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Genomic studies of pediatric cancer have primarily focused on specific tumor types or high-risk disease. Here, we used a three-platform sequencing approach, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and RNA sequencing (RNA-seq), to examine tumor and germline genomes from 309 prospectively identified children with newly diagnosed (85%) or relapsed/refractory (15%) cancers, unselected for tumor type. Eighty-six percent of patients harbored diagnostic (53%), prognostic (57%), therapeutically relevant (25%), and/or cancer-predisposing (18%) variants. Inclusion of WGS enabled detection of activating gene fusions and enhancer hijacks (36% and 8% of tumors, respectively), small intragenic deletions (15% of tumors), and mutational signatures revealing of pathogenic variant effects. Evaluation of paired tumor–normal data revealed relevance to tumor development for 55% of pathogenic germline variants. This study demonstrates the power of a three-platform approach that incorporates WGS to interrogate and interpret the full range of genomic variants across newly diagnosed as well as relapsed/refractory pediatric cancers.Significance:Pediatric cancers are driven by diverse genomic lesions, and sequencing has proven useful in evaluating high-risk and relapsed/refractory cases. We show that combined WGS, WES, and RNA-seq of tumor and paired normal tissues enables identification and characterization of genetic drivers across the full spectrum of pediatric cancers.This article is highlighted in the In This Issue feature, p. 2945

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Effective data sharing is key to accelerating research to improve diagnostic precision, treatment efficacy, and long-term survival in pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud-based data-sharing ecosystem for accessing, analyzing, and visualizing genomic data from >10,000 pediatric patients with cancer and long-term survivors, and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabytes are freely available, including 12,104 whole genomes, 7,697 whole exomes, and 2,202 transcriptomes. The resource is expanding rapidly, with regular data uploads from St. Jude's prospective clinical genomics programs. Three interconnected apps within the ecosystem—Genomics Platform, Pediatric Cancer Knowledgebase, and Visualization Community—enable simultaneously performing advanced data analysis in the cloud and enhancing the Pediatric Cancer knowledgebase. We demonstrate the value of the ecosystem through use cases that classify 135 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 pediatric cancer subtypes.Significance:To advance research and treatment of pediatric cancer, we developed St. Jude Cloud, a data-sharing ecosystem for accessing >1.2 petabytes of raw genomic data from >10,000 pediatric patients and survivors, innovative analysis workflows, integrative multiomics visualizations, and a knowledgebase of published data contributed by the global pediatric cancer community.This article is highlighted in the In This Issue feature, p. 995

Effective data sharing is key to accelerating research to improve diagnostic precision, treatment efficacy, and long-term survival in pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud-based data-sharing ecosystem for accessing, analyzing, and visualizing genomic data from >10,000 pediatric patients with cancer and long-term survivors, and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabytes are freely available, including 12,104 whole genomes, 7,697 whole exomes, and 2,202 transcriptomes. The resource is expanding rapidly, with regular data uploads from St. Jude's prospective clinical genomics programs. Three interconnected apps within the ecosystem—Genomics Platform, Pediatric Cancer Knowledgebase, and Visualization Community—enable simultaneously performing advanced data analysis in the cloud and enhancing the Pediatric Cancer knowledgebase. We demonstrate the value of the ecosystem through use cases that classify 135 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 pediatric cancer subtypes. Significance: To advance research and treatment of pediatric cancer, we developed St. Jude Cloud, a data-sharing ecosystem for accessing >1.2 petabytes of raw genomic data from >10,000 pediatric patients and survivors, innovative analysis workflows, integrative multiomics visualizations, and a knowledgebase of published data contributed by the global pediatric cancer community. This article is highlighted in the In This Issue feature, p. 995

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Here, using whole-genome, exome and transcriptome sequencing of 2,754 childhood patients with ALL, we find that, despite a generally low mutation burden, ALL cases harbor a median of four putative somatic driver alterations per sample, with 376 putative driver genes identified varying in prevalence across ALL subtypes. Most samples harbor at least one rare gene alteration, including 70 putative cancer driver genes associated with ubiquitination, SUMOylation, noncoding transcripts and other functions. In hyperdiploid B-ALL, chromosomal gains are acquired early and synchronously before ultraviolet-induced mutation. By contrast, ultraviolet-induced mutations precede chromosomal gains in B-ALL cases with intrachromosomal amplification of chromosome 21. We also demonstrate the prognostic significance of genetic alterations within subtypes. Intriguingly, DUX4- and KMT2A-rearranged subtypes separate into CEBPA/FLT3- or NFATC4-expressing subgroups with potential clinical implications. Together, these results deepen understanding of the ALL genomic landscape and associated outcomes.

