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

• Published in: British journal of cancer Vol. 119; no. 12; pp. 1527 - 1537
• Main Authors: Schmidt, Linnéa, Møller, Mia, Haldrup, Christa, Strand, Siri H, Vang, Søren, Hedegaard, Jakob, Høyer, Søren, Borre, Michael, Ørntoft, Torben, Sørensen, Karina Dalsgaard
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 01.12.2018; Nature Publishing Group UK
• Subjects: Aged; Cancer surgery; Disease Progression; Gene expression; Humans; Lymph; Lymph nodes; Lymphatic Metastasis; Lymphatic system; Male; Metastases; Metastasis; Middle Aged; Prognosis; Prostate; Prostate cancer; Prostatectomy; Prostatic Neoplasms - genetics; Prostatic Neoplasms - mortality; Prostatic Neoplasms - pathology; Ribonucleic acid; RNA; Sequence Analysis, RNA; Transcription; Transcriptome
• ISSN: 0007-0920; 1532-1827
• DOI: 10.1038/s41416-018-0321-5

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. 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). 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. 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.