{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"Research Square"}],"indexed":{"date-parts":[[2023,5,21]],"date-time":"2023-05-21T04:39:57Z","timestamp":1684643997783},"posted":{"date-parts":[[2023,4,21]]},"group-title":"In Review","reference-count":46,"publisher":"Research Square Platform LLC","license":[{"start":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T00:00:00Z","timestamp":1682035200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,4,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n        <jats:p><jats:bold>Purpose: <\/jats:bold>Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. \n<jats:bold>Methods: <\/jats:bold>We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma\u2014amounting to nearly 600 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. \n<jats:bold>Results: <\/jats:bold>We discovered that white blood cell count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of white blood cell count. By utilizing an objective PDL-1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PDL-1 expression in glioblastoma patients with high white blood cell counts. \n<jats:bold>Conclusion: <\/jats:bold>These findings suggest that in a subset of glioblastoma patients the incorporation of white blood cell count and PDL-1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, use of machine learning models allows us to visualize complex clinical datasets to uncover novel clinical relationships.<\/jats:p>","DOI":"10.21203\/rs.3.rs-2834239\/v1","type":"posted-content","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T22:32:59Z","timestamp":1682116379000},"source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised machine learning models reveal predictive markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation"],"prefix":"10.21203","author":[{"given":"Wesley","family":"Wang","sequence":"first","affiliation":[{"name":"The Ohio State University Wexner Medical Center"}]},{"given":"Zeynep Temerit","family":"Kumm","sequence":"additional","affiliation":[{"name":"The Ohio State University Wexner Medical Center"}]},{"given":"Cindy","family":"Ho","sequence":"additional","affiliation":[{"name":"The Ohio State University Wexner Medical Center"}]},{"given":"Ideli","family":"Zanesco-Fontes","sequence":"additional","affiliation":[{"name":"Barretos Cancer Hospital"}]},{"given":"Gustavo","family":"Texiera","sequence":"additional","affiliation":[{"name":"Barretos Cancer Hospital"}]},{"given":"Rui Manuel","family":"Reis","sequence":"additional","affiliation":[{"name":"Barretos Cancer Hospital"}]},{"given":"Horacio","family":"Martinetto","sequence":"additional","affiliation":[{"name":"Fundaci\u00f3n para la Lucha contra las Enfermedades Neurol\u00f3gicas de la Infancia"}]},{"given":"Javaria","family":"Khan","sequence":"additional","affiliation":[{"name":"University of Mississippi Medical Center"}]},{"given":"Mark D.","family":"Anderson","sequence":"additional","affiliation":[{"name":"University of Mississippi Medical Center"}]},{"given":"M Omar","family":"Chohan","sequence":"additional","affiliation":[{"name":"University of Mississippi Medical Center"}]},{"given":"Sasha","family":"Beyer","sequence":"additional","affiliation":[{"name":"The Ohio State University Wexner Medical Center"}]},{"given":"J Brad","family":"Elder","sequence":"additional","affiliation":[{"name":"The Ohio State University Wexner Medical Center"}]},{"given":"Pierre","family":"Giglio","sequence":"additional","affiliation":[{"name":"The Ohio State University Wexner Medical Center"}]},{"given":"Jos\u00e9 Javier","family":"Otero","sequence":"additional","affiliation":[{"name":"The Ohio State University Wexner Medical Center"}]}],"member":"8761","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"1. Ostrom QT, Cioffi G, Waite K, Kruchko C, Barnholtz-Sloan JS. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014\u20132018. Neuro-Oncology. 2021;23(Supplement_3):iii1-iii105. doi:10.1093\/neuonc\/noab200","DOI":"10.1093\/neuonc\/noab200"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"2. Silbergeld DL, Rostomily RC, Alvord EC Jr. The cause of death in patients with glioblastoma is multifactorial: clinical factors and autopsy findings in 117 cases of supratentorial glioblastoma in adults. Journal of Neuro-Oncology. 