Cp Case Study

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Aim 3. Validation of transcriptional drivers and therapeutic combinations in CRPC.
Rationale and strategy: To experimentally validate computationally predicted drivers of CRPC, we will use loss- and gain-of-function approaches for in vitro (i.e., cell lines) and in vivo (i.e., xenograft models) experimental validation to determine whether these genes are essential for drug-resistance. Computationally inferred drug combinations will be validated for their effect on MRs’ activity and ability to govern drug resistance and tumorigenicity. Furthermore, inferred drug combinations will be analyzed using RNA sequencing of treated tumors for their synergistic action and potential benefits for patients with castration-resistant prostate cancer.
Approach
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We will first pursue treatment administration in castration-resistant cell lines (i.e., CWR-R1, C4-2, C4-2AT6, PC3, or DU145 as above) which express MRs of CRPC and will perform analyses of cellular proliferation (using BrdU incorporation and Ki67 staining), apoptosis, and AR activity following drug administration. In addition, we will assess expression levels of MRs (and their transcriptional targets) that were computationally predicted to be affected by specific drug combination using qPCR, in triplicates. These experiments will be done in the presence and absence of androgens (i.e., using anti-androgen drugs, such as Enzalutamide) to investigate if such drug combinations can be administered alongside anti-androgen therapy. In vitro treatment effects will be evaluated after 48 hours of drug …show more content…
To overcome this limitation, we will over-express MRs of CRPC (that are not sufficiently expressed/active) in cell lines used for functional in vitro and in vivo validation in Aim 3.1 (Validation of drug combinations). Secondly, since therapeutic response is dose-dependent, we will perform a short pilot study to determine optimal drug dosages for single and combination drugs. Thirdly, we understand that survival analysis might depend on multiple factors, including age, Gleason score, pre-treatment etc. We will consider these factors in multivariate statistical analysis to evaluate independent predictive value for the transcriptional drivers of CRPC.

H. Projected Timeline
We anticipate to conduct the proposed R01 grant within a 5-year period, from July 1st 2017 to June 30th 2022 (Fig 9). We will begin with Aim 1, which will uncover transcriptional drivers of treatment failure in castration-resistant prostate cancer. While conducting Aim 1, we will start working on (i) Aim 2, where we will gather and analyze datasets with available single-agent treatment data to identify drugs and drug combinations that could be used to preclude or overcome resistance; and (ii) Aim 3.1, which will carry experimental validation of identified Figure 9. Time-line for

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