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Molecular and Spatial Profiling of Prostate Tumors

Time: Fri 2020-06-05 10.00

Location:, (English)

Subject area: Biotechnology

Doctoral student: Emelie Berglund , Genteknologi, Genteknologi

Opponent: Assoc. Prof. Päivi Östling, Karolinska Institutet

Supervisor: Professor Joakim Lundeberg, Genteknologi, Science for Life Laboratory, SciLifeLab, Genteknologi

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Every cancer tumor is unique, with characteristics that change over time. The evolution of a full-blown malignancy from a single cell that gives rise to a heterogeneous population of cancer cells is a complex process. The use of spatial information makes a big contribution to understanding the progression of tumors and how patients respond to treatment. Currently, the scientific community is taking a step further in order to understand gene expression heterogeneity in the context of tissue spatial organization to shed light on cell- to-cell interactions. Technological advances in recent years have increased the resolution at which heterogeneity can be observed. Spatial transcriptomics (ST) is an in situ capturing technique that uses a glass slide containing oligonucleotides to capture mRNAs while maintaining the spatial information of histological tissue sections. It combines histology and Illumina sequencing to detect and visualize the whole transcriptome information of tissue sections. In Paper I, an AI method was developed to create a computerized tissue anatomy. The rich source of information enables the AI method to identify genetic patterns that cannot be seen by the naked eye. This study also provided insights into gene expression in the environment surrounding the tumor, the tumor microenvironment, which interacts with tumor cells for cancer growth and progression. In Paper II, we investigate the cellular response of treatment. It is well known that virtually all patients with hormone naïve prostate cancer treated with GnRH agonists will relapse over time and that the cancer will transform into a castration-resistant form denoted castration-resistant prostate cancer. This study shows that by characterizing the non-responding cell populations, it may be possible to find an alternative way to target them in the early stages and thereby decrease the risk of relapse. In Paper III, we deal with scalability limitations, which in the ST method are represented by time- consuming workflow in the library preparation. This study introduces an automated library preparation protocol on the Agilent Bravo Automated Liquid Handling Platform to enable rapid and robust preparation of ST libraries. Finally, Paper IV expands on the first work and illustrates the utility of the ST technology by constructing, for the first time, a molecular view of a cross-section of a prostate organ.