Assessing Molecular Pathways Driving Conjunctival Melanoma
Abstract
The scientific objective of this proposal is to decrease the suffering and death due to late diagnosis of conjunctival melanoma, an aggressive cancer of the eye, by developing methods to more accurately and reproducibly screen for and detect the cancer earlier, and to analyze molecular pathways that contribute to the cancer’s growth. The rationale for this study is that, in the earliest stages of cancer development, the current methods of detecting the presence of cancer are not sensitive enough, and there is a need for more sensitive and accurate diagnostic methods. This proposal addresses the Fiscal Year 2020 Rare Cancer Research Program Focus Area of Biology and Etiology. We will use archived biopsy samples to analyze the molecular make-up of conjunctival melanomas and use that information to develop a diagnostic tool and to understand the molecular changes that lead to conjunctival melanoma progression. We will also use computer machine learning techniques to predict tumor stage and molecular state using microscope images of biopsy tissue. This research will help patients with conjunctival melanoma, which can occur in patients of all races. The successful completion of this study will help to prevent more advanced cancers and decrease the suffering and death associated with an advanced diagnosis. The anticipated clinical applications are: (1) a molecular classifier to diagnose early conjunctival melanoma and (2) a computer vision model that can diagnose conjunctival melanoma from images of biopsy tissue. The longer term outcome is a deeper understanding of the molecules involved in cancer progression, which can lead to more precise and targeted treatments. The projected time to a clinically relevant outcome is 3 years for both the classifier and computer vision model. The likely contribution of this study to advancing rare cancers research is to develop a multimodal way of approaching cancer diagnosis by combining molecular studies with computer vision methods.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2021
- Source ID
- W81XWH2110982
Entities
People
- Maria Wei
Organizations
- Northern California Institute for Research and Education
- United States Army