Chip-Based Diagnosis of Problematic Liver Tumors

但前提是。 准确的肿瘤诊断和分类是肿瘤治疗的第一步。虽然很多人 肿瘤活检是诊断并构成癌症治疗、肿瘤分类的基石 类型和来源，是一个永远存在的临床挑战。据估计，多达10%的人 肿瘤没有明确的原发部位，每个病例花费数千美元 确定发源地，但总体成功有限。按照目前的病理实践标准， 使用形态标准和一组半定量免疫组织化学(IHC)分析， 通常在确定肿瘤类型或起源部位的能力方面受到限制。此外，诊断出的 当尚未确定原发部位(未知)时，转移性病变可能非常困难 原发癌症)。由于治疗通常以起源地为基础，显然有必要 识别和验证将清楚地区分这些组织上相似的分类器 肿瘤类型和增强标准病理技术进行诊断呼叫。我们有 最近证明了利用基因表达谱来区分21个不同类型的 肿瘤分型准确率为88%。然而，这种分类器的性能主要是 受限于使用多个平台进行分析。这项提议试图构建一种新的基因 在单一的商业上可获得的全基因组寡核苷酸平台上的表达分类器， 重点放在最有问题的肝脏原发和转移肿瘤上。为了证明 这种方法的临床实用性，我们计划用独立的测试集来验证分类器 也将通过标准的IHC方法进行分析；分子分类的结果将 比较头颅与标准病理分型的诊断准确性。至 进一步翻译该技术，我们将选择核心分类器基因并使用REAL进行验证 IME定量聚合酶链式反应最后，分子分类器将使用预期获得的 已知和未知起源地的组织上的屈光样本。

PROVIDED. Precise tumor diagnosis and classification is the first step in cancer management. While many tumor biopsies are diagnostic and form the cornerstone of cancer therapy, classification of tumor type and site of origin, is an ever-present clinical challenge. It is estimated that up to 10% of all tumors have no defined primary site of origin and that thousands of dollars are spent per case to identify the site of origin with limited overall success. The current standard of pathologic practice, using morphologic criteria and a panel of semi-quantitative immunohistochemical (IHC) analyses, is often limited in its capacity to define tumor type or site of origin. Moreover, the diagnosis of metastatic lesions can be quite difficult when no primary site of origin has been identified (unknown primary cancers). Since therapy is often based on site of origin, there is a clear need for the identification and validation of a classifier that will cleanly distinguish these histologically similar tumor types and augment standard pathological techniques in making the diagnostic call. We have ¿ecently demonstratedthe feasibility of using gene expression profiling to discriminate 21 different tumor types with an accuracy of 88%. The performance of this classifier, however, was principally imited by the use of multiple platforms for analysis. This proposal seeks to build a new gene expression classifier on a single, commercially available, genome-wide oligonucleotide platform, with a focus on the most problematic primary and metastatic tumors of the liver. To demonstrate the clinical utility of this approach, we plan to validate the classifier with independent test sets that will also be profiled by standard IHC approaches; the results of molecular classification will then be compared head to head with standard pathological classification for accuracy of diagnosis. To further translate the technology, we will select core classifier genes and perform validation with real ime quantitative PCR. Finally, the molecular classifiers will be tested using prospectively acquired Diopsy samples on tissues of known and unknown sites of origin.