Detection of gene fusions is important for discovery of cancer drivers and clinical oncology testing, but existing software tools for fusion detection usually take hours to run and may fail to find lowly expressed fusions. To overcome these limitations, we developed the Fuzzion2 program, which uses pattern matching to detect known gene fusions in unmapped paired-read RNA-Seq data. Given a set of patterns representing fusion transcript breakpoints, Fuzzion2 finds every read pair matching any of the patterns. Both exact and inexact (fuzzy) matches are detected; the fuzzy matching tolerates variations caused by sequencing errors, SNVs, and indels. By employing a novel index of frequency minimizers, Fuzzion2 needs only minutes to process a sample. We have also developed pipelines to produce patterns for Fuzzion2, from fusion contig sequences, from genomic breakpoints in DNA and RNA, and from fusion protein sequences. To evaluate its applicability in clinical testing, we ran Fuzzion2 on ~2,000 RNA-seq samples profiled by the St. Jude clinical genomics program and confirmed its sensitivity in identifying lowly expressed fusions, such as KIAA1549-BRAF in low-grade glioma, which are frequently missed by commonly used fusion detection programs. Notably, Fuzzion2 detected a subclonal BCR-ABL1 fusion expressed at 1% and 6% of the wild-type BCR and ABL1 transcription level, respectively, in a B-lineage ALL sample that also has an IGH-CRLF fusion. Processing RNA-seq data from BCR-ABL1 cell lines, K562 with p210 fusion and OP1 with p190 fusion, diluted at 1:10, 1:100, and 1:1000 showed that Fuzzion2 can detect the fusion at 1:10-1:100 dilution, achieving a sensitivity 10 times greater than that of other fusion detection programs. We also evaluated the performance of Fuzzion2 for large-scale data mining in a study to compare the prevalence of gene fusions in pediatric versus adult cancers. We assembled a set of 15,474 patterns representing 5,480 fusions identified in the Pediatric Cancer Genome Project, NCI TARGET, clinical sequencing, and the COSMIC database. Fuzzion2 was deployed to the NCI Cancer Genomics Cloud and analyzed 9,464 TCGA RNA-seq samples from adult solid and brain tumors. Processing took an average of 6 minutes at a cost of only US$0.16 per sample. Among the 105 recurrent fusions identified in pediatric cancers, only 11 were also found in adult cancers. These shared fusions can be classified into two categories: 1) gene fusions present in cancers that occur in both children and young adults, e.g., synovial sarcoma, papillary thyroid cancer, and fibrolamellar hepatocellular carcinoma; and 2) kinase fusions involving ABL1, NTRK, and FGFR. Our experience with Fuzzion2 demonstrates that it is a powerful tool for time-critical clinical application and large-scale data mining. It is publicly available at https://github.com/stjude/fuzzion2. Citation Format: Stephen V. Rice, Michael N. Edmonson, Liqing Tian, Michael Rusch, David A. Wheeler, Jennifer L. Neary, Scott Newman, Lu Wang, Patrick R. Blackburn, Michael Macias, Andrew Thrasher, Jian Wang, Mark R. Wilkinson, Xin Zhou, Jinghui Zhang. Fuzzion2: Fast, sensitive detection of known gene fusions by fuzzy pattern matching for clinical testing and large-scale data mining [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4092.

Genomic studies of pediatric cancer have primarily focused on specific tumor types or high-risk disease. Here, we used a three-platform sequencing approach, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and RNA sequencing (RNA-seq), to examine tumor and germline genomes from 309 prospectively identified children with newly diagnosed (85%) or relapsed/refractory (15%) cancers, unselected for tumor type. Eighty-six percent of patients harbored diagnostic (53%), prognostic (57%), therapeutically relevant (25%), and/or cancer-predisposing (18%) variants. Inclusion of WGS enabled detection of activating gene fusions and enhancer hijacks (36% and 8% of tumors, respectively), small intragenic deletions (15% of tumors), and mutational signatures revealing of pathogenic variant effects. Evaluation of paired tumor–normal data revealed relevance to tumor development for 55% of pathogenic germline variants. This study demonstrates the power of a three-platform approach that incorporates WGS to interrogate and interpret the full range of genomic variants across newly diagnosed as well as relapsed/refractory pediatric cancers. Significance: Pediatric cancers are driven by diverse genomic lesions, and sequencing has proven useful in evaluating high-risk and relapsed/refractory cases. We show that combined WGS, WES, and RNA-seq of tumor and paired normal tissues enables identification and characterization of genetic drivers across the full spectrum of pediatric cancers. This article is highlighted in the In This Issue feature, p. 2945

Effective data sharing is key to accelerating research that will improve the precision of diagnoses, efficacy of treatments and long-term survival of pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud-based data sharing ecosystem developed via collaboration between St. Jude Children’s Research Hospital, DNAnexus, and Microsoft, for accessing, analyzing and visualizing genomic data from >10,000 pediatric cancer patients, long-term survivors of pediatric cancer and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabyes on St. Jude Cloud include 12,104 whole genomes, 7,697 whole exomes and 2,202 transcriptomes, which are freely available to researchers worldwide. The resource is expanding rapidly with regular data uploads from St. Jude’s prospective clinical genomics programs, providing public access as soon as possible rather than holding data back until publication. Three interconnected apps within the St. Jude Cloud ecosystem—Genomics Platform, Pediatric Cancer Knowledgebase (PeCan) and Visualization Community—provide a unique experience for simultaneously performing advanced data analysis in the cloud and enhancing the pediatric cancer knowledgebase. We demonstrate the value of the St. Jude Cloud ecosystem through use cases that classify 48 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 subtypes of pediatric cancer.

Genetic alterations that activate NOTCH1 signaling and T cell transcription factors, coupled with inactivation of the INK4/ARF tumor suppressors, are hallmarks of T-lineage acute lymphoblastic leukemia (T-ALL), but detailed genome-wide sequencing of large T-ALL cohorts has not been carried out. Using integrated genomic analysis of 264 T-ALL cases, we identified 106 putative driver genes, half of which had not previously been described in childhood T-ALL (for example, CCND3, CTCF, MYB, SMARCA4, ZFP36L2 and MYCN). We describe new mechanisms of coding and noncoding alteration and identify ten recurrently altered pathways, with associations between mutated genes and pathways, and stage or subtype of T-ALL. For example, NRAS/FLT3 mutations were associated with immature T-ALL, JAK3/STAT5B mutations in HOXA1 deregulated ALL, PTPN2 mutations in TLX1 deregulated T-ALL, and PIK3R1/PTEN mutations in TAL1 deregulated ALL, which suggests that different signaling pathways have distinct roles according to maturational stage. This genomic landscape provides a logical framework for the development of faithful genetic models and new therapeutic approaches.

