylome[25,26]or by bioinformatic analysis of gene expression data. NGS can be used to identify new marker and give insights in the basic changes of tumor development by interrogating the methylome.[27-29]Sequencing of the complete human methylome is cost and time-consuming, therefore other large-scale methods for the detection of promoter methylation are used. Illumina's Infinium HumanMethylation450 BeadChip is a standard method of choice to investigate the methylation status for over 450 000 sites.[30-32]The availability of such tools leads to the need of post discovery validation of the identified methylated sites. This validation can be done by different techniques like pyrosequencing or methylation specific PCR, and the latter remains the quick and easy method for such purposes.

DNA hypermethylation has been used to investigate PDAC extensively and it has been shown that it occurs in pancreatic intraepithelial neoplasia (PanIN) lesions indicating that epigenetic changes might be interesting candidates for the development of an early diagnosis marker.[22,33,34]Studies[33,35-52]revealed that hypermethylation without NGS in tumor tissue and body fluids might serve as more than 100 possible marker genes.

Genome

The biggest impact of NGS can be found in the area of DNA sequencing. NGS leads to a reduction in cost and time needed for the process of sequencing. Advancements in computer sciences made it easier to analyze the data produced by the NGS machines. The concept of NGS, which employs massive parallel shotgun sequencing was envisioned by the work of Craig Venter in the late 1990s.[53]It is based on the already obtained knowledge of the human genome since short read sequences are mapped against the known human DNA sequence. A typical NGS instrument goes through billions of fragmented DNAs (reads) simultaneously. The occurring sequencing errors in the reads are compensated by reading multiple (30-40×) DNA fragments for an accurate determination of the sequence, thereby determining somatic mutations and allelic status of polymorphisms at the same time. Typically around 90 Gb of sequence representing 30-fold coverage is obtained to call 99% of all variant alleles.[54,55]Additionally high coverage with multiple reads is needed to identify mutations which may exist only in low allele frequencies since multiple tumor sub-clones within a tumor, comprising intra-tumor heterogeneity, exist. This aggravates the need for even more coverage.[56]The pathogenesis of cancer is heterogeneous in terms of the spectrum of gene mutations within the same histology subtype. Sequencing of many different tumor samples is necessary to understand the full spectrum of causative mutations. Several ongoing projects are analyzing large numbers of cancer specimens; in The Cancer Genome Atlas (TCGA) initiated by NIH/NCI in the USA and the International Cancer Genome Consortium (ICGC) a network of institutes from multiple nations.[57]These efforts might not be adequate since the number of tumors analyzed is not enough to comprise a picture of all changes in the tumor genome for each subtype. Two numbers easily explain this fact. Each year roughly 40 000 new cases of pancreatic cancer are diagnosed in the USA, whereas the total number of sequenced pancreatic cancer is in the hundreds. This resembles the work done in the field of high throughput expression profiling which lead to the result, that each published dataset showed different candidate genes with only a small overlap.[12]At the moment sequencing of tumors might help to identify major clades of tumor subtypes but for the true delineation of cancers, more data are needed. Comparison of the two key data sets on pancreatic cancer sequencing revealed an overlap of only already well known mutated genes in pancreatic cancer, like KRAS, TP53 CDKN2A and SMAD4 and two less known mutated genes TGFBR2 and MLL3.[58,59]Since the technology in NGS is proceeding rapidly, sequencing of thousands of PDAC cancer genomes will occur in the following years and will give us new insights in the different subtypes during progression of this disease.

Precision medicine

The new scientific knowledge from the large NGS programs will ultimately have impact on the treatment decisions made in the hospital. From the past, we have learned that the progress gained from this insight will take 10 to 15 years until it reaches the practical medicine. However, NGS leads to new possibilities to radically change the treatment of such patients in a very short time. The combination of NGS to determine critical mutations in one patient and then to treat this mutation in this patient with already approved drugs might give small patient groups the opportunity to survive longer than the average. This has been demonstrated beautifully in lung cancer, where the treatment of patients with EML4/ALK positive cancer with crizotinib enhances their survival considerably.[60]This targeted therapy enables the clinicians to treat each patient with an oncogenic ALK activation regardless of the origin of the tumor.[61]NGS sequencing of cancer patients might focus on mutations with known susceptibilities for different drugs overcoming the major drawback in NGS, the need of large amounts of nucleic acid material. Systems for targeted resequencing of mutations need only minute amounts of DNA and will enable the clinician to analyze nucleic acids from pure tumor tissue in a short period.[62]This will also reduce the cost considerably.Analyzing the results of the combination of sequencing and adapted therapy will not be easy and might result in a large set of N-of-1 trials since the treatment is based on the genotype of an individual patient.[63]

In PDAC, such mutations still have to be identified. It has to be proven that pharmaceutical intervention adds to the survival time of such patients and precision medicine will shape the therapeutic landscape for PDAC patients into different treatment options with much better efficacy than the chemotherapies.

