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Published on: July 3, 2025
Pancancer modelling predicts the context-specific impact of somatic mutations on transcriptional programs
Hatice U Osmanbeyoglu1, Eneda Toska2, Carmen Chan2
1Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, Box No. 460, New York, New York 10065, USA.
Abstract:
Pancancer studies have identified many genes that are frequently somatically altered across multiple tumour types, suggesting that pathway-targeted therapies can be deployed across diverse cancers. However, the same 'actionable mutation' impacts distinct context-specific gene regulatory programs and signalling networks-and interacts with different genetic backgrounds of co-occurring alterations-in different cancers. Here we apply a computational strategy for integrating parallel (phospho)proteomic and mRNA sequencing data across 12 TCGA tumour data sets to interpret the context-specific impact of somatic alterations in terms of functional signatures such as (phospho)protein and transcription factor (TF) activities. Our analysis predicts distinct dysregulated transcriptional regulators downstream of somatic alterations in different cancers, and we validate the context-specific differential activity of TFs associated to mutant PIK3CA in isogenic cancer cell line models. These results have implications for the pancancer use of targeted drugs and potentially for the design of combination therapies.
Insights
Piecemeal cancer therapies targeting specific gene mutations may not work across all cancer types. This study reveals how the same mutation affects gene activity differently in various cancers, impacting targeted treatment strategies.
Area of Science:
- Genomics
- Cancer Biology
- Computational Biology
Background:
- Pancancer studies identify frequently altered genes, suggesting broad applicability of pathway-targeted therapies.
- However, the impact of 'actionable mutations' varies context-specifically across different tumor types and genetic backgrounds.
Purpose of the Study:
- To computationally integrate proteomic and mRNA sequencing data across 12 TCGA datasets.
- To interpret the context-specific impact of somatic alterations on functional signatures, including protein and transcription factor activities.
Main Methods:
- Applied a computational strategy to integrate parallel (phospho)proteomic and mRNA sequencing data.
- Analyzed 12 The Cancer Genome Atlas (TCGA) tumor datasets.
- Interpreted somatic alteration impacts using functional signatures like (phospho)protein and transcription factor (TF) activities.
Main Results:
- Predicted distinct dysregulated transcriptional regulators downstream of somatic alterations in different cancers.
- Validated context-specific differential TF activity associated with mutant PIK3CA in isogenic cancer cell line models.
Conclusions:
- Somatic alterations have context-specific impacts on gene regulatory programs and signaling networks.
- Findings have implications for the pancancer use of targeted drugs and combination therapy design.
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