| Literature DB >> 26345285 |
Jean-François Spinella1, Pauline Cassart2, Nicolas Garnier3, Philippe Rousseau4, Claire Drullion5, Chantal Richer6, Manon Ouimet7, Virginie Saillour8, Jasmine Healy9, Chantal Autexier10,11, Daniel Sinnett12,13.
Abstract
BACKGROUND: The 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.Entities:
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Year: 2015 PMID: 26345285 PMCID: PMC4562123 DOI: 10.1186/s12885-015-1639-5
Source DB: PubMed Journal: BMC Cancer ISSN: 1471-2407 Impact factor: 4.430
Fig. 1Whole-exome sequencing analysis workflow. Boxes represent the analysis/cleaning steps. Cylinders represent the SNV filtering steps used in the data reduction strategy to identify functional somatic mutations. The number of variations remaining after each step is shown in brackets. Note that only SNVs that passed a given filter were tested for in the subsequent step. Using public databases and variant annotation tools, we identified 6 top-ranked mutations among the pre-B cALL patients, including 3 SNVs referenced in COSMIC v71 and 3 candidate rare/private SNVs in ACD (p.G223V), DOT1L (p.V114F) and HCFC1 (p.Y103H) with a potential functional impact
Candidate somatic mutations identified in each patient
| Patient | Subgroup | Gene | Genomic change | Protein change | Class | VAF | COSMIC v71 |
|---|---|---|---|---|---|---|---|
| Case 1 | HD | NRAS | g.chr1:115258744C > T | p.G13D | Missense | 0.48 | Haematopoietic and Lymphoid tissue |
| ACD/TPP1 | g.chr16:67693443C > A | p.G223V | Missense | 0.51 | - | ||
| DOT1L | g.chr19:2191086G > T | p.V114F | Missense | 0.46 | - | ||
| Case 2 | t(12;21) | BRAF | g.chr7:140481411C > G | p.G466A | Missense | 0.15 | Thyroid |
| HCFC1 | g.chrX:153230064A > G | p.Y103H | Missense | 0.51 | - | ||
| Case 3 | CN | FLT3 | g.chr13:28592642G > T | p.D835Y | Missense | 0.44 | Haematopoietic and Lymphoid tissue |
Variant allele frequencies (VAF) (number of supporting reads/coverage) were calculated based on ultra-deep targeted re-sequencing data (mean coverage >1800X). Only tissue types harbouring the highest occurrence of the mutation in the COSMIC database (v71) are presented. HD hyperdiploid, CN Cytogenetically normal
Fig. 2ACD p.G223V protects from camptothecin-induced apoptosis and increases telomere length. a. Schematic representation of the ACD protein. The p.G223V mutation, depicted in black, is adjacent to the TEL patch of the OB-fold domain involved in telomerase recruitment and composed of seven critical amino acids located in a region defined by the curly bracket (E168, E169, E171, R180, L183, L212 and E215) [30]. p.Q320X, p.P491T and p.K170del, depicted in grey, are three germline mutations recently identified and associated with familial melanomas and bone marrow failure disorders [34–36]. TPP1C corresponds to the TIN2-binding domain. Together, the OB (oligonucleotide/oligosaccharide-binding) and PBD (POT1 binding domain) domains form the ACDN domain necessary for POT1 binding to telomeric DNA and the stimulation of telomerase processivity. b. In vitro apoptosis assays show overall reduced levels of apoptosis associated with ACD p.G223V. The c.659 g > t mutation was introduced into the ACD transgene by site-directed mutagenesis and expressed in Nalm-6 cells. The graph shows annexin V/PI staining for 3 h on Nalm-6 pLenti (empty vector), Nalm-6 ACD WT and Nalm-6 ACD G223V cells. c. The telomere restriction fragment assay (TRF) showed a quantitative increase in telomere size for Nalm-6 ACD G223V cells at passage 16 (p16) and p27 after selection. Mean TRF length = ∑ (ODi)/∑ (ODi/Li) where ODi is the radioactive signal, Li is the TRF fragment length at position i. The bar chart of Fig. 2c (bottom) represents the mean TRF length for each condition directly quantified from each corresponding lane of the TRF gel presented at the top of Fig. 2c Significance (in b and c) was determined by a Mann–Whitney U test; p-values <0.05 are represented by an asterisk