Abstract A013: NF1 loss is syntetic lethal with Trastuzumab emtansine
Bibliographic record
Abstract
Abstract There is great interest in the identification of biomarkers to guide development of antibody-drug conjugates (ADC). Most research has focused on target expression, but key predictors of payload efficacy have not been indeitifed. NF1 is a tumor suppressor classically considered as an inhibitor of RAS signaling, and often mutated in metastatic HER2+ breast cancer (BC). We screened multiple approved drugs for differential sensitivityin CRISPR-engineeredf NF1 KO cells. HER2-targeted agents (small molecules or antibodies) were found to be less effective upon NF1 loss; surprisingly, we identified increased sensitivity to the approved ADC T-DM1, but not to Trastuzumab Deruxtecan (T-Dxd). We then elucidated the underlying molecular cause employing in vivo, in vitro and in vitro reductionist systems. To measure in vivo MT dynamics, we transiently transfected the MT end-binding protein EB3- GFP and reconstructed MT trajectories by live-cell imaging. Upon DM1 treatment, KO cells showed a highly significant reduction in MT speed, demonstrating a direct role for NF1 on MT dynamics in cells. In tubulin polymerization assays, recombinant NF1 greatly accelerated polymerization and completely rescued DM1-induced inhibition. NF1 induced significant MT bundling, a defining feature of many MT-associated proteins, which generates signal indistinguishable from true MT polymerization in turbidity assays. To follow the dynamics of individual microtubules, we applied Total Internal Reflection (TIRF) microscopy on glass-immobilized MTs. As expected, polymerization in the presence of NF1 led to a dose-dependent significant increase in MT dynamics (fraction of elongating MTs, elongation speed, catastrophe rate). Expectedly, DM1 led to significant reduction in the fraction of elongating MTs and speed, but these defects were completely or partially rescued by NF1. Importantly, DM1 did not only lead to MT shortening (as proposed by the current model), but also to clear and frequent MT fracturing, indicating that the drug is not only engaging MT ends but also intra-tubular binding sites. This is consistent with recent models of MT formation which incorporate the frequent presence of areas of discontinuity or damage induced by mechanical stress, exposing intratubular DM1 binding sites. Interestingly, adding NF1 to DM1-treated MTs generated areas of de novo intra-tubular tubulin insertion, coincident with damaged sites, suggesting an entirely novel role for NF1 in MT repair. In conclusion, we provide evidence for a model in which maytansinoids bind not only to soluble tubulin dimers and MT ends, but also to intratubular damaged sites. Thus, the number of binding sites in cells would be proportional to MT damage, suggesting a mechanism for differential efficacy across tumor types and a potential avenue for combinatorial drug development. These results prompt the use of NF1 as a biomarker to select patients for ADC treatment. Funding: FIEO fellowship 2023, AIRC (n25791), Italian MoH-Ricerca Corrente di Rete (ACCORD) 2022, Next Generation EU – PNRR M6C2 – PNRR-MAD-2022-12376934 Citation Format: Eleonora Messuti, Bruno Achutti Duso, Alessia Castiglioni, Giulia Tini, Emanuele Bonetti, Giuseppe Ciossani, Silvia Monzani, Daria Khuntsariya, Marcus Braun, Zdenek Lansky, Luigi Scietti, Luca Mazzarella. NF1 loss is syntetic lethal with Trastuzumab emtansine [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A013.
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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) | 0.009 | 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".