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Updated: Jun 8, 2025
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
Published on: July 25, 2020
A framework for target discovery in rare cancers
Bingchen Li1, Ananthan Sadagopan1, Jiao Li1
1Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA 02215, USA.
Abstract:
While large-scale functional genetic screens have uncovered numerous cancer dependencies, rare cancers are poorly represented in such efforts and the landscape of dependencies in many rare cancers remains obscure. We performed genome-scale CRISPR knockout screens in an exemplar rare cancer, TFE3-translocation renal cell carcinoma (tRCC), revealing previously unknown tRCC-selective dependencies in pathways related to mitochondrial biogenesis, oxidative metabolism, and kidney lineage specification. To generalize to other rare cancers in which experimental models may not be readily available, we employed machine learning to infer gene dependencies in a tumor or cell line based on its transcriptional profile. By applying dependency prediction to alveolar soft part sarcoma (ASPS), a distinct rare cancer also driven by TFE3 translocations, we discovered and validated that MCL1 represents a dependency in ASPS but not tRCC. Finally, we applied our model to predict gene dependencies in tumors from the TCGA (11,373 tumors; 28 lineages) and multiple additional rare cancers (958 tumors across 16 types, including 13 distinct subtypes of kidney cancer), nominating potentially actionable vulnerabilities in several poorly-characterized cancer types. Our results couple unbiased functional genetic screening with a predictive model to establish a landscape of candidate vulnerabilities across cancers, including several rare cancers currently lacking in potential targets.
Insights
Researchers identified new cancer dependencies in rare cancers using CRISPR screens and machine learning. This approach revealed specific vulnerabilities in TFE3-translocation renal cell carcinoma (tRCC) and alveolar soft part sarcoma (ASPS), offering potential therapeutic targets.
Area of Science:
- Genomics
- Oncology
- Computational Biology
Background:
- Large-scale functional genetic screens have identified cancer dependencies, but rare cancers are underrepresented.
- The landscape of gene dependencies in many rare cancers remains largely unknown.
- TFE3-translocation renal cell carcinoma (tRCC) is a rare cancer with limited research on its genetic dependencies.
Purpose of the Study:
- To identify novel cancer dependencies in rare cancers, specifically TFE3-translocation renal cell carcinoma (tRCC).
- To develop and apply a machine learning model to predict gene dependencies in rare cancers lacking experimental models.
- To nominate actionable vulnerabilities in poorly-characterized cancer types.
Main Methods:
- Genome-scale CRISPR knockout screens were performed in tRCC models.
- Machine learning models were trained to infer gene dependencies from transcriptional profiles.
- Dependency prediction was applied to alveolar soft part sarcoma (ASPS) and a large cohort of TCGA tumors and other rare cancers.
Main Results:
- CRISPR screens in tRCC revealed dependencies in mitochondrial biogenesis, oxidative metabolism, and kidney lineage specification pathways.
- Machine learning successfully predicted gene dependencies, identifying MCL1 as a dependency in ASPS but not tRCC.
- The predictive model identified potential vulnerabilities across multiple rare cancers, including 13 subtypes of kidney cancer.
Conclusions:
- Functional genetic screening coupled with predictive modeling can establish a landscape of candidate vulnerabilities in rare cancers.
- This approach can uncover previously unknown, cancer-selective dependencies, such as MCL1 in ASPS.
- The findings provide a foundation for developing targeted therapies for rare cancers with limited treatment options.
