2026-10-12T19:13:54Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34026121 2026-09-29T17:28:23Z category_12 category_21 category_64 category_132 portal_5 item_type_6 month_year_09_2026

<p>PRISMA 2020 checklist.</p> Yu Xiao (150812) Jing Fu (177892) Juan Li (146180) Gang Hu (132827) Cell Biology Biotechnology Cancer Infectious Diseases required summary measures level summary statistics grade ae estimates available regimens differ analyzed exploratorily using 98 &# 8211 87 &# 8211 80 &# 8211 79 &# 8211 67 &# 8211 06 &# 8211 less favorable pfs analysis summarized pfs effect bayesian models connected overall networks bayesian network meta 24 &# 8211 19 &# 8211 16 ), ptn favorable survival profile xlink "> within highest confirmed orr analysis </ p indirect survival comparisons pneumonitis risk relative dominant survival networks compared therapies used xlink "> her2 dm1 vs lap xlink "> dominant pfs confirmed orr 19 ), pneumonitis networks targeted therapies sae comparisons nala network july 24 effects models survival ranking pneumonitis odds dominant trials treatment difference sparse sae quantitatively synthesized prespecified conversion phase ii odds ratios <div> <p>Background</p><p>HER2-positive advanced breast cancer remains challenging after trastuzumab-based therapy because available regimens differ in survival, response, and toxicity. We compared therapies used in second-line and later settings.</p> <p>Methods</p><p>This PRISMA-NMA-compliant review was registered with PROSPERO (CRD420251244047). PubMed, Embase, Web of Science, and CENTRAL were searched through July 24, 2026. Phase II or III randomized controlled trials after trastuzumab-based therapy were eligible. Bayesian network meta-analysis summarized PFS and OS as hazard ratios and confirmed ORR and safety outcomes as odds ratios. Random-effects models were used where heterogeneity was estimable; fixed-effect Bayesian models were used for the sparse SAE and ILD/pneumonitis networks. DoR was analyzed exploratorily using a fixed-effect Bayesian normal model after prespecified conversion of arm-level summary statistics; TTP was not quantitatively synthesized when the required summary measures were unavailable or clinically non-equivalent.</p> <p>Results</p><p>Sixteen reports representing seven independent randomized trial families and 3,339 unique randomized participants were included. Relative to T-DXd, T-DM1 had less favorable PFS (HR 3.30, 95% CrI 2.06–5.28) and OS (HR 1.52, 1.19–2.01). However, T-DXd was not represented in the second-line-only OS network or the Asia-dominant PFS/OS networks; therefore, its survival ranking reflects evidence primarily from mixed-line, non-Asia-dominant trials. T-DXd produced higher confirmed ORR than Lap-Cap (OR 7.43, 3.80–13.16), PTN-Cap (OR 3.35, 1.24–8.19), and T-DM1 (OR 6.31, 3.98–10.32). DoR showed no clear between-treatment difference in the EMILIA/NALA network, and TTP could not be quantitatively synthesized. Any-grade AE estimates did not indicate a safety advantage for T-DXd. SAE comparisons were also inconclusive: T-DM1 vs Lap-Cap OR 0.90 (0.67–1.21) and T-DXd vs T-DM1 OR 1.30 (0.87–1.93). T-DXd had higher ILD/pneumonitis odds than T-DM1 (OR 5.55, 2.79–12.16); LVD differences were unclear.</p> <p>Conclusion</p><p>Within the connected overall networks, T-DXd showed a favorable survival profile and the highest confirmed ORR, although its survival ranking was not estimable in the second-line-only OS or Asia-dominant survival networks. Its increased ILD/pneumonitis risk relative to T-DM1 is clinically important. Indirect survival comparisons and sparse safety networks require cautious interpretation.</p></div> 2026-09-29T17:28:10Z Text Journal contribution 10.1371/journal.pone.0358540.s010 https://figshare.com/articles/journal_contribution/_p_PRISMA_2020_checklist_p_/34026121 CC BY 4.0