Sequencing 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. By 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. We 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.

To identify the prognostic factors that are useful to improve central nervous system (CNS) control in children with acute lymphoblastic leukemia (ALL), we analyzed the outcome of 7640 consecutive patients treated on Chinese Children’s Cancer Group ALL-2015 protocol between 2015 and 2019. This protocol featured prephase dexamethasone treatment before conventional remission induction and subsequent risk-directed therapy, including 16 to 22 triple intrathecal treatments, without prophylactic cranial irradiation. The 5-year event-free survival was 80.3% (95% confidence interval [CI], 78.9-81.7), and overall survival 91.1% (95% CI, 90.1-92.1). The cumulative risk of isolated CNS relapse was 1.9% (95% CI, 1.5-2.3), and any CNS relapse 2.7% (95% CI, 2.2-3.2). The isolated CNS relapse rate was significantly lower in patients with B-cell ALL (B-ALL) than in those with T-cell ALL (T-ALL) (1.6%; 95% CI, 1.2-2.0 vs 4.6%; 95% CI, 2.9-6.3; P < .001). Independent risk factors for isolated CNS relapse included male sex (hazard ratio [HR], 1.8; 95% CI, 1.1-3.0; P = .03), the presence of BCR-ABL1 fusion (HR, 3.8; 95% CI, 2.0-7.3; P < .001) in B-ALL, and presenting leukocyte count ≥50×109/L (HR, 4.3; 95% CI, 1.5-12.2; P = .007) in T-ALL. Significantly lower isolated CNS relapse was associated with the use of total intravenous anesthesia during intrathecal therapy (HR, 0.2; 95% CI, 0.04-0.7; P = .02) and flow cytometry examination of diagnostic cerebrospinal fluid (CSF) (HR, 0.2; 95% CI, 0.06-0.6; P = .006) among patients with B-ALL. Prephase dexamethasone treatment, delayed intrathecal therapy, use of total intravenous anesthesia during intrathecal therapy, and flow cytometry examination of diagnostic CSF may improve CNS control in childhood ALL. This trial was registered with the Chinese Clinical Trial Registry (ChiCTR-IPR-14005706).

Leukemia is the most common malignancy affecting children. The morphologic analysis of bone marrow smears is an important initial step for diagnosis. Recent publications demonstrated that artificial intelligence is able to classify blood cells but a long way from clinical use. A total of 1,732 bone marrow images were used for the training of a convolutional neural network (CNN). New techniques of deep learning were integrated and an end-to-end leukemia diagnosis system was developed by using raw images without pre-processing. The system creatively imitated the workflow of a hematologist by detecting and excluding uncountable and crushed cells, then classifying and counting the remain cells to make a diagnosis. The performance of the CNN in classifying WBCs achieved an accuracy of 82.93%, precision of 86.07% and F1 score of 82.02%. And the performance in diagnosing acute lymphoid leukemia achieved an accuracy of 89%, sensitivity of 86% and specificity of 95%. The system also performs well at detecting the bone marrow metastasis of lymphoma and neuroblastoma, achieving an average accuracy of 82.93%. This is the first study which included a wider variety of cell types in leukemia diagnosis, and achieved a relatively high performance in real clinical scenarios.

Studies have revealed key genomic aberrations in pediatric acute myeloid leukemia (AML) based on Western populations. It is unknown to what extent the current genomic findings represent populations with different ethnic backgrounds. Here we present the genomic landscape of driver alterations of Chinese pediatric AML and discover previously undescribed genomic aberrations, including the XPO1-TNRC18 fusion. Comprehensively comparing between the Chinese and Western AML cohorts reveal a substantially distinct genomic alteration profile. For example, Chinese AML patients more commonly exhibit mutations in KIT and CSF3R, and less frequently mutated of genes in the RAS signaling pathway. These differences in mutation frequencies lead to the detection of previously uncharacterized co-occurring mutation pairs. Importantly, the distinct driver profile is clinical relevant. We propose a refined prognosis risk classification model which better reflected the adverse event risk for Chinese AML patients. These results emphasize the importance of genetic background in precision medicine.

Hematopoiesis is a finely regulated process in vertebrates under both homeostatic and stress conditions. By whole exome sequencing we have studied the genomics of acute lymphoid leukemia patients who needed multiple red blood cell transfusions after intensive chemotherapies. ARHGEF12, encoding a RhoA guanine nucleotide exchange factor, was found associated with chemotherapy-induced anemia by genome-wide association study analyses. A single nucleotide polymorphism (SNP) of ARHGEF12 located in an intron predicted to be a GATA1 binding site, rs10892563, is significantly associated with patients who need red blood cell transfusion (P=3.469E-03, odds ratio 5.864). A luciferase reporter assay revealed that this SNP impairs GATA1 mediated trans-regulation of ARHGEF12, and quantitative PCR studies confirmed that the homozygotes status is associated with ~61% reduction of ARHGEF12 expression (P=0.0088). Consequently, erythropoiesis was affected at the pro-erythroblast phase in the patients. A role of ARHGEF12 and its homologs in erythroid differentiation was confirmed in human K562 cells, mouse 32D cells and primary murine bone marrow cells. We further demonstrated in zebrafish by morpholino-mediated knockdown and CRISPR/Cas9-mediated knockout of arhgef12 that its reduction resulted in erythropoiesis defects. The p38 kinase pathway was affected by the ARHGEF12-RhoA signaling in K562 cells, and consistently, the arhgef12-rhoA-p38 pathway was also shown to be important for erythroid differentiation in zebrafish as active rhoA or p38 readily rescued the impaired erythropoiesis caused by arhgef12 knocking down. Finally, ARHGEF12 mediated p38 activity also appeared to be involved in phenotypes of patients of the rs10892563 homozygous genotype. Our findings present a novel SNP of ARHGEF12 that may involve ARHGEF12-RhoA-p38 signaling in erythroid regeneration in acute lymphoblastic leukemia patients after chemotherapy.

Acute lymphoblastic leukemia expressing the gamma delta T-cell receptor (γδ T-ALL) is a poorly understood disease. We studied 200 children with γδ T-ALL from 13 clinical study groups to understand the clinical and genetic features of this disease. We found age and genetic drivers were significantly associated with outcome. γδ T-ALL diagnosed in children under 3 years of age was extremely high-risk and enriched for genetic alterations that result in both LMO2 activation and STAG2 inactivation. Mechanistically, using patient samples and isogenic cell lines, we show that inactivation of STAG2 profoundly perturbs chromatin organization by altering enhancer–promoter looping, resulting in deregulation of gene expression associated with T-cell differentiation. High-throughput drug screening identified a vulnerability in DNA repair pathways arising from STAG2 inactivation, which can be targeted by poly(ADP-ribose) polymerase inhibition. These data provide a diagnostic framework for classification and risk stratification of pediatric γδ T-ALL. Significance: Patients with acute lymphoblastic leukemia expressing the gamma delta T-cell receptor under 3 years old or measurable residual disease ≥1% at end of induction showed dismal outcomes and should be classified as having high-risk disease. The STAG2/LMO2 subtype was enriched in this very young age group. STAG2 inactivation may perturb chromatin conformation and cell differentiation and confer vulnerability to poly(ADP-ribose) polymerase inhibition.