al Hinai As

Bio: al Hinai As is an academic researcher from Erasmus University Medical Center. The author has contributed to research in topics: Germline & Somatic hypermutation. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Germline loss of MBD4 predisposes to leukaemia due to a mutagenic cascade driven by 5mC

Abstract: Cytosine methylation is essential for normal mammalian development, yet also provides a major mutagenic stimulus. Methylcytosine (5mC) is prone to spontaneous deamination, which introduces cytosine to thymine transition mutations (C>T) upon replication. Cells endure hundreds of 5mC deamination events each day and an intricate repair network is engaged to restrict this damage. Central to this network are the DNA glycosylases MBD4 and TDG, which recognise T:G mispairing and initiate base excision repair (BER). Here we describe a novel cancer predisposition syndrome resulting from germline biallelic inactivation of MBD4 that leads to the development of acute myeloid leukaemia (AML). These leukaemias have an extremely high burden of C>T mutations, specifically in the context of methylated CG dinucleotides (CG>TG). This dependence on 5mC as a source of mutations may explain the remarkable observation that MBD4-deficient AMLs share a common set of driver mutations, including biallelic mutations in DNMT3A and hotspot mutations in IDH1/IDH2. By assessing serial samples taken over the course of treatment, we highlight a critical interaction with somatic mutations in DNMT3A that accelerates leukaemogenesis and accounts for the conserved path to AML. MBD4-deficiency was also detected, rarely, in sporadic cancers, which display the same mutational signature. Collectively these cancers provide a model of 5mC-dependent hypermutation and reveal factors that shape its mutagenic influence.

SuperFreq: Integrated mutation detection and clonal tracking in cancer

Abstract: Analysing multiple cancer samples from an individual patient can provide insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression. Existing approaches for clonal tracking from sequencing data typically require the user to combine multiple tools that are not purpose-built for this task. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application. We developed SuperFreq, a cancer exome sequencing analysis pipeline that integrates identification of somatic single nucleotide variants (SNVs) and copy number alterations (CNAs) and clonal tracking for both. SuperFreq does not require a matched normal and instead relies on unrelated controls. When analysing multiple samples from a single patient, SuperFreq cross checks variant calls to improve clonal tracking, which helps to separate somatic from germline variants, and to resolve overlapping CNA calls. To demonstrate our software we analysed 304 cancer-normal exome samples across 33 cancer types in The Cancer Genome Atlas (TCGA) and evaluated the quality of the SNV and CNA calls. We simulated clonal evolution through in silico mixing of cancer and normal samples in known proportion. We found that SuperFreq identified 93% of clones with a cellular fraction of at least 50% and mutations were assigned to the correct clone with high recall and precision. In addition, SuperFreq maintained a similar level of performance for most aspects of the analysis when run without a matched normal. SuperFreq is highly versatile and can be applied in many different experimental settings for the analysis of exomes and other capture libraries. We demonstrate an application of SuperFreq to leukaemia patients with diagnosis and relapse samples.

Finding cancer driver mutations in the era of big data research.

Abstract: In the last decade, the costs of genome sequencing have decreased considerably. The commencement of large-scale cancer sequencing projects has enabled cancer genomics to join the big data revolution. One of the challenges still facing cancer genomics research is determining which are the driver mutations in an individual cancer, as these contribute only a small subset of the overall mutation profile of a tumour. Focusing primarily on somatic single nucleotide mutations in this review, we consider both coding and non-coding driver mutations, and discuss how such mutations might be identified from cancer sequencing datasets. We describe some of the tools and database that are available for the annotation of somatic variants and the identification of cancer driver genes. We also address the use of genome-wide variation in mutation load to establish background mutation rates from which to identify driver mutations under positive selection. Finally, we describe the ways in which mutational signatures can act as clues for the identification of cancer drivers, as these mutations may cause, or arise from, certain mutational processes. By defining the molecular changes responsible for driving cancer development, new cancer treatment strategies may be developed or novel preventative measures proposed.

SuperFreq: Integrated mutation detection and clonal tracking in cancer

Abstract: Motivation Analysing multiple tumour samples from an individual cancer patient allows insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression; therefore, the ability to identify and track clones using genomics data is of great interest. Existing approaches for clonal tracking typically require the user to combine multiple tools that are not purpose-made. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application. Results We have built superFreq, a cancer exome sequencing analysis tool that calls and annotates somatic SNVs and CNAs and attributes them to clones. SuperFreq makes use of unrelated control samples and does not require matched normal samples. We demonstrate the ability of superFreq to track clones by combining real samples in known proportions to simulating a multi-sample analysis. In addition, we compared superFreq to other somatic SNV callers and CNA callers on exome sequencing data from cancer-normal pairs, including 304 participants gathered from 33 cancer types in The Cancer Genome Atlas (TCGA). SuperFreq offers a reliable platform to identify somatic mutations and to track clones. SuperFreq recalled 91% of somatic SNVs identified by a consensus of four other methods, with a median of 1 additional somatic SNV per sample that was not found by any other method. CNA calls from superFreq showed good agreement with those generated by Sequenza, or those from ASCAT generated using matched SNP arrays. Using our simulated data set for testing multi-sample clonal tracking, we found that superFreq identified 93% of clones with a cellular fraction of at least 50%, and mutations were assigned to clones with high recall and close to 100% precision. In addition, SuperFreq maintained a similar level of performance for most aspects of the analysis without a matched normal control. SuperFreq is a highly adaptable method and has already been used in multiple different projects. Availability SuperFreq is implemented in R and available on github at https://github.com/ChristofferFlensburg/superFreq.

Recapitulation of human germline coding variation in an ultra-mutated infant leukemia

Abstract: Background: Mixed lineage leukemia/Histone-lysine N-methyltransferase 2A gene rearrangements occur in 80% of infant acute lymphoblastic leukemia, but the role of cooperating events is unknown. While infant leukemias typically carry few somatic lesions, we identified a case with over 100 somatic point mutations per megabase and here report unique genomic-features of this case. Results: The patient presented at 82 days of age, one of the earliest manifestations of cancer hypermutation recorded. The transcriptional profile showed global similarities to canonical cases. Coding lesions were predominantly clonal and almost entirely targeting alleles reported in human genetic variation databases with a notable exception in the mismatch repair gene, MSH2 . There were no rare germline alleles or somatic mutations affecting proof-reading polymerase genes POLE or POLD1 , however there was a predicted damaging mutation in the error prone replicative polymerase, POLK . The patient9s diagnostic leukemia transcriptome was depleted of rare and low-frequency germline alleles due to loss-of-heterozygosity, while somatic point mutations targeted low-frequency and common human alleles in proportions that offset this discrepancy. Somatic signatures of ultra-mutations were highly correlated with germline single nucleotide polymorphic sites indicating a common role for 5-methylcytosine deamination, DNA mismatch repair and DNA adducts. Conclusions: These data suggest similar molecular processes shaping population-scale human genome variation also underlies the rapid evolution of an infant ultra-mutated leukemia.