Clinical characteristics and outcomes of children with newly diagnosed acute myeloid leukemia and hyperleukocytosis managed with different cytoreductive methods.

BACKGROUND: Hyperleukocytosis in patients with acute myeloid leukemia (AML) has been associated with worse outcomes. For cytoreduction, leukapheresis has been used but its clinical utility is unknown, and low-dose cytarabine (LD-cytarabine) is used as an alternative method. METHODS: Children with newly diagnosed AML treated between 1997 and 2017 in institutional protocols were studied. Hyperleukocytosis was defined as a leukocyte count of ≥100 × 109 /L at diagnosis. Clinical characteristics, early complications, survival data, and effects of cytoreductive methods were reviewed. Among 324 children with newly diagnosed AML, 49 (15.1%) presented with hyperleukocytosis. Initial management of hyperleukocytosis included leukapheresis or exchange transfusion (n = 16, considered as one group), LD-cytarabine (n = 18), hydroxyurea (n = 1), and no leukoreduction (n = 14). RESULTS: Compared with patients who received leukapheresis, the percentage decrease in leukocyte counts following intervention was greater among those who received LD-cytarabine (48% vs. 75%; p = .02), with longer median time from diagnosis to initiation of protocol therapy (28.1 vs. 95.2 hours; p < .001). The incidence of infection was higher in patients (38%) who had leukapheresis than those who receive LD-cytarabine (0%) or leukoreduction with protocol therapy (14%) (p = .008). No differences were noted in the outcomes among the intervention groups. Although patients with hyperleukocytosis had higher incidences of pulmonary and metabolic complications than did those without, no early deaths occurred, and the complete remission, event-free survival, overall survival rates, and outcomes of both groups were similar. CONCLUSION: LD-cytarabine treatment appears to be a safe and effective means of cytoreduction for children with AML and hyperleukocytosis.

Venetoclax in combination with cytarabine with or without idarubicin in children with relapsed or refractory acute myeloid leukaemia: a phase 1, dose-escalation study.

BACKGROUND: Outcomes for children with relapsed or refractory acute myeloid leukaemia remain poor. The BCL-2 inhibitor, venetoclax, has shown promising activity in combination with hypomethylating agents and low-dose cytarabine in older adults for whom chemotherapy is not suitable with newly diagnosed acute myeloid leukaemia. We aimed to determine the safety and explore the activity of venetoclax in combination with standard and high-dose chemotherapy in paediatric patients with relapsed or refractory acute myeloid leukaemia. METHODS: We did a phase 1, dose-escalation study at three research hospitals in the USA. Eligible patients were aged 2-22 years with relapsed or refractory acute myeloid leukaemia or acute leukaemia of ambiguous lineage with adequate organ function and performance status. During dose escalation, participants received venetoclax orally once per day in continuous 28-day cycles at either 240 mg/m2 or 360 mg/m2, in combination with cytarabine received intravenously every 12 h at either 100 mg/m2 for 20 doses or 1000 mg/m2 for eight doses, with or without intravenous idarubicin (12 mg/m2) as a single dose, using a rolling-6 accrual strategy. The primary endpoint was the recommended phase 2 dose of venetoclax plus chemotherapy and the secondary endpoint was the proportion of patients treated at the recommended phase 2 dose who achieved complete remission or complete remission with incomplete haematological recovery. Analyses were done on patients who received combination therapy. The study is registered with ClinicalTrials.gov (NCT03194932) and is now enrolling to address secondary and exploratory objectives. FINDINGS: Between July 1, 2017, and July 2, 2019, 38 patients were enrolled (aged 3-22 years; median 10 [IQR 7-13]), 36 of whom received combination therapy with dose escalation, with a median follow-up of 7·1 months (IQR 5·1-11·2). The recommended phase 2 dose of venetoclax was found to be 360 mg/m2 (maximum 600 mg) combined with cytarabine (1000 mg/m2 per dose for eight doses), with or without idarubicin (12 mg/m2 as a single dose). Overall responses were observed in 24 (69%) of the 35 patients who were evaluable after cycle 1. Among the 20 patients treated at the recommended phase 2 dose, 14 (70%, 95% CI 46-88) showed complete response with or without complete haematological recovery, and two (10%) showed partial response. The most common grade 3-4 adverse events were febrile neutropenia (22 [66%]), bloodstream infections (six [16%]), and invasive fungal infections (six [16%]). Treatment-related death occurred in one patient due to colitis and sepsis. INTERPRETATION: The safety and activity of venetoclax plus chemotherapy in paediatric patients with heavily relapsed and refractory acute myeloid leukaemia suggests that this combination should be tested in newly diagnosed paediatric patients with high-risk acute myeloid leukaemia. FUNDING: US National Institutes of Health, American Lebanese Syrian Associated Charities, AbbVie, and Gateway for Cancer Research.

