Abstract
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 microenvironment. 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.
Highlights
Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today
We investigate for the first time a multifocal Prostate cancer (PCa) simultaneously at tissue- and transcriptome-wide scale using the recently introduced Spatial Transcriptomics (ST) method[23], which allows for quantification of the mRNA population in the spatial context of intact tissue
Others overlap with regions annotated as cancerous or prostatic intraepithelial neoplasia (PIN) (Fig. 2a, b)
Summary
Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. While genetic changes are important to track cancer heterogeneity and clonal evolution they may be of clinical relevance This is exemplified by the high proportion of castration-resistant PCa cases being DNA repair deficient[10]. Pathological severity of prostate adenocarcinoma, despite progress with molecular markers and MRI, is generally scored according to the Gleason grading (Gs) system, which uses histological data only, frequently complemented with PSA measurement in blood and tumor staging[21]. This classification method has limitations and new alternatives have been proposed[22]. We use a novel computational procedure to elicit spatial, transcriptome-wide expression patterns enabling deconvolution of molecular events in cancer and associated microenvironment
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