Detection of minimal residual disease identifies differences in treatment response between T-ALL and precursor B-ALL

We performed sensitive polymerase chain reaction-based minimal residual disease (MRD) analyses on bone marrow samples at 9 follow-up time points in 71 children with T-lineage acute lymphoblastic leukemia (T-ALL) and compared the results with the precursor B-lineage ALL (B-ALL) results (n = 210) of our previous study. At the first 5 follow-up time points, the frequency of MRD-positive patients and the MRD levels were higher in T-ALL than in precursor-B-ALL, reflecting the more frequent occurrence of resistant disease in T-ALL. Subsequently, patients were classified according to their MRD level at time point 1 (TP1), taken at the end of induction treatment (5 weeks), and at TP2 just before the start of consolidation treatment (3 months). Patients were considered at low risk if TP1 and TP2 were MRD negative and at high risk if MRD levels at TP1 and TP2 were 10(-3) or higher; remaining patients were considered at intermediate risk. The relative distribution of patients with T-ALL (n = 43) over the MRD-based risk groups differed significantly from that of precursor B-ALL (n = 109). Twenty-three percent of patients with T-ALL and 46% of patients with precursor B-ALL were classified in the low-risk group (P =.01) and had a 5-year relapse-free survival (RFS) rate of 98% or greater. In contrast, 28% of patients with T-ALL were classified in the MRD-based high-risk group compared to only 11% of patients with precursor B-ALL (P =.02), and the RFS rates were 0% and 25%, respectively (P =.03). Not only was the distribution of patients with T-ALL different over the MRD-based risk groups, the prognostic value of MRD levels at TP1 and TP2 was higher in T-ALL (larger RFS gradient), and consistently higher RFS rates were found for MRD-negative T-ALL patients at the first 5 follow-up time points

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

Abstract The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non-pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. This article is protected by copyright. All rights reserved.Peer reviewe