Abstract
The current convergence of molecular and pharmacological data provides unprecedented opportunities to gain insights into the relationships between the two types of data. Multiple forms of large-scale molecular data, including but not limited to gene and microRNA transcript expression, DNA somatic and germline variations from next-generation DNA and RNA sequencing, and DNA copy number from array comparative genomic hybridization are all potentially informative when one attempts to recognize the panoply of potentially influential events both for cancer progression and therapeutic outcome. Concurrently, there has also been a substantial expansion of the pharmacological data being accrued in a systematic fashion. For cancer cell lines, the National Cancer Institute cell line panel (NCI-60), the Cancer Cell Line Encyclopedia (CCLE), and the collaborative Genomics of Drug Sensitivity in Cancer (GDSC) databases all provide subsets of these forms of data. For the patient-derived data, The Cancer Genome Atlas (TCGA) provides analogous forms of genomic information along with treatment histories. Integration of these data in turn relies on the fields of statistics and statistical learning. Multiple algorithmic approaches may be chosen, depending on the data being considered, and the nature of the question being asked. Combining these algorithms with prior biological knowledge, the results of molecular biological studies, and the consideration of genes as pathways or functional groups provides both the challenge and the potential of the field. The ultimate goal is to provide a paradigm shift in the way that drugs are selected to provide a more targeted and efficacious outcome for the patient.
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Abaan OD, Polley EC, Davis SR, Zhu YJ, Bilke S, Walker RL, Pineda M, Gindin Y, Jiang Y, Reinhold WC, Holbeck SL, Simon RM, Doroshow JH, Pommier Y, Meltzer PS (2013) The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology. Cancer Res 73:4372–4382. doi:10.1158/0008-5472.CAN-12-3342
Adams S, Robbins FM, Chen D, Wagage D, Holbeck SL, Morse HC 3rd, Stroncek D, Marincola FM (2005) HLA class I and II genotype of the NCI-60 cell lines. J Transl Med 3:11. doi:10.1186/1479-5876-3-11
Aksoy BA, Demir E, Babur O, Wang W, Jing X, Schultz N, Sander C (2014) Prediction of individualized therapeutic vulnerabilities in cancer from genomic profiles. Bioinformatics. doi:10.1093/bioinformatics/btu164
Algeciras-Schimnich A, Pietras EM, Barnhart BC, Legembre P, Vijayan S, Holbeck SL, Peter ME (2003) Two CD95 tumor classes with different sensitivities to antitumor drugs. Proc Natl Acad Sci USA 100:11445–11450. doi:10.1073/pnas.2034995100
Amundson SA, Do KT, Vinikoor LC, Lee RA, Koch-Paiz CA, Ahn J, Reimers M, Chen Y, Scudiero DA, Weinstein JN, Trent JM, Bittner ML, Meltzer PS, Fornace AJ Jr (2008) Integrating global gene expression and radiation survival parameters across the 60 cell lines of the National Cancer Institute Anticancer Drug Screen. Cancer Res 68:415–424. doi:10.1158/0008-5472.CAN-07-2120
Baggerly KA, Coombes KR (2009) Deriving chemosensitivity from cell lines: forensic bioinformatics and reproducible research in high-throughput biology. Ann Appl Stat 3:1309–1334. doi:10.1214/09-Aoas291
