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3 recordsLinked to original sources
PubMed2026

Quality-of-life assessment in the randomized JBCRG-M06/EMERALD study of eribulin plus dual HER2 blockade in HER2-positive locally advanced or metastatic breast cancer.

BACKGROUND: Although taxanes are a mainstay treatment for locally advanced or metastatic breast cancer (LABC/MBC), they often impair quality of life (QoL). Treatments that avoid taxane-related QoL deteriorations would be valuable. METHODS: The JBCRG-M06/EMERALD trial (NCT03264547, UMIN000027938) compared eribulin with a taxane, each combined with trastuzumab and pertuzumab, in patients with human epidermal growth factor receptor type 2 (HER2)-positive LABC/MBC. QoL was assessed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Module C30 (EORTC QLQ-C30) version 3.0. QoL deterioration was defined as a decrease in the Global Health Status (GHS) score by ≥ 10 points (minimum clinically important difference), disease progression, or death. RESULTS: QoL data were available for 210 (of 224 randomized) and 205 (of 222 randomized) patients in the eribulin and taxane groups, respectively. The median (95% confidence interval) time to QoL deterioration was 7.16 (6.28-8.34) months in the eribulin group versus 4.57 (4.17-6.14) months in the taxane group, with a hazard ratio of 0.80 (95% confidence interval 0.65-0.98; log-rank P = 0.08). QoL was maintained at 6 and 12 months in greater proportions of the eribulin group (62.7% and 30.5%) compared with the taxane group (43.5% and 25.5%). GHS scores remained stable over time in the eribulin group. GHS deteriorated between weeks 9 and 27 in the taxane group (i.e. during treatment) with subsequent recovery toward baseline. CONCLUSIONS: Eribulin could help avoid the early deteriorations in QoL that occur during taxane therapy and maintain QoL for longer in patents with HER2-positive LABC/MBC receiving trastuzumab and pertuzumab.

Adult↗
PubMed2026

Eribulin versus taxane as first-line chemotherapy combined with dual HER2 blockade in patients with HER2-positive locally advanced or metastatic breast cancer: final survival outcomes of the JBCRG-M06/EMERALD study.

BACKGROUND: The phase III JBCRG-M06/EMERALD study was the first to show noninferior progression-free survival (PFS) of eribulin to taxane, combined with dual human epidermal growth factor receptor 2 (HER2) blockade (trastuzumab plus pertuzumab), as a first-line treatment for HER2-positive locally advanced breast cancer or metastatic breast cancer (LABC/MBC). We report final survival outcomes and biomarker analyses of the EMERALD trial. PATIENTS AND METHODS: Patients with HER2-positive LABC/MBC were randomly assigned 1:1 to either eribulin or physician-choice taxane (docetaxel or paclitaxel), both combined with trastuzumab plus pertuzumab, as first-line chemotherapy. PFS and overall survival (OS) were assessed through 30 June 2023 for PFS and 31 December 2024 for OS. Survival outcomes were compared between the eribulin and taxane groups and according to circulating tumor DNA (ctDNA) detection of PIK3CA mutations (PIK3CAm+; E542K, E545K, H1047R, and N345K single nucleotide variants) or HER2 amplification (HER2 amp+; ERBB2 copy number >2.5). RESULTS: Median OS was 78.5 months [95% confidence interval (CI) 64.3-not reached (NR)] for eribulin and was NR for taxane, with a hazard ratio of 1.25 (95% CI 0.92-1.71, log-rank P = 0.19). The 60-month OS rates were 59.7% and 65.2% for eribulin and taxane, respectively. Median OS and 60-month OS rates were numerically lower in ctDNA PIK3CAm+ patients, and greater in ctDNA HER2 amp+ patients for all patients and with stratification by treatment group. There were no statistical interactions between treatment group with either ctDNA PIK3CAm or ctDNA HER2 amp status. Similar patterns were observed for PFS. CONCLUSION: Final survival analysis revealed that median OS exceeded 6 years with eribulin or physician-choice taxane, combined with trastuzumab plus pertuzumab, as first-line chemotherapy for HER2-positive LABC/MBC, with no significant differences between the two groups. ctDNA PIK3CAm+ status was a poor prognostic factor. ctDNA HER2 amp+ was associated with longer survival.

Aged↗
PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