Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer

Cancers

Anna Tanoglidi¹  Kimmo Kartasalo²  Tuomas Mirtti³  Alastair D. Lamb⁴  Andrew Erickson⁴  Richard Colling⁴  Maja Marklund⁵  Joakim Lundeberg⁵  Eduard Chelebian⁶  Christophe Avenel⁶  Carolina Wählby⁶ 

[1] Department of Clinical Pathology, Uppsala University Hospital, 752 37 Uppsala, Sweden;Department of Medical Epidemiology and Biostatistics, Karolinska Institute, 171 77 Stockholm, Sweden;Department of Pathology, Research Program in Systems Oncology, University of Helsinki, Helsinki University Hospital, 00100 Helsinki, Finland;Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 7DQ, UK;Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, 171 65 Solna, Sweden;Science for Life Laboratory, Department of Information Technology, Uppsala University, 752 37 Uppsala, Sweden;

关键词: prostate cancer;    morphological features;    spatial transcriptomics;    deep learning;   

DOI  :  10.3390/cancers13194837

来源: DOAJ

【 摘 要 】

Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H&E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H&E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out.

【 授权许可】

Unknown