Strategies for Overcoming Resistance in Tumours Harboring BRAF Mutations
Bibliographic record
Abstract
The development of resistance to previously effective treatments has been a challenge for health care providers and a fear for patients undergoing cancer therapy. This is an unfortunately frequent occurrence for patients undergoing targeted therapy for tumours harboring the activating V600E mutation of the BRAF gene. Since the initial identification of the BRAF mutation in 2002, a series of small molecular inhibitors that target the BRAFV600E have been developed, but intrinsic and acquired resistance to these drugs has presented an ongoing challenge. More recently, improvements in therapy have been achieved by combining the use of BRAF inhibitors with other drugs, such as inhibitors of the downstream effector mitogen activated protein kinase (MAPK)/extracellular-signal regulated kinase (ERK) kinase (MEK). Despite improved success in response rates and in delaying resistance using combination therapy, ultimately, the acquisition of resistance remains a concern. Recent research articles have shed light on some of the underlying mechanisms of this resistance and have proposed numerous strategies that might be employed to overcome or avoid resistance to targeted therapies. This review will explore some of the resistance mechanisms, compare what is known in melanoma cancer to colorectal cancer, and discuss strategies under development to manage the development of resistance.
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 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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".