Banerji S, Cibulskis K, Rangel-Escareno C, Brown KK, Carter SL, Frederick AM, Lawrence MS, Sivachenko AY, Sougnez C, Zou L, Cortes ML, Fernandez-Lopez JC, Peng S, Ardlie KG, Auclair D, Bautista-Pina V, Duke F, Francis J, Jung J, Maffuz-Aziz A, Onofrio RC, Parkin M, Pho NH, Quintanar-Jurado V, Ramos AH, Rebollar-Vega R, Rodriguez-Cuevas S, Romero-Cordoba SL, Schumacher SE, Stransky N, Thompson KM, Uribe-Figueroa L, Baselga J, Beroukhim R, Polyak K, Sgroi DC, Richardson AL, Jimenez-Sanchez G, Lander ES, Gabriel SB, Garraway LA, Golub TR, Melendez-Zajgla J, Toker A, Getz G, Hidalgo-Miranda A, Meyerson M (2012) Sequence analysis of mutations and translocations across breast cancer subtypes. Nature 486:405–409. doi:10.1038/nature11154
Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehar J, Kryukov GV, Sonkin D, Reddy A, Liu M, Murray L, Berger MF, Monahan JE, Morais P, Meltzer J, Korejwa A, Jane-Valbuena J, Mapa FA, Thibault J, Bric-Furlong E, Raman P, Shipway A, Engels IH, Cheng J, Yu GK, Yu J, Aspesi P Jr, de Silva M, Jagtap K, Jones MD, Wang L, Hatton C, Palescandolo E, Gupta S, Mahan S, Sougnez C, Onofrio RC, Liefeld T, MacConaill L, Winckler W, Reich M, Li N, Mesirov JP, Gabriel SB, Getz G, Ardlie K, Chan V, Myer VE, Weber BL, Porter J, Warmuth M, Finan P, Harris JL, Meyerson M, Golub TR, Morrissey MP, Sellers WR, Schlegel R, Garraway LA (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483:603–607. doi:10.1038/nature11003
Blower PE, Verducci JS, Lin S, Zhou J, Chung JH, Dai Z, Liu CG, Reinhold W, Lorenzi PL, Kaldjian EP, Croce CM, Weinstein JN, Sadee W (2007) MicroRNA expression profiles for the NCI-60 cancer cell panel. Mol Cancer Ther 6(5):1483–1491
Bussey KJ, Chin K, Lababidi S, Reimers M, Reinhold WC, Kuo WL, Gwadry F, Ajay, Kouros-Mehr H, Fridlyand J, Jain A, Collins C, Nishizuka S, Tonon G, Roschke A, Gehlhaus K, Kirsch I, Scudiero DA, Gray JW, Weinstein JN (2006) Integrating data on DNA copy number with gene expression levels and drug sensitivities in the NCI-60 cell line panel. Mol Cancer Ther 5:853–867
Cerami EG, Gross BE, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader GD, Sander C (2011) Pathway commons, a web resource for biological pathway data. Nucleic Acids Res 39:D685–D690. doi:10.1093/nar/gkq1039
Choi BO, Koo SK, Park MH, Rhee H, Yang SJ, Choi KG, Jung SC, Kim HS, Hyun YS, Nakhro K, Lee HJ, Woo HM, Chung KW (2012) Exome sequencing is an efficient tool for genetic screening of Charcot-Marie-Tooth Disease. Hum Mutat. doi:10.1002/humu.22143
Coombes KR, Wang J, Baggerly KA (2007) Microarrays: retracing steps. Nat Med 13:1276–1277. doi:10.1038/Nm1107-1276b
Covell DG (2008) Connecting chemosensitivity, gene expression and disease. Trends Pharmacol Sci 29:1–5. doi:10.1016/j.tips.2007.10.015
Covell DG (2012) Integrating constitutive gene expression and chemoactivity: mining the NCI60 anticancer screen. PLoS ONE 7:e44631. doi:10.1371/journal.pone.0044631
Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, Caudy M, Garapati P, Gopinath G, Jassal B, Jupe S, Kalatskaya I, Mahajan S, May B, Ndegwa N, Schmidt E, Shamovsky V, Yung C, Birney E, Hermjakob H, D’Eustachio P, Stein L (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res 39:D691–D697. doi:10.1093/nar/gkq1018
