Distinct and convergent effects of <i>SF3B1</i> mutations in human breast cancer
Abstract
Tumor genomic profiling has uncovered many cancer drivers whose implications in terms of tumor biology and therapeutic actionability remain understudied. Hotspot mutations in SF3B1 induce widespread transcriptomic alterations and occur across multiple cancer types. Despite this, the biological and clinical consequences of SF3B1 mutations remain elusive. Characterization of the largest SF3B1 mutant breast cancer clinical cohort to date identifies SF3B1 driver mutations in approximately 2.5% of HR+ HER2- breast cancer, with strong enrichment of K700E substitutions, substantial variation in variant allele fraction (VAF), and significantly improved overall survival due to enrichment in Luminal A disease. In vitro and in vivo studies in representative cell models suggest several of the most prevalent SF3B1 mutations have deleterious effects on cell growth, leading to selective loss of the mutation over time, providing a rationale for the low frequency and low VAF of SF3B1 mutations in breast cancer. Though all introduced hotspot mutations limit growth and are reverted to wildtype (WT) over time, mutations at position R625 have a more pronounced phenotype than K700E, providing an explanation for the clinically observed skew in mutation frequencies. RNA and DNA sequencing analyses were used to identify characteristic pathway-level transcriptomic changes in SF3B1 -mutant cells and identify copy number alterations as a mechanism of both improved mutation tolerability and a means to eliminate the mutation over time. These data suggest that while SF3B1 mutations are enriched in some clinical contexts, their role in breast tumorigenesis is highly complex and dependent on secondary events that overcome their deleterious effects on cell growth and survival.
Article Details
Journal Info
Proceedings of the National Academy of Sciences
National Academy of Sciences
Authors (22)
Riley E. Bergman
Medical Scientist Training Program, Vanderbilt University School of Medicine
Christin I. Keeton
Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center
Irika R. Sinha
Department of Neuroscience, Johns Hopkins School of Medicine
Jonathan P. Ling
Department of Pathology, Division of Neuropathology, Johns Hopkins University
Yu Wang
Lauren Ha
Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center
Eshani P. Mehta
Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center
Sarah C. Reed
Medical Scientist Training Program, Vanderbilt University School of Medicine
Ethan K. Fox
Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center
Tina M. O’Grady
Tempus AI, Inc.
Yingying Yu
Tempus AI, Inc.
Stamatina Fragkogianni
Tempus AI, Inc.
Calvin Chao
Tempus AI, Inc.
Brad A. Davidson
Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center
Adam X. Miranda
Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center
Sarah Croessmann
Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center
Shilin Zhao
Department of Biostatistics, Vanderbilt University Medical Center
Qi Liu
Yu Shyr
Department of Biostatistics, Vanderbilt University Medical Center
W. Brian Dalton
The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University
Paula J. Hurley
Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center
Ben H. Park
Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center