Eradicating Metastatic Prostate Cancer Through the Systematic Identification of Synergistic Drug Combinations
Abstract
Background: Despite the advances in our knowledge of cancer biology and the approval of new cancer drugs by the Food and Drug Administration, very few patients with solid tumors that have spread to distant sites (metastases) are cured of their disease. This is certainly true of prostate cancer (PC). Consequently, substantial research is directed toward identifying new targets and treatments. Our studies have demonstrated that the genetic/genomic features in a PC that arise in one individual may be very different in another individual. Importantly, these differences can predict whether a particular therapy is likely to work or to fail in one individual compared to another. This information has important implications for treatment selection. Similarly, the strategy of developing new drugs or combinations of drugs appears most fruitful when using many laboratory models that reflect the many differences seen in human patients, including subsets that will have a particular drug target and a subset that will not. To improve the likelihood that a laboratory discovery/finding will actually represent the outcome of a treatment in patients, relevant laboratory models are required. Recent studies indicate that a type of model, called a patient-derived xenograft (PDX), has the potential to greatly speed the drug development process and ensure that the output of drug studies will accurately reflect the clinical responses that will occur in patients. In part, this is due to advances in establishing PDX models with the retention of key cancer features, including 3D growth, the presence of blood vessels, and the maintenance of molecular features including mutations, structural genomic events, epigenetic features, and gene expression programs. An important aspect of PDX models is the ability to test combinations of drugs. A major current limitation in cancer treatment is the restriction of combining very promising agents in clinical trials due to (1) intellectual property issues when drugs are developed by different companies, (2) the multitude of possible combinations that could be tested, and (3) the long process of evaluating safety and efficacy in human clinical trials. Consequently, drugs are usually tested in a sequential fashion, and in most cancers, resistance is the common outcome. Hypotheses/Objectives: This proposal is designed to exploit the power of PC PDX models that are diverse for selecting effective combination drug therapy. We will test the hypothesis that rational combinations of drugs will avoid drug resistance and result in complete responses that far exceed the traditional strategy of giving one drug at a time. Promising combinations will be advanced for human clinical trials. Specific Aims: Aim 1. Conduct a systematic assessment of combination pharmacological therapy to eradicate CRPC using panels of PDX models that reflect the diversity of molecular aberrations found in mCRPC. In this aim, we will employ a “one animal per model per treatment” approach to rapidly screen promising drug combinations for antitumor effects across a well-characterized panel of prostate cancer PDXs capable of modeling inter-patient response differences. Aim 2. Identify molecular features (genotype/phenotype) of PDX responders to drug combination(s). Determining molecular predictors (biomarkers) of response serve to define the optimal clinical population to test for efficacy and advance clinical care. Aim 3. Establish an international consortium for evaluating and validating novel therapeutic combinations capable of eradicating CRPC. We will assemble an International coalition of groups with PDX models that can rapidly and independently validate promising drug combinations. Validating drug response findings will support moving specific combinations forward for clinical testing in men with advanced PC. Impact: This proposal has the potential to impact men with PC in at least three major ways. First,
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 29, 2018
- Source ID
- W81XWH1810348
Entities
People
- John T Isaacs
Organizations
- Johns Hopkins University
- United States Army