Kaupallisten eturauhassyövän riskinarviointipaneelien transkriptioanalyysi vaikeasti ennustettavissa erilaistumisryhmien 2-4 eturauhassyövissä

Tausta: Eturauhassyövän erilaistumisryhmien (grade group, GG) 2-4 prognoosin arvioinnissa tarvitaan edistysaskeleita ylihoidon minimoimiseksi. Tavoitteemme oli selvittää lähetti-RNA-tasolla kaupallisten paneelien, Decipher, Oncotype DX, Prolaris, ja mutaatiopaneeli MSK-IMPACTin suorituskykyä ennustaa etäpesäkevapaata ja eturauhassyöpäspesifistä selviytymistä GG 2-4 eturauhassyövässä radikaalin eturauhasen poiston jälkeen. Materiaalit ja metodologia: Retrospektiivinen kohortti koostui radikaalilla eturauhasen poistolla hoidetuista GG 2-4 potilaista (10,4 vuoden mediaaniseuranta-aika). Analysoimme 76 tapausta, joilla oli leikkauksen jälkeinen etäpesäke tai eturauhassyöpäspesifinen kuolema, sekä 84 verrokkia, joilla oli samanveroinen etenemisriski lähtötilanteessa, mutta syöpä ei edennyt. Indeksileesioiden lähetti-RNAt analysoitiin käyttäen NanoString-alustaa. Koulutimme satunnaismetsämalleja (random forest) ennustamaan kliinisiä päätetapahtumia käyttäen paneelien geenikokoelmia. Mittasimme käyrän alla olevaa pinta-alaa (area under the curve, AUC), sensitiivisyyttä, spesifisyyttä, Youden indeksiä sekä NND-lukua (number needed to diagnose). Selviytymistodennäköisyyttä arvioitiin käyttämällä Kaplan-Meier estimaattoria. Tulokset: Geenikokoelmien suorituskyky oli parempi etäpesäkevapaan ja eturauhassyöpäspesifisen selviytymisen ennustamisessa verrattuna kliinisiin muuttujiin. Paneelien välillä oli kuitenkin merkitseviä eroja. Oncotype DX ennusti metastaaseja heikommin (AUC=0,65) kuin muut paneelit (AUC=0,73-0,74). Decipherin, MSK-IMPACTin ja Prolariksen NND-luvut eivät eronneet merkittävästi (2,83-3,12), kun taas Oncotype DXn NND-luku oli korkein (4,79). Prolaris ennusti eturauhassyöpäspesifistä kuolemaa heikommin (AUC=0,66) kuin MSK-IMPACT tai Decipher (AUC=0,72). Oncotype DXn suorituskyky ei eronnut muista paneeleista (AUC=0,69, p>0,05), mutta sen NND-luku (2,79) oli matalampi kuin muilla paneeleilla (4,22-5,66). Pohdinta: Kaupallisten riskiluokituspaneelien geenien transkriptianalyysi ennustaa GG 2-4 potilaiden selviytymistä radikaalin eturauhasen poiston jälkeen ja saattaa olla avuksi kliinisessä päätöksenteossa. Paneelien välillä oli merkitseviä eroja, mutta paremmin ennustaville geenikokoelmille on tarvetta. Prospektiivinen tutkimus biopsia-aineistossa on tarpeen.Background: Improved prognostication is needed to minimize overtreatment in Grade Group (GG) 2-4 prostate cancer. Our aim was to determine, at messenger RNA (mRNA) level, the performance of the genes in the commercial panels Decipher, Oncotype DX, Prolaris and mutational panel MSK-IMPACT to predict metastasis-free and prostate cancer-specific death (PCSD) in patients with GG2-4 prostate cancer at radical prostatectomy. Methods: The retrospective cohort consisted of GG2-4 patients treated with radical prostatectomy (median follow-up 10.4 years). Seventy-six cases with post-operative metastasis or PCSD and 84 controls with similar clinical baseline risk, but without progression, were analyzed. Index lesion mRNA transcripts were analyzed using NanoString technology. Random forest models were trained using panel gene sets to predict clinical endpoints and area under the curve (AUC), sensitivity, specificity, Youden index and number needed to diagnose (NND) was measured. Survival probability was assessed with Kaplan-Meier estimator. Results: All gene sets outperformed clinical parameters and predicted metastasis-free and prostate cancer-specific survival. However, there were significant differences between the panels. In metastasis prediction, the genes in Oncotype DX had inferior performance (area under the curve [AUC] = 0.65) compared to other panels (AUC = 0.73-0.74). Decipher, MSK-IMPACT and Prolaris showed similar NND (2.83-3.12) with Oncotype DX having highest NND (4.79). In PCSD prediction, the Prolaris gene set performed worse (AUC = 0.66) than MSK-IMPACT or Decipher (AUC = 0.72). Oncotype DX performed similarly to other panels (AUC=0.69, p > .05). Oncotype DX demonstrated lowest NND (2.79) compared to other panels (4.22-5.66). Conclusion: Transcript analysis of genes included in commercial panels is feasible in survival prediction of GG2-4 patients after radical prostatectomy and may aid in clinical decision making. There were significant differences between the panels, and overall stronger predictive gene sets are needed. Prospective investigation is warranted in biopsy materials

