Research progress of biomarkers in lung cancer screening_West China Medical Journal

Authors：

LIU Li ,  TIAN Panwen

Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

TIAN Panwen, Email: mrascend@163.com

Keywords：

Biomarker; Lung cancer; Screening

DOI：

10.7507/1002-0179.201905065

Video：

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Abstract Full text Figures/Tables Video References Cited by

Lung cancer is a malignant tumor with the highest incidence and mortality in China. Early diagnosis and early treatment is the key to improve the survival and prognosis of patients with lung cancer. In recent years, many studies have focused on biomarkers of lung cancer. Emerging biomarkers tests have shown some potential in lung cancer screening. Combining biomarkers, imaging omics and artificial intelligence to establish a comprehensive model for lung cancer screening and prediction may be the development direction for improving lung cancer screening in the future. This paper summarizes the application of biomarkers in lung cancer screening, introduces the emerging biomarkers and new technologies, and discusses the application prospects of biomarkers in lung cancer screening, in order to providea theoretical basis for improving screening, early diagnosis and early treatment of lung cancer.

Citation： LIU Li, TIAN Panwen. Research progress of biomarkers in lung cancer screening. West China Medical Journal, 2020, 35(1): 78-83. doi: 10.7507/1002-0179.201905065 Copy

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2.	National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
3.	Henschke CI, Shaham D, Yankelevitz DF, et al. CT screening for lung cancer: significance of diagnoses in its baseline cycle. Clin Imaging, 2006, 30(1): 11-15.
4.	Yousaf-Khan U, van der Aalst C, de Jong PA, et al. Final screening round of the NELSON lung cancer screening trial: the effect ofa 2.5-year screening interval. Thorax, 2017, 72(1): 48-56.
5.	Pastorino U, Silva M, Sestini S, et al. Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy. Ann Oncol, 2019, 30(7): 1162-1169.
6.	Boyle P, Chapman CJ, Holdenrieder S, et al. Clinical validation of an autoantibody test for lung cancer. Ann Oncol, 2011, 22(2): 383-389.
7.	Massion PP, Healey GF, Peek LJ, et al. Autoantibody signature enhances the positive predictive power of computed tomography and nodule-based risk models for detection of lung cancer. J Thorac Oncol, 2017, 12(3): 578-584.
8.	Chapman CJ, Murray A, McElveen JE, et al. Autoantibodies in lung cancer: possibilities for early detection and subsequent cure. Thorax, 2008, 63(3): 228-233.
9.	Ajona D, Pajares MJ, Corrales L, et al. Investigation of complement activation product c4d as a diagnostic and prognostic biomarker for lung cancer. J Natl Cancer Inst, 2013, 105(18): 1385-1393.
10.	Ajona D, Okrój M, Pajares MJ, et al. Complement C4d-specific antibodies for the diagnosis of lung cancer. Oncotarget, 2018, 9(5): 6346-6355.
11.	Verri C, Borzi C, Holscher T, et al. Mutational profile from targeted NGS predicts survival in LDCT screening-detected lung cancers. J Thorac Oncol, 2017, 12(6): 922-931.
12.	Sozzi G, Boeri M, Rossi M, et al. Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: a correlative MILD trial study. J Clin Oncol, 2014, 32(8): 768-773.
