Top

BMC Medical Informatics and Decision Making

Published in:

Open Access 01-12-2018 | Research article

SwissMTB: establishing comprehensive molecular cancer diagnostics in Swiss clinics

Authors: Franziska Singer, Anja Irmisch, Nora C. Toussaint, Linda Grob, Jochen Singer, Thomas Thurnherr, Niko Beerenwinkel, Mitchell P. Levesque, Reinhard Dummer, Luca Quagliata, Sacha I. Rothschild, Andreas Wicki, Christian Beisel, Daniel J. Stekhoven

Published in: BMC Medical Informatics and Decision Making | Issue 1/2018

Abstract

Background

Molecular precision oncology is an emerging practice to improve cancer therapy by decreasing the risk of choosing treatments that lack efficacy or cause adverse events. However, the challenges of integrating molecular profiling into routine clinical care are manifold. From a computational perspective these include the importance of a short analysis turnaround time, the interpretation of complex drug-gene and gene-gene interactions, and the necessity of standardized high-quality workflows. In addition, difficulties faced when integrating molecular diagnostics into clinical practice are ethical concerns, legal requirements, and limited availability of treatment options beyond standard of care as well as the overall lack of awareness of their existence.

Methods

To the best of our knowledge, we are the first group in Switzerland that established a workflow for personalized diagnostics based on comprehensive high-throughput sequencing of tumors at the clinic. Our workflow, named SwissMTB (Swiss Molecular Tumor Board), links genetic tumor alterations and gene expression to therapeutic options and clinical trial opportunities. The resulting treatment recommendations are summarized in a clinical report and discussed in a molecular tumor board at the clinic to support therapy decisions.

Results

Here we present results from an observational pilot study including 22 late-stage cancer patients. In this study we were able to identify actionable variants and corresponding therapies for 19 patients. Half of the patients were analyzed retrospectively. In two patients we identified resistance-associated variants explaining lack of therapy response. For five out of eleven patients analyzed before treatment the SwissMTB diagnostic influenced treatment decision.

Conclusions

SwissMTB enables the analysis and clinical interpretation of large numbers of potentially actionable molecular targets. Thus, our workflow paves the way towards a more frequent use of comprehensive molecular diagnostics in Swiss hospitals.

Available only for authorised users

Shin SH, Bode AM, Dong Z. Addressing the challenges of applying precision oncology. NPJ Precision Onc. 2017;1:28. https://doi.org/10.1038/s41698-017-0032-z.CrossRef

Rehm HL. Disease-targeted sequencing: a cornerstone in the clinic. Nat Rev Genet. 2013;14:295–300. https://doi.org/10.1038/nrg3463.CrossRefPubMedPubMedCentral

Hovelson DH, McDaniel AS, Cani AK, Johnson B, Rhodes K, Williams PD, et al. Development and validation of a scalable next-generation sequencing system for assessing relevant somatic variants in solid tumors. Neoplasia. 2015;17:385–99. https://doi.org/10.1016/j.neo.2015.03.004.CrossRefPubMedPubMedCentral

Hamblin A, Wordsworth S, Fermont JM, Page S, Kaur K, Camps C, et al. Clinical applicability and cost of a 46-gene panel for genomic analysis of solid tumours: retrospective validation and prospective audit in the UK National Health Service. PLoS Med. 2017;14:e1002230. https://doi.org/10.1371/journal.pmed.1002230.CrossRefPubMedPubMedCentral

Tsongalis GJ, Peterson JD, de Abreu FB, Tunkey CD, Gallagher TL, Strausbaugh LD, et al. Routine use of the ion torrent AmpliSeq™ Cancer Hotspot panel for identification of clinically actionable somatic mutations. Clin Chem Lab Med. 2014;52:707–14. https://doi.org/10.1515/cclm-2013-0883.CrossRefPubMed

Beltran H, Eng K, Mosquera JM, Sigaras A, Romanel A, Rennert H, et al. Whole-exome sequencing of metastatic Cancer and biomarkers of treatment response. JAMA Oncol. 2015;1:466–74. https://doi.org/10.1001/jamaoncol.2015.1313.CrossRefPubMedPubMedCentral

