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 60 month milestones. Primary achievements for 60 months are: 1) Continued Case and control identification 52 Pure DCIS and 48 adjacent DCIS with invasion) through extensive database and searching at Duke 2) Deep and comprehensive full exome sequencing for 100 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 5 goals.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2022
Accession Number
AD1216094

Entities

People

  • Carlo Maley

Organizations

  • Arizona State University

Tags

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Breast Cancer
  • Carcinoma
  • Cells
  • Computational Biology
  • Computational Science
  • Computer Vision
  • Computers
  • Convolutional Neural Networks
  • Data Analysis
  • Deep Learning
  • Genetic Variation
  • Genetics
  • Identification
  • Information Science
  • Lymphocytes
  • Machine Learning
  • Medical Personnel
  • Neoplasms
  • Neural Networks

Fields of Study

  • Biology

Readers

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

Technology Areas

  • Biotechnology