據(jù)4月2日《美國醫(yī)學協(xié)會期刊》（JAMA）上的一則研究顯示,，將被稱作基因表達信號的遺傳資訊與乳腺癌的臨床及其他風險因子進行整合,，可能對改善無復發(fā)存活及預計化療反應的預后判斷有幫助,。
美國杜克大學的Chaitanya R. Acharya及其同事開展了一項研究,，旨在決定將基因組資訊與臨床及病理性風險因子結(jié)合起來,，以改善早期乳腺癌病患的預后及治療策略的價值,。該項研究包括作為補充化療人選的罹患早期乳腺癌的病人,，研究中使用了964例乳腺腫瘤的樣本,。所有的病人都根據(jù)其相應的臨床病理特征而被賦予一個復發(fā)風險分數(shù),。研究人員對這些樣本進行了遺傳測試及基因表達信號（特征性概況）的檢測，以獲取與復發(fā)風險分數(shù)相符的擺脫調(diào)節(jié)的模式,，旨在改善僅用臨床病理預后模型所得到的預后判斷,。
研究人員發(fā)現(xiàn)，將基因表達信號整合到臨床風險層級之中可以改善對三種風險亞組（即低度,、中度及高度風險組）病人的預后判斷,，并有助于對病患無復發(fā)存活時間及對化療反應的預測。
文章的作者得出結(jié)論：“盡管這些結(jié)果仍然有待于在未來獲得前瞻性的臨床證實,，但這些結(jié)果提供給人們初步的證據(jù),，即定義乳腺癌的豐富的基因表達信號，如果應用恰當?shù)脑?，所代表的將不是一種自相矛盾的現(xiàn)象,，而應該被看作是對目前臨床病理風險分級系統(tǒng)的一種重要的補充手段。此外,，不斷增加的常用于治療乳腺癌的藥品庫中有關特定化療藥物敏感性的知識,，可能會更快地應用于目前的臨床治療。要做到這一點必需先解決成本及應用便利性的問題,，而且是在FDA批準了在某些情形下對早期乳腺癌可以使用多種化療藥物或化療組合,，且這些療法已被看作是一種標準化的治療的時候。”（來源：EurekAlert!中文版）
生物谷推薦原始出處：
（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
