ArticleNature communications2020
Identification of relevant genetic alterations in cancer using topological data analysis.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 55 citations in OpenAlex.
- Article
- Integrating gene mutation spectra from tumors and the general population with gene expression topological networks to identify novel cancer driver genes.Human genetics · 2025Article
- Assessment of Methylation in SelectedInternational journal of molecular sciences · 2025Article
- Mutation impact on mRNA versus protein expression across human cancers.GigaScience · 2025Article
- Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology.Laboratory investigation; a journal of technical methods and pathology · 2024Review
- BAY-9835: Discovery of the First Orally Bioavailable ADAMTS7 Inhibitor.Journal of medicinal chemistry · 2024Article
- Lung Inflammatory Phenotype in Mice Deficient in Fibulin-2 and ADAMTS-12.International journal of molecular sciences · 2024Article
- Topological data analysis reveals core heteroblastic and ontogenetic programs embedded in leaves of grapevine (Vitaceae) and maracuyá (Passifloraceae).PLoS computational biology · 2024Article
- Topological data analysis reveals a core gene expression backbone that defines form and function across flowering plants.PLoS biology · 2023Article
- Article
- The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia.PLoS computational biology · 2023Article
- A novel approach to topological network analysis for the identification of metrics and signatures in non-small cell lung cancer.Scientific reports · 2023Article
- Raman spectroscopy and topological machine learning for cancer grading.Scientific reports · 2023Article
- Multiscale Methods for Signal Selection in Single-Cell Data.Entropy (Basel, Switzerland) · 2022Article
- TAaCGH Suite for Detecting Cancer-Specific Copy Number Changes Using Topological Signatures.Entropy (Basel, Switzerland) · 2022Article
- Exploration of the Mechanism of Lianhua Qingwen in Treating Influenza Virus Pneumonia and New Coronavirus Pneumonia with the Concept of "Different Diseases with the Same Treatment" Based on Network Pharmacology.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- An Integrated Pan-Cancer Analysis of ADAMTS12 and Its Potential Implications in Pancreatic Adenocarcinoma.Frontiers in oncology · 2022Article
- The role of microfibrillar-associated protein 2 in cancer.Frontiers in oncology · 2022Review
- Immunomodulatory role of metalloproteases in cancers: Current progress and future trends.Frontiers in immunology · 2022Review
- The topology of data: opportunities for cancer research.Bioinformatics (Oxford, England) · 2021Article
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 2 countries.
Funding
Abstract
Large-scale cancer genomic studies enable the systematic identification of mutations that lead to the genesis and progression of tumors, uncovering the underlying molecular mechanisms and potential therapies. While some such mutations are recurrently found in many tumors, many others exist solely within a few samples, precluding detection by conventional recurrence-based statistical approaches. Integrated analysis of somatic mutations and RNA expression data across 12 tumor types reveals that mutations of cancer genes are usually accompanied by substantial changes in expression. We use topological data analysis to leverage this observation and uncover 38 elusive candidate cancer-associated genes, including inactivating mutations of the metalloproteinase ADAMTS12 in lung adenocarcinoma. We show that ADAMTS12
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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.