Cancer Biomarker Discovery for Precision Medicine: New Progress

Author(s): Jinfeng Zou, Edwin Wang*

Journal Name: Current Medicinal Chemistry

Volume 26 , Issue 42 , 2019


  Journal Home
Translate in Chinese
Become EABM
Become Reviewer
Call for Editor

Abstract:

Background: Precision medicine puts forward customized healthcare for cancer patients. An important way to accomplish this task is to stratify patients into those who may respond to a treatment and those who may not. For this purpose, diagnostic and prognostic biomarkers have been pursued.

Objective: This review focuses on novel approaches and concepts of exploring biomarker discovery under the circumstances that technologies are developed, and data are accumulated for precision medicine.

Results: The traditional mechanism-driven functional biomarkers have the advantage of actionable insights, while data-driven computational biomarkers can fulfill more needs, especially with tremendous data on the molecules of different layers (e.g. genetic mutation, mRNA, protein etc.) which are accumulated based on a plenty of technologies. Besides, the technology-driven liquid biopsy biomarker is very promising to improve patients’ survival. The developments of biomarker discovery on these aspects are promoting the understanding of cancer, helping the stratification of patients and improving patients’ survival.

Conclusion: Current developments on mechanisms-, data- and technology-driven biomarker discovery are achieving the aim of precision medicine and promoting the clinical application of biomarkers. Meanwhile, the complexity of cancer requires more effective biomarkers, which could be accomplished by a comprehensive integration of multiple types of biomarkers together with a deep understanding of cancer.

Keywords: Precision medicine, diagnostic and prognostic biomarkers, cancer heterogeneity, integrative analysis, network, early cancer detection.

[1]
Vargas, A.J.; Harris, C.C. Biomarker development in the precision medicine era: lung cancer as a case study. Nat. Rev. Cancer, 2016, 16(8), 525-537.
[http://dx.doi.org/10.1038/nrc.2016.56] [PMID: 27388699]
[2]
Ansell, S.M.; Lesokhin, A.M.; Borrello, I.; Halwani, A.; Scott, E.C.; Gutierrez, M.; Schuster, S.J.; Millenson, M.M.; Cattry, D.; Freeman, G.J.; Rodig, S.J.; Chapuy, B.; Ligon, A.H.; Zhu, L.; Grosso, J.F.; Kim, S.Y.; Timmerman, J.M.; Shipp, M.A.; Armand, P. PD-1 blockade with nivolumab in relapsed or refractory Hodgkin’s lymphoma. N. Engl. J. Med., 2015, 372(4), 311-319.
[http://dx.doi.org/10.1056/NEJMoa1411087] [PMID: 25482239]
[3]
Antoniu, S.; Ulmeanu, R. Nivolumab for advanced non-small cell lung cancer: an immunologically-mediated tumor checkout. Ann. Transl. Med., 2016, 4(10), 201.
[http://dx.doi.org/10.21037/atm.2016.05.32] [PMID: 27294097]
[4]
Hamid, O.; Robert, C.; Daud, A.; Hodi, F.S.; Hwu, W.J.; Kefford, R.; Wolchok, J.D.; Hersey, P.; Joseph, R.W.; Weber, J.S.; Dronca, R.; Gangadhar, T.C.; Patnaik, A.; Zarour, H.; Joshua, A.M.; Gergich, K.; Elassaiss-Schaap, J.; Algazi, A.; Mateus, C.; Boasberg, P.; Tumeh, P.C.; Chmielowski, B.; Ebbinghaus, S.W.; Li, X.N.; Kang, S.P.; Ribas, A. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N. Engl. J. Med., 2013, 369(2), 134-144.
[http://dx.doi.org/10.1056/NEJMoa1305133] [PMID: 23724846]
[5]
Iwai, Y.; Hamanishi, J.; Chamoto, K.; Honjo, T. Cancer immunotherapies targeting the PD-1 signaling pathway. J. Biomed. Sci., 2017, 24(1), 26.
[http://dx.doi.org/10.1186/s12929-017-0329-9] [PMID: 28376884]
[6]
Powles, T.; Eder, J.P.; Fine, G.D.; Braiteh, F.S.; Loriot, Y.; Cruz, C.; Bellmunt, J.; Burris, H.A.; Petrylak, D.P.; Teng, S.L.; Shen, X.; Boyd, Z.; Hegde, P.S.; Chen, D.S.; Vogelzang, N.J. MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature, 2014, 515(7528), 558-562.
[http://dx.doi.org/10.1038/nature13904] [PMID: 25428503]
[7]
Reck, M.; Rodríguez-Abreu, D.; Robinson, A.G.; Hui, R.; Csőszi, T.; Fülöp, A.; Gottfried, M.; Peled, N.; Tafreshi, A.; Cuffe, S.; O’Brien, M.; Rao, S.; Hotta, K.; Leiby, M.A.; Lubiniecki, G.M.; Shentu, Y.; Rangwala, R.; Brahmer, J.R. KEYNOTE-024 investigators. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N. Engl. J. Med., 2016, 375(19), 1823-1833.
[http://dx.doi.org/10.1056/NEJMoa1606774] [PMID: 27718847]
[8]
Dagogo-Jack, I.; Shaw, A.T. Tumour heterogeneity and resistance to cancer therapies. Nat. Rev. Clin. Oncol., 2018, 15(2), 81-94.
[http://dx.doi.org/10.1038/nrclinonc.2017.166] [PMID: 29115304]
[9]
Wang, E.; Zou, J.; Zaman, N.; Beitel, L.K.; Trifiro, M.; Paliouras, M. Cancer systems biology in the genome sequencing era: part 1, dissecting and modeling of tumor clones and their networks. Semin. Cancer Biol., 2013, 23(4), 279-285.
[http://dx.doi.org/10.1016/j.semcancer.2013.06.002] [PMID: 23791722]
[10]
Wang, E.; Zou, J.; Zaman, N.; Beitel, L.K.; Trifiro, M.; Paliouras, M. Cancer systems biology in the genome sequencing era: part 2, evolutionary dynamics of tumor clonal networks and drug resistance. Semin. Cancer Biol., 2013, 23(4), 286-292.
[http://dx.doi.org/10.1016/j.semcancer.2013.06.001] [PMID: 23792107]
[11]
Li, J.; Lenferink, A.E.; Deng, Y.; Collins, C.; Cui, Q.; Purisima, E.O.; O’Connor-McCourt, M.D.; Wang, E. Identification of high-quality cancer prognostic markers and metastasis network modules. Nat. Commun., 2010, 1(4), 34.
