Assessment of Chronic Myeloid Leukaemia In Vitro Models Variability: Insights Into Extracellular Vesicles
Bibliographic record
Abstract
Chronic Myeloid Leukaemia is driven by the BCR::ABL1 fusion gene. Although Tyrosine Kinase Inhibitors have significantly improved patient outcomes, drug resistance and disease persistence remain challenges, highlighting the need for effective preclinical models. We observed cellular heterogeneity among CML models in response to TKIs, influencing viability, metabolism, and molecular markers. With growing interest in extracellular vesicles as mediators of leukaemia progression via oncogenic cargo like BCR::ABL1, we explored whether EVs from different CML cell lines exhibit distinct features. EVs from K562 and KCL22 cells were characterised under basal conditions using Fourier Transform Infrared spectroscopy, Atomic Force Microscopy, dot blotting, and Nanoparticle Tracking Analysis. We assessed EV release and BCR::ABL1 content before and after treatment with imatinib, nilotinib, or dasatinib, alongside Ki67 expression to gauge proliferation. Fourier Transform Infrared Spectroscopy with Principal Component Analysis revealed distinct clustering of EVs by cell line. While untreated K562 and KCL22 cells showed similar EV output and BCR::ABL1 content, post-treatment K562 cells released more EVs with elevated BCR::ABL1 transcripts. KCL22 cells showed earlier reduction in Ki67 expression. These findings highlight model-dependent EV behaviour, reflecting patient heterogeneity and reinforcing the need for careful model selection in CML research.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".