Amoy Diagnostics Co., Ltd.

Neoadjuvant therapies are essential for managing high-risk early-stage breast cancer, but their effectiveness is limited, necessitating the exploration of the optimal neoadjuvant treatment strategy based on innovative subtypes. Given the heterogeneity inherent in breast cancer, there is growing need for identifying novel molecular subtypes predictive of treatment response through multi-omic analyses. A comprehensive analysis was performed using data from 142 high-risk early breast cancer patients, including genomic, transcriptomic, proteomic, and phosphoproteomic profiles. The molecular subtypes were explored based on the biological characteristics and responses to neoadjuvant treatments. The results were also validated in the TCGA-breast cancer cohort and external dataset. Three molecular subtypes were identified, each associated with different optimal treatment strategies. The immune-activated (IA) subtype displayed a significantly higher pathologic complete response (pCR) rate when treated with platinum-based neoadjuvant regimens, and exhibited heightened immune cell infiltration. The vesicular transport pathway-activated (VT) subtype, characterized by vesicular transport pathway activation, showed a favorable pCR rate to anthracycline-based neoadjuvant chemotherapy. In contrast, the kinase activation (KA) subtype demonstrated limited responsiveness to both platinum and anthracycline-based treatments and featured enrichment in non-canonical TGF-β signaling, MAPK/ATM kinase activation, and angiogenic signatures. A seven-gene classifier linked to non-canonical TGF-β signaling was created to identify the KA subtype, achieving an area under the curve value of 90%. The drug vulnerability of breast cancer cells within the KA subtype indicated potential therapeutic effectiveness of ATR/ATM inhibitors and anti-angiogenic agents. This study defined three novel molecular subtypes in high-risk early-stage breast cancer patients and provided optimal therapeutic treatment strategies based on these subtypes. Therefore, important insights about intrinsic biological properties can form the basis for new cancer treatment strategies.

QL1706 has shown promising efficacy in solid tumors in a phase 1/1b study. Here, we report updated long-term survival outcomes and biomarker analyses. Among 468 patients treated with QL1706 (5 mg/kg), median progression-free survival (mPFS) and overall survival (mOS) are 1.5 and 14.2 months for non-small cell lung cancer (NSCLC), 1.9 and 20.2 months for nasopharyngeal carcinoma (NPC), and 4.2 and 18.6 months for cervical cancer (CC), respectively. Liver metastasis is correlated with poor progression-free survival (PFS) and overall survival (OS) in NSCLC and poor OS in CC, while elevated lactate dehydrogenase is linked to shorter PFS and OS in NPC. CDK4/11q13 diploid or the expression of GZMK^high & MYC^low distinguishes NPCs with the most favorable PFS. In NSCLC, PD-L1⁺/TIL⁺ or a low ARG1:CXCL13 ratio indicates better outcomes. QL1706 offers long-term survival benefits in solid tumors, with identified molecular markers aiding in selecting suitable candidates. This study has been registered on clinicaltrials.gov (NCT04296994 and NCT05171790).

High-quality, regulatory-grade databases for precise genetic variant interpretation are critically needed for Chinese populations, where existing fragmented databases impede clinical effectiveness evaluations. We developed BRCA-CN, a consortium blockchain-based governance framework specifically designed for BRCA gene variant interpretation in Chinese populations. Our framework compiled 66,485 variants from 6,031 samples across six Chinese laboratories. A 15-expert panel conducted systematic variant curation using unified interpretation standards based on ACMG/AMP guidelines. Smart contracts ensured data integrity and accountability throughout the consensus process. After deduplication, we established a comprehensive database of 950 unique variants (BRCA1: 365, BRCA2: 585), completing consensus reviews for 607 sites with 462 achieving definitive interpretations. Comparison with ClinVar revealed 83.6% concordance, with AI validation (PrimateAI, REVEL, EVE) confirming high interpretation accuracy. The blockchain framework successfully enabled secure cross-institutional collaboration while maintaining data sovereignty and regulatory compliance. BRCA-CN demonstrates the transformative potential of blockchain technology in genomic medicine, addressing critical challenges in data sharing, standardization, and regulatory oversight. This framework provides a robust foundation for clinical decision-making and establishes a replicable model for population-specific genomic databases. Access to the BRCA-CN portal, user guides, and test data is provided in the supplementary materials, available at: https://oxygen-chamber.mgi-tech.com/sdb2.