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Updated: Nov 16, 2025
Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
Published on: February 27, 2015
Inferring perturbation profiles of cancer samples
Martin Pirkl1,2, Niko Beerenwinkel1,2
1Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.
Motivation:
Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations.
Results:
We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens and breast cancer samples from The Cancer Genome Atlas.
Availability And Implementation:
The method is available as the R-package nempi at https://github.com/cbg-ethz/nempi and http://bioconductor.org/packages/nempi.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
This study introduces a new mathematical method to infer gene activity changes in cancer. The approach analyzes genetic data to reveal patient-specific gene perturbation profiles, improving cancer driver gene analysis.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Cancer is a prevalent disease driven by altered gene activity, which is difficult to observe directly.
- Molecular profiles offer indirect evidence of gene perturbations, but accurate inference is challenging due to data errors and incomplete coverage.
- Understanding gene perturbations is crucial for identifying cancer drivers and developing targeted therapies.
Purpose of the Study:
- To develop a novel mathematical method for analyzing cancer driver genes and inferring patient-specific gene perturbation profiles.
- To overcome the challenges of inferring gene perturbations from molecular alterations in cancer.
Main Methods:
- Developed a causal network model integrating genetic aberrations and gene expression data across patients.
- Utilized a novel mathematical approach to infer unobserved gene perturbations from integrated molecular data.
- Implemented the method as an R-package named 'nempi' for broader accessibility.
Main Results:
- The developed method successfully predicts gene perturbations in computational simulations.
- Validated the method's performance using CRISPR perturbation screens.
- Demonstrated the method's applicability to real-world cancer data, specifically breast cancer samples from The Cancer Genome Atlas (TCGA).
Conclusions:
- The novel mathematical method provides a robust way to infer patient-specific gene perturbation profiles from molecular data.
- This approach enhances the analysis of cancer driver genes and aids in understanding tumor biology.
- The 'nempi' R-package offers a valuable tool for researchers in cancer genomics and computational biology.
