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Analysis of BCR-ABL1 Tyrosine Kinase Domain Mutations In Primitive Chronic Myeloid Leukemia Cells Identifies a Unique Mutator Phenotype.

2010· article· en· W97160107 on OpenAlexaff
Xiaoyan Jiang, Hannah Grant, Justin Stebbing, Letizia Foroni, Charles Craddock, Mike Griffiths, Richard E. Clark, Stephen J. O’Brien, Jamshid S. Khorashad, Gareth Gerrard, Lihui Wang, Julie Irving, Meng Wang, Loraine Karran, Martin J.S. Dyer, Donna L. Forrest, Karen Page, Connie J. Eaves, Adrian Woolfson

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsImatinib mesylateMyeloid leukemiaBiologyMutationCancer researchPhenotypeTyrosine kinaseLeukemiaMolecular biologyABLImatinibGeneticsGeneReceptor

Abstract

fetched live from OpenAlex

Abstract Abstract 3397 The tyrosine kinase inhibitor (TKI), imatinib mesylate (IM), induces remissions in most chronic phase chronic myeloid leukaemia (CML-CP) patients, but emergence of drug resistance attributable to critical mutations in the BCR-ABL1 TK domain remains a significant clinical problem. We previously demonstrated that primitive (CD34+) CML-CP cells are both intrinsically insensitive to IM and genetically unstable. BCR-ABL1 TK domain mutations are, furthermore, readily detectable in CD34+ CML-CP cells even prior to IM treatment. However, the mechanisms by which they arise are unknown. In this study, we characterized the spectrum of mutations at each of the 3 codon positions in the BCR-ABL1 TK domain of IM-naïve CD34+ cells and used a mathematical model to compare these with those found in the same region of DNA in IM-resistant cells. A total of 460 TK mutations were identified in cells from 15 IM-naïve and from 316 IM-resistant patients. The mutations were non-randomly distributed across the 3 codon positions, with very few mutations at position 3 (1 of 136 mutations, 0.7%, in IM-naïve patients and 31 of 329, 9%, in IM-resistant patients). The remainder showed no bias in their distribution across codon positions 1 and 2 in IM-naïve patient cells but, in IM-resistant cells, a significant positive bias was observed at codon position 2 (p=0.042). Conversely, BCR-ABL1 TK domain sequences from IM-naïve patient cells had significantly more transitions relative to unselected regions of the genome across all codon positions, a significant positive A-to-G mutational bias (A>G, 1.12×10−10) and an under-representation of C-to-T mutations (C>T, 5.34×10−5) when these mutations were compared with unselected regions of the genome. In addition, we observed a T-to-C mutational hotspot (T>C, 1.23×10−4) at codon position 2. IM-naïve cells exhibited a transitional bias at position 2 and overall (1.99×10−4 and 6.19×10−5, respectively; at position 1, P=0.052). We observed a similar pattern of TK codon position mutations at position 1 and overall in sequences derived from IM-resistant cells. Interestingly, in IM-resistant cells, A-to-T (A>T) transversions were over-represented at position 2 and overall (1.41×10−11 and 3.77×10−5). These cells also had a profound transversional bias (1.45×10−4) at codon position 3, suggesting that positive selection occurs at a position where transversions are typically non-synonymous. However, a skewed transition:transversion ratio and transition and transversion frequency at TK codon positions 1 and 2 was observed in both IM-naïve and IM-selected cells with mutations distributed across these 2 codon positions in a significantly uneven fashion. In IM-resistant cells, the frequency of C-to-T mutations (C>T, 3.82×10−11) indicated a bias affecting codon position 2 more often than position 1, and A-to-T transversions occurred significantly more frequently at position 2 compared to position 1 (A>T, 7.45×10−9). This was also the case for T-to-C transitions in TK sequences of IM-naïve cells (T>C, 1.13×10−4). Frequencies of these TK mutations in IM-naïve and IM-resistant cells were also higher than in unselected regions of the genome, providing further evidence that the TK domain mutations in CML-CP cells have a distinct mutational profile. For example, the clinically observed M244V and D276G mutations result from A-to-G transitions and the F359L mutation arises from a T-to-C transition, both of which would be predicted by the activity of a CML-CP mutator. However, the most clinically important mutation, T315I, that confers resistance to most currently available TKIs, is generated by a C-to-T transition, suggesting a high mutational rate generating mutational escape around the principal mutator pattern and profound selection thereafter. Notably, the CML-CP mutational signature is distinct from that of the activation-induced cytidine deaminase (AID)-induced hypermutation reported in CML blast crisis, both in targeting specific codon positions and in the overall mutational pattern. This strongly favors the hypothesis that the genomic instability of primitive CML-CP cells is generated by a different, specific mutational process. The results also suggest that monitoring BCR-ABL mutational hotspots may be clinically useful in anticipating TKI resistance. Disclosures: No relevant conflicts of interest to declare.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.252
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2010
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