Correction to: H3.3 K27M depletion increases differentiation and extends latency of diffuse intrinsic pontine glioma growth in vivo.

The original article can be found online.

H3.3 K27M depletion increases differentiation and extends latency of diffuse intrinsic pontine glioma growth in vivo.

Histone H3 K27M mutation is the defining molecular feature of the devastating pediatric brain tumor, diffuse intrinsic pontine glioma (DIPG). The prevalence of histone H3 K27M mutations indicates a critical role in DIPGs, but the contribution of the mutation to disease pathogenesis remains unclear. We show that knockdown of this mutation in DIPG xenografts restores K27M-dependent loss of H3K27me3 and delays tumor growth. Comparisons of matched DIPG xenografts with and without K27M knockdown allowed identification of mutation-specific effects on the transcriptome and epigenome. The resulting transcriptional changes recapitulate expression signatures from K27M primary DIPG tumors and are strongly enriched for genes associated with nervous system development. Integrated analysis of ChIP-seq and expression data showed that genes upregulated by the mutation are overrepresented in apparently bivalent promoters. Many of these targets are associated with more immature differentiation states. Expression profiles indicate K27M knockdown decreases proliferation and increases differentiation within lineages represented in DIPG. These data suggest that K27M-mediated loss of H3K27me3 directly regulates a subset of genes by releasing poised promoters, and contributes to tumor phenotype and growth by limiting differentiation. The delayed tumor growth associated with knockdown of H3 K27M provides evidence that this highly recurrent mutation is a relevant therapeutic target.

Statistical selection of biological models for genome-wide association analyses.

Genome-wide association studies have discovered many biologically important associations of genes with phenotypes. Typically, genome-wide association analyses formally test the association of each genetic feature (SNP, CNV, etc) with the phenotype of interest and summarize the results with multiplicity-adjusted p-values. However, very small p-values only provide evidence against the null hypothesis of no association without indicating which biological model best explains the observed data. Correctly identifying a specific biological model may improve the scientific interpretation and can be used to more effectively select and design a follow-up validation study. Thus, statistical methodology to identify the correct biological model for a particular genotype-phenotype association can be very useful to investigators. Here, we propose a general statistical method to summarize how accurately each of five biological models (null, additive, dominant, recessive, co-dominant) represents the data observed for each variant in a GWAS study. We show that the new method stringently controls the false discovery rate and asymptotically selects the correct biological model. Simulations of two-stage discovery-validation studies show that the new method has these properties and that its validation power is similar to or exceeds that of simple methods that use the same statistical model for all SNPs. Example analyses of three data sets also highlight these advantages of the new method. An R package is freely available at www.stjuderesearch.org/site/depts/biostats/maew.

POST: A framework for set-based association analysis in high-dimensional data.

Evaluating the differential expression of a set of genes belonging to a common biological process or ontology has proven to be a very useful tool for biological discovery. However, existing gene-set association methods are limited to applications that evaluate differential expression across k⩾2 treatment groups or biological categories. This limitation precludes researchers from most effectively evaluating the association with other phenotypes that may be more clinically meaningful, such as quantitative variables or censored survival time variables. Projection onto the Orthogonal Space Testing (POST) is proposed as a general procedure that can robustly evaluate the association of a gene-set with several different types of phenotypic data (categorical, ordinal, continuous, or censored). For each gene-set, POST transforms the gene profiles into a set of eigenvectors and then uses statistical modeling to compute a set of z-statistics that measure the association of each eigenvector with the phenotype. The overall gene-set statistic is the sum of squared z-statistics weighted by the corresponding eigenvalues. Finally, bootstrapping is used to compute a p-value. POST may evaluate associations with or without adjustment for covariates. In simulation studies, it is shown that the performance of POST in evaluating the association with a categorical phenotype is similar to or exceeds that of existing methods. In evaluating the association of 875 biological processes with the time to relapse of pediatric acute myeloid leukemia, POST identified the well-known oncogenic WNT signaling pathway as its top hit. These results indicate that POST can be a very useful tool for evaluating the association of a gene-set with a variety of different phenotypes. We have developed an R package named POST which is freely available in Bioconductor.

