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Main applicant: Prof. Jean-Pierre Bourquin
Co-Applicant(s): PD. Dr. Emmanuella Guenova, Prof. Dr. Anne Müller, Prof. Dr. Beat Schäfer, PD. Dr. Alexandre Theocharides, Prof. Dr. Thorsten Zenz
Despite rapid progress in cancer genomics and molecular disease classification, it remains difficult to identify actionable targets and predict response to drugs based on this information. Most major institutional precision medicine programs rely mainly on genomic information. New approaches which identify cancer vulnerabilities and predict the response to treatment based on pre-treatment testing of individual cancer cells could improve response rates, reduce unnecessary treatments, and significantly increase cost-effectiveness. Based on the applicants` strong proof-of-concept data, we hypothesize that critical predictive and mechanistic information can be obtained from direct functional screening of primary patient samples` response to drugs. To this end, we propose to use phenotypic screens with drugs that target a range of mechanisms to capture the underlying response heterogeneity and perturbed biological systems in cancer cells. We join forces to develop a clinical platform for next-generation precision medicine at the University of Zurich (UZH), where we apply drug response profiling to personalize treatment of patients and investigate unexpected vulnerabilities at depth. We capture drug response phenotypes and oncogenic mechanisms, accelerate drug development and refine personalized treatment in cancer. We will address the following aims (Figure 1):
Aim 1. To develop next generation drug profiling platforms for clinical application in cancer
As a basis for this KFSP we will generate drug response profiling (DRP) from cohorts of patients with resistant leukemia, lymphoma and sarcoma and establish a corresponding repository of patient derived xenografts (PDX). We will expand this “knowledgebase” with samples acquired in clinical trials (aim 2). We capture the specific advantages and differences of imaging based screens of cancer cell co-cultures on stroma (e.g. for effects that require a longer exposure or detection of subpopulations) compared with a less complex cancer cell suspension format (higher throughput) and validate the critical observations using complex in vivo PDX systems. We will develop organoid-based readouts for sarcoma and multi-parametric image-based screening in particular to distinguish malignant from normal cells in patient samples, which will allow us to follow the fate of cancer cells in samples with lower infiltration of cancer cells without additional sample manipulation, a common obstacle in oncology. By profiling more than 500 samples with complementary technology, we expect to refine our library logistics, analytical and bioinformatics tools to provide a competitive platform for personalized medicine trials and contribute to position UZH and associated partners internationally.
Aim 2. To perform drug response profiling to guide treatment in trials
We will establish clinical trials to formally test the use of DRP for a) the prediction and b) treatment guidance in cancer. Our objectives are to 1) demonstrate feasibility of DRP in real time (to guarantee the applicability to donating patients), 2) assess the predictive value of DRP 3) identify new agents with remarkable activity in individual patients and 4) test the potential of DRP on clinical decision making and outcome. We will set up a common framework for prospective clinical trials. For adults with leukemia and lymphoma, we will open a protocol based on own preliminary work with the objective to offer this approach to all patients with leukemia and lymphoma at the University Hospital Zurich (USZ) and patients in Switzerland. For pediatric leukemia, we will develop a protocol to generate and provide DRP in the setting of a large pediatric clinical trial for the treatment of high risk relapsed acute lymphoblastic leukemia (ALL), given the poor outcomes in this patient population.
Aim 3. To gain mechanistic insights into the pathogenesis of cancer based on DRP data
Based on the functional screens of relevant customized libraries and models, we will specifically explore the role of epigenetic modifications/pathways and MAP kinase signaling in lymphoma and sarcoma. DRP will provide us insights into recurrent susceptibilities of patient samples to compounds targeting these pathways. The most active compounds will be validated individually in vitro, and in vivo using PDXs. We will focus on gaining a deeper mechanistic understanding of candidate drugs, using functional genomic tools that we have developed in our laboratories and assess the consequences of these perturbations on the epigenetic landscape using next generation sequencing to assess regions based on histone marks and chromatin conformation to unravel key components of the oncogenic dependencies involved.
Collectively, this KFSP will generate unique opportunities for improved cancer therapy that can be translated rapidly given the network of basic, translational and clinical research partners.
Figure 1. Schematic overview of the CRPP. Primary samples from patients with leukemia, lymphoma and sarcoma will be screened by DRP to identify compounds with activity against cancer cells (Aim 1). The data will be correlated to clinical parameters (e.g. outcome, survival) and tested for molecular associations. DRP will then be used to predict response to therapy in clinical interventional trials (Aim 2). In addition, DRP may identify compounds approved for the treatment of a specific cancer with activity in another disease (Drug re-purposing). Based on the track record of the investigators epigenetic mechanisms and MAP kinase-signaling will be further explored based on DRP-data in individual cancer patients (Aim 3). In all aims, PDX models will be used to develop patient-specific disease models and assess response to compounds identified by DRP. Ultimately, novel targets identified in aim 3 can be evaluated in clinical trials.