Biomarkers and Target Therapy for Lung Cancer

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Abstract

Precision (target) medicine is proposed as a new strategy to identify and develop new highly selective drugs against specific targets for the disease and more precise tailoring of medicines to the target populations of patients. Precision medicine can be an important approach to create more novel and safer therapeutics (tyrosine kinase inhibitors, tumour specific monoclonal antibodies) for patients with gene mutation, aberrations, or protein over-expression. Precision medicine requires an understanding mutational processes, and heterogeneity between cancer cells during tumor evolution. The present review briefly define various heterogeneities and potential associations with drug efficacy and resistance, emphasize the importance to develop functional biomarkers to monitor drug efficacy and resistance, and define opportunities and challenges of precision medicine for clinical practice.

About the authors

Olga V. Shneider

Saint Petersburg City Hospital No 40

Author for correspondence.
Email: o.shneider@gb40.ru
ORCID iD: 0000-0001-8341-2454
SPIN-code: 8405-1051

Cand. Sci. (Med.)

Russian Federation, Saint Petersburg

Tatyana A. Kamilova

Saint Petersburg City Hospital No 40

Email: kamilovaspb@mail.ru
ORCID iD: 0000-0001-6360-132X
SPIN-code: 2922-4404

Cand. Sci. (Biol.)

Russian Federation, Saint Petersburg

Alexander S. Golota

Saint Petersburg City Hospital No 40

Email: golotaa@yahoo.com
ORCID iD: 0000-0002-5632-3963
SPIN-code: 7234-7870

Cand. Sci. (Med.), Associate Professor

Russian Federation, Saint Petersburg

Andrey M. Sarana

Health Committee of Saint Petersburg

Email: asarana@mail.ru
ORCID iD: 0000-0003-3198-8990
SPIN-code: 7922-2751

Cand. Sci. (Med.)