The genetic basis of early T-cell precursor acute lymphoblastic leukaemia.

Early T-cell precursor acute lymphoblastic leukaemia (ETP ALL) is an aggressive malignancy of unknown genetic basis. We performed whole-genome sequencing of 12 ETP ALL cases and assessed the frequency of the identified somatic mutations in 94 T-cell acute lymphoblastic leukaemia cases. ETP ALL was characterized by activating mutations in genes regulating cytokine receptor and RAS signalling (67% of cases; NRAS, KRAS, FLT3, IL7R, JAK3, JAK1, SH2B3 and BRAF), inactivating lesions disrupting haematopoietic development (58%; GATA3, ETV6, RUNX1, IKZF1 and EP300) and histone-modifying genes (48%; EZH2, EED, SUZ12, SETD2 and EP300). We also identified new targets of recurrent mutation including DNM2, ECT2L and RELN. The mutational spectrum is similar to myeloid tumours, and moreover, the global transcriptional profile of ETP ALL was similar to that of normal and myeloid leukaemia haematopoietic stem cells. These findings suggest that addition of myeloid-directed therapies might improve the poor outcome of ETP ALL.

Contribution of the TP53 R337H mutation to the cancer burden in southern Brazil: Insights from the study of 55 families of children with adrenocortical tumors.

BACKGROUND: The tumor protein p53 (TP53) arginine-to-histidine mutation at codon 337 (R337H) predisposes children to adrenocortical tumors (ACTs) and, rarely, to other childhood tumors, but its impact on adult cancer remains undetermined. The objective of this study was to investigate the frequency and types of cancer in relatives of children with ACT who carry the TP53 R337H mutation. METHODS: TP53 R337H testing was offered to relatives of probands with ACT. The parental lineage segregating the R337H mutation was identified in all families. The frequency and distribution of cancer types were compared according to R337H status. The authors' data also were compared with those publicly available for children with TP53 mutations other than R337H. RESULTS: The mean and median follow-up times for the probands with ACT were 11.2 years and 9.7 years (range, 3-32 years), respectively. During this time, cancer was diagnosed in 12 of 81 first-degree relatives (14.8%) carrying the R337H mutation but in only 1 of 94 noncarriers (1.1%; P = .0022). At age 45 years, the cumulative risk of cancer was 21% (95% confidence interval, 5%-33%) in carriers and 2% (95% confidence interval, 0%-4%) in noncarriers (P = .008). The frequency of cancer was higher in the R337H segregating lineages than in the nonsegregating lineages (249 of 1410 vs 66 of 984 individuals; P < .001). Breast and gastric cancer were the most common types. CONCLUSIONS: TP53 R337H carriers have a lifelong predisposition to cancer with a bimodal age distribution: 1 peak, represented by ACT, occurs in the first decade of life, and another peak of diverse cancer types occurs in the fifth decade. The current findings have implications for genetic counseling and surveillance of R337H carriers. Cancer 2017;123:3150-58. © 2017 American Cancer Society.

CC-PROMISE effectively integrates two forms of molecular data with multiple biologically related endpoints.

BACKGROUND: As new technologies allow investigators to collect multiple forms of molecular data (genomic, epigenomic, transcriptomic, etc) and multiple endpoints on a clinical trial cohort, it will become necessary to effectively integrate all these data in a way that reliably identifies biologically important genes. METHODS: We introduce CC-PROMISE as an integrated data analysis method that combines components of canonical correlation (CC) and projection onto the most interesting evidence (PROMISE). For each gene, CC-PROMISE first uses CC to compute scores that represent the association of two forms of molecular data with each other. Next, these scores are substituted into PROMISE to evaluate the statistical evidence that the molecular data show a biologically meaningful relationship with the endpoints. RESULTS: CC-PROMISE shows outstanding performance in simulation studies and an example application involving pediatric leukemia. In simulation studies, CC-PROMISE controls the type I error (misleading significance) rate very near the nominal level across 100 distinct null settings in which no molecular-endpoint association exists. Also, CC-PROMISE has better statistical power than three other methods that control type I error in 396 of 400 (99 %) alternative settings for which a molecular-endpoint association is present; the power advantage of CC-PROMISE exceeds 30 % in 127 of the 400 (32 %) alternative settings. These advantages of CC-PROMISE are also observed in an example application. CONCLUSION: CC-PROMISE very effectively identifies genes for which some form of molecular data shows a biologically meaningful association with multiple related endpoints. AVAILABILITY: The R package CCPROMISE is currently available from www.stjuderesearch.org/site/depts/biostats/software .

