Recent genomic studies have identified chromosomal rearrangements defining new subtypes of B-progenitor acute lymphoblastic leukemia (B-ALL), however many cases lack a known initiating genetic alteration. Using integrated genomic analysis of 1,988 childhood and adult cases, we describe a revised taxonomy of B-ALL, incorporating 23 subtypes defined by chromosomal rearrangements, sequence mutations, or heterogeneous genomic alterations, many of which show marked variation in prevalence according to age. Two subtypes have frequent alterations of the B lymphoid transcription factor gene PAX5. One, PAX5alt (7.4%), has diverse PAX5 alterations (rearrangements, intragenic amplifications or mutations), and a second subtype is defined by PAX5 p.Pro80Arg and biallelic PAX5 alterations. We show that p.Pro80Arg impairs B lymphoid development and promotes the development of B-ALL with biallelic Pax5 alteration in vivo. These results demonstrate the utility of transcriptome sequencing to classify B-ALL and reinforce the central role of PAX5 as a checkpoint in B lymphoid maturation and leukemogenesis.

Infant acute lymphoblastic leukemia (ALL) with MLL rearrangements (MLL-R) represents a distinct leukemia with a poor prognosis. To define its mutational landscape, we performed whole genome, exome, RNA and targeted DNA sequencing on 65 infants (47 MLL-R and 18 non-MLL-R) and 20 older children (MLL-R cases) with leukemia. Our data demonstrated infant MLL-R ALL to have one of the lowest frequencies of somatic mutations of any sequenced cancer, with the predominant leukemic clone carrying a mean of 1.3 non-silent mutations. Despite the paucity of mutations, activating mutations in kinase/PI3K/RAS signaling pathways were detected in 47%. Surprisingly, however, these mutations were often sub-clonal and frequently lost at relapse. In contrast to infant cases, MLL-R leukemia in older children had more somatic mutations (a mean of 6.5/case versus 1.3/case, P=7.15×10−5) and contained frequent mutations (45%) in epigenetic regulators, a category of genes that with the exception of MLL was rarely mutated in infant MLL-R ALL.

Histiocytic neoplasms are clonal, hematopoietic disorders characterized by an accumulation of abnormal, monocyte-derived dendritic cells or macrophages in Langerhans Cell (LCH) and non-Langerhans (non-LCH) histiocytoses, respectively. The discovery of BRAFV600E mutations in ~50% of these patients provided the first molecular therapeutisc target in histiocytosis. However, recurrent driving mutations in the majority of BRAFV600E-wildtype, non-LCH patients are unknown, and recurrent cooperating mutations in non-MAP kinase pathways are undefined for the histiocytic neoplasms. Through combined whole exome and transcriptome sequencing, we identified recurrent kinase fusions involving BRAF, ALK, and NTRK1, as well as recurrent, activating MAP2K1 and ARAF mutations in BRAFV600E-wildtype, non-LCH patients. In addition to MAP kinase pathway lesions, recurrently altered genes involving diverse cellular pathways were identified. Treatment of MAP2K1- and ARAF-mutated, non-LCH patients using MEK and RAF inhibitors, respectively, resulted in clinical efficacy demonstrating the importance of detecting and targeting diverse kinase alterations in these disorders.

Acute myeloid leukemia (AML) comprises a heterogeneous group of leukemias frequently defined by recurrent cytogenetic abnormalities, including rearrangements involving subunits of the core-binding factor (CBF) transcriptional complex. To better understand the genomic landscape of CBF-AMLs, we analyzed both pediatric (n=87) and adult (n=78) samples, including cases with RUNX1-RUNX1T1 (n=85) or CBFB-MYH11 (n=80) rearrangements, by whole-genome or whole-exome sequencing. In addition to previously reported somatic mutations in the Ras signaling pathway, we identified recurrent stabilizing mutations in CCND2, suggesting a recurrent and previously unappreciated cooperating pathway in CBF-AML. Outside of signaling alterations, RUNX1-RUNX1T1 and CBFB-MYH11 AMLs demonstrated a remarkably different spectrum of cooperating mutations as RUNX1-RUNX1T1 cases harbored recurrent somatic mutations in DHX15 and ZBTB7A, as well as an enrichment of somatic mutations in epigenetic regulators, including ASXL2, and in components of the cohesin complex. This detailed analysis provides insights into the pathogenesis and development of CBF-AML, while highlighting dramatic differences in the landscape of cooperating mutations between these related AML subtypes.

BackgroundSequencing errors are key confounding factors for detecting low-frequency genetic variants that are important for cancer molecular diagnosis, treatment, and surveillance using deep next-generation sequencing (NGS). However, there is a lack of comprehensive understanding of errors introduced at various steps of a conventional NGS workflow, such as sample handling, library preparation, PCR enrichment, and sequencing. In this study, we use current NGS technology to systematically investigate these questions.ResultsBy evaluating read-specific error distributions, we discover that the substitution error rate can be computationally suppressed to 10−5 to 10−4, which is 10- to 100-fold lower than generally considered achievable (10−3) in the current literature. We then quantify substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing by using multiple deep sequencing datasets. We find that error rates differ by nucleotide substitution types, ranging from 10−5 for A>C/T>G, C>A/G>T, and C>G/G>C changes to 10−4 for A>G/T>C changes. Furthermore, C>T/G>A errors exhibit strong sequence context dependency, sample-specific effects dominate elevated C>A/G>T errors, and target-enrichment PCR led to ~ 6-fold increase of overall error rate. We also find that more than 70% of hotspot variants can be detected at 0.1 ~ 0.01% frequency with the current NGS technology by applying in silico error suppression.ConclusionsWe present the first comprehensive analysis of sequencing error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS analysis accuracy both experimentally and computationally, ultimately enhancing the precision of deep sequencing.Electronic supplementary materialThe online version of this article (10.1186/s13059-019-1659-6) contains supplementary material, which is available to authorized users.