Characterizing progression to subsequent lines of therapy in metastatic renal cell carcinoma (mRCC) after nivolumab plus ipilimumab (Nivo+Ipi): Results from the International mRCC Database Consortium (IMDC).
Bibliographic record
Abstract
4538 Background: Treatment patterns and number of lines of therapy for patients with mRCC are not well characterized in the era of immunoncology-based combinations. We aimed to quantify the attrition rates by line of therapy and to examine predictors of receiving second-line (2L) treatment. Methods: Using the IMDC, patients with mRCC who received first-line (1L) Nivo+Ipi were included. Clinical and pathologic characteristics and outcomes were extracted. Chi-square tests were used to compare categorical variables between patients who received 2L and those who did not. A logistic regression model was used to assess predictors of 2L therapy initiation in eligible patients. Results: 995 patients treated with 1L Nivo+Ipi were identified, of whom 704 stopped 1L and were thus eligible for 2L therapy. Reasons for stopping 1L included progressive disease (PD) in 39.5%, toxicity in 25.0%, death in 4.3%, complete response in 1.6% and other in 29.7%. Among 2L eligible patients, 410 (58.2%) received 2L whereas 294 (41.8%) did not. Patients who stopped 1L for PD were more likely to initiate 2L than those who stopped for other reasons (81.7% vs 43.0%, p <0.001). Patients who received 2L were more likely to have clear-cell histology (75.1 vs 62.9%, p=0.02), bone metastases (39.8 vs 29.6%, p=0.01), and only one site of metastases (18.3 vs 10.5%, p=0.01) and less likely to be poor risk by IMDC criteria (27.1 vs 34.4%, p=0.03). After adjusting for IMDC criteria, no predictors of receiving 2L therapy remained significant. The overall response rate to 1L therapy was lower in patients who received 2L than in those who did not: 18.5% (76/366) and 33.7% (99/245), respectively (p<0.001). Among 258 patients who stopped 2L, 145 (56.2%, overall 20.6%) started third-line (3L) therapy. Of the 98 patients who stopped 3L, 52 (53.1%, overall 7.4%) started fourth-line therapy. Conclusions: In our study, we found that over half of eligible patients received the subsequent line of therapy. We were unable to identify predictors of 2L therapy initiation. Attrition rates between lines of therapy have important implications for patient counseling, cost analyses, and clinical trial design. [Table: see text]
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".