Abstract 967: Establishing mechanisms of acquired resistance to Met inhibitor in Met-addicted gastric cancer cell lines.
Bibliographic record
Abstract
Abstract Signaling by the Met, hepatocyte growth factor (HGF) receptor tyrosine kinase (RTK) activates multiple downstream signaling pathways that promote cell migration and invasive growth. Cells that overexpress and are “addicted” to Met also require Met signaling to sustain cell survival. Thus, a number of specific small-molecule inhibitors have been developed to target Met in the clinic. Although several successes of RTK-targeted therapies are acknowledged, most have had limited long-term success clinically due to development of drug resistance. As MET amplification occurs in 10-20% of gastric cancers, and MET-amplified cell lines are often exquisitely dependent on Met signaling for proliferation and survival, we established Met inhibitor-resistant clones derived from two different gastric cancer cell lines (MKN45 and KATO II) that are sensitive to Met inhibition. Treatment with a small-molecule inhibitor, PHA-665752 (PHA), specific to Met abrogates cell proliferation and anchorage-independent growth in these cells. In vitro selection of the most tumorigenic and resistant cells was performed by isolating colonies in anchorage-independent conditions with increasing PHA concentrations. Colonies were picked and expanded in 2D cell culture conditions and PHA concentration gradually increased until the cells were able to proliferate in a PHA concentration of >1μM (10x greater than the dose required for abrogation of proliferation in the parental cell lines). DNA, RNA, and protein have been isolated from each of the clones and sequencing, microarray analyses, phospho-RTK arrays, reverse-phase protein arrays (RPPA) have been utilized to identify the changes in signalling, protein and transcript expression that allow these cells to escape PHA inhibition. The analyses from these high-throughput assays will be presented here. As several Met inhibitors are presently in all three phases of clinical trials, and Met signalling has emerged as a means of escaping treatment by other targeted small-molecule kinase inhibitors, there exists a great need to determine the mechanisms through which cells may circumvent Met inhibition. The identification of resistance mechanisms will be essential for developing multi-pronged therapeutic strategies that may prove more efficacious at combatting tumorigenesis. Citation Format: Andrea Z. Lai, Crista Thompson, Sean Cory, Hong Zhao, Naila Chughtai, Michael Hallett, Morag Park. Establishing mechanisms of acquired resistance to Met inhibitor in Met-addicted gastric cancer cell lines. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 967. doi:10.1158/1538-7445.AM2013-967
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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 teacher head, 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".