Early Detection of Breast Cancer Using Autoantibody Mar*

使用自身抗体 Mar* 早期检测乳腺癌

• 批准号: 7287417
• 负责人: MICHAEL A TAINSKY
• 金额: $ 68.33万
• 依托单位: WAYNE STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-02 至 2010-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7287417
• 关键词: Amino Acid Sequence; Antibodies; Antigen Targeting; Antigens; Autoantibodies; Autoimmune Diseases; Bacteriophages; Binding; Bioinformatics; Biological Assay; Biological Markers; Biological Neural Networks; Breast Cancer Early Detection; Breast Diseases; Cancer Detection; Cancer Patient; Characteristics; Classification; Clinical; Cloning; Collaborations; Communities; Complementary DNA; DNA Sequence; Data; Detection; Development; Diagnostic; Diagnostic Neoplasm Staging; Diagnostic tests; Differentiation Antigens; Disease; Disease Marker; Early Detection Research Network; Early Diagnosis; Epitopes; Felis catus; Foundations; Freezing; Funding; Goals; Immunoglobulin G; Institutes; Institution; Interview; Invasive; Libraries; Machine Learning; Malignant Neoplasms; Mammary Gland Parenchyma; Mammary Neoplasms; Methods; Michigan; Nature; Non-Malignant; Numbers; Operative Surgical Procedures; Outcome; Patients; Pattern Recognition; Peptide Sequence Determination; Phage Display; Phase; Planet Mars; Preparation; Principal Investigator; Printing; Probability; Prognostic Marker; Protein Microchips; Proteins; Protocols documentation; Reaction; Recording of previous events; Recurrence; Relapse; Reliance; Research; Rest; Sampling; Screening procedure; Sensitivity and Specificity; Serum; Specimen; Staging; Study of serum; Subgroup; Techniques; Technology; Testing; Time; Trees; Tumor Antigens; Tumor Tissue; Urban Hospitals; Validation; Woman; Women' s Group; antigen binding; cohort; concept; diagnosis standard; follow-up; malignant breast neoplasm; novel; prognostic; tumor

DESCRIPTION (provided by applicant): When cancer is identified at the earliest stages, the probability of cure is very high and therefore diagnostic screening tests that can detect these early stages are crucial. Efforts toward the development of early detection assays for cancers have traditionally depended on single biomarker molecules. Current technologies have been disappointing and have not resulted in diagnostic tests suitable for clinical practice. The core technology of this project rests on research from the PI's lab in which he has developed a high throughput method to identify large numbers of epitopes that can be used to identify the presence of breast cancer by detecting the presence of auto-antibodies to tumor proteins in the serum of the test subject. These biomarkers are cloned without a preconceived notion of their function and can be used as diagnostic and prognostic biomarkers. The essential features of the approach are acknowledging the heterogeneous nature of any specific kind of cancer, departing from the reliance on any single marker for disease detection, and using specialized bioinformatics techniques to interpret the results. The concept employs pattern recognition of multiple markers as a diagnostic rather than any single marker. This study is possible due to a remarkable specimen bank consisting of primary breast tumor tissues and sera collected frozen at the time of surgery from 1306 patients from Detroit metropolitan hospitals from 1975 to 1983. In addition, follow-up sera were collected from participating patients through 1992. Interview follow-ups were continued until 1996 and data forms from patient follow-ups are available. Over 11,000 sera have been maintained at -70¿ C at the KCI facility and are still currently available for all primary samples. In the discovery phase, the serum antibodies have been detected by screening of large numbers of potential epitope targets on protein microarrays. By a unique combination of techniques to enhance detection (microarrays of bacteriophage-bearing displayed tumor antigens), this study proposes to investigate a novel serum assay to detect and predict outcomes for breast cancer. The principle is that we can clone epitopes reacting with IgG in patients sera and use them to detect antibodies in sera to discriminate cancer and healthy subjects and detect disease prior to standard diagnosis. Such an approach should provide an early detection assay for asymptomatic women.

描述（由申请人提供）：当癌症在最早阶段被发现时，治愈的可能性非常高，因此可以检测这些早期阶段的诊断筛查测试至关重要。 传统上，开发癌症早期检测测定的努力依赖于单一生物标志物分子。 目前的技术一直令人失望，并没有导致适合临床实践的诊断测试。 该项目的核心技术依赖于PI实验室的研究，在该实验室中，他开发了一种高通量方法来鉴定大量表位，这些表位可用于通过检测受试者血清中肿瘤蛋白自身抗体的存在来鉴定乳腺癌的存在。 这些生物标志物在没有对其功能的先入为主的概念的情况下被克隆，并且可以用作诊断和预后生物标志物。 该方法的基本特征是承认任何特定类型癌症的异质性，脱离对任何单一疾病检测标记的依赖，并使用专门的生物信息学技术来解释结果。 该概念采用多个标记的模式识别作为诊断，而不是任何单一标记。 这项研究是可能的，由于一个显着的标本库，包括原发性乳腺肿瘤组织和血清收集在手术时从1306例患者从1975年至1983年从底特律大都会医院冷冻。 此外，随访血清收集参与患者通过1992年。 访谈随访一直持续到1996年，并提供了患者随访的数据表。 在KCI设施中，超过11，000份血清保持在-70 ℃，目前仍可用于所有初级样本。 在发现阶段，通过在蛋白质微阵列上筛选大量潜在的表位靶标来检测血清抗体。 通过一种独特的技术组合，以提高检测（微阵列的噬菌体展示肿瘤抗原），本研究提出了一种新的血清检测方法来检测和预测乳腺癌的结果。 其原理是，我们可以克隆与患者血清中IgG反应的表位，并使用它们来检测血清中的抗体，以区分癌症和健康受试者，并在标准诊断之前检测疾病。 这种方法应该为无症状妇女提供早期检测方法。