Abstract
Clinical researchers working in flow cytometry (FCM) nowadays experience increasing demands to perform experiments that involve high throughput, rare event analysis and detailed immunophenotyping. Beckman Coulter and Becton Dickinson offer multi-use flow cytometry sorters that can analyze up to 70K EPS (events per seconds) with more than nine parameters enabled. While this multi-parametric feature provides a great power for hypothesis testing, it also generates a vast amount of data, which is analyzed manually through a processing called gating. For large experiments, this manual gating turns out to be time consuming and requires intensive operator training and experience. The lack of required expertise leads to wrong interpretation of data, thus a wrong therapy course for the case of patients with acute lymphoblastic leukemia (ALL) is followed. This paper aims to present a pipeline-software, as a ready-to-use machine learning based automated FCM assessment tool for the daily clinical practice for patients with ALL. The new system increases accuracy in assessment of FCM based on minimal residual disease (MRD) method in samples analyzed by conventional operator-based gating since computer-aided analysis potentially has a higher power due to the use of the whole multi-parametric FCM-data space at once instead of using methods restricted to two-dimensional decision rules. The tool is implemented as a telemedical network for analysis, clinical follow-up, treatment monitoring of leukemia and allows dissemination of automated FCM-MRD analysis to medical centres in the world.
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References
Reiter, M., Kleber, F., Rota, P., Groeneveld-Krentz, S., Schumich, A., Dworzak, M., Gau, M.: An automated flow cytometry data analysis support system. In: CYTO 2015, Glasgow (2015)
Rota, P., Kleber, F., Reiter, M., Groeneveld-Krentz, S., Kampel, M.: The role of machine learning in medical data analysis. A case study: flow cytometry. In: Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP, vol. 3, pp. 303–310. ISBN 978-989-758-175-5. doi:10.5220/0005675903030310
Toedling, J., Rhein, P., Ratei, R., Karawajew, L., Spang, R.: Automated in-silico detection of cell populations in flow cytometry readouts and its application to leukemia disease monitoring. BMC Bioinform. 7, 282 (2006). doi:10.1186/1471-2105-7-282
PDQ® Pediatric Treatment Editorial Board: PDQ Childhood Acute Lymphoblastic Leukemia Treatment. National Cancer Institute, Bethesda. http://www.cancer.gov/types/leukemia/patient/child-all-treatment-pdq. [PMID: 26389385]. Accessed 28 Oct 2016
Costa, E.S., Arroyo, M.E., Pedreira, C.E., García-Marcos, M.A., Tabernero, M.D., Almeida, J., Orfao, A.: A new automated flow cytometry data analysis approach for the diagnostic screening of neoplastic B-cell disorders in peripheral blood samples with absolute lymphocytosis. J. Leukemia 20, 1221–1230 (2006). doi:10.1038/sj.leu.2404241. Published online 25 May 2006
Aghaeepour, N., Finak, G., Hoos, H., Mosmann, T.R., Brinkman, R., Gottardo, R., Scheuermann, R.H.: Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 10(3), 228–238 (2013). doi:10.1038/nmeth.2365
Rota, P., Groeneveld-Krentz, S., Reiter, M.: On automated flow cytometric analysis for MRD estimation of acute lymphoblastic leukaemia: a comparison among different approaches. In: The Proceedings of Bioinformatics and Biomedicine (BIBM 2015), pp. 438–441 (2015)
AIEOP-BFM ALL 2009 treatment protocol. http://www.bfm-international.org/aieop/aieop_index.html