Russian Federation, Saint Petersburg

Sergey G. Sсherbak

Saint Petersburg City Hospital No 40; Saint Petersburg State University

Email: b40@zdrav.spb.ru
ORCID iD: 0000-0001-5047-2792
SPIN-code: 1537-9822

Dr. Sci. (Med.), Professor

Russian Federation, Saint Petersburg

References

  1. Nitu R, Rogobete AF, Gundogdu F, et al. microRNAs expression as novel genetic biomarker for early prediction and continuous monitoring in pulmonary cancer. Biochem Genet. 2017;55(4):281–290. doi: 10.1007/s10528-016-9789-y
  2. Wu D, Wang DC, Cheng Y, et al. Roles of tumor heterogeneity in the development of drug resistance: A call for precision therapy. Semin Cancer Biol. 2017;42(1):13–19. doi: 10.1016/j.semcancer.2016.11.006
  3. Travis WD, Bambrilla E, Burke AP, et al. WHO classification of tumours of the lung, pleura, thymus and heart (IARC WHO classification of tumours). 4th edition. Geneva (Switzerland): World Health Organization; 2015.
  4. Devarakonda S, Morgensztern D, Govindan R. Genomic alterations in lung adenocarcinoma. Lancet Oncol. 16 (7) (2015) e342–351. doi: 10.1016/S1470-2045(15)00077-7
  5. Köhler J. Second-Line Treatment of NSCLC-The pan-ErbB inhibitor afatinib in times of shifting paradigms. Front Med (Lausanne). 2017;4:9. doi: 10.3389/fmed.2017.00009
  6. Thomas A, Liu SV, Subramaniam DS, Giaccone G. Refining the treatment of NSCLC according to histological and molecular subtypes. Nat Rev Clin Oncol. 2015;12(9): 511–526. doi: 10.1038/nrclinonc.2015.90
  7. Villalobos P, Wistuba II. Lung cancer biomarkers. Hematol Oncol Clin North Am. 2017;31(1):13–29. doi: 10.1016/j.hoc.2016.08.006
  8. Gridelli C, Ciardiello F, Gallo C, et al. First-line erlotinib followed by second-line cisplatin-gemcitabine chemotherapy in advanced non-small-cell lung cancer: the TORCH randomized trial. J Clin Oncol. 2012;30(24):3002–3011. doi: 10.1200/JCO.2011.41.2056
  9. Fortunato O, Verri C, Pastorino U et al. MicroRNA profile of lung tumor tissues is associated with a high risk plasma miRNA signature. Microarrays (Basel). 2016;5(3):E18. doi: 10.3390/microarrays5030018
  10. Schuler M, Wu YL, Hirsh V, et al. First-line afatinib versus chemotherapy in patients with non-small cell lung cancer and common epidermal growth factor receptor gene mutations and brain metastases. J Thorac Oncol. 2016;11(3): 380–390. doi: 10.1016/j.jtho.2015.11.014
  11. Park K, Tan EH, O’Byrne K, et al. Afatinib versus gefitinib as first-line treatment of patients with EGFR mutation-positive non-small-cell lung cancer (LUX-Lung 7): a phase 2B, open-label, randomised controlled trial. Lancet Oncol. 2016; 17(5):577–589. doi: 10.1016/ S1470-2045(16)30033-X52
  12. Zhang X, Ran YG, Wang KJ. Risk of severe rash in cancer patients treated with EGFR tyrosine kinase inhibitors: a systematic review and meta-analysis. Future Oncol. 2016; 12(23):2741–2753. doi: 10.2217/fon-2016-0180
  13. Yang JC, Sequist LV, Zhou C, et al. Effect of dose adjustment on the safety and efficacy of afatinib for EGFR mutation-positive lung adenocarcinoma: post hoc analyses of the randomized LUX-Lung 3 and 6 trials. Ann Oncol. 2016;27(11):2103–2110. doi: 10.1093/annonc/mdw322
  14. Oxnard GR, Thress KS, Alden RS, et al. Association between plasma genotyping and outc omes of treatment with osimertinib (AZD9291) in advanced non-small-cell lung cancer. J Clin Oncol. 2016;34(28):3375–3382. doi: 10.1200/JCO.2016.66.7162 59
  15. Sacher AG, Paweletz C, Dahlberg SE, et al. Prospective validation of rapid plasma genotyping for the detection of EGFR and KRAS mutations in advanced lung cancer. JAMA Oncol. 2016;2(8):1014–1022. doi: 10.1001/jamaoncol.2016.0173
  16. Yanagita M, Redig AJ, Paweletz CP, et al. A prospective evaluation of circulating tumor cells and cell-free DNA in EGFR mutant non-small cell lung cancer patients treated with erlotinib on a phase II trial. Clin Cancer Res. 2016;22(24): 6010–6020. doi: 10.1158/1078-0432.CCR-16-0909
  17. Iuchi T, Shingyoji M, Itakura M, et al. Frequency of brain metastases in non-small-cell lung cancer, and their association with epidermal growth factor receptor mutations. Int J Clin Oncol. 2015;20(4):674–679. doi: 10.1007/s10147-014-0760-9
