Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC | Scientific Reports – Nature.com

Howlader, N. et al. SEER Cancer Statistics Review, 19752017 Vol. 4 (National Cancer Institute, 2020).

Google Scholar

Liao, G. et al. Prognostic role of soluble programmed death ligand 1 in non-small cell lung cancer: A systematic review and meta-analysis. Front. Oncol. 11, 774131 (2021).

Article CAS PubMed PubMed Central Google Scholar

Tubin, S., Khan, M. K., Gupta, S. & Jeremic, B. Biology of NSCLC: Interplay between cancer cells, radiation and tumor immune microenvironment. Cancers 13, 775 (2021).

Article CAS PubMed PubMed Central Google Scholar

Barta, J. A., Powell, C. A. & Wisnivesky, J. P. Global epidemiology of lung cancer. Ann. Glob. Health 85, 2419 (2019).

Google Scholar

Howlader, N. et al. The effect of advances in lung-cancer treatment on population mortality. N. Engl. J. Med. 383, 640649 (2020).

Article CAS PubMed PubMed Central Google Scholar

Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2019. CA Cancer J. Clin. 69, 734 (2019).

Article PubMed Google Scholar

Varela, G. & Thomas, P. A. Surgical management of advanced non-small cell lung cancer. J. Thorac. Dis. 6, S217 (2014).

PubMed PubMed Central Google Scholar

Miller, K. D. et al. Cancer treatment and survivorship statistics, 2019. CA Cancer J. Clin. 69, 363385 (2019).

Article PubMed Google Scholar

Goldstraw, P. et al. Non-small-cell lung cancer. The Lancet 378, 17271740 (2011).

Article Google Scholar

Tang, C. et al. Development of an immune-pathology informed radiomics model for non-small cell lung cancer. Sci. Rep. 8, 19 (2018).

ADS Google Scholar

Azuma, K. et al. Association of PD-L1 overexpression with activating EGFR mutations in surgically resected nonsmall-cell lung cancer. Ann. Oncol. 25, 19351940 (2014).

Article CAS PubMed Google Scholar

Meyers, D., Bryan, P., Banerji, S. & Morris, D. Targeting the PD-1/PD-L1 axis for the treatment of non-small-cell lung cancer. Curr. Oncol. 25, 324334 (2018).

Article Google Scholar

Garon, E. B. et al. Pembrolizumab for the treatment of nonsmall-cell lung cancer. N. Engl. J. Med. 372, 20182028 (2015).

Article PubMed Google Scholar

Brahmer, J. R. et al. Safety and activity of antiPD-L1 antibody in patients with advanced cancer. N. Engl. J. Med. 366, 24552465 (2012).

Article CAS PubMed PubMed Central Google Scholar

Glatzel-Plucinska, N. et al. SATB1 level correlates with Ki-67 expression and is a positive prognostic factor in non-small cell lung carcinoma. Anticancer Res. 38, 723736 (2018).

CAS PubMed Google Scholar

Pawelczyk, K. et al. Role of PD-L1 expression in non-small cell lung cancer and their prognostic significance according to clinicopathological factors and diagnostic markers. Int. J. Mol. Sci. 20, 824 (2019).

Article CAS PubMed PubMed Central Google Scholar

Shimoji, M. et al. Clinical and pathologic features of lung cancer expressing programmed cell death ligand 1 (PD-L1). Lung Cancer 98, 6975 (2016).

Article PubMed Google Scholar

Sun, J.-M. et al. Prognostic significance of PD-L1 in patients with nonsmall cell lung cancer: A large cohort study of surgically resected cases. J. Thorac. Oncol. 11, 10031011 (2016).

Article CAS PubMed Google Scholar

Zhou, C. et al. PD-L1 expression as poor prognostic factor in patients with non-squamous non-small cell lung cancer. Oncotarget 8, 58457 (2017).

Article PubMed PubMed Central Google Scholar

Cooper, W. A. et al. PD-L1 expression is a favorable prognostic factor in early stage non-small cell carcinoma. Lung Cancer 89, 181188 (2015).

Article PubMed Google Scholar

Teng, M. W., Ngiow, S. F., Ribas, A. & Smyth, M. J. Classifying cancers based on T-cell infiltration and PD-L1. Cancer Res. 75, 21392145 (2015).

Article CAS PubMed PubMed Central Google Scholar

Guo, W., Ji, Y. & Catenacci, D. V. A subgroup cluster-based Bayesian adaptive design for precision medicine. Biometrics 73, 367377 (2017).

Article MathSciNet PubMed Google Scholar

Fisher, R., Pusztai, L. & Swanton, C. Cancer heterogeneity: Implications for targeted therapeutics. Br. J. Cancer 108, 479485 (2013).

Article CAS PubMed PubMed Central Google Scholar

Dagogo-Jack, I. & Shaw, A. T. Tumour heterogeneity and resistance to cancer therapies. Nat. Rev. Clin. Oncol. 15, 8194 (2018).

Article CAS PubMed Google Scholar

Yu, D. et al. Machine learning prediction of the adverse outcome for nontraumatic subarachnoid hemorrhage patients. Ann. Clin. Transl. Neurol. 7, 21782185 (2020).

Article PubMed PubMed Central Google Scholar

Luo, W. et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: A multidisciplinary view. J. Med. Internet Res. 18, e323 (2016).

Article PubMed PubMed Central Google Scholar

Sun, W., Jiang, M., Dang, J., Chang, P. & Yin, F.-F. Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis. Radiat. Oncol. 13, 18 (2018).

Article Google Scholar

Ou, F.-S., Michiels, S., Shyr, Y., Adjei, A. A. & Oberg, A. L. Biomarker discovery and validation: Statistical considerations. J. Thorac. Oncol. 16, 537545 (2021).

Article CAS PubMed PubMed Central Google Scholar

Heiden, B. T. et al. Analysis of delayed surgical treatment and oncologic outcomes in clinical stage I nonsmall cell lung cancer. JAMA Netw. Open 4, e2111613e2111613 (2021).

Article PubMed PubMed Central Google Scholar

Andersen, P. K. & Gill, R. D. Coxs regression model for counting processes: A large sample study. Ann. Stat. 10, 11001120 (1982).

Article MathSciNet Google Scholar

Kalbfleisch, J. D. & Prentice, R. L. The Statistical Analysis of Failure Time Data (Wiley, 2011).

Google Scholar

Binder, H., Allignol, A., Schumacher, M. & Beyersmann, J. Boosting for high-dimensional time-to-event data with competing risks. Bioinformatics 25, 890896 (2009).

Article CAS PubMed Google Scholar

Ishwaran, H., Kogalur, U. B., Blackstone, E. H. & Lauer, M. S. Random survival forests. Ann. Appl. Stat. 2, 841860 (2008).

Article MathSciNet Google Scholar

Jaeger, B. C. et al. Oblique random survival forests. Ann. Appl. Stat. 13, 18471883 (2019).

Article MathSciNet PubMed PubMed Central Google Scholar

Harrell, F. E., Califf, R. M., Pryor, D. B., Lee, K. L. & Rosati, R. A. Evaluating the yield of medical tests. Jama 247, 25432546 (1982).

Article PubMed Google Scholar

Lang, M. et al. mlr3: A modern object-oriented machine learning framework in R. J. Open Source Softw. 4, 1903 (2019).

Article ADS Google Scholar

Stekhoven, D. J. & Stekhoven, M. D. J. Package missForest. R package version 1 (2013).

Read the original post:
Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC | Scientific Reports - Nature.com

Related Posts

Comments are closed.