据4月2日《美国医学协会期刊》（JAMA）上的一则研究显示，将被称作基因表达信号的遗传资讯与乳腺癌的临床及其他风险因子进行整合，可能对改善无复发存活及预计化疗反应的预后判断有帮助。
美国杜克大学的Chaitanya R. Acharya及其同事开展了一项研究，旨在决定将基因组资讯与临床及病理性风险因子结合起来，以改善早期乳腺癌病患的预后及治疗策略的价值。该项研究包括作为补充化疗人选的罹患早期乳腺癌的病人，研究中使用了964例乳腺肿瘤的样本。所有的病人都根据其相应的临床病理特征而被赋予一个复发风险分数。研究人员对这些样本进行了遗传测试及基因表达信号（特征性概况）的检测，以获取与复发风险分数相符的摆脱调节的模式，旨在改善仅用临床病理预后模型所得到的预后判断。
研究人员发现，将基因表达信号整合到临床风险层级之中可以改善对三种风险亚组（即低度、中度及高度风险组）病人的预后判断，并有助于对病患无复发存活时间及对化疗反应的预测。
文章的作者得出结论：“尽管这些结果仍然有待于在未来获得前瞻性的临床证实，但这些结果提供给人们初步的证据，即定义乳腺癌的丰富的基因表达信号，如果应用恰当的话，所代表的将不是一种自相矛盾的现象，而应该被看作是对目前临床病理风险分级系统的一种重要的补充手段。此外，不断增加的常用于治疗乳腺癌的药品库中有关特定化疗药物敏感性的知识，可能会更快地应用于目前的临床治疗。要做到这一点必需先解决成本及应用便利性的问题，而且是在FDA批准了在某些情形下对早期乳腺癌可以使用多种化疗药物或化疗组合，且这些疗法已被看作是一种标准化的治疗的时候。”（来源：EurekAlert!中文版）
生物谷推荐原始出处：
（JAMA），299(13):1574-1587，Chaitanya R. Acharya，Anil Potti
Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer
Chaitanya R. Acharya, MS; David S. Hsu, MD, PhD; Carey K. Anders, MD; Ariel Anguiano, MD; Kelly H. Salter, BS; Kelli S. Walters, BS; Richard C. Redman, MD; Sascha A. Tuchman, MD; Cynthia A. Moylan, MD; Sayan Mukherjee, PhD; William T. Barry, PhD; Holly K. Dressman, PhD; Geoffrey S. Ginsburg, MD, PhD; Kelly P. Marcom, MD; Katherine S. Garman, MD; Gary H. Lyman, MD; Joseph R. Nevins, PhD; Anil Potti, MD
JAMA. 2008;299(13):1574-1587.
Context Gene expression profiling may be useful for prognostic and therapeutic strategies in breast carcinoma.
Objectives To demonstrate the value in integrating genomic information with clinical and pathological risk factors, to refine prognosis, and to improve therapeutic strategies for early stage breast cancer.
Design, Setting, and Patients Retrospective study of patients with early stage breast carcinoma who were candidates for adjuvant chemotherapy; 964 clinically annotated breast tumor samples (573 in the initial discovery set and 391 in the validation cohort) with corresponding microarray data were used. All patients were assigned relapse risk scores based on their respective clinicopathological features. Signatures representing oncogenic pathway activation and tumor biology/microenvironment status were applied to these samples to obtain patterns of deregulation that correspond with relapse risk scores to refine prognosis with the clinicopathological prognostic model alone. Predictors of chemotherapeutic response were also applied to further characterize clinically relevant heterogeneity in early stage breast cancer.
Main Outcome Measures Gene expression signatures and clinicopathological variables in early stage breast cancer to determine a refined estimation of relapse-free survival and sensitivity to chemotherapy.
Results In the initial data set of 573 patients, prognostically significant clusters
