Zhang Chenlu, Li Nan, Zhang Pengxia, Jiang Zhimei, Cheng Yichao, Li Huiqing, Pang Zhenfei
School of Basic Medical Sciences, Jiamusi University No. 258, Xuefu Street, Xiangyang District, Jiamusi 154007, Heilongjiang, China.
Heilongjiang Provincial Center for Prevention and Treatment of Cerebral Palsy in Children Postdoctoral Research Workstation No. 258, Xuefu Street, Xiangyang District, Jiamusi 154007, Heilongjiang, China.
Am J Cancer Res. 2024 Dec 15;14(12):5614-5627. doi: 10.62347/MWNZ5609. eCollection 2024.
Breast cancer is the most common malignant tumour in women, with more than 685,000 women dying of breast cancer each year. The heterogeneity of breast cancer complicates both treatment and diagnosis. Traditional methods based on histopathology and hormone receptor status are now no longer sufficient. Recently, advances in multi-omics techniques, including genomic, proteomic, and transcriptomic analyses, have deepened our understanding of breast cancer. Combining these approaches allows for precise molecular subtyping, which is essential for the detection of key mutations, protein interactions and gene expression patterns that are highly relevant to different therapeutic strategies. Genomic analyses have been effectively identifying key mutations in cancer. Meanwhile, proteomics and transcriptomics complement by identifying new therapeutic targets and elucidating gene expression dynamics. Integrating multi-omics and conventional diagnostics improves tumour characterisation and enables prognostic accuracy comparable to established standards and treatment response. Existing and emerging technologies enable real-time enhanced tumour follow-up and data analysis through liquid biopsy and artificial intelligence, respectively. Despite these clinical implementation challenges, multi-omics including clinical phenotyping offers significant potential for precision breast cancer treatment. This article describes recent advances in molecular subtyping and multi-omics technologies that are driving key innovations to optimise patient outcomes and further develop personalised medicine in the context of breast cancer care.
乳腺癌是女性中最常见的恶性肿瘤,每年有超过68.5万名女性死于乳腺癌。乳腺癌的异质性使治疗和诊断都变得复杂。基于组织病理学和激素受体状态的传统方法如今已不再足够。最近,包括基因组、蛋白质组和转录组分析在内的多组学技术取得的进展,加深了我们对乳腺癌的理解。结合这些方法能够实现精确的分子分型,这对于检测与不同治疗策略高度相关的关键突变、蛋白质相互作用和基因表达模式至关重要。基因组分析已有效地识别出癌症中的关键突变。与此同时,蛋白质组学和转录组学通过识别新的治疗靶点和阐明基因表达动态起到补充作用。整合多组学和传统诊断方法可改善肿瘤特征描述,并实现与既定标准相当的预后准确性和治疗反应。现有技术和新兴技术分别通过液体活检和人工智能实现实时增强的肿瘤随访和数据分析。尽管存在这些临床实施方面的挑战,但包括临床表型分析在内的多组学在精准乳腺癌治疗方面具有巨大潜力。本文介绍了分子分型和多组学技术的最新进展,这些进展推动了关键创新,以优化患者治疗效果,并在乳腺癌护理背景下进一步发展个性化医疗。