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A Deep-sequencing-assisted, Spontaneous Suppressor Screen in the Fission Yeast Schizosaccharomyces pombe

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Protocol for predicting suppressors of cell-death pathways based on transcriptomic and vulnerability data.

Yaron Vinik¹, Avi Maimon¹, Sima Lev¹

¹Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot 76100, Israel.

|May 31, 2025

Summary

This study introduces a computational method to predict repressors of cancer cell death. The protocol identifies potential cell death repressors by analyzing gene vulnerability and transcriptomic data, aiding in cancer research.

Keywords:

Bioinformatics Cancer RNAseq

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Area of Science:

Computational Biology
Cancer Research
Genomics

Background:

Understanding mechanisms that regulate cancer cell death is crucial for developing effective cancer therapies.
Identifying genes that protect cancer cells from death (repressors) can reveal new therapeutic targets.

Purpose of the Study:

To present a computational protocol for predicting cancer cell death repressors.
To combine gene vulnerability and transcriptomic responses to cell death inducers for prediction.

Main Methods:

Developed a protocol to calculate gene predictors based on vulnerability and transcriptomic data.
Aggregated predictors into a single metric to rank genes by their predictive power for cell death repression.
Established criteria for selecting candidate genes for experimental validation.

Main Results:

The computational protocol successfully predicts potential repressors of cancer cell death.
Several experimentally validated cell death repressors were identified using the developed protocol.

Conclusions:

The presented computational protocol is an effective tool for identifying cancer cell death repressors.
This method facilitates the discovery of novel therapeutic targets in cancer treatment.