Virginie Saillour

Whole-Exome Sequencing Reveals a Rapid Change in the Frequency of Rare Functional Variants in a Founding Population of Humans

Whole-exome or gene targeted resequencing in hundreds to thousands of individuals has shown that the majority of genetic variants are at low frequency in human populations. Rare variants are enriched for functional mutations and are expected to explain an important fraction of the genetic etiology of human disease, therefore having a potential medical interest. In this work, we analyze the whole-exome sequences of French-Canadian individuals, a founder population with a unique demographic history that includes an original population bottleneck less than 20 generations ago, followed by a demographic explosion, and the whole exomes of French individuals sampled from France. We show that in less than 20 generations of genetic isolation from the French population, the genetic pool of French-Canadians shows reduced levels of diversity, higher homozygosity, and an excess of rare variants with low variant sharing with Europeans. Furthermore, the French-Canadian population contains a larger proportion of putatively damaging functional variants, which could partially explain the increased incidence of genetic disease in the province. Our results highlight the impact of population demography on genetic fitness and the contribution of rare variants to the human genetic variation landscape, emphasizing the need for deep cataloguing of genetic variants by resequencing worldwide human populations in order to truly assess disease risk.

Integration of High-Resolution Methylome and Transcriptome Analyses to Dissect Epigenomic Changes in Childhood Acute Lymphoblastic Leukemia

B-cell precursor acute lymphoblastic leukemia (pre-B ALL) is the most common pediatric cancer. Although the genetic determinants underlying disease onset remain unclear, epigenetic modifications including DNA methylation are suggested to contribute significantly to leukemogenesis. Using the Illumina 450K array, we assessed DNA methylation in matched tumor-normal samples of 46 childhood patients with pre-B ALL, extending single CpG-site resolution analysis of the pre-B ALL methylome beyond CpG-islands (CGI). Unsupervised hierarchical clustering of CpG-site neighborhood, gene, or microRNA (miRNA) gene-associated methylation levels separated the tumor cohort according to major pre-B ALL subtypes, and methylation in CGIs, CGI shores, and in regions around the transcription start site was found to significantly correlate with transcript expression. Focusing on samples carrying the t(12;21) ETV6-RUNX1 fusion, we identified 119 subtype-specific high-confidence marker CpG-loci. Pathway analyses linked the CpG-loci-associated genes with hematopoiesis and cancer. Further integration with whole-transcriptome data showed the effects of methylation on expression of 17 potential drivers of leukemogenesis. Independent validation of array methylation and sequencing-derived transcript expression with Sequenom Epityper technology and real-time quantitative reverse transcriptase PCR, respectively, indicates more than 80% empirical accuracy of our genome-wide findings. In summary, genome-wide DNA methylation profiling enabled us to separate pre-B ALL according to major subtypes, to map epigenetic biomarkers specific for the t(12;21) subtype, and through a combined methylome and transcriptome approach to identify downstream effects on candidate drivers of leukemogenesis.

Rare allelic forms of PRDM9 associated with childhood leukemogenesis

One of the most rapidly evolving genes in humans, PRDM9, is a key determinant of the distribution of meiotic recombination events. Mutations in this meiotic-specific gene have previously been associated with male infertility in humans and recent studies suggest that PRDM9 may be involved in pathological genomic rearrangements. In studying genomes from families with children affected by B-cell precursor acute lymphoblastic leukemia (B-ALL), we characterized meiotic recombination patterns within a family with two siblings having hyperdiploid childhood B-ALL and observed unusual localization of maternal recombination events. The mother of the family carries a rare PRDM9 allele, potentially explaining the unusual patterns found. From exomes sequenced in 44 additional parents of children affected with B-ALL, we discovered a substantial and significant excess of rare allelic forms of PRDM9. The rare PRDM9 alleles are transmitted to the affected children in half the cases; nonetheless there remains a significant excess of rare alleles among patients relative to controls. We successfully replicated this latter observation in an independent cohort of 50 children with B-ALL, where we found an excess of rare PRDM9 alleles in aneuploid and infant B-ALL patients. PRDM9 variability in humans is thought to influence genomic instability, and these data support a potential role for PRDM9 variation in risk of acquiring aneuploidies or genomic rearrangements associated with childhood leukemogenesis.

SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing

BackgroundNext-generation sequencing (NGS) allows unbiased, in-depth interrogation of cancer genomes. Many somatic variant callers have been developed yet accurate ascertainment of somatic variants remains a considerable challenge as evidenced by the varying mutation call rates and low concordance among callers. Statistical model-based algorithms that are currently available perform well under ideal scenarios, such as high sequencing depth, homogeneous tumor samples, high somatic variant allele frequency (VAF), but show limited performance with sub-optimal data such as low-pass whole-exome/genome sequencing data. While the goal of any cancer sequencing project is to identify a relevant, and limited, set of somatic variants for further sequence/functional validation, the inherently complex nature of cancer genomes combined with technical issues directly related to sequencing and alignment can affect either the specificity and/or sensitivity of most callers.ResultsFor these reasons, we developed SNooPer, a versatile machine learning approach that uses Random Forest classification models to accurately call somatic variants in low-depth sequencing data. SNooPer uses a subset of variant positions from the sequencing output for which the class, true variation or sequencing error, is known to train the data-specific model. Here, using a real dataset of 40 childhood acute lymphoblastic leukemia patients, we show how the SNooPer algorithm is not affected by low coverage or low VAFs, and can be used to reduce overall sequencing costs while maintaining high specificity and sensitivity to somatic variant calling. When compared to three benchmarked somatic callers, SNooPer demonstrated the best overall performance.ConclusionsWhile the goal of any cancer sequencing project is to identify a relevant, and limited, set of somatic variants for further sequence/functional validation, the inherently complex nature of cancer genomes combined with technical issues directly related to sequencing and alignment can affect either the specificity and/or sensitivity of most callers. The flexibility of SNooPer’s random forest protects against technical bias and systematic errors, and is appealing in that it does not rely on user-defined parameters. The code and user guide can be downloaded at https://sourceforge.net/projects/snooper/.

Whole-exome sequencing of a rare case of familial childhood acute lymphoblastic leukemia reveals putative predisposing mutations in Fanconi anemia genes

BackgroundAcute lymphoblastic leukemia (ALL) is the most common pediatric cancer. While the multi-step model of pediatric leukemogenesis suggests interplay between constitutional and somatic genomes, the role of inherited genetic variability remains largely undescribed. Nonsyndromic familial ALL, although extremely rare, provides the ideal setting to study inherited contributions to ALL. Toward this goal, we sequenced the exomes of a childhood ALL family consisting of mother, father and two non-twinned siblings diagnosed with concordant pre-B hyperdiploid ALL and previously shown to have inherited a rare form of PRDM9, a histone H3 methyltransferase involved in crossing-over at recombination hotspots and Holliday junctions. We postulated that inheritance of additional rare disadvantaging variants in predisposing cancer genes could affect genomic stability and lead to increased risk of hyperdiploid ALL within this family.MethodsWhole exomes were captured using Agilent’s SureSelect kit and sequenced on the Life Technologies SOLiD System. We applied a data reduction strategy to identify candidate variants shared by both affected siblings. Under a recessive disease model, we focused on rare non-synonymous or frame-shift variants in leukemia predisposing pathways.ResultsThough the family was nonsyndromic, we identified a combination of rare variants in Fanconi anemia (FA) genes FANCP/SLX4 (compound heterozygote - rs137976282/rs79842542) and FANCA (rs61753269) and a rare homozygous variant in the Holliday junction resolvase GEN1 (rs16981869). These variants, predicted to affect protein function, were previously identified in familial breast cancer cases. Based on our in-house database of 369 childhood ALL exomes, the sibs were the only patients to carry this particularly rare combination and only a single hyperdiploid patient was heterozygote at both FANCP/SLX4 positions, while no FANCA variant allele carriers were identified. FANCA is the most commonly mutated gene in FA and is essential for resolving DNA interstrand cross-links during replication. FANCP/SLX4 and GEN1 are involved in the cleavage of Holliday junctions and their mutated forms, in combination with the rare allele of PRDM9, could alter Holliday junction resolution leading to nondisjunction of chromosomes and segregation defects.ConclusionTaken together, these results suggest that concomitant inheritance of rare variants in FANCA, FANCP/SLX4 and GEN1 on the specific genetic background of this familial case, could lead to increased genomic instability, hematopoietic dysfunction, and higher risk of childhood leukemia.

