The use of cellular thermal shift assay (CETSA) to study Crizotinib resistance in ALK-expressing human cancers
Bibliographic record
Abstract
Various forms of oncogenic ALK proteins have been identified in various types of human cancers. While Crizotinib, an ALK inhibitor, has been found to be therapeutically useful against a subset of ALK(+) tumours, clinical resistance to this drug has been well recognized and the mechanism of this phenomenon is incompletely understood. Using the cellular thermal shift assay (CETSA), we measured the Crizotinib-ALK binding in a panel of ALK(+) cell lines, and correlated the findings with the ALK structure and its interactions with specific binding proteins. The Crizotinib IC50 significantly correlated with Crizotinib-ALK binding. The suboptimal Crizotinib-ALK binding in Crizotinib-resistant cells is not due to the cell-specific environment, since transfection of NPM-ALK into these cells revealed substantial Crizotinib-NPM-ALK binding. Interestingly, we found that the resistant cells expressed higher protein level of β-catenin and siRNA knockdown restored Crizotinib-ALK binding (correlated with a significant lowering of IC50). Computational analysis of the crystal structures suggests that β-catenin exerts steric hindrance to the Crizotinib-ALK binding. In conclusion, the Crizotinib-ALK binding measurable by CETSA is useful in predicting Crizotinib sensitivity, and Crizotinib-ALK binding is in turn dictated by the structure of ALK and some of its binding partners.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".