Clonal hematopoiesis (CH), characterized by a fraction of peripheral blood cells carrying an acquired genetic variant, emerges with age. Although in general CH is associated with increased mortality ...and morbidity, no higher risk of death was observed for individuals ≥80 years. Here, we investigated CH in 621 individuals aged ≥80 years from the population-based LifeLines cohort. Sensitive error-corrected sequencing of 27 driver genes at a variant allele frequency ≥1% revealed CH in the majority (62%) of individuals, independent of gender. The observed mutational spectrum was dominated by DNMT3A and TET2 variants, which frequently (29%) displayed multiple mutations per gene. In line with previous results in individuals ≥80 years, the overall presence of CH did not associate with a higher risk of death (hazard ratio, 0.91; 95% confidence interval, 0.70-1.18; P = .48). Being able to assess the causes of death, we observed no difference between individuals with or without CH, except for deaths related to hematological malignancies. Interestingly, comparison of mutational spectra confined to DNMT3A and TET2 vs spectra containing other mutated genes, showed a higher risk of death when mutations other than DNMT3A or TET2 were present (hazard ratio, 1.48; 95% confidence interval, 1.06-2.08; P = .025). Surprisingly, no association of CH with cardiovascular morbidity was found, irrespective of clone size. Further, CH associated with chronic obstructive pulmonary disease. Data on estimated exposure to DNA damaging toxicities (ie, smoking, a history of cancer as a proxy for previous genotoxic therapy, and job-related pesticide exposure) showed an association with spliceosome and ASXL1 variants, but not with DNMT3A and TET2 variants.
•Clonal hematopoiesis is present in 62% of individuals ≥80 years and its prognostic implications are driver gene–specific.•ASXL1 and spliceosome variants are more frequent in individuals with estimated higher exposure to DNA damaging agents.
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Abstract
Background
Copy number variants (CNVs) are known to play an important role in the development and progression of several diseases. However, detection of CNVs with whole-exome sequencing ...(WES) experiments is challenging. Usually, additional experiments have to be performed.
Findings
We developed a novel algorithm for somatic CNV calling in matched WES data called “CopyDetective". Different from other approaches, CNV calling with CopyDetective consists of a 2-step procedure: first, quality analysis is performed, determining individual detection thresholds for every sample. Second, actual CNV calling on the basis of the previously determined thresholds is performed. Our algorithm evaluates the change in variant allele frequency of polymorphisms and reports the fraction of affected cells for every CNV. Analyzing 4 WES data sets (n = 100) we observed superior performance of CopyDetective compared with ExomeCNV, VarScan2, ControlFREEC, ExomeDepth, and CNV-seq.
Conclusions
Individual detection thresholds reveal that not every WES data set is equally apt for CNV calling. Initial quality analyses, determining individual detection thresholds—as realized by CopyDetective—can and should be performed prior to actual variant calling.
Evolution of clonal hematopoiesis van Zeventer, Isabelle A.; de Graaf, Aniek O.; Jansen, Joop H. ...
