Routine perioperative blood tests predict survival of resectable lung cancer.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
10 10 2023
10 10 2023
Historique:
received:
26
05
2023
accepted:
06
10
2023
medline:
12
10
2023
pubmed:
11
10
2023
entrez:
10
10
2023
Statut:
epublish
Résumé
There is growing evidence that inflammatory, immunologic, and metabolic status is associated with cancer patients survival. Here, we built a simple algorithm to predict lung cancer outcome. Perioperative routine blood tests (RBT) of a cohort of patients with resectable primary lung cancer (LC) were analysed. Inflammatory, immunologic, and metabolic profiles were used to create a single algorithm (RBT index) predicting LC survival. A concurrent cohort of patients with resectable lung metastases (LM) was used to validate the RBT index. Charts of 2088 consecutive LC and 1129 LM patients undergoing lung resection were evaluated. Among RBT parameters, C-reactive protein (CRP), lymphocytes, neutrophils, hemoglobin, albumin and glycemia independently correlated with survival, and were used to build the RBT index. Patients with a high RBT index had a higher 5-year mortality than low RBT patients (adjusted HR 1.93, 95% CI 1.62-2.31). High RBT patients also showed a fourfold higher risk of 30-day postoperative mortality (2.3% vs. 0.5%, p 0.0019). The LM analysis validated the results of the LC cohort. We developed a simple and easily available multifunctional tool predicting short-term and long-term survival of curatively resected LC and LM. Prospective external validation of RBT index is warranted.
Identifiants
pubmed: 37816885
doi: 10.1038/s41598-023-44308-y
pii: 10.1038/s41598-023-44308-y
pmc: PMC10564956
doi:
Substances chimiques
C-Reactive Protein
9007-41-4
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
17072Informations de copyright
© 2023. Springer Nature Limited.
Références
Siegel, R. L., Miller, K. D., Wagle, N. S. & Jemal, A. Cancer statistics, 2023. CA Cancer J. Clin. 73(1), 17–48. https://doi.org/10.3322/caac.21763 (2023).
doi: 10.3322/caac.21763
pubmed: 36633525
Associazione Italiana di Oncologia Medica (AIOM). Linee guida NEOPLASIA DEL POLMONE. s.l.: 2021. Available at: https://www.aiom.it/wp-content/uploads/2021/10/2021_NumeriCancro_web.pdf . Last accessed: April 15, 2023.
Amin, M. B. et al. The eighth edition AJCC cancer staging manual: Continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging. CA Cancer J. Clin. 67(2), 93–99. https://doi.org/10.3322/caac.21388 (2017).
doi: 10.3322/caac.21388
pubmed: 28094848
Mantovani, A., Allavena, P., Sica, A. & Balkwill, F. Cancer-related inflammation. Nature 454(7203), 436–444. https://doi.org/10.1038/nature07205 (2008).
doi: 10.1038/nature07205
pubmed: 18650914
Diakos, C. I., Charles, K. A., McMillan, D. C. & Clarke, S. J. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. 15(11), e493-503. https://doi.org/10.1016/S1470-2045(14)70263-3 (2014).
doi: 10.1016/S1470-2045(14)70263-3
pubmed: 25281468
Pastorino, U. et al. Inflammatory status and lung function predict mortality in lung cancer screening participants. Eur. J. Cancer Prev. 27(4), 289–295. https://doi.org/10.1097/CEJ.0000000000000342 (2018).
doi: 10.1097/CEJ.0000000000000342
pubmed: 28333763
pmcid: 6012047
Pastorino, U. et al. Baseline and postoperative C-reactive protein levels predict mortality in operable lung cancer. Eur. J. Cancer. 79, 90–97. https://doi.org/10.1016/j.ejca.2017.03.020 (2017).
doi: 10.1016/j.ejca.2017.03.020
pubmed: 28472743
Cupp, M. A. et al. Neutrophil to lymphocyte ratio and cancer prognosis: An umbrella review of systematic reviews and meta-analyses of observational studies. BMC Med. 18(1), 360. https://doi.org/10.1186/s12916-020-01817-1 (2020).
doi: 10.1186/s12916-020-01817-1
pubmed: 33213430
pmcid: 7678319
Jin, J., Yang, L., Liu, D. & Li, W. M. Prognostic value of pretreatment lymphocyte-to-monocyte ratio in lung cancer: A systematic review and meta-analysis. Technol. Cancer Res. Treat. 20, 1533033820983085. https://doi.org/10.1177/1533033820983085 (2021).