[http://dx.doi.org/10.1038/ncomms1033]
[12]
Zhang, M.; Yao, C.; Guo, Z.; Zou, J.; Zhang, L.; Xiao, H.; Wang, D.; Yang, D.; Gong, X.; Zhu, J.; Li, Y.; Li, X. Apparently low reproducibility of true differential expression discoveries in microarray studies. Bioinformatics, 2008, 24(18), 2057-2063.
[http://dx.doi.org/10.1093/bioinformatics/btn365] [PMID: 18632747]
[13]
Kumar-Sinha, C.; Chinnaiyan, A.M. Precision oncology in the age of integrative genomics. Nat. Biotechnol., 2018, 36(1), 46-60.
[http://dx.doi.org/10.1038/nbt.4017] [PMID: 29319699]
[14]
Hiom, S.C. Diagnosing cancer earlier: reviewing the evidence for improving cancer survival. Br. J. Cancer, 2015, 112(Suppl. 1), S1-S5.
[http://dx.doi.org/10.1038/bjc.2015.23]
[15]
Burgener, J.M.; Rostami, A.; De Carvalho, D.D.; Bratman, S.V. Cell-free DNA as a post-treatment surveillance strategy: current status. Semin. Oncol., 2017, 44(5), 330-346.
[http://dx.doi.org/10.1053/j.seminoncol.2018.01.009] [PMID: 29580435]
[16]
Butler, T.M.; Spellman, P.T.; Gray, J. Circulating-tumor DNA as an early detection and diagnostic tool. Curr. Opin. Genet. Dev., 2017, 42, 14-21.
[http://dx.doi.org/10.1016/j.gde.2016.12.003] [PMID: 28126649]
[17]
Cheng, F.; Su, L.; Qian, C. Circulating tumor DNA: a promising biomarker in the liquid biopsy of cancer. Oncotarget, 2016, 7(30), 48832-48841.
[http://dx.doi.org/10.18632/oncotarget.9453] [PMID: 27223063]
[18]
Pantel, K. Blood-based analysis of circulating cell-free DNA and tumor cells for early cancer detection. PLoS Med., 2016, 13(12)e1002205
[http://dx.doi.org/10.1371/journal.pmed.1002205] [PMID: 28027295]
[19]
Wan, J.C.M.; Massie, C.; Garcia-Corbacho, J.; Mouliere, F.; Brenton, J.D.; Caldas, C.; Pacey, S.; Baird, R.; Rosenfeld, N. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat. Rev. Cancer, 2017, 17(4), 223-238.
[http://dx.doi.org/10.1038/nrc.2017.7] [PMID: 28233803]
[20]
Kang, S.; Li, Q.; Chen, Q.; Zhou, Y.; Park, S.; Lee, G.; Grimes, B.; Krysan, K.; Yu, M.; Wang, W.; Alber, F.; Sun, F.; Dubinett, S.M.; Li, W.; Zhou, X.J. CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biol., 2017, 18(1), 53.
[http://dx.doi.org/10.1186/s13059-017-1191-5] [PMID: 28335812]
[21]
Zou, J.; Wang, E. eTumorType, an algorithm of discriminating cancer types for circulating tumor cells or cell-free DNAs in Blood. Genomics Proteomics Bioinformatics, 2017, 15(2), 130-140.
[http://dx.doi.org/10.1016/j.gpb.2017.01.004] [PMID: 28389380]
[22]
Zhang, J.; Baran, J.; Cros, A.; Guberman, J.M.; Haider, S.; Hsu, J.; Liang, Y.; Rivkin, E.; Wang, J.; Whitty, B.; Wong-Erasmus, M.; Yao, L.; Kasprzyk, A.A. International cancer genome consortium data portal--a one-stop shop for cancer genomics data. Database (Oxford), 2011, 2011bar026
[http://dx.doi.org/10.1093/database/bar026] [PMID: 21930502]
[23]
Weinstein, J.N.; Collisson, E.A.; Mills, G.B.; Shaw, K.R.; Ozenberger, B.A.; Ellrott, K.; Shmulevich, I.; Sander, C.; Stuart, J.M. Cancer genome atlas research network. The cancer genome atlas pan-cancer analysis project. Nat. Genet., 2013, 45(10), 1113-1120.
[http://dx.doi.org/10.1038/ng.2764] [PMID: 24071849]
[24]
Mailman, M.D.; Feolo, M.; Jin, Y.; Kimura, M.; Tryka, K.; Bagoutdinov, R.; Hao, L.; Kiang, A.; Paschall, J.; Phan, L.; Popova, N.; Pretel, S.; Ziyabari, L.; Lee, M.; Shao, Y.; Wang, Z.Y.; Sirotkin, K.; Ward, M.; Kholodov, M.; Zbicz, K.; Beck, J.; Kimelman, M.; Shevelev, S.; Preuss, D.; Yaschenko, E.; Graeff, A.; Ostell, J.; Sherry, S.T. The NCBI dbGaP database of genotypes and phenotypes. Nat. Genet., 2007, 39(10), 1181-1186.
[http://dx.doi.org/10.1038/ng1007-1181] [PMID: 17898773]
[25]
Zou, W.; Wolchok, J.D.; Chen, L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: Mechanisms, response biomarkers, and combinations. Sci. Transl. Med., 2016, 8(328)328rv4
[http://dx.doi.org/10.1126/scitranslmed.aad7118] [PMID: 26936508]
[26]
Topalian, S.L.; Taube, J.M.; Anders, R.A.; Pardoll, D.M. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat. Rev. Cancer, 2016, 16(5), 275-287.
[http://dx.doi.org/10.1038/nrc.2016.36] [PMID: 27079802]
[27]
Maleki Vareki, S.; Garrigós, C.; Duran, I. Biomarkers of response to PD-1/PD-L1 inhibition. Crit. Rev. Oncol. Hematol., 2017, 116, 116-124.
[http://dx.doi.org/10.1016/j.critrevonc.2017.06.001] [PMID: 28693793]
[28]
Ahn, M.J.; Sun, J.M.; Lee, S.H.; Ahn, J.S.; Park, K. EGFR TKI combination with immunotherapy in non-small cell lung cancer. Expert Opin. Drug Saf., 2017, 16(4), 465-469.
[http://dx.doi.org/10.1080/14740338.2017.1300656] [PMID: 28271729]
[29]
Akbay, E.A.; Koyama, S.; Carretero, J.; Altabef, A.; Tchaicha, J.H.; Christensen, C.L.; Mikse, O.R.; Cherniack, A.D.; Beauchamp, E.M.; Pugh, T.J.; Wilkerson, M.D.; Fecci, P.E.; Butaney, M.; Reibel, J.B.; Soucheray, M.; Cohoon, T.J.; Janne, P.A.; Meyerson, M.; Hayes, D.N.; Shapiro, G.I.; Shimamura, T.; Sholl, L.M.; Rodig, S.J.; Freeman, G.J.; Hammerman, P.S.; Dranoff, G.; Wong, K.K. Activation of the PD-1 pathway contributes to immune escape in EGFR-driven lung tumors. Cancer Discov., 2013, 3(12), 1355-1363.