Cross-species genomics matches driver mutations and cell compartments to model ependymoma.

Understanding the biology that underlies histologically similar but molecularly distinct subgroups of cancer has proven difficult because their defining genetic alterations are often numerous, and the cellular origins of most cancers remain unknown. We sought to decipher this heterogeneity by integrating matched genetic alterations and candidate cells of origin to generate accurate disease models. First, we identified subgroups of human ependymoma, a form of neural tumour that arises throughout the central nervous system (CNS). Subgroup-specific alterations included amplifications and homozygous deletions of genes not yet implicated in ependymoma. To select cellular compartments most likely to give rise to subgroups of ependymoma, we matched the transcriptomes of human tumours to those of mouse neural stem cells (NSCs), isolated from different regions of the CNS at different developmental stages, with an intact or deleted Ink4a/Arf locus (that encodes Cdkn2a and b). The transcriptome of human supratentorial ependymomas with amplified EPHB2 and deleted INK4A/ARF matched only that of embryonic cerebral Ink4a/Arf(-/-) NSCs. Notably, activation of Ephb2 signalling in these, but not other, NSCs generated the first mouse model of ependymoma, which is highly penetrant and accurately models the histology and transcriptome of one subgroup of human supratentorial tumour. Further, comparative analysis of matched mouse and human tumours revealed selective deregulation in the expression and copy number of genes that control synaptogenesis, pinpointing disruption of this pathway as a critical event in the production of this ependymoma subgroup. Our data demonstrate the power of cross-species genomics to meticulously match subgroup-specific driver mutations with cellular compartments to model and interrogate cancer subgroups.

SVSI: fast and powerful set-valued system identification approach to identifying rare variants in sequencing studies for ordered categorical traits.

In genetic association studies of an ordered categorical phenotype, it is usual to either regroup multiple categories of the phenotype into two categories and then apply the logistic regression (LG), or apply ordered logistic (oLG), or ordered probit (oPRB) regression, which accounts for the ordinal nature of the phenotype. However, they may lose statistical power or may not control type I error due to their model assumption and/or instable parameter estimation algorithm when the genetic variant is rare or sample size is limited. To solve this problem, we propose a set-valued (SV) system model to identify genetic variants associated with an ordinal categorical phenotype. We couple this model with a SV system identification algorithm to identify all the key system parameters. Simulations and two real data analyses show that SV and LG accurately controlled the Type I error rate even at a significance level of 10(-6) but not oLG and oPRB in some cases. LG had significantly less power than the other three methods due to disregarding of the ordinal nature of the phenotype, and SV had similar or greater power than oLG and oPRB. We argue that SV should be employed in genetic association studies for ordered categorical phenotype.

Feasibility, efficacy, and adverse effects of outpatient antibacterial prophylaxis in children with acute myeloid leukemia.

BACKGROUND: Intensive chemotherapy for pediatric acute myeloid leukemia incurs the risk of infectious complications, but the benefits of antibiotic prophylaxis remain unclear. METHODS: In the current study, among 103 children treated on the AML02 protocol between October 2002 and October 2008 at St. Jude Children's Research Hospital, the authors retrospectively assessed the effect of antibiotic prophylaxis on the frequency of febrile neutropenia, clinically or microbiologically confirmed infections (including bacteremia), and antibiotic resistance, as well as on the results of nasal and rectal surveillance cultures. Initially, patients received no prophylaxis or oral cephalosporin (group A). The protocol was then amended to administer intravenous cefepime alone or intravenous vancomycin plus either oral cephalosporin, oral ciprofloxacin, or intravenous cefepime (group B). RESULTS: There were 334 infectious episodes. Patients in group A had a significantly greater frequency of documented infections and bacteremia (both P < .0001) (including gram-positive and gram-negative bacteremia; P = .0003 and .001, respectively) compared with patients in group B, especially viridans streptococcal bacteremia (P = .001). The incidence of febrile neutropenia without documented infection was not found to be different between the 2 groups. Five cases of bacteremia with vancomycin-resistant enterococci (VRE) occurred in group B (vs none in group A), without related mortality. Two of these cases were preceded by positive VRE rectal surveillance cultures. CONCLUSIONS: Outpatient intravenous antibiotic prophylaxis is feasible in children with acute myeloid leukemia and reduces the frequency of documented infection but not of febrile neutropenia. Despite the emergence of VRE bacteremia, the benefits favor antibiotic prophylaxis. Creative approaches to shorten the duration of prophylaxis and thereby minimize resistance should be explored.

