Genomic Diversity and the Microenvironment as Drivers of Progression in DCIS

Abstract

The project is designed to test whether genetic and/or tumor environmental heterogeneity is a driving force in progression of breast DCIS. Our project, a collaboration between Duke and ASU, has made substantial progress on all 4 aims and we met our 48 month milestones. Primary achievements for 48 months are: 1) Continued Case and control identification (43 Pure DCIS and 43 adjacent DCIS with invasion) through extensive database and searching at Duke 2) Deep and comprehensive full exome sequencing for 86 cases from 30-160ng of DNA isolated from archival FFPE specimens, 3) Comparison of analytic methods to characterize somatic mutations from this full exome sequencing, 4) Application of sequencing data for copy number assessment 5) Development of dual immune-staining on DCIS lesions using 7 pairs of antibodies, 6) Imaging analysis of these stains, including quantitative analysis, 7) Identification of upstaged DCIS cases for the radiology aim, 8) Development of image analysis methods for digital mammograms, 9) Validation Aim (4) approval of the Duke IRB/TBCRC038 protocol at 13 sites, including DOD approval to initiate collection of DCIS that either did or did not progress to invasive cancer, 10) Full integration of team members over the past year via frequent conferencing, face to face meetings, and constant communication. This multi-disciplinary progress puts our group into an ideal position to fully implement the aims of the project and reach our year 4 goals.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2018
Accession Number
AD1072022

Entities

People

  • Eun-sil Hwang

Organizations

  • Duke University

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Breast Cancer
  • Cancer
  • Carcinoma
  • Cell Physiological Processes
  • Computational Biology
  • Computational Science
  • Computer Vision
  • Computers
  • Deep Learning
  • Department Of Defense
  • Diagnostic Imaging
  • Genetic Variation
  • Identification
  • Machine Learning
  • Medical Personnel
  • Mutations
  • Neoplasms

Fields of Study

  • Biology

Readers

  • Molecular and genetic basis of cancer.
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • Biotechnology