Associations of PTEN and ERG with Magnetic Resonance Imaging Visibility and Assessment of Non–organ-confined Pathology and Biochemical Recurrence After Radical Prostatectomy

Background: Diagnosing clinically significant prostate cancer (PCa) is challenging, but may be facilitated by biomarkers and multiparametric magnetic resonance imaging (MRI). Objective: To determine the association between biomarkers phosphatase and tensin homolog (PTEN) and ETS-related gene (ERG) with visible and invisible PCa lesions in MRI, and to predict biochemical recurrence (BCR) and non-organ-confined (non-OC) PCa by integrating clinical, MRI, and biomarker-related data. Design, setting, and participants: A retrospective analysis of a population-based cohort of men with PCa, who underwent preoperative MRI followed by radical prostatectomy (RP) during 2014-2015 in Helsinki University Hospital (n = 346), was conducted. A tissue microarray corresponding to the MRI-visible and MRI-invisible lesions in RP specimens was constructed and stained for PTEN and ERG. Outcome measurements and statistical analysis: Associations of PTEN and ERG with MRI-visible and MRI-invisible lesions were examined (Pearson's chi 2 test), and predictions of non-OC disease together with clinical and MRI parameters were determined (area under the receiver operating characteristic curve and logistic regression analyses). BCR prediction was analyzed by Kaplan-Meier and Cox proportional hazard analyses. Results and limitations: Patients with MRI-invisible lesions (n = 35) had less PTEN loss and ERG-positive expression compared with patients (n = 90) with MRI-visible lesions (17.2% vs 43.3% [p = 0.006]; 8.6% vs 20.0% [p = 0.125]). Patients with invisible lesions had better, but not statistically significantly improved, BCR-free survival probability in Kaplan-Meier analyses (p = 0.055). Rates of BCR (5.7% vs 21.1%; p = 0.039), extraprostatic extension (11.4% vs 44.6%; p < 0.001), seminal vesicle invasion (0% vs 21.1%; p = 0.003), and lymph node metastasis (0% vs 12.2%; p = 0.033) differed between the groups in favor of patients with MRI-invisible lesions. Biomarkers had no independent role in predicting non-OC disease or BCR. The short follow-up period was a limitation. Conclusions: PTEN loss, BCR, and non-OC RP findings were more often encountered with MRI-visible lesions. Patient summary: Magnetic resonance imaging (MRI) of the prostate misses some cancer lesions. MRI-invisible lesions seem to be less aggressive than MRI-visible lesions. (C) 2020 European Association of Urology. Published by Elsevier B.V. All rights reserved.Peer reviewe

Prognostic impact of kallikrein-related peptidase transcript levels in prostate cancer

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