Strom SP, Lee H, Das K, Vilain E, Nelson SF, Grody WW, et al. Assessing the necessity of confirmatory testing for exome-sequencing results in a clinical molecular diagnostic laboratory. Genet Med. 2014;16:510–5. https://doi.org/10.1038/gim.2013.183.CrossRefPubMedPubMedCentral

Hyman DM, Taylor BS, Baselga J. Implementing genome-driven oncology. Cell. 2017;168:584–99. https://doi.org/10.1016/j.cell.2016.12.015.CrossRefPubMedPubMedCentral

Stewart BW, Wild CP. World Cancer Report 2014. Lyon, France: International Agency for Research on Cancer. World Health Organization; 2014:630.

10.

Zahreddine H, Borden KLB. Mechanisms and insights into drug resistance in cancer. Front Pharmacol. 2013;4:28. https://doi.org/10.3389/fphar.2013.00028.CrossRefPubMedPubMedCentral

11.

Housman G, Byler S, Heerboth S, Lapinska K, Longacre M, Snyder N, et al. Drug resistance in cancer: an overview. Cancers (Basel). 2014;6:1769–92. https://doi.org/10.3390/cancers6031769.CrossRef

12.

Lackner MR, Wilson TR, Settleman J. Mechanisms of acquired resistance to targeted cancer therapies. Future Oncol. 2012;8:999–1014. https://doi.org/10.2217/fon.12.86.CrossRefPubMed

13.

Sabarinathan R, Pich O, Martincorena I, Rubio-Perez C, Juul M, Wala J, et al. The whole-genome panorama of cancer drivers. BioRxiv. 2017. https://doi.org/10.1101/190330.

14.

Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell. 2015;161:1681–96. https://doi.org/10.1016/j.cell.2015.05.044.CrossRef

15.

Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P, Larkin J, et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med. 2011;364:2507–16. https://doi.org/10.1056/NEJMoa1103782.CrossRefPubMedPubMedCentral

16.

Cuello M, Ettenberg SA, Clark AS, Keane MM, Posner RH, Nau MM, et al. Down-regulation of the erbB-2 receptor by trastuzumab (herceptin) enhances tumor necrosis factor-related apoptosis-inducing ligand-mediated apoptosis in breast and ovarian cancer cell lines that overexpress erbB-2. Cancer Res. 2001;61:4892–900.PubMed

17.

Gómez-López G, Dopazo J, Cigudosa JC, Valencia A, Al-Shahrour F. Precision medicine needs pioneering clinical bioinformaticians. Brief Bioinformatics. 2017. https://doi.org/10.1093/bib/bbx144.

18.

MD Anderson. MD Anderson Cancer Center. https://www.mdanderson.org/. Accessed 6 Apr 2018.

19.

Bryce AH, Egan JB, Borad MJ, Stewart AK, Nowakowski GS, Chanan-Khan A, et al. Experience with precision genomics and tumor board, indicates frequent target identification, but barriers to delivery. Oncotarget. 2017;8:27145–54. https://doi.org/10.18632/oncotarget.16057.CrossRefPubMedPubMedCentral

20.

Rennert H, Eng K, Zhang T, Tan A, Xiang J, Romanel A, et al. Development and validation of a whole-exome sequencing test for simultaneous detection of point mutations, indels and copy-number alterations for precision cancer care. NPJ Genomic Med. 2016;1. https://doi.org/10.1038/npjgenmed.2016.19.

21.

NCT Heidelberg. NCT MASTER. http://www.nct-heidelberg.de/forschung/nct-master/clinical-translation.html. Accessed 6 Apr 2018.

22.

Britschgi C, Diehl C, Rechsteiner M, Valtcheva N, Freiberger S, Wong C, et al. 38Implications of routine NGS testing for daily clinical practice--the Zurich experience with the OFA panel. Annals of Oncology. 2017;28(7).

23.

