Detailed information

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Project title:

Conditionally-Reprogrammed Primary Prostate Cancer Cells as Novel Models for Patient Therapy Prediction

Principal investigator(s):

Wright, Jessica A

Co-investigator(s):

N/A

Supervisors:

Liu, Stanley K

Institution paid:

Sunnybrook Research Institute (Toronto, Ontario)

Research institution:

Sunnybrook Research Institute (Toronto, Ontario)

Department:

Biological Sciences

Program:

Doctoral Research Award: Canada Graduate Scholarships

Competition (year/month):

201911

Assigned peer review committee:

Doctoral Research Awards - A

Primary institute:

Cancer Research

Primary theme:

Biomedical

Term (yrs/mths):

3 yrs 0 mth

CIHR contribution:

Contributors:

Amount:

$105,000

Equipment:

$0

External funding partner(s):

Partner Name:

N/A

Amount:

N/A

Equipment:

N/A

External applicant partner(s):

Partner Name:

N/A

Amount:

N/A

Equipment:

N/A

External in-kind partner(s):

Partner Name:

N/A

Amount:

N/A

Equipment:

N/A

Keywords:

Cancer Models; Predicting Treatment Response; Primary Cell Culture; Prostate Cancer

Abstract/Summary:

There are an ever-increasing number of treatments available for prostate cancer, however they can have considerable side-effects. A significant clinical problem is predicting how a prostate cancer patient will respond to a treatment. Experimental models to study prostate cancer are extremely limited compared to other cancer types which limits research into how prostate cancer is able to resist anti-cancer treatments. My research tackles this problem by growing prostate cancer cells from patient biopsies, using a specialized growth technique. These patient-derived tumor cells (PDCs) represent a promising solution since they are quick to grow and retain the original genetic and biological characteristics of patient tumors. Additionally, their lower cost and ability to be used in a range of experiments, including anti-cancer sensitivity testing, makes them an attractive tool for the early prediction of treatment response. Using this innovative technique, I have successfully generated 25 novel PDCs from both early stage and advanced prostate cancers. The aims of my project are to continue deriving a large representative panel of PDCs and to use them to predict clinical response to various treatments. I will treat these cells with anti-cancer treatments used in the clinic (hormone therapies, chemotherapy, radiation treatment), measure their survival, and then determine if this can predict how a patient will respond to a given treatment in the clinic. PDCs which have an exceptional response or resistance to anti-cancer treatments will be of particular interest for additional study. I will search for changes in the genetic material or proteins in these PDCs that may explain the altered response to treatments. Ultimately, my research will determine if PDCs are a promising approach for predicting how a prostate cancer patient will respond to different treatments. It will also provide new insights into why certain prostate cancer patients respond differently to treatments.

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Version:

20250311.1