First-Line Immune Checkpoint Inhibitor-Based Therapy for Metastatic Renal Cell Carcinoma: A Systematic Review
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (CPIs) have come to the forefront as a major component of the management of metastatic renal cell carcinoma ( mRCC). Over a short period of time, several studies have shown benefit in using these agents in the first-line setting. Objective: In this systematic review, the available evidence regarding the use of CPI-based regimens in previously untreated mRCC was reviewed. Methods: A systematic search for phase II and III studies was conducted of the PubMed and Embase databases as per the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. The search retrieved abstracts to February 1, 2020. Data was compiled and summarized in narrative and tabular formats. Results: Fifty-five abstracts from 11 clinical trials were included, including four phase III clinical trials and seven phase II trials. The most recent phase III data demonstrates overall survival (OS) benefit for ipilimumab plus nivolumab (for intermediate and poor risk patients) and pembrolizumab plus axitinib combination regimens over sunitinib. Two other regimens (avelumab plus axitinib and atezolizumab plus bevacizumab) have shown benefits in progression free survival, but not in OS to date. Toxicity data shows varying patterns of adverse events between the four treatments. Phase II data indicate CPI has activity as a single agent, and in patients with non-clear cell subtypes of RCC. Conclusions: CPI-based regimens improve outcomes in virtually all subgroups of mRCC patients when used as front-line therapy. This is certain to change the landscape of mRCC 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 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".