R01CA262147

Noninvasive Bladder Cancer Diagnostics via Machine Learning Analysis of Nanoscale Surface Images of Epithelial Cells Extracted from Voided Urine Samples - Project Summary/Abstract

Bladder cancer is a common cancer with an estimated 81,190 new cases and 17,240 deaths in 2018 (with over 500,000 survivors) in the US. The gold standard for diagnosis of bladder cancer includes an invasive optical bladder examination (cystoscopy) and tumor resection for pathology examination. Due to the high recurrence rate of this cancer (50-80%), frequent and costly cystoscopy exams are required to monitor patients for recurrence and/or progression to a more advanced stage. This makes bladder cancer the most expensive cancer to monitor, follow up, and treat per patient. Additionally, the invasive nature of cystoscopy results in low patient compliance.

There is an urgent unmet need for a bladder cancer screening and monitoring test that is noninvasive, rapid, objective, reproducible, easy to perform and interpret, and highly accurate. Such a test would reduce the need for frequent cystoscopies and greatly increase patient participation in screening and early detection programs by decreasing patient discomfort and post-procedural complications.

In this project, we propose to develop a noninvasive test for the identification of bladder cancer presence and aggressiveness (grade). The test will be based on the non-invasive analysis of individual cells extracted from urine, using extraction technology that already exists in hospitals for voided urine cytology tests (VUC), the current standard of care for non-invasive examination of cells in urine used to assist with cancer diagnosis and surveillance.

We will utilize a novel modality of atomic force microscopy (AFM) for nanoscale imaging of cells extracted from urine, specifically mapping and imaging the physical properties of the cell surface. The collected images will then be analyzed using machine-learning methods and novel advanced statistical approaches to identify a "digital signature" of cancer. This 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, rather than cell bulk or the presence of biochemical markers or genetic analysis.

Our strong preliminary results demonstrate the feasibility of the proposed approach and its presumed superiority compared to currently used non-invasive methods. These results 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 visual analysis of many cells.

Supported by the preliminary data, our proposed research aims to (1) optimize and expand the method, (2) define the accuracy of cancer detection on a large cohort of patients, and (3) 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 accurately detecting the 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 for bladder and upper urinary tract cancer, stool for colorectal cancer, sputum for aerodigestive cancer, cervical smears for cervical cancer, etc.). These methods will be based on the analysis of physical characteristics of the cell surface. The proposed research serves as the first step in pursuit of this overarching goal.

Trustees Of Tufts College

TO IMPROVE SCREENING AND EARLY DETECTION STRATEGIES AND TO DEVELOP ACCURATE DIAGNOSTIC TECHNIQUES AND METHODS FOR PREDICTING THE COURSE OF DISEASE IN CANCER PATIENTS. SCREENING AND EARLY DETECTION RESEARCH INCLUDES DEVELOPMENT OF STRATEGIES TO DECREASE CANCER MORTALITY BY FINDING TUMORS EARLY WHEN THEY ARE MORE AMENABLE TO TREATMENT. DIAGNOSIS RESEARCH FOCUSES ON METHODS TO DETERMINE THE PRESENCE OF A SPECIFIC TYPE OF CANCER, TO PREDICT ITS COURSE AND RESPONSE TO THERAPY, BOTH A PARTICULAR THERAPY OR A CLASS OF AGENTS, AND TO MONITOR THE EFFECT OF THE THERAPY AND THE APPEARANCE OF DISEASE RECURRENCE. THESE METHODS INCLUDE DIAGNOSTIC IMAGING AND DIRECT ANALYSES OF SPECIMENS FROM TUMOR OR OTHER TISSUES. SUPPORT IS ALSO PROVIDED FOR ESTABLISHING AND MAINTAINING RESOURCES OF HUMAN TISSUE TO FACILITATE RESEARCH. SMALL BUSINESS INNOVATION RESEARCH (SBIR) PROGRAM: TO EXPAND AND IMPROVE THE SBIR PROGRAM, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, TO INCREASE SMALL BUSINESS PARTICIPATION IN FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION. SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM: TO STIMULATE AND FOSTER SCIENTIFIC AND TECHNOLOGICAL INNOVATION THROUGH COOPERATIVE RESEARCH AND DEVELOPMENT CARRIED OUT BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO FOSTER TECHNOLOGY TRANSFER BETWEEN SMALL BUSINESS CONCERNS AND RESEARCH INSTITUTIONS, TO INCREASE PRIVATE SECTOR COMMERCIALIZATION OF INNOVATIONS DERIVED FROM FEDERAL RESEARCH AND DEVELOPMENT, AND TO FOSTER AND ENCOURAGE PARTICIPATION OF SOCIALLY AND ECONOMICALLY DISADVANTAGED SMALL BUSINESS CONCERNS AND WOMEN-OWNED SMALL BUSINESS CONCERNS IN TECHNOLOGICAL INNOVATION.

93.394 - Cancer Detection and Diagnosis Research

National Cancer Institute (NCI) [HHS - NIH]

Medford, Massachusetts 021555530 United States

Single Zip Code

NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed) (PA-20-185)

Amendment Since initial award the total obligations have increased 362% from $674,628 to $3,120,020.