Meric-Bernstam F, Brusco L, Shaw K, Horombe C, Kopetz S, Davies MA, et al. Feasibility of large-scale genomic testing to facilitate enrollment onto Genomically matched clinical trials. J Clin Oncol. 2015;33:2753–62. https://doi.org/10.1200/JCO.2014.60.4165.CrossRefPubMedPubMedCentral

24.

Merriman B, Ion Torrent R&D Team, Rothberg JM. Progress in ion torrent semiconductor chip based sequencing. Electrophoresis. 2012;33:3397–417. https://doi.org/10.1002/elps.201200424.CrossRefPubMed

25.

Swissmedic. Swissmedic. https://www.swissmedic.ch/swissmedic/de/home/ueber-uns/publikationen/swissmedic-journal.html. Accessed 6 Apr 2018.

26.

ThermoFisher. Oncomine Assays. http://www.thermofisher.com/ch/en/home/clinical/preclinical-companion-diagnostic-development/oncomine-oncology.html. Accessed 6 Apr 2018.

27.

Singer J, Irmisch A, Ruscheweyh H-J, Singer F, Toussaint NC, Levesque MP, et al. Bioinformatics for precision oncology. Brief Bioinformatics. 2017. https://doi.org/10.1093/bib/bbx143.

28.

Roychowdhury S, Chinnaiyan AM. Translating cancer genomes and transcriptomes for precision oncology. CA Cancer J Clin. 2016;66:75–88. https://doi.org/10.3322/caac.21329.CrossRefPubMed

29.

Wagner AH, Coffman AC, Ainscough BJ, Spies NC, Skidmore ZL, Campbell KM, et al. DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res. 2016;44:D1036–44. https://doi.org/10.1093/nar/gkv1165.CrossRefPubMed

30.

Griffith M, Griffith OL, Coffman AC, Weible JV, McMichael JF, Spies NC, et al. DGIdb: mining the druggable genome. Nat Methods. 2013;10:1209–10. https://doi.org/10.1038/nmeth.2689.CrossRefPubMedPubMedCentral

31.

Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34(Database issue):D668–72. https://doi.org/10.1093/nar/gkj067.CrossRefPubMed

32.

Abrahamson R. Overview of targeted therapies for Cancer. My Cancer Genome. 2017. https://www.mycancergenome.org/content/molecular-medicine/overview-of-targeted-therapies-for-cancer/. Accessed 20 Mar 2018.

33.

Roychowdhury S, Iyer MK, Robinson DR, Lonigro RJ, Wu Y-M, Cao X, et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci Transl Med. 2011;3:111ra121. https://doi.org/10.1126/scitranslmed.3003161.CrossRefPubMedPubMedCentral

34.

O’Rawe J, Jiang T, Sun G, Wu Y, Wang W, Hu J, et al. Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing. Genome Med. 2013;5:28. https://doi.org/10.1186/gm432.CrossRefPubMedPubMedCentral

35.

Hofmann AL, Behr J, Singer J, Kuipers J, Beisel C, Schraml P, et al. Detailed simulation of cancer exome sequencing data reveals differences and common limitations of variant callers. BMC Bioinformatics. 2017;18:8. https://doi.org/10.1186/s12859-016-1417-7.CrossRefPubMedPubMedCentral

36.

Ewing AD, Houlahan KE, Hu Y, Ellrott K, Caloian C, Yamaguchi TN, et al. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nat Methods. 2015;12:623–30. https://doi.org/10.1038/nmeth.3407.CrossRefPubMedPubMedCentral

37.

TCGA. TCGA-SKCM. https://portal.gdc.cancer.gov/projects/TCGA-SKCM. Accessed 6 Apr 2018.

38.

Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2014;371:2189–99. https://doi.org/10.1056/NEJMoa1406498.CrossRefPubMedPubMedCentral

39.

Legat A, Maby-El Hajjami H, Baumgaertner P, Cagnon L, Abed Maillard S, Geldhof C, et al. Vaccination with LAG-3Ig (IMP321) and peptides induces specific CD4 and CD8 T-cell responses in metastatic melanoma patients--report of a phase I/IIa clinical trial. Clin Cancer Res. 2016;22:1330–40. https://doi.org/10.1158/1078-0432.CCR-15-1212.CrossRefPubMed

40.

