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Clinical benefit of systemic therapies for recurrent ovarian cancer-ESMO-MCBS scores
K E Broekman1, M van Kruchten1, H van Tinteren2
1Department of Medical Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Background:
Licensed systemic treatment options for platinum-sensitive recurrent ovarian cancer are platinum-based chemotherapy and maintenance treatment with bevacizumab and poly (ADP-ribose) polymerase inhibitors. For platinum-resistant disease, several non-platinum options are available. We aimed to assess the clinical benefit of these treatments according to the European Society of Medical Oncology (ESMO)-Magnitude of Clinical Benefit Scale (MCBS).
Materials And Methods:
A PubMed search was carried out including all studies evaluating systemic treatment of recurrent epithelial ovarian cancer, from 1990 onwards. Randomised trials with an adequate comparator and design showing a statistically significant benefit of the study arm were independently scored by two blinded observers using the ESMO-MCBS.
Results:
A total of 1127 papers were identified, out of which 61 reported results of randomised trials of sufficient quality. Nineteen trials showed statistically significant results and the studied treatments were graded according to ESMO-MCBS. Only three treatments showed substantial benefit (score of 4 on a scale of 1-5) according to the ESMO-MCBS: platinum-based chemotherapy with paclitaxel in the platinum-sensitive setting and the addition of bevacizumab to chemotherapy in the platinum-resistant setting. The WEE1 inhibitor adavosertib (not licensed) also scores a 4, based on a recent small phase II study. Assessment of quality-of-life data and toxicity using the ESMO-MCBS showed to be complex, which should be taken into account in using this score for clinical decision making.
Conclusion:
Only a few licensed systemic therapies for recurrent ovarian cancer show substantial clinical benefit based on ESMO-MCBS scores. Trials demonstrating overall survival benefit are sparse.
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