[http://dx.doi.org/10.1158/2159-8290.CD-13-0310] [PMID: 24078774]
[30]
Moya-Horno, I.; Viteri, S.; Karachaliou, N.; Rosell, R. Combination of immunotherapy with targeted therapies in advanced non-small cell lung cancer (NSCLC). Ther. Adv. Med. Oncol., 2018, 101758834017745012
[http://dx.doi.org/10.1177/1758834017745012] [PMID: 29383034]
[31]
Tabchi, S.; Kourie, H.R.; Kattan, J. Adding checkpoint inhibitors to tyrosine kinase inhibitors targeting EGFR/ALK in non-small cell lung cancer: a new therapeutic strategy. Invest. New Drugs, 2016, 34(6), 794-796.
[http://dx.doi.org/10.1007/s10637-016-0383-2] [PMID: 27562868]
[32]
Zhang, Y.; Xiang, C.; Wang, Y.; Duan, Y.; Liu, C.; Zhang, Y. PD-L1 promoter methylation mediates the resistance response to anti-PD-1 therapy in NSCLC patients with EGFR-TKI resistance. Oncotarget, 2017, 8(60), 101535-101544.
[http://dx.doi.org/10.18632/oncotarget.21328] [PMID: 29254184]
[33]
Aguiar, P.N., Jr; De Mello, R.A.; Hall, P.; Tadokoro, H.; Lima Lopes, G. PD-L1 expression as a predictive biomarker in advanced non-small-cell lung cancer: updated survival data. Immunotherapy, 2017, 9(6), 499-506.
[http://dx.doi.org/10.2217/imt-2016-0150] [PMID: 28472902]
[34]
Daud, A.I.; Wolchok, J.D.; Robert, C.; Hwu, W.J.; Weber, J.S.; Ribas, A.; Hodi, F.S.; Joshua, A.M.; Kefford, R.; Hersey, P.; Joseph, R.; Gangadhar, T.C.; Dronca, R.; Patnaik, A.; Zarour, H.; Roach, C.; Toland, G.; Lunceford, J.K.; Li, X.N.; Emancipator, K.; Dolled-Filhart, M.; Kang, S.P.; Ebbinghaus, S.; Hamid, O. Programmed death-ligand 1 expression and response to the anti-programmed death 1 antibody pembrolizumab in melanoma. J. Clin. Oncol., 2016, 34(34), 4102-4109.
[http://dx.doi.org/10.1200/JCO.2016.67.2477] [PMID: 27863197]
[35]
Tumeh, P.C.; Harview, C.L.; Yearley, J.H.; Shintaku, I.P.; Taylor, E.J.; Robert, L.; Chmielowski, B.; Spasic, M.; Henry, G.; Ciobanu, V.; West, A.N.; Carmona, M.; Kivork, C.; Seja, E.; Cherry, G.; Gutierrez, A.J.; Grogan, T.R.; Mateus, C.; Tomasic, G.; Glaspy, J.A.; Emerson, R.O.; Robins, H.; Pierce, R.H.; Elashoff, D.A.; Robert, C.; Ribas, A. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature, 2014, 515(7528), 568-571.
[http://dx.doi.org/10.1038/nature13954] [PMID: 25428505]
[36]
Rizvi, N.A.; Hellmann, M.D.; Snyder, A.; Kvistborg, P.; Makarov, V.; Havel, J.J.; Lee, W.; Yuan, J.; Wong, P.; Ho, T.S.; Miller, M.L.; Rekhtman, N.; Moreira, A.L.; Ibrahim, F.; Bruggeman, C.; Gasmi, B.; Zappasodi, R.; Maeda, Y.; Sander, C.; Garon, E.B.; Merghoub, T.; Wolchok, J.D.; Schumacher, T.N.; Chan, T.A. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science, 2015, 348(6230), 124-128.
[http://dx.doi.org/10.1126/science.aaa1348] [PMID: 25765070]
[37]
Lu, Y.C.; Robbins, P.F. Cancer immunotherapy targeting neoantigens. Semin. Immunol., 2016, 28(1), 22-27.
[http://dx.doi.org/10.1016/j.smim.2015.11.002] [PMID: 26653770]
[38]
Schumacher, T.N.; Schreiber, R.D. Neoantigens in cancer immunotherapy. Science, 2015, 348(6230), 69-74.
[http://dx.doi.org/10.1126/science.aaa4971] [PMID: 25838375]
[39]
Ott, P.A.; Hu, Z.; Keskin, D.B.; Shukla, S.A.; Sun, J.; Bozym, D.J.; Zhang, W.; Luoma, A.; Giobbie-Hurder, A.; Peter, L.; Chen, C.; Olive, O.; Carter, T.A.; Li, S.; Lieb, D.J.; Eisenhaure, T.; Gjini, E.; Stevens, J.; Lane, W.J.; Javeri, I.; Nellaiappan, K.; Salazar, A.M.; Daley, H.; Seaman, M.; Buchbinder, E.I.; Yoon, C.H.; Harden, M.; Lennon, N.; Gabriel, S.; Rodig, S.J.; Barouch, D.H.; Aster, J.C.; Getz, G.; Wucherpfennig, K.; Neuberg, D.; Ritz, J.; Lander, E.S.; Fritsch, E.F.; Hacohen, N.; Wu, C.J. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature, 2017, 547(7662), 217-221.
