Phase Ib Study of Atezolizumab Plus Interferon-α with or without Bevacizumab in Patients with Metastatic Renal Cell Carcinoma and Other Solid Tumors
Bibliographic record
Abstract
This Phase Ib study combined programmed death-ligand 1 inhibitor, atezolizumab, with other immunomodulatory agents in locally advanced and metastatic solid tumors. Arms B-D evaluated atezolizumab plus interferon-α, with/without vascular endothelial growth factor inhibitor, bevacizumab, in renal cell carcinoma (RCC) and other solid tumors. Arm B predominantly recruited patients with previously treated RCC or melanoma to receive atezolizumab plus interferon α-2b. Arm C investigated atezolizumab plus polyethylene glycol (PEG)-interferon α-2a in previously treated RCC. Arm D evaluated atezolizumab plus PEG-interferon α-2a and bevacizumab. Primary objectives were safety and tolerability; secondary objectives included clinical activity. Combination therapy was well tolerated, with safety profiles consistent with known risks of individual agents. The most frequent treatment-related toxicities were fatigue, chills, and pyrexia. The objective response rate (ORR) in arm B was 20.0% overall and 17.8% in patients with previously treated checkpoint inhibitor–naive RCC (n = 45). No responses were reported in arm C. The highest ORR in arm D was 46.7% in patients with treatment-naive RCC (n = 15). Data showed preliminary clinical activity and acceptable tolerability of atezolizumab plus interferon α-2b in patients with previously treated checkpoint inhibitor–naive RCC and of atezolizumab plus PEG-interferon α-2a and bevacizumab in patients with treatment-naive RCC.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".