Variant interpretation in the era of massively parallel sequencing is challenging. Although many resources and guidelines are available to assist with this task, few integrated end-to-end tools exist. Here, we present the Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE), a web- and cloud-based platform for annotation, identification, and classification of variations in known or putative disease genes. Starting from a set of variants in variant call format (VCF), variants are annotated, ranked by putative pathogenicity, and presented for formal classification using a decision-support interface based on published guidelines from the American College of Medical Genetics and Genomics (ACMG). The system can accept files containing millions of variants and handle single-nucleotide variants (SNVs), simple insertions/deletions (indels), multiple-nucleotide variants (MNVs), and complex substitutions. PeCanPIE has been applied to classify variant pathogenicity in cancer predisposition genes in two large-scale investigations involving >4000 pediatric cancer patients and serves as a repository for the expert-reviewed results. PeCanPIE was originally developed for pediatric cancer but can be easily extended for use for nonpediatric cancers and noncancer genetic diseases. Although PeCanPIE's web-based interface was designed to be accessible to non-bioinformaticians, its back-end pipelines may also be run independently on the cloud, facilitating direct integration and broader adoption. PeCanPIE is publicly available and free for research use.

Variant interpretation in the era of next-generation sequencing (NGS) is challenging. While many resources and guidelines are available to assist with this task, few integrated end-to-end tools exist. Here we present “PeCanPIE” – the Pediatric Cancer Variant Pathogenicity Information Exchange, a web- and cloud-based platform for annotation, identification, and classification of variations in known or putative disease genes. Starting from a set of variants in Variant Call Format (VCF), variants are annotated, ranked by putative pathogenicity, and presented for formal classification using a decision-support interface based on published guidelines from the American College of Medical Genetics and Genomics (ACMG). The system can accept files containing millions of variants and handle single-nucleotide variants (SNVs), simple insertions/deletions (indels), multiple-nucleotide variants (MNVs), and complex substitutions. PeCanPIE has been applied to classify variant pathogenicity in cancer predisposition genes in two large-scale investigations involving >4,000 pediatric cancer patients, and serves as a repository for the expert-reviewed results. While PeCanPIE’s web-based interface was designed to be accessible to non-bioinformaticians, its back end pipelines may also be run independently on the cloud, facilitating direct integration and broader adoption. PeCanPIE is publicly available and free for research use.

Pharmacogenetics is frequently cited as an area for initial focus of the clinical implementation of genomics. Through the PG4KDS protocol, St. Jude Children's Research Hospital pre-emptively genotypes patients for 230 genes using the Affymetrix Drug Metabolizing Enzymes and Transporters (DMET) Plus array supplemented with a CYP2D6 copy number assay. The PG4KDS protocol provides a rational, stepwise process for implementing gene/drug pairs, organizing data, and obtaining consent from patients and families. Through August 2013, 1,559 patients have been enrolled, and four gene tests have been released into the electronic health record (EHR) for clinical implementation: TPMT, CYP2D6, SLCO1B1, and CYP2C19. These genes are coupled to 12 high-risk drugs. Of the 1,016 patients with genotype test results available, 78% of them had at least one high-risk (i.e., actionable) genotype result placed in their EHR. Each diplotype result released to the EHR is coupled with an interpretive consult that is created in a concise, standardized format. To support-gene based prescribing at the point of care, 55 interruptive clinical decision support (CDS) alerts were developed. Patients are informed of their genotyping result and its relevance to their medication use through a letter. Key elements necessary for our successful implementation have included strong institutional support, a knowledgeable clinical laboratory, a process to manage any incidental findings, a strategy to educate clinicians and patients, a process to return results, and extensive use of informatics, especially CDS. Our approach to pre-emptive clinical pharmacogenetics has proven feasible, clinically useful, and scalable.

