RESUMEN
PURPOSE: BCR::ABL1 quantification is widely regarded as the standard for monitoring measurable residual disease (MRD) in Philadelphia chromosome-positive (Ph+) ALL. However, recent evidence of BCR::ABL1 multilineage involvement questions the significance of BCR::ABL1 MRD. We aimed to define the prognostic role of MRD as assessed by BCR::ABL1 or lymphoid-specific immunoglobulin/T-cell receptor (IG/TR) gene markers. PATIENTS AND METHODS: We conducted BCR::ABL1 and IG/TR quantification after each treatment cycle in 264 patients treated in the GRAAPH-2014 trial, which used four cycles of reduced-intensity chemotherapy with nilotinib, followed by hematopoietic stem-cell transplantation (HSCT). RESULTS: Comparing BCR::ABL1 and IG/TR MRD revealed residual BCR::ABL1-positive non-ALL cells in 98 (43%) of 228 patients, defining multilineage Ph+ ALL. Despite poorer BCR::ABL1 responses, patients with multilineage Ph+ ALL had similar disease-free survival (DFS; hazard ratio [HR], 0.83 [95% CI, 0.49 to 1.41]; P = .50). Although BCR::ABL1 response failed to predict outcomes, IG/TR positivity (≥0.01%) was strongly associated with lower DFS (after cycle 2, HR, 2.49 [95% CI, 1.40 to 4.40]; P = .002; after cycle 4, HR, 4.13 [95% CI, 1.82 to 9.38]; P = .001). In multivariable analysis, both IG/TR positivity after cycle 2 and initial WBC count ≥30 × 109/L predicted poorer DFS, enabling to define a high-risk group having a 4-year DFS of 56.5% compared with 87.6% (HR, 3.72 [95% CI, 1.93 to 7.15]; P < .001). Moreover, allogeneic HSCT significantly improved DFS in the high-risk group (HR, 0.33 [95% CI, 0.18 to 0.60]; P < .001), whereas the standard-risk group had favorable outcomes regardless of allogeneic HSCT. CONCLUSION: Our findings challenge the significance of BCR::ABL1 monitoring in adult Ph+ ALL and demonstrate the prognostic role of IG/TR MRD. This study provides a framework for using MRD to guide treatment strategies in adults with Ph+ ALL.
RESUMEN
High-throughput sequencing plays a pivotal role in hematological malignancy diagnostics, but interpreting missense mutations remains challenging. In this study, we used the newly available AlphaMissense database to assess the efficacy of machine learning to predict missense mutation effects and its impact to improve our ability to interpret them. Based on the analysis of 2073 variants from 686 patients analyzed for clinical purpose, we confirmed the very high accuracy of AlphaMissense predictions in a large real-life data set of missense mutations (AUC of ROC curve 0.95), and provided a comprehensive analysis of the discrepancies between AlphaMissense predictions and state of the art clinical interpretation.
