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 36 month milestones. Primary achievements for 36 months are: 1) Continued Case and control identification (45 Pure DCIS and 36 adjacent DCIS with invasion) through extensive database and searching at Duke 2) Deep and comprehensive full exome sequencing for 32 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 12 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, 2017
Accession Number
AD1046997

Entities

People

  • E. Shelley Hwang

Organizations

  • Duke University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Breast Cancer
  • Cancer
  • Carcinoma
  • Cells
  • Computational Biology
  • Computational Science
  • Computer Vision
  • Computers
  • Databases
  • Deep Learning
  • Genetic Variation
  • Identification
  • Information Science
  • Medical Personnel
  • Neoplasms
  • Supervised Machine Learning

Fields of Study

  • Biology

Readers

  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • Biotechnology