Clinical and translational medicine,
10/2023, Letnik:
13, Številka:
10
Journal Article
Recenzirano
Odprti dostop
Different from myeloid malignancies, mutations in CH generally occur in a small percentage of blood cells as reflected by the variant allele frequency (VAF), which is the number of variant reads ...relative to the total number of reads at a given mutation position.1,2 Individuals with CH have a higher risk of developing haematological malignancies, including acute myeloid leukaemia.3,5,6 Based on these findings, CH is increasingly recognized as a precursor condition for myeloid malignancies, representing an early stage in the stepwise process of clonal selection, with subsequent mutations that may result in full-blown leukaemia.7 The acronym “Clonal Hematopoiesis of Indeterminate Potential” (CHIP) was first proposed to describe the presence of CH in otherwise healthy individuals, at a VAF ≥2%.8 CH occurs at higher frequencies when mutation screening by Next Generation Sequencing (NGS) is ordered for patients presenting with cytopenias that remain unexplained after careful evaluation and nondiagnostic bone marrow.9 “Clonal Cytopenia of Undetermined Significance” (CCUS) is coined to describe the presence of CH clones in such individuals with unexplained cytopenia, that do not meet established criteria for myeloid malignancy. Gene-specific fitness effects determining clonal outgrowth were shown in the Lothian Birth Cohort.16 Fabre et al. further tracked gene-specific clonal dynamics in peripheral blood samples from 385 adults in the Sardinia longitudinal study.17 We recently reported growth rates for common myeloid driver gene mutations based on sequential VAF measurements in 3359 individuals ≥60 years from the population-based Lifelines cohort.18 These studies show that DNMT3A and TP53-mutated clones are characterized by very limited clonal expansion, whereas highest growth rates are observed for clones with mutations in the splicing factor genes (SF3B1, SRSF2 and U2AF1) and JAK2. Other markers of prognostic relevance include mean corpuscular volume (MCV) and red blood cell distribution width (RDW).6,20 Finally, the joint role of gene mutations and clonal chromosomal abnormalities in haematological malignancy development warrants further study.24 ‘GENOTYPE (G) + ENVIRONMENT (E) = PHENOTYPE (P)’ Apart from gene-specific trajectories, there is still considerable inter-individual variation in growth trajectories, even when such individuals carry the exact same mutation.
Clonal hematopoiesis (CH) is defined by the presence of somatic mutations that may cause clonal expansion of hematopoietic cells. Here, we investigated the association between platelet count ...abnormalities, CH and consequences on overall survival and the development of hematological malignancies. Individuals with thrombocytopenia (n = 631) or thrombocytosis (n = 178) ≥60 years, and their age‐ and sex‐matched controls, were selected within the population‐based Lifelines cohort (n = 167,729). Although the prevalence of CH was not increased in thrombocytopenia cases compared with their controls (37.9% vs 39.3%; P = 0.639), mutations in spliceosome genes (SF3B1, SRSF2, U2AF1) were significantly enriched in thrombocytopenia cases (P = 0.007). Overall, CH in combination with thrombocytopenia did not impact on survival, but thrombocytopenia in combination with multiple mutated genes (hazard ratio HR = 2.08, 95% confidence interval CI, 1.24‐3.50; P = 0.006), mutations in TP53 (HR = 5.83, 95% CI, 2.49‐13.64; P < 0.001) or spliceosome genes (HR = 2.69, 95% CI, 1.29‐5.63; P = 0.009) increased the risk of death. The prevalence of CH in thrombocytosis cases was higher compared with controls (55.8% vs 37.7%; P < 0.001). Especially mutations in JAK2 (P < 0.001) and CALR (P = 0.003) were enriched in individuals with thrombocytosis. The presence of CH in individuals with thrombocytosis did not impact on overall survival. However, during follow‐up of 11 years 23% of the individuals with thrombocytosis and CH were diagnosed with hematological malignancies. From these, 81% were diagnosed with myeloproliferative disease and 76% carried driver mutations JAK2, CALR, or MPL.
Deriving valid variant calling results from raw next-generation sequencing data is a particularly challenging task, especially with respect to clinical diagnostics and personalized medicine. However, ...when using classic variant calling software, the user usually obtains nothing more than a list of variants that pass the corresponding caller's internal filters. Any expected mutations (e.g. hotspot mutations), that have not been called by the software, need to be investigated manually.
BBCAnalyzer (Bases By CIGAR Analyzer) provides a novel visual approach to facilitate this step of time-consuming, manual inspection of common mutation sites. BBCAnalyzer is able to visualize base counts at predefined positions or regions in any sequence alignment data that are available as BAM files. Thereby, the tool provides a straightforward solution for evaluating any list of expected mutations like hotspot mutations, or even whole regions of interest. In addition to an ordinary textual report, BBCAnalyzer reports highly customizable plots. Information on the counted number of bases, the reference bases, known mutations or polymorphisms, called mutations and base qualities is summarized in a single plot. By uniting this information in a graphical way, the user may easily decide on a variant being present or not - completely independent of any internal filters or frequency thresholds.
