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Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers
Daniel Heim1, Grégoire Montavon2, Peter Hufnagl1
1Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.
Genome Medicine
|November 17, 2018
Summary
This study introduces a computational method to analyze how cancer mutations affect protein profiles across different cancer types. The approach identifies cross-cancer mutation effects, aiding in targeted therapy development and basket trial design.
Area of Science:
- Oncology
- Genomics
- Proteomics
- Computational Biology
Background:
- Comprehensive mutational profiling is driving new molecular tumor classifications beyond traditional histology.
- Molecular classifications aim to predict therapy response based on genetic alterations.
- Basket trials test targeted therapies across histotypes based on specific mutations, with variable success.
Purpose of the Study:
- To develop a computational approach for analyzing the histological context dependency of mutational effects.
- To integrate genomic and proteomic tumor profiles across diverse cancer types.
- To systematically identify proteins characteristic of oncogenic mutation effects.
Main Methods:
- Utilized the energy distance to compare protein profiles in tumors with and without oncogenic mutations.
- Employed Monte Carlo simulations for statistical significance analysis.
- Ranked proteins by their contribution to profile differences to identify mutation-specific markers.
Main Results:
- Applied the approach to four molecular classification proposals and 12 actionable genes.
- Observed protein-level effects for all evaluated actionable genes in corresponding tumor types.
- Identified consistent cross-cancer protein profile effects for 4 genes (FGFR1, ERRB2, IDH1, KRAS/NRAS) in 14 tumor types.
- Validated findings using cell line drug response data.
Conclusions:
- The computational approach can identify mutational signatures with protein-level effects.
- Supports preclinical in silico testing of molecular classifications and mutation druggability.
- Facilitates the identification of novel, cross-cancer targeted therapies.
- Guides efficient design of basket trials for targeted cancer treatments.
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