ArticleJCO clinical cancer informatics2021
My Cancer Genome: Coevolution of Precision Oncology and a Molecular Oncology Knowledgebase.
Article in JCO clinical cancer informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- The GA4GH Categorical Variation Representation Specification: A Unified Computational Framework for Reasoning over Genomic Variant Categories.bioRxiv : the preprint server for biology · 2026Article
- A context-augmented large language model for accurate precision oncology medicine recommendations.Cancer cell · 2026Article
- Review
- MarkerPredict: predicting clinically relevant predictive biomarkers with machine learning.NPJ systems biology and applications · 2025Article
- Shared neoantigens for cancer immunotherapy.Molecular therapy. Oncology · 2025Review
- Biological Data Resources and Machine Learning Frameworks for Hematology Research.Genomics, proteomics & bioinformatics · 2025Review
- DNA Methylation Array Analysis Identifies Biological Subgroups of Cutaneous Melanoma and Reveals Extensive Differences with Benign Melanocytic Nevi.Diagnostics (Basel, Switzerland) · 2025Article
- Unveiling the Digital Evolution of Molecular Tumor Boards.Targeted oncology · 2025Review
- Cancer genomics and bioinformatics in Latin American countries: applications, challenges, and perspectives.Frontiers in oncology · 2025Review
- Precision oncology platforms: practical strategies for genomic database utilization in cancer treatment.Molecular cytogenetics · 2024Review
- Longitudinal ultra-sensitive mutation burden sequencing for precise minimal residual disease assessment in AML.Nature communications · 2024Article
- Interpretation knowledge extraction for genetic testing via question-answer model.BMC genomics · 2024Article
- Ku70 senses cytosolic DNA and assembles a tumor-suppressive signalosome.Science advances · 2024Article
- Article
- Tri©DB: an integrated platform of knowledgebase and reporting system for cancer precision medicine.Journal of translational medicine · 2023Article
- Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal.Cancer research · 2023Article
- OncoCTMiner: streamlining precision oncology trial matching via molecular profile analysis.Database : the journal of biological databases and curation · 2023Article
- MRE11:p.K464R mutation mediates olaparib resistance by enhancing DNA damage repair in HGSOC.Cell & bioscience · 2023Article
- Identification of neoepitope reactive T-cell receptors guided by HLA-A*03:01 and HLA-A*11:01 immunopeptidomics.Journal for immunotherapy of cancer · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe My Cancer Genome (MCG) knowledgebase and resulting website were launched in 2011 with the purpose of guiding clinicians in the application of genomic testing results for treatment of patients with cancer. Both knowledgebase and website were originally developed using a wiki-style approach that relied on manual evidence curation and synthesis of that evidence into cancer-related biomarker, disease, and pathway pages on the website that summarized the literature for a clinical audience. This approach required significant time investment for each page, which limited website scalability as the field advanced. To address this challenge, we designed and used an assertion-based data model that allows the knowledgebase and website to expand with the field of precision oncology.
methodsAssertions, or computationally accessible cause and effect statements, are both manually curated from primary sources and imported from external databases and stored in a knowledge management system. To generate pages for the MCG website, reusable templates transform assertions into reconfigurable text and visualizations that form the building blocks for automatically updating disease, biomarker, drug, and clinical trial pages.
resultsCombining text and graph templates with assertions in our knowledgebase allows generation of web pages that automatically update with our knowledgebase. Automated page generation empowers rapid scaling of the website as assertions with new biomarkers and drugs are added to the knowledgebase. This process has generated more than 9,100 clinical trial pages, 18,100 gene and alteration pages, 900 disease pages, and 2,700 drug pages to date.
conclusionLeveraging both computational and manual curation processes in combination with reusable templates empowers automation and scalability for both the MCG knowledgebase and MCG website.
Indexed as
Identifiers
What OpenQuestion holds
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.