Computational Analysis of Breast Cancer Finds That Many Cell Types Correlate with Patient Survival and Genomic Features
Article ID: 646507
Released: 21-Jan-2016 2:05 PM EST
Source Newsroom: Norris Cotton Cancer Center Dartmouth-Hitchcock Medical Center
Newswise — LEBANON, N.H. – A study by researchers at Norris Cotton Cancer Center has shown that a very sensitive computational method can be used to reveal the human immune system’s effect on cancer. The Dartmouth study was the first to perform an in-depth computational examination of how immune activity translates to prognosis in breast cancer.
The research team was able to infer the presence of different types of immune cells in cancer using gene expression data, thereby allowing for a systematic analysis of the role immune cells play in cancer. The method shows promise in studying the immune system’s effect on cancer for computational biologists, as gene expression data is publicly available for most tumor types. Understanding how the immune system treats different tumor types, and why it treats them this way, can provide information that can be used to predict patient prognosis and immunotherapy response in a cancer-specific manner
The research team, led by Chao Cheng, Ph.D., Assistant Professor in the Department of Genetics at The Geisel School of Medicine at Dartmouth, used gene expression data from breast cancer patients to computationally infer the presence of different types of immune cells. The findings were then correlated with patient survival and other genomic data, characterizing the role these immune cells play in breast tumors. The primary findings were that a variety of cell types were correlated with patient prognosis in breast cancer.
Specifically, presence of natural killer cells and plasmacytoid dendritic cells correlated with good survival, while presence of CD8+ effector T cells correlated with bad survival. The Dartmouth researchers’ data suggested that CD8+ effector T cells, which are known to be tumor-killing cells, likely correlate with bad survival due to being present in high quantities in immunosuppressive environments (the tumor shuts them off to escape their cytotoxic attacks). Additionally, the data indicated that plasmacytoid dendritic cells and natural killer cells likely mediate the tumor-killing protein TRAIL as part of their protective effect. The data also suggested that different molecular subtypes of breast cancer have different immune response profiles.Collectively, these results allow for a detailed and personalized assessment of the patient immune response to a cancerous tumor. When combined with routinely collected patient biopsy genomic data, this method can enable a richer understanding of the complex interplay between the host immune system and cancer.
“Understanding what immune cells are present in a patient’s tumor can inform whether they will respond to immunotherapy. There is currently much interest in developing computational approaches that allow for a quick, easy assessment of the immune environment of a patient tumor,” said one of the authors of the study, Frederick Varn. “Down the road, we are hopeful that our approach will be used to predict patient immunotherapy response, in addition to general patient prognosis,” he added.
The study was published in the Nature Communications and authored by Frederick S. Varn, Chao Cheng, Erik H. Andrews and David W. Mullins, all of the Norris Cotton Cancer Center and Dartmouth’s Geisel School of Medicine.
About Dartmouth-Hitchcock Norris Cotton Cancer CenterNorris Cotton Cancer Center combines advanced cancer research at Dartmouth College and the Geisel School of Medicine at Dartmouth with patient-centered cancer care provided at Dartmouth-Hitchcock Medical Center, at Dartmouth-Hitchcock regional locations in Manchester, Nashua, and Keene, NH, and St. Johnsbury, VT, and at 12 partner hospitals throughout New Hampshire and Vermont. It is one of 45 centers nationwide to earn the National Cancer Institute's "Comprehensive Cancer Center" designation. Learn more about Norris Cotton Cancer Center research, programs, and clinical trials online at www.cancer.dartmouth.edu.
Funding SourcesTo Chao Cheng: American Cancer Society Research Grant IRG-82-003-30; National Center for Advancing Translational Sciences of the NIH UL1TR001086;Geisel School of Medicine at Dartmouth College start-up funding packageTo Frederick S. Varn: National Institute of General Medicine Sciences of the NIH T32GM008704