Introduction: Although recent studies have refined the classification of B-progenitor and T-lineage acute lymphoblastic leukemia into gene-expression based subgroups, a comprehensive integration of significantly mutated genes and pathways for each subgroup is needed to understand disease etiology. Methods: We studied 2789 children, adolescents and young adults (AYA) with newly diagnosed B-ALL (n=2,322 cases) or T-ALL (n=467) treated on Children's Oncology Group (n=1,872) and St. Jude Children's Research Hospital trials (n=917). The cohort comprised childhood NCI standard-risk (41.8%; age range 1-9.99 yrs, WBC ≤ 50,000/ml), childhood NCI high-risk (44.5%; age range ≥10 to 15.99 yrs) and AYA (9.9%; age range 16-30.7 yrs). Genomic analysis was performed on tumor and matched-remission samples using whole transcriptome sequencing (RNA-seq; tumor only; n=1,922), whole exome sequencing (n=1,659), whole genome sequencing (n=757), and single nucleotide polymorphism array (n=1,909). Results: For B-ALL, 2104 cases (90.6%) were classified into 26 subgroups based on RNA-seq gene expression data and aneuploidy or other gross chromosomal abnormalities (iAMP21, Down syndrome, dicentric), deregulation of known transcription factors by rearrangement or mutation (PAX5 P80R, IKZF1 N159Y), or activation of kinase alterations (Ph+, Ph-like). For T-ALL, cases were classified into 9 previously described subtypes based on dysregulation of transcription factor genes and gene expression. In 1,659 cases subject to exome sequencing (1259 B-ALL, 405 T-ALL) we identified 18,954 nonsynonymous single nucleotide variants (SNV) and 2,329 insertion-deletion mutations (indels) in 8,985 genes. Overall, 161 potential driver genes were identified by the mutation-significance detection tool MutSigCV or by presence of pathogenic variants in known cancer genes. Integration of sequence mutations and DNA copy number alteration data in B-ALL identified 7 recurrently mutated pathways: transcriptional regulation (40.6%), cell cycle and tumor suppression (38.0%), B-cell development (34.5%), epigenetic regulation (24.7%), Ras signaling (33.0%), JAK-STAT signaling (12.0%) and protein modification (ubiquitination or SUMOylation, 5.0%). The top 10 genes altered by deletion or mutation in B-ALL were CDKN2A/B (30.1%), ETV6 (27.0%), PAX5 (24.6%), CDKN1B (20.3%), IKZF1 (17.6%), KRAS (16.5%), NRAS (14.6%), BTG1 (7.5%) histone genes on chromosome 6 (6.9%) and FLT3 (6.1%), and for T-ALL, CDKN2A/B (74.7%), NOTCH1 (68.2%), FBXW7 (21.3%), PTEN (20.5%) and PHF6 (18.2%) (Figure 1A). We identified 17 putative novel driver genes involved in ubiquitination (UBE2D3, UBE2A, UHRF1, and USP1), SUMOylation (SAE1, UBE2I), transcriptional regulation (ZMYM2, HMGB1), immune function (B2M), migration (CXCR4), epigenetic regulation (DOT1L) and mitochondrial function (LETM1). We also observed variation in the frequency of genes and pathways altered across B-ALL subtypes (Figure 1B). Interestingly, alteration of SAE1 and UBA2, novel genes that form a heterodimeric complex important for SUMOylation, and UHRF1 were enriched in ETV6-RUNX1 cases. Deletions of LETM1, ZMYM2 and CHD4 were associated with near haploid and low hypodiploid cases. Deletion of histone genes on chromosome 6 and alterations of HDAC7 were enriched in Ph+ and Ph-like ALL. Mutations in the RNA-binding protein ZFP36L2 were observed in PAX5alt, DUX4 and MEF2D subgroups. Genomic subtypes were prognostic. ETV6-RUNX1, hyperdiploid, DUX4 and ZNF384 ALL were associated with good outcome (5-yr EFS 91.1%, 87.2%, 91.9% and 85.7%, respectively), ETV6-RUNX1-like, iAMP21, low hyperdiploid, PAX5 P80R and PAX5alt were associated with intermediate outcome (5-yr EFS 68.6%, 72.2%, 70.8%, 77.0% and 70.9%, respectively), whilst KMT2A, MEF2D, Ph-like CRLF2 and Ph-like other conferred a poor prognosis (55.5%, 67.1%, 51.5% and 62.1%, respectively). TCF3-HLF and near haploid had the worst outcome with 5-yr EFS rates of 27.3% and 47.2%, respectively. Conclusions: These findings provide a comprehensive landscape of genomic alterations in childhood ALL. The associations of mutations with ALL subtypes highlights the need for specific patterns of cooperating mutations in the development of leukemia, which may help identify vulnerabilities for therapy intervention. Disclosures Gastier-Foster: Bristol Myers Squibb (BMS): Other: Commercial Research; Incyte Corporation: Other: Commercial Research. Willman:to come: Patents & Royalties; to come: Membership on an entity's Board of Directors or advisory committees; to come: Research Funding. Raetz:Pfizer: Research Funding. Borowitz:Beckman Coulter: Honoraria. Zweidler-McKay:ImmunoGen: Employment. Angiolillo:Servier Pharmaceuticals: Consultancy. Relling:Servier Pharmaceuticals: Research Funding. Hunger:Jazz: Honoraria; Amgen: Consultancy, Equity Ownership; Bristol Myers Squibb: Consultancy; Novartis: Consultancy. Loh:Medisix Therapeutics, Inc.: Membership on an entity's Board of Directors or advisory committees. Mullighan:Amgen: Honoraria, Other: speaker, sponsored travel; Loxo Oncology: Research Funding; AbbVie: Research Funding; Pfizer: Honoraria, Other: speaker, sponsored travel, Research Funding; Illumina: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: sponsored travel.

Advances in pharmacogenetic testing will expand the number of clinically important pharmacogenetic variants. Communication and interpretation of these test results are critical steps in implementation of pharmacogenetics into the clinic. Computational tools that integrate directly into the electronic medical record (EMR) are needed to translate the growing number of genetic variants into interpretive consultations to facilitate gene-based drug prescribing. Herein, we describe processes for incorporating pharmacogenetic tests and interpretations into the EMR for clinical practice. Clinical Pharmacology & Therapeutics (2012); 92 5, 563–566. doi:10.1038/clpt.2012.140

BackgroundThe prevalence and spectrum of predisposing mutations among children and adolescents with cancer are largely unknown. Knowledge of such mutations may improve the understanding of tumorigenesis, direct patient care, and enable genetic counseling of patients and families. MethodsIn 1120 patients younger than 20 years of age, we sequenced the whole genomes (in 595 patients), whole exomes (in 456), or both (in 69). We analyzed the DNA sequences of 565 genes, including 60 that have been associated with autosomal dominant cancer-predisposition syndromes, for the presence of germline mutations. The pathogenicity of the mutations was determined by a panel of medical experts with the use of cancer-specific and locus-specific genetic databases, the medical literature, computational predictions, and second hits identified in the tumor genome. The same approach was used to analyze data from 966 persons who did not have known cancer in the 1000 Genomes Project, and a similar approach was used to analyze data...

