A protein interaction landscape of breast cancer

Author:

Kim Minkyu1234ORCID,Park Jisoo45ORCID,Bouhaddou Mehdi1234ORCID,Kim Kyumin1234ORCID,Rojc Ajda1234ORCID,Modak Maya1234ORCID,Soucheray Margaret1234ORCID,McGregor Michael J.1234ORCID,O’Leary Patrick46ORCID,Wolf Denise46,Stevenson Erica1234,Foo Tzeh Keong7ORCID,Mitchell Dominique368ORCID,Herrington Kari A.9ORCID,Muñoz Denise P.46ORCID,Tutuncuoglu Beril1234ORCID,Chen Kuei-Ho1234ORCID,Zheng Fan45ORCID,Kreisberg Jason F.45ORCID,Diolaiti Morgan E.46ORCID,Gordan John D.368ORCID,Coppé Jean-Philippe46,Swaney Danielle L.1234ORCID,Xia Bing7ORCID,van ’t Veer Laura46,Ashworth Alan46ORCID,Ideker Trey4510ORCID,Krogan Nevan J.1234ORCID

Affiliation:

1. Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.

2. The J. David Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.

3. Quantitative Biosciences Institute, University of California, San Francisco, CA, USA.

4. The Cancer Cell Map Initiative, San Francisco and La Jolla, CA, USA.

5. Department of Medicine, University of California, San Diego, CA, USA.

6. Department of Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.

7. Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.

8. Division of Hematology/Oncology, University of California, San Francisco, CA, USA.

9. Department of Biochemistry and Biophysics, Center for Advanced Light Microscopy, University of California, San Francisco, CA, USA.

10. Department of Bioengineering, University of California, San Diego, CA, USA.

Abstract

Mapping protein interactions driving cancer Cancer is a genetic disease, and much cancer research is focused on identifying carcinogenic mutations and determining how they relate to disease progression. Three papers demonstrate how mutations are processed through networks of protein interactions to promote cancer (see the Perspective by Cheng and Jackson). Swaney et al . focus on head and neck cancer and identify cancer-enriched interactions, demonstrating how point mutant–dependent interactions of PIK3CA, a kinase frequently mutated in human cancers, are predictive of drug response. Kim et al . focus on breast cancer and identify two proteins functionally connected to the tumor-suppressor gene BRCA1 and two proteins that regulate PIK3CA. Zheng et al . developed a statistical model that identifies protein networks that are under mutation pressure across different cancer types, including a complex bringing together PIK3CA with actomyosin proteins. These papers provide a resource that will be helpful in interpreting cancer genomic data. —VV

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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