Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse

Abstract

Insight into the cancer cell populations that are responsible for relapsed disease is needed to improve outcomes. Here we report a single-cell-based study of B cell precursor acute lymphoblastic leukemia at diagnosis that reveals hidden developmentally dependent cell signaling states that are uniquely associated with relapse. By using mass cytometry we simultaneously quantified 35 proteins involved in B cell development in 60 primary diagnostic samples. Each leukemia cell was then matched to its nearest healthy B cell population by a developmental classifier that operated at the single-cell level. Machine learning identified six features of expanded leukemic populations that were sufficient to predict patient relapse at diagnosis. These features implicated the pro-BII subpopulation of B cells with activated mTOR signaling, and the pre-BI subpopulation of B cells with activated and unresponsive pre-B cell receptor signaling, to be associated with relapse. This model, termed 'developmentally dependent predictor of relapse' (DDPR), significantly improves currently established risk stratification methods. DDPR features exist at diagnosis and persist at relapse. By leveraging a data-driven approach, we demonstrate the predictive value of single-cell 'omics' for patient stratification in a translational setting and provide a framework for its application to human cancer.

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Figure 1: Mass cytometry analysis of samples from patients with BCP-ALL reveals phenotypic heterogeneity of leukemic cells.
Figure 2: Single-cell developmental classifier for BCP-ALL.
Figure 3: Developmental classification reveals that BCP-ALL results in an expansion of cells across the pre-pro-B to pre-BI transition.
Figure 4: DDPR predicts which patients will go on to relapse based on the features of expanded BCP-ALL populations at the time of diagnosis.
Figure 5: DDPR synergizes with existing risk-stratification methods to improve relapse-free survival prediction for patients with BCP-ALL.
Figure 6: Cells with DDPR features are present at the time of diagnosis and persist at relapse.

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Acknowledgements

We thank G. Fragiadakis, M.H. Spitzer, P.F. Gherardini, A. Tsai, R.M. Angelo, J. Levine, M. O'Brien, D. Pe'er, M. Shipp and members of the ASH–EHA TRTH program for discussions. This work received funding from the Stanford Immunology NIH Training Program (grant no. 5T32AI007290-29, 5T32AI007290-30, 5T32AI007290-32 and 2T32AI007290-31; all to Z.G.), the Fondazione Italiana per la Ricerca sul Cancro (FIRC-AIRC; grant no. 19488; J.S.), the M. Tettamanti Foundation and Benedetta è la vita ONLUS Foundation (J.S.), a Damon Runyon Cancer Research Foundation Fellowship (DRG-2017-09; S.C.B.), the US National Institutes of Health (NIH) (grant no. K99GM104148-01; S.C.B.) and the Associazione Italiana per la Ricerca sul Cancro (grant no. 20564; A.B.). G.P.N. is supported by NIH grants R01CA184968, 1R01GM10983601, 1R01NS08953301, 1R01CA19665701, R01HL120724, 1R21CA183660, R33CA0183692, 1R33CA183654-01, U19AI057229, 1U19AI100627, U54-UCA149145A, N01-HV-00242 HHSN26820100034C and 5UH2AR067676, NIH Northrop–Grumman Corporation subcontract 7500108142, US Food and Drug Administration (FDA) grants HHSF223201210194C and BAA-15-00121, US Department of Defense (DOD) grants OC110674 and W81XWH-14-1-0180, the NWCRA Entertainment Industry Foundation, and the Bill and Melinda Gates Foundation grant OPP1113682. K.L.D. is supported by the NetApp St. Baldrick's Foundation Scholar award and a CureSearch Young Investigator award. Z.G. and G.P.N. are members of the Parker Institute for Cancer Immunotherapy, which supported the Stanford Cancer Immunotherapy Program.

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G.P.N. and K.L.D. conceptualized the study; Z.G., S.C.B., E.F.S., L.W., N.S., N.A., G.F., W.J.F., R.T. and K.L.D. developed the methods; Z.G., J.S., A.J. and K.L.D. performed all experiments; Z.G., N.S. and R.T. developed the software; Z.G. and R.T. performed formal data analysis for all of the data generated; N.J.L., G.G., A.B., K.L.D. and G.P.N. provided funding and patient samples supporting the study; and Z.G., J.S., S.C.B., R.T., G.G., K.L.D. and G.P.N. wrote and edited the manuscript.

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Correspondence to Kara L Davis.

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Competing interests

S.C.B. and G.P.N. are paid consultants for Fluidigm, the manufacturer that produced some of the reagents and instrumentation used in this study.

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Good, Z., Sarno, J., Jager, A. et al. Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse. Nat Med 24, 474–483 (2018). https://doi.org/10.1038/nm.4505

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