Prognostic prediction models based on peripheral biomarkers for non-small cell lung cancer: a systematic review_Chinese Journal of Evidence-Based Medicine

Authors：

TIAN Xueqi ¹ , JIAO Lijing ^1,2 , BI Ling ^1,2 ,  XU Ling ^1,2

1. Department I of Oncology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, P. R. China;
2. Institute of Tumor Transformation of Integrated Traditional Chinese and Western Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, P. R. China;

Corresponding author：

XU Ling, Email: xulq67@aliyun.com

Keywords：

Non-small cell lung cancer; Blood-based immune score; Risk prediction model; Systematic review

DOI：

10.7507/1672-2531.202306071

Video：

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Objective To systematically review the prediction models of blood-based biomarkers for non-small cell lung cancer (NSCLC). Methods The PubMed, Embase, Cochrane Library, Web of Science, VIP, WanFang Data and CNKI databases were electronically searched to collect studies related to the objectives from inception to June, 2023. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.4.1 software. Results A total of 8 studies were included and all of them were retrospective cohort studies. The models were internally validated in 2 studies and externally validated in 4 studies. The performances of the eight predictive models were stable, which was measured by the area under the curve of receiver operating characteristic curve lying between 0.664 and 0.783. However, the risk of bias was high, which may mainly be reflected in data processing, model validation and performance adjustment. Meta-analysis showed that LDH (HR=1.86, 95%CI 41.32 to 2.63, P<0.01), dNLR (HR=2.15, 95%CI 1.56 to 2.96, P<0.01) and NLR (HR=1.71, 95%CI 1.08 to 2.69, P=0.02) were independent factors of prognosis for NSCLC patients. Conclusion Current evidence shows that the NSCLC prediction models based on peripheral blood biomarkers are still in the development stage, and the models have a high risk of bias.

Citation： TIAN Xueqi, JIAO Lijing, BI Ling, XU Ling. Prognostic prediction models based on peripheral biomarkers for non-small cell lung cancer: a systematic review. Chinese Journal of Evidence-Based Medicine, 2023, 23(12): 1407-1412. doi: 10.7507/1672-2531.202306071 Copy

