“Nothing in biology makes sense except in the light of evolution”
- Theodosius Dobzhansky

Welcome to the Evolution of Genetic Immune Escape Viewer

Immune escape is a critical hallmark of cancer progression, yet its evolutionary path largely remains unclear. When do genetic alterations in immunomodulatory pathways arise during cancer development? What precedes them, and what happens afterwards? We seek to build a pan-cancer atlas that maps the genetic evolution of immune escape. How can we get there? Click below to learn more.

Methodology

Learn how immunomodulatory genes and mutation timing are identified from large-scale CRISPR screens and WGS data.

Tutorials

Step-by-step guidance on how to use EvoGIE to explore immune escape evolution across different cancer WGS cohorts.

The Shiny App is developed by Wenjie Chen. The results are generated by Dr. Shengqing Gu lab and Dr. Peter Van Loo lab.

For more details, please refer to our BioRxiv preprint.

Identifying Immunomodulatory Genes

The public CRISPR screen studies are comprehensively collected to identify the potential mechanisms for immune escape in cancers.

  • 1. Published CRISPR screens were collected for identifying the regulators of MHC-I expression, response to CD8 T-cell-mediated killing, NK-cell-mediated killing, macrophage-mediated phagocytosis and γδ T-cell-mediated killing.
  • 2. The top 100 positive/negative regulators in each study were respectively unitized for Gene Set Enrichment Analysis.
  • 3. Next, we selected the featured enrichments for further analysis based on the frequencies of studies reporting these enrichments.
  • 4. We then combined the immunomodulatory genes in each frequently enriched pathway as a gene set for that pathway.

Inferring Mutation Timing

GRITIC is used to estimate when clonal copy number gains happened in a tumor's evolution. (PMID: 38943574)

  • 1. Posterior gain timing distributions for clonal copy number segments are calculated based on the copy number, tumor purity and the read counts for SNVs in the region of the gain.
  • 2. For each SNV, GRITIC samples when the gain happened, how many copies the SNV has, and then estimates when the SNV occurred.
  • 3. The exact timing of each SNV is sampled within a time window defined by nearby gains.
  • 4. The timing of SNVs is measured on a 'mutation time' scale that goes from 0 (representing conception) to 1 (the end of clonal evolution). Each SNV is sampled 250 times to create a full timing distribution.




Manuscript under preparation:

Wenjie Chen, Toby Baker, Zhihui Zhang, Huw A Ogilvie, Peter Van Loo, Shengqing Stan Gu. Evolutionary trajectories of immune escape across cancers. BioRxiv preprint


For issues with the app, please contact: Dr. Wenjie Chen, wchen20@mdanderson.org.