1991;10(2):179\u2013185. doi:10.1007\/BF00146880","DOI":"10.1007\/BF00146880"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"3. Stupp R, Mason WP, van den Bent MJ, et al. Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma. New England Journal of Medicine. 2005;352(10):987\u2013996. doi:10.1056\/nejmoa043330","DOI":"10.1056\/NEJMoa043330"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"4. Louis DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncology. 2021;23(8). doi:10.1093\/neuonc\/noab106","DOI":"10.1093\/neuonc\/noab106"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"5. Hegi ME, Diserens AC, Gorlia T, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. The New England Journal of Medicine. 2005;352(10):997\u20131003. doi:10.1056\/NEJMoa043331","DOI":"10.1056\/NEJMoa043331"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"6. Gomes I, Moreno DA, Dos Reis MB, et al. Low MGMT digital expression is associated with a better outcome of IDH1 wildtype glioblastomas treated with temozolomide. Journal of Neuro-Oncology. 2021;151(2):135\u2013144. doi:10.1007\/s11060-020-03675-6","DOI":"10.1007\/s11060-020-03675-6"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"7. Ma S, Rudra S, Campian JL, et al. Prognostic impact of CDKN2A\/B deletion, TERT mutation, and EGFR amplification on histological and molecular IDH-wildtype glioblastoma. Neuro-Oncology Advances. 2020;2(1). doi:10.1093\/noajnl\/vdaa126","DOI":"10.1093\/noajnl\/vdaa126"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"8. Heimberger AB, Hlatky R, Suki D, et al. Prognostic effect of epidermal growth factor receptor and EGFRvIII in glioblastoma multiforme patients. Clinical Cancer Research. 2005;11(4):1462\u20131466. doi:10.1158\/1078-0432.CCR-04-1737","DOI":"10.1158\/1078-0432.CCR-04-1737"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"9. Cevik L, Landrove MV, Aslan MT, et al. Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology. Brain Pathology. 2022;32(5). doi:10.1111\/bpa.13050","DOI":"10.1111\/bpa.13050"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"10. Wang W, Howard D, Giglio P, Thomas D, Javier Otero J. Bioethical implications of current state practices of molecular diagnostics in neuropathology. Neuro-Oncology. 2022;24(6):853\u2013854. doi:10.1093\/neuonc\/noac058","DOI":"10.1093\/neuonc\/noac058"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"11. Dundas NE, Ziadie MS, Revell PA, et al. A lean laboratory: operational simplicity and cost effectiveness of the Luminex xTAG\u2122 respiratory viral panel. Journal of Molecular Diagnostics. 2011;13(2):175\u2013179. doi:10.1016\/j.jmoldx.2010.09.003","DOI":"10.1016\/j.jmoldx.2010.09.003"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"12. Gorlia T, van den Bent MJ, Hegi ME, et al. Nomograms for predicting survival of patients with newly diagnosed glioblastoma: prognostic factor analysis of EORTC and NCIC trial 26981\u2009\u2212\u200922981\/CE.3. The Lancet Oncology. 2008;9(1):29\u201338. doi:10.1016\/s1470-2045(07)70384-4","DOI":"10.1016\/S1470-2045(07)70384-4"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"13. Johnson DR, Sawyer AM, Meyers CA, O\u2019Neill BP, Wefel JS. Early measures of cognitive function predict survival in patients with newly diagnosed glioblastoma. Neuro-Oncology. 2012;14(6):808\u2013816. doi:10.1093\/neuonc\/nos082","DOI":"10.1093\/neuonc\/nos082"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"14. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)\u2014A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics. 2009;42(2):377\u2013381. doi:10.1016\/j.jbi.2008.08.010","DOI":"10.1016\/j.jbi.2008.08.010"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"15. Harris PA, Taylor R, Minor BL, et al. The REDCap consortium: Building an international community of software platform partners. Journal of Biomedical Informatics. 2019;95:103208. doi:10.1016\/j.jbi.2019.103208","DOI":"10.1016\/j.jbi.2019.103208"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"16. Charlson ME, Pompei P, Ales KL, MacKenzie CRonald. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases. 1987;40(5):373\u2013383. doi:10.1016\/0021-9681(87)90171-8","DOI":"10.1016\/0021-9681(87)90171-8"},{"key":"ref17","unstructured":"17. Tierney N, Cook D, McBain M, et al. Data Structures, Summaries, and Visualisations for Missing Data. R-Packages. Published online 2019. https:\/\/CRAN.R-project.org\/package=naniar"},{"key":"ref18","unstructured":"18. Buuren S van, Groothuis-Oudshoorn K, Vink G, et al. mice: Multivariate Imputation by Chained Equations. R-Packages. Published online November 19, 2022. https:\/\/CRAN.R-project.org\/package=mice"},{"key":"ref19","unstructured":"19. Kassambara A, Mundt F. factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R-Packages. Published online April 1, 2020. https:\/\/CRAN.R-project.org\/package=factoextra"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"20. Wang W, Alzate-Correa D, Alves MJ, et al. Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution. Respiratory Physiology & Neurobiology. 2021;283:103558. doi:10.1016\/j.resp.2020.103558","DOI":"10.1016\/j.resp.2020.103558"},{"key":"ref21","unstructured":"21. Therneau TM, Lumley TL, Elizabeth A, Cynthia C. survival: Survival Analysis. R-Packages. Published online March 3, 2022. https:\/\/CRAN.R-project.org\/package=survival"},{"key":"ref22","unstructured":"22. Kassambara A, Kosinski M, Biecek P, Fabian S. Drawing Survival Curves using \u201cggplot2\u201d [R package survminer version 0.4.5]. R-Packages. Published online 2019. https:\/\/CRAN.R-project.org\/package=survminer"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"23. Hadley Wickham. Ggplot2 Elegant Graphics for Data Analysis. Cham Springer International Publishing; 2016.","DOI":"10.1007\/978-3-319-24277-4_9"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"24. Pau G, Fuchs F, Sklyar O, Boutros M, Huber W. EBImage\u2014an R package for image processing with applications to cellular phenotypes. Bioinformatics. 2010;26(7):979\u2013981. doi:10.1093\/bioinformatics\/btq046","DOI":"10.1093\/bioinformatics\/btq046"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"25. van der Walt S, Sch\u00f6nberger JL, Nunez-Iglesias J, et al. scikit-image: image processing in Python. PeerJ. 2014;2:e453. doi:10.7717\/peerj.453","DOI":"10.7717\/peerj.453"},{"key":"ref26","unstructured":"26. Kalinowski T, Ushey K, Allaire JJ, et al. reticulate: Interface to \u201cPython.\u201d R-Packages. Published online January 27, 2023. https:\/\/CRAN.R-project.org\/package=reticulate"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"27. Otsu N. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics. 1979;9(1):62\u201366. doi:10.1109\/tsmc.1979.4310076","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"28. Tin Kam Ho. Random decision forests. IEEE Xplore. doi:10.1109\/ICDAR.1995.598994","DOI":"10.1109\/ICDAR.1995.598994"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"29. Igarashi T, Teramoto K, Ishida M, Hanaoka J, Daigo Y. Scoring of PD-L1 expression intensity on pulmonary adenocarcinomas and the correlations with clinicopathological factors. ESMO Open. 2016;1(4):e000083. Published 2016 Aug 26. doi:10.1136\/esmoopen-2016-000083","DOI":"10.1136\/esmoopen-2016-000083"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"30. Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics. 2012;13(6):395\u2013405. doi:10.1038\/nrg3208","DOI":"10.1038\/nrg3208"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"31. Perry JR, Laperriere N, O\u2019Callaghan CJ, et al. Short-Course Radiation plus Temozolomide in Elderly Patients with Glioblastoma. New England Journal of Medicine. 2017;376(11):1027\u20131037. doi:10.1056\/nejmoa1611977","DOI":"10.1056\/NEJMoa1611977"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"32. Pierscianek D, Ahmadipour Y, Michel A, et al. Preoperative Survival Prediction in Patients With Glioblastoma by Routine Inflammatory Laboratory Parameters. Anticancer Research. 2020;40(2):1161\u20131166. doi:10.21873\/anticanres.1405","DOI":"10.21873\/anticanres.14058"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"33. Jarmuzek P, Kot M, Defort P, et al. Prognostic Values of Combined Ratios of White Blood Cells in Glioblastoma: A Retrospective Study. Journal of Clinical Medicine. 