Proteomic Perspectives on KRAS-Driven Cancers and Emerging Therapeutic Approaches
Bibliographic record
Abstract
KRAS mutations are implicated in approximately 23% of all human malignancies, with particularly high prevalence in pancreatic ductal adenocarcinoma (PDAC) (~92%), colorectal cancer (CRC) (~49%), and non-small cell lung cancer (NSCLC) (~35%). The recent approval of the KRASG12C-specific inhibitors for NSCLC represents a pivotal advancement in KRAS-targeted therapy. Nevertheless, the emergence of intrinsic and acquired resistance to KRAS-targeted therapies poses a significant clinical obstacle to targeting KRAS, which necessitates a deeper understanding of the resistance mechanisms. Recent progress in proteomic studies has enabled comprehensive profiling of the proteomic alterations driven by KRAS mutations, offering valuable insights into the disrupted KRAS interactome, aberrant signaling pathways and dysregulated cellular processes contributing to tumorigenesis. This review discusses current knowledge on proteomic alterations associated with oncogenic KRAS mutations, with particular focus on allele-specific proteome signatures and the roles of post-translational modifications (PTMs) of KRAS in modulating the functional networks. Furthermore, we highlight recent therapeutic advances targeting KRAS variants and discuss emerging resistance mechanisms from a proteomics-informed perspective.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".