Alaoui-Jamali Moulay A, Dupré Isabelle, Qiang He
Department of Medicine, Centre for Translational Research in Cancer, Lady Davis Institute for Medical Research, McGill University, Montreal, Canada.
Drug Resist Updat. 2004 Aug-Oct;7(4-5):245-55. doi: 10.1016/j.drup.2004.06.004.
The oncologist's challenges, particularly with advanced cancers, are (a) how to predict tumor response to a given drug or regimen; (b) how to predict which tumors of identical histology will remain indolent and which will be likely to progress; and (c) how to determine the appropriate timing of the emergence of drug-resistant cancer cells and hence switch to appropriate therapy. These issues are still unresolved; current clinical practice is hampered by the complexity and heterogeneity of anti-tumor drug resistance where multiple cellular, tumor microenvironment and host factors operate simultaneously. The rapid accumulation of genomic and proteomic databases for complex biological systems, such as cancer, together with advances in technology platforms, have paved the way to an increased molecular understanding and prediction of antitumor drug response. The complex phenotype of drug resistance can now be dissected and specific, clinically relevant markers pinpointed. Several microarray studies of genetic patterns from untreated and pre-treated cancers have provided "fingerprints" that can predict response to therapeutics. Nevertheless, such approaches require further validation in experimental models and in large clinical trials before their routine clinical use. Moreover, comparative transcriptional profiling alone is unlikely to predict drug sensitivity/resistance, a dynamic process where protein phosphorylation, protein trafficking, and protein-protein interactions with secondary effectors play key roles in the fate of cancer cells following therapeutic stress. Functional proteomics is potentially more predictive, but still faces technical challenges with regards to sampling, tumor heterogeneity, and lack of standardized methodologies. These obstacles are surmountable with current concerted research efforts and availability of powerful high-throughput genomic and proteomic instrumentations, and thus approaches to predict and overcome drug resistance could be rationalized.
肿瘤学家面临的挑战，尤其是对于晚期癌症，包括：（a）如何预测肿瘤对特定药物或治疗方案的反应；（b）如何预测相同组织学类型的肿瘤哪些会保持惰性，哪些可能会进展；以及（c）如何确定耐药癌细胞出现的合适时机，从而转向合适的治疗方法。这些问题仍未解决；当前的临床实践因抗肿瘤耐药性的复杂性和异质性而受到阻碍，其中多种细胞、肿瘤微环境和宿主因素同时起作用。针对癌症等复杂生物系统的基因组和蛋白质组数据库的快速积累，以及技术平台的进步，为增强对抗肿瘤药物反应的分子理解和预测铺平了道路。现在可以剖析耐药的复杂表型，并确定特定的、与临床相关的标志物。几项对未经治疗和预处理癌症的基因模式进行的微阵列研究提供了可以预测治疗反应的“指纹”。然而，在其常规临床应用之前，此类方法需要在实验模型和大型临床试验中进一步验证。此外，仅比较转录谱不太可能预测药物敏感性/耐药性，这是一个动态过程，其中蛋白质磷酸化、蛋白质运输以及与二级效应器的蛋白质 - 蛋白质相互作用在治疗应激后癌细胞的命运中起关键作用。功能蛋白质组学可能更具预测性，但在采样、肿瘤异质性和缺乏标准化方法方面仍面临技术挑战。通过当前的协同研究努力以及强大的高通量基因组和蛋白质组仪器的可用性，这些障碍是可以克服的，因此预测和克服耐药性的方法可以更加合理。
