Real-World Analysis of Immunotherapy Related Toxicities in Patients with Triple Negative Breast Cancer Receiving Therapy per Keynote-522
Bibliographic record
Abstract
Early-stage triple-negative breast cancer (TNBC) carries a high risk of early recurrence and is associated with elevated mortality and morbidity. The KEYNOTE-522 trial, a randomized phase I study, investigated the efficacy of pembrolizumab, an immunotherapy agent, combined with chemotherapy in treating TNBC (Schmid et al., 2022). The trial demonstrated a pathological complete response rate of 64.8% in the chemotherapy-immunotherapy group compared to 51.2% in the chemotherapy-placebo group. Following these findings, pembrolizumab with neoadjuvant chemotherapy received Health Canada approval and has since become the standard of care for TNBC. While the KEYNOTE-522 trial provided pivotal insights, real-world data on the adverse events and toxicities of this regimen remain limited but are perceived significant (Marhold et al., n.d.). Emerging evidence suggests that toxicity rates in clinical practice may exceed those reported in the trial (SABCS 2022, n.d.). In this study, we retrospectively analyzed data from patients treated according to the KEYNOTE-522 protocol, examining demographic characteristics, treatment outcomes, and rates of immunotherapy-related toxicities to compare with trial data. Our analysis identified higher rates of grade 1, 2, and 3 toxicities in our cohort than those reported in KEYNOTE-522. Furthermore, a significant proportion of patients required discontinuation of immunotherapy due to adverse events. These findings highlight the challenges of translating clinical trial protocols into real-world practice, as well as the need for robust toxicity monitoring tools to optimize patient outcomes. Future research will focus on implementing an intuitive reporting tool to assist patients and healthcare providers in promptly identifying and managing grade 2 toxicities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".