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2.	Shukuya T, Carbone DP. Predictive markers for the efficacy of Anti-PD-1/PD-L1 antibodies in lung cancer. J Thorac Oncol, 2016, 11(7): 976-988.
3.	Lee JS, Ruppin E. Multiomics prediction of response rates to therapies to inhibit programmed cell death 1 and programmed cell death 1 ligand 1. JAMA Oncol, 2019, 5(11): 1614-1618.
4.	Tanizaki J, Haratani K, Hayashi H, et al. Peripheral blood biomarkers associated with clinical outcome in non-small cell lung cancer patients treated with nivolumab. J Thorac Oncol, 2018, 13(1): 97-105.
5.	Zhang Z, Li Y, Yan X, et al. Pretreatment lactate dehydrogenase may predict outcome of advanced non small-cell lung cancer patients treated with immune checkpoint inhibitors: a meta-analysis. Cancer Med, 2019, 8(4): 1467-1473.
6.	陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
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8.	Aldea M, Benitez JC, Mezquita L. The lung immune prognostic index (LIPI) stratifies prognostic groups in advanced non-small cell lung cancer (NSCLC) patients. Transl Lung Cancer Res, 2020, 9(4): 967-970.
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10.	Jafri SH, Shi R, Mills G. Advance lung cancer inflammation index (ALI) at diagnosis is a prognostic marker in patients with metastatic non-small cell lung cancer (NSCLC): a retrospective review. BMC Cancer, 2013, 13: 158.
11.	Chen T, Song C, Liang G, et al. Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and their variations as a basis for a prediction model in advanced NSCLC patients receiving anlotinib. Dis Markers, 2022, 2022: 5879137.
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15.	Ogundimu EO, Altman DG, Collins GS. Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol, 2016, 76: 175-182.
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17.	Mulder FI, Candeloro M, Kamphuisen PW, et al. The Khorana score for prediction of venous thromboembolism in cancer patients: a systematic review and meta-analysis. Haematologica, 2019, 104(6): 1277-1287.
18.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
19.	Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med, 2016, 35(2): 214-226.
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26.	Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart, 2012, 98(9): 683-690.
27.	Doherty JR, Cleveland JL. Targeting lactate metabolism for cancer therapeutics. J Clin Invest, 2013, 123(9): 3685-3692.
28.	Xu K, Yin N, Peng M, et al. Glycolysis fuels phosphoinositide 3-kinase signaling to bolster T cell immunity. Science, 2021, 371(6527): 405-410.
29.	Zhang T, Yang X, Zhao J, et al. The application of combined immune checkpoint inhibitor modalities in previously treated non-small cell lung cancer patients and the associations thereof with the lung immune prognostic index. Front Oncol, 2021, 11: 690093.
30.	Gong T, Liu J, Jiang J, et al. The role of lactate deshydrogenase levels on non-small cell lung cancer prognosis: a meta-analysis. Cell Mol Biol, 2019, 65(1): 89-93.
31.	Han Z, Hu Z, Zhao Q, et al. The advanced lung cancer inflammation index predicts outcomes of patients with non-small cell lung cancer following video-assisted thoracic surgery. J Int Med Res, 2021, 49(12): 3000605211062442.
32.	Demb J, Wei EK, Izano M, et al. Chronic inflammation and risk of lung cancer in older adults in the health, aging and body composition cohort study. J Geriatr Oncol, 2019, 10(2): 265-271.
33.	Russo A, Franchina T, Ricciardi GRR, et al. Baseline neutrophilia, derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non-small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. J Cell Physiol, 2018, 233(10): 6337-6343.
34.	Petrelli F, Cabiddu M, Coinu A, et al. Prognostic role of lactate dehydrogenase in solid tumors: a systematic review and meta-analysis of 76 studies. Acta Oncol, 2015, 54(7): 961-970.
35.	Minami S, Ihara S, Ikuta S, et al. Gustave roussy immune score and royal marsden hospital prognostic score are biomarkers of immune-checkpoint inhibitor for non-small cell lung cancer. World J Oncol, 2019, 10(2): 90-100.

