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Updated: May 22, 2025

Defining Gene Functions in Tumorigenesis by Ex vivo Ablation of Floxed Alleles in Malignant Peripheral Nerve Sheath Tumor Cells
Published on: August 25, 2021
Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis
Yongqiang Zheng1, Kai Yu1,2, Jin-Fei Lin1,3
1State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Abstract:
Genetic variants can affect protein function by driving aberrant subcellular localization. However, comprehensive analysis of how mutations promote tumor progression by influencing nuclear localization is currently lacking. Here, we systematically characterize potential shuttling-attacking mutations (SAMs) across cancers through developing the deep learning model pSAM for the ab initio decoding of the sequence determinants of nucleocytoplasmic shuttling. Leveraging cancer mutations across 11 cancer types, we find that SAMs enrich functional genetic variations and critical genes in cancer. We experimentally validate a dozen SAMs, among which R14M in PTEN, P255L in CHFR, etc. are identified to disrupt the nuclear localization signals through interfering their interactions with importins. Further studies confirm that the nucleocytoplasmic shuttling altered by SAMs in PTEN and CHFR rewire the downstream signaling and eliminate their function of tumor suppression. Thus, this study will help to understand the molecular traits of nucleocytoplasmic shuttling and their dysfunctions mediated by genetic variants.
Insights
Genetic variants called shuttling-attacking mutations (SAMs) disrupt nuclear protein import, driving cancer progression. This study identifies SAMs, revealing their role in tumor suppression loss and offering insights into cancer genetics.
Area of Science:
- Genomics
- Molecular Biology
- Cancer Research
Background:
- Genetic variants can alter protein function through aberrant subcellular localization.
- The impact of mutations on nuclear localization and cancer progression remains incompletely understood.
Purpose of the Study:
- To systematically characterize potential shuttling-attacking mutations (SAMs) across various cancer types.
- To develop a deep learning model (pSAM) for predicting sequence determinants of nucleocytoplasmic shuttling.
- To investigate the functional consequences of SAMs on tumor suppressor genes.
Main Methods:
- Development of the deep learning model pSAM for ab initio decoding of nucleocytoplasmic shuttling determinants.
- Systematic analysis of cancer mutations across 11 cancer types to identify SAMs.
- Experimental validation of identified SAMs, including disruption of nuclear localization signals and importin interactions.
Main Results:
- SAMs were found to be enriched in functional genetic variations and critical cancer genes.
- Several SAMs, including R14M in PTEN and P255L in CHFR, were experimentally validated to disrupt nuclear localization signals by interfering with importin interactions.
- Altered nucleocytoplasmic shuttling in PTEN and CHFR due to SAMs was shown to rewire downstream signaling and abolish tumor suppressor functions.
Conclusions:
- This study provides a comprehensive characterization of SAMs and their role in cancer.
- The findings highlight the importance of nucleocytoplasmic shuttling in maintaining tumor suppression.
- Understanding SAMs offers new insights into the molecular mechanisms of genetic variants in cancer progression.
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