Genomic characterization of pediatric T-cell acute lymphoblastic leukemia reveals novel recurrent driver mutations

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy with variable prognosis. It represents 15% of diagnosed pediatric ALL cases and has a threefold higher incidence among males. Many recurrent alterations have been identified and help define molecular subgroups of T-ALL, however the full range of events involved in driving transformation remain to be defined. Using an integrative approach combining genomic and transcriptomic data, we molecularly characterized 30 pediatric T-ALLs and identified common recurrent T-ALL targets such as FBXW7, JAK1, JAK3, PHF6, KDM6A and NOTCH1 as well as novel candidate T-ALL driver mutations including the p.R35L missense mutation in splicesome factor U2AF1 found in 3 patients and loss of function mutations in the X-linked tumor suppressor genes MED12 (frameshit mutation p.V167fs, splice site mutation g.chrX:70339329T>C, missense mutation p.R1989H) and USP9X (nonsense mutation p.Q117*). In vitro functional studies further supported the putative role of these novel T-ALL genes in driving transformation. U2AF1 p.R35L was shown to induce aberrant splicing of downstream target genes, and shRNA knockdown of MED12 and USP9X was shown to confer resistance to apoptosis following T-ALL relevant chemotherapy drug treatment in Jurkat leukemia cells. Interestingly, nearly 60% of novel candidate driver events were identified among immature T-ALL cases, highlighting the underlying genomic complexity of pediatric T-ALL, and the need for larger integrative studies to decipher the mechanisms that contribute to its various subtypes and provide opportunities to refine patient stratification and treatment.

A novel somatic mutation in ACD induces telomere lengthening and apoptosis resistance in leukemia cells

BackgroundThe identification of oncogenic driver mutations has largely relied on the assumption that genes that exhibit more mutations than expected by chance are more likely to play an active role in tumorigenesis. Major cancer sequencing initiatives have therefore focused on recurrent mutations that are more likely to be drivers. However, in specific genetic contexts, low frequency mutations may also be capable of participating in oncogenic processes. Reliable strategies for identifying these rare or even patient-specific (private) mutations are needed in order to elucidate more personalized approaches to cancer diagnosis and treatment.MethodsHere we performed whole-exome sequencing on three cases of childhood pre-B acute lymphoblastic leukemia (cALL), representing three cytogenetically-defined subgroups (high hyperdiploid, t(12;21) translocation, and cytogenetically normal). We applied a data reduction strategy to identify both common and rare/private somatic events with high functional potential. Top-ranked candidate mutations were subsequently validated at high sequencing depth on an independent platform and in vitro expression assays were performed to evaluate the impact of identified mutations on cell growth and survival.ResultsWe identified 6 putatively damaging non-synonymous somatic mutations among the three cALL patients. Three of these mutations were well-characterized common cALL mutations involved in constitutive activation of the mitogen-activated protein kinase pathway (FLT3 p.D835Y, NRAS p.G13D, BRAF p.G466A). The remaining three patient-specific mutations (ACD p.G223V, DOT1L p.V114F, HCFC1 p.Y103H) were novel mutations previously undescribed in public cancer databases. Cytotoxicity assays demonstrated a protective effect of the ACD p.G223V mutation against apoptosis in leukemia cells. ACD plays a key role in protecting telomeres and recruiting telomerase. Using a telomere restriction fragment assay, we also showed that this novel mutation in ACD leads to increased telomere length in leukemia cells.ConclusionThis study identified ACD as a novel gene involved in cALL and points to a functional role for ACD in enhancing leukemia cell survival. These results highlight the importance of rare/private somatic mutations in understanding cALL etiology, even within well-characterized molecular subgroups.

