Precision Therapy In Early Stages For Triple Negative Breast Cancer Interview with:
Eran Andrechek, PhD

Eran Andrechek, PhD Associate Professor Department of Physiology Michigan State University East Lansing, MI

Associate Professor
Department of Physiology
Michigan State University
East Lansing, MI What is the background for this study?

Response: Of the various types of breast cancer, triple negative breast cancer (lacking estrogen receptor, progesterone receptor and HER2) has the worst outcome and is largely limited to chemotherapy for treatment.  Other types can be treated with personalized medicine, resulting in better outcome.  For instance, a HER2+ve breast cancer can be treated with Herceptin, which targets HER2 itself.  The fact that triple negative breast cancer lacks these sort of targeted treatments presents a clear need in breast cancer therapy.

The goal of this study was to bring together our computational work using large databases from breast cancer with research into therapeutic options.  Essentially we wanted to ask if we could use patterns in what genes were being expressed to predict optimal therapy for triple negative breast cancer. What are the main findings?

Response: We found that we could make predictions in both pre-clinical models as well as in human breast cancer samples for combination therapy that was tailored to individual samples.  We then demonstrated that these therapies were effective and specific for the individual tumors.  Essentially we were able to predict what drugs should work for a human breast cancer sample and then treated these tumors when grown in a pre-clinical model.  This treatment was successful and resulted in reduction of tumor size. What should readers take away from your report?

Response: Precision therapy is possible for triple negative breast cancer, but this will require additional research and clinical trials before it is applied clinically. What recommendations do you have for future research as a result of this study?

Response: Our next steps are to compare standard of care therapy with our computationally directed therapy.  Eventually the goal is to transition our findings into clinical trials. Thank you for your contribution to the community.


J-R Jhan, E R Andrechek. Effective personalized therapy for breast cancer based on predictions of cell signaling pathway activation from gene expression analysis. Oncogene, 2017; DOI: 10.1038/onc.2016.503

Note: Content is Not intended as medical advice. Please consult your health care provider regarding your specific medical condition and questions.

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