Tracking Peripheral T-Cell Repertoire Changes for Preoperative and Early Ovarian Cancer Diagnosis

追踪外周 T 细胞库的变化以进行术前和早期卵巢癌诊断

• 批准号: 10542809
• 负责人: Jayanthi S Lea
• 金额: --
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10542809
• 关键词: Adnexal Mass; Affect; Age; Antigens; Artificial Intelligence; Benign; Biological Markers; Biometry; Blood; CA-125 Antigen; Cancer Detection; Cancer Etiology; Cancer Patient; Cancerous; Cessation of life; Clinical; Clinical Research; Data; Data Set; Detection; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Diagnostic Specificity; Disease; Early Diagnosis; Evaluation; Excision; Female; Future; Goals; Gynecologic Oncology; Histologic; Human; Image; Immune response; Immune system; Immunogenomics; Immunology; Irrigation; Lesion; Life; Logistic Regressions; Low Prevalence; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of ovary; Measures; Methods; Modeling; Neoplasm Metastasis; Operative Surgical Procedures; Ovarian; Ovarian Mass; Ovarian Serous Adenocarcinoma; Ovary; Patients; Pelvis; Performance; Peripheral; Pilot Projects; Planned Pregnancy; Process; Proliferating; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; Public Domains; Reporter; Representational Oligonucleotide Microarray Analysis; Research Personnel; Risk; Role; Route; Sampling; Screening for Ovarian Cancer; Sensitivity and Specificity; Serum; Signal Transduction; Skates; Specificity; Specimen; Stage at Diagnosis; Symptoms; T cell receptor repertoire sequencing; T-Cell Receptor; T-Lymphocyte; T-cell receptor repertoire; Testing; Tissues; Training; Tumor Antigens; Tumor Markers; Ultrasonography; Unnecessary Surgery; Uterus; Vagina; Validation; Woman; accurate diagnosis; antigen-specific T cells; biobank; biomarker development; cancer biomarkers; cancer diagnosis; cohort; collaborative trial; detection method; detection sensitivity; diagnosis standard; diagnostic accuracy; diagnostic biomarker; diagnostic criteria; disease diagnosis; immunogenicity; improved; indexing; innovation; machine learning method; machine learning model; machine learning prediction; mortality; multimodality; multiplex assay; noninvasive diagnosis; novel; preservation; prospective; recruit; reproductive; screening; sequencing platform; software development; specific biomarkers; tool; transcriptome sequencing; tumor; tumor progression; young woman

Project Summary Ovarian cancer is the most lethal female cancer. When the disease can be diagnosed at early stage, there is striking survival improvement (five year survival ≥ 90%), compared to late stages (≤ 40%). However, currently no early detection method for ovarian cancer has enough accuracy, and most tumors already progressed to advanced stages at diagnosis. Furthermore, over 70% of the adnexal masses detected on preoperative imaging are found to be benign after pelvic surgery. Current clinical tests rely on serum CA-125 and sonograms to diagnose the ovarian adnexal masses. However, CA-125 is elevated by many common benign conditions; and ultrasound imaging of ovary frequently misses small but malignant lesions. As a result, surgical removal of the lesion and histologic evaluation remains the only gold standard for diagnosis. These limitations dictate an urgent clinical need of a better preoperative diagnostic method with high detection accuracy, to lower the mortality rate, reduce unnecessary surgeries and preserve the life choices for many patients, especially young women at reproductive age planning for pregnancies. Here, we propose a completely different route to detect ovarian cancer signals from the blood T cell repertoire. This is feasible because the T lymphocytes recognize tumor antigens at initial stages, proliferate and alter the peripheral T cell repertoire. Therefore, detection of cancer-associated T cells (CAT) in the blood provides an exciting novel opportunity for non-invasive cancer diagnosis. However, no prior studies have achieved this goal because it is difficult to identify CAT in high-throughput, as most of the cancer antigens remain unknown. To prepare for this task, we developed the software TRUST and iSMART, to obtain antigen-specific TCRs from cancer datasets. These tools have enabled us to produce a large training set of CATs, which allowed us to identify diagnostic TCRs for the ovarian cancer patients. Following this result, we further developed DeepCAT, for pan-cancer prediction using blood TCR sequencing data, and demonstrated over 99% specificity and 86% sensitivity in a pilot study to predict ovarian cancer patients (n=14) from healthy donors (n=176). To develop this approach into a novel ovarian cancer specific biomarker, we have established a biorepository to prospectively collect specimens from patients with benign or malignant ovarian lesions and from healthy donors of similar age span, with related clinical information. In Aim 1, we will generate TCR sequencing data of the new patient samples to develop a novel, TCR-based ovarian cancer predictor using machine learning method. In Aim 2, we will combine this approach with existing clinical tests to obtain a multi-modality biomarker, and independently test it using the samples from the Uterine Lavage cohort led by Dr. Steven Skates. These Aims will be delivered by the PIs and co-investigators with complementary expertise covering gynecological oncology, clinical cohort recruitment, biostatistics, artificial intelligence, immunology and ovarian cancer biomarker development.

