Publications by authors named "Thomas V Yu"
Article Synopsis
- International cancer registries, like AACR Project GENIE, provide access to genomic and clinical data from over 130,000 cancer patients, but analyzing this combined data can be tricky.
- The cBioPortal for Cancer Genomics has improved its features to help visualize and analyze longitudinal clinical and genomic data, allowing users to see how treatment impacts patient outcomes over time.
- These enhancements enable researchers and clinicians to explore complex datasets, fostering discoveries on how specific genomic changes affect cancer prognosis and treatment effectiveness.
Importance: An automated, accurate method is needed for unbiased assessment quantifying accrual of joint space narrowing and erosions on radiographic images of the hands and wrists, and feet for clinical trials, monitoring of joint damage over time, assisting rheumatologists with treatment decisions. Such a method has the potential to be directly integrated into electronic health records.
Objectives: To design and implement an international crowdsourcing competition to catalyze the development of machine learning methods to quantify radiographic damage in rheumatoid arthritis (RA).
Unlabelled: The American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE) is an international pan-cancer registry with the goal to inform cancer research and clinical care worldwide. Founded in late 2015, the milestone GENIE 9.1-public release contains data from >110,000 tumors from >100,000 people treated at 19 cancer centers from the United States, Canada, the United Kingdom, France, the Netherlands, and Spain.
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- Many methods for identifying neoantigens rely on tumor sequencing paired with bioinformatics, but there's a lack of reference data and clarity on what makes tumor epitopes immunogenic.
- A global consortium was formed to predict immunogenic epitopes from shared tumor sequencing, leading to the assessment of 608 epitopes for T cell binding in patient samples.
- A new model for tumor epitope immunogenicity was created, which was able to accurately filter out non-immunogenic peptides and improve prediction performance, providing valuable data for understanding anti-tumor immunity.