To construct a population pharmacokinetic model for temozolomide (TMZ), a novel imidazo-tetrazine methylating agent and its metabolites MTIC and AIC in infants and children with primary central nervous system tumors.We evaluated the pharmacokinetics of TMZ and MTIC in 39 children (20 boys and 19 girls) with 132 pharmacokinetic studies (109 in the training set and 23 in the validation set). The median age was 7.1 years (range 0.7 to 21.9 years). Children received oral TMZ dosages ranging from 145 to 200 mg/m(2) per day for 5 days in each course of therapy. Serial plasma samples were collected after the first and fifth doses of the first and third courses. Approximately eight plasma samples were collected up to 8 h after each dose, and assayed for TMZ, MTIC, and AIC by HPLC with UV detection. A one-compartment model was fitted to the TMZ and metabolite plasma concentrations using maximum likelihood estimation. Covariates, including demographics and biochemical data were tested for their effects on TMZ clearance (CL/F) and MTIC AUC utilizing a two-stage approach via linear mixed-effects modeling.The population mean (inter- and intrapatient variability expressed as %CV) for the pharmacokinetic parameters (based on the training set) were as follows: TMZ CL/F 5.4 l/h (53.4, 17.5), Vc/F 14.0 l (48.5, 39.2), C(max) 9.1 mg/l (20.8, 29.1), and MTIC AUC 1.0 microg/ml.h (13.9, 30.0). Covariate analysis showed that increasing age and body surface area (BSA) were associated with a significant increases in TMZ CL, Vc, and C(max) ( P0.05), and that increasing age was associated with significant decreases in TMZ and MTIC AUC. Indicators of liver and renal function were not significantly associated with TMZ pharmacokinetics or MTIC AUC. The final model with the significant covariates was validated using the remaining 23 pharmacokinetic studies.This study extends previous work done in adults, and identified BSA and age as covariates that account for variability in TMZ disposition in infants and children with primary CNS malignancies.

Glucocorticoids are universally used in the treatment of acute lymphoblastic leukemia (ALL), and resistance to glucocorticoids in leukemia cells confers poor prognosis. To elucidate mechanisms of glucocorticoid resistance, we determined the prednisolone sensitivity of primary leukemia cells from 444 patients newly diagnosed with ALL and found significantly higher expression of CASP1 (encoding caspase 1) and its activator NLRP3 in glucocorticoid-resistant leukemia cells, resulting from significantly lower somatic methylation of the CASP1 and NLRP3 promoters. Overexpression of CASP1 resulted in cleavage of the glucocorticoid receptor, diminished the glucocorticoid-induced transcriptional response and increased glucocorticoid resistance. Knockdown or inhibition of CASP1 significantly increased glucocorticoid receptor levels and mitigated glucocorticoid resistance in CASP1-overexpressing ALL. Our findings establish a new mechanism by which the NLRP3-CASP1 inflammasome modulates cellular levels of the glucocorticoid receptor and diminishes cell sensitivity to glucocorticoids. The broad impact on the glucocorticoid transcriptional response suggests that this mechanism could also modify glucocorticoid effects in other diseases.

Etoposide is used to treat childhood malignancies, and its plasma pharmacokinetics have been related to pharmacodynamic endpoints. Limiting the number of samples should facilitate the assessment of etoposide pharmacokinetics in children. We compared limited sampling strategies using multiple linear regression of plasma concentrations and clearance with Bayesian methods of estimating clearance using compartmental pharmacokinetic models. Optimal sampling times were estimated in the multiple linear regression method by determining the combination of two samples which maximized the correlation coefficient, and in the Bayesian estimation approach by minimizing the variance in estimates of clearance. Clearance estimates were compared to the actual clearances from Monte Carlo-simulated data and predicted clearances estimated using all available plasma concentrations in clinical data from children with acute lymphoblastic leukemia. Multiple linear regression poorly predicted clearance (mean bias 8.3%, precision 17.5%), but improved if plasma concentrations were logarithmically transformed (mean bias 1.4%, precision 12.5%). Bayesian estimation methods with optimal samples gave the best overall prediction (mean bias 2.5%, precision 6.8%) and also performed better than regression methods for abnormally high or low clearances. We conclude that Bayesian estimation with limited sampling gives the best estimates of etoposide clearance.