doi: 10.1177/1533033820983085
pubmed: 33576324
pmcid: 7887688
Jafri, S. H., 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 27(13), 158. https://doi.org/10.1186/1471-2407-13-158 (2013).
doi: 10.1186/1471-2407-13-158
He, X. et al. Advanced lung cancer inflammation index, a new prognostic score, predicts outcome in patients with small-cell lung cancer. Clin. Lung Cancer 16(6), e165–e171. https://doi.org/10.1016/j.cllc.2015.03.005 (2015).
doi: 10.1016/j.cllc.2015.03.005
pubmed: 25922292
Hu, Z. et al. Advanced lung cancer inflammation index is a prognostic factor of patients with small-cell lung cancer following surgical resection. Cancer Manag. Res. 26(13), 2047–2055. https://doi.org/10.2147/CMAR.S295952 (2021).
doi: 10.2147/CMAR.S295952
Zhang, L. et al. The prognostic value of the advanced lung cancer inflammation index in patients with gastrointestinal malignancy. BMC Cancer. 23(1), 101. https://doi.org/10.1186/s12885-023-10570-6 (2023).
doi: 10.1186/s12885-023-10570-6
pubmed: 36717809
pmcid: 9885705
Li, Q., Ma, F., Tsilimigras, D. I., Åberg, F. & Wang, J. F. The value of the Advanced Lung Cancer Inflammation Index (ALI) in assessing the prognosis of patients with hepatocellular carcinoma treated with camrelizumab: A retrospective cohort study. Ann. Transl. Med. 10(22), 1233. https://doi.org/10.21037/atm-22-5099 (2022).
doi: 10.21037/atm-22-5099
pubmed: 36544677
pmcid: 9761123
Valero, C. et al. Host factors independently associated with prognosis in patients with oral cavity cancer. JAMA Otolaryngol. Head Neck Surg. 146(8), 699–707. https://doi.org/10.1001/jamaoto.2020.1019 (2020).
doi: 10.1001/jamaoto.2020.1019
pubmed: 32525545
Sansa, A. et al. External validation of the H-index (host index) in patients with head and neck squamous cell carcinomas. Head Neck. 45(1), 178–186. https://doi.org/10.1002/hed.27224 (2023).
doi: 10.1002/hed.27224
pubmed: 36225167
Pastorino, U. et al. Baseline and postoperative C-reactive protein levels predict long-term survival after lung metastasectomy. Ann. Surg. Oncol. 26(3), 869–875. https://doi.org/10.1245/s10434-018-07116-7 (2019).
doi: 10.1245/s10434-018-07116-7
pubmed: 30607764
Goldstraw, P. et al. The IASLC lung cancer staging project: Proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J. Thorac. Oncol. 11(1), 39–51. https://doi.org/10.1016/j.jtho.2015.09.009 (2016).
doi: 10.1016/j.jtho.2015.09.009
pubmed: 26762738
Kuo, Y. Statistical methods for determining single or multiple cutpoints of risk factors in survival data analysis. Dissertation, Division of Biometrics and Epidemiology, School of Public Health, The Ohio State University. 1997.
Contal, C. & O’Quigley, J. An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput. Stat. Data Anal. 30(3), 253–270 (1999).
doi: 10.1016/S0167-9473(98)00096-6
Mandrekar, J. N., Mandrekar, S. J. & Cha, S. S. Cutpoint determination methods in survival analysis using SAS®. (Paper 261–28). Proceedings of the 28th SAS Users Group International Conference (SUGI 28), 2003.
Rea, F., Corrao, G., Ludergnani, M., Cajazzo, L. & Merlino, L. A new population-based risk stratification tool was developed and validated for predicting mortality, hospital admissions, and health care costs. J. Clin. Epidemiol. 116, 62–71 (2019).
doi: 10.1016/j.jclinepi.2019.08.009
pubmed: 31472207
Gagne, J. J., Glynn, R. J., Avorn, J., Levin, R. & Schneeweiss, S. A combined comorbidity score predicted mortality in elderly patients better than existing scores. J. Clin. Epidemiol. 64, 749e59 (2011).
doi: 10.1016/j.jclinepi.2010.10.004
Kaplan, E. L. & Meier, P. Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53(282), 457–481 (1958).
doi: 10.1080/01621459.1958.10501452
Hong, T. H. et al. Programmed Death-ligand 1 copy number alteration as an adjunct biomarker of response to immunotherapy in advanced non-small cell lung cancer. J. Thorac. Oncol. https://doi.org/10.1016/j.jtho.2023.03.024 (2023).
