Abstract 3580: Understanding and overcoming innate and acquired resistance to type I and II RAF inhibitors in anaplastic thyroid cancer using translational functional genomics
Bibliographic record
Abstract
Abstract Anaplastic thyroid cancer (ATC) is one of the most lethal human cancers, with some patients succumbing to the disease within weeks of diagnosis. Despite a subset of BRAFV600E mutant ATC patients responding to monomeric type I RAF inhibitor (RAFi) dabrafenib in combination with MEK inhibitor (MEKi) trametinib, almost all patients rapidly develop adaptive or acquired resistance. These patients, along with those who do not harbor the BRAFV600E alteration, have limited treatment options. To understand the mechanism of resistance to dabrafenib and trametinib, we utilized multi-region whole genome sequencing and single nuclei profiling of ATC patient tumors to unravel genomic, transcriptomic, and microenvironmental evolution during type I RAFi and MEKi therapy. Single-cell nuclei sequencing of matched primary and resistant ATC patient tumors identified transcriptomic reactivation of MAPK-pathway, along with immunosuppressive macrophage proliferation, underlie the development of acquired resistance. Our translation genomics led us to hypothesize that deeper inhibition of the MAPK-pathway can be efficacious in overcoming treatment resistance. Screening of a panel of type II RAFis revealed that ATC cell lines are exquisitely sensitive to the type II RAFi naporafenib. We further demonstrate that naporafenib in combination with MEKi trametinib can durably and robustly overcome both innate and acquired treatment resistance to dabrafenib and trametinib using ATC cell lines and patient-derived xenograft models, including in a PDX model from an ATC patient who developed acquired resistance to dabrafenib and trametinib. Finally, we describe a novel mechanism of acquired resistance to type II RAF inhibitor and MEK inhibitor through compensatory mutations in MAST1. Taken together, our work using translational and functional genomics have unraveled the differential mechanisms of treatment resistance to type I and type II RAFi in combination with trametinib, and rationalizes the clinical investigation of type II RAFi in the setting of thyroid cancer. Citation Format: Peter Zeng, Jalna Meens, John Barrett, Matthew J. Cecchini, Sarah B. Ryan, Nachuan Pan, Amir Karimi, Laura Jarycki, Alice E. Dawson, Mushfiq Shaikh, David Palma, Eric Winquist, Paul C. Boutros, Laurie Ailles, Anthony C. Nichols. Understanding and overcoming innate and acquired resistance to type I and II RAF inhibitors in anaplastic thyroid cancer using translational functional genomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3580.
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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) | 0.001 | 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".