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. 2018 Dec;119(12):1527-1537.
doi: 10.1038/s41416-018-0321-5. Epub 2018 Nov 19.

Exploring the transcriptome of hormone-naive multifocal prostate cancer and matched lymph node metastases

Linnéa Schmidt  1 , Mia Møller  1 , Christa Haldrup  1 , Siri H Strand  1 , Søren Vang  1 , Jakob Hedegaard  1 , Søren Høyer  2 , Michael Borre  3 , Torben Ørntoft  1 , Karina Dalsgaard Sørensen  4
Affiliations

Exploring the transcriptome of hormone-naive multifocal prostate cancer and matched lymph node metastases

Linnéa Schmidt et al. Br J Cancer. 2018 Dec.

Abstract

Background: The current inability to predict whether a primary prostate cancer (PC) will progress to metastatic disease leads to overtreatment of indolent PCs as well as undertreatment of aggressive PCs. Here, we explored the transcriptional changes associated with metastatic progression of multifocal hormone-naive PC.

Methods: Using total RNA-sequencing, we analysed laser micro-dissected primary PC foci (n = 23), adjacent normal prostate tissue samples (n = 23) and lymph node metastases (n = 9) from ten hormone-naive PC patients. Genes important for PC progression were identified using differential gene expression and clustering analysis. From these, two multi-gene-based expression signatures (models) were developed, and their prognostic potential was evaluated using Cox-regression and Kaplan-Meier analyses in three independent radical prostatectomy (RP) cohorts (>650 patients).

Results: We identified several novel PC-associated transcripts deregulated during PC progression, and these transcripts were used to develop two novel gene-expression-based prognostic models. The models showed independent prognostic potential in three RP cohorts (n = 405, n = 107 and n = 91), using biochemical recurrence after RP as the primary clinical endpoint.

Conclusions: We identified several transcripts deregulated during PC progression and developed two new prognostic models for PC risk stratification, each of which showed independent prognostic value beyond routine clinicopathological factors in three independent RP cohorts.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Workflow describing sample preparation. a An illustration of HE stained sections of prostate and lymph node FFPE tissue from ten radical prostatectomy patients (PT1-PT10). Firstly, FFPE tissue blocks were sectioned for HE staining and for laser microdissection. Secondly, marked tissue areas (colored regions in figure) were used for laser microdissection, RNA extraction, and RNA-sequencing. DAN distant adjacent normal, PAN proximal adjacent normal, CAN primary tumor, MET lymph node metastasis, LYMPH non-malignant lymph node. NA not available. b Heatmap based on RNA expression for all samples. Hierarchical clustering of patient samples, using the 500 most variable transcripts, revealed a distinct clustering pattern where samples types clustered closer together than intrapatient samples. Rows correspond to patient samples (patient numbers and sample types; CAN, DAN, PAN, MET and LYMPH are illustrated by the colored bars above the heatmap). Columns correspond to the 500 transcripts with the largest variation between samples. c Progression scores in five external PC patient cohorts (Methods). The sum of the expression values of the genes included were validated to be lower in the more aggressive tissue types in Strand (6 AN and 14 CAN), Haldrup (13 AN and 29 CAN), TCGA (52 AN and 499 CAN), Taylor (29 AN and 150 CAN) and in Grasso (28 AN, 59 CAN, and 35 MET), as assessed by two-sided t-tests (overlap of the 19 genes in each cohort can be found in Methods)
Fig. 2
Fig. 2
Kaplan–Meier analysis of the progression model in the TCGA, Taylor and Long cohorts. Kaplan–Meier analysis of the progression model in TCGA (top), Taylor (middle), and Long (bottom) cohorts
Fig. 3
Fig. 3
Identification of seeding foci. a Using clustering analysis, the most likely seeding focus was identified in two of the four patients (patient 7 and 5) with a minimum of two CAN samples and one matched MET sample. b Kaplan–Meier analysis of the seeding model in the TCGA, Taylor, and Long cohorts
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