Immunotherapy efficacy and toxicity in a real-world elderly population.
Bibliographic record
Abstract
e15137 Background: Immunotherapy has emerged as an effective treatment option for the management of advanced cancers. However, the immune system modulation of this class of medication can cause immune related adverse events (irAEs). In this study, we explored the impact of aging on CTLA-4 and PDL-1 inhibitors efficacy and irAE in the context of real-world management of solid cancers. Methods: This retrospective study involved all non-study patients with histologically-confirmed metastatic or inoperable solid cancers receiving immunotherapy at Kingston Health Sciences Centre. We defined ‘Elderly’ as age ≥75. We collected treatment responses, efficacy, and irAEs as study outcomes. All statistical analyses were conducted under SPSS IBM for Windows version 24.0. Results: Our study (N = 78) had 29 (37%) patients age < 65, 26 (33%) patients age 65-74, and 29 (30%) patients age ≥75. Melanoma, non-small cell lung cancer, and renal cell carcinoma accounted for 70%, 22%, and 8% of the study population respectively. Distributions of ipilimumab (32%), nivolumab (33%), and pembrolizumab (35%) were similar in the study. The response rates were 28%, 27%, and 39% in the age < 65, age 64-74, age ≥75 groups respectively (P = 0.585). Kaplan-Meier curve showed a median survival of 28 months (12.28-43.9, 95% CI) and 17 months (0-36.9, 95% CI) in the age < 65 and age 64-74 groups respectively, and it has not been reached in the age > 75 (P = 0.319). There were no statistically significant differences found in terms of irAEs, multiple irAEs, severity of grade 3 or higher, types of irAEs, and irAEs resolution status when comparing between different age groups. Conclusions: Elderly patients are able to tolerate and gain significant benefit from immunotherapy at least as much as or even more than younger patients. The toxicity of the single agent immunotherapy is mild in this population, and the treatment is well-tolerated. Future studies in evaluating aging and combination immunotherapy would be required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".