Defining and Targeting Adaptations to Oncogenic KRASG12C Inhibition Using Quantitative Temporal Proteomics
Bibliographic record
Abstract
Covalent inhibitors of the KRAS G12C oncoprotein have recently been developed and are being evaluated in clinical trials. Resistance to targeted therapies is common and may limit long-term efficacy of KRAS inhibitors (KRASi). To identify pathways of adaptation to KRASi and predict drug combinations that circumvent resistance, we use mass-spectrometry-based quantitative temporal proteomics to profile the proteomic response to KRASi in pancreatic and lung cancer 2D and 3D cellular models. We quantify 10,805 proteins, representing the most comprehensive KRASi proteome (https://manciaslab.shinyapps.io/KRASi/). Our data reveal common mechanisms of acute and long-term response between KRAS G12C -driven tumors. Based on these proteomic data, we identify potent combinations of KRASi with phosphatidylinositol 3-kinase (PI3K), HSP90, CDK4/6, and SHP2 inhibitors, in some instances converting a cytostatic response to KRASi monotherapy to a cytotoxic response to combination treatment. Overall, using quantitative temporal proteomics, we comprehensively characterize adaptations to KRASi and identify combinatorial regimens with potential therapeutic utility.
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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 | 0.001 | 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".