Primary resistance to tyrosine kinase inhibitors in patients with advanced renal cell carcinoma: state-of-the-science
Bibliographic record
Abstract
Although over 70% of patients with metastatic renal cell carcinoma (RCC) respond to initial therapy with tyrosine kinase inhibitors (disease control rate 70-80%), approximately 20-30% of patients do not respond to first-line therapy and progress within ≤3 months. Understanding the mechanisms of resistance to targeted therapies is vital in the development of prospectively defined sequences and because the choice of first-line therapy determines that of second and subsequent line therapy, identification of the optimal first-line therapy is a priority for clinicians treating patients with metastatic RCC. By preselecting those patients most likely to respond to antivascular endothelial growth factor therapy, clinicians can begin to optimize therapeutic strategies. This review focuses on primary antivascular endothelial growth factor-refractory patients and the move towards individualizing treatment for RCC. The authors include a review of the growing number of studies, as yet retrospective, which provide important information on the group of primary refractory patients with advanced RCC for whom the prognosis is not good. First, the percentage of primary refractory patients (26%) is in agreement with disease control rate - the sum of objective responses and disease stabilization, observed in registration studies for a range of tyrosine kinase inhibitors. Second, the prognosis for these patients is poor as they do not respond to first-line nor to second-line therapy, and changing the mechanism of action (with inhibition of mTOR pathway) does not appear to produce additional benefits. Third and most importantly, the results of these studies demonstrate the need to better characterize the mechanism of primary resistance to current therapeutic agents with the ultimate aim of developing a therapeutic strategy for this important subgroup of patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".