T-lineage acute lymphoblastic leukemia (T-ALL) accounts for about 15% of pediatric and about 25% of adult ALL cases. Minimal/measurable residual disease (MRD) assessed by flow cytometry (FCM) is an important prognostic indicator for risk stratification. In order to assess the MRD a limited number of antibodies directed against the most discriminative antigens must be selected. We propose a pipeline for evaluating the influence of different markers for cell population classification in FCM data. We use linear support vector machine, fitted to each sample individually to avoid issues with patient and laboratory variations. The best separating hyperplane direction as well as the influence of omitting specific markers is considered. Ninety-one bone marrow samples of 43 pediatric T-ALL patients from five reference laboratories were analyzed by FCM regarding marker importance for blast cell identification using combinations of eight different markers. For all laboratories, CD48 and CD99 were among the top three markers with strongest contribution to the optimal hyperplane, measured by median separating hyperplane coefficient size for all samples per center and time point (diagnosis, Day 15, Day 33). Based on the available limited set tested (CD3, CD4, CD5, CD7, CD8, CD45, CD48, CD99), our findings prove that CD48 and CD99 are useful markers for MRD monitoring in T-ALL. The proposed pipeline can be applied for evaluation of other marker combinations in the future.

Advanced Diagnostics and Target Discovery in ALL Fondazione istituto di Ricerca pediatrica Città della Speranza Padova Italy

Computer Vision Lab Faculty of Informatics Technical University of Vienna Vienna Austria

Department of Pediatric Haematology and Oncology University Hospital Motol Prague Czech Republic

Immunological Diagnostics St Anna Children's Cancer Research Institute Vienna Austria

Labdia Labordiagnostik GmbH Vienna Austria

M Tettamanti Foundation Research Center Department of Pediatrics University of Milano Bicocca Monza Italy

Pediatric Hematology Oncology and Stem Cell Transplant Division Maternal and Child Health Department University of Padova Padova Italy

The Rina Zaizov Division of Pediatric Hematology Oncology Schneider's Children's Medical Center Petah Tikva Israel

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Möricke A, Zimmermann M, Valsecchi MG, Stanulla M, Biondi A, Mann G, et al. Dexamethasone vs prednisone in induction treatment of pediatric ALL: results of the randomized trial AIEOP-BFM ALL 2000. Blood. 2016;127(17):2101-2112.

Vora A, Goulden N, Wade R, Mitchell C, Hancock J, Hough R, et al. Treatment reduction for children and young adults with low-risk acute lymphoblastic leukaemia defined by minimal residual disease (UKALL 2003): a randomised controlled trial. Lancet Oncol. 2013;14(3):199-209.

Place AE, Stevenson KE, Vrooman LM, Harris MH, Hunt SK, O'Brien JE, et al. Intravenous pegylated asparaginase versus intramuscular native Escherichia coli L-asparaginase in newly diagnosed childhood acute lymphoblastic leukaemia (DFCI 05-001): a randomised, open-label phase 3 trial. Lancet Oncol. 2015;16(16):1677-1690.

Reismüller B, Attarbaschi A, Peters C, Dworzak MN, Pötschger U, Urban C, et al. Long-term outcome of initially homogenously treated and relapsed childhood acute lymphoblastic leukaemia in Austria-a population-based report of the Austrian Berlin-Frankfurt-Münster (BFM) Study Group. Br J Haematol. 2009;144(4):559-570.

Modvig S, Madsen H, Siitonen S, Rosthøj S, Tierens A, Juvonen V, et al. Minimal residual disease quantification by flow cytometry provides reliable risk stratification in T-cell acute lymphoblastic leukemia. Leukemia. 2019;33(6):1324-1336.

Conter V, Valsecchi MG, Buldini B, Parasole R, Locatelli F, Colombini A, et al. Early T-cell precursor acute lymphoblastic leukaemia in children treated in AIEOP centres with AIEOP-BFM protocols: a retrospective analysis. Lancet Haematol. 2016;3(2):e80-e86.