Nagorsen D, Thiel E. HLA typing demands for peptide-based anti-cancer vaccine. Cancer Immunol Immunother. 2008;57:1903–10. https://doi.org/10.1007/s00262-008-0493-6.CrossRefPubMed

41.

Singer J, Ruscheweyh H-J, Hofmann AL, Thurnherr T, Singer F, Toussaint NC, et al. NGS-pipe: a flexible, easily extendable and highly configurable framework for NGS analysis. Bioinformatics. 2018;34:107–8. https://doi.org/10.1093/bioinformatics/btx540.CrossRefPubMed

42.

Köster J, Rahmann S. Snakemake--a scalable bioinformatics workflow engine. Bioinformatics. 2012;28:2520–2. https://doi.org/10.1093/bioinformatics/bts480.CrossRefPubMed

43.

Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, et al. From FastQ data to high confidence variant calls: the genome analysis toolkit best practices pipeline. Curr Protoc Bioinformatics. 2013;11:11.10.1–11.10.33. https://doi.org/10.1002/0471250953.bi1110s43.CrossRef

44.

Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31:213–9. https://doi.org/10.1038/nbt.2514.CrossRefPubMedPubMedCentral

45.

Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 2012;22:568–76. https://doi.org/10.1101/gr.129684.111.CrossRefPubMedPubMedCentral

46.

Saunders CT, Wong WSW, Swamy S, Becq J, Murray LJ, Cheetham RK. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics. 2012;28:1811–7. https://doi.org/10.1093/bioinformatics/bts271.CrossRefPubMed

47.

Xi R, Lee S, Xia Y, Kim T-M, Park PJ. Copy number analysis of whole-genome data using BIC-seq2 and its application to detection of cancer susceptibility variants. Nucleic Acids Res. 2016;44:6274–86. https://doi.org/10.1093/nar/gkw491.CrossRefPubMedPubMedCentral

48.

Magi A, Tattini L, Cifola I, D’Aurizio R, Benelli M, Mangano E, et al. EXCAVATOR: detecting copy number variants from whole-exome sequencing data. Genome Biol. 2013;14:R120. https://doi.org/10.1186/gb-2013-14-10-r120.CrossRefPubMedPubMedCentral

49.

TCGA. UNC mRNAseq summary. 2013. https://webshare.bioinf.unc.edu/public/mRNAseq_TCGA/UNC_mRNAseq_summary.pdf. Accessed 6 Apr 2018.

50.

Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. https://doi.org/10.1093/bioinformatics/bts635.CrossRefPubMed

51.

TCGA. TCGA-UVM. https://portal.gdc.cancer.gov/projects/TCGA-UVM. Accessed 6 Apr 2018.

52.

GDAC. GDAC Firehose. 2016. https://gdac.broadinstitute.org/. Accessed 6 Apr 2018.

53.

Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SAJR, Behjati S, Biankin AV, et al. Signatures of mutational processes in human cancer. Nature. 2013;500:415–21. https://doi.org/10.1038/nature12477.CrossRefPubMedPubMedCentral

54.

Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, Boutselakis H, et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2015;43(Database issue):D805–11. https://doi.org/10.1093/nar/gku1075.CrossRefPubMed

55.

Sherry ST, Ward M, Sirotkin K. dbSNP-database for single nucleotide polymorphisms and other classes of minor genetic variation. Genome Res. 1999;9:677–9.PubMed

56.

Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29:308–11. https://doi.org/10.1093/nar/29.1.308.CrossRefPubMedPubMedCentral

57.

Landrum MJ, Lee JM, Riley GR, Jang W, Rubinstein WS, Church DM, et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 2014;42(Database issue):D980–5. https://doi.org/10.1093/nar/gkt1113.CrossRefPubMed

58.

Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6:pl1. https://doi.org/10.1126/scisignal.2004088.CrossRefPubMedPubMedCentral

59.

Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2:401–4. https://doi.org/10.1158/2159-8290.CD-12-0095.CrossRefPubMed

60.

