Computationally Validation Of Transcriptional Drivers And Therapeutic Combinations In CRPC

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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For this, we will utilize mouse (NKP) as well as human castration-resistant (e.g., CWR-R1, C4-2, C4-2AT6, PC3, DU145) (citation)cell lines. We will perform shRNA-mediated silencing of the activated MRs by lentiviral infection(citation), which will be confirmed by quantitative RT-PCR at 48 hours. To analyze ability of MRs to abrogate castration-resistance, we will perform analyses of cellular proliferation, using BrdU incorporation and Ki67 staining, colony formation essays and invasion essays after silencing. As a control, we will silence 3-5 genes that are neither differentially expressed nor differentially active in castration-resistant tumors and are not regulated by any MR. Conversely, MRs whose activity decreases in castration-resistant state will be tested in over-expression essays using lentiviral cDNA expression …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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