Vascular endothelial growth factor-targeted therapy in patients with renal cell carcinoma pretreated with immune checkpoint inhibitors: A systematic literature review
Bibliographic record
Abstract
INTRODUCTION: We conducted a systematic literature review to identify evidence for use of vascular endothelial growth factor (VEGF)-targeted (anti-VEGF) treatment in patients with renal cell carcinoma (RCC) following prior checkpoint inhibitor (CPI)-based therapy. METHODS: This was a PRISMA-standard systematic literature review; registered with PROSPERO (CRD42021255568). Literature searches were conducted in MEDLINE®, Embase, and the Cochrane Library (January 28, 2021; updated September 13, 2022) to identify publications reporting efficacy/effectiveness and safety/tolerability evidence for anti-VEGF treatment in patients with RCC who had received prior CPI therapy. RESULTS: Of 2,639 publications screened, 48 were eligible and featured 2,759 patients treated in trials and 2,209 in real-world studies (RWS). Most patients with available data were treated with anti-VEGF tyrosine kinase inhibitor-based regimens (trials: 93 %; RWS: 100 %), most commonly cabozantinib, which accounted for 46 % of trial and 62 % of RWS patients in publications with available data. Collectively, there was consistent evidence of anti-VEGF treatment activity after prior CPI therapy. Activity was reported for all anti-VEGF regimens and regardless of prior CPI-based regimen. No new safety signals were detected for subsequent anti-VEGF therapy; no studies suggested increased immune-related adverse events associated with prior CPI therapy. The results were limited by data quality; study heterogeneity prohibited meta-analyses. CONCLUSION: Based on the available data (most commonly for cabozantinib), anti-VEGF therapy appears to be a rational treatment choice in patients with RCC who have progressed despite prior CPI-based therapy. Results from ongoing trials of combination anti-VEGF plus CPI regimen post prior CPI therapy trials will contribute more definitive evidence. PLAIN LANGUAGE SUMMARY: Anticancer treatments that work by reducing levels of a substance in the body called Vascular Endothelial Growth Factor are known as anti-VEGF drugs. Reducing VEGF levels helps to reduce blood supply to tumors, which can slow the speed at which the cancer grows. Some other types of anticancer drugs that help the immune system to fight cancer cells are called checkpoint inhibitors. Here, we looked at published studies that investigated how anti-VEGF drugs work, and what side effects they cause, in people who have already been treated with checkpoint inhibitors for a type of kidney cancer called renal cell carcinoma. We aimed to summarize the available evidence to help doctors decide how best to use anti-VEGF drugs in these patients. We found 48 studies that included almost 5,000 patients. The results of the studies showed that anti-VEGF drugs have anticancer effects in people with renal cell carcinoma who had already been treated with checkpoint inhibitors. All of the VEGF-targeting drugs had anticancer effects, irrespective of what checkpoint inhibitor treatment people had received before. There were different amounts of evidence available for the different anti-VEGF drugs. The anti-VEGF cabozantinib had the largest amount of evidence. Importantly, previous checkpoint inhibitor treatment did not seem to affect the number or type of side-effects associated with anti-VEGF drugs. Results from ongoing, well-designed studies will be helpful to confirm these results. Our findings may be useful for doctors considering using anti-VEGF drugs in patients with renal cell carcinoma who have received checkpoint inhibitor treatment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".