[http://dx.doi.org/10.1038/nature22991] [PMID: 28678778]
[40]
Balachandran, V.P.; Łuksza, M.; Zhao, J.N.; Makarov, V.; Moral, J.A.; Remark, R.; Herbst, B.; Askan, G.; Bhanot, U.; Senbabaoglu, Y.; Wells, D.K.; Cary, C.I.O.; Grbovic-Huezo, O.; Attiyeh, M.; Medina, B.; Zhang, J.; Loo, J.; Saglimbeni, J.; Abu-Akeel, M.; Zappasodi, R.; Riaz, N.; Smoragiewicz, M.; Kelley, Z.L.; Basturk, O.; Gönen, M.; Levine, A.J.; Allen, P.J.; Fearon, D.T.; Merad, M.; Gnjatic, S.; Iacobuzio-Donahue, C.A.; Wolchok, J.D.; DeMatteo, R.P.; Chan, T.A.; Greenbaum, B.D.; Merghoub, T.; Leach, S.D. Australian Pancreatic Cancer Genome Initiative; Garvan Institute of Medical Research; Prince of Wales Hospital; Royal North Shore Hospital; University of Glasgow; St Vincent’s Hospital; QIMR Berghofer Medical Research Institute; University of Melbourne, Centre for Cancer Research; University of Queensland, Institute for Molecular Bioscience; Bankstown Hospital; Liverpool Hospital; Royal Prince Alfred Hospital, Chris O’Brien Lifehouse; Westmead Hospital; Fremantle Hospital; St John of God Healthcare; Royal Adelaide Hospital; Flinders Medical Centre; Envoi Pathology; Princess Alexandria Hospital; Austin Hospital; Johns Hopkins Medical Institutes; ARC-Net Centre for Applied Research on Cancer. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature, 2017, 551(7681), 512-516.
[http://dx.doi.org/10.1038/nature24462] [PMID: 29132146]
[41]
Alexandrov, L.B.; Nik-Zainal, S.; Wedge, D.C.; Aparicio, S.A.; Behjati, S.; Biankin, A.V.; Bignell, G.R.; Bolli, N.; Borg, A.; Børresen-Dale, A.L.; Boyault, S.; Burkhardt, B.; Butler, A.P.; Caldas, C.; Davies, H.R.; Desmedt, C.; Eils, R.; Eyfjörd, J.E.; Foekens, J.A.; Greaves, M.; Hosoda, F.; Hutter, B.; Ilicic, T.; Imbeaud, S.; Imielinski, M.; Jäger, N.; Jones, D.T.; Jones, D.; Knappskog, S.; Kool, M.; Lakhani, S.R.; López-Otín, C.; Martin, S.; Munshi, N.C.; Nakamura, H.; Northcott, P.A.; Pajic, M.; Papaemmanuil, E.; Paradiso, A.; Pearson, J.V.; Puente, X.S.; Raine, K.; Ramakrishna, M.; Richardson, A.L.; Richter, J.; Rosenstiel, P.; Schlesner, M.; Schumacher, T.N.; Span, P.N.; Teague, J.W.; Totoki, Y.; Tutt, A.N.; Valdés-Mas, R.; van Buuren, M.M.; van ’t Veer, L.; Vincent-Salomon, A.; Waddell, N.; Yates, L.R.; Zucman-Rossi, J.; Futreal, P.A.; McDermott, U.; Lichter, P.; Meyerson, M.; Grimmond, S.M.; Siebert, R.; Campo, E.; Shibata, T.; Pfister, S.M.; Campbell, P.J.; Stratton, M.R. Australian Pancreatic Cancer Genome Initiative; ICGC Breast Cancer Consortium; ICGC MMML-Seq Consortium; ICGC PedBrain. Signatures of mutational processes in human cancer. Nature, 2013, 500(7463), 415-421.
[http://dx.doi.org/10.1038/nature12477] [PMID: 23945592]
[42]
Alexandrov, L.B.; Nik-Zainal, S.; Wedge, D.C.; Campbell, P.J.; Stratton, M.R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep., 2013, 3(1), 246-259.
[http://dx.doi.org/10.1016/j.celrep.2012.12.008] [PMID: 23318258]
[43]
Huang, X.; Wojtowicz, D.; Przytycka, T.M. Detecting presence of mutational signatures in cancer with confidence. Bioinformatics, 2017, 34(2), 330-337.
[http://dx.doi.org/10.1093/bioinformatics/btx604] [PMID: 29028923]
[44]
Xiang, Y.; Gubian, S.; Suomela, B.; Hoeng, J. Generalized simulated annealing for efficient global optimization: the GenSA package for R., Available at: http://cran.r-project.org/web/packages/GenSA/2016.
[45]
Nik-Zainal, S.; Davies, H.; Staaf, J.; Ramakrishna, M.; Glodzik, D.; Zou, X.; Martincorena, I.; Alexandrov, L.B.; Martin, S.; Wedge, D.C.; Van Loo, P.; Ju, Y.S.; Smid, M.; Brinkman, A.B.; Morganella, S.; Aure, M.R.; Lingjærde, O.C.; Langerød, A.; Ringnér, M.; Ahn, S.M.; Boyault, S.; Brock, J.E.; Broeks, A.; Butler, A.; Desmedt, C.; Dirix, L.; Dronov, S.; Fatima, A.; Foekens, J.A.; Gerstung, M.; Hooijer, G.K.; Jang, S.J.; Jones, D.R.; Kim, H.Y.; King, T.A.; Krishnamurthy, S.; Lee, H.J.; Lee, J.Y.; Li, Y.; McLaren, S.; Menzies, A.; Mustonen, V.; O’Meara, S.; Pauporté, I.; Pivot, X.; Purdie, C.A.; Raine, K.; Ramakrishnan, K.; Rodríguez-González, F.G.; Romieu, G.; Sieuwerts, A.M.; Simpson, P.T.; Shepherd, R.; Stebbings, L.; Stefansson, O.A.; Teague, J.; Tommasi, S.; Treilleux, I.; Van den Eynden, G.G.; Vermeulen, P.; Vincent-Salomon, A.; Yates, L.; Caldas, C.; van’t Veer, L.; Tutt, A.; Knappskog, S.; Tan, B.K.; Jonkers, J.; Borg, Å.; Ueno, N.T.; Sotiriou, C.; Viari, A.; Futreal, P.A.; Campbell, P.J.; Span, P.N.; Van Laere, S.; Lakhani, S.R.; Eyfjord, J.E.; Thompson, A.M.; Birney, E.; Stunnenberg, H.G.; van de Vijver, M.J.; Martens, J.W.; Børresen-Dale, A.L.; Richardson, A.L.; Kong, G.; Thomas, G.; Stratton, M.R. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature, 2016, 534(7605), 47-54.
[http://dx.doi.org/10.1038/nature17676] [PMID: 27135926]
[46]
Polak, P.; Kim, J.; Braunstein, L.Z.; Karlic, R.; Haradhavala, N.J.; Tiao, G.; Rosebrock, D.; Livitz, D.; Kübler, K.; Mouw, K.W.; Kamburov, A.; Maruvka, Y.E.; Leshchiner, I.; Lander, E.S.; Golub, T.R.; Zick, A.; Orthwein, A.; Lawrence, M.S.; Batra, R.N.; Caldas, C.; Haber, D.A.; Laird, P.W.; Shen, H.; Ellisen, L.W.; D’Andrea, A.D.; Chanock, S.J.; Foulkes, W.D.; Getz, G. A mutational signature reveals alterations underlying deficient homologous recombination repair in breast cancer. Nat. Genet., 2017, 49(10), 1476-1486.