  18. Ballard P, Yates JW, Yang Z, et al. Preclinical comparison of osimertinib with other EGFR-TKIs in EGFR-Mutant NSCLC brain metastases models, and early evidence of clinical brain metastases activity. Clin Cancer Res. 2016; 22(20):5130–5140. doi: 10.1158/1078-0432.CCR- 16-0399
  19. Hata A, Katakami N, Yoshioka H, et al. Spatiotemporal T790M heterogeneity in individual patients with EGFR- mutant non-small-cell lung cancer after acquired resistance to EGFR-TKI. J Thorac Oncol. 2015;10(11):1553–1559. doi: 10.1097/JTO.0000000000000647
  20. Banno E, Togashi Y, Nakamura Y, et al. Sensitivities to various epidermal growth factor receptor-tyrosine kinase inhibitors of uncommon epidermal growth factor receptor mutations L861Q and S768I: what is the optimal epidermal growth factor receptor-tyrosine kinase inhibitor? Cancer Sci. 2016;107(8):1134–1140. doi: 10.1111/cas.12980
  21. Kobayashi Y, Togashi Y, Yatabe Y, et al. EGFR Exon 18 mutations in lung cancer: molecular predictors of augmented sensitivity to afatinib or neratinib as compared with first- or third-generation TKIs. Clin Cancer Res. 2015;21(23): 5305–5313. doi: 10.1158/1078-0432.CCR-15-1046
  22. Saxon JA, Sholl LM, Janne PA. Brief report: EGFR L858M/L861Q cis mutations confer selective sensitivity to afatinib. J Thorac Oncol. 2017;12(5):884–889. doi: 10.1016/j.jtho.2017.01.006
  23. Niederst MJ, Hu H, Mulvey HE, et al. The allelic context of the C797S mutation acquired upon treatment with third-generation EGFR inhibitors impacts sensitivity to subsequent treatment strategies. Clin Cancer Res. 2015;21(17): 3924–3933. doi: 10.1158/1078- 0432.CCR-15-0560
  24. Yu HA, Tian SK, Drilon AE, et al. Acquired resistance of egfr-mutant lung cancer to a T790M-specific EGFR inhibitor: emergence of a third mutation (C797S) in the EGFR tyrosine kinase domain. JAMA Oncol. 2015;1(7): 982–984. doi: 10.1001/jamaoncol.2015.1066
  25. Sholl LM. Biomarkers in lung adenocarcinoma: a decade of progress. Arch Pathol Lab Med. 2015;139(4):469–480. doi: 10.5858/arpa.2014-0128-RA
  26. Sullivan I, Planchard D. ALK inhibitors in non-small cell lung cancer: the latest evidence and developments. Ther Adv Med Oncol. 2016;8(1):32–47. doi: 10.1177/1758834015617355
  27. Kempf E, Rousseau B, Besse B, et al. KRAS oncogene in lung cancer: focus on molecularly driven clinical trials. Eur Respir Rev. 2016;25(139):71–76. doi: 10.1183/16000617.0071-2015.
  28. Finocchiaro G, Toschi L, Gianoncelli L, et al. Prognostic and predictive value of MET deregulation in non-small cell lung cancer. Ann Transl Med. 2015;3(6):83. doi: 10.3978/j.issn.2305-5839.2015.03.43
  29. Ko B, He T, Gadgeel S, Halmos B. MET/HGF pathway activation as a paradigm of resistance to targeted therapies. Ann Transl Med. 2017;5(1):4. doi: 10.21037/atm.2016.12.09
  30. Byers LA, Diao L, Wang J, et al, An epithelial-mesenchymal transition gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance. Clin Cancer Res. 2013;19(1)279–290. doi: 10.1158/1078-0432.CCR-12-1558
  31. Drilon A, Cappuzzo F, Ou SI, Camidge DR. Targeting MET in lung cancer: will expectations finally be MET? J Thorac Oncol. 2017;12(1):15–26. doi: 10.1016/j.jtho.2016.10.014
  32. Awad MM, Oxnard GR, Jackman DM, et al. MET exon 14 mutations in non-small-cell lung cancer are associated with advanced age and stage-dependent met genomic amplification and c-Met overexpression. J Clin Oncol. 2016; 34(7):721–730. doi: 10.1200/JCO.2015.63.4600
  33. Liu X, Jia Y, Stoopler MB, et al. Next-generation sequencing of pulmonary sarcomatoid carcinoma reveals high frequency of actionable MET gene mutations. J Clin Oncol. 2016;34(8):794–802. doi: 10.1200/JCO.2015.62.0674
  34. Tong JH, Yeung SF, Chan AW, et al. MET amplification and exon 14 splice site mutation define unique molecular subgroups of non-small cell lung carcinoma with poor prognosis. Clin Cancer Res. 2016;15;22(12):3048–3056. doi: 10.1158/1078-0432.CCR-15-2061
