進行メラノーマにおけるマルチオミクス誘導療法の安全性:一致させた比較コホート分析
Benedict Gosztonyi1, Tarun Mehra1, Gabriele Gut1
1Department of Medical Oncology and Hematology, University Hospital Zurich, Medical Faculty, University of Zurich, Zurich, Switzerland.
Purpose:
Melanoma treatment has advanced with targeted therapies and immunotherapies, but challenges like resistance and response variability remain. Traditional clinical trials and diagnostics struggle to address the molecular heterogeneity of cancer, limiting their effectiveness in overcoming these challenges. The prospective, multicentric observational Tumor Profiler (TuPro) precision oncology project demonstrated the feasibility and the potential of multi-omics-guided therapy selection to account for individual tumor complexity and improve clinical outcomes, particularly in later treatment lines. However, the safety of this multi-omics-guided approach has not yet been established.
Materials And Methods:
We compared the TuPro melanoma cohort (n = 98), which received multi-omics-guided selection of adjuvant, standard of care and beyond standard-of-care therapies in patients with advanced melanoma, to a matched, retrospective synchronous cohort that did not undergo multi-omics tumor profiling. Adverse events, hospitalizations, and adverse event-related costs were analyzed to assess safety and clinical impact.
Results:
TuPro-guided therapy selection enabled more treatments beyond standard of care (SOC) without increasing high-grade adverse events, hospitalizations, or adverse event-related costs. However, we observed a higher incidence and earlier onset of predominantly low-grade adverse events, primarily within the SOC setting, with shorter durations and higher recovery rates.
Conclusion:
These findings support the clinical feasibility and safety of multi-omics-guided therapies, demonstrating their potential to expand treatment options and efficacy without necessarily increasing toxicity. Prospective studies are needed to validate these results, and further research should focus on leveraging multi-omics data to predict and mitigate adverse events.
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