[http://dx.doi.org/10.1038/ng.3934] [PMID: 28825726]
[47]
Davies, H.; Glodzik, D.; Morganella, S.; Yates, L.R.; Staaf, J.; Zou, X.; Ramakrishna, M.; Martin, S.; Boyault, S.; Sieuwerts, A.M.; Simpson, P.T.; King, T.A.; Raine, K.; Eyfjord, J.E.; Kong, G.; Borg, Å.; Birney, E.; Stunnenberg, H.G.; van de Vijver, M.J.; Børresen-Dale, A.L.; Martens, J.W.; Span, P.N.; Lakhani, S.R.; Vincent-Salomon, A.; Sotiriou, C.; Tutt, A.; Thompson, A.M.; Van Laere, S.; Richardson, A.L.; Viari, A.; Campbell, P.J.; Stratton, M.R.; Nik-Zainal, S. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat. Med., 2017, 23(4), 517-525.
[http://dx.doi.org/10.1038/nm.4292] [PMID: 28288110]
[48]
Telonis, A.G.; Magee, R.; Loher, P.; Chervoneva, I.; Londin, E.; Rigoutsos, I. Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types. Nucleic Acids Res., 2017, 45(6), 2973-2985.
[http://dx.doi.org/10.1093/nar/gkx082] [PMID: 28206648]
[49]
Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 2015, 43(7)e47
[http://dx.doi.org/10.1093/nar/gkv007] [PMID: 25605792]
[50]
Noble, W.S. What is a support vector machine? Nat. Biotechnol., 2006, 24(12), 1565-1567.
[http://dx.doi.org/10.1038/nbt1206-1565] [PMID: 17160063]
[51]
Chen, B.; Huang, S. Circular RNA: An emerging non-coding RNA as a regulator and biomarker in cancer. Cancer Lett., 2018, 418, 41-50.
[http://dx.doi.org/10.1016/j.canlet.2018.01.011] [PMID: 29330104]
[52]
Ransohoff, J.D.; Wei, Y.; Khavari, P.A. The functions and unique features of long intergenic non-coding RNA. Nat. Rev. Mol. Cell Biol., 2018, 19(3), 143-157.
[http://dx.doi.org/10.1038/nrm.2017.104] [PMID: 29138516]
[53]
Yang, S.; Xu, J.; Zeng, X. A six-long non-coding RNA signature predicts prognosis in melanoma patients. Int. J. Oncol., 2018, 52(4), 1178-1188.
[http://dx.doi.org/10.3892/ijo.2018.4268] [PMID: 29436619]
[54]
Meng, X.; Jin-Cheng, G.; Jue, Z.; Quan-Fu, M.; Bin, Y.; Xu-Feng, W. Protein-coding genes, long non-coding RNAs combined with microRNAs as a novel clinical multi-dimension transcriptome signature to predict prognosis in ovarian cancer. Oncotarget, 2017, 8(42), 72847-72859.
[http://dx.doi.org/10.18632/oncotarget.20457] [PMID: 29069830]
[55]
Yang, F.; Liu, D.Y.; Guo, J.T.; Ge, N.; Zhu, P.; Liu, X.; Wang, S.; Wang, G.X.; Sun, S.Y. Circular RNA circ-LDLRAD3 as a biomarker in diagnosis of pancreatic cancer. World J. Gastroenterol., 2017, 23(47), 8345-8354.
[http://dx.doi.org/10.3748/wjg.v23.i47.8345] [PMID: 29307994]
[56]
Okholm, T.L.H.; Nielsen, M.M.; Hamilton, M.P.; Christensen, L.L.; Vang, S.; Hedegaard, J.; Hansen, T.B.; Kjems, J.; Dyrskjot, L.; Pedersen, J.S. Circular RNA expression is abundant and correlated to aggressiveness in early-stage bladder cancer. NPJ Genom. Med., 2017, 2(36)
[http://dx.doi.org/10.1038/s41525-017-0038-z] [PMID: 29263845]
[57]
Venet, D.; Dumont, J.E.; Detours, V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLOS Comput. Biol., 2011, 7(10) e1002240
[http://dx.doi.org/10.1371/journal.pcbi.1002240] [PMID: 22028643]
[58]
Hanahan, D.; Weinberg, R.A. The hallmarks of cancer. Cell, 2000, 100(1), 57-70.
[http://dx.doi.org/10.1016/S0092-8674(00)81683-9] [PMID: 10647931]
[59]
Hanahan, D.; Weinberg, R.A. Hallmarks of cancer: the next generation. Cell, 2011, 144(5), 646-674.
[http://dx.doi.org/10.1016/j.cell.2011.02.013] [PMID: 21376230]
[60]
Gao, S.; Tibiche, C.; Zou, J.; Zaman, N.; Trifiro, M.; O’Connor-McCourt, M.; Wang, E. Identification and construction of combinatory cancer hallmark-based gene signature sets to predict recurrence and chemotherapy benefit in stage II colorectal cancer. JAMA Oncol., 2016, 2(1), 37-45.
[http://dx.doi.org/10.1001/jamaoncol.2015.3413] [PMID: 26502222]
[61]
Choi, J.; Park, S.; Yoon, Y.; Ahn, J. Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers. Bioinformatics, 2017, 33(22), 3619-3626.
[http://dx.doi.org/10.1093/bioinformatics/btx487] [PMID: 28961949]
[62]
Sohn, I.; Kim, J.; Jung, S.H.; Park, C. Gradient lasso for Cox proportional hazards model. Bioinformatics, 2009, 25(14), 1775-1781.
[http://dx.doi.org/10.1093/bioinformatics/btp322] [PMID: 19447787]
[63]
Roy, J.; Winter, C.; Isik, Z.; Schroeder, M. Network information improves cancer outcome prediction. Brief. Bioinform., 2014, 15(4), 612-625.
[http://dx.doi.org/10.1093/bib/bbs083] [PMID: 23255167]
[64]
Wu, G.; Stein, L. A network module-based method for identifying cancer prognostic signatures. Genome Biol., 2012, 13(12), R112.
[http://dx.doi.org/10.1186/gb-2012-13-12-r112] [PMID: 23228031]
[65]
Langfelder, P.; Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9(559), 1-13.
[http://dx.doi.org/10.1186/1471-2105-9-559]
[66]
Ahmad, A.; Fröhlich, H. Towards clinically more relevant dissection of patient heterogeneity via survival-based Bayesian clustering. Bioinformatics, 2017, 33(22), 3558-3566.