Cromer MK, Starker LF, Choi M, Udelsman R, Nelson-Williams C, Lifton RP, Carling T (2012) Identification of somatic mutations in parathyroid tumors using whole-exome sequencing. J Clin Endocrinol Metab. doi:10.1210/jc.2012-1743
Cronin M, Ross JS (2011) Comprehensive next-generation cancer genome sequencing in the era of targeted therapy and personalized oncology. Biomark Med 5:293–305. doi:10.2217/bmm.11.37
Cunningham L, Finckbeiner S, Hyde RK, Southall N, Marugan J, Yedavalli VR, Dehdashti SJ, Reinhold WC, Alemu L, Zhao L, Yeh JR, Sood R, Pommier Y, Austin CP, Jeang KT, Zheng W, Liu P (2012) Identification of benzodiazepine Ro5-3335 as an inhibitor of CBF leukemia through quantitative high throughput screen against RUNX1-CBFbeta interaction. Proc Natl Acad Sci USA 109:14592–14597. doi:10.1073/pnas.1200037109
Cupelli LA, Hsu MC (1995) The human immunodeficiency virus type 1 Tat antagonist, Ro 5-3335, predominantly inhibits transcription initiation from the viral promoter. J Virol 69:2640–2643
Doherty D, Bamshad MJ (2012) Exome sequencing to find rare variants causing neurologic diseases. Neurology. doi:10.1212/WNL.0b013e3182617170
Dyment DA, Cader MZ, Chao MJ, Lincoln MR, Morrison KM, Disanto G, Morahan JM, De Luca GC, Sadovnick AD, Lepage P, Montpetit A, Ebers GC, Ramagopalan SV (2012) Exome sequencing identifies a novel, multiple sclerosis susceptibility variant in the TYK2 gene. Neurology. WNL.0b013e3182616fc4 [pii]
Folger O, Jerby L, Frezza C, Gottlieb E, Ruppin E, Shlomi T (2011) Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 7:501. doi:10.1038/msb.2011.35
Garner KM, Eastman A (2011) Variations in Mre11/Rad50/Nbs1 status and DNA damage-induced S-phase arrest in the cell lines of the NCI60 panel. BMC Cancer 11(206):1–13. doi:10.1186/1471-2407-11-206
Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, Greninger P, Thompson IR, Luo X, Soares J, Liu Q, Iorio F, Surdez D, Chen L, Milano RJ, Bignell GR, Tam AT, Davies H, Stevenson JA, Barthorpe S, Lutz SR, Kogera F, Lawrence K, McLaren-Douglas A, Mitropoulos X, Mironenko T, Thi H, Richardson L, Zhou W, Jewitt F, Zhang T, O’Brien P, Boisvert JL, Price S, Hur W, Yang W, Deng X, Butler A, Choi HG, Chang JW, Baselga J, Stamenkovic I, Engelman JA, Sharma SV, Delattre O, Saez-Rodriguez J, Gray NS, Settleman J, Futreal PA, Haber DA, Stratton MR, Ramaswamy S, McDermott U, Benes CH (2012) Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483:570–575. doi:10.1038/nature11005
Gillis N, Patel J, Innocenti F (2013) Clinical implementation of germ line cancer pharmacogenetic variants during the next-generation sequencing era. Clin Pharmacol Ther. doi:10.1038/clpt.2013.214
Gmeiner WH, Reinhold WC, Pommier Y (2010) Genome-wide mRNA and microRNA profiling of the NCI 60 cell-line screen and comparison of FdUMP[10] with fluorouracil, floxuridine, and topoisomerase 1 poisons. Mol Cancer Ther 9:3105–3114. doi:10.1158/1535-7163.MCT-10-0674
Haibe-Kains B, El-Hachem N, Birkbak NJ, Jin AC, Beck AH, Aerts HJ, Quackenbush J (2013) Inconsistency in large pharmacogenomic studies. Nature 504:389–393. doi:10.1038/nature12831
Hertz DL (2013) Germline pharmacogenetics of paclitaxel for cancer treatment. Pharmacogenomics 14:1065–1084. doi:10.2217/pgs.13.90
