Targeting the adaptive capacity of prostate cancer through the manipulation of transcriptional and metabolic traits: CancerADAPT

Programme(s): H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)

Topic(s): ERC-2018-COG - ERC Consolidator Grant

Call for proposal: ERC-2018-COG

Funding Scheme: ERC-COG - Consolidator Grant

Grant agreement ID: 819242

Project description: Understanding the adaptive properties of cancer cells in disease progression
The composition and molecular features of tumours change in the course of the disease, but the mechanisms underlying these changes remain poorly understood. The EU-funded CancerADAPT project is based on the postulate that cancer cells rely on adaptive transcriptional and metabolic mechanisms, rapidly creating new microenvironments during disease progression. The project aims to predict the molecular cues that govern the adaptive properties of prostate cancer (PC) cells. Researchers will employ single-cell RNA-Seq, spatial transcriptomics and multiregional omics to identify the transcriptional and metabolic diversity within tumours in the course of the disease. The computational analyses will allow them to identify and classify the predicted adaptation strategies of PC cells in response to variations in the tumour microenvironment.

Objective
The composition and molecular features of tumours vary during the course of the disease, and the selection pressure imposed by the environment is a central component in this process. Evolutionary principles have been exploited to explain the genomic aberrations in cancer. However, the phenotypic changes underlying disease progression remain poorly understood. In the past years, I have contributed to identify and characterise the therapeutic implications underlying metabolic alterations that are intrinsic to primary tumours or metastasis. In CancerADAPT I postulate that cancer cells rely on adaptive transcriptional & metabolic mechanisms [converging on a Metabolic Phenotype] in order to rapidly succeed in their establishment in new microenvironments along disease progression. I aim to predict the molecular cues that govern the adaptive properties in prostate cancer (PCa), one of the most commonly diagnosed cancers in men and an important source of cancer-related deaths. I will exploit single cell RNASeq, spatial transcriptomics and multiregional OMICs in order to identify the transcriptional and metabolic diversity within tumours and along disease progression. I will complement experimental strategies with computational analyses that identify and classify the predicted adaptation strategies of PCa cells in response to variations in the tumour microenvironment. Metabolic phenotypes postulated to sustain PCa adaptability will be functionally and mechanistically deconstructed. We will identify therapeutic strategies emanating from these results through in silico methodologies and small molecule high-throughput screening, and evaluate their potential to hamper the adaptability of tumour cells in vitro and in vivo, in two specific aspects: metastasis and therapy response. CancerADAPT will generate fundamental understanding on how cancer cells adapt in our organism, in turn leading to therapeutic strategies that increase the efficacy of current treatments.