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Data-driven spatio-temporal analysis of prostate tumours in situ suggests the pre-existence of ADT-resistant expression clones
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
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Abstract [en]

The molecular heterogeneity and development of castration-resistant prostate cancer (CRPC) in advanced metastatic prostate cancer is still unresolved, which is the reason why the majority of affected men reach a lethal stage within a few years. In this study, we challenge the conventional notion that castration-resistant prostate cancer cells evolve during treatment. We use Spatial Transcriptomics to analyse the tumour heterogeneity of core needle biopsies pre- and post-treatment to enhance our understanding of the underlying processes of resistance. Hereby we are able to link morphological information (and clinical information such as Gleason scores) to transcriptome-wide analysis in situ. Our data-driven analysis of transcriptomes in situ identifies multiple distinct cell populations based on unique expression signatures within the same prostate both pre- and post-treatment. Strikingly, we found that cells with gene expression profiles matching resistant tumour cell clusters exist already before treatment. Such resistant clusters still express the androgen receptor (AR) in nuclei of their cancer cells after treatment. We also demonstrate that the adjacent stromal cells in resistant tumor clusters do not express AR before treatment, which can explain the poor prognosis seen in this type of tumour.

Analyses of expression profiles and pathway annotation suggest that non-responsiveness is associated with an epithelial-to-mesenchymal transition, leading to migration and metastasis. We also present a molecular sub-categorisation of the same Gleason Score based on the ST analysis. In addition, we show that the stromal component can be convoluted into several subcategories that provide additional layers to the interpretation of tumour aggressiveness. This demonstrates that connecting histology to spatially resolved gene expression patterns in situ enhances our ability to predict resistance and promises to improve personalised medicine.

National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry towards Bioinformatics
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URN: urn:nbn:se:su:diva-177920OAI: oai:DiVA.org:su-177920DiVA, id: diva2:1384890
Available from: 2020-01-12 Created: 2020-01-12 Last updated: 2020-01-24Bibliographically approved
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Friedrich, StefanieSonnhammer, Erik
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CiteExportLink to record
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Citation style
  • apa
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