Holbeck S, Chang J, Best AM, Bookout AL, Mangelsdorf DJ, Martinez ED (2010a) Expression profiling of nuclear receptors in the NCI60 cancer cell panel reveals receptor–drug and receptor–gene interactions. Mol Endocrinol 24:1287–1296. doi:10.1210/me.2010-0040
Holbeck SL, Collins JM, Doroshow JH (2010b) Analysis of food and drug administration-approved anticancer agents in the NCI60 panel of human tumor cell lines. Mol Cancer Ther 9:1451–1460. doi:10.1158/1535-7163.MCT-10-0106
Jain M, Nilsson R, Sharma S, Madhusudhan N, Kitami T, Souza AL, Kafri R, Kirschner MW, Clish CB, Mootha VK (2012) Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336:1040–1044. doi:10.1126/science.1218595
Johnston JJ, Rubinstein WS, Facio FM, Ng D, Singh LN, Teer JK, Mullikin JC, Biesecker LG (2012) Secondary variants in individuals undergoing exome sequencing: screening of 572 individuals identifies high-penetrance mutations in cancer-susceptibility genes. Am J Hum Genet. doi:10.1016/j.ajhg.2012.05.021
Kohn KW (2001) Molecular interaction maps as information organizers and simulation guides. Chaos 11:84–97
Kohn KW, Aladjem MI, Kim S, Weinstein JN, Pommier Y (2006) Depicting combinatorial complexity with the molecular interaction map notation. Mol Syst Biol 2:51
Koo GC, Tan SY, Tang T, Poon SL, Allen GE, Tan L, Chong SC, Ong WS, Tay K, Tao M, Quek R, Loong S, Yeoh KW, Yap SP, Lee KA, Lim LC, Tan D, Goh C, Cutcutache I, Yu W, Ng CC, Rajasegaran V, Heng HL, Gan A, Ong CK, Rozen S, Tan P, Teh BT, Lim ST (2012) Janus kinase 3-activating mutations identified in natural killer/T-cell lymphoma. Cancer Discov. 2159-8290.CD-12-0028 [pii]
Kwei KA, Baker JB, Pelham RJ (2012) Modulators of sensitivity and resistance to inhibition of PI3K identified in a pharmacogenomic screen of the NCI-60 human tumor cell line collection. PLoS One 7:e46518. doi:10.1371/journal.pone.0046518
Lee JS, Paull K, Alvarez M, Hose C, Monks A, Grever M, Fojo AT, Bates SE (1994) Rhodamine efflux patterns predict P-glycoprotein substrates in the National Cancer Institute drug screen. Mol Pharmacol 46:627–638
Li C, Li H (2008) Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics 24:1175–1182. doi:10.1093/bioinformatics/btn081
Liu H, D’Andrade P, Fulmer-Smentek S, Lorenzi P, Kohn KW, Weinstein JN, Pommier YG, Reinhold WC (2010) mRNA and microRNA expression profiles of the NCI-60 integrated drug activities. MCT 9(5):1080–1091
Lorenzi P, Reinhold W, Varma S, Hutchinson A, Pommier Y, Chanock S, Weinstein J (2009) DNA fingerprinting of the NCI-60 cell line panel. Mol Cancer Ther 8:713–724
Ma Y, Ding Z, Qian Y, Shi X, Castranova V, Harner EJ, Guo L (2006) Predicting cancer drug response by proteomic profiling. Clin Cancer Res 12:4583–4589. doi:10.1158/1078-0432.CCR-06-0290
Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7(Suppl 1):S7. doi:10.1186/1471-2105-7-S1-S7
Masica DL, Karchin R (2013) Collections of simultaneously altered genes as biomarkers of cancer cell drug response. Cancer Res 73:1699–1708. doi:10.1158/0008-5472.Can-12-3122
McDermott U, Settleman J (2009) Personalized cancer therapy with selective kinase inhibitors: an emerging paradigm in medical oncology. J Clin Oncol 27:5650–5659. doi:10.1200/JCO.2009.22.9054
