Characterizing the Tumor Microenvironment and Its Prognostic Impact in Breast Cancer

The tumor microenvironment (TME) is crucial in cancer development and therapeutic response. Immunotherapy is increasingly recognized as a critical component of cancer treatment. While immunotherapies have shown efficacy in various cancers, including breast cancer, patient responses vary widely. Some patients receive significant benefits, while others experience minimal or no improvement. This disparity underscores the complexity and diversity of the immune system. In this study, we investigated the immune landscape and cell-cell communication within the TME of breast cancer through integrated analysis of bulk and single-cell RNA sequencing data. We established profiles of tumor immune infiltration that span across a broad spectrum of adaptive and innate immune cells. Our clustering analysis of immune infiltration identified three distinct patient groups: high T cell abundance, moderate infiltration, and low infiltration. Patients with low immune infiltration exhibited the poorest survival rates, while those in the moderate infiltration group showed better outcomes than those with high T cell abundance. Moreover, the high cell abundance group was associated with a greater tumor burden and higher rates of TP53 mutations, whereas the moderate infiltration group was characterized by a lower tumor burden and elevated PIK3CA mutations. Analysis of an independent single-cell RNA-seq breast cancer dataset confirmed the presence of similar infiltration patterns. Further investigation into ligand-receptor interactions within the TME unveiled significant variations in cell-cell communication patterns among these groups. Notably, we found that the signaling pathways SPP1 and EGF were exclusively active in the low immune infiltration group, suggesting their involvement in immune suppression. This work comprehensively characterizes the composition and dynamic interplay in the breast cancer TME. Our findings reveal associations between the extent of immune infiltration and clinical outcomes, providing valuable prognostic information for patient stratification. The unique mutations and signaling pathways associated with different patient groups offer insights into the mechanisms underlying diverse tumor immune infiltration and the formation of an immunosuppressive tumor microenvironment.

Authors

Institutions

Publication Details

Journal
Cells
Published
2024-09-10
DOI
https://doi.org/10.3390/cells13181518
Citations
10
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
2.83

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Characterizing the Tumor Microenvironment and Its Prognostic Impact in Breast Cancer

Amit Kumar Tiwari, Wenjuan Zhang, Alex Pui‐Wai Lee, Mary Qu Yang
10 citations
Cells
Cancer Immunotherapy and Biomarkers
2.83
article

Characterizing the Tumor Microenvironment and Its Prognostic Impact in Breast Cancer

Amit Kumar Tiwari, Wenjuan Zhang, Alex Pui‐Wai Lee, Mary Qu Yang
article en
10 citations

Abstract

The tumor microenvironment (TME) is crucial in cancer development and therapeutic response. Immunotherapy is increasingly recognized as a critical component of cancer treatment. While immunotherapies have shown efficacy in various cancers, including breast cancer, patient responses vary widely. Some patients receive significant benefits, while others experience minimal or no improvement. This disparity underscores the complexity and diversity of the immune system. In this study, we investigated the immune landscape and cell-cell communication within the TME of breast cancer through integrated analysis of bulk and single-cell RNA sequencing data. We established profiles of tumor immune infiltration that span across a broad spectrum of adaptive and innate immune cells. Our clustering analysis of immune infiltration identified three distinct patient groups: high T cell abundance, moderate infiltration, and low infiltration. Patients with low immune infiltration exhibited the poorest survival rates, while those in the moderate infiltration group showed better outcomes than those with high T cell abundance. Moreover, the high cell abundance group was associated with a greater tumor burden and higher rates of TP53 mutations, whereas the moderate infiltration group was characterized by a lower tumor burden and elevated PIK3CA mutations. Analysis of an independent single-cell RNA-seq breast cancer dataset confirmed the presence of similar infiltration patterns. Further investigation into ligand-receptor interactions within the TME unveiled significant variations in cell-cell communication patterns among these groups. Notably, we found that the signaling pathways SPP1 and EGF were exclusively active in the low immune infiltration group, suggesting their involvement in immune suppression. This work comprehensively characterizes the composition and dynamic interplay in the breast cancer TME. Our findings reveal associations between the extent of immune infiltration and clinical outcomes, providing valuable prognostic information for patient stratification. The unique mutations and signaling pathways associated with different patient groups offer insights into the mechanisms underlying diverse tumor immune infiltration and the formation of an immunosuppressive tumor microenvironment.

CellsVol. 13(18)
University of Arkansas at Little Rock (US), University of Arkansas for Medical Sciences (US)
National Science Foundation, Arkansas Research Alliance, National Institutes of Health
No poverty
Openalex Percentile: Top 8%
Cancer Immunotherapy and Biomarkers
2.83
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.