Immune Response in Triple-Negative Breast Cancer - Machine Learning-based Insights from Histology and -Omics

Breast cancer is the most common type of cancer in women worldwide. Triple-negative breast cancer (TNBC) is a subtype of breast cancer that lacks the expression of the oestrogen receptor, progesterone receptor, and amplification of HER2. This subtype is the most aggressive subtype and a heterogeneous subgroup of breast cancer, making up around 10-15% of the cases, often affecting younger patients and presenting with a higher risk of relapse. One factor that seems to positively impact patient outcomes in this heterogeneous subtype is the presence of an active immune response. This thesis focuses on improving the understanding of immune infiltration in TNBC and deriving approaches for better prognostication and treatment prediction. For this, we developed an automated image analysis pipeline and applied it to (among others) six immunohistochemical markers of immune cells. Digital cell counts extracted from five tissue microarray blocks showed how immune cells are often co-expressed in the tumour micro-environment. Furthermore, we demonstrate how a combination of immune status with DNA repair deficiency status can improve prognostication in (chemotherapy-treated) patients. In addition, we created a stand-alone classifier for gene expression data, based on the immunomodulatory subtype, that reflects immune response in TNBC. Our classifier was borderline non-significant in the stratification of neoadjuvant- treated chemotherapy patients and could stratify adjuvant chemotherapy-treated patients into subgroups of better or worse prognosis. Lastly, we studied the spatial heterogeneity of the tumour immune microenvironment in TNBC and its connection to molecular and genomic subtypes. We connected ecosystems in the tumour micro-environment with patterns of immune infiltration and could show TNBC specific differences. In conclusion, this thesis advances the quantitative and integrative study of the tumour (immune) micro-environment in TNBC, offering new approaches and insights to bridge the gap between immune infiltration, molecular heterogeneity, and clinical outcome.

Publication Details

Journal
Lund University Publications (Lund University)
Published
2025-01-01
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Immune Response in Triple-Negative Breast Cancer - Machine Learning-based Insights from Histology and -Omics

Lund University Publications (Lund University)
Cancer Immunotherapy and Biomarkers
article

Immune Response in Triple-Negative Breast Cancer - Machine Learning-based Insights from Histology and -Omics

article en

Abstract

Breast cancer is the most common type of cancer in women worldwide. Triple-negative breast cancer (TNBC) is a subtype of breast cancer that lacks the expression of the oestrogen receptor, progesterone receptor, and amplification of HER2. This subtype is the most aggressive subtype and a heterogeneous subgroup of breast cancer, making up around 10-15% of the cases, often affecting younger patients and presenting with a higher risk of relapse. One factor that seems to positively impact patient outcomes in this heterogeneous subtype is the presence of an active immune response. This thesis focuses on improving the understanding of immune infiltration in TNBC and deriving approaches for better prognostication and treatment prediction. For this, we developed an automated image analysis pipeline and applied it to (among others) six immunohistochemical markers of immune cells. Digital cell counts extracted from five tissue microarray blocks showed how immune cells are often co-expressed in the tumour micro-environment. Furthermore, we demonstrate how a combination of immune status with DNA repair deficiency status can improve prognostication in (chemotherapy-treated) patients. In addition, we created a stand-alone classifier for gene expression data, based on the immunomodulatory subtype, that reflects immune response in TNBC. Our classifier was borderline non-significant in the stratification of neoadjuvant- treated chemotherapy patients and could stratify adjuvant chemotherapy-treated patients into subgroups of better or worse prognosis. Lastly, we studied the spatial heterogeneity of the tumour immune microenvironment in TNBC and its connection to molecular and genomic subtypes. We connected ecosystems in the tumour micro-environment with patterns of immune infiltration and could show TNBC specific differences. In conclusion, this thesis advances the quantitative and integrative study of the tumour (immune) micro-environment in TNBC, offering new approaches and insights to bridge the gap between immune infiltration, molecular heterogeneity, and clinical outcome.

Lund University Publications (Lund University)
Good health and well-being
Openalex Percentile: Top 38%
Cancer Immunotherapy and Biomarkers
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.

Immune Response in Triple-Negative Breast Cancer - Machine Learning-based Insights from Histology and -Omics · Lund University Publications (Lund University) (2025) | TGRS Research Map | TGRS