Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours.

Analysis of molecular aberrations across multiple cancer types, known as pan-cancer analysis, identifies commonalities and differences in key biological processes that are dysregulated in cancer cells from diverse lineages. Pan-cancer analyses have been performed for adult but not paediatric cancers, which commonly occur in developing mesodermic rather than adult epithelial tissues. Here we present a pan-cancer study of somatic alterations, including single nucleotide variants, small insertions or deletions, structural variations, copy number alterations, gene fusions and internal tandem duplications in 1,699 paediatric leukaemias and solid tumours across six histotypes, with whole-genome, whole-exome and transcriptome sequencing data processed under a uniform analytical framework. We report 142 driver genes in paediatric cancers, of which only 45% match those found in adult pan-cancer studies; copy number alterations and structural variants constituted the majority (62%) of events. Eleven genome-wide mutational signatures were identified, including one attributed to ultraviolet-light exposure in eight aneuploid leukaemias. Transcription of the mutant allele was detectable for 34% of protein-coding mutations, and 20% exhibited allele-specific expression. These data provide a comprehensive genomic architecture for paediatric cancers and emphasize the need for paediatric cancer-specific development of precision therapies.

Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE): a cloud-based platform for curating and classifying germline variants.

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.

Germline Mutations in Predisposition Genes in Pediatric Cancer.

BACKGROUND: The 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. METHODS: In 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 from an autism study (from 515 persons with autism and 208 persons without autism). RESULTS: Mutations that were deemed to be pathogenic or probably pathogenic were identified in 95 patients with cancer (8.5%), as compared with 1.1% of the persons in the 1000 Genomes Project and 0.6% of the participants in the autism study. The most commonly mutated genes in the affected patients were TP53 (in 50 patients), APC (in 6), BRCA2 (in 6), NF1 (in 4), PMS2 (in 4), RB1 (in 3), and RUNX1 (in 3). A total of 18 additional patients had protein-truncating mutations in tumor-suppressor genes. Of the 58 patients with a predisposing mutation and available information on family history, 23 (40%) had a family history of cancer. CONCLUSIONS: Germline mutations in cancer-predisposing genes were identified in 8.5% of the children and adolescents with cancer. Family history did not predict the presence of an underlying predisposition syndrome in most patients. (Funded by the American Lebanese Syrian Associated Charities and the National Cancer Institute.).

MicroRNAs Form Triplexes with Double Stranded DNA at Sequence-Specific Binding Sites; a Eukaryotic Mechanism via which microRNAs Could Directly Alter Gene Expression.

MicroRNAs are important regulators of gene expression, acting primarily by binding to sequence-specific locations on already transcribed messenger RNAs (mRNA) and typically down-regulating their stability or translation. Recent studies indicate that microRNAs may also play a role in up-regulating mRNA transcription levels, although a definitive mechanism has not been established. Double-helical DNA is capable of forming triple-helical structures through Hoogsteen and reverse Hoogsteen interactions in the major groove of the duplex, and we show physical evidence (i.e., NMR, FRET, SPR) that purine or pyrimidine-rich microRNAs of appropriate length and sequence form triple-helical structures with purine-rich sequences of duplex DNA, and identify microRNA sequences that favor triplex formation. We developed an algorithm (Trident) to search genome-wide for potential triplex-forming sites and show that several mammalian and non-mammalian genomes are enriched for strong microRNA triplex binding sites. We show that those genes containing sequences favoring microRNA triplex formation are markedly enriched (3.3 fold, p<2.2 × 10(-16)) for genes whose expression is positively correlated with expression of microRNAs targeting triplex binding sequences. This work has thus revealed a new mechanism by which microRNAs could interact with gene promoter regions to modify gene transcription.

PG4KDS: a model for the clinical implementation of pre-emptive pharmacogenetics.

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.

Genomic and global gene expression profiling in pediatric and young adult acute leukemia with PICALM::MLLT10 Fusion.

PICALM: MLLT10 fusion is a rare but recurrent genetic driver in acute leukemias. To better understand the genomic landscape of PICALM::MLLT10 (PM) positive acute leukemia, we performed genomic profiling and gene expression profiling in twenty PM-positive patients, including AML (n = 10), T-ALL/LLy (n = 8), Mixed-phenotype acute leukemia (MPAL), T/B (n = 1) and acute undifferentiated leukemia (AUL) (n = 1). Besides confirming the known activation of HOXA, differential gene expression analysis compared to hematopoietic stem cells demonstrated the enrichment of genes associated with cell proliferation-related pathways and relatively high expression of XPO1 in PM-AML and PM-T-ALL/LLy. Our study also suggested PHF6 disruption as a key cooperating event in PICALM::MLLT10-positive leukemias. In addition, we demonstrated differences in gene expression profiles as well as remarkably different spectra of co-occurring mutations between PM-AML and PM-T-ALL/LLy. Alterations affecting TP53 and NF1, hallmarks of PM-AML, are strongly associated with disease progression and relapse, whereas EZH2 alterations are highly enriched in PM-T-ALL/LLy. This comprehensive genomic and transcriptomic profiling provides insights into the pathogenesis and development of PICALM::MLLT10 positive acute leukemia.

The genomic landscape of pediatric acute lymphoblastic leukemia.

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.

St. Jude Cloud: A Pediatric Cancer Genomic Data-Sharing Ecosystem.

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.

Genomes for Kids: The Scope of Pathogenic Mutations in Pediatric Cancer Revealed by Comprehensive DNA and RNA Sequencing.

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.

The genomic landscape of pediatric and young adult T-lineage acute lymphoblastic leukemia.

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.

NALP3 inflammasome upregulation and CASP1 cleavage of the glucocorticoid receptor cause glucocorticoid resistance in leukemia cells.

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.

Development and use of active clinical decision support for preemptive pharmacogenomics.

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.

Lymphoid gene expression as a predictor of risk of secondary brain tumors.

Gene expression profiles are tissue-specific but may also reflect germ-line-driven expression patterns across tissue types. Previously, using a targeted pharmacologic approach, we identified germ-line polymorphisms in a single gene (thiopurine methyltransferase) associated with the risk of irradiation- and chemotherapy-induced secondary brain tumors in children with acute lymphoblastic leukemia (ALL). To identify additional candidate genetic risk factors, in identically treated patients, we compared the gene expression profiles of diagnostic ALL blasts of those who did develop irradiation-associated brain tumors (n = 9) with the profiles from those who did not (n = 33). Weighted rank regression was used to identify 33 probe sets associated with the time-dependent development of brain tumors; k-means clustering (k = 2) identified 2 groups that differed significantly in cumulative incidence of brain tumors (P = 0.012). Permutation analysis was used to estimate the probability (P = 0.18) of obtaining 2 such clusters by chance. Linear discriminant analysis (time-independent categorization of outcome) was used to identify 70 probe sets whose expression differentiated between the 2 groups of patients. Permutation analyses (n = 1,000) was used to estimate the probability of selecting these probe sets by chance (P = 0.055). Five probe sets were in common between the time-independent and time-dependent methods. The distinguishing genes are involved in neural growth (FGFR1) and in nuclear trafficking (HNRPL, KPNB1). These data suggest that gene expression profiling from accessible tissues may identify targets involved in therapy-related malignancies in unrelated tissues.

Global gene expression as a function of germline genetic variation.

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.