Molecular alterations in prostate cancer and association with MRI features

Deficiency in SPOP-mediated ubiquitination and degradation of TIAM1 promotes gastric cancer progression

It was well known that SPOP is highly mutated in various cancers especially the prostate cancer and SPOP mutation dramatically impaired its tumor suppressive function. However, the detailed role and underlying mechanisms of SPOP in regulating the growth of gastric cancer is not fully studied. Here, we found that Cullin3SPOP promoted the ubiquitination and degradation of TIAM1 protein in gastric cancer setting. Gastric cancer and prostate cancer derived SPOP mutation failed to suppress the proliferation, migration and invasion of gastric cancer cells partially due to the elevated level of TIAM1 protein. Notably, SPOP protein were negatively associated with TIAM1 protein in human gastric cancer tissue specimens. In conclusion, our results elucidate a molecular mechanism by which SPOP regulates the stability of TIAM1, and further demonstrate that SPOP inhibits the progression of gastric cancer by promoting the ubiquitination and degradation of TIAM1 protein.

Genetic Profiling of African American Patients With Prostatic Adenocarcinoma Metastatic to the Lymph Nodes: A Pilot Study

CONTEXT.—

Genetic profiling data of prostatic adenocarcinoma are derived from predominantly White patients. In African Americans, prostatic adenocarcinoma has a poorer prognosis, raising the possibility of distinct genetic alterations.

OBJECTIVE.—

To investigate the genomic alterations of prostatic adenocarcinoma metastatic to regional lymph nodes in African American patients, with an emphasis on SPOP mutation.

DESIGN.—

We retrospectively reviewed African American patients with pN1 prostatic adenocarcinoma managed with radical prostatectomy and lymph node dissection. Comprehensive molecular profiling was performed, and androgen receptor signaling scores were calculated.

RESULTS.—

Nineteen patients were included. The most frequent genetic alteration was SPOP mutations (5 of 17; 29.4% [95% CI: 10.3-56.0]). While most alterations were associated with a high androgen receptor signaling score, mutant SPOP was exclusively associated with a low median and interquartile range (IQR) androgen receptor signaling score (0.788 [IQR 0.765-0.791] versus 0.835 [IQR 0.828-0.842], P = .003). In mutant SPOP, mRNA expression of SPOP inhibitor G3BP1 and SPOP substrates showed a significantly decreased expression of AR (33.40 [IQR 28.45-36.30] versus 59.53 [IQR 53.10-72.83], P = .01), TRIM24 (3.95 [IQR 3.28-5.03] versus 9.80 [IQR 7.39-11.70], P = .008), and NCOA3 (15.19 [IQR 10.59-15.93] versus 21.88 [IQR 18.41-28.33], P = .046).

CONCLUSIONS.—

African American patients with metastatic prostate adenocarcinoma might have a higher prevalence of mutant SPOP (30%), compared to ∼10% in unselected cohorts with lower expressions of SPOP substrates. In our study, in patients with mutant SPOP, the mutation was associated with decreased SPOP substrate expression and androgen receptor signaling, raising concern for suboptimal efficacy of androgen deprivation therapy in this subset of patients.

Prognostic Features of Biochemical Recurrence of Prostate Cancer Following Radical Prostatectomy Based on Multiparametric MRI and Immunohistochemistry Analysis of MRI-guided Biopsy Specimens

