Breast cancer (BC) is the most common cancer among women and is the leading cause of cancer death in women worldwide, with 1.6 million new cases and 500,000 BC deaths annually. Patients diagnosed in low- resource settings (LRS) account for half of new cases, and the majority of deaths from BC worldwide. The first critical step to starting life-saving treatment for BC is the accurate and timely pathologic confirmation of a cancer diagnosis, a task which remains challenging in many LRS. Traditional pathology assessment involves processing surgically excised specimens with cell-block methods for: (1) cellular histopathology, which identifies abnormal cellular morphologies indicative of malignancy, and (2) molecular pathology, which identifies tumor biomarkers, specifically estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), and the proliferation maker Ki67. Breast cancer subtyping using these markers is essential for determining prognosis, as well as for selecting subtype-specific therapies. Unfortunately, histology-based pathology services require a strong pathology infrastructure and trained pathologists, limiting access to these services in many LRS. For example, there are only 15 trained pathologists in Tanzania, a country of over 55 million people. There is hence an urgent need for new methods to accurately diagnose cancer, as well as to analyze expression levels of molecular biomarkers for tumor subtyping. A technology driven solution that could automate cellular pathology with minimal user-intervention and virtually no infrastructure requirements could thus enormously impact the management of breast cancer in LRS. Motivated by this need, the objective of this proposal is to finalize the development of the EpiView-D4 point-of-care test (POCT) to analyze both the cellular and molecular features of breast cancer from needle aspiration specimens. The EpiView component of the device enables easily accessible, low-cost, smart-phone based brightfield cellular imaging of fine needle aspirate breast biopsies without the need for pathologist assessment. In parallel, the D4 POCT component of the device images a point-of-care antibody microarray for the quantification of ER/PR/Her2/Ki67 levels from breast FNA lysate with picomolar sensitivity within 30 minutes at point-of-care, eliminating the need for additional visits before a treatment plan can be initiated. The EpiView-D4 will enable automated readout of both cytopathology and the molecular profiles of breast cancer, using machine learning algorithms integrated into a smartphone application. In this proposal, we will conduct final device development and training of ML algorithms, followed by pre-clinical validation and clinical investigation of the Epiview-D4 POCT, first at Duke University Medical Center, and then in the intended LRS of Kilimanjaro Christian Medical Center. The impact of this technology lies in its potential to dramatically improve breast cancer management worldwide by enabling rapid and accurate diagnosis and subtyping of breast cancers, thereby driving timely and appropriate treatment for breast cancer patients and hence improving the outcomes for hundreds of thousands of women with BC annually in LRS.
In less developed parts of the world, there is an urgent need for accurate and more informative diagnosis of breast cancer as trained pathologists are scarce? and pathology infrastructure is often very limited. The proposed research will complete the currently ongoing development of a cell-phone based device that: (1) images cells from breast tumors and analyzes them by software to identify whether a person has breast cancer, and (2) measures levels of clinically-relevant protein biomarkers to help guide treatment. If successful, this technology can be widely by health care workers to provide the same level of care in low-resource settings as is currently available in the US and will thereby save many lives.