Development of a High-Throughput Three-Dimensional H&E Platform for the Characterization of Breast Cancer Biopsies

PROJECT SUMMARY The purpose of this project is to develop a prognostic imaging tool for breast cancer that can be used to predict specific clinical outcomes such as tumor reoccurrence and drug resistance based upon a tumor s histomorphology featuresThe current histopathology paradigm for evaluating tissues is focused on diagnosing disease wherein these same tissues if evaluated in their entirety and digitizedprovide an opportunity to correlate clinical outcomes to specific tissue featuresThrough this projectwe are developing a highthroughput breast tumor imaging approach that is capable of imaging breast tumor tissue in its entirety and generating virtual Handamp E optical Z sections inD of equivalent quality to traditional Handamp E sections so that all of the cells within a biopsy are characterizedTo achieve this objectivewe are combining our patented tissue clearing approach with fluorescent labelinghigh content confocal microscopy and an unbiased machine learning approachThis approach allows for biopsies to be digitized in their entirety and for all of the features and heterogeneity of tumors to be assessed instead of just looking at a few ultra thinD slidesThrough the combination of this unique imaging approach with hierarchical agglomerative clusteringspecific histomorphological features can be correlated to clinical outcomes using a detailed sample library with corresponding clinical outcome dataThe main objectives of this project are todevelop a robustD Handamp E labeling approachdemonstrate that tissues can be imaged inD using a fluorescent approach to generateHandamp E likeimages of equivalent quality to traditional Handamp E andshow that this tissue analysis approach can be transferred to an automated highcontent confocal microscopeAdditionallywe will show the ability to cluster tissues based on their histomorphological features and will with a small data set ofbreast tumor biopsies show how these features are correlated to clinical outcomesIf successfulwe will develop this proof of concept into a robust CLIACFR Partcompliant assay that complies with the ICH guidelines for analytical assaysThis assay would ultimately allow clinicians to better predict how a tumor will respond to certain treatments and best tailor a treatment for a specific patientThis type of precision medicine approach will lead to improved patient outcomes and a more efficacious treatment regimen PROJECT NARRATIVE The purpose of this project is to develop a prognostic imaging tool for breast cancer that evaluates breast tumor tissues in their entirety inD and uses a machine learning based approach to predict specific clinical outcomes such as tumor reoccurrence and drug resistanceUpon successful development and commercialization of this technologyclinicians will be able to better tailor treatment regimens for patients based upon their specific disease which will result in improved patient outcomes