Using protein interaction networks to identify cancer dependencies from tumor genome data
Horn, H.; Fagre, C.; Gupta, A.; Tsafou, K.; Fornelos, N.; Neal, J. T.; Lage, K.
Show abstract
Genes required for tumor proliferation and survival (dependencies) are challenging to predict from cancer genome data, but are of high therapeutic value. We developed an algorithm (network purifying selection [NPS]) that aggregates weak signals of purifying selection across a genes first order protein-protein interaction network. We applied NPS to 4,742 tumor genomes to show that a genes NPS score is predictive of whether it is a dependency and validated 58 NPS-predicted dependencies in six cancer cell lines. Importantly, we demonstrate that leveraging NPS predictions to execute targeted CRISPR screens is a powerful, highly cost-efficient approach for identifying and validating dependencies quickly, because it eliminates the substantial experimental overhead required for whole-genome screening.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Identifying transcription factor-bound gene activators and silencers in the chromatin accessible human genome using ATAC-STARR-seq 95%
- De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data 95%
- Dynamic Analysis of Alternative Polyadenylation from Single-Cell RNA-Seq(scDaPars) Reveals Cell Subpopulations Invisible to Gene Expression Analysis 94%
Similar papers in this journal
- Machine-learning analysis of factors that shape cancer aneuploidy landscapes reveals an important role for negative selection 97%
- Multiplex enCas12a screens show functional buffering by paralogs is systematically absent from genome-wide CRISPR/Cas9 knockout screens 96%
- A global high-density chromatin interaction network reveals functional long-range and trans-chromosomal relationships 95%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.