Abstract LB187: EGFR signaling and pharmacology in oncology revealed with an innovative RTK biosensor technology
Bibliographic record
Abstract
Abstract EGFR is involved in key biological processes and its deregulation is associated with the development of many cancers. EGFR is implicated in tumor invasion, metastasis and angiogenesis. Moreover, intrinsic and acquired mutations of EGFR have been described to modify receptor signaling and be responsible for the appearance of drug resistance during treatment in a clinical setting. Development of tools providing new insight into RTK complex mechanisms is crucial to develop more effective RTK-targeting drugs. In this study, we present a live-cell ebBRET-based biosensor platform which includes 12 distinct biosensors for monitoring SH2-domain containing proteins-mediated downstream signaling of RTKs, hence following the activation of MAPK, Akt and PKC pathways. These biosensors are designed to measure receptor proximal events that are engaged upon receptor activation at the plasma membrane (PM) and early endosomal compartments (EE). Using EGFR and two of its ligands as a model system, we showed the capacity of these biosensors to differentiate unique signaling signatures of EGFR, at the PM or the EE, with EGF and Epiregulin ligands displaying differences of efficacy and potency. Indeed, EGF was more potent and efficacious than Epiregulin on all the studied pathways. Also, we detected activities at the EE with EGF stimulation but not with Epiregulin. Our data highlight the platform's capacity to follow the trafficking of RTK biosensors into different compartments and to reveal internalization selectivity and bias which can be observed with different ligands. Overcoming resistance has become an important challenge when developing new therapeutics for the treatment of cancer. We further demonstrated that EGFR deletions or single point mutations, found in Gliobastoma or NSCLC, have an impact on the constitutive activity of EGFR and the signaling profiles but also on inhibitor efficacies which could impact receptor trafficking from the PM to the EE. In addition, the sensitivity of the RTK platform allowed us to measure the signaling of endogenously expressed EGFR in pathophysiologically-relevant cell lines commonly used for their oncogenic properties, such as A431 human epidermoid carcinoma cells, N87 gastric carcinoma cells and T47D, MCF7 and SK-BR-3 breast adenocarcinoma cells. Finally, we illustrated, using BRET-based imaging, the recruitment of SH2 effectors at the PM or at the EE after a 10-minute or 60-minute incubation respectively with EGF, highlighting the translatability of the biosensor platform to microscopy. The ebBRET-based biosensor technology displayed new insights in RTK biology and revealed different modes of action, extensive RTK signal profiling, and trafficking of RTK effectors. It represents a powerful tool for the analysis of RTK mutations and for the identification of novel generation of TKIs and antibodies directed against RTKs. Citation Format: Florence Gross, Guilhem Dugast, Arturo Mancini, Hiroyuki Kobayashi, Michel Bouvier, Stephan Schann, Xavier Leroy, Laurent Sabbagh. EGFR signaling and pharmacology in oncology revealed with an innovative RTK biosensor technology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB187.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".