Role of ERBB signaling in RET-rearranged lung cancer and contribution of EGFR amplification to cabozantinib resistance.
Bibliographic record
Abstract
11583 Background: Lung cancers driven by oncogenic RET fusions have lower response rates to targeted monotherapy such as cabozantinib (28%) relative to response rates typically observed in ALK- or ROS1- rearranged lung adenocarcinomas (60-80%). Methods: To identify targetable co-dependencies or cooperating pathways for RET fusion-positive lung cancers, we performed high-throughput chemical and genetic screens to find FDA-approved drugs or genes that when inhibited, would synergize with cabozantinib in RET fusion-positive lung cancer cell lines. In addition we performed NGS of a pair of pre-treatment and post-cabozantinib progression samples. Results: We identified EGFR siRNAs and anti-EGFR drugs as synergistic with cabozantinib. Combinations of drugs that target EGFR (cetuximab, afatinib, erlotinib, gefitinib, neratinib) and RET (cabozantinib, CEP-32496, lenvatinib, vandetanib) were more effective at reducing growth of RET cell lines than any single agent in vitro and in xenograft models. Cabozantinib treatment of RET fusion-positive cell lines inhibited EGFR and RET phosphorylation, an observation not seen in RET wild-type cell lines. Co-immunoprecipitation studies reveal that RET and EGFR interact. Ectopic expression of CCDC6-RET in NIH-3T3 or human bronchial epithelial cells resulted in upregulation of multiple ERBB receptors and ligands (not seen in a ROS1 fusion-positive cell line) and a concomitant increase in EGFR stability. Treatment with ERBB pathway ligands or overexpression of EGFR decreased sensitivity to cabozantinib in two RET fusion-positive cell lines. Finally, sequencing of a pair of pre-treatment and post-progression samples from a lung cancer patient treated with cabozantinib revealed acquired amplification of EGFR in the latter sample. Conclusions: Taken together, these results suggest that the tumorigenic potential of RET fusion oncogenes is dependent on deregulation of ERBB-activated pathways and that a combination of RET and EGFR drugs could be more effective in treating RET fusion-positive tumors. Moreover, amplification of EGFR is a potential driver of resistance to cabozantinib in RET-rearranged lung cancers.
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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) | 0.002 | 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".