Reiter, M., Kleber, F., Hoffmann, J., Groeneveld-Krentz, S., Groeneveld, T., Dworzak, M., Schuumich, A., Kampel, M., Gau, M.: Status report of the automated software-based MRD-assessment procedure. In: 28th Annual Meeting of the International BFM (Berlin-Frankfurt-Muenster) Study Group, Budapest, Hungary (2015)
Reiter, M., Ho-mann, J., Kleber, F., Schumich, A., Peter, G., Kromp, F., Kampel, M., Dworzak, M.: Towards automation of flow cytometric analysis for quality assured follow-up assessment to guide curative therapy for acute lymphoblastic leukaemia in children. Memo Mag. Eur. Med. Oncol. 7(4), 219–226 (2014). Springer
Bonner, W.A., Hulett, H.R., Sweet, R.G., Herzenberg, L.A.: Fluorescence activated cell sorting. Rev. Sci. Instrum. 43(3), 404–409 (1972). doi:10.1063/1.1685647
Campana, D., Coustan-Smith, E.: Detection of minimal residual disease in acute leukemia by flow cytometry. J. Cytometry Part A 38(4), 139–152 (1999). doi:10.1002/(SICI)1097-0320(19990815)38:4<139:AID-CYTO1>3.0.CO;2-H
Karawajew, L., Dworzak, M., et al.: Minimal residual disease analysis by eight-color flow cytometry in relapsed childhood acute lymphoblastic leukemia. J. Haematol. 100, 935–944 (2015). doi:10.3324/haematol.2014.116707
Verschoor, C.P., Lelic, A., Bramson, J.L., Bowdish, D.M.E.: An introduction to automated flow cytometry gating tools and their implementation. J. Front. Immunol. 8, 1–8 (2015). doi:10.3389/fimmu.2015.00380. http://www.frontiersin.org
Nunes, C., Wong, R., Mason, M., Fegan, C., Man, S., Pepper, C.: Expansion of a CD8(+)PD-1(+) replicative senescence phenotype in early stage CLL patients is associated with inverted CD4: CD8 ratios and disease progression. Clin. Cancer Res. 18(3), 678–687 (2012). doi:10.1158/1078-0432.CCR-11-2630
Dalmazzo, L.F.F., Jácomo, R.H., Marinato, A.F., Figueiredo-Pontes, L.L., Cunha, R.L.G., Garcia, A.B., et al.: The presence of CD56/CD16 in T-cell acute lymphoblastic leukaemia correlates with the expression of cytotoxic molecules and is associated with worse response to treatment. Br. J. Haematol. 144(2), 223–229 (2009). doi:10.1111/j.1365-2141.2008.07457
Reiter, M., Rota, P., Kleber, F., Diem, M., Groeneveld-Krentz, S., Dworzak, M.: Clustering of cell populations in flow cytometry data using a combination of Gaussian mixtures. J. Pattern Recogn. 60, 1029–1040 (2016). Elsevier
Chu, H., Yang, Y., Li, Q., Xu, Y., Wei, H.: A scalable clinical intelligent decision support system. In: Chang, C.K., Chiari, L., Cao, Yu., Jin, H., Mokhtari, M., Aloulou, H. (eds.) ICOST 2016. LNCS, vol. 9677, pp. 159–165. Springer, Cham (2016). doi:10.1007/978-3-319-39601-9_14
Berndt, R.D., Takenga, C., et. al.: SaaS-platform for mobile health applications. In: Proceedings of the IEEE-International Multi-conference on Systems, Signals and Devices, Chemnitz, Germany, pp. 1–4, March 2012, doi:10.1109/SSD.2012.6198120
FlowVIEW Functionalities. https://www.youtube.com/watch?v=fu0V76Cppa4
Cario, G., Rhein, P.: High CD45 surface expression determines relapse risk in children with precursor B-cell and T-cell acute lymphoblastic leukemia treated according to the ALL-BFM 2000 protocol. Haematologica 99(1), 103–110 (2014). doi:10.3324/haematol.2013.090225
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The AutoFLOW project is funded by Marie Curie Industry Academia Partnerships & Pathways (FP7-MarieCurie-PEOPLE-2013- IAPP) under the grant no.610872.
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Takenga, C. et al. (2017). A Clinical Tool for Automated Flow Cytometry Based on Machine Learning Methods. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10209. Springer, Cham. https://doi.org/10.1007/978-3-319-56154-7_48
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