13.	Sestini S, Boeri M, Marchiano A, et al. Circulating microRNA signature as liquid-biopsy to monitor lung cancer in low-dose computed tomography screening. Oncotarget, 2015, 6(32): 32868-32877.
14.	Jenkins S, Yang JC, Ramalingam SS, et al. Plasma ctDNA analysis for detection of the EGFR^T790M mutation in patients with advanced non-small cell lung cancer. J Thorac Oncol, 2017, 12(7): 1061-1070.
15.	Abbosh C, Birkbak NJ, Wilson GA, et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature, 2017, 545(7655): 446-451.
16.	Cohen JD, Li L, Wang Y, et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science, 2018, 359(6378): 926-930.
17.	Blackhall F, Frese KK, Simpson K, et al. Will liquid biopsies improve outcomes for patients with small-cell lung cancer?. Lancet Oncol, 2018, 19(9): e470-e481.
18.	Ilie M, Hofman V, Long-Mira E, et al. “Sentinel” circulating tumor cells allow early diagnosis of lung cancer in patients with chronic obstructive pulmonary disease. PLoS One, 2014, 9(10): e111597.
19.	Chen YY, Xu GB. Effect of circulating tumor cells combined with negative enrichment and CD45-FISH identification in diagnosis, therapy monitoring and prognosis of primary lung cancer. Med Oncol, 2014, 31(12): 240.
20.	Pan BT, Teng K, Wu C, et al. Electron microscopic evidence for externalization of the transferrin receptor in vesicular form in sheep reticulocytes. J Cell Biol, 1985, 101(3): 942-948.
21.	Li A, Zhang T, Zheng M, et al. Exosomal proteins as potential markers of tumor diagnosis. J Hematol Oncol, 2017, 10(1): 175.
22.	Zhang JT, Qin H, Man Cheung FK, et al. Plasma extracellular vesicle microRNAs for pulmonary ground-glass nodules. J Extracell Vesicles, 2019, 8(1): 1663666.
23.	Li W, Li C, Zhou T, et al. Role of exosomal proteins in cancer diagnosis. Mol Cancer, 2017, 16(1): 145.
24.	Esteller M, Sanchez-Cespedes M, Rosell R, et al. Detection of aberrant promoter hypermethylation of tumor suppressor genes in serum DNA from non-small cell lung cancer patients. Cancer Res, 1999, 59(1): 67-70.
25.	Wielscher M, Vierlinger K, Kegler U, et al. Diagnostic performance of plasma DNA methylation profiles in lung cancer, pulmonary fibrosis and COPD. EBioMedicine, 2015, 2(8): 929-936.
26.	Hulbert A, Jusue-Torres I, Stark A, et al. Early detection of lung cancer using DNA promoter hypermethylation in plasma and sputum. Clin Cancer Res, 2017, 23(8): 1998-2005.
27.	Doseeva V, Colpitts T, Gao G, et al. Performance of a multiplexed dual analyte immunoassay for the early detection of non-small cell lung cancer. J Transl Med, 2015, 13: 55.
28.	Mazzone PJ, Wang XF, Han X, et al. Evaluation of a serum lung cancer biomarker panel. Biomark Insights, 2018: 13.
29.	Molina R, Marrades RM, Augé JM, et al. Assessment of a combined panel of six serum tumor markers for lung cancer. Am J Respir Crit Care Med, 2016, 193(4): 427-437.
30.	Silvestri GA, Tanner NT, Kearney P, et al. Assessment of plasma proteomics biomarker’s ability to distinguish benign from malignant lung nodules: results of the PANOPTIC (pulmonary nodule plasma proteomic classifier) trial. Chest, 2018, 154(3): 491-500.
