Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
Cancer Research UK Clinical Trials Unit, The Institute of Cancer Research, Sutton, SM2 5NG, UK.
Cancer Res. 2014 Jun 1;74(11):2946-2961. doi: 10.1158/0008-5472.CAN-13-3375. Epub 2014 Apr 4.
Gene signatures have failed to predict responses to breast cancer therapy in patients to date. In this study, we used bioinformatic methods to explore the hypothesis that the existence of multiple drug resistance mechanisms in different patients may limit the power of gene signatures to predict responses to therapy. In addition, we explored whether substratification of resistant cases could improve performance. Gene expression profiles from 1,550 breast cancers analyzed with the same microarray platform were retrieved from publicly available sources. Gene expression changes were introduced in cases defined as sensitive or resistant to a hypothetical therapy. In the resistant group, up to five different mechanisms of drug resistance causing distinct or overlapping gene expression changes were generated bioinformatically, and their impact on sensitivity, specificity, and predictive values of the signatures was investigated. We found that increasing the number of resistance mechanisms corresponding to different gene expression changes weakened the performance of the predictive signatures generated, even if the resistance-induced changes in gene expression were sufficiently strong and informative. Performance was also affected by cohort composition and the proportion of sensitive versus resistant cases or resistant cases that were mechanistically distinct. It was possible to improve response prediction by substratifying chemotherapy-resistant cases from actual datasets (non-bioinformatically perturbed datasets) and by using outliers to model multiple resistance mechanisms. Our work supports the hypothesis that the presence of multiple resistance mechanisms in a given therapy in patients limits the ability of gene signatures to make clinically useful predictions.
到目前为止，基因标记未能预测患者对乳腺癌治疗的反应。在这项研究中，我们使用生物信息学方法来探索假设，即不同患者中存在多种耐药机制可能会限制基因标记预测治疗反应的能力。此外，我们还探讨了是否可以对耐药病例进行细分以提高性能。从公开来源检索了在相同微阵列平台上分析的 1550 个乳腺癌的基因表达谱。在敏感或耐药病例中引入了基因表达变化。在耐药组中，生物信息学生成了多达五个不同的耐药机制，这些机制导致了不同或重叠的基因表达变化，并研究了它们对签名的敏感性、特异性和预测值的影响。我们发现，增加与不同基因表达变化对应的耐药机制的数量会削弱预测签名的性能，即使耐药诱导的基因表达变化足够强且信息量丰富。性能还受到队列组成、敏感与耐药病例的比例或耐药病例的机制差异的影响。通过对实际数据集（非生物信息学扰动数据集）中的化疗耐药病例进行细分，并使用异常值来模拟多种耐药机制，可以改善反应预测。我们的工作支持了这样的假设，即在患者中给定治疗中存在多种耐药机制会限制基因标记做出临床有用预测的能力。
