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Updated: Jul 11, 2025
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
Computational analysis of protein-protein interactions of cancer drivers in renal cell carcinoma
Jimin Pei1,2,3, Jing Zhang1,2,3, Qian Cong1,2,3
1Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Abstract:
Renal cell carcinoma (RCC) is the most common type of kidney cancer with rising cases in recent years. Extensive research has identified various cancer driver proteins associated with different subtypes of RCC. Most RCC drivers are encoded by tumor suppressor genes and exhibit enrichment in functional categories such as protein degradation, chromatin remodeling, and transcription. To further our understanding of RCC, we utilized powerful deep-learning methods based on AlphaFold to predict protein-protein interactions (PPIs) involving RCC drivers. We predicted high-confidence complexes formed by various RCC drivers, including TCEB1, KMT2C/D and KDM6A of the COMPASS-related complexes, TSC1 of the MTOR pathway, and TRRAP. These predictions provide valuable structural insights into the interaction interfaces, some of which are promising targets for cancer drug design, such as the NRF2-MAFK interface. Cancer somatic missense mutations from large datasets of genome sequencing of RCCs were mapped to the interfaces of predicted and experimental structures of PPIs involving RCC drivers, and their effects on the binding affinity were evaluated. We observed more than 100 cancer somatic mutations affecting the binding affinity of complexes formed by key RCC drivers such as VHL and TCEB1. These findings emphasize the importance of these mutations in RCC pathogenesis and potentially offer new avenues for targeted therapies.
Insights
Deep learning predicted protein interactions for kidney cancer drivers, revealing mutation impacts on protein binding. These findings highlight key mutations in renal cell carcinoma (RCC) pathogenesis and suggest new therapeutic targets.
Area of Science:
- Oncology
- Structural Biology
- Computational Biology
Background:
- Renal cell carcinoma (RCC) is the most common kidney cancer, with increasing incidence.
- RCC development is linked to various cancer driver proteins, often encoded by tumor suppressor genes.
- These drivers are involved in critical cellular processes like protein degradation, chromatin remodeling, and transcription.
Purpose of the Study:
- To predict protein-protein interactions (PPIs) of RCC driver proteins using deep learning.
- To gain structural insights into RCC driver complexes and identify potential drug targets.
- To analyze the impact of cancer somatic mutations on PPIs involving RCC drivers.
Main Methods:
- Utilized deep learning models, including AlphaFold, for predicting protein-protein interactions (PPIs).
- Predicted high-confidence protein complexes involving key RCC drivers (e.g., TCEB1, KMT2C/D, KDM6A, TSC1, TRRAP).
- Mapped cancer somatic missense mutations from RCC genome sequencing data to predicted and experimental PPI interfaces.
Main Results:
- Generated high-confidence structural predictions for complexes of multiple RCC drivers.
- Identified specific interaction interfaces, such as NRF2-MAFK, as potential drug design targets.
- Observed over 100 cancer somatic mutations impacting the binding affinity of complexes involving VHL and TCEB1.
Conclusions:
- Predicted PPIs offer valuable structural insights into RCC pathogenesis.
- Mutations affecting driver protein complex binding affinity are significant in RCC.
- These findings may pave the way for novel targeted therapies for renal cell carcinoma.
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