  35. Camidge DR, Moran T, Demedts I, et al. A randomized, open-label, phase 2 study of emibetuzumab plus erlotinib (LY+E) and emibetuzumab monotherapy (LY) in patietns with acquired resistance to erlotinib and MET diagnostic positive (MET Dx+) metastatic NSCLC. J Clin Oncol. 2016; 34(15 suppl):9070. doi: 10.1200/jco.2016.34.15_suppl.9070
  36. Planchard D, Kim TM, Mazieres J, et al. Dabrafenib in patients with BRAFV600Epositive advanced non-small-cell lung cancer: a single-arm, multicentre, openlabel, phase 2 trial. Lancet Oncol. 2016;17(5):642–650. doi: 10.1016/S1470-2045(16)00077-2
  37. Chuang JC, Liang Y, Wakelee HA. Neoadjuvant and adjuvant therapy for non-small cell lung cancer. Hematol Oncol Clin North Am. 2017;31(1):31–44. doi: 10.1016/j.hoc.2016.08.011
  38. Zwitter M, Rajer M, Stanic K, et al. Intercalated chemotherapy and erlotinibfor non-small cell lung cancer (NSCLC) with activating epidermal growth factor receptor (EGFR) mutations. Cancer Biol Ther. 2016;17(8):833–839. doi: 10.1080/15384047.2016.1195049
  39. Planchard D, Loriot Y, Andre F, et al. EGFR-independent mechanisms of acquired resistance to AZD9291 in EGFR T790M-positive NSCLC patients. Ann Oncol. 2015;26(10): 2073 2078. doi: 10.1093/annonc/mdv319
  40. Jänne PA, Yang JC, Kim DW, et al. AZD9291 in EGFR inhibitor-resistant non-small-cell lung cancer. N Engl J Med. 2015;372(18):1689–1699. doi: 10.1056/NEJMoa1411817
  41. Shi P, Oh YT, Zhang G, et al. Met gene amplification and protein hyperactivation is a mechanism of resistance to both first and third generation EGFR inhibitors in lung cancer treatment. Cancer Lett. 2016;380(2):494–504. doi: 10.1016/j.canlet.2016.07.021
  42. Thress KS, Paweletz CP, Felip E, et al. Acquired EGFR C797S mutation mediates resistance to AZD9291 in non-small cell lung cancer harboring EGFR T790M. Nat Med. 2015;21(6):560–562. doi: 10.1038/nm.3854
  43. Gainor JF, Niederst MJ, Lennerz JK, et al. Dramatic response to combination erlotinib and crizotinib in a patient with advanced, EGFR-mutant lung cancer harboring de novo MET amplification. J Thorac Oncol. 2016;11(7): e83–85. doi: 10.1016/j.jtho.2016.02.021
  44. Womack JP, Varella-Garcia M, Camidge DR. Waxing and waning of MET amplification in EGFR-mutated NSCLC in response to the presence and absence of erlotinib selection pressure. J Thorac Oncol. 2015;10(12):e115–118. doi: 10.1097/JTO.0000000000000642
  45. Oxnard GR, Yang JC, Yu H, et al. TATTON: a multi-arm, phase Ib trial of osimertinib combined with selumetinib, savolitinib, or durvalumab in EGFR-mutant lung cancer. Ann Oncol. 2020;31(4):507–516. doi: 10.1016/j.annonc.2020.01.013
  46. Khozin S, Blumenthal GM, Zhang L, et al. FDA approval: ceritinib for the treatment of metastatic anaplastic lymphoma kinase-positive non-small cell lung cancer. Clin Cancer Res. 2015; 21(11):2436–2439. doi: 10.1158/1078-0432.CCR-14-3157
  47. Balar AV, Weber JS. PD-1 and PD-L1 antibodies in cancer: current status and future directions. Cancer Immunol Immunother. 2017;66(5):551–564. doi: 10.1007/s00262-017-1954-6
  48. Sun JM, Zhou W, Choi YL, et al. Prognostic significance of programmed cell death ligand 1 in patients with non-small-cell lung cancer: a large cohort study of surgically resected cases. J Thorac Oncol. 2016;11(7):1003–1011. doi: 10.1016/j.jtho.2016.04.007
  49. Fehrenbacher L, Spira A, Ballinger M, et al. Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet. 2016; 387(10030):1837–1846. doi: 10.1016/S0140-6736(16)00587-0
  50. Gettinger SN, Horn L, Gandhi L, et al. Overall survival and long-term safety of Nivolumab (Anti-Programmed Death 1 Antibody, BMS-936558, ONO-4538) in patients with previously treated advanced non-small-cell lung cancer. J Clin Oncol. 2015;33(18):2004–2012. doi: 10.1200/JCO.2014.58.3708
  51. Borghaei H, Paz-Ares L, Horn L, et al. Nivolumab versus Docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med. 2015;373(17):1627–1639. doi: 10.1056/NEJMoa1507643
  52. Brahmer J, Reckamp KL, Baas P, et al. Nivolumab versus docetaxel in advanced squamouscell non-small-cell lung cancer. N Engl J Med. 2015;373(2):123–135. doi: 10.1056/NEJMoa1504627
