Metastatic triple-negative breast cancer (mTNBC) is a poor prognostic disease with limited treatments and un-certain therapeutic algorithms. We performed a systematic review and multiple Bayesian network meta-analyses according to treatment line to establish an optimal therapeutic sequencing strategy for this lethal disease. We included 125 first-line trials (37,812 patients) and 33 s/further-lines trials (11,321 patients). The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall response rates (ORR), overall survival (OS) and safety, for first and further lines, separately. We also estimated separate treatment rankings for the first and subsequent lines according to each endpoint, based on (surface under the cumulative ranking curve) SUCRA values. No first-line treatment was associated with superior PFS and OS than paclitaxel +/- bevacizumab. Platinum-based polychemotherapies were generally superior in terms of ORR, at the cost of higher toxicity.. PARP-inhibitors in germline-BRCA1/2-mutant patients, and immunotherapy + chemotherapy in PD-L1 -positive mTNBC, performed similar to paclitaxel +/- bevacizumab. In PD-L1-positive mTNBC, pembrolizumab + chemotherapy was better than atezolizumab + nab-paclitaxel in terms of OS according to SUCRA values. In second/further-lines, sacituzumab govitecan outperformed all other treatments on all endpoints, followed by PARP-inhibitors in germline-BRCA1/2-mutant tumors. Trastuzumab deruxtecan in HER2-low mTNBC performed similarly and was the best advanced-line treatment in terms of PFS and OS after sacituzumab govitecan, ac-cording to SUCRA values. Moreover, comparisons with sacituzumab govitecan, talazoparib and olaparib were not statistically significant. The most effective alternatives or candidates for subsequent lines were represented by nab-paclitaxel (in ORR), capecitabine (in PFS) and eribulin (in PFS and OS).

Multiple Bayesian network meta-analyses to establish therapeutic algorithms for metastatic triple negative breast cancer

Mustacchi, Giorgio;Generali, Daniele
2022-01-01

Abstract

Metastatic triple-negative breast cancer (mTNBC) is a poor prognostic disease with limited treatments and un-certain therapeutic algorithms. We performed a systematic review and multiple Bayesian network meta-analyses according to treatment line to establish an optimal therapeutic sequencing strategy for this lethal disease. We included 125 first-line trials (37,812 patients) and 33 s/further-lines trials (11,321 patients). The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall response rates (ORR), overall survival (OS) and safety, for first and further lines, separately. We also estimated separate treatment rankings for the first and subsequent lines according to each endpoint, based on (surface under the cumulative ranking curve) SUCRA values. No first-line treatment was associated with superior PFS and OS than paclitaxel +/- bevacizumab. Platinum-based polychemotherapies were generally superior in terms of ORR, at the cost of higher toxicity.. PARP-inhibitors in germline-BRCA1/2-mutant patients, and immunotherapy + chemotherapy in PD-L1 -positive mTNBC, performed similar to paclitaxel +/- bevacizumab. In PD-L1-positive mTNBC, pembrolizumab + chemotherapy was better than atezolizumab + nab-paclitaxel in terms of OS according to SUCRA values. In second/further-lines, sacituzumab govitecan outperformed all other treatments on all endpoints, followed by PARP-inhibitors in germline-BRCA1/2-mutant tumors. Trastuzumab deruxtecan in HER2-low mTNBC performed similarly and was the best advanced-line treatment in terms of PFS and OS after sacituzumab govitecan, ac-cording to SUCRA values. Moreover, comparisons with sacituzumab govitecan, talazoparib and olaparib were not statistically significant. The most effective alternatives or candidates for subsequent lines were represented by nab-paclitaxel (in ORR), capecitabine (in PFS) and eribulin (in PFS and OS).
2022
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https://www.sciencedirect.com/science/article/pii/S0305737222001372?via=ihub
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/3053378
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