[http://dx.doi.org/10.1093/bioinformatics/btx464] [PMID: 28961917]
[67]
Witten, D.M.; Tibshirani, R. A framework for feature selection in clustering. J. Am. Stat. Assoc., 2010, 105(490), 713-726.
[http://dx.doi.org/10.1198/jasa.2010.tm09415] [PMID: 20811510]
[68]
van ’t Veer, L.J.; Dai, H.; van de Vijver, M.J.; He, Y.D.; Hart, A.A.; Mao, M.; Peterse, H.L.; van der Kooy, K.; Marton, M.J.; Witteveen, A.T.; Schreiber, G.J.; Kerkhoven, R.M.; Roberts, C.; Linsley, P.S.; Bernards, R.; Friend, S.H. Gene expression profiling predicts clinical outcome of breast cancer. Nature, 2002, 415(6871), 530-536.
[http://dx.doi.org/10.1038/415530a] [PMID: 11823860]
[69]
Zhang, W.; Le, T.D.; Liu, L.; Zhou, Z.H.; Li, J. Mining heterogeneous causal effects for personalized cancer treatment. Bioinformatics, 2017, 33(15), 2372-2378.
[http://dx.doi.org/10.1093/bioinformatics/btx174] [PMID: 28369195]
[70]
Breiman, L.; Friedman, J.H.; Olshen, R.A.; Stone, C.G. Classification and Regression Trees; Wadsworth In-ternational Group: Belmont, California, USA, 1984.
[71]
Goeman, J.J. L1 penalized estimation in the Cox proportional hazards model. Biom. J., 2010, 52(1), 70-84.
[http://dx.doi.org/ 10.1002/bimj.200900028] [PMID: 19937997]
[72]
Liu, X.; Chang, X.; Liu, R.; Yu, X.; Chen, L.; Aihara, K. Quantifying critical states of complex diseases using single-sample dynamic network biomarkers. PLOS Comput. Biol., 2017, 13(7) e1005633
[http://dx.doi.org/10.1371/journal.pcbi.1005633] [PMID: 28678795]
[73]
Liu, X.; Wang, Y.; Ji, H.; Aihara, K.; Chen, L. Personalized characterization of diseases using sample-specific networks. Nucleic Acids Res., 2016, 44(22)e164
[http://dx.doi.org/10.1093/nar/gkw772] [PMID: 27596597]
[74]
Hou, J.P.; Ma, J. DawnRank: discovering personalized driver genes in cancer. Genome Med., 2014, 6(7), 56.
[http://dx.doi.org/10.1186/s13073-014-0056-8] [PMID: 25177370]
[75]
Brin, S.; Page, L. The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst., 1998, 30(1- 7), 107-117.
[http://dx.doi.org/10.1016/S0169-7552(98)00110-X]
[76]
Page, L.; Brin, S.; Motwani, R.; Winograd, T. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report; Stanford InfoLab: Stanford, CA, 1999.
[77]
Bertrand, D.; Chng, K.R.; Sherbaf, F.G.; Kiesel, A.; Chia, B.K.; Sia, Y.Y.; Huang, S.K.; Hoon, D.S.; Liu, E.T.; Hillmer, A.; Nagarajan, N. Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles. Nucleic Acids Res., 2015, 43(7)e44
[http://dx.doi.org/10.1093/nar/gku1393] [PMID: 25572314]
[78]
Guo, W.F.; Zhang, S.W.; Liu, L.L.; Liu, F.; Shi, Q.Q.; Zhang, L.; Tang, Y.; Zeng, T.; Chen, L. Discovering personalized driver mutation profiles of single samples in cancer by network control strategy. Bioinformatics, 2018, 34(11), 1893-1903.
[http://dx.doi.org/10.1093/bioinformatics/bty006] [PMID: 29329368]
[79]
Zaman, N.; Li, L.; Jaramillo, M.L.; Sun, Z.; Tibiche, C.; Banville, M.; Collins, C.; Trifiro, M.; Paliouras, M.; Nantel, A.; O’Connor-McCourt, M.; Wang, E. Signaling network assessment of mutations and copy number variations predict breast cancer subtype-specific drug targets. Cell Rep., 2013, 5(1), 216-223.
[http://dx.doi.org/10.1016/j.celrep.2013.08.028] [PMID: 24075989]
[80]
Costello, J.C.; Heiser, L.M.; Georgii, E.; Gönen, M.; Menden, M.P.; Wang, N.J.; Bansal, M. Ammad-ud-din, M.; Hintsanen, P.; Khan, S.A.; Mpindi, J.P.; Kallioniemi, O.; Honkela, A.; Aittokallio, T.; Wennerberg, K.; Collins, J.J.; Gallahan, D.; Singer, D.; Saez-Rodriguez, J.; Kaski, S.; Gray, J.W.; Stolovitzky, G. NCI DREAM Community. A community effort to assess and improve drug sensitivity prediction algorithms. Nat. Biotechnol., 2014, 32(12), 1202-1212.
[http://dx.doi.org/10.1038/nbt.2877] [PMID: 24880487]
[81]
Wang, E.; Zaman, N.; McGee, S.; Milanese, J.S.; Masoudi-Nejad, A.; O’Connor-McCourt, M. Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data. Semin. Cancer Biol., 2015, 30, 4-12.
[http://dx.doi.org/10.1016/j.semcancer.2014.04.002] [PMID: 24747696]
[82]
Milanese, J.S.; Tabchi, S.; Zaman, N.; Zou, J.; Han, P.; Meng, Z.; Nantel, A.; Droit, A.; Wang, E. eTumorMetastasis, a network-based algorithm predicts clinical outcomes using whole-exome sequencing data of cancer patients. bioRxiv, 2018.
[http://dx.doi.org/10.1101/268680]
[83]
Zhang, C.; Liu, J.; Shi, Q.; Zeng, T.; Chen, L. Comparative network stratification analysis for identifying functional interpretable network biomarkers. BMC Bioinformatics, 2017, 18(Suppl. 3), 48.
[http://dx.doi.org/10.1186/s12859-017-1462-x] [PMID: 28361683]
[84]
Hanzelmann, S.; Castelo, R.; Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 2013, 14(7)
[http://dx.doi.org/ 10.18129/B9.bioc.GSVA]
[85]
Drier, Y.; Sheffer, M.; Domany, E. Pathway-based personalized analysis of cancer. Proc. Natl. Acad. Sci. USA, 2013, 110(16), 6388-6393.
