A protein interaction landscape of breast cancer

• DOI: 10.1126/science.abf3066
• Publication Date: 2021-09-30T19:08:17Z
• Authors: Minkyu Kim; Jisoo Park; Mehdi Bouhaddou; Kyumin Kim; Ajda Rojc; Maya Modak; Margaret Soucheray; Michael J. McGregor; Patrick O’Leary; Denise Wolf; Erica Stevenson; Tzeh Keong Foo; Dominique Mitchell; Kari A. Herrington; Denise P. Muñoz; Beril Tutuncuoglu; Kuei-Ho Chen; Fan Zheng; Jason F. Kreisberg; Morgan E. Diolaiti; John D. Gordan; Jean-Philippe Coppé; Danielle L. Swaney; Bing Xia; Laura van ’t Veer; Alan Ashworth; Trey Ideker; Nevan J. Krogan
• Keywords: Tumor; Tandem Affinity Purification; General Science & Technology; Breast Neoplasms; Mass Spectrometry; Cell Line; Neoplasm Proteins; 3. Good health; Cell Line, Tumor; Breast Cancer; Mutation; Genetics; 2.1 Biological and endogenous factors; Humans; Female; Protein Interaction Maps; Aetiology; Biotechnology; Cancer

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