Integrated tumour-microenvironment biomarkers for improved targeting of breast cancer therapy


Competition: FOCUS Program 2011
Funding: $1,300,0003 years
Début : September 2011

The aim of this project is to develop enabling tools for better stratification of breast cancer patients by integrating information from the tumor and the surrounding stromal tissu. This will allow the development of personalized therapies relevant to specific tumor subtypes.

The genetic heterogeneity of breast cancer tumors has long been recognized. Tumorintrinsic features and subtypes are associated with differential outcomes and are used to personalize cancer treatment regimes. It has become increasingly evident however that interactions between a primary tumor and its surrounding stromal environment, in addition to genetic changes within the tumor itself, are significant predictors of disease progression. Prior to the CQDM funding of this project, Dr. Park’s team showed that sub-classification of the stromal environment can predict disease outcome. Moreover, combining stromal information with tumor-intrinsic data increased prognostic accuracy. As there are currently no accurate and standardized tests for this purpose, optimization of stromal signatures to classify tumor samples is critical for breast cancer research with direct implications in treatment regime decisions.

With this in mind, the goal of the CQDMfunded project is to generate and validate distinct tumor-microenvironment profiles. The identification of new tissue and blood biomarkers, which integrate the intrinsic and stromal characteristics of the tumor, will allow for better stratification of patients with breast cancer with potential to predict responders to treatments. In the first year of this project, the team completed laser capture microdissection and tissue isolation of a total of 48 HER2‐positive and triple‐negative breast cancer patients. The generation of samplespecific gene expression profiles is ongoing.

Impact on the drug discovery process

  • Stratify breast tumors by combining tumor-intrinsic and stromal features to identify specific groups of patients for responders to therapies.
  • Selection of most appropriate treatments based on integrated tumor-microenvironment classification.
  • Identify stromal features as targets for the development of new therapies.

Morag Park

Université McGill


Michael Hallet and Atilla Omeroglu
Université McGill


Paul Rejto
Director and Head, Computational Biology Group,
Pfizer Global R&D, New York City, CA, USA