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

Eineluoto, Juho; Sandeman, Kevin; Pohjonen, Joona; Sopyllo, Konrad; Nordling, Stig; Stürenberg, Carolin; Malén, Adrian; Kilpeläinen, Tuomas P.; Santti, Henrikki; Pétas, Anssi; Matikainen, Mika; Pellinen, Teijo; Järvinen, Petrus; Kenttämies, Anu; Rannikko, Antti; Mirtti, Tuomas

doi:10.1016/j.euf.2020.06.016

Cited by 7 publications

(7 citation statements)

References 29 publications

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“…Eineluoto et al [ 160 ] determined the association between PTEN and ETS-related gene (ERG), with visible and invisible PCa lesions on MRI. A retrospective analysis of 346 patients with pre-RP MRI, PTEN and ERG tissue microarray staining, was performed.…”

Section: Resultsmentioning

confidence: 99%

“…A study reported that quantitative mpMRI features and gene expression in biopsy tissue and in radiogenomic analysis genes were significantly associated with radiomic features [ 158 ], while the other identified biomarkers were able to differentiate between the T2c and T3b prostate cancer stages [ 27 ]. Some retrospective radiogenomic research identified those visible mpMRI lesions, and the genes that can predict visibility, and allows the identification of high risk patients of harboring aggressive disease [ 130 , 159 , 160 ]. Research being performed on the association between prostate MRI and tissue-based gene expression shows that genomic testing can reveal more about the disease biological processes [ 168 , 169 , 170 ].…”

Section: Discussionmentioning

confidence: 99%

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Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Ferro

Cobelli

Vartolomei

et al. 2021

IJMS

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Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Ferro

Cobelli

Vartolomei

et al. 2021

IJMS

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“…The cohort consisted of 387 patients with preoperative multiparametric MRI and subsequent robot-assisted laparoscopic prostatectomy (RALP) as their primary treatment at the Department of Urology in Helsinki University Hospital (Helsinki, Finland) between January 2014 and September 2015. The design and generation of tissue microarrays (TMA) for cohort I has been previously described ( 24 ). After linking tumor location data, preoperative MRI data, and mfIHC data, 38 of the 387 patients were excluded due to insufficient matching data.…”

Section: Methodsmentioning

confidence: 99%

Stromal FAP Expression is Associated with MRI Visibility and Patient Survival in Prostate Cancer

Pellinen

Sandeman

Blom

et al. 2022

Cancer Research Communications

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Some clinically significant prostate cancers (PCa) are missed by magnetic resonance imaging (MRI). We asked if the tumour stroma in surgically treated localized PCa lesions positive or negative with MRI are different in their cellular and molecular properties, and whether the differences are reflected to the clinical course of the disease. We profiled the stromal and immune cell composition of MRI-classified tumour lesions by applying multiplexed fluorescence immunohistochemistry (mfIHC) and automated image analysis in a clinical cohort of 343 patients (Cohort I). We compared stromal variables between MRI-visible, invisible lesions, and benign tissue and assessed the predictive significance for biochemical recurrence (BCR) and disease-specific survival (DSS) using Cox regression and Log rank analysis. Subsequently, we carried out a prognostic validation of the identified biomarkers in a population-based cohort of 319 patients (Cohort II). MRI true-positive lesions are different from benign tissue and MRI false-negative lesions in their stromal composition. CD163-positive cells (macrophages) and fibroblast activation protein (FAP)-positive cells were more abundant in MRI true-positive than in MRI false-negative lesions or benign areas. In MRI true-visible lesions, a high proportion of stromal FAP-positive cells was associated with PTEN status and increased immune infiltration (CD8+, CD163+), and predicted elevated risk for BCR. High FAP phenotype was confirmed to be a strong indicator of poor prognosis in two independent patient cohorts using also conventional IHC. The molecular composition of the tumour stroma may determine whether early prostate lesions are detectable by MRI and associates with survival after surgical treatment.

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“…20,21 Our dataset showed 87.35% positive and 12.65% negative PTEN in NPCs, with an expression rate close to HNSCC. The reported PTEN positive rates of other types of malignant tumors, such as colorectal cancer 22 or prostate cancer, 23 were lower than NPCs. This may be a contributing factor to the longer veyear survival rate and overall survival of NPC patients than other common types of malignancies.…”

Section: Discussionmentioning

confidence: 88%

Dynamic contrast-enhanced MRI predicts PTEN protein expression which can function as a prognostic measure of progression-free survival in NPC patients

Huang

et al. 2021

J Cancer Res Clin Oncol

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Objectives: The objective of our study was to investigate whether a phosphatase and tensin homolog deleted on chromosome 10 (PTEN) expression was associated with dynamic contrast-enhanced MRI (DCE-MRI) parameters and prognosis in nasopharyngeal carcinoma (NPC). Methods: Two hundred and forty-ve (245) patients with NPC who underwent pretreatment biopsy, PTEN immunohistochemistry of biopsy, and radical intensity-modulated radiation therapy (IMRT) with or without chemotherapy were included. Tumor segmentations were delineated on pretreatment MRI manually. The pharmacokinetic parameters (Ktrans, Kep, Ve, and Vp) derived from dynamic contrastenhanced MRI (DCE-MRI) using the extended Toft's model within the tumor segmentations were estimated. The following demographics and clinical features were assessed and correlated against each other: gender, age, TNM stage, clinical-stage, Epstein-Barr virus (EBV), pathological type, progression-free survival (PFS), and prognosis status. DCE parameter evaluation and clinical feature comparison between the PTEN positive and negative groups was performed and correlation between PTEN expression with the PFS and prognosis status using Cox regression for survival analysis was assessed. Results: A signi cantly lower K trans and K ep were found in NPC tumors in PTEN negative patients than in PTEN positive patients. K trans performed better than K ep in detecting PTEN expression with the ROC AUC of 0.752. PTEN negative was associated with later TNM stage, later clinical-stage, shorter PFS, and worse prognosis. Moreover, N stage, pathological type, K ep , and prognostic status can be considered as independent variables in discrimination of PTEN negative expression in NPCs. Conclusions: PTEN negative indicated a shorter PFS and worse prognosis than PTEN positive in NPC patients. K trans and K ep derived from DCE-MRI, which yielded reliable capability, should be considered as novel image markers that are correlated with PTEN expression and may be used to predict PTEN expression noninvasively. Combined radiological and clinical features can improve the performance of the classi cation of PTEN expression.

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

Cited by 7 publications

References 29 publications

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Stromal FAP Expression is Associated with MRI Visibility and Patient Survival in Prostate Cancer

Dynamic contrast-enhanced MRI predicts PTEN protein expression which can function as a prognostic measure of progression-free survival in NPC patients

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