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. 2023 Oct 3;22(1):162.
doi: 10.1186/s12943-023-01863-2.

Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of tissue heterogeneity on genomic signatures

Sandy Figiel  1 , Wencheng Yin  1 , Dimitrios Doultsinos  1 , Andrew Erickson  1 , Ninu Poulose  1 , Reema Singh  1 , Anette Magnussen  1 , Thineskrishna Anbarasan  1   2 , Renuka Teague  1   3 , Mengxiao He  4 , Joakim Lundeberg  4 , Massimo Loda  1   5 , Clare Verrill  1   3   6 , Richard Colling  1   3 , Pelvender S Gill  1   3 , Richard J Bryant  1   2   6 , Freddie C Hamdy  1   2   6 , Dan J Woodcock  1 , Ian G Mills  1 , Olivier Cussenot  1 , Alastair D Lamb  7   8   9
Affiliations

Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of tissue heterogeneity on genomic signatures

Sandy Figiel et al. Mol Cancer. .

Abstract

Genetic signatures have added a molecular dimension to prognostics and therapeutic decision-making. However, tumour heterogeneity in prostate cancer and current sampling methods could confound accurate assessment. Based on previously published spatial transcriptomic data from multifocal prostate cancer, we created virtual biopsy models that mimic conventional biopsy placement and core size. We then analysed the gene expression of different prognostic signatures (OncotypeDx®, Decipher®, Prostadiag®) using a step-wise approach with increasing resolution from pseudo-bulk analysis of the whole biopsy, to differentiation by tissue subtype (benign, stroma, tumour), followed by distinct tumour grade and finally clonal resolution. The gene expression profile of virtual tumour biopsies revealed clear differences between grade groups and tumour clones, compared to a benign control, which were not reflected in bulk analyses. This suggests that bulk analyses of whole biopsies or tumour-only areas, as used in clinical practice, may provide an inaccurate assessment of gene profiles. The type of tissue, the grade of the tumour and the clonal composition all influence the gene expression in a biopsy. Clinical decision making based on biopsy genomics should be made with caution while we await more precise targeting and cost-effective spatial analyses.

Keywords: Prognostic genetic signatures; Prostate cancer; Spatial transcriptomics; Virtual biopsy.

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Conflict of interest statement

MH and JL are scientific consultants to 10x Genomics, Inc.

Figures

Fig. 1
Fig. 1
Spatial visualisation of virtual biopsies. We used our recently published organ-wide spatial transcriptomic data [9] to construct virtual biopsy models (2 tumour biopsies and 1 benign biopsy) that mimic conventional biopsy placement and core size. Visualisation of histology and tissue status (GG: Gleason grade group; PIN: prostatic intraepithelial neoplasia) and tumour clones from each tumour biopsy. Spatial visualisation of gene expression (ANPEP, ANO7, TPM2 and REPS2) in each tumour biopsy. Violin plots representing gene expression according to histological status. TRUS = transrectal ultrasound guided prostate biospy. LATP = local anaesthetic transperineal prostate biospy. ST = spatial transcriptomics. GG = Gleason grade group. PIN = prostatic intra-epithelial neoplasia. 18G = 18 gauge core biopsy needle
Fig. 2
Fig. 2
Gene expression profile of the OncotypeDx® signature. Heatmap of gene expression (logFC) of the OncotypeDx® signature at different levels of precision (whole biopsy, tissue subtype, tumour grade and clonal level) in tumour biopsy 1 (A), tumour biopsy 2 (B) and section H2_1 (C). The histograms represent the number of spatial transcriptomic spots for each entity. False discovery rate (FDR) is indicated: *FDR < 0.01; •FDR < 0.05, °FDR < 0.1. FC: fold change; BB: benign biopsy; B1: tumour biopsy 1; B2: tumour biopsy 2; GG: Gleason grade group
See this image and copyright information in PMC

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