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。当疾病可以在早期诊断时， 与晚期（≤ 40%）相比，生存率显著提高（5年生存率≥ 90%）。但目前 卵巢癌的早期检测方法没有足够的准确性，大多数肿瘤已经进展到 诊断时处于晚期。此外，超过70%的附件肿块在术前检测到， 影像学检查发现盆腔手术后是良性的。目前的临床试验依赖于血清CA-125和 超声波检查以诊断卵巢附件肿块。然而，CA-125在许多常见的良性肿瘤中升高， 卵巢的超声成像经常错过小但恶性的病变。因此，手术 切除病变和组织学评价仍然是诊断的唯一金标准。这些限制 因此，临床上迫切需要一种更好的术前诊断方法， 降低死亡率，减少不必要的手术，为许多患者保留生活选择， 特别是处于生育年龄的年轻妇女的怀孕计划。在这里，我们提出了一个完全 通过不同的途径从血液T细胞库中检测卵巢癌信号。这是可行的，因为T 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）的检测为癌症相关T细胞的治疗提供了令人兴奋的新机会。 非侵入性癌症诊断。然而，没有先前的研究已经实现了这一目标，因为它是困难的， 高通量鉴定CAT，因为大多数癌症抗原仍然未知。为了完成这项任务，我们 开发了软件TRUST和iSMART，以从癌症数据集中获得抗原特异性TCR。这些 工具使我们能够产生一个大型的CAT训练集，这使我们能够识别诊断TCR， 卵巢癌患者。根据这一结果，我们进一步开发了DeepCAT，用于泛癌症预测 使用血液TCR测序数据，并在初步研究中证明了超过99%的特异性和86%的灵敏度 从健康供体（n=176）预测卵巢癌患者（n=14）。把这种方法发展成一部小说 卵巢癌特异性生物标志物，我们已经建立了一个生物储存库， 良性或恶性卵巢病变患者和来自相似年龄段的健康供体， 临床信息。在目标1中，我们将生成新患者样本的TCR测序数据，以开发 使用机器学习方法的新的基于TCR的卵巢癌预测器。在目标2中，我们将联合收割机结合起来 与现有的临床测试方法，以获得多模态生物标志物，并独立测试它使用 来自史蒂文·斯贝茨博士领导的子宫灌洗队列的样本。这些目标将由PI实现， 具有妇科肿瘤学、临床队列招募等互补专业知识的合作研究者， 生物统计学、人工智能、免疫学和卵巢癌生物标志物开发。