Wednesday, January 15, 2025
1/15/2025
Abstract Ductal carcinomas in situ (DCIS) of the breast are a heterogeneous group of neoplastic lesions that are usually detected by screening mammography. Workup generally includes a percutaneous (core) Biopsy (Bx) for histologic confirmation, followed by multiparametric MRI (mpMRI), followed by breast-conserving excision, and adjuvant radiation. Approximately 20-25% of patients with core Bx-confirmed DCIS are upstaged to invasive carcinoma upon pathology of resected tissue. Foreknowledge of this would dictate a more aggressive surgical intervention, including sentinel node biopsy for axillary staging. Further, another 20-25% of patients are judged to have low-risk disease and current thought is that such women may have better outcomes in an active surveillance setting, and this is being tested in clinical trials. The ultimate goal and the overall impact of this project is to use machine learning to identify biochemical (SA1) or imaging (SA2) biomarkers, as well as their combination (SA3) to discriminate indolent from aggressive DCIS, as determined by upstaging upon excisional biopsy. The major hypothesis to be tested in this work is that hypoxia and expression of hypoxia-related proteins (HRPs) can discriminate aggressive from more indolent DCIS, and that this can be used for decision support. Expression of HRPs is optimally characterized by immunohistochemistry (IHC), and we have deployed methods for multiplexed IHC, as well as methods for advanced analytics using machine learning (pathomics). We have also shown that hypoxic habitats within breast cancers can be identified from mpMRI using machine learning (radiomics). We thus propose to use pathomics of core biopsies and radiomics of mpMRI to determine the presence and extent of hypoxic habitats in DCIS prior to surgery to predict subsequent upstaging after surgical resection. This work will be performed in Aim 1 for pathomics and Aim 2 for radiomics, and Aim 3 will develop combined radio-pathomics predictors. Each aim will contain: (a) retrospective arms for training, tuning, and testing; and (b) prospective internal and external cohorts for rigorous validation. For the retrospective studies, we have identified 604 cases wherein women with DCIS obtained core Bx, mpMRI, and surgery with pathology at Moffitt in the last 10 years. Internal prospective studies will accrue ~6 women/month who have consented to the total Cancer Care® protocol and who have their complete workup at Moffitt. External validation cohorts will be accrued at UCSF and at Advent Health. At the end of this work we will have developed a risk model for DCIS that can be deployed prior to surgery to guide decisions along the spectrum from active surveillance at one end to more extensive surgical intervention at the other. This is expected to lay a foundation for subsequent interventional trials. Additionally, the inclusion of hypoxia as a central hypothesis has high potential to illuminate components of the natural history of this disease.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).