Ahronian LG, Sennott EM, Van Allen EM, Wagle N, Kwak EL, Faris JE, et al. Clinical acquired resistance to RAF inhibitor combinations in BRAF-mutant colorectal Cancer through MAPK pathway alterations. Cancer Discov. 2015;5:358–67. https://doi.org/10.1158/2159-8290.CD-14-1518.CrossRefPubMedPubMedCentral

61.

Cotto KC, Wagner AH, Feng Y-Y, Kiwala S, Coffman AC, Spies G, et al. DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Res. 2017. https://doi.org/10.1093/nar/gkx1143.CrossRefPubMedCentral

62.

Thurnherr T, Singer F, Stekhoven DJ, Beerenwinkel N. Genomic variant annotation workflow for clinical applications. F1000Research. 2016;5:1963.CrossRefPubMedPubMedCentral

63.

MyCancerGenome. MyCancerGenome. 2018. https://www.mycancergenome.org/. Accessed 6 Apr 2018.

64.

Shneiderman B. The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings 1996 IEEE Symposium on Visual Languages: IEEE Comput. Soc. Press; 1996. p. 336–43. https://doi.org/10.1109/VL.1996.545307.

65.

Chia PL, Mitchell P, Dobrovic A, John T. Prevalence and natural history of ALK positive non-small-cell lung cancer and the clinical impact of targeted therapy with ALK inhibitors. Clin Epidemiol. 2014;6:423–32. https://doi.org/10.2147/CLEP.S69718.CrossRefPubMedPubMedCentral

66.

Kang HJ, Chun S-M, Kim K-R, Sohn I, Sung CO. Clinical relevance of gain-of-function mutations of p53 in high-grade serous ovarian carcinoma. PLoS One. 2013;8:e72609. https://doi.org/10.1371/journal.pone.0072609.CrossRefPubMedPubMedCentral

67.

Li MM, Datto M, Duncavage EJ, Kulkarni S, Lindeman NI, Roy S, et al. Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a joint consensus recommendation of the association for molecular pathology, american society of clinical oncology, and college of american pathologists. J Mol Diagn. 2017;19:4–23. https://doi.org/10.1016/j.jmoldx.2016.10.002.CrossRefPubMedPubMedCentral

68.

Chakravarty D, Gao J, Phillips S, Kundra R, Zhang H, Wang J, et al. OncoKB: A Precision Oncology Knowledge Base. JCO Precis Oncol. 2017:1–16.

69.

Massard C, Michiels S, Ferté C, Le Deley M-C, Lacroix L, Hollebecque A, et al. High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: results of the MOSCATO 01 trial. Cancer Discov. 2017;7:586–95. https://doi.org/10.1158/2159-8290.CD-16-1396.CrossRefPubMed

70.

Larkin J, Chiarion-Sileni V, Gonzalez R, Grob JJ, Cowey CL, Lao CD, et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N Engl J Med. 2015;373:23–34. https://doi.org/10.1056/NEJMoa1504030.CrossRefPubMedPubMedCentral

71.

Della Corte CM, Fasano M, Papaccio F, Ciardiello F, Morgillo F. Role of HGF-MET signaling in primary and acquired resistance to targeted therapies in Cancer. Biomedicine. 2014;2:345–58. https://doi.org/10.3390/biomedicines2040345.CrossRef

72.

Sun C, Wang L, Huang S, Heynen GJJE, Prahallad A, Robert C, et al. Reversible and adaptive resistance to BRAF(V600E) inhibition in melanoma. Nature. 2014;508:118–22. https://doi.org/10.1038/nature13121.CrossRefPubMed

73.

Raaijmakers MIG, Widmer DS, Narechania A, Eichhoff O, Freiberger SN, Wenzina J, et al. Co-existence of BRAF and NRAS driver mutations in the same melanoma cells results in heterogeneity of targeted therapy resistance. Oncotarget. 2016;7:77163–74. https://doi.org/10.18632/oncotarget.12848.CrossRefPubMedPubMedCentral

74.

