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Single-cell mass cytometry and machine learning predict relapse in childhood leukemia

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Article: e1472057 | Received 18 Apr 2018, Accepted 27 Apr 2018, Published online: 12 Sep 2018

Figures & data

Figure 1. Single-cell developmental classification and DDPR prediction to model relapse in BCP-ALL.

At diagnosis, expanded leukemic cells have the closest phenotypic similarity to cells across the pre-pro-B to pre-BI transitional populations of normal B-cell development. BCP-ALL cells with distinct cellular features and the highest phenotypic similarity to pro-BII and pre-BI cells exist at diagnosis in patients who will go on to relapse. Specifically, pro-BII-like cells with high basal activation of either prpS6 or 4EBP1, and pre-BI-like cells with high basal activation of SYK and lack of CREB and rpS6 response to pre-B cell receptor engagement. These cells exist at diagnosis but persist despite the pressure of treatment to mediate eventual relapse.

Figure 1. Single-cell developmental classification and DDPR prediction to model relapse in BCP-ALL.At diagnosis, expanded leukemic cells have the closest phenotypic similarity to cells across the pre-pro-B to pre-BI transitional populations of normal B-cell development. BCP-ALL cells with distinct cellular features and the highest phenotypic similarity to pro-BII and pre-BI cells exist at diagnosis in patients who will go on to relapse. Specifically, pro-BII-like cells with high basal activation of either prpS6 or 4EBP1, and pre-BI-like cells with high basal activation of SYK and lack of CREB and rpS6 response to pre-B cell receptor engagement. These cells exist at diagnosis but persist despite the pressure of treatment to mediate eventual relapse.