Comorbidity burden and effectiveness of immunotherapy in patients with metastatic renal cell carcinoma.
Bibliographic record
Abstract
509 Background: Comorbidities pose a challenge in the treatment of patients with cancer and present a barrier to inclusion in clinical trials. While the effect of comorbidities on the efficacy of immunotherapy (IO) has been studied in various malignancies, it remains unclear in patients with metastatic renal cell carcinoma (mRCC) treated with IO-based combinations. Methods: Data from patients with mRCC receiving IO-based combinations (IO+IO or IO+anti-vascular endothelial growth factor (VEGF)) as first-line treatment were collected from the Dana-Farber Cancer Institute and the Tom Baker Cancer Centre-University of Calgary. The comorbidity burden was assessed at baseline using the Charlson Comorbidity Index (CCI). Patients were stratified into two groups: CCI-low (≤3) or CCI-high (>3), to predict overall survival (OS) through maximally selected rank statistics. The effect of CCI on OS and time-to-treatment failure (TTF) was assessed using multivariable Cox regression models. Results: Overall, 311 patients were included. The median age was 63 years (Q1-Q3: 58-69), and most patients had clear-cell mRCC (89.7%). A total of 167 (53.7%) and 144 (46.3%) patients were treated with IO+IO and IO+anti-VEGF, respectively. The most prevalent comorbidities were cardiovascular disease (20.2%) and diabetes (18.6%). In terms of CCI, 241 (77.5%) and 70 (22.5%) patients were categorized as CCI-low and CCI-high, respectively. Median follow-up was 40.8 months (Q1-Q3: 34.4-47.2) for OS. OS (aHR: 1.89, 95% CI: 1.16-3.06, p=0.010) and TTF (aHR: 1.56, 95% CI: 1.04-2.33, p=0.029) were worse in the CCI-high group (vs. CCI-low group) after adjusting for covariates (IMDC groups, age, Karnofsky score, histology, sarcomatoid features, sites of metastases, treatment type, and nephrectomy status). Rates of all adverse events (AEs) (66% vs. 65.5%) and immune-related AEs (42.1% vs. 35.9%) were comparable between the CCI-low and CCI-high groups, respectively. Conclusions: We report for the first time that comorbidity burden is an adverse prognostic factor in patients with mRCC undergoing IO-based combinations, highlighting the need for multidisciplinary care and tailored treatment strategies in this vulnerable patient population. Interestingly, despite the observed difference in survival outcomes, the incidence and profile of AEs were similar between the high- and low-comorbidity groups, suggesting that comorbidity burden does not substantially alter the safety profile of IO-based treatments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".