31.	Meyer MG, Hayenga JW, Neumann T, et al. The Cell-CT 3-dimensional cell imaging technology platform enables the detection of lung cancer using the noninvasive LuCED sputum test. Cancer Cytopathol, 2015, 123(9): 512-523.
32.	Wilbur DC, Meyer MG, Presley C, et al. Automated 3-dimensional morphologic analysis of sputum specimens for lung cancer detection: performance characteristics support use in lung cancer screening. Cancer Cytopathol, 2015, 123(9): 548-556.
33.	Puchades-Carrasco L, Jantus-Lewintre E, Pérez-Rambla C, et al. Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer. Oncotarget, 2016, 7(11): 12904-12916.
34.	Mathé EA, Patterson AD, Haznadar M, et al. Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Res, 2014, 74(12): 3259-3270.
35.	Rezola A, Pey J, Rubio Á, et al. In-silico prediction of key metabolic differences between two non-small cell lung cancer subtypes. PLoS One, 2014, 9(8): e103998.
36.	Caiola E, Brunelli L, Marabese M, et al. Different metabolic responses to PI3K inhibition in NSCLC cells harboring wild-type and G12C mutant KRAS. Oncotarget, 2016, 7(32): 51462-51472.
37.	Ten Haaf K, Jeon J, Tammemägi MC, et al. Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study. PLoS Med, 2017, 14(4): e1002277.
38.	Wang J, Liu Q, Yuan S, et al. Genetic predisposition to lung cancer: comprehensive literature integration, meta-analysis, and multiple evidence assessment of candidate-gene association studies. Sci Rep, 2017, 7(1): 8371.
39.	McKay JD, Hung RJ, Han Y, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat Genet, 2017, 49(7): 1126-1132.
40.	Mieth B, Kloft M, Rodríguez JA, et al. Combining multiple hypothesis testing with machine learning increases the statistical power of genome-wide association studies. Sci Rep, 2016, 6: 36671.
41.	Schrider DR, Kern AD. Supervised machine learning for population genetics: a new paradigm. Trends Genet, 2018, 34(4): 301-312.
42.	Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
43.	Chen B, Zhang R, Gan Y, et al. Development and clinical application of radiomics in lung cancer. Radiat Oncol, 2017, 12(1): 154.
44.	Shen W, Zhou M, Yang F, et al. Multi-scale convolutional neural networks for lung nodule classification. Inf Process Med Imaging, 2015, 24: 588-599.
45.	Rios Velazquez E, Parmar C, Liu Y, et al. Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res, 2017, 77(14): 3922-3930.
46.	Lee J, Li B, Cui Y, et al. A quantitative CT imaging signature predicts survival and complements established prognosticators in stage I non-small cell lung cancer. Int J Radiat Oncol Biol Phys, 2018, 102(4): 1098-1106.
47.	Lin Y, Leng Q, Jiang Z, et al. A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules. Int J Cancer, 2017, 141(6): 1240-1248.
48.	Ma J, Guarnera MA, Zhou W, et al. A prediction model based on biomarkers and clinical characteristics for detection of lung cancer in pulmonary nodules. Transl Oncol, 2017, 10(1): 40-45.
49.	Jiang R, Dong X, Zhu W, et al. Combining PET/CT with serum tumor markers to improve the evaluation of histological type of suspicious lung cancers. PLoS One, 2017, 12(9): e0184338.
50.	Jiang F, Todd NW, Qiu Q, et al. Combined genetic analysis of sputum and computed tomography for noninvasive diagnosis of non-small-cell lung cancer. Lung Cancer, 2009, 66(1): 58-63.

