Dwivedi Shailendra, Purohit Purvi, Misra Radhieka, Lingeswaran Malavika, Vishnoi Jeewan Ram, Pareek Puneet, Misra Sanjeev, Sharma Praveen
1Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, 342005 India.
2Under-graduate Medical Scholar, Era's Lucknow Medical College and Hospital, Lucknow, 226003 India.
Indian J Clin Biochem. 2019 Jan;34(1):3-18. doi: 10.1007/s12291-019-0811-0. Epub 2019 Jan 8.
Breast cancer is recognized for its different clinical behaviors and patient outcomes, regardless of common histopathological features at diagnosis. The heterogeneity and dynamics of breast cancer undergoing clonal evolution produces cells with distinct degrees of drug resistance and metastatic potential. Presently, single cell analysis have made outstanding advancements, overshadowing the hurdles of heterogeneity linked with vast populations. The speedy progression in sequencing analysis now allow unbiased, high-output and high-resolution elucidation of the heterogeneity from individual cell within a population. Classical therapeutics strategies for individual patients are governed by the presence and absence of expression pattern of the estrogen and progesterone receptors and human epidermal growth factor receptor 2. However, such tactics for clinical classification have fruitfulness in selection of targeted therapies, short-term patient responses but unable to predict the long-term survival. In any phenotypic alterations, like breast cancer disease, molecular signature have proven its implication, as we aware that individual cell's state is regulated at diverse levels, such as DNA, RNA and protein, by multifaceted interplay of intrinsic biomolecules pathways existing in the organism and extrinsic stimuli such as ambient environment. Thus for complete understanding, complete profiling of single cell requires a synchronous investigations from different levels (multi-omics) to avoid incomplete information produced from single cell. In this article, initially we briefed on novel updates of various methods available to explore omics and then we finally pinpointed on various omics (i.e. genomics, transcriptomics, epigenomics, proteomics and metabolomics) data and few special aspects of circulating tumor cells, disseminated tumor cells and cancer stem cells, so far available from various studies that can be used for better management of breast cancer patients.
乳腺癌因其不同的临床行为和患者预后而被认知，无论诊断时的常见组织病理学特征如何。经历克隆进化的乳腺癌的异质性和动态性产生了具有不同耐药程度和转移潜能的细胞。目前，单细胞分析取得了显著进展，克服了与大量细胞群体相关的异质性障碍。测序分析的快速发展现在能够对群体中单个细胞的异质性进行无偏倚、高产量和高分辨率的阐释。针对个体患者的传统治疗策略由雌激素和孕激素受体以及人表皮生长因子受体2的表达模式的有无来决定。然而，这种临床分类策略在选择靶向治疗、短期患者反应方面有成效，但无法预测长期生存。在任何表型改变中，如乳腺癌疾病，分子特征已证明其重要性，因为我们知道个体细胞的状态在不同水平上受到调节，如DNA、RNA和蛋白质，这是由生物体中存在的内在生物分子途径和外部刺激（如周围环境）的多方面相互作用所导致的。因此，为了全面理解，对单个细胞进行完整的分析需要从不同水平（多组学）进行同步研究，以避免单个细胞产生的不完整信息。在本文中，我们首先简要介绍了可用于探索组学的各种方法的新进展，然后最后指出了各种组学（即基因组学、转录组学、表观基因组学、蛋白质组学和代谢组学）数据以及循环肿瘤细胞、播散肿瘤细胞和癌症干细胞的一些特殊方面，这些是目前从各种研究中可获得的，可用于更好地管理乳腺癌患者。
