Early CRP kinetics to predict long‐term efficacy of first‐line immune‐checkpoint inhibition combination therapies in metastatic renal cell carcinoma: an updated multicentre real‐world experience applying different CRP kinetics definitions
Bibliographic record
Abstract
Abstract Objectives Although biomarkers predicting therapy response in first‐line metastatic renal carcinoma (mRCC) therapy remain to be defined, C‐reactive protein (CRP) kinetics have recently been associated with immunotherapy (IO) response. Here, we aimed to assess the predictive and prognostic power of two contemporary CRP kinetics definitions in a large, real‐world first‐line mRCC cohort. Methods Metastatic renal carcinoma patients treated with IO‐based first‐line therapy within 5 years were retrospectively included in this multicentre study. According to Fukuda et al. , patients were defined as ‘CRP flare‐responder’, ‘CRP responder’ and ‘non‐CRP responder’; according to Ishihara et al. , patients were defined as ‘normal’, ‘normalised’ and ‘non‐normalised’ based on their early CRP kinetics. Patient and tumor characteristics were compared, and treatment outcome was measured by overall (OS) and progression‐free survival (PFS), including multivariable Cox regression analyses. Results Out of 316 mRCC patients, 227 (72%) were assigned to CRP groups according to Fukuda. Both CRP flare‐ (HR [Hazard ratio]: 0.59) and CRP responders (HR: 0.52) had a longer PFS, but not OS, than non‐CRP responders. According to Ishihara, 276 (87%) patients were assigned to the respective groups, and both normal and normalised patients had a significantly longer PFS and OS, compared with non‐normalised group. Conclusion Different early CRP kinetics may predict therapy response in first‐line mRCC therapy in a large real‐world cohort. However, further research regarding the optimal timing and frequency of measurement is needed.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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; 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".