doi: 10.1016/j.jtho.2023.03.024
pubmed: 37924973
Juliette, P. et al. Prognostic relevance of sarcopenia, geriatric, and nutritional assessments in older patients with diffuse large B-cell lymphoma: Results of a multicentric prospective cohort study. Ann. Hematol. https://doi.org/10.1007/s00277-023-05200-x (2023).
doi: 10.1007/s00277-023-05200-x
Zhang, C. L. et al. Research progress and value of albumin-related inflammatory markers in the prognosis of non-small cell lung cancer: A review of clinical evidence. Ann. Med. 55(1), 1294–1307. https://doi.org/10.1080/07853890.2023.2192047 (2023).
doi: 10.1080/07853890.2023.2192047
pubmed: 37036321
pmcid: 10088931
Kumar, A. et al. Inflammatory and nutritional serum markers as predictors of peri-operative morbidity and survival in ovarian cancer. Anticancer Res. 37(7), 3673–3677. https://doi.org/10.21873/anticanres.11738 (2017).
doi: 10.21873/anticanres.11738
pubmed: 28668859
Torres, M. L. et al. Nutritional status, CT body composition measures and survival in ovarian cancer. Gynecol. Oncol. 129(3), 548–553. https://doi.org/10.1016/j.ygyno.2013.03.003 (2013).
doi: 10.1016/j.ygyno.2013.03.003
pubmed: 23523419
Mourtzakis, M. et al. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl. Physiol. Nutr. Metab. 33(5), 997–1006. https://doi.org/10.1139/H08-075 (2008).
doi: 10.1139/H08-075
pubmed: 18923576
Hacker, U. T. et al. Modified Glasgow prognostic score (mGPS) is correlated with sarcopenia and dominates the prognostic role of baseline body composition parameters in advanced gastric and esophagogastric junction cancer patients undergoing first-line treatment from the phase III EXPAND trial. Ann. Oncol. 33(7), 685–692. https://doi.org/10.1016/j.annonc.2022.03.274 (2022).
doi: 10.1016/j.annonc.2022.03.274
pubmed: 35395383
Fang, E., Wang, X., Feng, J. & Zhao, X. The prognostic role of glasgow prognostic score and C-reactive protein to albumin ratio for sarcoma: A system review and meta-analysis. Dis. Markers 7(2020), 8736509. https://doi.org/10.1155/2020/8736509 (2020).
doi: 10.1155/2020/8736509
Leuzzi, G. et al. Baseline C-reactive protein level predicts survival of early-stage lung cancer: Evidence from a systematic review and meta-analysis. Tumori 102(5), 441–449. https://doi.org/10.5301/tj.5000522 (2016).
doi: 10.5301/tj.5000522
pubmed: 27292573
Subramanian, H., Knight, J., Sultan, I., Kaczorowski, D. J. & Subramaniam, K. Pre-habilitation of cardiac surgical patients, Part 2: Frailty, malnutrition, respiratory disease, alcohol/smoking cessation and depression. Semin. Cardiothorac. Vasc. Anesth. 26(4), 295–303. https://doi.org/10.1177/10892532221130922 (2022).
doi: 10.1177/10892532221130922
pubmed: 36189933
Vernieri, C. et al. Fasting-mimicking diet is safe and reshapes metabolism and antitumor immunity in patients with cancer. Cancer Discov. 12(1), 90–107. https://doi.org/10.1158/2159-8290.CD-21-0030 (2022).
doi: 10.1158/2159-8290.CD-21-0030
pubmed: 34789537
Cortellino, S. et al. Fasting renders immunotherapy effective against low-immunogenic breast cancer while reducing side effects. Cell Rep. 40(8), 111256. https://doi.org/10.1016/j.celrep.2022.111256 (2022).
doi: 10.1016/j.celrep.2022.111256
pubmed: 36001966
Vanguri, R. S. et al. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat. cancer 3(10), 1151–1164. https://doi.org/10.1038/s43018-022-00416-8 (2022).
doi: 10.1038/s43018-022-00416-8
pubmed: 36038778
pmcid: 9586871
Wu, Y. et al. Using machine learning for mortality prediction and risk stratification in atezolizumab-treated cancer patients: Integrative analysis of eight clinical trials. Cancer Med. 12(3), 3744–3757. https://doi.org/10.1002/cam4.5060 (2023).
doi: 10.1002/cam4.5060
pubmed: 35871390
Prelaj, A. et al. Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients. Front. Oncol. 12, 1078822. https://doi.org/10.3389/fonc.2022.1078822 (2023).
doi: 10.3389/fonc.2022.1078822
pubmed: 36755856
pmcid: 9899835