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
基本信息
- 批准号:7935183
- 负责人:
- 金额:$ 42.03万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AntigensAppearanceAutoantibodiesBiological MarkersBreast Cancer GeneticsCancer DiagnosticsCancer PatientCell surfaceClinicalCustomDevelopmentDiagnosticDiagnostic testsDiseaseEvaluationHousingImmunologistIndividualLaboratoriesLipidsMachine LearningMalignant - descriptorMalignant NeoplasmsMethodsNon-Small-Cell Lung CarcinomaOncologistOvarianPatientsPolysaccharidesPremalignantPrintingProtein GlycosylationResearchResearch PersonnelRiskScreening for cancerScreening procedureSerumStagingStructureTechniquesTestingTumor-Associated Carbohydrate AntigensValidationbasecancer riskcomputerized data processingmalignant breast neoplasmmelanomanoveloutcome forecastpopulation basedtooltumortumor progression
项目摘要
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(申请人提供):这项研究致力于开发和临床验证一种基于血清的诊断测试,用于癌症和癌症风险的早期检测。我们已经开发了一种印刷的多糖阵列(PGA),它可以检测癌症患者和高危患者血清中一组强大的癌症特异性抗糖蛋白自身抗体。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)的特定糖链结构。在癌变过程中,TACA的组合总是存在的,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患者,可以使用PGA同时检测到针对这些TACA的多种血清自身抗体。使用专门的统计和机器学习方法,我们已经识别了一组自身抗体,这些抗体可以区分转移性乳腺癌患者和健康人。虽然这些结果是初步的,但我们可以得出结论,PGAs和我们专门的数据处理方法可以用作发现糖生物标志物的工具,用于开发基于临床血清的筛查试验,以早期发现癌症和癌症风险,并用于评估恶性肿瘤状态。我们的实验室已经确定了推进研究的三个关键组成部分:(1)定制多聚糖阵列的内部打印,(2)PGA专用的数学数据处理工具和(3)肿瘤相关多聚糖发现工具。继续的研究将使用打印的多糖阵列作为主要的生物标记物发现技术。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在内的联合专业人员将在大型人群研究中使用PGAs,其具体目标如下:1.识别和验证乳腺癌疾病特定阶段的抗葡聚糖自身抗体标志,包括:乳腺癌风险增加、浸润性前期(DCIS)和早期乳腺癌、乳腺癌进展。2.扩大研究范围,以识别和验证至少其他三种主要恶性肿瘤,包括卵巢癌、黑色素瘤和非小细胞肺癌(NSCLC)的抗糖自身抗体签名。3.鉴定、分离和/或合成作为癌症诊断抗原的血清和细胞材料中的新型肿瘤相关多糖,并进行初步测试。
项目成果
期刊论文数量(0)
专著数量(0)
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Margaret Elisabeth Huflejt其他文献
Margaret Elisabeth Huflejt的其他文献
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{{ truncateString('Margaret Elisabeth Huflejt', 18)}}的其他基金
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:
7281031 - 财政年份:2007
- 资助金额:
$ 42.03万 - 项目类别:
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:
8141269 - 财政年份:2007
- 资助金额:
$ 42.03万 - 项目类别:
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:
7691340 - 财政年份:2007
- 资助金额:
$ 42.03万 - 项目类别:
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 - 财政年份:2007
- 资助金额:
$ 42.03万 - 项目类别:
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