Skip to main content
Premium Trial:

Request an Annual Quote

TGen Gets Funding for Inflammatory Breast Cancer Study

NEW YORK (GenomeWeb News) – The Translational Genomics Research Institute has received a $50,000 award to conduct DNA analysis studies focused on identifying the genetic origins of inflammatory breast cancer (IBC).

The funding from the Inflammatory Breast Cancer Foundation (IBCRF) will support efforts to analyze samples from IBC tumors for genetic similarities that could reveal a therapeutic vulnerability, TGen said today.

IBC is a particularly deadly but rare type of cancer that causes around five percent of breast cancers, and it is often misdiagnosed and swiftly moves to an advanced stage.

This study will focus on the triple-negative form of the disease, which does not express clinically significant levels of estrogen receptor, progesterone receptor, or human epidermal growth factor.

Research on this type of cancer is difficult because cells within an IBC tumor are diffuse throughout the breast and are mixed with normal cells and a significant number of immune system cells, Heather Cunliffe, head of TGen's Breast and Ovarian Cancer Research Unit, said in a statement.

"This makes isolation of tumor-specific DNA samples for research exceedingly difficult," Cunliffe added.

She explained that this study will use technology developed at TGen that allows researchers to purify and examine triple-negative IBC accurately at high resolution without contamination from healthy cells.

TGen hopes that the study will generate findings that can be translated into new therapeutic approaches for treating IBC patients.

The Scan

Study Links Genetic Risk for ADHD With Alzheimer's Disease

A higher polygenic risk score for attention-deficit/hyperactivity disorder is also linked to cognitive decline and Alzheimer's disease, a new study in Molecular Psychiatry finds.

Study Offers Insights Into Role of Structural Variants in Cancer

A new study in Nature using cell lines shows that structural variants can enable oncogene activation.

Computer Model Uses Genetics, Health Data to Predict Mental Disorders

A new model in JAMA Psychiatry finds combining genetic and health record data can predict a mental disorder diagnosis before one is made clinically.

Study Tracks Off-Target Gene Edits Linked to Epigenetic Features

Using machine learning, researchers characterize in BMC Genomics the potential off-target effects of 19 computed or experimentally determined epigenetic features during CRISPR-Cas9 editing.