Germline-specific RNA helicase DDX4 forms cytoplasmic granules in cancer cells and promotes tumor growth.

Cancer cells undergo major epigenetic alterations and transcriptomic changes, including ectopic expression of tissue- and cell-type-specific genes. Here, we show that the germline-specific RNA helicase DDX4 forms germ-granule-like cytoplasmic ribonucleoprotein granules in various human tumors, but not in cultured cancer cells. These cancerous DDX4 complexes contain RNA-binding proteins and splicing regulators, including many known germ granule components. The deletion of DDX4 in cancer cells induces transcriptomic changes and affects the alternative splicing landscape of a number of genes involved in cancer growth and invasiveness, leading to compromised capability of DDX4-null cancer cells to form xenograft tumors in immunocompromised mice. Importantly, the occurrence of DDX4 granules is associated with poor survival in patients with head and neck squamous cell carcinoma and higher histological grade of prostate cancer. Taken together, these results show that the germ-granule-resembling cancerous DDX4 granules control gene expression and promote malignant and invasive properties of cancer cells.

External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study.

The tall cell subtype (TC-PTC) is an aggressive subtype of papillary thyroid carcinoma (PTC). The TC-PTC is defined as a PTC comprising at least 30% epithelial cells that are three times as tall as they are wide. In practice, this definition is difficult to adhere to, resulting in high inter-observer variability. In this multicenter study, we validated a previously trained deep learning (DL)-based algorithm for detection of tall cells on 160 externally collected hematoxylin and eosin (HE)-stained PTC whole-slide images. In a test set of 360 manual annotations of regions of interest from 18 separate tissue sections in the external dataset, the DL-based algorithm detected TCs with a sensitivity of 90.6% and a specificity of 88.5%. The DL algorithm detected non-TC areas with a sensitivity of 81.6% and a specificity of 92.9%. In the validation datasets, 20% and 30% TC thresholds correlated with a significantly shorter relapse-free survival. In conclusion, the DL algorithm detected TCs in unseen, external scanned HE tissue slides with high sensitivity and specificity without any retraining.

Low nuclear expression of HIF-hydroxylases PHD2/EGLN1 and PHD3/EGLN3 are associated with poor recurrence-free survival in clear cell renal cell carcinoma.

BACKGROUND: Hypoxia inducible factors, HIF-1α and HIF-2α, and their main regulators, the prolyl hydroxylase domain proteins (PHDs), mediate cellular response to hypoxia and contribute to tumor progression in clear cell renal cell carcinoma (ccRCC). These biomarkers may improve the value of traditional histopathological features in predicting disease progression after nephrectomy for localized ccRCC and guide patient selection for adjuvant treatments. PATIENTS AND METHODS: In this study, we analyzed the associations of PHD2 and PHD3 with histopathological tumor features and recurrence-free survival (RFS) in a retrospective cohort of 173 patients who had undergone surgery for localized ccRCC at Helsinki University Hospital (HUH), Finland. An external validation cohort of 191 patients was obtained from Turku University Hospital (TUH), Finland. Tissue-microarrays (TMA) were constructed using the primary tumor samples. Clinical parameters and follow-up information from 2006 to 2019 were obtained from electronic medical records. The cytoplasmic and nuclear expression of PHD2, and PHD3 were scored based on immunohistochemical staining and their associations with histopathological features and RFS were evaluated. RESULTS: Nuclear PHD2 and PHD3 expression in cancer cells were associated with lower pT-stage and Fuhrman grade compared with negative nuclei. Patients with positive nuclear expression of PHD2 and PHD3 in cancer cells had favorable RFS compared with patients having negative tumors. The nuclear expression of PHD2 was independently associated with a decreased risk of disease recurrence or death from RCC in multivariable analysis. These results were observed in both cohorts. CONCLUSIONS: The absence of nuclear PHD2 and PHD3 expression in ccRCC was associated with poor RFS and the nuclear expression of PHD2 predicted RFS regardless of other known histopathological prognostic factors. Nuclear PHD2 and PHD3 are potential prognostic biomarkers in patients with localized ccRCC and should be further investigated and validated in prospective studies.

Tumor-to-tumor metastasis to Warthin tumor presenting as an initial sign of breast carcinoma: A case report.

Metastases of distant primary tumors on the parotid gland are very rare. Tumor-to-tumor metastasis to salivary gland tumors is extremely rare. A case of a 69-year-old woman with a large left parotid gland mass with no previous salivary gland problems or cancer. Fine-needle aspiration (FNA) showed evidence of a Warthin tumor. A total left parotidectomy was performed. The final pathology report showed a Warthin tumor containing two metastases of adenocarcinoma. The immunohistochemistry of the metastases led to the diagnosis of primary breast cancer. A negative FNA does not rule out the possibility of a malignant parotid tumor or metastasis, and a parotid lump should be presumed to be a secondary tumor until proven otherwise if the patient has a history of any distant primary cancer. Our patient case serves as a reminder that a new distant primary cancer might be the cause of a parotid lump.

Prognostic Factors for Localized Clear Cell Renal Cell Carcinoma and Their Application in Adjuvant Therapy.