Masuyama H, Nakatsukasa H, Takamoto N, Hiramatsu Y. Down-regulation of pregnane X receptor contributes to cell growth inhibition and apoptosis by anticancer agents in endometrial cancer cells. Mol Pharmacol. 2007;72:1045–53. https://doi.org/10.1124/mol.107.037937.CrossRefPubMed

75.

Homsi J, Bedikian AY, Papadopoulos NE, Kim KB, Hwu W-J, Mahoney SL, et al. Phase 2 open-label study of weekly docosahexaenoic acid-paclitaxel in patients with metastatic uveal melanoma. Melanoma Res. 2010;20:507–10. https://doi.org/10.1097/CMR.0b013e3283403ce9.CrossRefPubMed

76.

Isakoff SJ, Wang D, Campone M, Calles A, Leip E, Turnbull K, et al. Bosutinib plus capecitabine for selected advanced solid tumours: results of a phase 1 dose-escalation study. Br J Cancer. 2014;111:2058–66. https://doi.org/10.1038/bjc.2014.508.CrossRefPubMedPubMedCentral

77.

Soria JC, Massard C, Magné N, Bader T, Mansfield CD, Blay JY, et al. Phase 1 dose-escalation study of oral tyrosine kinase inhibitor masitinib in advanced and/or metastatic solid cancers. Eur J Cancer. 2009;45:2333–41. https://doi.org/10.1016/j.ejca.2009.05.010.CrossRefPubMed

78.

Babina IS, Turner NC. Advances and challenges in targeting FGFR signalling in cancer. Nat Rev Cancer. 2017;17:318–32. https://doi.org/10.1038/nrc.2017.8.CrossRefPubMed

79.

Yang Y, Wu N, Shen J, Teixido C, Sun X, Lin Z, et al. MET overexpression and amplification define a distinct molecular subgroup for targeted therapies in gastric cancer. Gastric Cancer. 2015;19:778–88. https://doi.org/10.1007/s10120-015-0545-5.CrossRefPubMed

80.

Yao JC, Fazio N, Singh S, Buzzoni R, Carnaghi C, Wolin E, et al. Everolimus for the treatment of advanced, non-functional neuroendocrine tumours of the lung or gastrointestinal tract (RADIANT-4): a randomised, placebo-controlled, phase 3 study. Lancet. 2016;387:968–77. https://doi.org/10.1016/S0140-6736(15)00817-X.CrossRefPubMed

81.

Jardim DL, Wheler JJ, Hess K, Tsimberidou AM, Zinner R, Janku F, et al. FBXW7 mutations in patients with advanced cancers: clinical and molecular characteristics and outcomes with mTOR inhibitors. PLoS One. 2014;9:e89388. https://doi.org/10.1371/journal.pone.0089388.CrossRefPubMedPubMedCentral

82.

Brachova P, Thiel KW, Leslie KK. The consequence of oncomorphic TP53 mutations in ovarian cancer. Int J Mol Sci. 2013;14:19257–75. https://doi.org/10.3390/ijms140919257.CrossRefPubMedPubMedCentral

83.

Lawrie TA, Bryant A, Cameron A, Gray E, Morrison J. Pegylated liposomal doxorubicin for relapsed epithelial ovarian cancer. Cochrane Database Syst Rev. 2013:CD006910. https://doi.org/10.1002/14651858.CD006910.pub2.

84.

Pisano C, Cecere SC, Di Napoli M, Cavaliere C, Tambaro R, Facchini G, et al. Clinical trials with pegylated liposomal doxorubicin in the treatment of ovarian cancer. J Drug Deliv. 2013;2013:898146. https://doi.org/10.1155/2013/898146.CrossRefPubMedPubMedCentral

85.

González-Cao M, Rodón J, Karachaliou N, Sánchez J, Santarpia M, Viteri S, et al. Other targeted drugs in melanoma. Ann Transl Med. 2015;3:266. https://doi.org/10.3978/j.issn.2305-5839.2015.08.12.CrossRefPubMedPubMedCentral

86.

