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 24 month milestones. Primary achievements for 24 months are: 1) Continued Case and control identification through extensive database and medical record searching at Duke, 2) Development of methods for isolating DNA from archival DCIS lesions, 3) Deep and comprehensive full exome sequencing for 20 cases from 20-160ng of DNA isolated from these archival FFPE specimens, 4) Comparison of analytic methods to characterizesomatic mutations from this full exome sequencing, 5) Application of sequencing library DNA to Illumina SNP arrays for copy number assessment 6) Development of dual immune-staining on DCIS lesions using 6 pairs of antibodies, 7) Sharing of images from these stains with collaborators for quantitative analysis, 8) Identification of a series of upstaged DCIS cases for the radiology aim,9) Development of image analysis methods for digital mammograms, 10) Approval of both the TBCRC and Duke IRB protocol for the validation aim to initiate collection of DCIS that either did or did not progress to invasive cancer, 11) 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 year3 and 4 goals.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2017
Accession Number
AD1054022

Entities

People

  • Carlo Maley

Organizations

  • Arizona State University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

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

Fields of Study

  • Biology

Readers

  • Molecular and genetic basis of cancer.
  • Oncology and Biomarker-Based Cancer Detection.
  • Technical Research and Report Writing.

Technology Areas

  • Biotechnology