Predicting clinical response to breast cancer therapy – the immune signature approach

Professor Barbara D Fazekas de St Groth

University of Sydney (NSW)

$449,839

2024 - 2027

‍

The Research

Breast cancer is the most common cancer in women and is still a major cause of cancer death despite significant improvements in treatment over the past 30 years. Those improvements include the introduction of adjuvant chemotherapy following primary surgery, and specific therapies directed to factors expressed by some but not all breast cancers, including estrogen and progesterone receptors, and the Her2 molecule. A minority of cancers, termed triple negative, have neither hormone receptors nor Her2, and treatment options are very limited if chemotherapy proves ineffective.

‍

In this project we will determine the distribution of >200 different cell subpopulations in pre- and post-therapy blood samples from breast cancer patients and use machine learning statistical techniques to identify those that can predict patient response to a particular therapy with the greatest accuracy.

‍