Kim ES, Scott LJ. Palbociclib: a review in HR-positive, HER2-negative, advanced or metastatic breast Cancer. Target Oncol. 2017;12:373–83. https://doi.org/10.1007/s11523-017-0492-7.CrossRefPubMed

87.

Fontana D, Ceccon M, Gambacorti-Passerini C, Mologni L. Activity of second-generation ALK inhibitors against crizotinib-resistant mutants in an NPM-ALK model compared to EML4-ALK. Cancer Med. 2015;4:953–65. https://doi.org/10.1002/cam4.413.CrossRefPubMedPubMedCentral

88.

Lovly CM, Pao W. Escaping ALK inhibition: mechanisms of and strategies to overcome resistance. Sci Transl Med. 2012;4:120ps2. https://doi.org/10.1126/scitranslmed.3003728.CrossRefPubMed

89.

Friboulet L, Li N, Katayama R, Lee CC, Gainor JF, Crystal AS, et al. The ALK inhibitor ceritinib overcomes crizotinib resistance in non-small cell lung cancer. Cancer Discov. 2014;4:662–73. https://doi.org/10.1158/2159-8290.CD-13-0846.CrossRefPubMedPubMedCentral

90.

King TC, Akerley W, Fan AC, Moore T, Mangray S, Hsiu Chen M, et al. p53 mutations do not predict response to paclitaxel in metastatic nonsmall cell lung carcinoma. Cancer. 2000;89:769–73. https://doi.org/10.1002/1097-0142(20000815)89:4<769::AID-CNCR8>3.0.CO;2-6.CrossRefPubMed

91.

Muller PAJ, Vousden KH. Mutant p53 in cancer: new functions and therapeutic opportunities. Cancer Cell. 2014;25:304–17. https://doi.org/10.1016/j.ccr.2014.01.021.CrossRefPubMedPubMedCentral

92.

Chang FL, Lai MD. Various forms of mutant p53 confer sensitivity to cisplatin and doxorubicin in bladder cancer cells. J Urol. 2001;166:304–10. https://doi.org/10.1016/S0022-5347(05)66150-2.CrossRefPubMed

93.

Minor DR, Kashani-Sabet M, Garrido M, O’Day SJ, Hamid O, Bastian BC. Sunitinib therapy for melanoma patients with KIT mutations. Clin Cancer Res. 2012;18:1457–63. https://doi.org/10.1158/1078-0432.CCR-11-1987.CrossRefPubMed

94.

Clinical trial. Phase II clinical trial NCT02307500: Regorafenib in Patients With Metastatic Solid Tumors Who Have Progressed After Standard Therapy (RESOUND) . clinicaltrials.gov. 2014. https://clinicaltrials.gov/ct2/show/NCT02307500?term=NCT02307500&rank=1. Accessed 23 Mar 2018.

95.

Oberg JA, Glade Bender JL, Sulis ML, Pendrick D, Sireci AN, Hsiao SJ, et al. Implementation of next generation sequencing into pediatric hematology-oncology practice: moving beyond actionable alterations. Genome Med. 2016;8:133. https://doi.org/10.1186/s13073-016-0389-6.CrossRefPubMedPubMedCentral

96.

Mody RJ, Wu Y-M, Lonigro RJ, Cao X, Roychowdhury S, Vats P, et al. Integrative clinical sequencing in the management of refractory or relapsed cancer in youth. JAMA. 2015;314:913–25. https://doi.org/10.1001/jama.2015.10080.CrossRefPubMedPubMedCentral

97.

Sun JX, He Y, Sanford E, Montesion M, Frampton GM, Vignot S, et al. A computational approach to distinguish somatic vs germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal. PLoS Comput Biol. 2018;14:e1005965. https://doi.org/10.1371/journal.pcbi.1005965.CrossRefPubMedPubMedCentral

98.

Macías M, Alegre E, Díaz-Lagares A, Patiño A, Pérez-Gracia JL, Sanmamed M, et al. Liquid biopsy: from basic research to clinical practice. Adv Clin Chem. 2018;83:73–119. https://doi.org/10.1016/bs.acc.2017.10.003.CrossRefPubMed

99.

