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Updated: Jun 26, 2026
Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures
Published on: December 26, 2016
Integrating large-scale in vitro functional genomic screen and multi-omics data to identify novel breast cancer
Hao-Kuen Lin1, Jiawei Dai2, Lajos Pusztai3
1Danbury Hospital, Danbury, CT, 06810, USA.
Purpose:
Our goal is to leverage publicly available whole transcriptome and genome-wide CRISPR-Cas9 screen data to identify and prioritize novel breast cancer therapeutic targets.
Methods:
We used DepMap dependency scores > 0.5 to identify genes that are potential therapeutic targets in 48 breast cancer cell lines. We removed genes that were pan-essential or were not expressed in TCGA breast cancer cohort. Genes were prioritized based on druggability using the Drug-Gene Interaction Database. Targets were defined separately for ER+, HER2+, and TNBC. A broader list of genes with dependency score > 0.25 were used to assess the associations between dependency scores and mutations and copy number variations (CNV) to identify potential synthetic lethal relationships and to map survival critical genes into biological pathways.
Results:
66, 53, and 29 genes were prioritized as targets in ER+, HER2+, and TNBC, respectively. These included known actionable targets and many novel targets. ER+ included FOXA1, GATA3, LDB1, TRPS1, NAMPT, WDR26, and ZNF217; HER2+ cancers included STX4, HECTD1, and TBL1XR1; and TNBC included GFPT1 and GPX4. Synthetic lethal associations revealed 5 and 19 significant associations between potential survival critical genes and mutations in HER2+ and TNBC, respectively. For example, PIK3CA mutation increased dependency on NDUFS3 in HER2+ cancers, and CNTRL mutation increased dependency on electron transport chain (ETC) genes in TNBC. 329, 747, and 622 CNVs showed synthetic lethal association in ER+, HER2+, and TNBC, respectively.
Conclusion:
We provide a genome-wide drug target prioritization list for breast cancer derived from integrated large-scale omics data.
Insights
This study identifies novel breast cancer drug targets using CRISPR screens and omics data. It prioritizes genes for ER+, HER2+, and TNBC subtypes, revealing potential synthetic lethal relationships for new therapies.
Area of Science:
- Genomics
- Cancer Biology
- Pharmacogenomics
Background:
- Breast cancer comprises heterogeneous subtypes (ER+, HER2+, TNBC) requiring tailored therapeutic strategies.
- Identifying novel drug targets is crucial for improving treatment outcomes in breast cancer.
- Large-scale omics data and functional screens offer powerful tools for target discovery.
Purpose of the Study:
- To identify and prioritize novel therapeutic targets for breast cancer subtypes using publicly available data.
- To integrate whole transcriptome and genome-wide CRISPR-Cas9 screen data for target discovery.
- To generate a prioritized list of druggable targets specific to ER+, HER2+, and triple-negative breast cancer (TNBC).
Main Methods:
- Utilized DepMap dependency scores (>0.5) in 48 breast cancer cell lines to identify essential genes.
- Filtered essential genes by removing pan-essential genes and those not expressed in TCGA breast cancer cohort.
- Prioritized genes based on druggability and analyzed associations between dependency scores, mutations, and copy number variations (CNVs) for synthetic lethality.
Main Results:
- Prioritized 66, 53, and 29 potential therapeutic targets for ER+, HER2+, and TNBC, respectively.
- Identified novel targets such as FOXA1, GATA3, GFPT1, and GPX4, alongside known actionable targets.
- Revealed significant synthetic lethal associations, including PIK3CA mutations with NDUFS3 dependency in HER2+ cancers and CNTRL mutations with electron transport chain (ETC) gene dependency in TNBC.
Conclusions:
- Developed a comprehensive, genome-wide prioritization list of drug targets for breast cancer subtypes.
- Integrated large-scale omics and CRISPR screen data to identify novel therapeutic opportunities.
- The findings provide a valuable resource for guiding future breast cancer drug development efforts.
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