Background Although prostate MRI is routinely used for the detection and staging of localized prostate cancer, imaging-based assessment and targeted molecular sampling for risk stratification are an active area of research. Purpose To evaluate features of preoperative MRI and MRI-guided biopsy immunohistochemistry (IHC) findings associated with biochemical recurrence (BCR) of prostate cancer after surgery. Materials and Methods In this retrospective case-control study, patients underwent multiparametric MRI before MRI-guided biopsy followed by radical prostatectomy between 2008 and 2016. Lesions were retrospectively scored with the Prostate Imaging Reporting and Data System (PI-RADS) (version 2) by radiologists who were blinded to the clinical-pathologic results. The IHC staining, including stains for the ETS-related gene, phosphatase and tensin homolog, androgen receptor, prostate specific antigen, and p53, was performed with targeted biopsy specimens of the index lesion (highest suspicion at MRI and pathologic grade) and scored by pathologists who were blinded to clinical-pathologic outcomes. Cox proportional hazards regression analysis was used to evaluate associations with recurrence-free survival (RFS). Results The median RFS was 31.7 months (range, 1-101 months) for 39 patients (median age, 62 years; age range, 47-76 years) without BCR and 14.6 months (range, 1-61 months) for 40 patients (median age, 59 years; age range, 47-73 years) with BCR. MRI features that showed a significant relationship with the RFS interval included an index lesion with a PI-RADS score of 5 (hazard ratio [HR], 2.10; 95% CI: 1.05, 4.21; P = .04); index lesion burden, defined as ratio of index lesion volume to prostate volume (HR, 1.55; 95% CI: 1.2, 2.1; P = .003); and suspicion of extraprostatic extension (EPE) (HR, 2.18; 95% CI: 1.1, 4.2; P = .02). Presurgical multivariable analysis indicated that suspicion of EPE at MRI (adjusted HR, 2.19; 95% CI: 1.1, 4.3; P = .02) and p53 stain intensity (adjusted HR, 2.22; 95% CI: 1.0, 4.7; P = .04) were significantly associated with RFS. Conclusion MRI features, including Prostate Imaging Reporting and Data System score, index lesion burden, extraprostatic extension, and preoperative guided biopsy p53 immunohistochemistry stain intensity are associated with biochemical relapse of prostate cancer after surgery. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Costa in this issue.

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Simple Summary

The increasing interest in implementing artificial intelligence in radiomic models has occurred alongside advancement in the tools used for computer-aided diagnosis. Such tools typically apply both statistical and machine learning methodologies to assess the various modalities used in medical image analysis. Specific to prostate cancer, the radiomics pipeline has multiple facets that are amenable to improvement. This review discusses the steps of a magnetic resonance imaging based radiomics pipeline. Present successes, existing opportunities for refinement, and the most pertinent pending steps leading to clinical validation are highlighted.

Abstract

The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.

Genetic Landscape of Prostate Cancer Conspicuity on Multiparametric Magnetic Resonance Imaging: A Systematic Review and Bioinformatic Analysis

Context

Multiparametric magnetic resonance imaging (mpMRI) detects most, but not all, clinically significant prostate cancer. The genetic basis of prostate cancer visibility and invisibility on mpMRI remains uncertain.

Objective

To systematically review the literature on differential gene expression between mpMRI-visible and mpMRI-invisible prostate cancer, and to use bioinformatic analysis to identify enriched processes or cellular components in genes validated in more than one study.

Evidence acquisition

We performed a systematic literature search of the Medline, EMBASE, PubMed, and Cochrane databases up to January 2020 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. The primary endpoint was differential genetic features between mpMRI-visible and mpMRI-invisible tumours. Secondary endpoints were explanatory links between gene function and mpMRI conspicuity, and the prognostic value of differential gene enrichment.

Evidence synthesis

We retrieved 445 articles, of which 32 met the criteria for inclusion. Thematic synthesis from the included studies showed that mpMRI-visible cancer tended towards enrichment of molecular features associated with increased disease aggressivity, including phosphatase and tensin homologue (PTEN) loss and higher genomic classifier scores, such as Oncotype and Decipher. Three of the included studies had accompanying publicly available data suitable for further bioinformatic analysis. An over-representation analysis of these datasets revealed increased expression of genes associated with extracellular matrix components in mpMRI-visible tumours.

Conclusions

Prostate cancer that is visible on mpMRI is generally enriched with molecular features of tumour development and aggressivity, including activation of proliferative signalling, DNA damage, and inflammatory processes. Additionally, there appears to be concordant cellular components and biological processes associated with mpMRI conspicuity, as highlighted by bioinformatic analysis of large genetic datasets.

Patient summary

Prostate cancer that is detected by magnetic resonance imaging (MRI) tends to have genetic features that are associated with more aggressive disease. This suggests that MRI can be used to assess the likelihood of aggressive prostate cancer, based on tumour visibility.