[http://dx.doi.org/10.1073/pnas.1219651110] [PMID: 23547110]
[86]
Cun, Y.; Fröhlich, H. Network and data integration for biomarker signature discovery via network smoothed T-statistics. PLoS One, 2013, 8(9)e73074
[http://dx.doi.org/10.1371/journal.pone.0073074] [PMID: 24019896]
[87]
Winter, C.; Kristiansen, G.; Kersting, S.; Roy, J.; Aust, D.; Knösel, T.; Rümmele, P.; Jahnke, B.; Hentrich, V.; Rückert, F.; Niedergethmann, M.; Weichert, W.; Bahra, M.; Schlitt, H.J.; Settmacher, U.; Friess, H.; Büchler, M.; Saeger, H.D.; Schroeder, M.; Pilarsky, C.; Grützmann, R. Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes. PLOS Comput. Biol., 2012, 8(5)e1002511
[http://dx.doi.org/10.1371/journal.pcbi.1002511] [PMID: 22615549]
[88]
Guo, Z.; Zhang, T.; Li, X.; Wang, Q.; Xu, J.; Yu, H.; Zhu, J.; Wang, H.; Wang, C.; Topol, E.J.; Rao, S. Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinformatics, 2005, 6, 58.
[http://dx.doi.org/ 10.1186/1471-2105-6-58] [PMID: 15774002]
[89]
Zhang, F.; Ren, C.; Lau, K.K.; Zheng, Z.; Lu, G.; Yi, Z.; Zhao, Y.; Su, F.; Zhang, S.; Zhang, B.; Sobie, E.A.; Zhang, W.; Walsh, M.J. A network medicine approach to build a comprehensive atlas for the prognosis of human cancer. Brief. Bioinform., 2016, 17(6), 1044-1059.
[http://dx.doi.org/10.1093/bib/bbw076] [PMID: 27559151]
[90]
Leiserson, M.D.; Vandin, F.; Wu, H.T.; Dobson, J.R.; Eldridge, J.V.; Thomas, J.L.; Papoutsaki, A.; Kim, Y.; Niu, B.; McLellan, M.; Lawrence, M.S.; Gonzalez-Perez, A.; Tamborero, D.; Cheng, Y.; Ryslik, G.A.; Lopez-Bigas, N.; Getz, G.; Ding, L.; Raphael, B.J. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet., 2015, 47(2), 106-114.
[http://dx.doi.org/10.1038/ng.3168] [PMID: 25501392]
[91]
Bettegowda, C.; Sausen, M.; Leary, R.J.; Kinde, I.; Wang, Y.; Agrawal, N.; Bartlett, B.R.; Wang, H.; Luber, B.; Alani, R.M.; Antonarakis, E.S.; Azad, N.S.; Bardelli, A.; Brem, H.; Cameron, J.L.; Lee, C.C.; Fecher, L.A.; Gallia, G.L.; Gibbs, P.; Le, D.; Giuntoli, R.L.; Goggins, M.; Hogarty, M.D.; Holdhoff, M.; Hong, S.M.; Jiao, Y.; Juhl, H.H.; Kim, J.J.; Siravegna, G.; Laheru, D.A.; Lauricella, C.; Lim, M.; Lipson, E.J.; Marie, S.K.; Netto, G.J.; Oliner, K.S.; Olivi, A.; Olsson, L.; Riggins, G.J.; Sartore-Bianchi, A.; Schmidt, K.; Shih, M.; Oba-Shinjo, S.M.; Siena, S.; Theodorescu, D.; Tie, J.; Harkins, T.T.; Veronese, S.; Wang, T.L.; Weingart, J.D.; Wolfgang, C.L.; Wood, L.D.; Xing, D.; Hruban, R.H.; Wu, J.; Allen, P.J.; Schmidt, C.M.; Choti, M.A.; Velculescu, V.E.; Kinzler, K.W.; Vogelstein, B.; Papadopoulos, N.; Diaz, L.A. Jr. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl. Med., 2014, 6(224)224ra24
[http://dx.doi.org/10.1126/scitranslmed.3007094] [PMID: 24553385]
[92]
Heitzer, E.; Perakis, S.; Geigl, J.B.; Speicher, M.R. The potential of liquid biopsies for the early detection of cancer. NPJ Precise Oncol., 2017, 1(1), 36.
[http://dx.doi.org/ 10.1038/s41698-017-0039-5] [PMID: 29872715]
[93]
Han, X.; Wang, J.; Sun, Y. Circulating tumor DNA as biomarkers for cancer detection. Genomics Proteomics Bioinformatics, 2017, 15(2), 59-72.
[http://dx.doi.org/10.1016/j.gpb.2016.12.004] [PMID: 28392479]
[94]
Vychytilova-Faltejskova, P.; Radova, L.; Sachlova, M.; Kosarova, Z.; Slaba, K.; Fabian, P.; Grolich, T.; Prochazka, V.; Kala, Z.; Svoboda, M.; Kiss, I.; Vyzula, R.; Slaby, O. Serum-based microRNA signatures in early diagnosis and prognosis prediction of colon cancer. Carcinogenesis, 2016, 37(10), 941-950.
[http://dx.doi.org/10.1093/carcin/bgw078] [PMID: 27485599]
[95]
Diehl, F.; Schmidt, K.; Choti, M.A.; Romans, K.; Goodman, S.; Li, M.; Thornton, K.; Agrawal, N.; Sokoll, L.; Szabo, S.A.; Kinzler, K.W.; Vogelstein, B.; Diaz, L.A. Jr. Circulating mutant DNA to assess tumor dynamics. Nat. Med., 2008, 14(9), 985-990.
[http://dx.doi.org/10.1038/nm.1789] [PMID: 18670422]
[96]
Phallen, J.; Sausen, M.; Adleff, V.; Leal, A.; Hruban, C.; White, J.; Anagnostou, V.; Fiksel, J.; Cristiano, S.; Papp, E.; Speir, S.; Reinert, T.; Orntoft, M.W.; Woodward, B.D.; Murphy, D.; Parpart-Li, S.; Riley, D.; Nesselbush, M.; Sengamalay, N.; Georgiadis, A.; Li, Q.K.; Madsen, M.R.; Mortensen, F.V.; Huiskens, J.; Punt, C.; van Grieken, N.; Fijneman, R.; Meijer, G.; Husain, H.; Scharpf, R.B.; Diaz, L.A. Jr.; Jones, S.; Angiuoli, S.; Ørntoft, T.; Nielsen, H.J.; Andersen, C.L.; Velculescu, V.E. Direct detection of early-stage cancers using circulating tumor DNA. Sci. Transl. Med., 2017, 9(403)eaan2415
[http://dx.doi.org/10.1126/scitranslmed.aan2415] [PMID: 28814544]
[97]
Forbes, S.A.; Tang, G.; Bindal, N.; Bamford, S.; Dawson, E.; Cole, C.; Kok, C.Y.; Jia, M.; Ewing, R.; Menzies, A.; Teague, J.W.; Stratton, M.R.; Futreal, P.A. COSMIC (the Catalogue of Somatic Mutations in Cancer): a resource to investigate acquired mutations in human cancer. Nucleic Acids Res., 2010, 38(Database issue), D652-D657.