BBCAnalyzer provides a unique, novel approach to facilitate variant calling where classical tools frequently fail to call. The R package is freely available at http://bioconductor.org . The local web application is available at Additional file 2. A documentation of the R package (Additional file 1) as well as the web application (Additional file 2) with detailed descriptions, examples of all input- and output elements, exemplary code as well as exemplary data are included. A video demonstrates the exemplary usage of the local web application (Additional file 3). Additional file 3: Supplement_3. Video demonstrating the exemplary usage of the web application "BBCAnalyzer". (MP4 11571 kb).
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Motivation
The application of next-generation sequencing in research and particularly in clinical routine requires valid variant calling results. However, evaluation of several commonly used ...tools has pointed out that not a single tool meets this requirement. False positive as well as false negative calls necessitate additional experiments and extensive manual work. Intelligent combination and output filtration of different tools could significantly improve the current situation.
Results
We developed appreci8, an automatic variant calling pipeline for calling single nucleotide variants and short indels by combining and filtering the output of eight open-source variant calling tools, based on a novel artifact- and polymorphism score. Appreci8 was trained on two data sets from patients with myelodysplastic syndrome, covering 165 Illumina samples. Subsequently, appreci8's performance was tested on five independent data sets, covering 513 samples. Variation in sequencing platform, target region and disease entity was considered. All calls were validated by re-sequencing on the same platform, a different platform or expert-based review. Sensitivity of appreci8 ranged between 0.93 and 1.00, while positive predictive value ranged between 0.65 and 1.00. In all cases, appreci8 showed superior performance compared to any evaluated alternative approach.
Availability and implementation
Appreci8 is freely available at https://hub.docker.com/r/wwuimi/appreci8/. Sequencing data (BAM files) of the 678 patients analyzed with appreci8 have been deposited into the NCBI Sequence Read Archive (BioProjectID: 388411; https://www.ncbi.nlm.nih.gov/bioproject/PRJNA388411).
Supplementary information
Supplementary data are available at Bioinformatics online.
A genomic locus 8 kb downstream of the transcription factor GFI1B (Growth Factor Independence 1B) predisposes to clonal hematopoiesis and myeloproliferative neoplasms. One of the most significantly ...associated polymorphisms in this region is rs621940-G. GFI1B auto-represses GFI1B, and altered GFI1B expression contributes to myeloid neoplasms. We studied whether rs621940-G affects GFI1B expression and growth of immature cells. GFI1B ChIP-seq showed clear binding to the rs621940 locus. Preferential binding of various hematopoietic transcription factors to either the rs621940-C or -G allele was observed, but GFI1B showed no preference. In gene reporter assays the rs621940 region inhibited GFI1B promoter activity with the G-allele having less suppressive effects compared to the C-allele. However, CRISPR-Cas9 mediated deletion of the locus in K562 cells did not alter GFI1B expression nor auto-repression. In healthy peripheral blood mononuclear cells GFI1B expression did not differ consistently between the rs621940 alleles. Long range and targeted deep sequencing did not detect consistent effects of rs621940-G on allelic GFI1B expression either. Finally, we observed that myeloid colony formation was not significantly affected by either rs621940 allele in 193 healthy donors. Together, these findings show no evidence that rs621940 or its locus affect GFI1B expression, auto-repression or growth of immature myeloid cells.
•A genomic locus surrounding rs621940 associates with myeloproliferative neoplasms.•GFI1B binds to the genomic region 8 kb downstream of GFI1B adjacent to rs621940.•Removal of the genomic region does not affect GFI1B expression levels.•GFI1B expression is not significantly affected by either rs621940 allele.•Clonogenic growth of myeloid progenitors is not affected by rs621940 in 193 donors.
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