ABSTRACT
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.