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Record W4393095493 · doi:10.1158/1538-7445.am2024-1216

Abstract 1216: Pan-RAS inhibition by a tumor-targeted biotherapeutic

2024· article· en· W4393095493 on OpenAlexaff
Greg L. Beilhartz, Huazhu Liang, Molly L. Udaskin, Christine Ng, Nikolina Radulovich, Roman A. Melnyk

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsUniversity Health NetworkHospital for Sick Children
Fundersnot available
KeywordsCancer researchInternal medicineMedicine

Abstract

fetched live from OpenAlex

Abstract Oncogenic RAS signaling drives tumor growth in about 30% of human malignancies. In recent years the first mutant-specific inhibitors of KRAS G12C (sotorasib and adagrasib) entered the clinic. While some durable responses were noted in KRAS-mutant non-small cell lung cancer, the clinical experience of KRAS G12C inhibitors is marked by rapid drug resistance, either intrinsic or acquired, causing re-activation of the MAPK pathway. Common resistance mechanisms include compensatory signaling through the related RAS isoforms NRAS and HRAS, as well as the selection of non-G12C KRAS mutations, among others. The bacterial peptidase RRSP potently cleaves all RAS mutants and isoforms in the conserved Switch I region. We have shown that receptor-mediated intracellular delivery of RRSP using an engineered protein delivery system (RASx) can inactivate all RAS signaling in a cell, causing apoptosis in RAS-addicted tumor cells in vitro and in vivo. Since systemic pan-RAS inhibition is not likely to be tolerated therapeutically, precise tumor targeting is a necessary feature of a true pan-RAS inhibitor. Here, we describe the unique ability of the RASx system to specify the cell surface receptor used by RRSP to enter cells, allowing exquisite targeting and killing of KRAS, NRAS and HRAS-driven tumor cells while sparing healthy tissues. We screened a panel of patient-derived tumor organoids (PDOs), to demonstrate broad tumor cell killing across pancreatic and colorectal cancer-derived PDOs. 97% of KRAS mutant organoids were sensitive to RASx, independent of the specific mutation. Intriguingly, 46% of wildtype RAS PDOs were sensitive to RASx, including organoids with activating mutations in EGFR. In contrast, PDOs with mutations downstream of RAS such as BRAF V600E were resistant to RASx. Indeed, RASx is effective not only against RAS-addicted tumors, but also wildtype RAS tumors driven by upstream receptor tyrosine kinases, potentially expanding the clinical utility of this platform. RASx is a first-in-class, tumor-targeted pan-RAS inhibitor that represents a unique entry into the growing armamentarium against oncogenic RAS. Citation Format: Greg L. Beilhartz, Huazhu Liang, Molly Udaskin, Christine Ng, Nikolina Radulovich, Roman A. Melnyk. Pan-RAS inhibition by a tumor-targeted biotherapeutic [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 1216.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.043
GPT teacher head0.410
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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