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 χ² 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.

The diverse roles of SPOP in prostate cancer and kidney cancer

Multiple studies have confirmed that speckle-type pox virus and zinc finger (POZ) protein (SPOP) functions as a substrate adaptor of cullin 3-based E3 ligase and has a crucial role in various cellular processes via specific targeting of proteins for ubiquitination and subsequent proteasomal degradation. Dysregulation of SPOP-mediated proteolysis might be involved in the development and progression of human prostate and kidney cancers. In prostate cancer, SPOP seems to function as a tumour suppressor by targeting several proteins, including androgen receptor (AR), steroid receptor coactivator 3 (SRC3) and BRD4, for degradation, whereas it might function as an oncoprotein in kidney cancer, for example, by targeting phosphatase and tensin homologue (PTEN) for proteasomal degradation. In addition, nuclear SPOP targets AR for degradation and has a role as a tumour suppressor in prostate cancer; however, in kidney cancer, SPOP largely accumulates in the cytoplasm and fails to promote degradation of AR located in the nucleus, resulting in activation of AR-driven pathways and cancer progression. Owing to the context-dependent function of SPOP in human malignancies, further assessment of the molecular mechanisms involving SPOP in prostate and kidney cancers is needed to improve our understanding of its role in the development of these cancer types. Treatments that target SPOP might become therapeutic strategies in these malignancies in the future.

Biologic Significance of Magnetic Resonance Imaging Invisibility in Localized Prostate Cancer

PURPOSE

Multiparametric magnetic resonance imaging (mpMRI) is used widely for prostate cancer (PCa) evaluation. Approximately 35% of aggressive tumors, however, are not visible on mpMRI. We sought to identify the molecular alterations associated with mpMRI-invisible tumors and determine whether mpMRI visibility is associated with PCa prognosis.

METHODS

Discovery and validation cohorts included patients who underwent mpMRI before radical prostatectomy and were found to harbor both mpMRI-visible (Prostate Imaging and Reporting Data System 3 to 5) and -invisible (Prostate Imaging and Reporting Data System 1 or 2) foci on surgical pathology. Next-generation sequencing was performed to determine differential gene expression between mpMRI-visible and -invisible foci. A genetic signature for tumor mpMRI visibility was derived in the discovery cohort and assessed in an independent validation cohort. Its association with long-term oncologic outcomes was evaluated in a separate testing cohort.

RESULTS

The discovery cohort included 10 patients with 26 distinct PCa foci on surgical pathology, of which 12 (46%) were visible and 14 (54%) were invisible on preoperative mpMRI. Next-generation sequencing detected prioritized genetic mutations in 14 (54%) tumor foci (n = 8 mpMRI visible, n = 6 mpMRI invisible). A nine-gene signature (composed largely of cell organization/structure genes) associated with mpMRI visibility was derived (area under the curve = 0.89), and the signature predicted MRI visibility with 75% sensitivity and 100% specificity (area under the curve = 0.88) in the validation cohort. In the testing cohort (n = 375, median follow-up 8 years) there was no significant difference in biochemical recurrence, distant metastasis, or cancer-specific mortality in patients with predicted mpMRI-visible versus -invisible tumors (all P > .05).

CONCLUSION

Compared with mpMRI-invisible disease, mpMRI-visible tumors are associated with underexpression of cellular organization genes. mpMRI visibility does not seem to be predictive of long-term cancer outcomes, highlighting the need for biopsy strategies that detect mpMRI-invisible tumors.

PTEN Expression in Prostate Cancer: Relationship With Clinicopathologic Features and Multiparametric MRI Findings

