A Transcriptome Atlas of a Cross Section of a Multifocal Prostate Cancer

A Transcriptome Atlas of a Cross Section of a Multifocal Prostate Cancer Emelie Berglund1,§, Niklas Schultz2,§, Maja Marklund1,¤, Ludvig Bergensthråhle1,¤, Joseph Bergenstråhle1, Reza Mirzazadeh1, Ludvig Larsson1, Firaz Tarish2, Anna Tanoglidi3, Jonas Maaskola1, Thomas Helleday2 and Joakim Lundeberg1,* 1Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology (KTH), Science for Life Laboratory, Solna, Sweden. 2 Division of Translational Medicine & Chemical Biology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden. 3 Department of Clinical Pathology, University Uppsala Hospital, Uppsala, Sweden. §These authors contributed equally to this work. ¤These authors contributed equally to this work. *Correspondence should be addressed to J.L. ([email protected]) SUMMARY 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. INTRODUCTION Prostate cancer (PCa) is the most commonly diagnosed non-skin cancer (Grönberg 2003, Siegel et al. 2019). If the tumor is organ-confined at the time of diagnosis, prognosis is often favorable (Chi et al., 2009). The serum biomarker, Prostate Specific Antigen (PSA), is often the first and only sign that cancer is present. The current approach for validation and detection of PCa in situ involves sampling 12 needle biopsies using transrectal ultrasound (TRUS) guidance. Clinical risk assessment is primarily determined by pathologic grade based on the Gleason histological grading system, initially proposed in the 1960s and modified multiple times thereafter. The grade as reported by the Gleason system is based on the combination of the most dominant and second most dominant histological patterns observed (Gleason et al., 1966, Gordetsky and Epstein, 2016). Since 2014, a new contemporary PCa grading system stratifies PCa into five prognostic Grade Groups based on the modified Gleason score groups: Grade Group 1 = Gleason score ≤6, Grade Group 2 = Gleason score 3 + 4 = 7, Grade Group 3 = Gleason score 4 + 3 = 7, Grade Group 4 = Gleason score 8, Grade Group 5 = Gleason scores 9 and 10 (Gordetsky and Epstein, 2016). Still, the Gleason scoring system is known to suffer from high variability across pathologists, and even within the same pathologist (Egevad et al., 2013). Moreover, it has been estimated that the Gleason grade assigned to TRUS biopsies differs compared to the whole radical prostatectomy specimen in around 50% of cases (Kvåle et al., 2009, Humphrey 2004). Although not fully understood, evidence shows that PCa exhibits intra-tumor heterogeneity (ITH) (VanderWeele et al., 2018, Berglund et al., 2018, Yadav et al., 2018), and it is not unusual to find three or four different grades present within the same prostate (multifocal) (Andreoiu et al., 2010, Wise et al., 2002). The prostate consists of four zones: anterior fibromuscular stroma, central zone (CZ), peripheral zone (PZ), and transition zone (TZ) (McNeal 1981). Studies have characterized the prevalence of cancer arising in these zones, and the majority of prostate cancers (70%–75%) originate from the PZ with the remaining 20%–30% originating from the TZ. Cancers in the CZ are uncommon (McNeal et al., 1988, Patel et al., 2011). Generally, the TZ is regarded as the site of benign prostatic hyperplasia (BPH), i.e. low Gleason grades. While PCa arising in the TZ are frequently found incidentally in TURP specimens, the detection of TZ and anterior stromal tumors pose a clinical challenge (McNeal et al., 1988, Noguchi et al., 2000, Augustin et al., 2003). Indeed, no 1 report has, to our knowledge, surveyed the gene expression differences from the PZ to CZ region in PCa to better define co-existing tumor entities. Most studies of the genome landscape of tumors focus on aberrations in epithelial cancer cells. However, emerging evidence puts emphasis on the crucial role of the tumor microenvironment (TME) and the immune system, which can both promote and suppress cancer progression (Martin et al., 2016, Hanahan and Weinberg 2011, Chen and Mellman, 2017). The current histological grading system of PCa does not capture relevant information from the TME. Ayala and coworkers (2003) were the first to develop a grading system for reactive stroma. They showed that higher levels of reactive stroma correlate with tumor grade and prognosis, which highlights an active role of tumor-stroma in PCa progression (Ayala et al., 2003). Several studies have subsequently confirmed the association of the amount of reactive stroma with PCa aggressiveness (Yanagisawa et al., 2007, McKenney et al., 2016). However, we lack a more comprehensive transcriptome analysis of the stroma and tumor interaction that would potentially advance our understanding of tumorigenesis in PCa. A molecular map of the prostate may also provide us a useful tool to complement existing tools to subcategorize the reactive stroma in the context of different tumor classifications. An increasing number of high-throughput RNA-seq based methods that assess molecular conditions with spatial resolution are emerging (Junker et al., 2014, Crosetto et al., 2015, Chen et al., 2015, Moffitt et al., 2016, Ståhl et al., 2016, Lein et al., 2017, Rodriques et al., 2019, Eng et al., 2019). Here, we analyze a cross section of a whole prostate using the Spatial Transcriptomics (ST) method. In this RNA sequencing based technique, fixed tissue sections are placed onto barcoded reverse transcription primers providing both a bright field image of the tissue as well a spatially resolved transcriptome. We carried out this study to capture gradual and spatial differences in gene expression between pathological and normal tissue compartments of a prostate. Altogether, this work represents a resource and foundation of knowledge to study PCa in the context of its tissue environment at organ-scale. 2 RESULTS Spatial Transcriptomics establishes a detailed atlas of cellular heterogeneity in the prostate cancer organ Intra-tumor heterogeneity (ITH) and its consequences over time challenges diagnosis and effective treatment of PCa (VanderWeele et al., 2018, Berglund et al., 2018, Yadav et al., 2018). Therefore, we aimed to present an initial effort to structure and molecularly define a cross section of a prostate organ containing cancer. We took advantage of recent advancements in the field of transcriptomics that are beginning to provide spatial resolution ranging from a few cells to subcellular resolution (Vickovic et al., 2019, Rodriques et al., 2019). First, we generated high- quality Spatial Transcriptomic libraries (Salmén et al., 2018) from barcoded regions (spots) to provide an overview of a cross section of the prostate and its multifocal tumors (Figure 1). The prostate was resected by open radical prostatectomy and horizontally cut into two pieces. The upper part was used in this study and subdivided, in two steps, into 21 cubes (Figure 1, Figure S1) and then snapfrozen. We cryosectioned 10 µm sections from the bottom of each cube (two sections per cube) for ST analysis. The sections (areas) were mounted onto spatially barcoded microarray slides. The sections were fixed, hematoxylin-eosin (HE) stained and imaged by bright field microscopy. Sections were later annotated by a trained pathologist (Figure S2). The annotations were used for comparison with results from data-driven ST gene expression analyses. The slides were then processed with the ST protocol (Berglund et al 2018) and sequencing data were analyzed using the ST pipeline (Navarro et al., 2017). Transcriptional profiling was acquired from more than 23 000 barcoded regions from the entire cross section of the prostate, and approximately 4000 UMIs and 2100 genes were identified on average per spot (Figure S3). In this study, we define low- grade cancers as Gleason score ≤ 7 and high-grade cancer as Gleason score ≥8. Next, we developed a method for analyzing the barcoded gene expression data using factorized negative binomial regression (Methods). Our method yields

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