To discover common and sub-type specific somatic alterations affecting key biological processes in pediatric cancers, we analyzed point mutations, copy number alterations, gene fusions and structural alterations detected from paired tumor-normal whole genome sequencing (n=655), whole exome sequencing (n=1,108), and RNA-seq data (n=913) of 1,705 leukemia and solid tumors. Our cohort consists of 693 B-lineage Acute Lymphoblastic Leukemia (B-ALL), 264 T-ALL, 211 Acute Myeloid Leukemia (AML), 318 Neuroblastoma (NBL), 128 Wilms Tumor (WT), and 91 Osteosarcoma (OS) with a median mutation rate of 0.28-0.58 per Mb. We identified 130 potential driver genes based on significance of variant recurrence and pathogenicity within each cancer type and across all cancer types. Seventy-two (55%) driver genes were significant in one cancer type, thirty eight were significant in > 1 leukemia subtype, thirteen (NRAS, WT1, MYCN, PTEN, TP53, KRAS, RB1, ATRX, PTPN11, MLLT1, BCOR, SETD2, NF1) were significant in both leukemia and solid tumor while the remaining seven (MGA, SF3B1, ASXL1, BCORL1, STAG2, ACTB, NIPBL) were significant only in pan-cancer analysis. The number of mutated driver genes per sample ranged from 0.8 in WT to 5.8 in T-ALL, lower when considering only point mutations (from 0.3 in NBL to 3.1 in T-ALL). The most frequently mutated biological processes affecting both leukemia and solid tumor were transcription factors (56% of samples), cell cycle (41%), epigenetic regulators (36%), Ras signaling (21%), PI-3K (11%), and the MYC complex (7%). By contrast, the JAK signaling pathway was mutated only in leukemia (16%) while mutations in the NOTCH signaling pathway were exclusive to T-ALL (77%). Aberrant transcription may also affect the normal function of a driver gene. For example, the RAS signaling pathway was mutated in B-ALL (35%), T-ALL (15%), AML (37%) and NBL (4.3%). Aside from the known KRAS 4a isoform found in all cancer types, we discovered two novel KRAS isoforms present in 71.1% of B-ALL, 67.9% of T-ALL, 71.3% of AML and 3.0% of NBL but not in WT or OS. Allele-specific expression (ASE) was detected in 205 (6.8%) of 3,016 expressed somatic mutations, and 97% (32 out of 33) of truncation mutations on autosomes exhibit reduced expression of the mutant allele likely due to nonsense mediated decay. Two ASE mutations, WT1 D447N in a cytogenetically normal AML and JAK2 D873N in a B-ALL, were selected for single-cell sequencing and successfully validated. Only 44% of our driver genes match those identified in adult cancer. This, coupled with our finding that point mutations only accounted for 48% of the driver alterations, may provide new insight into the design of precision treatment for pediatric cancer. Our presented data will be made public at NCI’s Genome Data Commons (gdc.cancer.gov) and can be explored on our ProteinPaint data portal (pecan.stjude.org). Citation Format: Xiaotu Ma, Yu Liu, Yanling Liu, Michael Edmonson, Charles Gawad, Xin Zhou, Yongjin Li, Michael Rusch, John Easton, Mark Wilkinson, Leandro C. Hermida, Sean Davis, Malcolm Smith, Jaime Guidry Auvil, Paul Meltzer, Ching C. Lau, Elizabeth Perlman, John M. Maris, Soheil Meshinchi, Stephen P. Hunger, Daniela S. Gerhard, Jinghui Zhang. Comparison of somatic alterations in the genome and transcriptome of 1,705 pediatric leukemia and solid tumors: a report from the Children’s Oncology Group (COG) - NCI TARGET Project [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3004. doi:10.1158/1538-7445.AM2017-3004

Asparaginase is an essential component of pediatric acute lymphoblastic leukemia (ALL) therapy. However, asparaginase-induced hypersensitivity reactions can compromise its efficacy either by directly influencing the pharmacokinetics of asparaginase or by leading to a discontinuation of asparaginase treatment. Here, we report successful challenges using native Escherichia coli asparaginase after previous hypersensitivity reactions to both PEGylated E. coli asparaginase and Erwinia asparaginase. The two patients included in this case report were diagnosed with B-precursor ALL at St. Jude Children’s Research Hospital and were treated with a common regimen. Both patients developed hypersensitivity reactions to PEGylated E. coli asparaginase and Erwinia asparaginase early in treatment, and they were challenged with native E. coli asparaginase. Serum samples were collected for estimating the pharmacokinetic parameters of each patient during native E. coli asparaginase therapy. Challenges with native E. coli asparaginase were successful, and asparaginase serum concentrations above therapeutic levels were attained in both patients. These two cases suggest that some patients can be given native E. coli asparaginase after hypersensitivity reactions to PEGylated asparaginase and achieve therapeutic concentrations of the drug in serum.

Background Active clinical decision support (CDS) delivered through an electronic health record (EHR) facilitates gene-based drug prescribing and other applications of genomics to patient care. Objective We describe the development, implementation, and evaluation of active CDS for multiple pharmacogenetic test results reported preemptively. Materials and methods Clinical pharmacogenetic test results accompanied by clinical interpretations are placed into the patient's EHR, typically before a relevant drug is prescribed. Problem list entries created for high-risk phenotypes provide an unambiguous trigger for delivery of post-test alerts to clinicians when high-risk drugs are prescribed. In addition, pre-test alerts are issued if a very-high risk medication is prescribed (eg, a thiopurine), prior to the appropriate pharmacogenetic test result being entered into the EHR. Our CDS can be readily modified to incorporate new genes or high-risk drugs as they emerge. Results Through November 2012, 35 customized pharmacogenetic rules have been implemented, including rules for TPMT with azathioprine, thioguanine, and mercaptopurine, and for CYP2D6 with codeine, tramadol, amitriptyline, fluoxetine, and paroxetine. Between May 2011 and November 2012, the pre-test alerts were electronically issued 1106 times (76 for thiopurines and 1030 for drugs metabolized by CYP2D6), and the post-test alerts were issued 1552 times (1521 for TPMT and 31 for CYP2D6). Analysis of alert outcomes revealed that the interruptive CDS appropriately guided prescribing in 95% of patients for whom they were issued. Conclusions Our experience illustrates the feasibility of developing computational systems that provide clinicians with actionable alerts for gene-based drug prescribing at the point of care.

