Abstract 1232: Overcoming resistance to EGFR-tyrosine kinase inhibitor therapy in non-small cell lung cancer
Bibliographic record
Abstract
Abstract Lung cancer is the leading cause of cancer death in developed countries both for men and women; and the second most common type of cancer diagnosed, with less than 15% of patients surviving beyond 5 years. Since more than 60% of lung adenocarcinomas and more than 90% of lung squamous cell carcinomas express high levels of EGFR mRNA and protein, it has been the focus of efforts to develop new agents that target the EGFR pathway. Clinical trials have demonstrated that patient selection plays a major role for patient response. Patients that present amplification or activating mutations (L878R or exon 19 deletions) of EGFR, have higher response rates. Selection improved response rates from less than 10% to over 60-80%. Despite the promising results, all patients that receive TKI therapy develop resistance to treatment in less than one year. The mechanisms of resistance to EGFR TKi's described so far are those dependant on the overactivation/amplification of other receptor tyrosine kinases (RTK) capable to sustain anti-apoptotic signaling pathways. Our study proposes the use of genome-wide screenings using an 80,000 shRNA library on EGFR resistant cells can identify new genes that mediate the resistance to EGFR targeted therapy and provide the basis for designing new therapeutic approaches to avoid the emergence of resistance. So far, we have generated a shortlist of gene candidates that confer synergistic synthetic lethal interactions with EGFR targeted therapies and that are also highly expressed in resistant cells. We are currently overexpressing candidate genes in sensitive cells searching for those that can confer resistance to EGFR targeted therapies. Our study proposes target genes that can synergize with the current treatments to create new therapeutical strategies that can avoid the acquisition of resistance to targeted therapies. Overall, the information gained from this type of study can be applicable to tumors where ERBB receptor(s) play an important role and targeted therapies are currently used. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1232. doi:1538-7445.AM2012-1232
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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 | 0.001 |
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".