TCGA_PanCancer_Immune_Genetics

Last updated: 03/20/2022

Description

Germline genetic variants shape immune response and may explain some of the immunological heterogeneity observed across patients’ tumors. We present a workflow to assess the contribution of hosts’ genetic background to the immune landscape of solid tumors using harmonized data from more than 9000 patients enrolled in the TCGA. The protocol is developed around the structure of TCGA, but it can be adapted to explore other repositories, for instance in the context of cancer immunotherapy.

The code and data deposited here were used to generate the results and resource files for the Sayaman, Saad et al., 2021 paper.

Code contained herein are meant as a guide and should be modified and adapted to match your server specifications and directories.

Citations

Please cite Sayaman, Saad et al., Immunity 2021 when using the data and code contained here in, and downloading the TCGA QC and HRC imputed genotyping data.

• Sayaman, Saad et al., Immunity (2021). Germline genetic contribution to the immune landscape of cancer. https://doi.org/10.1016/j.immuni.2021.01.011
• Sayaman, Saad, Heimann, et al., Analytic pipelines to assess the relationship between intratumoral immune response and germline genetics.

Please additionally cite: Thorsson et al., Immunity 2018 when referencing the immune traits.

• Thorsson et al., Immunity (2018). The Immune Landscape of Cancer. https://doi.org/10.1016/j.immuni.2018.03.023

Please additionally cite: Carrot-Zhang et al., Cancer Cell 2020 when referencing ancestry assignments and downloading the TCGA QC and HRC imputed genotyping data.

• Carrot-Zhang et al., Cancer Cell (2020). Comprehensive Analysis of Genetic Ancestry and Its Molecular Correlates in Cancer. https://doi.org/10.1016/j.ccell.2020.04.012

Code Contributors

• Rosalyn W. Sayaman, Ph.D., University of California, San Francisco: rwsayaman@gmail.com
• Mohamad Saad, Ph.D., Qatar Computing Research Institute: msaad@hbku.edu.qa
• Carolina Heimann, Institute for Systems Biology
• Donglei Hu, Ph.D., University of California, San Francisco
• Khalid Kunji, Qatar Computing Research Institute
• Scott Huntsman, University of California, San Francisco
• Denise M. Wolf, Ph.D., University of California, San Francisco
• Vésteinn Thorsson, Ph.D., Institute for Systems Biology

Acknowledgment

• David L. Gibbs, Ph.D., Institute for Systems Biology
• Michele Ceccarelli, Ph.D., University of Naples "Federico II", Istituto di Ricerche Genetiche "G. Salvatore"

Senior Authors

• Davide Bedognetti, Ph.D., Sidra Medicine
• Elad Ziv, Ph.D., University of California, San Francisco

Contents

Before you begin

The protocol describes specific steps using TCGA datasets However, this can be applied in other datasets with similar structure.

• Apply for dbGaP Authorization

• Software Installation

  • PLINK
  • GCTA GREML
  • R/Bioconductor
  • LocusZoom
  • eCAVIAR

• Download Datasets

  • Sample List (Sayaman et al., Immunity 2021)
  • Immune Traits (Sayaman et al., Immunity 2021)
  • TCGA QC and HRC Imputed Genotyping Data
  • TCGA Whole Exome Sequencing Data
  • GTEx Data
  • VEP Annotation
  • Roadmap Epigenomics Project Epigenomic State Model

Analysis

• Code
  • Pre-processing of Imputed Data
  • Immune Traits
  • Heritability Analysis
  • Genome-Wide Association Study (GWAS)
  • Epigenomic Analysis
  • Expression and splicing quantitative trait locus analysis (eQTLs and sQTL)
  • Colocalization with E-Caviar colocalization and manual curation of the expanded region
  • Rare Variant Analysis

Visualization

• Interactive visualization of results
  • CRI iAtlas
  • PheWeb

Resources

1. TCGA QC and HRC Imputed Genotyping Data

   • Access to the TCGA original birdseed (GDC Data Portal: https://portal.gdc.cancer.gov/) and pre-processed quality controlled genotyping data imputed to HRC generated in Sayaman et al., Immunity 2021 requires GDC controlled access permission approval.

   • The quality controlled and HRC imputed genotyping data and the UCSF ancestry assignments were contributed towards ancestry analyses (Carrot-Zhang et al., 2020) and are accessible at the GDC publication page (https://gdc.cancer.gov/about-data/publications/CCG-AIM-2020).

   • See folder: "TCGA QC HRC Imputed Genotyping Data used by the AIM AWG (from Sayaman et al)"

     • Controlled Access data include:
       • QC Unimputed Genotyping Data
       • HRC Stranded Unimputed Genotyping Data
       • 1000 Genomes Stranded Unimputed Genotyping Data
       • HRC Imputed Genotyping Data

   • Download:

     • Subfolder: "QC Unimputed Genotyping Data", and read the READ_ME_1.txt file.
     • Subfolder: "HRC Imputed Genotyping Data", and read the READ_ME_4.txt file.

2. TCGA Germline Immune Response GWAS Summary Statistics. https://doi.org/10.6084/m9.figshare.13077920.v1

   • Figshare: https://figshare.com/articles/dataset/Sayaman_et_al_TCGA_Germline-Immune_GWAS_Summary_Statistics/13077920/1

3. Interactive visualization of results

   • CRI iAtlas (https://www.cri-iatlas.org/)
   • PheWeb tool (https://pheweb-tcga.qcri.org/)