Drug Discovery and Development

  • Home Drug Discovery and Development
  • Drug Discovery
  • Women in Pharma and Biotech
  • Oncology
  • Neurological Disease
  • Infectious Disease
  • Resources
    • Video features
    • Podcast
    • Voices
    • Webinars
  • Pharma 50
    • 2025 Pharma 50
    • 2024 Pharma 50
    • 2023 Pharma 50
    • 2022 Pharma 50
    • 2021 Pharma 50
  • Advertise
  • SUBSCRIBE

Computerized Tissue Image Analysis Reveals Underlying Genomics of ER+ Breast Cancer

By Case Western Reserve University | September 8, 2016

Breast cancer tissue shows high (left) and low (right) tubule formation. The lumen is delineated by blue lines, tubules by orange lines. Green dots mark tubule nuclei and red dots nontubule nuclei determined by the machine learning classifier. In the right panel, which was also identified as low risk by a genomic assay, the cells appear to have lost their capacity to form tubules with well-defined lumen. Credit: Case Western Reserve University

The number of tubules in tumors may predict which women with estrogen receptor positive (ER+) breast cancer will benefit from hormone therapy alone and which require chemotherapy, researchers at Case Western Reserve University have found.

Tubules represent the tumor’s vasculature, providing tumors with oxygen and nutrition. The more of them there are, the more likely a patient will need chemotherapy.

In a study published in Scientific Reports, the researchers developed a computer program to automatically count the number of tubules found in whole slide images of breast cancer tissue specimens. They found the number of tubules correlated with the scores produced by the current best test differentiating between indolent and aggressive ER+ cancers.

“This is the first large-scale validation that fundamental prognostic information is in the tissue data, and that it predicts the underlying genomics of the tumor,” said Anant Madabhushi, F. Alex Nason professor II of biomedical engineering and an author of the study.

“If we can mirror the genomics of the tumor, we can predict who responds to hormone therapy only and who doesn’t,” he said.

Madabhushi teamed with David Romo Bucheli and Eduardo Romero, engineering faculty from the Universidad Nacional de Colombia, in Bogota; Andrew Janowczyk biomedical engineering research associate at Case Western Reserve; and Hannah Gilmore, associate professor of pathology at Case Western Reserve School of Medicine.

Pathologists currently use three features to grade tumors: mitoses, distinct forms of cell nuclei, called nuclear pleomorphism, and tubules. More of each increases the risk the cancer is aggressive and requires chemotherapy.

The researchers used machine learning to allow a computer to best quantify the number of tubules automatically and quickly.

Using slide images from 174 ER+ patients, they found that the number of tubules correlates with patients’ Oncotype DX gene expression test risk scores.

The gene expression test has been shown to be predictive in identifying which ER+ breast cancer patients would benefit from chemotherapy, but the cost is out of reach for low- and middle-income patients around the world, and the wait time can be weeks, Madabhushi said. His group at the Center for Computational Imaging and Personalized Diagnostics is exploring alternative means to cheaply and quickly provide the same answers.

This study is the first of three by his lab in this area of research. The next quantifies the number of mitoses in whole image slides and correlates the numbers with the genomic test. The last quantifies nuclear pleomorphism and architecture and correlates them with the genomic test.

If all contribute to accurate predictions, Madabhushi plans to integrate them and develop one program that provides a coherent prediction.


Filed Under: Genomics/Proteomics

 

Related Articles Read More >

Spatial biology: Transforming our understanding of cellular environments
DNA double helix transforming into bar graphs, blue and gold, crisp focus on each strand, scientific finance theme --ar 5:4 --personalize 3kebfev --v 6.1 Job ID: f40101e1-2e2f-4f40-8d57-2144add82b53
Biotech in 2025: Precision medicine, smarter investments, and more emphasis on RWD in clinical trials
DNA helix 3D illustration. Mutations under microscope. Decoding genome. Virtual modeling of chemical processes. Hi-tech in medicine
Genomics in 2025: How $500 whole genome sequencing could democratize genomic data
A media release and Scientific Report image of Elizabeth Kellogg. - Camera Settings: ILCE-9M2, 12mm, ISO 1000, 1/80, f/3.2, Fri, 04-19-2024 at 10:10. v.12.01.23
St. Jude pioneers gene editing and structural biology to advance pediatric research
“ddd
EXPAND YOUR KNOWLEDGE AND STAY CONNECTED
Get the latest news and trends happening now in the drug discovery and development industry.

MEDTECH 100 INDEX

Medtech 100 logo
Market Summary > Current Price
The MedTech 100 is a financial index calculated using the BIG100 companies covered in Medical Design and Outsourcing.
Drug Discovery and Development
  • MassDevice
  • DeviceTalks
  • Medtech100 Index
  • Medical Design Sourcing
  • Medical Design & Outsourcing
  • Medical Tubing + Extrusion
  • Subscribe to our E-Newsletter
  • Contact Us
  • About Us
  • R&D World
  • Drug Delivery Business News
  • Pharmaceutical Processing World

Copyright © 2025 WTWH Media LLC. All Rights Reserved. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of WTWH Media
Privacy Policy | Advertising | About Us

Search Drug Discovery & Development

  • Home Drug Discovery and Development
  • Drug Discovery
  • Women in Pharma and Biotech
  • Oncology
  • Neurological Disease
  • Infectious Disease
  • Resources
    • Video features
    • Podcast
    • Voices
    • Webinars
  • Pharma 50
    • 2025 Pharma 50
    • 2024 Pharma 50
    • 2023 Pharma 50
    • 2022 Pharma 50
    • 2021 Pharma 50
  • Advertise
  • SUBSCRIBE