Contributors:GR proposed the study. PC and GR performed research and wrote the first draft. All authors contributed to the design and interpretation of the study and to further drafts. GR is the guarantor.

Funding:None.

Ethical approval:Not needed.

Competing interest:No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.

1 Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin 2013;63:11-30.

2 Schneider G, Siveke JT, Eckel F, Schmid RM. Pancreatic cancer: basic and clinical aspects. Gastroenterology 2005;128: 1606-1625.

3 Ehehalt F, Rümmele P, Kersting S, Lang-Schwarz C, Rückert F, Hartmann A, et al. Hepatocyte nuclear factor (HNF) 4α expression distinguishes ampullary cancer subtypes and prognosis after resection. Ann Surg 2011;254:302-310.

4 Ehehalt F, Saeger HD, Schmidt CM, Grützmann R. Neuroendocrine tumors of the pancreas. Oncologist 2009;14:456-467.

5 Distler M, Kersting S, Niedergethmann M, Aust DE, Franz M, Rückert F, et al. Pathohistological subtype predicts survival in patients with intraductal papillary mucinous neoplasm (IPMN) of the pancreas. Ann Surg 2013;258:324-330.

6 Reid MD, Saka B, Balci S, Goldblum AS, Adsay NV. Molecular genetics of pancreatic neoplasms and their morphologic correlates: an update on recent advances and potential diagnostic applications. Am J Clin Pathol 2014;141:168-180.

7 Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144:646-674.

8 Pilarsky C, Ammerpohl O, Sipos B, Dahl E, Hartmann A, Wellmann A, et al. Activation of Wnt signalling in stroma from pancreatic cancer identified by gene expression profiling. J Cell Mol Med 2008;12:2823-2835.

9 Kristiansen G. Manual microdissection. Methods Mol Biol 2010;576:31-38.

10 Vandewoestyne M, Goossens K, Burvenich C, Van Soom A, Peelman L, Deforce D. Laser capture microdissection: should an ultraviolet or infrared laser be used? Anal Biochem 2013;439:88-98.

11 Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366:883-892.

12 Grützmann R, Boriss H, Ammerpohl O, Lüttges J, Kalthoff H, Schackert HK, et al. Meta-analysis of microarray data on pancreatic cancer defines a set of commonly dysregulated genes. Oncogene 2005;24:5079-5088.

13 Alldinger I, Dittert D, Peiper M, Fusco A, Chiappetta G, Staub E, et al. Gene expression analysis of pancreatic cell lines reveals genes overexpressed in pancreatic cancer. Pancreatology 2005;5:370-379.

14 Grützmann R, Pilarsky C, Ammerpohl O, Lüttges J, B?hme A, Sipos B, et al. Gene expression profiling of microdissected pancreatic ductal carcinomas using high-density DNA microarrays. Neoplasia 2004;6:611-622.

15 Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A 2006;103:2257-2261.

16 Adams MD, Dubnick M, Kerlavage AR, Moreno R, Kelley JM, Utterback TR, et al. Sequence identification of 2,375 human brain genes. Nature 1992;355:632-634.

17 Grützmann R, Pilarsky C, Staub E, Schmitt AO, Foerder M, Specht T, et al. Systematic isolation of genes differentially expressed in normal and cancerous tissue of the pancreas. Pancreatology 2003;3:169-178.

18 Shah SP, K?bel M, Senz J, Morin RD, Clarke BA, Wiegand KC, et al. Mutation of FOXL2 in granulosa-cell tumors of the ovary. N Engl J Med 2009;360:2719-2729.

19 Mastrokolias A, den Dunnen JT, van Ommen GB, 't Hoen PA, van Roon-Mom WM. Increased sensitivity of next generation sequencing-based expression profiling after globin reduction in human blood RNA. BMC Genomics 2012;13:28.

20 Mooney M, Bond J, Monks N, Eugster E, Cherba D, Berlinski P, et al. Comparative RNA-Seq and microarray analysis of gene expression changes in B-cell lymphomas of Canis familiaris. PLoS One 2013;8:e61088.

21 Edgren H, Murumagi A, Kangaspeska S, Nicorici D, Hongisto V, Kleivi K, et al. Identification of fusion genes in breast cancer by paired-end RNA-sequencing. Genome Biol 2011;12:R6.