1. Xia C, Dong X, Li H, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J (Engl), 2022, 135(5): 584-590.
2. Shukuya T, Carbone DP. Predictive markers for the efficacy of Anti-PD-1/PD-L1 antibodies in lung cancer. J Thorac Oncol, 2016, 11(7): 976-988.
3. Lee JS, Ruppin E. Multiomics prediction of response rates to therapies to inhibit programmed cell death 1 and programmed cell death 1 ligand 1. JAMA Oncol, 2019, 5(11): 1614-1618.
4. Tanizaki J, Haratani K, Hayashi H, et al. Peripheral blood biomarkers associated with clinical outcome in non-small cell lung cancer patients treated with nivolumab. J Thorac Oncol, 2018, 13(1): 97-105.
5. Zhang Z, Li Y, Yan X, et al. Pretreatment lactate dehydrogenase may predict outcome of advanced non small-cell lung cancer patients treated with immune checkpoint inhibitors: a meta-analysis. Cancer Med, 2019, 8(4): 1467-1473.
6. 陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
7. Navani V, Meyers DE, Ruan Y, et al. Lung immune therapy evaluation (LITE) risk, a novel prognostic model for patients with advanced non-small cell lung cancer treated with immune checkpoint blockade. Clin Lung Cancer, 2023, 24(3): e152-e159.
8. Aldea M, Benitez JC, Mezquita L. The lung immune prognostic index (LIPI) stratifies prognostic groups in advanced non-small cell lung cancer (NSCLC) patients. Transl Lung Cancer Res, 2020, 9(4): 967-970.
9. Park W, Kwon D, Saravia D, et al. Developing a predictive model for clinical outcomes of advanced non-small cell lung cancer patients treated with nivolumab. Clin Lung Cancer, 2018, 19(3): 280-288.
10. Jafri SH, Shi R, Mills G. Advance lung cancer inflammation index (ALI) at diagnosis is a prognostic marker in patients with metastatic non-small cell lung cancer (NSCLC): a retrospective review. BMC Cancer, 2013, 13: 158.
11. Chen T, Song C, Liang G, et al. Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and their variations as a basis for a prediction model in advanced NSCLC patients receiving anlotinib. Dis Markers, 2022, 2022: 5879137.
12. Ulas A, Turkoz FP, Silay K, et al. A laboratory prognostic index model for patients with advanced non-small cell lung cancer. PLoS One, 2014, 9(12): e114471.
13. Kogo M, Sunaga T, Nakamura S, et al. Prognostic index for survival in patients with advanced non-small-cell lung cancer treated with third-generation agents. Chemotherapy, 2017, 62(4): 239-245.
14. Zhu L, Li X, Shen Y, et al. A new prognostic score based on the systemic inflammatory response in patients with inoperable non-small-cell lung cancer. Onco Targets Ther, 2016, 9: 4879-4886.
15. Ogundimu EO, Altman DG, Collins GS. Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol, 2016, 76: 175-182.
16. Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol, 2007, 165(6): 710-718.
17. Mulder FI, Candeloro M, Kamphuisen PW, et al. The Khorana score for prediction of venous thromboembolism in cancer patients: a systematic review and meta-analysis. Haematologica, 2019, 104(6): 1277-1287.
18. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
19. Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med, 2016, 35(2): 214-226.
20. Zhou ZR, Wang WW, Li Y, et al. In-depth mining of clinical data: the construction of clinical prediction model with R. Ann Transl Med, 2019, 7(23): 796.
21. Haribhakti N, Agarwal P, Vida J, et al. A simple scoring tool to predict medical intensive care unit readmissions based on both patient and process factors. J Gen Intern Med, 2021, 36(4): 901-907.
22. Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J, 2014, 35(29): 1925-1931.
23. Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: users' guides to the medical literature. JAMA, 2017, 318(14): 1377-1384.
24. Luo W, Phung D, Tran T, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res, 2016, 18(12): e323.
25. Steyerberg EW, Harrell FE. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol, 2016, 69: 245-247.
26. Moons KG, Kengne AP, Woodward M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart, 2012, 98(9): 683-690.
27. Doherty JR, Cleveland JL. Targeting lactate metabolism for cancer therapeutics. J Clin Invest, 2013, 123(9): 3685-3692.
28. Xu K, Yin N, Peng M, et al. Glycolysis fuels phosphoinositide 3-kinase signaling to bolster T cell immunity. Science, 2021, 371(6527): 405-410.
29. Zhang T, Yang X, Zhao J, et al. The application of combined immune checkpoint inhibitor modalities in previously treated non-small cell lung cancer patients and the associations thereof with the lung immune prognostic index. Front Oncol, 2021, 11: 690093.
30. Gong T, Liu J, Jiang J, et al. The role of lactate deshydrogenase levels on non-small cell lung cancer prognosis: a meta-analysis. Cell Mol Biol, 2019, 65(1): 89-93.
31. Han Z, Hu Z, Zhao Q, et al. The advanced lung cancer inflammation index predicts outcomes of patients with non-small cell lung cancer following video-assisted thoracic surgery. J Int Med Res, 2021, 49(12): 3000605211062442.
32. Demb J, Wei EK, Izano M, et al. Chronic inflammation and risk of lung cancer in older adults in the health, aging and body composition cohort study. J Geriatr Oncol, 2019, 10(2): 265-271.
33. Russo A, Franchina T, Ricciardi GRR, et al. Baseline neutrophilia, derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non-small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. J Cell Physiol, 2018, 233(10): 6337-6343.
34. Petrelli F, Cabiddu M, Coinu A, et al. Prognostic role of lactate dehydrogenase in solid tumors: a systematic review and meta-analysis of 76 studies. Acta Oncol, 2015, 54(7): 961-970.
35. Minami S, Ihara S, Ikuta S, et al. Gustave roussy immune score and royal marsden hospital prognostic score are biomarkers of immune-checkpoint inhibitor for non-small cell lung cancer. World J Oncol, 2019, 10(2): 90-100.

Chinese Journal of Evidence-Based Medicine

Prognostic prediction models based on peripheral biomarkers for non-small cell lung cancer: a systematic review

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