Senft D, Leiserson MDM, Ruppin E, Ronai ZA. Precision oncology: the road ahead. Trends Mol Med. 2017;23:874–98. https://doi.org/10.1016/j.molmed.2017.08.003.CrossRefPubMedPubMedCentral

100.

Wang Y, Navin NE. Advances and applications of single-cell sequencing technologies. Mol Cell. 2015;58:598–609. https://doi.org/10.1016/j.molcel.2015.05.005.CrossRefPubMedPubMedCentral

101.

Lu H, Li G, Zhou C, Jin W, Qian X, Wang Z, et al. Regulation and role of post-translational modifications of enhancer of zeste homologue 2 in cancer development. Am J Cancer Res. 2016;6:2737–54.PubMedPubMedCentral

102.

Khan SA, Reddy D, Gupta S. Global histone post-translational modifications and cancer: biomarkers for diagnosis, prognosis and treatment? World J Biol Chem. 2015;6:333–45. https://doi.org/10.4331/wjbc.v6.i4.333.CrossRefPubMedPubMedCentral

103.

Casado P, Hijazi M, Britton D, Cutillas PR. Impact of phosphoproteomics in the translation of kinase-targeted therapies. Proteomics. 2017;17. https://doi.org/10.1002/pmic.201600235.CrossRef

104.

Bedard PL, Hansen AR, Ratain MJ, Siu LL. Tumour heterogeneity in the clinic. Nature. 2013;501:355–64. https://doi.org/10.1038/nature12627.CrossRefPubMedPubMedCentral

105.

Genomics England. Genomics England. https://www.genomicsengland.co.uk/the-100000-genomes-project/. Accessed 6 Apr 2018.

106.

Soh KP, Szczurek E, Sakoparnig T, Beerenwinkel N. Predicting cancer type from tumour DNA signatures. Genome Med. 2017;9:104. https://doi.org/10.1186/s13073-017-0493-2.CrossRefPubMedPubMedCentral

107.

Gerstung M, Papaemmanuil E, Martincorena I, Bullinger L, Gaidzik VI, Paschka P, et al. Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat Genet. 2017;49:332–40. https://doi.org/10.1038/ng.3756.CrossRefPubMedPubMedCentral

108.

Coyne GO, Takebe N, Chen AP. Defining precision: the precision medicine initiative trials NCI-MPACT and NCI-MATCH. Curr Probl Cancer. 2017;41:182–93. https://doi.org/10.1016/j.currproblcancer.2017.02.001.CrossRefPubMed

109.

Hyman DM, Piha-Paul SA, Won H, Rodon J, Saura C, Shapiro GI, et al. HER kinase inhibition in patients with HER2- and HER3-mutant cancers. Nature. 2018;554:189–94. https://doi.org/10.1038/nature25475.CrossRefPubMedPubMedCentral

Title: SwissMTB: establishing comprehensive molecular cancer diagnostics in Swiss clinics
Authors: Franziska Singer
Anja Irmisch
Nora C. Toussaint
Linda Grob
Jochen Singer
Thomas Thurnherr
Niko Beerenwinkel
Mitchell P. Levesque
Reinhard Dummer
Luca Quagliata
Sacha I. Rothschild
Andreas Wicki
Christian Beisel
Daniel J. Stekhoven
Publication date: 01-12-2018
Publisher: BioMed Central
Published in: BMC Medical Informatics and Decision Making / Issue 1/2018
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-018-0680-0

Keynote webinar | Spotlight on medication adherence

Springer Medicine

SwissMTB: establishing comprehensive molecular cancer diagnostics in Swiss clinics

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2018

Inventory of oncologists’ unmet needs for tools to support decision-making about palliative treatment for metastatic colorectal cancer

Use of feedback to improve mental number line representations in primary care clinics

Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation

Smart medical beds in patient-care environments of the twenty-first century: a state-of-art survey

Analyzing hidden populations online: topic, emotion, and social network of HIV-related users in the largest Chinese online community

Informing evaluation of a smartphone application for people with acquired brain injury: a stakeholder engagement study