22 Kulis M, Esteller M. DNA methylation and cancer. Adv Genet 2010;70:27-56.

23 Nakhasi HL, Lynch KR, Dolan KP, Unterman RD, Feigelson P. Covalent modification and repressed transcription of a gene in hepatoma cells. Proc Natl Acad Sci U S A 1981;78:834-837.

24 Wissmann C, Wild PJ, Kaiser S, Roepcke S, Stoehr R, Woenckhaus M, et al. WIF1, a component of the Wnt pathway, is down-regulated in prostate, breast, lung, and bladder cancer. J Pathol 2003;201:204-212.

25 Lofton-Day C, Model F, Devos T, Tetzner R, Distler J, Schuster M, et al. DNA methylation biomarkers for blood-based colorectal cancer screening. Clin Chem 2008;54:414-423.

26 Tan AC, Jimeno A, Lin SH, Wheelhouse J, Chan F, Solomon A, et al. Characterizing DNA methylation patterns in pancreatic cancer genome. Mol Oncol 2009;3:425-438.

27 Murphy PJ, Cipriany BR, Wallin CB, Ju CY, Szeto K, Hagarman JA, et al. Single-molecule analysis of combinatorial epigenomic states in normal and tumor cells. Proc Natl Acad Sci U S A 2013;110:7772-7777.

28 Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 2009;462:315-322.

29 Stirzaker C, Taberlay PC, Statham AL, Clark SJ. Miningcancer methylomes: prospects and challenges. Trends Genet 2014;30:75-84.

30 Slieker RC, Bos SD, Goeman JJ, Bovée JV, Talens RP, van der Breggen R, et al. Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array. Epigenetics Chromatin 2013;6:26.

31 Sánchez-Vega F, Gotea V, Petrykowska HM, Margolin G, Krivak TC, DeLoia JA, et al. Recurrent patterns of DNA methylation in the ZNF154, CASP8, and VHL promoters across a wide spectrum of human solid epithelial tumors and cancer cell lines. Epigenetics 2013;8:1355-1372.

32 Morris TJ, Butcher LM, Feber A, Teschendorff AE, Chakravarthy AR, Wojdacz TK, et al. ChAMP: 450k Chip Analysis Methylation Pipeline. Bioinformatics 2014;30:428-430.

33 Dutruel C, Bergmann F, Rooman I, Zucknick M, Weichenhan D, Geiselhart L, et al. Early epigenetic downregulation of WNK2 kinase during pancreatic ductal adenocarcinoma development. Oncogene 2014;33:3401-3410.

34 Sato N, Fukushima N, Hruban RH, Goggins M. CpG island methylation profile of pancreatic intraepithelial neoplasia. Mod Pathol 2008;21:238-244.

35 Wehrum D, Grützmann R, Hennig M, Saeger HD, Pilarsky C. Recent patents concerning diagnostic and therapeutic applications of aberrantly methylated sequences in pancreatic cancer. Recent Pat DNA Gene Seq 2008;2:97-106.

36 Thun M, Jemal A, Desantis C, Blackard B, Ward E. An overview of the cancer burden for primary care physicians. Prim Care 2009;36:439-454.

37 Kuroki T, Yendamuri S, Trapasso F, Matsuyama A, Aqeilan RI, Alder H, et al. The tumor suppressor gene WWOX at FRA16D is involved in pancreatic carcinogenesis. Clin Cancer Res 2004;10:2459-2465.

38 Zhao G, Qin Q, Zhang J, Liu Y, Deng S, Liu L, et al. Hypermethylation of HIC1 promoter and aberrant expression of HIC1/SIRT1 might contribute to the carcinogenesis of pancreatic cancer. Ann Surg Oncol 2013;20:S301-311.

39 Yao F, Sun M, Dong M, Jing F, Chen B, Xu H, et al. NPTX2 hypermethylation in pure pancreatic juice predicts pancreatic neoplasms. Am J Med Sci 2013;346:175-180.

40 Yang L, Yang H, Li J, Hao J, Qian J. ppENK Gene Methylation Status in the Development of Pancreatic Carcinoma. Gastroenterol Res Pract 2013;2013:130927.

41 Yamamura A, Miura K, Karasawa H, Morishita K, Abe K, Mizuguchi Y, et al. Suppressed expression of NDRG2 correlates with poor prognosis in pancreatic cancer. Biochem Biophys Res Commun 2013;441:102-107.

42 Wang P, Chen L, Zhang J, Chen H, Fan J, Wang K, et al. Methylation-mediated silencing of the miR-124 genes facilitates pancreatic cancer progression and metastasis by targeting Rac1. Oncogene 2014;33:514-524.

