Validation of an Antibody Test for Early Diagnosis of Ovarian Cancer

描述（由申请人提供）：当在I期发现卵巢癌时，90%的病例可以通过手术和化疗治愈。每年有21，000例卵巢癌新发病例，但其中75%的病例在III期或IV期被诊断出来，5年生存率约为20%。这导致美国每年约有14，000人死亡。在北美，每200名女性中就有1名携带BRCA 1或BRCA 2突变，500万名女性在一级或二级亲属中有卵巢癌家族史。这些类别中卵巢癌风险较高的妇女显然将从筛查测试中受益，因此代表了将从该项目中受益的社区。卵巢癌的筛查测试将通过将诊断时的卵巢癌阶段降低到更有可能治愈的阶段来改变医疗保健提供的方法。 我们开发了一种高通量蛋白质组学技术，我们称之为“表位组学”（epitope-omics），其中我们从卵巢癌组织和细胞的RNA制成的cDNA文库中免疫选择数千种噬菌体展示抗原。我们使用蛋白质微阵列作为大规模平行免疫测定系统来鉴定由T7噬菌体cDNA文库编码的蛋白质，所述蛋白质与卵巢癌患者血清中特异性抗体结合，而不与良性妇科疾病妇女中存在的抗体结合。我们建议使用体外合成的蛋白质，使用Luminex珠阵列上的免疫测定来验证这些生物标志物。我们的假设是，它需要一组生物标志物来准确区分卵巢癌患者和妇科良性疾病患者。生物标志物领域的大多数人都会同意这一假设。我们将使用来自我们选择的生物标志物噬菌体克隆的PCR模板在体外合成蛋白质，我们已经证明这些噬菌体克隆是与卵巢癌患者血清中特异性抗体反应的肿瘤抗原。我们将使用早期和晚期卵巢癌的血清来验证这些生物标志物。此外，为了确保我们正在开发的诊断测试的特异性，我们将针对患有良性妇科疾病，自身免疫性疾病和患有其他癌症的女性的血清测定这些生物标志物。 然而，如果使用这些生物标志物仍对一部分患者进行错误分类，我们将使用其他方法，例如肿瘤抗原的全长重组蛋白。如有必要，将寻求替代方法，靶向针对肿瘤细胞过表达或突变的肿瘤抗原形成的自身抗体。这些方法应该为我们提供一个足够强大的抗原生物标志物分析物的珠阵列检测卵巢癌的早期检测。 该项目的重点是将我们对肿瘤相关抗原的大规模免疫蛋白质组学发现转化为Luminex微珠阵列检测，以检测血清自身抗体，用于检测高危女性和有卵巢癌家族史的女性的早期卵巢癌。

DESCRIPTION (provided by applicant): When discovered at stage I ovarian cancer is curable in 90% of the cases using surgery and chemotherapy. There are 21,000 new cases per year of ovarian cancer however 75% of those cases are diagnosed at stage III or IV and their 5-year survival is about 20%. This results in about 14,000 deaths per year in the US. About 1 in 200 women in North America carries BRCA1 or BRCA2 mutations and 5 million women with a family history of ovarian cancer in a first or second degree relative. Women in these categories who are at higher risk of ovarian cancer would clearly benefit from a screening test and therefore represent the communities that would benefit from this project. A screening test for ovarian cancer will change the approach to healthcare delivery by reducing stage of ovarian cancer at diagnosis to stage at which it is more likely to be curable. We developed a high-throughput proteomics technology we call "Epitomics" for (epitope-omics) in which we immunoselect thousands of phage displayed antigens from cDNA libraries made from RNA from ovarian cancer tissue and cells. We used protein microarrays as a massively parallel immunoassay system to identify the proteins coded by the T7 bacteriophage cDNA libraries that bind to antibodies specifically in the serum of ovarian cancer patients and not to antibodies present in women with benign gynecological diseases. We propose to validate these biomarkers using in vitro synthesized proteins using immunoassays on Luminex bead arrays. Our hypothesis is that it requires panels of biomarkers to accurately distinguish patients with ovarian cancer from those with gynecological benign diseases. Most people in the biomarker field would agree with this hypothesis. We will synthesize proteins in vitro using PCR templates from our selected biomarker bacteriophage clones that we have shown are tumor antigens reacting with antibodies specifically in the serum of ovarian cancer patients. We will validate these biomarkers using sera from early and late stage ovarian cancer. In addition, to ensure the specificity of the diagnostic test we are developing we will assay these biomarkers against sera from women with benign gynecological diseases, autoimmune diseases, and women with other cancers. However, if a fraction of patients are still misclassified using these biomarkers we will use additional approaches such as full length recombinant proteins of the tumor antigens. If necessary alternative approaches will be pursued targeting autoantibodies that are formed against tumor antigens that are overexpressed or mutated by tumor cells. These approaches should provide us with a sufficiently robust panel of antigen biomarker analytes for a bead array assay for the early detection of ovarian cancer. The focus of this project is to translate our large scale immuno-proteomics discoveries of tumor associated antigens into Luminex bead array assays to detect serum autoantibodies for the detection of early stage cancer of the ovary in high risk women and women with a family history of ovarian cancer.