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Comprehensive Tumor Microenvironment Based Prediction Models in Prostate Cancer

Gustavo Ayala

1 Collaborator(s)

Funding source

National Cancer Institute (NIH)
Developing new prognostic tools for prostate cancer (PCa) is vital owing to the disease's high incidence and mortality as well as morbidity resulting from current forms of therapy. Moreover, there are problematic limitations in our ability to predict patients who are most likely to die from PCa. If identified, these patients would benefit from additional therapies along with standard of care therapies. The ability to identify these patients in both in the biopsy setting and post-surgically would represent an important advancement in the field. The Baylor tumor microenvironment group has been at the forefront of establishing the predictive value of microenvironment-derived biomarkers. Our previous studies have shown that the tumor microenvironment reactive stroma promotes angiogenesis, tumor incidence and rate of tumor growth. Furthermore, we have reported that reactive stroma grading (RSG) is a significant predictor of PSA recurrence and PCa-specific mortality. Reliability and reproducibility are critical issues in biomarker development. To overcome this, it is essential to convert a biomarker to a truly quantitative test by focusing on analytical conditions. The overall goal of the proposed project is to produce a rigorous quantitative tumor microenvironment-based test, reproducible across populations that will supplement and improve currently used predictive tools and models. This test will be useful for both prostatectomy and biopsy specimens, to predict clinical outcomes and contribute to clinical decision-making. Our group is uniquely positioned to carry this work forward. Baylor College of Medicine has unique tissue and data resources. This will represent a major advancement. We hypothesize that we can improve on current standard-of-care predictive models, through the addition of novel tumor microenvironment-based biomarkers that are quantitative and reproducible, to support clinical decisions on whether a PCa patient would benefit by receiving more aggressive additional therapies. We propose three Specific Aims to address this hypothesis: Specific Aim 1: To develop predictive tools that will select patients who need adjuvant treatments above surgical therapy standard of care Specific Aim 2: To develop pre-treatment predictive tools, using biopsy tissues, for the selection of patients who could potentially benefit from additional treatments along with surgery or radiation Specific Aim 3: To identify key regulators of the microenvironment response in human PCa.

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