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Molecular and Spatial Profiling of Prostate Tumors
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. (Genteknologi)ORCID iD: 0000-0003-1857-307X
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Abstract [sv]

Varje cancertumör är unik med egenskaper som förändras över tid. Utvecklingen av en fullständig malignitet från en enda cell som ger upphov till en heterogen population av cancerceller är en komplex process. Att få tillgång till spatial information är viktigt för att förstå utvecklingen av tumörer och hur patienter svarar på behandling. För närvarande har forskare världen över tagit ett steg längre för att förstå gener och heterogenitet genom att titta på alla komponenter inom en vävnad för att belysa interaktion mellan celler. Under de senaste åren har tekniska framsteg ökat upplösningen vid vilken heterogenitet kan observeras. Spatial transcriptomics (ST) är en in situ-teknik, som använder sig av en array av glas. Den innehåller oligonukleotider för att fånga mRNA, samtidigt som den spatiella informationen om histologin bibehålls. Kombinationen av histologi och Illumina-sekvensering gör att man kan visualisera hela transkriptomet inom ett vävnadssnitt. I den första studien utvecklades en AI-metod för att skapa en datoriserad vävnads anatomi. Den rika informationskällan gör det möjligt för AI-metoden att identifiera genetiska mönster som inte kan ses med blotta ögat. Denna studie gav också insikter om genuttryck i miljön omkring tumören; tumörens mikromiljö, som interagerar med tumörceller för att cancern ska växa och sprida sig till andra organ. I papper II undersöker vi hur patienter svarar på behandlingen. Det är välkänt att praktiskt taget alla patienter med avancerad prostatacancer som behandlas med GnRH-agonist kommer över tid att få återfall. Denna studie visar; genom att karakterisera de icke-svarande celler så kan det vara möjligt att hitta ett alternativt sätt att behandla tidigt och därmed minska risken för återfall. I papper III vill vi förbättra de tidigare tidskrävande delarna i ST protokollet som krävs för att förbereda proverna för sekvensering. Denna studie introducerar ett automatiserat protokoll för på den så kallade “Agilent Bravo Automated Liquid Handling Platform” och möjliggör snabb och robust preparering av ST- bibliotek. Papper IV bygger vidare på det första arbetet och illustrerar ST- teknikens användbarhet genom att för första gången konstruera en atlas av ett tvärsnitt från ett helt organ (prostata).

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2020. , p. 56
Series
TRITA-CBH-FOU ; 2020:16
National Category
Engineering and Technology
Research subject
Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-273575ISBN: 978-91-7873-488-7 (print)OAI: oai:DiVA.org:kth-273575DiVA, id: diva2:1431124
Public defence
2020-06-05, https://kth-se.zoom.us/s/68861340458, 10:00 (English)
Opponent
Supervisors
Funder
AstraZenecaSwedish Cancer Society
Note

QC 2020-05-19

Available from: 2020-05-19 Created: 2020-05-19 Last updated: 2020-05-19Bibliographically approved
List of papers
1. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity
Open this publication in new window or tab >>Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity
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2018 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 9, no 1Article in journal (Refereed) Published
Abstract [en]

Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal prostate cancer using the spatial transcriptomics (ST) technology. Utilizing a novel approach for deconvolution, we analyze the transcriptomes of nearly 6750 tissue regions and extract distinct expression profiles for the different tissue components, such as stroma, normal and PIN glands, immune cells and cancer. We distinguish healthy and diseased areas and thereby provide insight into gene expression changes during the progression of prostate cancer. Compared to pathologist annotations, we delineate the extent of cancer foci more accurately, interestingly without link to histological changes. We identify gene expression gradients in stroma adjacent to tumor regions that allow for re-stratification of the tumor micro- environment. The establishment of these profiles is the first step towards an unbiased view of prostate cancer and can serve as a dictionary for future studies.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-273011 (URN)10.1038/s41467-018-04724-5 (DOI)000435650800010 ()29925878 (PubMedID)2-s2.0-85048864922 (Scopus ID)
Available from: 2020-05-05 Created: 2020-05-05 Last updated: 2020-05-19Bibliographically approved
2. Spatio-temporal analysis of prostate tumors in situ suggests the pre-existence of ADT-resistance
Open this publication in new window or tab >>Spatio-temporal analysis of prostate tumors in situ suggests the pre-existence of ADT-resistance
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

