Improvements in the detection of cancerous changes at the pre-invasive stage have the potential to make a significant impact in the prognosis and treatment of most cancer patients. Despite important advances in our understanding of cancer pathobiology, the most prevalent diagnostic methods continue to rely on low magnification tissue visualization, followed by biopsy and histopathology. This process is invasive, often limited in its sensitivity and/or specificity, time-consuming, and relies heavily on the expertise of highly trained physicians, who in turn exploit primarily morphological tissue changes for their assessments. These limitations present barriers to effective treatment and raise monetary and psychological costs. Our long-term objective is to transform pre- and early epithelial cancer diagnosis through the use of functional (metabolic) and morphological tissue biomarkers that are extracted non-invasively, automatically, and in near real time from fiber-probe-based endogenous two-photon (2P) images. In this proposal, we aim to establish and validate such measurements and biomarkers for the detection of human cervical pre-cancers in vivo. The cervix is an ideal organ for developing and testing our approach as it relaxes some of the size limitations presented for endoscopic applications, enabling us to focus on demonstrating the principles of this innovative platform. In addition, we expect that our proposed methods will enable significant near-term improvements in the sensitivity and specificity of detection of cervical pre-cancerous lesions during colposcopy and triage with therapy. To achieve our goals, we have forged a strong partnership among colleagues in academia and the clinic, leveraging strengths and expertise of multiple teams. Specifically, we will exploit our experience in laser development (Xu, Cornell) and multiphoton imaging probe design (Ben-Yakar, UT Austin) to develop a probe-based 2P imaging system that is portable and enables fast image acquisition throughout the cervical epithelium depth with submicron resolution (Aim 1). The final design specifications will be optimized to enable automated, near-real time analysis of images that provides quantitative metrics of metabolic function and morphology (Aim 2). In particular, we will assess: a) multiple quantitative biomarkers of cellular metabolism based on redox ratio and mitochondrial organization parameters extracted from endogenous NAD(P)H and FAD 2P excited fluorescence images of the epithelium, and b) morphological metrics associated with depth-dependent variations of the nuclear to cytoplasmic area ratio and epithelial thickness (Georgakoudi, Tufts). The real time algorithms will be established using freshly excised normal and pre-cancerous human cervical tissues (Aim 2). They will be tested and further optimized in vivo when the innovative 2P imaging platform will be used to perform the first-in-human 2P probe-based imaging during colposcopy (Thieu/Genega, Tufts Medical Center) (Aim3). We expect these studies will provide high sensitivity and specificity of cervical pre-cancer detection and will enable further studies that have the potential to change the paradigm of diagnosis and ultimately prognosis for a broad range of cancers that are accessible via a probe.
In 2018 alone, there were over 600,000 cancer-related deaths despite over 130 billion US dollars in cancer care costs. This project pursued by a strong team of partners in academia and the clinic aims to transform pre-cancer diagnosis and prognosis by enabling the non-invasive and automated extraction of a combination of metrics that report on important functional and morphological tissue aspects that are known to be altered during cancer development. We expect that this approach will improve significantly the accuracy of detection of pre- and early cancer lesions, which can be treated effectively and efficiently in most cases.