Abstract 4882: Identification and optimization of chemical inhibitors that directly target KRAS G12D mutant
Bibliographic record
Abstract
Abstract KRAS mutations are enriched in three lethal cancers, such as pancreatic ductal adenocarcinoma (PDAC) (> 90% of cases), colorectal cancer (CRC) (~50%) and lung cancer (~25%). Within these cancers, specific KRAS mutations dominate. KRAS G12D is the most common mutation in pancreatic and colorectal cancer. To date, accumulating studies have firmly established that constitutive activation of KRAS mutants is a driver of PDAC and CRC. Inhibitors of the downstream components of KRAS signaling only have limited success. Due to the high affinity of KRAS with GTP and no other obvious binding pocket on the KRAS surface for the design of inhibitors, KRAS has been considered ‘undruggable' for a long period of time. As a breakthrough, a novel direct inhibitor of the KRASG12C mutant was discovered in 2013, which forms a covalent bond with the cysteine at residue 12. However, such KRASG12C inhibitor is inactive against the G12D mutant as there is no Cysteine at residue 12. In this project, we have learnt from the excellent work of the G12C inhibitors and identified a novel compound as a direct inhibitor of the KRASG12D mutant. Importantly, this compound is active against the G12D mutant, but inactive against the KRAR wild-type. We demonstrated that our compound at 10 uM significantly inhibits the KRAS signaling in PANC10.05 pancreatic cancer cells and SNU-C2B colorectal cancer cells, both of which express endogenous KRASG12D mutant. Citation Format: Xiaohong Tian, Guoyan Geng, Jian Hui Wu. Identification and optimization of chemical inhibitors that directly target KRAS G12D mutant [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4882.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".