Summary
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.
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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101125051 |
| Start date: | 01-03-2024 |
| End date: | 28-02-2029 |
| Total budget - Public funding: | 1 999 455,00 Euro - 1 999 455,00 Euro |
Cordis data
Original description
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.
Status
SIGNEDCall topic
ERC-2023-COGUpdate Date
12-03-2024
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