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

Roostee, Suze Julia LU orcid (2025) In Lund University, Faculty of Medicine Doctoral Dissertation Series
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Dr. Ali, Raza, Cambridge University
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Triple-negative breast cancer (TNBC), Immune, Tumour micro-environment (TME), Machine Learning (ML)
in
Lund University, Faculty of Medicine Doctoral Dissertation Series
issue
2025:18
pages
105 pages
publisher
Lund University, Faculty of Medicine
defense location
Belfragesalen, BMC D15, Klinikgatan 32 i Lund. Join by Zoom: https://lu-se.zoom.us/j/62674981170?pwd=4avEmvQ3pE2BCe9bxVQkIdy6kS6ENN.1
defense date
2025-02-25 09:00:00
ISSN
1652-8220
ISBN
978-91-8021-671-5
language
English
LU publication?
yes
id
8b2c1a23-a5f4-4236-b086-6cc741657d04
date added to LUP
2025-01-27 13:28:01
date last changed
2025-02-05 09:29:44
@phdthesis{8b2c1a23-a5f4-4236-b086-6cc741657d04,
  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.}},
  author       = {{Roostee, Suze Julia}},
  isbn         = {{978-91-8021-671-5}},
  issn         = {{1652-8220}},
  keywords     = {{Triple-negative breast cancer (TNBC); Immune; Tumour micro-environment (TME); Machine Learning (ML)}},
  language     = {{eng}},
  number       = {{2025:18}},
  publisher    = {{Lund University, Faculty of Medicine}},
  school       = {{Lund University}},
  series       = {{Lund University, Faculty of Medicine Doctoral Dissertation Series}},
  title        = {{Immune Response in Triple-Negative Breast Cancer - Machine Learning-based Insights from Histology and -Omics}},
  url          = {{https://lup.lub.lu.se/search/files/207116176/Thesis_Suze_R_LUCRIS.pdf}},
  year         = {{2025}},
}