项目概要 卵巢癌是最致命的女性癌症，当这种疾病能够在早期诊断出来时，就有可能发生。 与晚期阶段（≤ 40%）相比，生存率显着提高（五年生存率≥ 90%）。 卵巢癌的早期检测方法还没有足够的准确性，并且大多数肿瘤已经进展为 此外，超过 70% 的附件肿块是在术前发现的。 盆腔手术后影像学检查发现良性，目前的临床测试依赖于血清 CA-125 和 超声检查可诊断卵巢附件肿块 然而，许多常见良性肿瘤的 CA-125 会升高。 卵巢超声检查经常会漏掉微小但恶性的病变，因此需要进行手术。 切除病变和组织学评估仍然是诊断的唯一金标准。 临床迫切需要一种更好、检测精度高的术前诊断方法 降低死亡率，减少不必要的手术并保留许多患者的生活选择， 尤其是年轻育龄女性的怀孕计划。 从血液 T 细胞库中检测卵巢癌信号的不同途径这是可能的，因为 T 细胞。 淋巴细胞在初始阶段识别肿瘤抗原、增殖并改变外周 T 细胞库。 因此，血液中癌症相关 T 细胞 (CAT) 的检测为以下方面提供了一个令人兴奋的新机会： 然而，之前的研究还没有实现这一目标，因为它很难实现。 由于大多数癌症抗原仍然未知，因此我们需要以高通量鉴定 CAT。 开发了软件 TRUST 和 iSMART，从癌症数据集中获取抗原特异性 TCR。 工具使我们能够生成大量 CAT 训练集，这使我们能够识别诊断 TCR 根据这一结果，我们进一步开发了 DeepCAT，用于泛癌预测。 使用血液 TCR 测序数据，并在一项试点研究中证明了超过 99% 的特异性和 86% 的敏感性 从健康捐赠者 (n=176) 中预测卵巢癌患者 (n=14) 将这种方法发展为一种新颖的方法。 卵巢癌特异性生物标志物，我们建立了一个生物样本库，前瞻性地收集样本 患有良性或恶性卵巢病变的患者以及来自相似年龄跨度的健康捐赠者，具有相关的 在目标 1 中，我们将生成新患者样本的 TCR 测序数据，以开发 在目标 2 中，我们将结合使用机器学习方法的新型基于 TCR 的卵巢癌预测器。 与现有的临床测试方法相结合以获得多模态生物标志物，并使用 这些目标将由 PI 和 Steven Skates 博士领导的子宫灌洗队列中提供。 共同研究者具有互补的专业知识，涵盖妇科肿瘤学、临床队列招募、 生物统计学、人工智能、免疫学和卵巢癌生物标志物开发。