OBJECTIVE. The objective of our study was to investigate whether phosphatase and tensin homolog (PTEN) expression is associated with clinicopathologic features and multiparametric MRI findings in prostate cancer. MATERIALS AND METHODS. Forty-three patients with prostate cancer who underwent radical prostatectomy were included. Index tumor was identified on pretreatment MRI and delineated in the area that correlated best with histopathology results. The apparent diffusion coefficient (ADC) from DWI and pharmacokinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) using the extended Tofts model (K^trans, k_ep, v_e, and v_p) within the tumor were estimated. The following clinicopathologic parameters were assessed: pretreatment serum levels of prostate-specific antigen, disseminated tumor cell status, age, Gleason score, tumor size, extraprostatic extension (EPE), tumor location, and lymph node metastases. Gene expression profiles were acquired in biopsies from the tumor using bead arrays, and validated using reverse transcription quantitative polymerase chain reaction (RT-qPCR) on a different part of the biopsy. RESULTS. Based on bead arrays (p = 0.006) and RT-qPCR (p = 0.03) data, a significantly lower ADC was found in tumors with low PTEN expression. Moreover, PTEN expression was negatively associated with lymph node metastases (bead arrays, p = 0.008; RT-qPCR, p < 0.001). A weak but significant association between PTEN expression, EPE (p = 0.048), and Gleason score (p = 0.028) was revealed on bead arrays. ADC was negatively correlated with Gleason score (p = 0.001) and tumor size (p = 0.023). No association among DCE parameters, PTEN expression, and clinicopathologic features was found. CONCLUSION. ADC derived from DWI may be useful in selecting patients with potentially aggressive tumor caused by PTEN deficiency.

A TMEFF2-regulated cell cycle derived gene signature is prognostic of recurrence risk in prostate cancer

Background

The clinical behavior of prostate cancer (PCa) is variable, and while the majority of cases remain indolent, 10% of patients progress to deadly forms of the disease. Current clinical predictors used at the time of diagnosis have limitations to accurately establish progression risk. Here we describe the development of a tumor suppressor regulated, cell-cycle gene expression based prognostic signature for PCa, and validate its independent contribution to risk stratification in several radical prostatectomy (RP) patient cohorts.

Methods

We used RNA interference experiments in PCa cell lines to identify a gene expression based gene signature associated with Tmeff2, an androgen regulated, tumor suppressor gene whose expression shows remarkable heterogeneity in PCa. Gene expression was confirmed by qRT-PCR. Correlation of the signature with disease outcome (time to recurrence) was retrospectively evaluated in four geographically different cohorts of patients that underwent RP (834 samples), using multivariate logistical regression analysis. Multivariate analyses were adjusted for standard clinicopathological variables. Performance of the signature was compared to previously described gene expression based signatures using the SigCheck software.

Results

Low levels of TMEFF2 mRNA significantly (p < 0.0001) correlated with reduced disease-free survival (DFS) in patients from the Memorial Sloan Kettering Cancer Center (MSKCC) dataset. We identified a panel of 11 TMEFF2 regulated cell cycle related genes (TMCC11), with strong prognostic value. TMCC11 expression was significantly associated with time to recurrence after prostatectomy in four geographically different patient cohorts (2.9 ≤ HR ≥ 4.1; p ≤ 0.002), served as an independent indicator of poor prognosis in the four RP cohorts (1.96 ≤ HR ≥ 4.28; p ≤ 0.032) and improved the prognostic value of standard clinicopathological markers. The prognostic ability of TMCC11 panel exceeded previously published oncogenic gene signatures (p = 0.00017).

Conclusions

This study provides evidence that the TMCC11 gene signature is a robust independent prognostic marker for PCa, reveals the value of using highly heterogeneously expressed genes, like Tmeff2, as guides to discover prognostic indicators, and suggests the possibility that low Tmeff2 expression marks a distinct subclass of PCa.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5592-6) contains supplementary material, which is available to authorized users.

Artificial intelligence for aging and longevity research: Recent advances and perspectives

The applications of modern artificial intelligence (AI) algorithms within the field of aging research offer tremendous opportunities. Aging is an almost universal unifying feature possessed by all living organisms, tissues, and cells. Modern deep learning techniques used to develop age predictors offer new possibilities for formerly incompatible dynamic and static data types. AI biomarkers of aging enable a holistic view of biological processes and allow for novel methods for building causal models-extracting the most important features and identifying biological targets and mechanisms. Recent developments in generative adversarial networks (GANs) and reinforcement learning (RL) permit the generation of diverse synthetic molecular and patient data, identification of novel biological targets, and generation of novel molecular compounds with desired properties and geroprotectors. These novel techniques can be combined into a unified, seamless end-to-end biomarker development, target identification, drug discovery and real world evidence pipeline that may help accelerate and improve pharmaceutical research and development practices. Modern AI is therefore expected to contribute to the credibility and prominence of longevity biotechnology in the healthcare and pharmaceutical industry, and to the convergence of countless areas of research.