Wood BL, Levin G, Wilson M, Winter SS, Dunsmore K, Loh ML, et al. High-throughput screening by flow cytometry identifies reduced expression of CD48 as a universal characteristic of T-ALL and a suitable target for minimal residual disease (MRD) detection. Blood. 2011;118(21):2547.

Bhandoola A, von Boehmer H, Petrie HT, Zúñiga-Pflücker JC. Commitment and developmental potential of extrathymic and intrathymic T cell precursors: plenty to choose from. Immunity. 2007;26(6):678-689.

Tembhare PR, Chatterjee G, Khanka T, Ghogale S, Badrinath Y, Deshpande N, et al. Eleven-marker 10-color flow cytometric assessment of measurable residual disease for T-cell acute lymphoblastic leukemia using an approach of exclusion. Cytometry B Clin Cytom. 2021;100(4):421-433.

Porwit-MacDonald A, Björklund E, Lucio P, Van Lochem E, Mazur J, Parreira A, et al. BIOMED-1 concerted action report: flow cytometric characterization of CD7+ cell subsets in normal bone marrow as a basis for the diagnosis and follow-up of T cell acute lymphoblastic leukemia (T-ALL). Leukemia. 2000;14(5):816-825.

Nakamura A, Tsurusawa M, Kato A, Taga T, Hatae Y, Miyake M, et al. Prognostic impact of CD45 antigen expression in high-risk, childhood B-cell precursor acute lymphoblastic leukemia: Children's cancer and leukemia study group (CCLSG). Leuk Lymphoma. 2001;42(3):393-398.

DiGiuseppe JA, Wood BL. Applications of flow cytometric immunophenotyping in the diagnosis and posttreatment monitoring of B and T lymphoblastic leukemia/lymphoma. Cytometry B Clin Cytom. 2019;96(4):256-265.

Azzam HS, Grinberg A, Lui K, Shen H, Shores EW, Love PE. CD5 expression is developmentally regulated by T cell receptor (TCR) signals and TCR avidity. J Exp Med. 1998;188(12):2301-2311.

Fischer L, Gökbuget N, Schwartz S, Burmeister T, Rieder H, Brüggemann M, et al. CD56 expression in T-cell acute lymphoblastic leukemia is associated with non-thymic phenotype and resistance to induction therapy but no inferior survival after risk-adapted therapy. Haematologica. 2009;94(2):224-229.

Fuhrmann S, Schabath R, Möricke A, Zimmermann M, Kunz JB, Kulozik AE, et al. Expression of CD56 defines a distinct subgroup in childhood T-ALL with inferior outcome. Results of the ALL-BFM 2000 trial. Br J Haematol. 2018;183(1):96-103.

Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127(20):2391-2405.

Patel JL, Smith LM, Anderson J, Abromowitch M, Campana D, Jacobsen J, et al. The immunophenotype of T-lymphoblastic lymphoma in children and adolescents: a Children's Oncology Group report. Br J Haematol. 2012;159(4):454-461.

Dworzak M, Fröschl G, Printz D, De Zen L, Gaipa G, Ratei R, et al. CD99 expression in T-lineage ALL: implications for flow cytometric detection of minimal residual disease. Leukemia. 2004;18(4):703-708.

Gupta S, Devidas M, Loh ML, Raetz EA, Chen S, Wang C, et al. Flow-cytometric vs.-morphologic assessment of remission in childhood acute lymphoblastic leukemia: a report from the Children's Oncology Group (COG). Leukemia. 2018;32(6):1370-1379.

Roshal M, Fromm JR, Winter S, Dunsmore K, Wood BL. Immaturity associated antigens are lost during induction for T cell lymphoblastic leukemia: implications for minimal residual disease detection. Cytom B: Clinic Cytom. 2010;78(3):139-146.

Basso G, Veltroni M, Valsecchi MG, Dworzak M, Ratei R, Silvestri D, et al. Risk of relapse of childhood acute lymphoblastic leukemia is predicted by flow cytometric measurement of residual disease on day 15 bone marrow. J Clin Oncol. 2009;27(31):5168-5174.

Bene M, Castoldi G, Knapp W, Ludwig WD, Matutes E, Orfao A, et al. Proposals for the immunological classification of acute leukemias. European Group for the Immunological Characterization of Leukemias (EGIL). Leukemia. 1995;9(10):1783-1786.

