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.

项目摘要 卵巢癌是最致命的女性癌症。 与晚期相比，惊人的生存改善（五年生存≥90％）（≤40％） 没有针对卵巢癌的早期检测方法具有足够的准确性，大多数肿瘤都会发展为 诊断时的高级阶段。 在骨盆手术后发现成像是良性的。 诊断卵巢附件质量的超声图。 条件；卵巢的超声成像经常错过小但恶性病变。 切除病变和组织学评估仍然是诊断的唯一金标准。 决定具有高检测精度的更好术前诊断方法的紧急临床需求， 降低死亡率，减少不必要的手术并为许多患者保留生活选择， 尤其是在怀孕的生殖年龄计划中的年轻妇女。 检测血液T细胞再生的卵巢癌信号的不同途径是可行的 淋巴细胞在初始阶段识别肿瘤抗体，增殖并改变周围细胞库。 因此，血液中与癌症相关的T细胞（CAT）的检测为令人兴奋的新机会提供了 但是，非侵入性癌症的诊断。 在高脚齿中识别CAT，因为大多数癌症抗原仍然是未知的。 开发了软件信任和ISMART，以从癌症数据集中获得特异性的TCR。 工具使我们能够生产大型训练赛猫，这使我们能够确定诊断性的TCR 卵巢癌患者。 使用血液TCR测序数据，并在一项初步研究中证明了超过99％的特异性和86％的敏感性 预测健康供体的卵巢癌患者（n = 14）（n = 176）。 卵巢癌特异性生物标志物，我们建立了一个生物座席，以前瞻性地收集标本 患有良性或恶性卵巢病变的患者以及与年龄相似的健康供体的患者，相关的患者 AIM 1中的临床信息，我们将生成新患者样本的TCR测序数据 基于TCR的新型卵巢癌预测器使用机器学习方法。 现有临床测试的方法以获得多模式生物标志物，并使用the tht thth the Intional进行测试 史蒂文·斯凯特（Steven Skates）领导的子宫灌洗队列的样本将由PIS和 涉及妇科肿瘤学，临床队列招聘的综合专业知识的共同投资者， 生物统计学，人工智能，免疫学和卵巢癌生物标志物发展。