Department of Systems Biology, MD Anderson Cancer Center, The University of Texas, Houston, TX, USA.
Cancer Res Treat. 2011 Dec;43(4):205-11. doi: 10.4143/crt.2011.43.4.205. Epub 2011 Dec 27.
Molecular classification of cancers has been significantly improved patient outcomes through the implementation of treatment protocols tailored to the abnormalities present in each patient's cancer cells. Breast cancer represents the poster child with marked improvements in outcome occurring due to the implementation of targeted therapies for estrogen receptor or human epidermal growth factor receptor-2 positive breast cancers. Important subtypes with characteristic molecular features as potential therapeutic targets are likely to exist for all tumor lineages including hepatocellular carcinoma (HCC) but have yet to be discovered and validated as targets. Because each tumor accumulates hundreds or thousands of genomic and epigenetic alterations of critical genes, it is challenging to identify and validate candidate tumor aberrations as therapeutic targets or biomarkers that predict prognosis or response to therapy. Therefore, there is an urgent need to devise new experimental and analytical strategies to overcome this problem. Systems biology approaches integrating multiple data sets and technologies analyzing patient tissues holds great promise for the identification of novel therapeutic targets and linked predictive biomarkers allowing implementation of personalized medicine for HCC patients.
通过实施针对每个患者癌细胞中存在的异常情况量身定制的治疗方案,癌症的分子分类显著改善了患者的预后。乳腺癌是一个典型的例子,由于针对雌激素受体或人类表皮生长因子受体 2 阳性乳腺癌的靶向治疗的实施,其预后得到了显著改善。包括肝细胞癌 (HCC) 在内的所有肿瘤谱系中,很可能存在具有潜在治疗靶点特征的重要亚型,但尚未被发现和验证为靶点。由于每个肿瘤都会积累成百上千个关键基因的基因组和表观遗传改变,因此识别和验证候选肿瘤异常作为治疗靶点或预测预后或对治疗反应的生物标志物具有挑战性。因此,迫切需要设计新的实验和分析策略来克服这一问题。整合多个数据集和分析患者组织的技术的系统生物学方法为识别新的治疗靶点和相关预测生物标志物提供了巨大的希望,从而为 HCC 患者实施个性化医疗。