1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2018, 68(6): 394-424.
2. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
3. Henschke CI, Shaham D, Yankelevitz DF, et al. CT screening for lung cancer: significance of diagnoses in its baseline cycle. Clin Imaging, 2006, 30(1): 11-15.
4. Yousaf-Khan U, van der Aalst C, de Jong PA, et al. Final screening round of the NELSON lung cancer screening trial: the effect ofa 2.5-year screening interval. Thorax, 2017, 72(1): 48-56.
5. Pastorino U, Silva M, Sestini S, et al. Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy. Ann Oncol, 2019, 30(7): 1162-1169.
6. Boyle P, Chapman CJ, Holdenrieder S, et al. Clinical validation of an autoantibody test for lung cancer. Ann Oncol, 2011, 22(2): 383-389.
7. Massion PP, Healey GF, Peek LJ, et al. Autoantibody signature enhances the positive predictive power of computed tomography and nodule-based risk models for detection of lung cancer. J Thorac Oncol, 2017, 12(3): 578-584.
8. Chapman CJ, Murray A, McElveen JE, et al. Autoantibodies in lung cancer: possibilities for early detection and subsequent cure. Thorax, 2008, 63(3): 228-233.
9. Ajona D, Pajares MJ, Corrales L, et al. Investigation of complement activation product c4d as a diagnostic and prognostic biomarker for lung cancer. J Natl Cancer Inst, 2013, 105(18): 1385-1393.
10. Ajona D, Okrój M, Pajares MJ, et al. Complement C4d-specific antibodies for the diagnosis of lung cancer. Oncotarget, 2018, 9(5): 6346-6355.
11. Verri C, Borzi C, Holscher T, et al. Mutational profile from targeted NGS predicts survival in LDCT screening-detected lung cancers. J Thorac Oncol, 2017, 12(6): 922-931.
12. Sozzi G, Boeri M, Rossi M, et al. Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: a correlative MILD trial study. J Clin Oncol, 2014, 32(8): 768-773.
13. Sestini S, Boeri M, Marchiano A, et al. Circulating microRNA signature as liquid-biopsy to monitor lung cancer in low-dose computed tomography screening. Oncotarget, 2015, 6(32): 32868-32877.
14. Jenkins S, Yang JC, Ramalingam SS, et al. Plasma ctDNA analysis for detection of the EGFR^T790M mutation in patients with advanced non-small cell lung cancer. J Thorac Oncol, 2017, 12(7): 1061-1070.
15. Abbosh C, Birkbak NJ, Wilson GA, et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature, 2017, 545(7655): 446-451.
16. Cohen JD, Li L, Wang Y, et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science, 2018, 359(6378): 926-930.
17. Blackhall F, Frese KK, Simpson K, et al. Will liquid biopsies improve outcomes for patients with small-cell lung cancer?. Lancet Oncol, 2018, 19(9): e470-e481.
18. Ilie M, Hofman V, Long-Mira E, et al. “Sentinel” circulating tumor cells allow early diagnosis of lung cancer in patients with chronic obstructive pulmonary disease. PLoS One, 2014, 9(10): e111597.
19. Chen YY, Xu GB. Effect of circulating tumor cells combined with negative enrichment and CD45-FISH identification in diagnosis, therapy monitoring and prognosis of primary lung cancer. Med Oncol, 2014, 31(12): 240.
20. Pan BT, Teng K, Wu C, et al. Electron microscopic evidence for externalization of the transferrin receptor in vesicular form in sheep reticulocytes. J Cell Biol, 1985, 101(3): 942-948.
21. Li A, Zhang T, Zheng M, et al. Exosomal proteins as potential markers of tumor diagnosis. J Hematol Oncol, 2017, 10(1): 175.
22. Zhang JT, Qin H, Man Cheung FK, et al. Plasma extracellular vesicle microRNAs for pulmonary ground-glass nodules. J Extracell Vesicles, 2019, 8(1): 1663666.
23. Li W, Li C, Zhou T, et al. Role of exosomal proteins in cancer diagnosis. Mol Cancer, 2017, 16(1): 145.
24. Esteller M, Sanchez-Cespedes M, Rosell R, et al. Detection of aberrant promoter hypermethylation of tumor suppressor genes in serum DNA from non-small cell lung cancer patients. Cancer Res, 1999, 59(1): 67-70.
25. Wielscher M, Vierlinger K, Kegler U, et al. Diagnostic performance of plasma DNA methylation profiles in lung cancer, pulmonary fibrosis and COPD. EBioMedicine, 2015, 2(8): 929-936.
26. Hulbert A, Jusue-Torres I, Stark A, et al. Early detection of lung cancer using DNA promoter hypermethylation in plasma and sputum. Clin Cancer Res, 2017, 23(8): 1998-2005.
