Related Experiment Video
Updated: Oct 29, 2025
05:17
Micromanipulation of Circulating Tumor Cells for Downstream Molecular Analysis and Metastatic Potential Assessment
Published on: May 14, 2019
8.8K
OncoThreads: visualization of large-scale longitudinal cancer molecular data
Theresa A Harbig1, Sabrina Nusrat1, Tali Mazor2
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
Bioinformatics (Oxford, England)
|July 12, 2021
Summary
OncoThreads visualizes longitudinal cancer data, enabling researchers to explore temporal patterns in patient cohorts. This tool aids in understanding mutation impact on treatment response and identifying resistant clones.
Area of Science:
- Computational biology
- Bioinformatics
- Cancer research
Background:
- Longitudinal molecular profiling of tumors and liquid biopsies is crucial in cancer research.
- Next-generation sequencing and immuno-profile assays generate vast amounts of temporal data.
- Effective visualization tools are needed to interpret complex longitudinal cancer data across patient cohorts.
Purpose of the Study:
- To develop a visualization tool, OncoThreads, for exploring longitudinal clinical and molecular data in cancer patient cohorts.
- To enable interactive analysis of temporal patterns, including mutation impact on treatment response and resistance.
- To facilitate the discovery of insights from multi-timepoint cancer data.
Main Methods:
- OncoThreads visualizes patient cohorts using temporal heatmaps and Sankey diagrams.
- The tool supports interactive exploration and ranking of clinical and molecular features.
- It is implemented in JavaScript utilizing the cBioPortal web API.
Main Results:
- OncoThreads allows for the discovery of temporal patterns in longitudinal cancer data.
- The tool facilitates understanding the impact of mutations on treatment response, such as the emergence of resistant clones.
- Demonstrated functionality using a cohort of 23 glioma patients with 2-4 timepoints.
Conclusions:
- OncoThreads provides a valuable visualization solution for longitudinal cancer data analysis.
- The tool aids researchers in uncovering temporal trends and relationships within patient cohorts.
- Facilitates deeper understanding of cancer evolution and treatment dynamics.
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