Analytical protocol to identify local ancestry-associated molecular features in cancer

Jian Carrot-Zhang; Seunghun Han; Wanding Zhou; Jeffrey S Damrauer; Anab Kemal; Cancer Genome Atlas Analysis Network; Andrew D Cherniack; Rameen Beroukhim

doi:10.1016/j.xpro.2021.100766

Analytical protocol to identify local ancestry-associated molecular features in cancer

STAR Protoc. 2021 Sep 20;2(4):100766. doi: 10.1016/j.xpro.2021.100766. eCollection 2021 Dec 17.

Authors

Jian Carrot-Zhang^{1

2

3}, Seunghun Han^{2

3}, Wanding Zhou^{4

5}, Jeffrey S Damrauer⁶, Anab Kemal⁷; Cancer Genome Atlas Analysis Network; Andrew D Cherniack^{1

2

3}, Rameen Beroukhim^{1

2

3

8

9}

Collaborators

Affiliations

¹ The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
² Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
³ Harvard Medical School, Boston, MA 02115, USA.
⁴ Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, PA, 19104, USA.
⁵ Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
⁶ Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
⁷ National Cancer Institute, Bethesda, MD 20892, USA.
⁸ Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
⁹ Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.

Abstract

People of different ancestries vary in cancer risk and outcome, and their molecular differences may indicate sources of these variations. Determining the "local" ancestry composition at each genetic locus across ancestry-admixed populations can suggest causal associations. We present a protocol to identify local ancestry and detect the associated molecular changes, using data from the Cancer Genome Atlas. This workflow can be applied to cancer cohorts with matched tumor and normal data from admixed patients to examine germline contributions to cancer. For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020).

Keywords: Bioinformatics; Cancer; Genomics.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Genetics, Population / methods*
Genome, Human / genetics*
Genomics / methods*
Genotyping Techniques
Humans
Neoplasms / genetics*
Phenotype

Abstract

Publication types

MeSH terms

Grants and funding