A Single-Step, High-Dose Selection Scheme Reveals Distinct Mechanisms of Acquired Resistance to Oncogenic Kinase Inhibition in Cancer Cells
Bibliographic record
Abstract
Abstract Despite the remarkable clinical efficacy demonstrated by molecularly targeted cancer therapeutics, the benefits are typically temporary due to the emergence of acquired drug resistance. This has spurred a massive effort by the cancer research community to identify mechanisms used by cancer cells to evade treatment. Among the various methodologies developed and employed to identify such mechanisms, the most commonly used approach has been to model acquired resistance by exposing cancer cells in culture to gradually increasing concentrations of drug over an extended period of time. Here, we employed a less commonly used variation on this approach, wherein resistant cells are selected by immediately exposing cancer cells to a continuous, high concentration of drug. Using this approach, we isolated clones representing three distinct mechanisms of resistance to inhibition of MET kinase activity from a single clonally derived cancer cell line. The emergent clones had acquired resistance through engagement of alternative receptor tyrosine kinases either through upregulation of FGF3 or HBEGF or increased MAPK signaling through an activating V600E mutation in BRAF. Importantly, these mechanisms were not identified using the conventional “ramp-up” approach in previous studies that employed the same cell line. These results suggest that the particular nature of the selection scheme employed in cell culture modeling studies can determine which potential resistance mechanisms are identified and which ones may be missed, highlighting the need for careful consideration of the specific approach used to model resistance in cultured cells. Significance: Through modeling resistance to MET kinase inhibition in cultured cancer cells using single-step, high-dose selection, these findings highlight that the specific nature of the selection protocol impacts which resistance mechanisms are identified. See related commentary by Floros et al., p. 25
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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 | 0.000 |
| 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".