  53. Gettinger S, Rizvi NA, Chow LQ, et al. Nivolumab monotherapy for first-line treatment of advanced non-small-cell lung cancer. J Clin Oncol. 2016;34(25):2980–2987. doi: 10.1200/JCO.2016.66.9929
  54. Rizvi NA, Hellmann MD, Brahmer JR, et al. Nivolumab in combination with platinum-based doublet chemotherapy for first-line treatment of advanced non-small-cell lung cancer. J Clin Oncol. 2016;34(25):2969–2979. doi: 10.1200/JCO.2016.66.9861
  55. Garon EB, Rizvi NA, Hui R, et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med. 2015;372(21):2018–2028. doi: 10.1056/NEJMoa1501824
  56. Herbst RS, Baas P, Kim DW, et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced nonsmall-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet. 2016;387(10027):1540–1550. doi: 10.1016/S0140-6736(15)01281-7
  57. Barlesi FP, Ciardiello F, Pawel JV, et al. Primary analysis from OAK, a randomized phase III study comparing atezolizumab with docetaxel in 2L/3L NSCLC. In: ESMO Congress, Vol. LBA 44. Copenhagen, Denmark; 2016.
  58. Wang P, Yang D, Zhang H, et al. Early detection of lung cancer in serum by a panel of MicroRNA biomarkers. Clin Lung Cancer. 2015;16(4):313–319. doi: 10.1016/j.cllc.2014.12.006
  59. Nadal E, Truini A, Nakata A, et al. A novel serum 4-microRNA signature for lung cancer detection. Sci Rep. 2015;5:12464. doi: 10.1038/srep12464
  60. Wozniak MB, Scelo G, Muller DC, et al. Circulating microRNAs as non-invasive biomarkers for early detection of non-small-cell lung cancer. PLoS ONE. 2015; 10(5):e0125026. doi: 10.1371/journal.pone.0125026
  61. Xing L, Su J, Guarnera MA, et al. Sputum microRNA biomarkers for identifying lung cancer in indeterminate solitary pulmonary nodules. Clin Cancer Res. 2015;21(2): 484–489. doi: 10.1158/1078-0432.CCR-14-1873
  62. Sun L, Chen Y, Su Q, et al. Increased plasma miRNA-30a as a biomarker for non-small cell lung cancer. Med Sci Monit. 2016;22:647–655. doi: 10.12659/MSM.897330
  63. Tian F, Li R, Chen Z, et al. Differentially expressed miRNAs in tumor, adjacent, and normal tissues of lung adenocarcinoma. Biomed Res Int. 2016;2016:1428271. doi: 10.1155/2016/1428271
  64. Su K, Zhang T, Wang Y, Hao G. Diagnostic and prognostic value of plasma microRNA-195 in patients with non-small cell lung cancer. World J Surg Oncol. 2016;14(1):224. doi: 10.1186/s12957-016-0980-8
  65. Chen S-W, Wang T-B, Tian Y-H, Zheng Y-G. Down-regulation of microRNA-126 and microRNA-133b acts as novel predictor biomarkers in progression and metastasis of non small cell lung cancer. Int J Clin Exp Pathol. 2015; 8(11):14983–14988.
  66. Wang X, Zhi X, Zhang Y, et al. Role of plasma MicroRNAs in the early diagnosis of non-small-cell lung cancers: a case-control study. J Thorac Dis. 2016;8(7):1645–1652. doi: 10.21037/jtd.2016.06.21
  67. Halvorsen AR, Bjaanæs MM, Holm A, et. al. Unique combination of 6 circulating microRNAs for early detection of lung cancer. J Thorac Oncol. 2015;10:S736.
  68. Nakamura H, Nishimura T. History, molecular features, and clinical importance of conventional serum biomarkers in lung cancer. Surg Today. 2017;47(9):1037–1059. doi: 10.1007/s00595-017-1477-y
  69. Wang XB, Li J, Han Y. Prognostic significance of preoperative serum carcinoembryonic antigen in non-small cell lung cancer: a meta-analysis. Tumour Biol. 2014;35(10): 10105–10110. doi: 10.1007/s13277-014-2301-6
  70. Inomata M, Hayashi R, Yamamoto A, et al. Plasma neuron-specific enolase level as a prognostic marker in patients with non-small cell lung cancer receiving gefitinib. Mol Clin Oncol. 2015;3(4):802–806. doi: 10.3892/mco.2015.568
  71. Suh KJ, Keam B, Kim M, et al. Serum neuron-specific enolase levels predict the efficacy of first-line epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors in patients with non-small cell lung cancer harboring EGFR mutations. Clin Lung Cancer. 2016;17(4):245–252.e1. doi: 10.1016/j.cllc.2015.11.012
  72. Yu D, Du K, Liu T, Chen G. Prognostic value of tumor markers, NSE, CA125 and SCC, in operable NSCLC Patients. Int J Mol Sci. 2013;14(6):11145–11156. doi: 10.3390/ijms140611145

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