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, have met our 60 month milestones in all 4 aims. Primary achievements for 60 months are:1) Completed Case and control identification of 57 Pure DCIS and 61 Synchronous DCIS (DCIS with adjacent invasion) through extensive database and searching at Duke 2) Completed deep whole exome sequencing (WES) 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) Completed dual immune-staining on DCIS lesions using 6 pairs and 3 single antibodies, 6) Completed Image analysis of these stains, including quantitative analysis, 7) Completed identification of upstaged DCIS cases for the radiology aim, 8) Development of image analysis methods for digital mammograms, 9)Completed the validation Aim 4, including collection of DCIS that either did not progress or progressed to DCIS or invasive cancer,10) Completed Aim 4 WES and Whole genome sequencing for 110 validation cases from 30-160ng of DNA isolated from archival FFPE 11) Full integration of team members over the past year via frequent conferencing, face to face meetings, and constant communication. This multi-disciplinary approach allowed our group to fully implement and we reach our year 5 project goals.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2020
Accession Number
AD1179801

Entities

People

  • Carlo Maley

Organizations

  • Arizona State University

Tags

DTIC Thesaurus Topics

  • Breast Cancer
  • Carcinoma
  • Cells
  • Computational Biology
  • Computational Science
  • Computer Vision
  • Computers
  • Convolutional Neural Networks
  • Data Analysis
  • Deep Learning
  • Detection
  • Genetic Variation
  • Genetics
  • Histology
  • 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