Computing Power and Sample Size for the False Discovery Rate in Multiple Applications.

The false discovery rate (FDR) is a widely used metric of statistical significance for genomic data analyses that involve multiple hypothesis testing. Power and sample size considerations are important in planning studies that perform these types of genomic data analyses. Here, we propose a three-rectangle approximation of a p-value histogram to derive a formula to compute the statistical power and sample size for analyses that involve the FDR. We also introduce the R package FDRsamplesize2, which incorporates these and other power calculation formulas to compute power for a broad variety of studies not covered by other FDR power calculation software. A few illustrative examples are provided. The FDRsamplesize2 package is available on CRAN.

Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes.

Motivation: Large datasets containing multiple clinical and omics measurements for each subject motivate the development of new statistical methods to integrate these data to advance scientific discovery. Model: We propose bootstrap evaluation of association matrices (BEAM), which integrates multiple omics profiles with multiple clinical endpoints. BEAM associates a set omic features with clinical endpoints via regression models and then uses bootstrap resampling to determine statistical significance of the set. Unlike existing methods, BEAM uniquely accommodates an arbitrary number of omic profiles and endpoints. Results: In simulations, BEAM performed similarly to the theoretically best simple test and outperformed other integrated analysis methods. In an example pediatric leukemia application, BEAM identified several genes with biological relevance established by a CRISPR assay that had been missed by univariate screens and other integrated analysis methods. Thus, BEAM is a powerful, flexible, and robust tool to identify genes for further laboratory and/or clinical research evaluation. Availability: Source code, documentation, and a vignette for BEAM are available on GitHub at: https://github.com/annaSeffernick/BEAMR. The R package is available from CRAN at: https://cran.r-project.org/package=BEAMR. Contact: Stanley.Pounds@stjude.org. Supplementary Information: Supplementary data are available at the journal's website.

Pharmacogenomics, Race, and Treatment Outcome in Pediatric Acute Myeloid Leukemia.

Importance: Disparities in outcomes exist between Black and White patients with acute myeloid leukemia (AML), with Black patients experiencing poorer prognosis compared with their White counterparts. Objective: To assess whether varying intensity of induction therapy to treat pediatric AML is associated with reduced disparities in treatment outcome by race. Design, Setting, and Participants: A comparative effectiveness analysis was conducted of 86 Black and 359 White patients with newly diagnosed AML who were enrolled in the AML02 trial from 2002 to 2008 or the AML08 trial from 2008 to 2017. Statistical analysis was conducted from July 2023 through January 2024. Interventions: Patients in AML02 were randomly assigned to receive standard low-dose cytarabine-based induction therapy or augmented high-dose cytarabine-based induction therapy, whereas patients in AML08 received high-dose cytarabine-based therapy. Main Outcomes and Measures: Cytarabine pharmacogenomic 10-single-nucleotide variant (ACS10) scores were evaluated for association with outcome according to race and treatment arm. Results: This analysis included 86 Black patients (mean [SD] age, 8.8 [6.5] years; 54 boys [62.8%]; mean [SD] leukocyte count, 52 600 [74 000] cells/µL) and 359 White patients (mean [SD] age, 9.1 [6.2] years; 189 boys [52.6%]; mean [SD] leukocyte count, 54 500 [91 800] cells/µL); 70 individuals with other or unknown racial and ethnic backgrounds were not included. Among all patients without core binding factor AML who received standard induction therapy, Black patients had significantly worse outcomes compared with White patients (5-year event-free survival rate, 25% [95% CI, 9%-67%] compared with 56% [95% CI, 46%-70%]; P = .03). By contrast, among all patients who received augmented induction therapy, there were no differences in outcome according to race (5-year event-free survival rate, Black patients, 50% [95% CI, 38%-67%]; White patients, 48% [95% CI, 42%-55%]; P = .78). Among patients who received standard induction therapy, those with low ACS10 scores had a significantly worse 5-year event-free survival rate compared with those with high scores (42.4% [95% CI, 25.6%-59.3%] and 70.0% [95% CI, 56.6%-83.1%]; P = .004); however, among patients who received augmented induction therapy, there were no differences in 5-year event-free survival rates according to ACS10 score (low score, 60.6% [95% CI, 50.9%-70.2%] and high score, 54.8% [95% CI, 47.1%-62.5%]; P = .43). Conclusions and Relevance: In this comparative effectiveness study of pediatric patients with AML treated in 2 consecutive clinical trials, Black patients had worse outcomes compared with White patients after treatment with standard induction therapy, but this disparity was eliminated by treatment with augmented induction therapy. When accounting for ACS10 scores, no outcome disparities were seen between Black and White patients. Our results suggest that using pharmacogenomics parameters to tailor induction regimens for both Black and White patients may narrow the racial disparity gap in patients with AML.

