Immune infiltration and activation in breast tumors

Open Access
Authors
  • I. Nederlof
Supervisors
  • M.J. van de Vijver
Cosupervisors
  • M. Kok
  • H.M. Horlings
Award date 11-06-2025
Number of pages 321
Organisations
  • Faculty of Medicine (AMC-UvA)
Abstract
In this thesis, we explore the antitumor response of immune cells against breast tumors and investigate how these findings can be clinically applied to improve patient treatment. Tumor cells are continuously in contact with other cells in their environment and can exhibit characteristics that the immune system perceives as foreign. This enables immune cells to infiltrate the microenvironment, recognize the tumor cells, initiate an immune response, and facilitate the clearance of the tumor cells. However, tumor cells can also manipulate their surrounding microenvironment in a way that inhibits the activity of immune cells, thereby preventing an immune response. With the advent of immune checkpoint inhibition therapy, it has become possible to activate immune cells that are suppressed by the tumor, thereby enabling the clearance of the tumor.
Adding immune checkpoint inhibitors to chemotherapy has markedly increased response rates for patients with primary triple-negative breast cancer. To better tailor treatment options to individual patients and more accurately predict disease progression, it is necessary to gain more knowledge about the complex properties of the tumor microenvironment.
This thesis first compares methods for investigating and profiling the tumor microenvironment in patients with early-stage breast cancer. We examine the comparability of different techniques and employ methods in clinical cohort studies that go beyond merely quantifying tumor infiltrating lymphocytes. Next, we examine various determinants of the tumor microenvironment and explore how these characteristics can be used to improve immunotherapy for patients with triple-negative breast cancer (BELLINI trial, Nederlof et al. and TONIC trial Voorwerk et al.) and whether elements of the microenvironment can be used to predict clinical response.
Document type PhD thesis
Language English
Downloads
Thesis (complete) (Embargo up to 2027-06-11)
Chapter 5: Deep-learning spatial and phenotypical analysis of sTILs in young breast cancer patients (Embargo up to 2026-06-11)
Chapter 6: Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition (Embargo up to 2027-06-11)
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