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

项目摘要 卵巢癌是最致命的女性癌症。当疾病可以在早期阶段被诊断出来时，有 生存率显著提高(5年生存率≥为90%)，而晚期(≤为40%)。不过，目前 没有一种早期发现卵巢癌的方法有足够的准确性，大多数肿瘤已经进展到 诊断已进入晚期。此外，超过70%的附件肿块在术前被发现 骨盆手术后的成像被发现是良性的。目前的临床检测依赖于血清CA-125和 超声对卵巢附件肿块的诊断。然而，CA-125在许多常见良性疾病中升高 卵巢的超声成像常常会漏掉微小但恶性的病变。因此，外科手术 切除病变和组织学评估仍然是诊断的唯一金标准。这些限制 说明临床迫切需要一种更好的、具有高检测准确性的术前诊断方法，以 降低死亡率，减少不必要的手术，保留许多患者的生活选择， 特别是处于生育年龄的年轻女性为怀孕做好准备。在这里，我们提出了一个完整的 从血液T细胞谱系中检测卵巢癌信号的不同途径。这是可行的，因为T 淋巴细胞在最初阶段识别肿瘤抗原，增殖并改变外周T细胞库。 因此，检测血液中的癌症相关T细胞(CAT)提供了一个令人兴奋的新机会 非侵入性癌症诊断。然而，以前的研究都没有达到这一目标，因为很难 高通量鉴定CAT，因为大多数癌症抗原尚不清楚。为了准备这项任务，我们 开发了软件TRUST和iSmart，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大量的猫训练集，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，用于泛癌预测 使用血液TCR测序数据，并在初步研究中显示出99%以上的特异性和86%的敏感性 从健康人(n=176)中预测卵巢癌患者(n=14)。把这种方法发展成一部小说 卵巢癌特异性生物标记物，我们已经建立了一个生物库，以前瞻性地从 卵巢良性或恶性病变患者和年龄相仿的健康捐赠者，与 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基于TCR的卵巢癌预测新方法。在目标2中，我们将结合这一点 与现有的临床试验相结合，以获得多模式生物标志物，并使用 样本来自史蒂文·斯科特斯博士领导的子宫灌洗队列。这些目标将由私人投资总监和 具有互补专业知识的联合调查员，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记物的开发。

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.