Hoffmann J, Rother M, Kaiser U, Thrun MC, Wilhelm C, Gruen A, et al. Determination of CD43 and CD200 surface expression improves accuracy of B-cell lymphoma immunophenotyping. Cytometry B Clin Cytom. 2020;98(6):476-482.

Pedreira C, da Costa ES, Lecrevise Q, Grigore G, Fluxá R, Verde J, et al. From big flow cytometry datasets to smart diagnostic strategies: the EuroFlow approach. J Immunol Methods. 2019;475:112631.

Costa E, Pedreira CE, Barrena S, Lecrevisse Q, Flores J, Quijano S, et al. Automated pattern-guided principal component analysis vs expert-based immunophenotypic classification of B-cell chronic lymphoproliferative disorders: a step forward in the standardization of clinical immunophenotyping. Leukemia. 2010;24(11):1927-1933.

Peltier C, Visalli M, Schlich P. Comparison of canonical variate analysis and principal component analysis on 422 descriptive sensory studies. Food Qual Prefer. 2015;40:326-333.

Qiu P. Computational prediction of manually gated rare cells in flow cytometry data. Cytometry A. 2015;87(7):594-602.

Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Machine Learn. 2002;46(1):389-422.

Vapnik V, Chervonenkis A. Theory of pattern recognition, 1974. Russian 1974.

Chang YW, Lin CJ. Feature ranking using linear SVM. Causation and prediction challenge PMLR, Maastricht, Netherlands undefined; 2008. p. 53-64.

Breiman L. Random forests. Machine Learn. 2001;45:5-32.

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn machine learning in python. J Machine Learn Res. 2011;12:2825-2830.

Vapnik V. Principles of risk minimization for learning theory. Advances in neural information processing systems, Holmdel, New Jersey; 1992. p. 831-838.

Bishop CM. Pattern recognition and machine learning. Springer, New York, USA; 2006.

Bradstock K, Janossy G, Tidman N, Papageorgiou E, Prentice H, Willoughby M, et al. Immunological monitoring of residual disease in treated thymic acute lymphoblastic leukaemia. Leuk Res. 1981;5(4-5):301-309.

Enein AAA, Rahman HAA, Sharkawy NE, Elhamid SA, Abbas S, Abdelfaatah R, et al. Significance of CD99 expression in T-lineage acute lymphoblastic leukemia. Cancer Biomark. 2016;17(2):117-123.

Gaipa G, Basso G, Maglia O, Leoni V, Faini A, Cazzaniga G, et al. Drug-induced immunophenotypic modulation in childhood ALL: implications for minimal residual disease detection. Leukemia. 2005;19(1):49-56.

Stahnke K, Eckhoff S, Mohr A, Meyer L, Debatin KM. Apoptosis induction in peripheral leukemia cells by remission induction treatment in vivo: selective depletion and apoptosis in a CD34+ subpopulation of leukemia cells. Leukemia. 2003;17(11):2130-2139.

Janeliūnienė M, Matuzevičienė R, Griškevičius L, Kučinskienė ZA. Monitoring of T-cell acute lymphoblastic leukemia by flow cytometry. Central Europ J Med. 2010;5(6):651-658.

Gujral S, Tembhare P, Badrinath Y, Subramanian P, Kumar A, Sehgal K, et al. Intracytoplasmic antigen study by flow cytometry in hematolymphoid neoplasm. Indian J Pathol Microbiol. 2009;52(2):135-144.

Illingworth A, Liu L, Rolf N. Current Status of TdT Testing by Flow Cytometry (ICCS Module 15). Sponsored and reviewed by the Quality and Standards Committee of the International Clinical Cytometry Society (ICCS). 2019. 12.

Dworzak MN, Frooeschl G, Printz D, Mann G, Poetschger U, Muuehlegger N, et al. Prognostic significance and modalities of flow cytometric minimal residual disease detection in childhood acute lymphoblastic leukemia. Blood. 2002;99(6):1952-1958.

Flow-cytometric MRD detection in pediatric T-ALL: a multicenter AIEOP-BFM consensus-based guided standardized approach