Direct GDP-KRAS <sup>G12C</sup> inhibitors and mechanisms of resistance: the tip of the iceberg
Bibliographic record
Abstract
Kirsten rat sarcoma viral oncogene homolog mutations are observed in 25% of lung adenocarcinoma and 40% of these are G12C mutations. Historically, no approved targeted agents were available for patients with any KRAS mutation, and response rates to standard-of-care therapies were suboptimal. Newly developed inhibitors directed toward KRAS G12C have been successful in clinical trials with overall response rates ranging between 32% and 46%, and two FDA approvals were granted in May 2021 and December 2022 as second-line or later monotherapies. However, rapid tumor resistance complicates their use as a monotherapy. With the rapid development of this novel class of inhibitors, it is important to discern the different types of tumor resistance that may arise and how each can differently contribute to tumor growth and survival. G12C inhibitor resistance is under investigation and combinations of therapies with G12C inhibitors have been proposed. Much of this insight is gleaned from preclinical investigations, as our knowledge of clinical resistance is in its infancy. In this review, we summarize the preclinical development of KRAS G12C inhibitors, their clinical evaluations, different types of resistance mechanisms to these compounds, and ways of overcoming them. Finally, we underscore the importance of basic and translational investigations of these molecules in a landscape where their clinical evaluations garner the most attention, and we set the stage for what is to come.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".