Noninvasive bladder cancer diagnostics via machine learning analysis of nanoscale surface images of epithelial cells extracted from voided urine samples

项目摘要/摘要 膀胱癌是一种常见的癌症，2018年估计有81,190例新病例和17,240例死亡(与&gt； 500,000名幸存者)仅在美国。诊断膀胱癌的黄金标准包括侵袭性 光性膀胱检查(膀胱镜检查)和肿瘤切除病理检查。因为一个很高的 这种癌症的复发率(50%-80%)，经常(每3-6-12个月一次)昂贵的侵入性膀胱镜检查 需要进行检查以监测患者的复发和/或进展到更晚期。它让人 膀胱癌是每名患者监测/跟踪和治疗费用最高的癌症。此外，入侵者 当前护理标准的性质，即膀胱镜检查，导致患者依从性较低。 程序。对膀胱癌筛查和监测测试的迫切需求尚未得到满足，这将是 非侵入性、快速、客观、可重复性、易于操作和解释，且准确度高。这样的测试将会 减少频繁的膀胱镜检查的需要，并极大地扩大患者对筛查和 早期检测方案，因为它减少了患者的不适和术后并发症。 在这里，我们建议开发这样的测试来识别膀胱癌的存在和它的 攻击性(年级)。它将基于对从尿液中提取的单个细胞的非侵入性分析 (医院已经存在用于尿液细胞学检测的提取技术，(VUC)目前的标准是- CARE，一种对尿液中细胞的非侵入性检查，用于辅助癌症诊断和监测)。一个 新型原子力显微镜(AFM)将用于提取细胞的纳米级成像 尿液，绘制/成像细胞表面的物理属性。收集的图像将进一步 使用机器学习方法和新的高级统计方法进行分析，以识别 癌症的“特征”。这项拟议的技术与之前研究的尿液有根本的不同 生物标志物和所有现有的物理方法，因为它是基于对生物标志物的物理性质的分析 细胞表面，不是细胞块状，也不是生化标记或遗传分析。 我们强大的初步结果证明了所提出的方法的可行性，其假设 比目前使用的非侵入性方法更具优势，并将我们引向中心假设 膀胱癌可以通过分析从尿样中随机选择的少量细胞来识别， 具有较低的采样误差。这与VUC测试有很大不同，VUC测试需要对 很多细胞。在初步数据的支持下，我们建议(1)优化和扩展该方法，(2) 确定在大量患者队列中检测癌症的准确性，以及(3)评估 膀胱癌侵袭性(低级别与高级别)的鉴定。 我们的长期目标是开发一种非侵入性的临床方法，用于准确检测存在和 监测膀胱癌和许多其他癌症，在这些癌症中，细胞可以很容易地提取出来 无需组织活检即可获得体液(例如尿囊癌和上尿道癌、大便- 结直肠癌、痰-空气消化癌、宫颈涂片-宫颈癌等)，使用基于 对细胞表面的物理特性进行分析。拟议的研究，这是 对这一首要目标的追求。

PROJECT SUMMARY/ABSTRACT Bladder cancer is common cancer with an estimated 81,190 new cases and 17,240 deaths in 2018 (with > 500,000 survivors) only in the US. The gold standard for diagnosis of bladder cancer includes an invasive optical bladder examination (cystoscopy) and tumor resection for pathology examination. Because of a high recurrence rate of this cancer (50-80%), frequent (once every 3-6-12 months) costly and invasive cystoscopy exams are required to monitor patients for recurrence and/or progression to a more advanced stage. It makes bladder cancer the most expensive cancer to monitor/follow up and treat per patient. Moreover, the invasive nature of the current standard of care, cystoscopy, causes rather low compliance of patient to follow this procedure. There is an urgent unmet need for a bladder cancer screening and monitoring test, which will be noninvasive, rapid, objective, reproducible, easy to perform and interpret, and highly accurate. Such a test will reduce the need in frequent cystoscopies and greatly expand the participation of patients in screening and early detection programs because it decreases the patient discomfort and post-procedural complications. Here we propose to develop such a test for identification of the presence of bladder cancer and its aggressiveness (grade). It will be based on non-invasive analysis of individual cells extracted from urine (extraction technology already exists in hospitals for voided urine cytology tests, (VUC) the current standard-of- care, a non-invasive examination of cells in urine used to assist with cancer diagnosis and surveillance). A novel modality of Atomic Force Microscopy (AFM) will be used for nanoscale imaging of cells extracted from urine, mapping/imaging of the physical properties of the cell surface. The collected images will further be analyzed using machine-learning methods and novel advanced statistical approaches to identify a “digital signature” of cancer. The proposed technology is fundamentally different from previously studied urine biomarkers and all existing physical methods because it is based on the analysis of physical properties of the cell surface, not cell bulk or presence of biochemical markers or genetic analysis. Our strong preliminary results demonstrate the feasibility of the proposed approach, its presumed superiority compared to the currently used non-invasive methods, and lead us to the central hypothesis that bladder cancer can be identified by analyzing a small number of cells randomly chosen from urine samples, with a low sampling error. This is a substantial departure from VUC tests, which require a visual analysis of many cells. Supported by the preliminary data, we propose (1) to optimize and expand the method, (2) to define the accuracy of cancer detection on a large cohort of patients, and (3) to assess the accuracy of identification of aggressiveness (low versus high grade) of bladder cancer. Our long-term goal is to develop a non-invasive clinical method for accurate detecting of presence and monitoring bladder cancer as well as many other cancers, in which cells can be extracted from easily accessible bodily fluids without the need for tissue biopsy (e.g urine-bladder & upper urinary tract cancer, stool- colorectal cancer, sputum-aerodigestive cancer, cervical smears-cervical cancer etc.), using methods based on the analysis of physical characteristics of the cell surface. The proposed research, which is the first step in pursuit of this overarching goal.