Automating Tissue Microarray Analysis: extending PathGrid

2003年英国有超过44,000人被诊断出患有乳腺癌。这是目前英国最常见的癌症。一生中患乳腺癌的风险为九分之一，虽然大多数患乳腺癌的女性已经过了更年期，但每年有近8，000名确诊的女性年龄在50岁以下。改善结果是治疗的一个关键挑战。高通量基因组学方法，例如使用微阵列对冷冻组织样品进行表达谱分析，已经发现了许多与癌症治疗中的临床结果相关的新基因特征。这些潜在的生物标志物在临床使用之前必须在大量独立样本上进行验证。从大型临床试验的石蜡包埋肿瘤样本中创建的组织微阵列（TMA）是这些验证实验的理想试剂。然而，对可能包含数千个患者样本的TMA上的基于抗体的标记物的分析和评分对病理报告和图像处理提出了重大挑战。我们最初的试点PathGrid研究（2007年10月至2008年10月）正在使用一系列天文学中开发的技术来分析成像数据并处理和操纵由此产生的数据产品。这些已被应用于分析TMA图像数据所涉及的挑战，这些数据来自于基于人群的乳腺癌研究。我们利用天文学的“虚拟天文台”组件，专门改编自AstroGrid虚拟天文台eScience计划（http：//www.example.com）内开发的组件，以促进安全的数据传输，通过适当的元数据，数据采集，进入数据库系统的资源发现，并安全地分布式访问这些数据和信息资源。www.astrogrid.org图像分析已应用于输入TMA数据，利用一系列最初为各种天文用例开发的算法。由此产生的数据产品将依次与通过CancerGrid系统开发的临床试验系统（见http://www.cancergrid.org）接口（尽管这里计划了工作）。在我们最初的测试案例中，我们已经自动化了雌激素受体（ER）评估的评分。ER是乳腺生长的重要调节因子，也是乳腺癌预后和治疗的关键靶点。在诊断乳腺癌时评估ER状态，确定患者应遵循哪些治疗方案。特别是，那些ER阳性乳腺癌患者将接受雌激素拮抗剂治疗，如他莫昔芬。在CR-UK，利用基因组学工具的乳腺癌研究正在进行中，以验证现有的和新的预后和/或预测标志物。TMAs是从一项基于大规模人群的临床试验（Anglia乳腺癌研究的一部分）中创建的，用于分析一系列候选标志物。免疫组织化学用于评估细胞核ER表达水平。我们最初试点的重点是算法开发和验证。对于miniPIPSS计划，工作将转向通过在管道中运行所有处理操作来增加分析系统的效用。这种自动化，我们的Pathgrid系统的操作基础，已经通过使用AstroGrid的应用网格基础设施组件在原型中实现。并提供以开放和可扩展的方式处理和处理临床图像数据的方法。这是一个长期合作伙伴关系的阶段，旨在进一步开发这些分析技术，使其可用于更广泛的医学研究界，并进一步用于临床环境。

In 2003. over 44,000 people in the UK were diagnosed with breast cancer. This is now the commonest cancer occurring in the UK. The lifetime risk of developing breast cancer is 1 in 9, and while most of the women who get breast cancer are past their menopause, almost 8,000 diagnosed each year are under 50 years old. Improving outcomes is a key challenge in treatment. High throughput genomic methods, such as expression profiling of frozen tissue samples using microarrays, have resulted in the discovery of many novel gene signatures that are correlated with clinical outcomes in cancer treatment. It is essential that these potential biomarkers are validated on large numbers of independent samples prior to clinical use. Tissue microarrays (TMA) created from paraffin-embedded tumour samples from large clinical trials are the ideal reagent for these validation experiments. However, the analysis and scoring of antibody-based markers on TMAs that may contain thousands of patient samples presents major challenges for pathology reporting and image handling. Our initial pilot PathGrid study (Oct 2007 to Oct 2008) is using a range of techniques which have been developed in astronomy to both analyse imaging data and to handle and manipulate the resulting data products. These have been applied to the challenges involved in analysing the TMA image data taken from the SEARCH study population-based study of breast cancer. We have utilised astronomy 'Virtual Observatory' components specifically adapted from those developed within the AstroGrid Virtual Observatory eScience programme (http://www.astrogrid.org), to facilitate secure data transport, resource discovery through appropriate metadata, data acquisition, ingression to a database system, and secure distributed access to those data and information resources. Image analysis has been applied to the input TMA data utilising a range of algorithms originally developed for diverse astronomical use cases. The resulting data products will in turn be interfaced (though work planned here) to the clinical trials systems developed through the CancerGrid system (see http://www.cancergrid.org). In our initial test case we have automated the scoring of Estrogen Receptor (ER) assessments. ER is an important regulator of mammary growth, but is also a key prognostic and therapeutic target in breast cancer. Assessing ER status at time of diagnosis of breast cancer, determines which treatment programmes should be followed by patients. In particular, those patients who have ER-positive breast cancer will be offered estrogen antagonist therapies such as tamoxifen. At CR-UK, breast cancer studies utilising genomics tools are underway to validate existing and new prognostic and/or predictive markers. TMAs have been created from a large population-based clinical trial (SEARCH; part of the Anglia Breast Cancer study) for analysis with a range of candidate markers. Immunohistochemistry is used to assess the level of nuclear ER expression. The focus of our initial pilot has been on the algorithm development and validation. For the miniPIPSS programme the work will move to increasing the utility of the analysis system by running all processing operations in a pipeline. This automation, the operational basis of our Pathgrid system, has been implemented in prototype by making use of the application-grid infrastructural components from AstroGrid This miniPIPSS project will facilitate the further interchange of ideas and technologies between the physical and medical sciences, and provide methods for the handling and processing of clinical image data in an open and extensible manner The interaction with Oracle represents the initial stage of a longer term partnership, aiming to further develop these analysis techniques such that they are available to he wider medical research community - and further ahead for use in a clinical environment.