SPOP-Mutated/CHD1-Deleted Lethal Prostate Cancer and Abiraterone Sensitivity

Purpose: CHD1 deletions and SPOP mutations frequently cooccur in prostate cancer with lower frequencies reported in castration-resistant prostate cancer (CRPC). We monitored CHD1 expression during disease progression and assessed the molecular and clinical characteristics of CHD1-deleted/SPOP-mutated metastatic CRPC (mCRPC).Experimental Design: We identified 89 patients with mCRPC who had hormone-naive and castration-resistant tumor samples available: These were analyzed for CHD1, PTEN, and ERG expression by IHC. SPOP status was determined by targeted next-generation sequencing (NGS). We studied the correlations between these biomarkers and (i) overall survival from diagnosis; (ii) overall survival from CRPC; (iii) duration of abiraterone treatment; and (iv) response to abiraterone. Relationship with outcome was analyzed using Cox regression and log-rank analyses.Results: CHD1 protein loss was detected in 11 (15%) and 13 (17%) of hormone-sensitive prostate cancer (HSPC) and CRPC biopsies, respectively. Comparison of CHD1 expression was feasible in 56 matched, same patient HSPC and CRPC biopsies. CHD1 protein status in HSPC and CRPC correlated in 55 of 56 cases (98%). We identified 22 patients with somatic SPOP mutations, with six of these mutations not reported previously in prostate cancer. SPOP mutations and/or CHD1 loss was associated with a higher response rate to abiraterone (SPOP: OR, 14.50 P = 0.001; CHD1: OR, 7.30, P = 0.08) and a longer time on abiraterone (SPOP: HR, 0.37, P = 0.002, CHD1: HR, 0.50, P = 0.06).Conclusions: SPOP-mutated mCRPCs are strongly enriched for CHD1 loss. These tumors appear highly sensitive to abiraterone treatment. Clin Cancer Res; 24(22); 5585-93. ©2018 AACR.

Multiparametric Magnetic Resonance Imaging Features Identify Aggressive Prostate Cancer at the Phenotypic and Transcriptomic Level

PURPOSE

Multiparametric magnetic resonance imaging is a diagnostic tool for prostate cancer with limited data on prognostic use. We sought to determine whether multiparametric magnetic resonance could predict aggressive prostate cancer features.

MATERIALS AND METHODS

We retrospectively analyzed the records of 206 patients who underwent radical prostatectomy between 2013 and 2017. All patients had available RNA expression data on the final pathology specimen obtained from a location corresponding to a lesion location on multiparametric magnetic resonance imaging. The association between the PIRADS™ (Prostate Imaging Reporting and Data System) score and adverse pathology features were analyzed. We also performed differential transcriptomic analysis between the PIRADS groups. Factors associated with adverse pathology were analyzed using a multivariable logistic regression model.

RESULTS

Lesion size (p = 0.03), PIRADS score (p = 0.02) and extraprostatic extension (p = 0.01) associated significantly with the Decipher® score. Multivariable analysis showed that the PIRADS score (referent PIRADS 3, OR 8.1, 95% CI 1.2-57.5, p = 0.04), the Gleason Grade Group (referent 3, OR 5.6, 95% CI 1.5-21.1, p = 0.01) and prostate specific antigen (OR 1.103, 95% CI 1.011-1.203) were risk factors for adverse pathology findings. The difference between PIRADS 4 and 5 did not reach significance (OR 1.9, 95% CI 0.8-4.5, p = 0.12). However, the PI3K-AKT-mTOR, WNT-β and E2F signaling pathways were more active in PIRADS 5 than in PIRADS 4 cases.

CONCLUSIONS

The PIRADS score is associated with adverse pathology results, increased metastatic risk and differential genomic pathway activation.

Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare

The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.