Project description
A closer look at cancer’s survival secrets
Cancer’s survival mechanisms present a formidable obstacle in treatment. Following the inactivation of essential cancer genes, a complex interplay of signals occurs, impacting cell survival pathways. Dubbing this intricate phenomenon cancer dependency shock, which remains enigmatic in therapeutic targeting, the ERC-funded DepSHOCK project aims to address this challenge. Specifically, the research team seeks to decipher these footprints to reveal markers crucial for cancer cell survival. Using innovative computational methods and analysing unprecedented transcriptional data sets at single-cell resolution, they aim to identify therapeutic targets. The project aspires to lead the way in early anti-cancer drug discovery by providing grounded markers and insights for developing combinatorial therapies.
Objective
The oncogenic shock model has been introduced to describe the interplay between pro-survival and pro-apoptotic signals that follow an oncogene's inactivation and lead to cancer cell death.
In my recent work, I demonstrated that identifying novel cancer dependencies through systematic computational analyses of CRISPR functional genetic screens and multi-omic data is an excellent means to discover and prioritise new anti-cancer therapeutic targets. Here, I extend the oncogenic shock concept to any cancer dependency gene. I call cancer dependency shock (DepSHOCK) the signal dynamics interplay that follows the ablation of any gene essential for cancer cell survival, i.e. cancer dependency gene. Any DepSHOCK triggers the sudden inactivation of pro-survival pathways and activates death- inducing signals, with both events impacting down-streaming transcription factors, leaving a measurable gene expression footprint. My hypothesis is that each DepSHOCK footprint can be deconvoluted to infer transcriptional markers of signals that emanate from a dependency gene and drive cancer cell survival through the constitutive activity of specific biological pathways. Here I propose to investigate this hypothesis across tissues, canonical oncogenic addictions, and novel candidate cancer therapeutic targets. To this aim, I plan to establish an experimental/computational platform for the generation and the analysis of unprecedented transcriptional datasets obtained at single-cell resolution upon genetic perturbation of a rationally selected panel of cancer cell lines and therapeutically relevant dependency genes. My overarching goal will be to study the information content of the DepSHOCK footprints and their potential use as computational tools in early anti-cancer drug discovery through new statistical and machine-learning methods delivering mechanistically grounded therapeutic markers and hints for the design of combinatorial therapies.
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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(opens in new window) ERC-2023-COG
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20157 Milano
Italy
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