VLA-4 Induces Chemoresistance of T Cell Acute Lymphoblastic Leukemia Cells via PYK2-Mediated Drug Efflux
Bibliographic record
Abstract
Cell adhesion plays a critical role in the development of chemoresistance, which is a major issue in anti-cancer therapies. In this study, we have examined the role of the VLA-4 integrin, a major adhesion molecule of the immune system, in the chemoresistance of T-ALL cells. We found that attachment of Jurkat and HSB-2 T-ALL cells to VCAM-1, a VLA-4 ligand, inhibits doxorubicin-induced apoptosis. However, their adhesion to fibronectin, which is mainly mediated via VLA-5, had no effect. Even the presence of the chemoattractant SDF1α (Stromal cell-derived factor-1α), which enhances the adhesion of T-ALL cells to fibronectin, did not modify the sensitivity of the cells attached on fibronectin towards doxorubicin-induced apoptosis. Mechanistically, we found that VLA-4 promoted T-ALL chemoresistance by inducing doxorubicin efflux. Our results showed that cell adhesion to both fibronectin and VCAM-1-induced Focal adhesion kinase (FAK) phosphorylation in T-ALL cells. However, only cell adhesion to VCAM-1 led to PYK2 phosphorylation. Inhibition studies indicated that FAK is not involved in doxorubicin efflux and chemoresistance, whereas PYK2 inhibition abrogated both VLA-4-induced doxorubicin efflux and chemoresistance. Together, these results indicate that the VLA-4/PYK2 pathway could participate in T-ALL chemoresistance and its targeting could be beneficial to limit/avoid chemoresistance and patient relapse.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".