The molecular mechanisms by which potentially lethal castration-resistant prostate cancer emerge in advanced metastatic prostate cancer are still poorly understood. Intratumor heterogeneity is believed to contribute to the fact that a majority of affected men succumb to the disease within a few years. In this study, we will challenge the conventional notion that castration-resistant prostate cancer cells evolve as a consequence of treatment. To examine the temporal aspects of resistance, we analyze tumor heterogeneity in core needle biopsies collected pre-and post-treatment. By doing so, we are able to couple clinical responsiveness and morphological information such as Gleason score to transcriptome- wide data. Our data-driven analysis of transcriptomes identified several distinct intratumoral cell populations, characterized by their unique gene expression profiles. Strikingly, certain minor cell populations present before treatment exhibited gene expression profiles that matched those of resistant tumor cell clusters. Such resistant clusters were confirmed by the localization of the androgen receptor to the nuclei in cancer cells present after treatment. Our data also demonstrate that stromal cells adjacent to resistant tumor factors do not express AR before treatment (or after), which can be used to increase the power in predicting resistant tumors.

National Category
Medical and Health Sciences
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-273013 (URN)
Note

QC 20200511

Available from: 2020-05-05 Created: 2020-05-05 Last updated: 2020-05-19Bibliographically approved
3. Automation of Spatial Transcriptomics library preparation to enable rapid and robust insights into spatial organization of tissues
Open this publication in new window or tab >>Automation of Spatial Transcriptomics library preparation to enable rapid and robust insights into spatial organization of tissues
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2020 (English)In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 21, no 298Article in journal (Refereed) Published
Abstract [en]

Background: Interest in studying the spatial distribution of gene expression in tissues is rapidly increasing. Spatial Transcriptomics is a novel sequencing-based technology that generates high-throughput information on the distribution, heterogeneity and co-expression of cells in tissues. Unfortunately, manual preparation of high-quality sequencing libraries is time-consuming and subject to technical variability due to human error during manual pipetting, which results in sample swapping and the accidental introduction of batch effects. All these factors complicate the production and interpretation of biological datasets.

Results: We have integrated an Agilent Bravo Automated Liquid Handling Platform into the Spatial Transcriptomics workflow. Compared to the previously reported Magnatrix 8000+ automated protocol, this approach increases the number of samples processed per run, reduces sample preparation time by 35%, and minimizes batch effects between samples. The new approach is also shown to be highly accurate and almost completely free from technical variability between prepared samples.

Conclusions: The new automated Spatial Transcriptomics protocol using the Agilent Bravo Automated Liquid Handling Platform rapidly generates high-quality Spatial Transcriptomics libraries. Given the wide use of the Agilent Bravo Automated Liquid Handling Platform in research laboratories and facilities, this will allow many researchers to quickly create robust Spatial Transcriptomics libraries.

National Category
Engineering and Technology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-273016 (URN)10.1186/s12864-020-6631-z (DOI)000529208400002 ()32293264 (PubMedID)2-s2.0-85083405329 (Scopus ID)
Note

QC 20200512

Available from: 2020-05-05 Created: 2020-05-05 Last updated: 2020-05-25Bibliographically approved
4. A Transcriptome Atlas of a Cross Section of a Multifocal Prostate Cancer
Open this publication in new window or tab >>A Transcriptome Atlas of a Cross Section of a Multifocal Prostate Cancer
Show others...
(English)Manuscript (preprint) (Other academic)
National Category
Engineering and Technology
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-273014 (URN)
Note

Understanding the heterogeneous molecular landscape of prostate cancer is fundamental to improve treatment of the disease. Here, we aim to provide a broad molecular view of a cross- section of a prostate organ. This study manifests several unique tumor gene expression subtypes, with adjacent stroma, occupying distinct and different anatomical regions. The interplay between multiple molecularly defined tumor gene expression factors and/or Gleason scoring and the corresponding tumor microenvironment was hereby studied in detail. We perform a molecular sub-categorization of the tumor microenvironment throughout the whole prostate, using three different molecular principles; (i) AR staining, since loss of stromal AR is directly proportional to the degree of differentiation (Gleason score), (ii) Masson staining for its role in marking reactive stroma, and (iii) spatial transcriptomics analysis. In particular, we performed a detailed analysis of spatial distribution in histologic sections at the invasive border of a tumor foci contrasting it to the tumor core. Here, we show spatially confined DEPDC1, CHN1 and CRISP3 upregulation at the invasive border with implications of signal transduction pathways such as; PTEN, TGF-beta Receptor Complex, NOTCH4, ERBB2, and VEGF signaling, some of which are druggable. Overall, our results show an unprecedented view of the molecular heterogeneity of a prostate cancer not evident by other means. Here, we reveal patient-specific gene expression hallmarks from low to aggressive cancer within the same cross section of a prostate.

Available from: 2020-05-05 Created: 2020-05-05 Last updated: 2020-05-19Bibliographically approved

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