Outcomes of Patients with Metastatic Renal Cell Carcinoma Treated with Targeted Therapy After Immuno-oncology Checkpoint Inhibitors
Bibliographic record
Abstract
BACKGROUND: Immuno-oncology (IO) therapies have changed the treatment standards of metastatic renal cell carcinoma (mRCC). However, the effectiveness of targeted therapy following discontinuation of IO therapy in real-world settings has not been well studied. OBJECTIVE: To describe treatment sequence and assess clinical effectiveness of targeted therapy for mRCC patients who received prior IO therapy. DESIGN, SETTING, AND PARTICIPANTS: A retrospective, longitudinal cohort study using data from eight international cancer centers was conducted. Patients with mRCC were ≥18yr old, received IO therapy in any line, and initiated targeted therapy following IO therapy discontinuation. INTERVENTION: Patients were treated with vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFR-TKIs) or mammalian target of rapamycin inhibitors (mTORIs). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Outcomes were time to treatment discontinuation (TTD), overall survival (OS), and objective response rate (ORR). Crude and adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) were estimated using Cox proportional hazard models. Models were adjusted for age, sex, therapy line, and International Metastatic RCC Database Consortium risk group. RESULTS AND LIMITATIONS: Among 314 patients, 276 (87.9%) and 38 (12.1%) were treated with VEGFR-TKI and mTORI therapy, respectively. The most common tyrosine kinase inhibitor treatments were axitinib, cabozantinib, and sunitinib following IO therapy. In adjusted models, patients treated with VEGFR-TKI versus mTORI therapy had lower hazard of TTD after IO treatment (aHR=0.46; 95% CI: 0.30-0.71; p < 0.01). One-year OS probability (65% vs 47%, p < 0.01) and proportion of ORR (29.8% vs 3.6%, p < 0.01) were significantly greater for patients treated with VEGFR-TKIs versus those treated with mTORIs. CONCLUSIONS: Targeted therapy has clinical activity following IO treatment. Patients who received VEGFR-TKIs versus mTORIs following IO therapy had improved clinical outcomes. These findings may help inform treatment guidelines and clinical practice for patients post-IO therapy. PATIENT SUMMARY: Patients may continue to experience clinical benefits from targeted therapies after progression on immuno-oncology treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".