McDermott U, Sharma SV, Dowell L, Greninger P, Montagut C, Lamb J, Archibald H, Raudales R, Tam A, Lee D, Rothenberg SM, Supko JG, Sordella R, Ulkus LE, Iafrate AJ, Maheswaran S, Njauw CN, Tsao H, Drew L, Hanke JH, Ma XJ, Erlander MG, Gray NS, Haber DA, Settleman J (2007) Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling. Proc Natl Acad Sci USA 104:19936–19941. doi:10.1073/pnas.0707498104
Moghaddas Gholami A, Hahne H, Wu Z, Auer FJ, Meng C, Wilhelm M, Kuster B (2013) Global proteome analysis of the NCI-60 cell line panel. Cell Rep 4:609–620. doi:10.1016/j.celrep.2013.07.018
Mountzios G, Rampias T, Psyrri A (2014) The mutational spectrum of squamous-cell carcinoma of the head and neck: Targetable genetic events and clinical impact. Ann Oncol. doi:10.1093/annonc/mdu143
Munoz JL, Rodriguez-Cruz V, Greco SJ, Ramkissoon SH, Ligon KL, Rameshwar P (2014) Temozolomide resistance in glioblastoma cells occurs partly through epidermal growth factor receptor-mediated induction of connexin 43. Cell Death Dis 5:e1145. doi:10.1038/cddis.2014.111
Nishizuka S, Charboneau L, Young L, Major S, Reinhold W, Waltham M, Kouros-Mehr H, Bussey K, Lee J, Espina V, Munson P, Petricoin IE, Liotta L, Weinstein J (2003) Proteomic profiling of the NCI60 cancer cell lines using new high-density ‘reverse-phase’ lysate microarrays. Proc Natl Acad Sci USA 100:14229–14234
Papillon-Cavanagh S, De Jay N, Hachem N, Olsen C, Bontempi G, Aerts HJ, Quackenbush J, Haibe-Kains B (2013) Comparison and validation of genomic predictors for anticancer drug sensitivity. J Am Med Inform Assoc 20:597–602. doi:10.1136/amiajnl-2012-001442
Paull K, Shoemaker R, Hodes L, Monks A, Scudiero D, Rubinstein L, Plowman J, Boyd M (1989) Display and analysis of patterns of differential activity of drugs against human tumor cell lines: development of mean graph and COMPARE algorithm. J Natl Cancer Inst 81:1088–1092
Reinhold WC, Mergny JL, Liu H, Ryan M, Pfister TD, Kinders R, Parchment R, Doroshow J, Weinstein JN, Pommier Y (2010) Exon array analyses across the NCI-60 reveal potential regulation of TOP1 by transcription pausing at guanosine quartets in the first intron. Cancer Res 70:2191–2203 (0008-5472.CAN-09-3528 [pii])
Reinhold WC, Sunshine M, Liu H, Varma S, Kohn KW, Morris J, Doroshow J, Pommier Y (2012) CellMiner: A web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set. Cancer Res
Reinhold WC, Varma S, Sousa F, Sunshine M, Abaan OD, Davis SR, Reinhold SW, Kohn KW, Morris J, Doroshow J, Pommier Y (2014) NCI-60 whole exome sequencing and pharmacological CellMiner analyses. PLOS One. Submitted
Roschke A, Tonon G, Gehlhaus K, McTyre N, Bussey K, Lababidi S, Scudiero D, Weinstein J, Kirsch I (2003) Karyotypic complexity of the NCI-60 drug-screening panel. Cancer Res 63:8634–8647
Ross DT, Scherf U, Eisen MB, Perou CM, Rees C, Spellman P, Iyer V, Jeffrey SS, Van de Rijn M, Waltham M, Pergamenschikov A, Lee JC, Lashkari D, Shalon D, Myers TG, Weinstein JN, Botstein D, Brown PO (2000) Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet 24:227–235. doi:10.1038/73432
Ruan X, Kocher JP, Pommier Y, Liu H, Reinhold WC (2012) Mass homozygotes accumulation in the NCI-60 cancer cell lines as compared to HapMap trios, and relation to fragile site location. PLoS One 7:e31628. doi:10.1371/journal.pone.0031628 (PONE-D-11-09941 [pii])