Pharmacogenomic Score Effectively Personalizes Treatment of Acute Myeloid Leukemia.

PURPOSE: Cytarabine (also known as ara-C) has been the backbone of acute myeloid leukemia (AML) chemotherapy for over five decades. Recent pharmacogenomics-based 10-SNP ara-C score (ACS10) showed low ACS10 (£0) to be associated with poor outcome in AML patients treated with standard chemotherapy. Here, we evaluated ACS10 score in the context of three different induction 1 regimens in pediatric AML patients. EXPERIMENTAL DESIGN: ACS10 score groups (low,£0 or high,>0) were evaluated for association with event-free survival (EFS) and overall survival (OS) by three randomized treatment arms in patients treated on the AML02 (NCT00136084) and AML08 (NCT00703820) clinical trials: AML02 low-dose cytarabine (LDAC arm, n=91), AML02+AML08 high-dose cytarabine (HDAC arm, n=194) and AML08 clofarabine+ cytarabine (Clo/Ara-C arm, n=105) induction 1 regimens. RESULTS: Within the low-ACS10 score (£0) group, significantly improved EFS and OS was observed among patients treated with Clo/Ara-C as compared to LDAC (EFS, HR=0.45, 95% CI, 0.23-0.88, p=0.020; OS, HR=0.44, 95% CI, 0.19-0.99, p=0.048). In contrast, within the high-ACS10 score group (score >0) augmentation with Clo/Ara-C was not favorable as compared to LDAC (Clo/Ara-C vs. LDAC, EFS, HR=1.95, 95% CI: 1.05-3.63, p=0.035; OS HR=2.17, 95%CI: 1.05-4.49; p=0.037). Personalization models predicted 9% improvement in outcome in ACS10 score-based tailored induction (Clo/Ara-C for low and LDAC for high-ACS10 groups) as compared to non-personalized approaches (p<0.002). CONCLUSIONS: Our findings suggest that tailoring induction regimens using ACS10 scores can significantly improve outcome in patients with AML. Given the SNPs are germline, preemptive genotyping can accelerate matching the most effective remission induction regimen.

Genomic Determinants of Outcome in Acute Lymphoblastic Leukemia.

PURPOSE: Although cure rates for childhood acute lymphoblastic leukemia (ALL) exceed 90%, ALL remains a leading cause of cancer death in children. Half of relapses arise in children initially classified with standard-risk (SR) disease. MATERIALS AND METHODS: To identify genomic determinants of relapse in children with SR ALL, we performed genome and transcriptome sequencing of diagnostic and remission samples of children with SR (n = 1,381) or high-risk B-ALL with favorable cytogenetic features (n = 115) enrolled on Children's Oncology Group trials. We used a case-control study design analyzing 439 patients who relapsed and 1,057 who remained in complete remission for at least 5 years. RESULTS: Genomic subtype was associated with relapse, which occurred in approximately 50% of cases of PAX5-altered ALL (odds ratio [OR], 3.31 [95% CI, 2.17 to 5.03]; P = 3.18 × 10-8). Within high-hyperdiploid ALL, gain of chromosome 10 with disomy of chromosome 7 was associated with favorable outcome (OR, 0.27 [95% CI, 0.17 to 0.42]; P = 8.02 × 10-10; St Jude Children's Research Hospital validation cohort: OR, 0.22 [95% CI, 0.05 to 0.80]; P = .009), and disomy of chromosomes 10 and 17 with gain of chromosome 6 was associated with relapse (OR, 7.16 [95% CI, 2.63 to 21.51]; P = 2.19 × 10-5; validation cohort: OR, 21.32 [95% CI, 3.62 to 119.30]; P = .0004). Genomic alterations were associated with relapse in a subtype-dependent manner, including alterations of INO80 in ETV6::RUNX1 ALL, IKZF1, and CREBBP in high-hyperdiploid ALL and FHIT in BCR::ABL1-like ALL. Genomic alterations were also associated with the presence of minimal residual disease, including NRAS and CREBBP in high-hyperdiploid ALL. CONCLUSION: Genetic subtype, patterns of aneuploidy, and secondary genomic alterations determine risk of relapse in childhood ALL. Comprehensive genomic analysis is required for optimal risk stratification.

