Randomized Phase II Trial of Sapanisertib ± TAK-117 vs. Everolimus in Patients With Advanced Renal Cell Carcinoma After VEGF-Targeted Therapy
Bibliographic record
Abstract
BACKGROUND: Sapanisertib, a dual mTORC1/2 inhibitor, may offer more complete inhibition of the PI3K/AKT/mTOR pathway than mTORC1 inhibitors, such as everolimus. This phase II study evaluated the efficacy and safety of single-agent sapanisertib and sapanisertib plus the PI3Kα inhibitor TAK-117, vs. everolimus in patients with advanced clear cell renal cell carcinoma (ccRCC) that had progressed on or after VEGF-targeted therapy. MATERIALS AND METHODS: Patients with histologically confirmed, advanced ccRCC were randomized 1:1:1 to receive single-agent everolimus 10 mg once daily, single-agent sapanisertib 30 mg once weekly, or sapanisertib 4 mg plus TAK-117 200 mg, both once daily for 3 days/week, in 28-day cycles. The primary endpoint was progression-free survival (PFS). RESULTS: Ninety-five patients were treated with everolimus or sapanisertib (n = 32 each), or sapanisertib plus TAK-117 (n = 31). There were no significant differences in PFS among the 3 groups or across any subgroups. Median PFS was 3.8 months with everolimus vs. 3.6 months with sapanisertib (HR, 1.33; 95% CI, 0.75-2.36), and 3.1 months with sapanisertib plus TAK-117 (HR, 1.37; 95% CI, 0.75-2.52). No significant differences in overall survival were seen among groups. Overall response rate was 16.7%, 0%, and 7.1%, respectively. Discontinuations due to treatment-emergent adverse events were 15.6%, 28.1%, and 29.0%. CONCLUSION: Sapanisertib with or without TAK-117 was less tolerable and did not improve efficacy vs. everolimus in patients with advanced ccRCC who had relapsed after or were refractory to VEGF-targeted therapies. Dual mTORC1/2 inhibition may not be an effective therapeutic approach for these patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".