使用trastuzumab治療乳癌時 可能的決策工具


  July 28, 2009 — 根據8月15日癌症研究期刊上的新研究結果,表現磷酸分解酶與張力同系物(PTEN)蛋白可能預測乳癌病患是否會對trastuzumab(Herceptin,Genetech藥廠)產生抗藥性。
  
  這項研究使用電腦以數學模擬出檢驗這項治療效果的生化途徑,這個方法稱為系統生化學,是相對較新且尚未在臨床上實際應用的,但是在決定乳癌病患是否將會因anti-HER2治療而受益上被讚美為很大的進步。
  
  主要作者、英國愛丁堡大學病理學臨床講師Dana Faratian醫師在一篇新聞稿中表示,trastuzumab已經讓數千位HER-2陽性乳癌女性受益,但是僅有三分之一到二分之一接受這個藥物的病患有反應。我們必須知道哪些病患不會對治療產生反應,而這項研究是為了瞭解該目標的一項進步。
  
  Faratian與其共同同事們表示,以電腦的力量評估標靶治療的效果,端看這些途徑是否容易轉換為數學模式。
  
  安納堡密西根大學乳癌計劃科學主任Sofia Merajver醫師表示,這項研究是前進的一大進步,雖然trastuzumab使用於乳癌患者是項進步,仍然有許多病患治療失敗。這將可以協助了解何以如此,且如果沒有這個新的數學技術,則將無法達到。Merajver是癌症研究期刊的資深編輯,且未參與這項研究。
  
  【系統生化學在腫瘤學上的成功】
  Faratian博士與同事們解釋,使用標靶治療對抗細胞致癌基因在訊息傳遞路徑的產物,讓這個方法有機會進行研究,因為這些途徑可以輕而易舉地使用原本微分方程式處理。
  
  研究者們使用56種微分方程式建立一個模式,且分析56個個別生化實體的濃度變化,包括蛋白質。他們發現定量PTEN蛋白表現是決定是否對抗HER-2治療產生抗藥性的決定關鍵,不論是在數學模式或是活體外研究都是一樣。
  
  除此之外,相較於其他途徑部分分開考慮,以及當122位乳癌患者接受trastuzumab以多變項分析時,定量PTEN蛋白表現量更可以預測反應(相對風險3.0;95%信賴區間為1.6-5.5;P<0.0001)。
  
  Faratian博士建議,這些數學技術還沒被臨床醫師廣泛接納。他向Medscape腫瘤學表示,系統性生化學被大量宣傳,但應用於臨床問題而成功的例子相對較少,因為如此,這被臨床醫師質疑。這是第一次,該項技術被成功應用於癌症治療抗藥性的難題,且提供了一個可作為臨床決策上準則的工具。
  
  Faratian博士也表示,系統性生化學要應用在臨床上仍有漫漫長路,但這在藥物研發上有其他用途。對協助決定哪些標記應該在大型臨床研究中量測,這個方法可以提供一個基本且有用的資訊。舉例來說,美國食品藥物管理局將系統性生化學使用來作為部分藥物安全性資料證據,因此讓心臟藥物ranolazine於診間使用。
  
  這項研究作者們接受突破性乳癌、乳癌陣營、蘇格蘭基金委員會(策略性研究發展經費),以及生化科技及生化科學研究委員會贊助。

A Potential Decision-Making Tool for Trastuzumab Use in Breast Cancer

By Nick Mulcahy
Medscape Medical News

July 28, 2009 — Expression of the phosphatase and tensin homolog (PTEN) protein might help predict which breast cancer patients will be resistant to treatment with trastuzumab (Herceptin, Genentech), according to a new study published in the August 15 issue of Cancer Research.

The research uses computers to mathematically model biologic pathways that test the efficacy of a therapy. This method, known as systems biology, is relatively new and not yet clinically applicable, but has been hailed as a "great step forward" in the effort to determine which breast cancer patients will benefit from anti-HER2 therapy.

Trastuzumab "has benefited thousands of women with HER2-positive breast cancer, but only a third to half of patients treated with this agent respond," said lead author Dana Faratian, MD, a clinical lecturer in pathology at the University of Edinburgh in the United Kingdom, in a press statement. "We need to know which patients will [and which patients] won't respond to treatment, and this research is a step toward realizing that aim."

The key to using computational power to evaluate the efficacy of a targeted cancer therapy lies in the fact that pathways can be translated relatively easily into mathematical models, suggest Dr. Faratian and his coauthors.

This study "is a major step forward because, as revolutionary as [trastuzumab] has been, there are many patients who fail. This helps us understand why, and it would not have been possible without the new mathematical techniques," said Sofia Merajver, MD, PhD, scientific director of the Breast Oncology Program at the University of Michigan Comprehensive Cancer Center in Ann Arbor. She is a senior editorial board member of Cancer Research and was not involved in the study.

First Success of Systems Biology in Oncology

"The use of therapies targeted against the products of cellular oncogenes in signaling pathways lends itself to this approach because these pathways can readily be modeled using ordinary differential equations," Dr. Faratian and colleagues explain.

The investigators used 56 differential equations to build a model and analyze the change in concentrations of 56 separate biologic entities, including proteins. They found that quantitative PTEN protein expression was the key determinant of resistance to the anti-HER2 therapy, both in the math model and in vitro.

Furthermore, this quantitative PTEN protein expression was more predictive of response (relative risk, 3.0; 95% confidence interval, 1.6?- 5.5; P?< .0001) than other pathway components taken in isolation and when 122 breast cancers treated with trastuzumab were tested by multivariate analysis.

Dr. Faratian suggested that these mathematical techniques have not been embraced by clinicians. "Systems biology is much hyped, but there are relatively few success stories where it has been applied to clinical problems, and it has been viewed with a degree of skepticism by clinicians because of this," he told Medscape Oncology. "This is the first time it has been successfully applied to the problem of therapeutic resistance in cancer, and it provides a framework in which it can be used as a tool in clinical decision-making."

Dr. Faratian also said that systems biology is a "long way off" from being used in the clinic, but that it has other utility in drug development. "For helping decisions on which markers should be measured in large clinical trials, the approach can provide essential and valuable information. For example, systems-biology models were used, [in a US Food and Drug Administration] application that saw the heart drug ranolazine enter the clinic, as part of the evidence on its safety profile."

The study authors received grant support from Breakthrough Breast Cancer, the Breast Cancer Campaign, the Scottish Funding Council (Strategic Research Development Grant), and the Biotechnology and Biological Sciences Research Council.

Cancer Res. 2009;69(16). DOI: 10.1158/0008-5472.CAN-09-0777.

    
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