43 Thu KL, Radulovich N, Becker-Santos DD, Pikor LA, Pusic A, Lockwood WW, et al. SOX15 is a candidate tumor suppressor in pancreatic cancer with a potential role in Wnt/β-catenin signaling. Oncogene 2014;33:279-288.

44 Park JS, Park YN, Lee KY, Kim JK, Yoon DS. P16 hypermethylation predicts surgical outcome following curative resection of mid/distal bile duct cancer. Ann Surg Oncol 2013;20:2511-2517.

45 Zhao L, Cui Q, Lu Z, Chen J. Aberrant methylation of RASSF2A in human pancreatic ductal adenocarcinoma and its relation to clinicopathologic features. Pancreas 2012;41:206-211.

46 Zhang L, Gao J, Li Z, Gong Y. Neuronal pentraxin II (NPTX2) is frequently down-regulated by promoter hypermethylation in pancreatic cancers. Dig Dis Sci 2012;57:2608-2614.

47 Wu Y, Li J, Sun CY, Zhou Y, Zhao YF, Zhang SJ. Epigenetic inactivation of the canonical Wnt antagonist secreted frizzled-related protein 1 in hepatocellular carcinoma cells. Neoplasma 2012;59:326-332.

48 Li M, Zhao ZW. Clinical implications of mismatched repair gene promoter methylation in pancreatic cancer. Med Oncol 2012;29:970-976.

49 Hong SM, Omura N, Vincent A, Li A, Knight S, Yu J, et al. Genome-wide CpG island profiling of intraductal papillary mucinous neoplasms of the pancreas. Clin Cancer Res 2012;18:700-712.

50 Heichman KA, Warren JD. DNA methylation biomarkers and their utility for solid cancer diagnostics. Clin Chem Lab Med 2012;50:1707-1721.

51 Giovannetti E, Erozenci A, Smit J, Danesi R, Peters GJ. Molecular mechanisms underlying the role of microRNAs (miRNAs) in anticancer drug resistance and implications for clinical practice. Crit Rev Oncol Hematol 2012;81:103-122.

52 Park JK, Ryu JK, Yoon WJ, Lee SH, Lee GY, Jeong KS, et al. The role of quantitative NPTX2 hypermethylation as a novel serum diagnostic marker in pancreatic cancer. Pancreas 2012;41:95-101.

53 Venter JC, Adams MD, Sutton GG, Kerlavage AR, Smith HO, Hunkapiller M. Shotgun sequencing of the human genome. Science 1998;280:1540-1542.

54 Li H, Ruan J, Durbin R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 2008;18:1851-1858.

55 Yoshida K, Sanada M, Ogawa S. Deep sequencing in cancer research. Jpn J Clin Oncol 2013;43:110-115.

56 Martinez P, Birkbak NJ, Gerlinger M, McGranahan N, Burrell RA, Rowan AJ, et al. Parallel evolution of tumour subclones mimics diversity between tumours. J Pathol 2013;230:356-364.

57 Cancer Genome Atlas Research Network, Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 2013;45:1113-1120.

58 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.

59 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.

60 Kwak EL, Bang YJ, Camidge DR, Shaw AT, Solomon B, Maki RG, et al. Anaplastic lymphoma kinase inhibition in nonsmall-cell lung cancer. N Engl J Med 2010;363:1693-1703.

61 Godbert Y, Henriques de Figueiredo B, Bonichon F, Chibon F, Hostein I, Pérot G, et al. Remarkable response to crizotinib in woman with anaplastic lymphoma kinase-rearranged anaplastic thyroid carcinoma. J Clin Oncol 2014 Mar 31.

62 Mendelsohn J. Personalizing oncology: perspectives and prospects. J Clin Oncol 2013;31:1904-1911.

63 Brannon AR, Sawyers CL. "N of 1" case reports in the era of whole-genome sequencing. J Clin Invest 2013;123:4568-4570.

Received April 10, 2014

Accepted after revision May 6, 2014

(Hepatobiliary Pancreat Dis Int 2014;13:381-385)

Author Affiliations: Department of Vascular-, Thoracic and Visceral Surgery, University Hospital Dresden, Technische Universit?t Dresden, Fetscherstr. 74, Dresden 01307, Germany (Pilarsky C and Grützmann R)

Christian Pilarsky, PhD, Department of Vascular-, Thoracic and Visceral Surgery, University Hospital Dresden, Technische Universit?t Dresden, Fetscherstr. 74, Dresden 01307, Germany (Tel: +49-351-4587020; Fax: +49-351-4587338; Email: christian.pilarsky@gmail.com) ? 2014, Hepatobiliary Pancreat Dis Int. All rights reserved.

10.1016/S1499-3872(14)60281-2

Published online July 21, 2014.