2022;11(12):3397. doi:10.3390\/jcm11123397","DOI":"10.3390\/jcm11123397"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"34. Schernberg A, Nivet A, Dhermain F, et al. Neutrophilia as a biomarker for overall survival in newly diagnosed high-grade glioma patients undergoing chemoradiation. Clinical and Translational Radiation Oncology. 2018;10:47\u201352. doi:10.1016\/j.ctro.2018.04.002","DOI":"10.1016\/j.ctro.2018.04.002"},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"35. Brown NF, Ottaviani D, Tazare J, et al. Survival Outcomes and Prognostic Factors in Glioblastoma. Cancers. 2022;14(13):3161. doi:10.3390\/cancers14133161","DOI":"10.3390\/cancers14133161"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"36. Johnson DR, O\u2019Neill BP. Glioblastoma survival in the United States before and during the temozolomide era. Journal of Neuro-Oncology. 2011;107(2):359\u2013364. doi:10.1007\/s11060-011-0749-4","DOI":"10.1007\/s11060-011-0749-4"},{"key":"ref37","doi-asserted-by":"crossref","unstructured":"37. Marenco-Hillembrand L, Wijesekera O, Suarez-Meade P, et al. Trends in glioblastoma: outcomes over time and type of intervention: a systematic evidence based analysis. Journal of Neuro-Oncology. 2020;147(2):297\u2013307. doi:10.1007\/s11060-020-03451-6","DOI":"10.1007\/s11060-020-03451-6"},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"38. Kim WJ, Dho YS, Ock CY, et al. Clinical observation of lymphopenia in patients with newly diagnosed glioblastoma. Journal of Neuro-Oncology. 2019;143(2):321\u2013328. doi:10.1007\/s11060-019-03167-2","DOI":"10.1007\/s11060-019-03167-2"},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"39. Vaios EJ, Winter SF, Muzikansky A, Nahed BV, Dietrich J. Eosinophil and lymphocyte counts predict bevacizumab response and survival in recurrent glioblastoma. Neuro-oncology advances. 2020;2(1):vdaa031. doi:10.1093\/noajnl\/vdaa031","DOI":"10.1093\/noajnl\/vdaa031"},{"key":"ref40","unstructured":"40. Boggs DR, Athens JW, Cartwright GE, Wintrobe MM. The Effect of Adrenal Glucocorticosteroids Upon the Cellular Composition of Inflammatory Exudates. The American Journal of Pathology. 1964;44(5):763\u2013773"},{"key":"ref41","doi-asserted-by":"crossref","unstructured":"41. Mishler JM, Emerson PM. Development of Neutrophilia by Serially Increasing Doses of Dexamethasone. British Journal of Haematology. 1977;36(2):249\u2013257. doi:10.1111\/j.1365-2141.1977.tb00646.x","DOI":"10.1111\/j.1365-2141.1977.tb00646.x"},{"key":"ref42","doi-asserted-by":"crossref","unstructured":"42. Dubinski D, Won SY, Gessler F, et al. Dexamethasone-induced leukocytosis is associated with poor survival in newly diagnosed glioblastoma. Journal of Neuro-Oncology. 2018;137(3):503\u2013510. doi:10.1007\/s11060-018-2761-4","DOI":"10.1007\/s11060-018-2761-4"},{"key":"ref43","doi-asserted-by":"crossref","unstructured":"43. Roth P, Happold C, Weller M. Corticosteroid use in neuro-oncology: an update. Neuro-Oncology Practice. 2014;2(1):6\u201312. doi:10.1093\/nop\/npu029","DOI":"10.1093\/nop\/npu029"},{"key":"ref44","doi-asserted-by":"crossref","unstructured":"44. Herold MJ, McPherson KG, Reichardt HM. Glucocorticoids in T cell apoptosis and function. Cellular and Molecular Life Sciences. 2005;63(1). doi:10.1007\/s00018-005-5390-y","DOI":"10.1007\/s00018-005-5390-y"},{"key":"ref45","doi-asserted-by":"crossref","unstructured":"45. Dietrich J, Rao K, Pastorino S, Kesari S. Corticosteroids in brain cancer patients: benefits and pitfalls. Expert review of clinical pharmacology. 2011;4(2):233\u2013242. doi:10.1586\/ecp.11.1","DOI":"10.1586\/ecp.11.1"},{"key":"ref46","unstructured":"Main Figures and Tables"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/www.researchsquare.com\/article\/rs-2834239\/v1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.researchsquare.com\/article\/rs-2834239\/v1.html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,20]],"date-time":"2023-05-20T20:59:41Z","timestamp":1684616381000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.researchsquare.com\/article\/rs-2834239\/v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,21]]},"references-count":46,"URL":"https:\/\/doi.org\/10.21203\/rs.3.rs-2834239\/v1","relation":{},"subject":[],"published":{"date-parts":[[2023,4,21]]},"subtype":"preprint"}}