Current cancer genome data portals have focused primarily on presenting data generated from adult cancer studies. These portals typically lack features for exploring pathogenic germline mutations, somatic gene fusions, and gene expression profiling, all of which are important biomarkers for risk stratification of pediatric cancer. We have developed ProteinPaint (https://pecan.stjude.org/proteinpaint/), a web service hosting 30,000+ validated somatic SNV/indels and fusion transcripts detected in 1,654 pediatric tumor samples from 17 subtypes, 252 pathogenic or loss-of-function germline lesions detected in >1000 pediatric cancer patients of 21 subtypes, and gene expression profiles derived from RNA-Seq of 928 pediatric tumors. Cancer genomic alterations are shown on novel “disc-on-stem” skewer graphs which were designed to depict the diverse prevalence, complex allelic alteration, and temporal origin of sequence mutations and gene fusions. Adult somatic cancer mutation data from the COSMIC database can be displayed in parallel with pediatric cancer data sets for cross-study comparison. We will demonstrate examples of how ProteinPaint's integrative view of genomic alteration, gene expression and pediatric-adult data comparison has facilitated the evaluation of somatic and germline mutation pathogenicity in a clinical setting. Custom data including sequence mutations in the MAF format used by the Cancer Genome Atlas (TCGA) project, copy number alterations, and structural variations can all be imported and visualized alongside published pediatric and adult cancer data sets. Furthermore, ProteinPaint supports curation and annotation of fusion transcripts predicted from RNASeq data and analysis of tumor clonal evolution with a 2-D plot of mutation frequency of paired diagnosis and relapse samples. ProteinPaint delivers a premium user experience with animation and interactive features for visualizing large cancer mutation datasets, and can serve as a workbench to import, explore and interpret user data. Its framework continues to expand as its intuitive visualization has enabled non-bioinformatics scientists and clinicians to access and manipulate genomic data for discovery and clinical reporting. Citation Format: Xin Zhou, Michael N. Edmonson, Mark R. Wilkinson, Aman Patel, Gang Wu, Yu Liu, Yongjin Li, Zhaojie Zhang, Michael Rusch, Matthew Parker, Jared Becksfort, James R. Downing, Jinghui Zhang. Exploring genomic alterations in pediatric cancer using ProteinPaint. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2436.

Comprehensive studies examining the genomic landscape of T-lineage ALL are lacking, but are important to identify all oncogenic drivers. Here we report sequencing of 264 T-ALL consecutive cases treated on the Children's Oncology Group AALL0434 clinical trial. Whole exome sequencing, copy number analysis using exome and single nucleotide polymorphism array analysis of tumor and remission DNA, and RNA-sequencing of tumor RNA were performed. Cases with immunophenotypic data (N=189) included 19 early T-cell precursor (ETP) cases, 24 near-ETP (with normal CD5 expression) and 146 Non-ETP cases. Median exomic coverage was 89% (72%-96%) of exons with at least 20-fold coverage. We identified 4657 non-synonymous clonal and subclonal somatic mutations (3926 single nucleotide variants (SNV) and 731 insertion-deletion mutations; indels) in 3030 genes, with a mean of 17.6 per case (range 1-50). 176 potential driver genes were identified statistical analysis or by known pathogenic role in cancer. These included NOTCH1 (n=194, 73%), FBXW7 (n=64, 24%), PHF6 (n=50, 19%), PTEN (n=37, 14%), USP7 (n=32, 12%), DNM2 (n=29, 11%) and BCL11B (n=27, 10%). New mutations in T-ALL included CCND3 (n=15, 6%), MYB (n=13, 5%), CTCF (n=13, 5%), MED12 (n=7, 3%), USP9X (n=7, 3%), SMARCA4 (n=7, 3%) and CREBBP (n=6, 2%). In addition to MYB amplification, we identified missense mutations and in-frame protein insertions at the N-terminus of MYB, with a hotspot at codon 14 in a region of six acidic residues in an otherwise hydrophilic N-terminal tail. These mutations resulted in a disordered region that is predicted to affect nuclear localization. The MYB mutations detected were enriched in non-ETP cases (n=13; 8 non-ETP, 1 near-ETP, 4 unknown). Other genes enriched in non-ETP cases included RPL10, CNOT3, MYCN and DDX3X. MED12 mutations were more common in ETP ALL. Sub-clonal mutations (mutant allele fraction of less than 30%) were identified in 111 of 176 driver genes in 198 (75%) cases including NOTCH1 (n=94), FBXW7 (n=29) and PTEN (n=17) indicating that sub-clonal evolution is a hallmark of T-ALL. In addition, multiple mutations in individual genes were commonly observed in single cases. For example, up to 3 different somatic NOTCH1 mutations were detected in each of 9 patients, with 2 different NOTCH1 mutations in 49 cases. Integration of sequence mutations with copy number aberration data showed the following pathways to be most frequently mutated: cell cycle/tumor suppression (N=225; CDKN2A/B (n=206), CDKN1B (n=35), RB1 (n=28)); NOTCH1/FBXW7 (n=212), PI3K-AKT (n=130), JAK-STAT (n=99) and Ras (n=51). Mutations in the PI3K-AKT, JAK-STAT and Ras signaling pathways were mutually exclusive. We identified a high frequency of mutations in transcriptional regulators in 222 cases, including 108 cases with mutations in a core regulatory complex comprising TAL1 (n=51), MYB (n=45) RUNX1 (n=18) and GATA3 (n=13). In 90 of these 108 cases (83%), only a single mutation was present in any of the four genes, consistent with a central role of this complex in leukemogenesis. Epigenetic alterations were identified in 178 cases, including PHF6 (n=63), SMARCA4 (n=23), KDM6A (n=22) and EZH2 (n=18), and new deletions and mutations in KMT2A (MLL; n=11). Interim analysis of transcriptome sequencing data of 126 T-ALL cases detected fusions in 61 (48%) samples, which could be separated into two categories. One weres in-frame fusions resulting in a chimeric protein. The most frequent included MLLT10 fusions (PICALM-MLLT10 (n=3), DDX3X-MLLT10 (n=2) and NAP1L1-MLLT10 (n=1)), KMT2A fusions (KMT2A-MLLT1 (n=4), MLLT6-KMT2A (n=1) and MKT2A-MLLT4 (n=1)), as well as internal tandem duplication mutations involving FLT3 (n=6; 3 ETP, 1 near-ETP, 1 non-ETP, 1 unknown) and NOTCH1 (n=2). We also identified novel fusions including ETV6-CTNNB1 and STMN1-SPI1 (n=1 each). The other category contains rearrangement-driven aberrant expression, including rearrangements in TLX1 (n=11), TLX3 (n=4), TAL1 (n=2), and TAL2 (n=3). Moreover, we found a novel TAL2 transcript in all the 3 cases with TAL2 rearrangement, hijacking a new exon 6kb upstream of the canonical TAL2 transcription start site and extending approximate 3.5kb downstream. These findings provide the first comprehensive landscape of genomic alterations in T-ALL and have provided new insights into the genes and pathways mutated in this disease, their interaction, and the nature of clonal heterogeneity in T-ALL. Disclosures Hunger: Spectrum Pharmaceuticals: Consultancy; Jazz Pharmaceuticals: Consultancy; Sigma Tau: Consultancy; Merck: Equity Ownership. Mullighan:Cancer Science Institute: Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Speakers Bureau; Incyte: Consultancy, Honoraria; Loxo Oncology: Research Funding.

The Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for HLA-B Genotype and Abacavir Dosing were originally published in April 2012. We reviewed recent literature and concluded that none of the evidence would change the therapeutic recommendations in the original guideline; therefore, the original publication remains clinically current. However, we have updated the Supplementary Material online and included additional resources for applying CPIC guidelines to the electronic health record. Up-to-date information can be found at PharmGKB (http://www.pharmgkb.org).

Common, functional, germline genetic polymorphisms have been associated with clinical cancer outcomes. Little attention has been paid to the potential phenotypic consequences of germline genetic variation on downstream genes. We determined the germline status of 16 well-characterized functional polymorphisms in 126 children with newly diagnosed acute lymphoblastic leukemia (ALL). We assessed whether global gene expression profiles of diagnostic ALL blasts from the same patients differed by these germline polymorphic genotypes. Gene expression values were adjusted for ALL-subtype-specific patterns. Of the 16 loci, only the UGT1A1 promoter repeat polymorphism [A(TA)nTAA] (UGT1A1*28) and GSTM1 deletion were significant predictors of global gene expression in a supervised approach, which divided patients based on their germline genotypes [UGT1A1: 124 probe sets, false discovery rate (FDR)=13%, P< or =0.0031; GSTM1: 112 probe sets, FDR=42.5%, P< or =0.0084]. Genes whose expression distinguished the UGT1A1 (TA) 7/7 genotype from the other UGT1A1 genotypes included HDAC1, RELA and SLC2A1; those that distinguished the GSTM1 null genotype from non-null genotype included NBS1 and PRKR. In an unsupervised approach, the gene expression profiles using the entire array delineated two major clusters of patients. The only germline genotype frequency that differed between the two clusters was UGT1A1 (P=0.002; Fisher's exact test). Although their expression is limited to specific tissues, both GSTM1 and UGT1A1 are involved in the conjugation (and thus transport, excretion and lipophilicity) of a broad range of endobiotics and xenobiotics, which could plausibly have consequences for gene expression in different tissues.

The possible impact of common, functional, germline polymorphisms on clinical outcome in patients with leukemia is under active investigation. However, little attention has been paid to the possible consequences of germline genetic variation on genome-wide phenotypic variation in human tissues. We determined the germline status of 16 well-characterized functional genetic polymorphisms (using normal leukocyte cell DNA) in unrelated children with newly diagnosed acute lymphoblastic leukemia (ALL). We assessed whether global gene expression (using oligonucleotide arrays that interrogated approximately 10,000 genes) of diagnostic ALL blasts from the same patients differed by germline polymorphic genotypes. Gene expression values were adjusted for ALL-subtype-specific patterns. In a supervised analysis that divided patients based on their germline genotypes, we identified two polymorphisms that were significant predictors of global gene expression: the UGT1A1 promoter repeat polymorphism [A(TA)nTAA] (181 probe sets, p=0.01, false discovery rate [FDR] = 35.6%) and the GSTM1 deletion (112 probe sets, p=0.008, FDR = 42.5%). Genes whose expression differed significantly in ALL cells from patients with and without the UGT1A1 (TA) 7/7 genotype included ATM and ABL1; genes whose expression differed significantly between patients with the GSTM1 null genotype and the non-null genotype included PCNA, PRIM1, CDC6 and SKP1A. In an unsupervised analysis, patients were separated into two clusters; only the UGT1A1 promoter repeat polymorphism differed in frequency between the two clusters (p=0.001, Fisher’s exact test). These findings illustrate that polymorphisms in genes whose direct products are restricted to other tissues (e.g. UGT1A1 is primarily expressed hepatically) may affect gene expression elsewhere (e.g. ALL blasts). Because UGT1A1 is involved in the conjugation (and thus the transport and excretion) of numerous endogenous steroids and hormones, it is plausible that genotypic variation in its activity could have consequences for gene expression in distant tissues, such as ALL blasts.