[http://dx.doi.org/10.1093/nar/gkp995] [PMID: 19906727]
[98]
Newman, A.M.; Bratman, S.V.; To, J.; Wynne, J.F.; Eclov, N.C.; Modlin, L.A.; Liu, C.L.; Neal, J.W.; Wakelee, H.A.; Merritt, R.E.; Shrager, J.B.; Loo, B.W. Jr.; Alizadeh, A.A.; Diehn, M. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med., 2014, 20(5), 548-554.
[http://dx.doi.org/10.1038/nm.3519] [PMID: 24705333]
[99]
Newman, A.M.; Lovejoy, A.F.; Klass, D.M.; Kurtz, D.M.; Chabon, J.J.; Scherer, F.; Stehr, H.; Liu, C.L.; Bratman, S.V.; Say, C.; Zhou, L.; Carter, J.N.; West, R.B.; Sledge, G.W.; Shrager, J.B.; Loo, B.W., Jr; Neal, J.W.; Wakelee, H.A.; Diehn, M.; Alizadeh, A.A. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat. Biotechnol., 2016, 34(5), 547-555.
[http://dx.doi.org/10.1038/nbt.3520] [PMID: 27018799]
[100]
Cohen, J.D.; Li, L.; Wang, Y.; Thoburn, C.; Afsari, B.; Danilova, L.; Douville, C.; Javed, A.A.; Wong, F.; Mattox, A.; Hruban, R.H.; Wolfgang, C.L.; Goggins, M.G.; Dal Molin, M.; Wang, T.L.; Roden, R.; Klein, A.P.; Ptak, J.; Dobbyn, L.; Schaefer, J.; Silliman, N.; Popoli, M.; Vogelstein, J.T.; Browne, J.D.; Schoen, R.E.; Brand, R.E.; Tie, J.; Gibbs, P.; Wong, H.L.; Mansfield, A.S.; Jen, J.; Hanash, S.M.; Falconi, M.; Allen, P.J.; Zhou, S.; Bettegowda, C.; Diaz, L.A., Jr; Tomasetti, C.; Kinzler, K.W.; Vogelstein, B.; Lennon, A.M.; Papadopoulos, N. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science, 2018, 359(6378), 926-930.
[http://dx.doi.org/10.1126/science.aar3247] [PMID: 29348365]
[101]
Heim, D.; Budczies, J.; Stenzinger, A.; Treue, D.; Hufnagl, P.; Denkert, C.; Dietel, M.; Klauschen, F. Cancer beyond organ and tissue specificity: next-generation-sequencing gene mutation data reveal complex genetic similarities across major cancers. Int. J. Cancer, 2014, 135(10), 2362-2369.
[http://dx.doi.org/10.1002/ijc.28882] [PMID: 24706491]
[102]
Chaudhuri, A.A.; Chabon, J.J.; Lovejoy, A.F.; Newman, A.M.; Stehr, H.; Azad, T.D.; Khodadoust, M.S.; Esfahani, M.S.; Liu, C.L.; Zhou, L.; Scherer, F.; Kurtz, D.M.; Say, C.; Carter, J.N.; Merriott, D.J.; Dudley, J.C.; Binkley, M.S.; Modlin, L.; Padda, S.K.; Gensheimer, M.F.; West, R.B.; Shrager, J.B.; Neal, J.W.; Wakelee, H.A.; Loo, B.W. Jr.; Alizadeh, A.A.; Diehn, M. Early detection of molecular residual disease in localized lung cancer by circulating tumor DNA profiling. Cancer Discov., 2017, 7(12), 1394-1403.
[http://dx.doi.org/10.1158/2159-8290.CD-17-0716] [PMID: 28899864]
[103]
Tie, J.; Wang, Y.; Tomasetti, C.; Li, L.; Springer, S.; Kinde, I.; Silliman, N.; Tacey, M.; Wong, H.L.; Christie, M.; Kosmider, S.; Skinner, I.; Wong, R.; Steel, M.; Tran, B.; Desai, J.; Jones, I.; Haydon, A.; Hayes, T.; Price, T.J.; Strausberg, R.L.; Diaz, L.A. Jr.; Papadopoulos, N.; Kinzler, K.W.; Vogelstein, B.; Gibbs, P. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci. Transl. Med., 2016, 8(346)346ra92
[http://dx.doi.org/10.1126/scitranslmed.aaf6219] [PMID: 27384348]
[104]
Kasimir-Bauer, S.; Bittner, A.K.; König, L.; Reiter, K.; Keller, T.; Kimmig, R.; Hoffmann, O. Does primary neoadjuvant systemic therapy eradicate minimal residual disease? Analysis of disseminated and circulating tumor cells before and after therapy. Breast Cancer Res., 2016, 18(1), 20.
[http://dx.doi.org/10.1186/s13058-016-0679-3] [PMID: 26868521]
[105]
Kuske, A.; Gorges, T.M.; Tennstedt, P.; Tiebel, A.K.; Pompe, R.; Preißer, F.; Prues, S.; Mazel, M.; Markou, A.; Lianidou, E.; Peine, S.; Alix-Panabières, C.; Riethdorf, S.; Beyer, B.; Schlomm, T.; Pantel, K. Improved detection of circulating tumor cells in non-metastatic high-risk prostate cancer patients. Sci. Rep., 2016, 6, 39736.
[http://dx.doi.org/10.1038/srep39736] [PMID: 28000772]
[106]
Obermayr, E.; Bednarz-Knoll, N.; Orsetti, B.; Weier, H.U.; Lambrechts, S.; Castillo-Tong, D.C.; Reinthaller, A.; Braicu, E.I.; Mahner, S.; Sehouli, J.; Vergote, I.; Theillet, C.; Zeillinger, R.; Brandt, B. Circulating tumor cells: potential markers of minimal residual disease in ovarian cancer? a study of the OVCAD consortium. Oncotarget, 2017, 8(63), 106415-106428.
[http://dx.doi.org/10.18632/oncotarget.22468] [PMID: 29290959]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 26
ISSUE: 42
Year: 2019
Published on: 08 January, 2020
Page: [7655 - 7671]
Pages: 17
DOI: 10.2174/0929867325666180718164712
Price: $65

Article Metrics

PDF: 50
HTML: 6