Empirical bayesian selection of hypothesis testing procedures for analysis of sequence count expression data.

Differential expression analysis of sequence-count expression data involves performing a large number of hypothesis tests that compare the expression count data of each gene or transcript across two or more biological conditions. The assumptions of any specific hypothesis-testing method will probably not be valid for each of a very large number of genes. Thus, computational evaluation of assumptions should be incorporated into the analysis to select an appropriate hypothesis-testing method for each gene. Here, we generalize earlier work to introduce two novel procedures that use estimates of the empirical Bayesian probability (EBP) of overdispersion to select or combine results of a standard Poisson likelihood ratio test and a quasi-likelihood test for each gene. These EBP-based procedures simultaneously evaluate the Poisson-distribution assumption and account for multiple testing. With adequate power to detect overdispersion, the new procedures select the standard likelihood test for each gene with Poisson-distributed counts and the quasi-likelihood test for each gene with overdispersed counts. The new procedures outperformed previously published methods in many simulation studies. We also present a real-data analysis example and discuss how the framework used to develop the new procedures may be generalized to further enhance performance. An R code library that implements the methods is freely available at www.stjuderesearch.org/depts/biostats/software.

Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia.

Chromosomal aberrations are a hallmark of acute lymphoblastic leukaemia (ALL) but alone fail to induce leukaemia. To identify cooperating oncogenic lesions, we performed a genome-wide analysis of leukaemic cells from 242 paediatric ALL patients using high-resolution, single-nucleotide polymorphism arrays and genomic DNA sequencing. Our analyses revealed deletion, amplification, point mutation and structural rearrangement in genes encoding principal regulators of B lymphocyte development and differentiation in 40% of B-progenitor ALL cases. The PAX5 gene was the most frequent target of somatic mutation, being altered in 31.7% of cases. The identified PAX5 mutations resulted in reduced levels of PAX5 protein or the generation of hypomorphic alleles. Deletions were also detected in TCF3 (also known as E2A), EBF1, LEF1, IKZF1 (IKAROS) and IKZF3 (AIOLOS). These findings suggest that direct disruption of pathways controlling B-cell development and differentiation contributes to B-progenitor ALL pathogenesis. Moreover, these data demonstrate the power of high-resolution, genome-wide approaches to identify new molecular lesions in cancer.

Statistical Methods Inspired by Challenges in Pediatric Cancer Multi-omics.

Pediatric cancer multi-omics is a uniquely rewarding and challenging domain of biomedical research. Public generosity bestows an abundance of resources for the study of extremely rare diseases; this unique dynamic creates a research environment in which problems with high-dimension and low sample size are commonplace. Here, we present a few statistical methods that we have developed for our research setting and believe will prove valuable in other biomedical research settings as well. The genomic random interval (GRIN) method evaluates the loci and frequency of genomic abnormalities in the DNA of tumors to identify genes that may drive the development of malignancies. The association of lesions with expression (ALEX) method evaluates the impact of genomic abnormalities on the RNA transcription of nearby genes to inform the formulation of biological hypotheses on molecular mechanisms. The projection onto the most interesting statistical evidence (PROMISE) method identifies omic features that consistently associate with better prognosis or consistently associate with worse prognosis across multiple measures of clinical outcome. We have shown that these methods are statistically robust and powerful in the statistical bioinformatic literature and successfully used these methods to make fundamental biological discoveries that have formed the scientific rationale for ongoing clinical trials. We describe these methods and illustrate their application on a publicly available T-cell acute lymphoblastic leukemia (T-ALL) data set. A companion github site ( https://github.com/stjude/TALL-example ) provides the R code and data necessary to recapitulate the example data analyses of this chapter.