First-line mTOR inhibition in metastatic renal cell carcinoma (mRCC): An analysis from the International mRCC Database Consortium.
Bibliographic record
Abstract
430 Background: mTOR inhibitors (mTORi) are an important class of targeted therapies for mRCC that may act through regulation of mTOR as well as hypoxia-inducible factor and its resultant downstream angiogenesis pathways. FDA approval was based on efficacy in poor risk patients in the first-line setting for temsirolimus (T) and in sunitinib- and sorafenib-refractory patients for everolimus (E). Little is known about T’s effectiveness in good and intermediate risk patients and E’s outcomes in the first-line setting. Methods: We evaluated our international mRCC database to evaluate the outcomes of patients who received mTORi as their first-line targeted therapy. Results: Of the 2,370 patients in the database, 49 received a first-line mTORi (7 E, 42 T). Median age was 61 years and median KPS was 80%. 63% had clear cell and 37% had non-clear cell histology. 65% had prior nephrectomy. Of the 38 patients with available Heng prognostic risk criteria, 21%, 21%, and 58% were good, intermediate, and poor risk respectively. Median PFS and OS are detailed below. Objective responses and disease stabilization were achieved in 5% and 58%. Second-line therapy was administered in 21 patients of which 17 received VEGF inhibitors. Conclusions: Outcomes for good and intermediate risk patients treated with first-line mTORi were lower than historically expected for patients treated with VEGF targeted therapies. These results may be due to inclusion of non-clear cell histologies, treatment biases, small sample size, or other undefined confounders that need further exploration. Greater data capture of this important cohort of patients to confirm these results is planned and will be presented. [Table: see text]
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".