HER-2 negative

P2, N=366, Active, not recruiting, European Organisation for Research and Treatment of Cancer - EORTC | Trial completion date: Sep 2032 --> Sep 2026 | Trial primary completion date: Jun 2026 --> Sep 2026

P=N/A, N=6097, Completed, Brigham and Women's Hospital | Active, not recruiting --> Completed

P=N/A, N=100, Recruiting, N.N. Petrov National Medical Research Center of Oncology

P1, N=354, Active, not recruiting, AstraZeneca | Phase classification: P1/2 --> P1

The percentage of individuals with stage IV vs stages I to III diagnoses also increased. Although OS improved, research is warranted to determine factors contributing to increased incidence, including potential changes in natural history of breast cancer, disease screening, and incidence and mortality of other conditions.

Significant heterogeneity in HER2 expression between primary and metastatic gastric cancer underscores the need for reassessment of HER2 status in metastatic sites. Relying solely on primary tumor samples may lead to underestimation of HER2 positivity and suboptimal therapeutic decisions. Incorporating site-specific HER2 testing into clinical practice may enhance patient selection for HER2-targeted therapies and improve treatment outcomes.

De novo and metachronous MBC have different tumor biology, treatment patterns, and survival. In metachronous MBC patients, prior (neo)adjuvant systemic treatment was associated with worse survival compared to de novo MBC or patients with metachronous MBC without prior (neo)adjuvant treatment.

DCIS with high nuclear grade, comedonecrosis, a solid pattern, and the ER-/HER2+ or ER-/HER2- immunoprofile should be considered high risk for invasion. They should be approached as invasive tumors and evaluated in terms of lymph node staging and neoadjuvant treatment.

P1, N=175, Not yet recruiting, Immunome, Inc.

P1/2, N=1410, Active, not recruiting, Klus Pharma Inc. | Trial completion date: Jul 2026 --> Dec 2026 | Trial primary completion date: Dec 2025 --> Dec 2026

Kaplan-Meier analysis demonstrated that patients classified as low-risk by the model had a significantly better prognosis than those classified as high-risk, with a hazard ratio of 4.1 (P < 0.001) and 2.0 (P < 0.01) on the two external cohorts that include patient outcomes. This artificial intelligence-driven solution offers a rapid, cost-effective, and scalable alternative to genomic testing, with the potential to enhance personalized treatment planning, especially in resource-constrained settings.

However, these findings should be interpreted with caution given the study's retrospective nature and inherent limitations. Prospective studies are needed to confirm these observations and identify patient subgroups most likely to benefit from each treatment sequence.