Su Derrick W, Nieva Jorge
Norris Cancer Center, University of Southern California, Los Angeles, USA.
Transl Lung Cancer Res. 2017 Aug;6(4):473-485. doi: 10.21037/tlcr.2017.05.08.
An understanding of cancer evolution in lung cancer with its associated resistance to therapy can only be achieved with repeated sampling and analysis of the cancer. Given the high risks and costs associated with repeat physical biopsy, alternative technologies must be applied. Several modalities exist for analysis and re-analysis of cancer biology. Among them are the CellSearch platform, the CTC chip, and the high-definition CTC platform. While the former is primarily able to provide prognosticating information in the form of CTC enumeration, the latter two have the advantage of serving as a platform to study tumor biology. Techniques for analysis of single cell genomics, as well as protein expression on a single cell basis provide scientists with the capacity to understand cancer cell populations as a collection of individual cells, rather than as an average of all cells. A multimodal combination of circulating tumor DNAs (ctDNAs), CTCs, proteomics, and CTC-derived xenografts (CDXs) to create computational models useful in diagnosis, prognostication, and predictiveness to treatment is likely the future of tailoring individualized cancer care.
只有通过对癌症进行反复采样和分析，才能了解肺癌的癌症演变及其相关的治疗耐药性。鉴于重复进行实体活检存在高风险和高成本，必须应用替代技术。存在多种用于分析和重新分析癌症生物学的方法。其中包括CellSearch平台、CTC芯片和高清CTC平台。虽然前者主要能够以循环肿瘤细胞（CTC）计数的形式提供预后信息，但后两者具有作为研究肿瘤生物学平台的优势。单细胞基因组学分析技术以及单细胞水平的蛋白质表达分析技术，使科学家能够将癌细胞群体理解为单个细胞的集合，而不是所有细胞的平均值。循环肿瘤DNA（ctDNA）、CTC、蛋白质组学和CTC衍生异种移植（CDX）的多模态组合，以创建有助于诊断、预后和预测治疗效果的计算模型，可能是定制个性化癌症治疗的未来方向。
