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Published on: February 8, 2018
Comprehensive single-cell analysis of triple-negative breast cancer based on cDC1 immune-related genes: prognostic
Linan Huang1, Yiran Liu1, Yulin Shi2
1College of Traditional Chinese Medicine, Shandong Second Medical University, Weifang, 261000, China.
Background:
Various components of the immunological milieu surrounding tumors have become a key focus in cancer immunotherapy research. There are currently no reliable biomarkers for triple-negative breast cancer (TNBC), leading to limited clinical benefits. However, some studies have indicated that patients with TNBC may achieve better outcomes after immunotherapy. Therefore, this study aimed to identify molecular features potentially associated with conventional type 1 dendritic cell (cDC1) immunity to provide new insights into TNBC prognostication and immunotherapy decision-making.
Methods:
Single-cell ribonucleic acid sequencing data from the Gene Expression Omnibus database were analyzed to determine which genes are differentially expressed genes (DEGs) in cDC1s. We then cross-referenced cDC1-related DEGs with gene sets linked to immunity from the ImmPort and InnateDB databases to screen for the genes linked to the immune response and cDC1s. We used univariate Cox and least absolute shrinkage and selection operator regression analyses to construct a risk assessment model based on four genes in patients with TNBC obtained from the Cancer Genome Atlas, which was validated in a testing group. This model was also used to assess immunotherapy responses among the IMvigor210 cohort. We subsequently utilized single sample Gene Set Enrichment Analysis, CIBERSORT, and ESTIMATE to analyze the immunological characteristics of the feature genes and their correlation with drug response.
Results:
We identified 93 DEGs related to the immune response and cDC1s, of which four (IDO1, HLA-DOB, CTSD, and IL3RA) were substantially linked to the overall survival rate of TNBC patients. The risk assessment model based on these genes stratified patients into high- and low-risk groups. Low-risk patients exhibited enriched ''hot tumor'' phenotypes, including higher infiltration of memory-activated CD4 + T cells, CD8 + T cells, gamma delta T cells, and M1 macrophages, as well as elevated immune checkpoint expression and tumor mutational burden, suggesting potential responsiveness to immunotherapy. Conversely, high-risk patients displayed "cold tumor" characteristics, with higher infiltration of M0 and M2 macrophages and lower immune scores, which may be poorer in response to immunotherapy. However, experimental validation and larger clinical studies are necessary to confirm these findings and explore the underlying mechanisms of the identified genes.
Conclusion:
This study developed a robust risk assessment model using four genes that effectively forecast the outcome of patients with TNBC and have the potential to guide immunotherapy. This model provided new theoretical insights for knowing the TNBC immune microenvironment and developing personalized treatment strategies.
Insights
Researchers developed a four-gene risk model to predict triple-negative breast cancer (TNBC) patient outcomes and guide immunotherapy. This model identifies potential responders based on immune microenvironment characteristics.
Area of Science:
- Immunology
- Oncology
- Genomics
Background:
- The tumor immune microenvironment is crucial for cancer immunotherapy efficacy.
- Triple-negative breast cancer (TNBC) lacks reliable biomarkers, limiting treatment options.
- TNBC patients may benefit from immunotherapy, necessitating predictive biomarkers.
Purpose of the Study:
- Identify molecular features associated with conventional type 1 dendritic cell (cDC1) immunity in TNBC.
- Develop a prognostic and predictive model for TNBC patient outcomes and immunotherapy response.
- Gain insights into the TNBC immune microenvironment for personalized treatment strategies.
Main Methods:
- Analyzed single-cell RNA sequencing data to identify differentially expressed genes (DEGs) in cDC1s.
- Cross-referenced DEGs with immune gene sets to identify key immune-related genes.
- Constructed and validated a four-gene risk assessment model using Cox and LASSO regression analyses.
Main Results:
- Identified four genes (IDO1, HLA-DOB, CTSD, IL3RA) significantly associated with TNBC patient survival.
- The four-gene model stratified patients into high-risk ('cold tumor') and low-risk ('hot tumor') groups.
- Low-risk patients showed 'hot tumor' phenotypes suggesting immunotherapy responsiveness; high-risk patients showed 'cold tumor' phenotypes.
Conclusions:
- A robust four-gene risk model was developed to predict TNBC patient outcomes.
- The model shows potential for guiding immunotherapy decisions in TNBC.
- This study offers new insights into the TNBC immune microenvironment and personalized treatment.
