Spatial Profiling of Tumor Microenvironments in Breast Cancer

Master Thesis (2023)
Author(s)

N. Brouwer (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

L.F.A. Wessels – Mentor (TU Delft - Pattern Recognition and Bioinformatics)

Daniël J. Vis – Mentor (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)

C.C.S. Liem – Graduation committee member (TU Delft - Multimedia Computing)

Joana Gonçalves – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)

Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
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Publication Year
2023
Language
English
Graduation Date
04-09-2023
Awarding Institution
Delft University of Technology
Programme
['Computer Science | Bioinformatics']
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

The tumor composition of breast cancer determines how tumors behave. Yet, there is a limited understanding of the arrangement of tumor cells in relation to cells in the tumor microenvironment (TME). In this research, we have characterized distance relationships between 324 cell-type pairs in 749 tissue samples of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) study using Weibull distribution estimations, summarizing comprehensive spatial relationships with two parameters. The research showcased the first application of the method to a dataset of this substantial size and a dataset acquired with imaging mass cytometry. We identified distinct spatial relationships among breast cancer subtypes, particularly for basal, HER2-enriched, and luminal A tumors. The spatial relationships indicate attractive and repulsive interactions between different cell types and define cellular arrangements regardless of cellular abundance.
Moreover, several spatial relationships had significant associations with survival outcomes. Both findings could improve patient stratification and prognosis and emphasize the wealth of information that spatial analyses can retrieve. The results also confirm that Weibull distribution estimations are a suitable and effective method to summarize distance distributions. The application to other cohorts could lead to new insights into the tumor composition of different cancer types. Finally, the spatial profiling method was used to characterize neighborhoods and revealed distinct spatial relationships consistent with neighborhood characteristics but also provided new hallmarks.

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