ArticlePloS one2014
Inferring tree causal models of cancer progression with probability raising.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Cancerous time estimation for interpreting the evolution of lung adenocarcinoma.Briefings in bioinformatics · 2024Article
- HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures.PLoS computational biology · 2024Article
- A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer.bioRxiv : the preprint server for biology · 2024Article
- Article
- HyperHMM: efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs.Bioinformatics (Oxford, England) · 2023Article
- ToMExO: A probabilistic tree-structured model for cancer progression.PLoS computational biology · 2022Article
- Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.Current genomics · 2021Review
- Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?PLoS computational biology · 2021Article
- Every which way? On predicting tumor evolution using cancer progression models.PLoS computational biology · 2019Article
- Collaborative intra-tumor heterogeneity detection.Bioinformatics (Oxford, England) · 2019Article
- Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data.BMC bioinformatics · 2019Article
- Detecting repeated cancer evolution from multi-region tumor sequencing data.Nature methods · 2018Article
- Cancer progression models and fitness landscapes: a many-to-many relationship.Bioinformatics (Oxford, England) · 2018Article
- Modeling Cumulative Biological Phenomena with Suppes-Bayes Causal Networks.Evolutionary bioinformatics online · 2018Article
- Variable selection for disease progression models: methods for oncogenetic trees and application to cancer and HIV.BMC bioinformatics · 2017Article
- Progression inference for somatic mutations in cancer.Heliyon · 2017Article
- Toward Precision Healthcare: Context and Mathematical Challenges.Frontiers in physiology · 2017Article
- Algorithmic methods to infer the evolutionary trajectories in cancer progression.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data.Bioinformatics (Oxford, England) · 2016Article
- Integrating mutation and gene expression cross-sectional data to infer cancer progression.BMC systems biology · 2016Article
Corrections and comments
- Erratum issuedLoohuis, Loes Olde [corrected to Olde Loohuis, Loes]
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Existing techniques to reconstruct tree models of progression for accumulative processes, such as cancer, seek to estimate causation by combining correlation and a frequentist notion of temporal priority. In this paper, we define a novel theoretical framework called CAPRESE (CAncer PRogression Extraction with Single Edges) to reconstruct such models based on the notion of probabilistic causation defined by Suppes. We consider a general reconstruction setting complicated by the presence of noise in the data due to biological variation, as well as experimental or measurement errors. To improve tolerance to noise we define and use a shrinkage-like estimator. We prove the correctness of our algorithm by showing asymptotic convergence to the correct tree under mild constraints on the level of noise. Moreover, on synthetic data, we show that our approach outperforms the state-of-the-art, that it is efficient even with a relatively small number of samples and that its performance quickly converges to its asymptote as the number of samples increases. For real cancer datasets obtained with different technologies, we highlight biologically significant differences in the progressions inferred with respect to other competing techniques and we also show how to validate conjectured biological relations with progression models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.