Rubinstein LV, Shoemaker RH, Paull KD, Simon RM, Tosini S, Skehan P, Scudiero DA, Monks A, Boyd MR (1990) Comparison of in vitro anticancer-drug-screening data generated with a tetrazolium assay versus a protein assay against a diverse panel of human tumor cell lines. J Natl Cancer Inst 82:1113–1118
Shankavaram UT, Varma S, Kane D, Sunshine M, Chary KK, Reinhold WC, Pommier Y, Weinstein JN (2009) Cell Miner: a relational database and query tool for the NCI-60 cancer cell lines. BMC Genomics 10:277. doi:10.1186/1471-2164-10-277
Shoemaker RH (2006) The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 6:813–823
Stuelten CH, Mertins SD, Busch JI, Gowens M, Scudiero DA, Burkett MW, Hite KM, Alley M, Hollingshead M, Shoemaker RH, Niederhuber JE (2010) Complex display of putative tumor stem cell markers in the NCI60 tumor cell line panel. Stem Cells 28:649–660. doi:10.1002/stem.324
Stults DM, Killen MW, Shelton BJ, Pierce AJ (2011) Recombination phenotypes of the NCI-60 collection of human cancer cells. BMC Mol Biol 12:23. doi:10.1186/1471-2199-12-23
Szakacs G, Annereau J, Lababidi S, Shankavaram U, Arciello A, Bussey K, Reinhold W, Guo Y, Kruh G, Reimers M, Weinstein J, Gottesman M (2004) Predicting drug sensitivity and resistance: profiling ABC transporter genes in cancer cells. Cancer Cell 6(2):129–137
Weinberg R (2010) Point: hypotheses first. Nature 464:678. doi:10.1038/464678a
Weinstein JN (2012) Drug discovery: cell lines battle cancer. Nature 483:544–545. doi:10.1038/483544a
Weinstein JN, Lorenzi PL (2013) Cancer: discrepancies in drug sensitivity. Nature 504:381–383. doi:10.1038/nature12839
Weinstein JN, Myers TG, O’Connor PM, Friend SH, Fornace AJ Jr, Kohn KW, Fojo T, Bates SE, Rubinstein LV, Anderson NL, Buolamwini JK, van Osdol WW, Monks AP, Scudiero DA, Sausville EA, Zaharevitz DW, Bunow B, Viswanadhan VN, Johnson GS, Wittes RE, Paull KD (1997) An information-intensive approach to the molecular pharmacology of cancer. Science 275:343–349
Zeeberg B, Feng W, Wang G, Wang M, Fojo A, Sunshine M, Narasimhan S, Kane D, Reinhold W, Lababidi S, Bussey K, Riss J, Barrett J, Weinstein J (2003) GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol 4(4):R28
Zoppoli G, Regairaz M, Leo E, Reinhold WC, Varma S, Ballestrero A, Doroshow JH, Pommier Y (2012) Putative DNA/RNA helicase Schlafen-11 (SLFN11) sensitizes cancer cells to DNA-damaging agents. Proc Natl Acad Sci USA 109:15030–15035. doi:10.1073/pnas.1205943109
Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol 67:301–320. doi:10.1111/J.1467-9868.2005.00503.X
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Our studies are supported by the Center for Cancer Research, Intramural Program of the National Cancer Institute (Z01 BC 006150). This work was also supported in part by a MSKCC Genome Data Analysis Center Grant (U24 CA143840) awarded as part of the National Cancer Institute (NCI)/National Human Genome Research Institute (NHGRI).
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Reinhold, W.C., Varma, S., Rajapakse, V.N. et al. Using drug response data to identify molecular effectors, and molecular “omic” data to identify candidate drugs in cancer. Hum Genet 134, 3–11 (2015). https://doi.org/10.1007/s00439-014-1482-9
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DOI: https://doi.org/10.1007/s00439-014-1482-9