Joint genotype inference with germline and somatic mutations

The joint sequencing of related genomes has become an important means to discover rare variants. Normal-tumor genome pairs are routinely sequenced together to find somatic mutations and their associations with different cancers. Parental and sibling genomes reveal de novo germline mutations and inheritance patterns related to Mendelian diseases.Acute lymphoblastic leukemia (ALL) is the most common paediatric cancer and the leading cause of cancer-related death among children. With the aim of uncovering the full spectrum of germline and somatic genetic alterations in childhood ALL genomes, we conducted whole-exome re-sequencing on a unique cohort of over 120 exomes of childhood ALL quartets, each comprising a patients tumor and matched-normal material, and DNA from both parents. We developed a general probabilistic model for such quartet sequencing reads mapped to the reference human genome. The model is used to infer joint genotypes at homologous loci across a normal-tumor genome pair and two parental genomes.We describe the algorithms and data structures for genotype inference, model parameter training. We implemented the methods in an open-source software package (QUAD GT) that uses the standard file formats of the 1000 Genomes Project. Our methods utility is illustrated on quartets from the ALL cohort.

KMT2E-ASNS: a novel relapse-specific fusion gene in early T-cell precursor acute lymphoblastic leukemia

To the editor: Early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) is a recently characterized subtype accounting for up to 15% of childhood T-cell acute lymphoblastic leukemia (T-ALL).[1][1][⇓][2]-[3][3] This heterogeneous subgroup shows a distinctive immature immunophenotype

ngALL database: a flexible framework for the management and integration of childhood leukemia next generation sequencing data

The massive datasets generated by next-generation sequencing (NGS) studies present major challenges for data management and analysis. In our ongoing pediatric oncogenomics study, we have sequenced over 120 childhood acute lymphoblastic (ALL) quartets (matched normal-tumor, father, mother). ALL is the most common pediatric cancer and leading cause of cancer-related deaths among children, however its underlying causes remain largely unknown. We set out to build a comprehensive catalogue of genomic (sequence and structural) as well as epigenomic variations involved in childhood ALL, through deep exome resequencing, as well as transcriptome analysis (RNA-seq), genome-wide genotyping, as well as array-based methylation profiling. In response to the important challenge of integrating these various sources of information, we have created a flexible database system to effectively manage these ambitious datasets. In particular, we have implemented a custom Next-Generation childhood Acute Lymphoblastic Leukemia relational Database (ngALL DB) to report workflow analyses and integrate whole-exome sequencing data. This database also provides progress reports, allowing the user to track the samples through the project pipeline from the biospecimen repository to the annotated SNP list output. Moreover, it provides information about the different bioinformatics tools, sequencing runs and computing platforms used for mapping, cleaning and analyzing the data. The database designs flexibility and reusability facilitates data integration and allows for a customizable analytical approach and execution of custom structured queries. Complex queries and procedures can be written to interrogate the database and analyse virtually any aspect of the integrated NGS, genomic and epigenomic data. Furthermore, as the main goal of our project is to identify the full complement of genetic variants (inherited and somatic) involved in childhood ALL, the ngALL DB is linked to a high-resolution annotation database where each position in the genome is represented with detailed functional annotation. This integrated database structure offers a flexible framework to compile and characterize the large amounts of data generated from NGS studies, providing a powerful research tool for the identification of causal variants in childhood ALL.