Discovery and Clinical Validation of Cancer Biomarkers Using Printed Glycan Array

使用印刷聚糖阵列发现癌症生物标志物并进行临床验证

• 批准号: 7494050
• 负责人: Margaret Elisabeth Huflejt
• 金额: $ 38.04万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-07 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7494050
• 关键词: Antigens; Appearance; Autoantibodies; Biological Markers; Breast Cancer Genetics; Cancer Diagnostics; Cancer Patient; Cell surface; Clinical; Custom; Development; Diagnostic; Diagnostic tests; Disease; Evaluation; Housing; Immunologist; Individual; Invasive; Laboratories; Lipids; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Methods; Non-Small-Cell Lung Carcinoma; Oncologist; Ovarian; Patients; Polysaccharides; Population; Premalignant; Printing; Protein Glycosylation; Research; Research Personnel; Risk; Screening for cancer; Screening procedure; Serum; Staging; Structure; Techniques; Testing; Tumor-Associated Carbohydrate Antigens; Validation; base; cancer risk; computerized data processing; malignant breast neoplasm; melanoma; novel; outcome forecast; tool; tumor; tumor progression

DESCRIPTION (provided by applicant): This research is dedicated to the development and clinical validation of a serum-based diagnostic test for early detection of cancer and cancer risk. We have developed a printed glycan array (PGA) that detects a robust panel of cancer-specific anti-glycan autoantibodies in sera from cancer patients and at-risk patients. Aberrant glycosylation of proteins and lipids during malignant transformation results in the appearance of specific glycan structures known as Tumor Associated Carbohydrate Antigens, or TACAs, on cell surfaces and serum components. Combinations of TACAs are always present during malignant transformation and we have already demonstrated that multiple serum autoantibodies against these TACAs can be simultaneously detected using a PGA for patients with all stages of breast cancer including individuals with premalignant diseases. Using dedicated statistical and machine learning methods, we have identified sets of autoantibodies allowing differentiation of patients with metastatic breast cancer from healthy individuals. Although preliminary, these results allow us to conclude that PGAs together with our dedicated data processing methods can be used as a tool for the discovery of glyco-biomarkers, for the development of clinical serum-based screening tests for early detection of cancer and cancer risk, and for the evaluation of malignancy status. The three key components for advancing research have been established in our labratory: (1) in-house printing of custom glycan arrays, (2) PGA-dedicated mathematical data processing tools and (3) tumor-associated glycan discovery tools. The continued research will use the printed glycan array as a major biomarker-discovery technique. Further, the combined expertise of our team of investigators including glycobiologists, oncologists, mathematicians, clinical immunologists and chemists will use PGAs in large population-based studies with the following Specific Aims: 1. To identify and validate anti-glycan autoantibody signatures of the specific stages of breast cancer disease, including: increased breast cancer risk, pre-invasive (DCIS) and early breast cancer, breast cancer progression. 2. To expand the study to the identification and validation of anti-glycan autoantibody signatures for at least three other major malignancies, including ovarian, melanoma and Non-Small Cell Lung Cancer (NSCLC). 3. To identify, isolate and/or synthesize, and perform preliminary testing of novel tumor-associated glycans in serum and cellular materials as cancer-diagnostic antigens.

描述（由申请人提供）：本研究致力于开发和临床验证基于血清的诊断测试，以早期检测癌症和癌症风险。我们开发了一种印刷聚糖阵列 (PGA)，可检测癌症患者和高危患者血清中的一组强大的癌症特异性抗聚糖自身抗体。恶性转化过程中蛋白质和脂质的异常糖基化导致细胞表面和血清成分上出现被称为肿瘤相关碳水化合物抗原（TACA）的特定聚糖结构。 TACA 的组合在恶性转化过程中始终存在，我们已经证明，对于所有阶段的乳腺癌患者（包括患有癌前疾病的个体），可以使用 PGA 同时检测针对这些 TACA 的多种血清自身抗体。使用专门的统计和机器学习方法，我们已经确定了一组自身抗体，可以将转移性乳腺癌患者与健康个体区分开来。尽管是初步的，但这些结果让我们得出结论，PGA 与我们专用的数据处理方法一起可以用作发现糖生物标记物的工具，用于开发基于临床血清的筛查测试，以早期检测癌症和癌症风险，并用于评估恶性肿瘤状态。我们的实验室已经建立了推进研究的三个关键组成部分：(1) 内部打印定制聚糖阵列，(2) PGA 专用数学数据处理工具和 (3) 肿瘤相关聚糖发现工具。后续研究将使用印刷聚糖阵列作为主要的生物标志物发现技术。此外，我们的研究团队（包括糖生物学家、肿瘤学家、数学家、临床免疫学家和化学家）的综合专业知识将在基于大规模人群的研究中使用 PGA，其具体目标如下： 1. 识别和验证特定的抗聚糖自身抗体特征乳腺癌疾病的阶段，包括：乳腺癌风险增加、浸润前 (DCIS) 和早期乳腺癌、乳腺癌进展。 2. 将研究扩展到至少三种其他主要恶性肿瘤的抗聚糖自身抗体特征的识别和验证，包括卵巢癌、黑色素瘤和非小细胞肺癌（NSCLC）。 3. 鉴定、分离和/或合成血清和细胞材料中新型肿瘤相关聚糖作为癌症诊断抗原，并进行初步测试。