ArticleProceedings of the National Academy of Sciences of the United States of America2016
Algorithmic methods to infer the evolutionary trajectories in cancer progression.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Recapitulating the adenoma-carcinoma sequence by selection of four spontaneous oncogenic mutations in mismatch-repair-deficient human colon organoids.Nature cancer · 2024Article
- A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer.bioRxiv : the preprint server for biology · 2024Article
- Computational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc.Genome biology · 2024Article
- Source, co-occurrence, and prognostic value of PTEN mutations or loss in colorectal cancer.NPJ genomic medicine · 2023Article
- Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients.Nature communications · 2023Article
- Mathematical modeling the order of driver gene mutations in colorectal cancer.PLoS computational biology · 2023Article
- Tumor heterogeneity: preclinical models, emerging technologies, and future applications.Frontiers in oncology · 2023Review
- Stepwise evolutionary genomics of early-stage lung adenocarcinoma manifesting as pure, heterogeneous and part-solid ground-glass nodules.British journal of cancer · 2022Article
- J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments.BMC bioinformatics · 2022Article
- Chemotherapy after PD-1 inhibitors in relapsed/refractory Hodgkin lymphoma: Outcomes and clonal evolution dynamics.British journal of haematology · 2022Article
- Early Breast Cancer Evolution by Autosomal Broad Copy Number Alterations.International journal of genomics · 2022Article
- Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer.Frontiers in oncology · 2022Review
- Conditional prediction of consecutive tumor evolution using cancer progression models: What genotype comes next?PLoS computational biology · 2021Article
- VERSO: A comprehensive framework for the inference of robust phylogenies and the quantification of intra-host genomic diversity of viral samples.Patterns (New York, N.Y.) · 2021Article
- On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples.Current genomics · 2021Article
- Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study.Journal of medical Internet research · 2020Article
- The MOBSTER R package for tumour subclonal deconvolution from bulk DNA whole-genome sequencing data.BMC bioinformatics · 2020Article
- Genomic Underpinnings of Tumor Behavior inAmerican journal of respiratory and critical care medicine · 2020Article
- An Optimal Control Framework for the Automated Design of Personalized Cancer Treatments.Frontiers in bioengineering and biotechnology · 2020Article
- Smoking alters the evolutionary trajectory of non-small cell lung cancer.Experimental and therapeutic medicine · 2019Article
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9 authors.
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Abstract
The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses.
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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.