27. Doseeva V, Colpitts T, Gao G, et al. Performance of a multiplexed dual analyte immunoassay for the early detection of non-small cell lung cancer. J Transl Med, 2015, 13: 55.
28. Mazzone PJ, Wang XF, Han X, et al. Evaluation of a serum lung cancer biomarker panel. Biomark Insights, 2018: 13.
29. Molina R, Marrades RM, Augé JM, et al. Assessment of a combined panel of six serum tumor markers for lung cancer. Am J Respir Crit Care Med, 2016, 193(4): 427-437.
30. Silvestri GA, Tanner NT, Kearney P, et al. Assessment of plasma proteomics biomarker’s ability to distinguish benign from malignant lung nodules: results of the PANOPTIC (pulmonary nodule plasma proteomic classifier) trial. Chest, 2018, 154(3): 491-500.
31. Meyer MG, Hayenga JW, Neumann T, et al. The Cell-CT 3-dimensional cell imaging technology platform enables the detection of lung cancer using the noninvasive LuCED sputum test. Cancer Cytopathol, 2015, 123(9): 512-523.
32. Wilbur DC, Meyer MG, Presley C, et al. Automated 3-dimensional morphologic analysis of sputum specimens for lung cancer detection: performance characteristics support use in lung cancer screening. Cancer Cytopathol, 2015, 123(9): 548-556.
33. Puchades-Carrasco L, Jantus-Lewintre E, Pérez-Rambla C, et al. Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer. Oncotarget, 2016, 7(11): 12904-12916.
34. Mathé EA, Patterson AD, Haznadar M, et al. Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Res, 2014, 74(12): 3259-3270.
35. Rezola A, Pey J, Rubio Á, et al. In-silico prediction of key metabolic differences between two non-small cell lung cancer subtypes. PLoS One, 2014, 9(8): e103998.
36. Caiola E, Brunelli L, Marabese M, et al. Different metabolic responses to PI3K inhibition in NSCLC cells harboring wild-type and G12C mutant KRAS. Oncotarget, 2016, 7(32): 51462-51472.
37. Ten Haaf K, Jeon J, Tammemägi MC, et al. Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study. PLoS Med, 2017, 14(4): e1002277.
38. Wang J, Liu Q, Yuan S, et al. Genetic predisposition to lung cancer: comprehensive literature integration, meta-analysis, and multiple evidence assessment of candidate-gene association studies. Sci Rep, 2017, 7(1): 8371.
39. McKay JD, Hung RJ, Han Y, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat Genet, 2017, 49(7): 1126-1132.
40. Mieth B, Kloft M, Rodríguez JA, et al. Combining multiple hypothesis testing with machine learning increases the statistical power of genome-wide association studies. Sci Rep, 2016, 6: 36671.
41. Schrider DR, Kern AD. Supervised machine learning for population genetics: a new paradigm. Trends Genet, 2018, 34(4): 301-312.
42. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
43. Chen B, Zhang R, Gan Y, et al. Development and clinical application of radiomics in lung cancer. Radiat Oncol, 2017, 12(1): 154.
44. Shen W, Zhou M, Yang F, et al. Multi-scale convolutional neural networks for lung nodule classification. Inf Process Med Imaging, 2015, 24: 588-599.
45. Rios Velazquez E, Parmar C, Liu Y, et al. Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res, 2017, 77(14): 3922-3930.
46. Lee J, Li B, Cui Y, et al. A quantitative CT imaging signature predicts survival and complements established prognosticators in stage I non-small cell lung cancer. Int J Radiat Oncol Biol Phys, 2018, 102(4): 1098-1106.
47. Lin Y, Leng Q, Jiang Z, et al. A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules. Int J Cancer, 2017, 141(6): 1240-1248.
48. Ma J, Guarnera MA, Zhou W, et al. A prediction model based on biomarkers and clinical characteristics for detection of lung cancer in pulmonary nodules. Transl Oncol, 2017, 10(1): 40-45.
49. Jiang R, Dong X, Zhu W, et al. Combining PET/CT with serum tumor markers to improve the evaluation of histological type of suspicious lung cancers. PLoS One, 2017, 12(9): e0184338.
50. Jiang F, Todd NW, Qiu Q, et al. Combined genetic analysis of sputum and computed tomography for noninvasive diagnosis of non-small-cell lung cancer. Lung Cancer, 2009, 66(1): 58-63.

West China Medical Journal

Research progress of biomarkers in lung cancer screening

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