Approximately 20% of patients with renal cell carcinoma (RCC) present with primarily metastatic disease and over 30% of patients with localized RCC will develop distant metastases later, after complete resection of the primary tumor. Accurate postoperative prognostic models are essential for designing personalized surveillance programs, as well as for designing adjuvant therapy and trials. Several clinical and histopathological prognostic factors have been identified and adopted into prognostic algorithms to assess the individual risk for disease recurrence after radical or partial nephrectomy. However, the prediction accuracy of current prognostic models has been studied in retrospective patient cohorts and the optimal set of prognostic features remains unclear. In addition to traditional histopathological prognostic factors, novel biomarkers, such as gene expression profiles and circulating tumor DNA, are extensively studied to supplement existing prognostic algorithms to improve their prediction accuracy. Here, we aim to give an overview of existing prognostic features and prediction models for localized postoperative clear cell RCC and discuss their role in the adjuvant therapy trials. The results of ongoing placebo-controlled adjuvant therapy trials may elucidate prognostic factors and biomarkers that help to define patients at high risk for disease recurrence.

How to read biparametric MRI in men with a clinical suspicious of prostate cancer: Pictorial review for beginners with public access to imaging, clinical and histopathological database.

Prostate Magnetic Resonance Imaging (MRI) is increasingly being used in men with a clinical suspicion of prostate cancer (PCa). Performing prostate MRI without the use of an intravenous contrast (IV) agent in men with a clinical suspicion of PCa can lead to reduced MRI scan time. Enabling a large array of different medical providers (from mid-level to specialized radiologists) to evaluate and potentially report prostate MRI in men with a clinical suspicion of PCa with a high accuracy could be one way to enable wide adoption of prostate MRI in men with a clinical suspicion of PCa. The aim of this pictorial review is to provide an insight into acquisition, quality control and reporting of prostate MRI performed without IV contrast agent in men with a clinical suspicion of PCa, aimed specifically at radiologists starting reporting prostate MRI, urologists, urology/radiology residents and mid-level medical providers without experience in reporting prostate MRI. Free public access (http://petiv.utu.fi/improd/and http://petiv.utu.fi/multiimprod/) to complete datasets of 161 and 338 men is provided. The imaging datasets are accompanied by clinical, laboratory and histopathological findings. Several topics are simplified in order to provide a solid base for the development of skills needed for an unsupervised review and potential reporting of prostate MRI in men with a clinical suspicion of PCa. The current review represents the first step towards enabling a large array of different medical providers to review and report accurately prostate MRI performed without IV contrast agent in men with a clinical suspicion of PCa.

A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma.

After surgery of localized renal cell carcinoma, over 20% of the patients will develop distant metastases. Our aim was to develop an easy-to-use prognostic model for predicting metastasis-free survival after radical or partial nephrectomy of localized clear cell RCC. Model training was performed on 196 patients. Right-censored metastasis-free survival was analysed using LASSO-regularized Cox regression, which identified three key prediction features. The model was validated in an external cohort of 714 patients. 55 (28%) and 134 (19%) patients developed distant metastases during the median postoperative follow-up of 6.3 years (interquartile range 3.4-8.6) and 5.4 years (4.0-7.6) in the training and validation cohort, respectively. Patients were stratified into clinically meaningful risk categories using only three features: tumor size, tumor grade and microvascular invasion, and a representative nomogram and a visual prediction surface were constructed using these features in Cox proportional hazards model. Concordance indices in the training and validation cohorts were 0.755 ± 0.029 and 0.836 ± 0.015 for our novel model, which were comparable to the C-indices of the original Leibovich prediction model (0.734 ± 0.035 and 0.848 ± 0.017, respectively). Thus, the presented model retains high accuracy while requiring only three features that are routinely collected and widely available.

Molecular subtype diagnosis of endometrial carcinoma: comparison of the next-generation sequencing panel and Proactive Molecular Risk Classifier for Endometrial Cancer classifier.

The Cancer Genome Atlas-based molecular classification of endometrial carcinoma (EC) has the potential to better identify those patients whose disease is likely to behave differently than predicted when using traditional risk stratification; however, the optimal approach to molecular subtype assignment in routine practice remains undetermined. The aim of this study was to compare the results of two different widely available approaches to diagnosis the EC molecular subtype. EC specimens from 60 patients were molecularly subclassified using two different methods, by using the FoundationOne CDx next-generation sequencing (NGS) panel and using the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) classifier and performing immunostaining for mismatch repair proteins and p53. POLE mutation status was derived from FoundationOne results in both settings. Molecular classification based on ProMisE was successful for all 60 tumors. Microsatellite instability status could be determined based on the NGS panel results in 53 of 60 tumors, so ProMisE and NGS molecular subtype assignment could be directly compared for these 53 tumors. Molecular subtype diagnosis based on NGS and ProMisE was in agreement for 52 of 53 tumors. One tumor was microsatellite stable but showed loss of MLH1 and PMS2 expression. Molecular subtype diagnosis of EC based on the NGS panel of formalin-fixed paraffin-embedded ECs and based primarily on immunostaining (ProMisE) yields identical results in 98.1% (52/53, kappa = 0.97) of cases. Although results obtained using these two approaches are comparable, each has advantages and disadvantages that will influence the choice of the method to be used in clinical practice.

Performance of Finnish biobanks in nationwide pulmonary carcinoid tumour research.

Finnish hospital-integrated biobanks administer millions of formalin-fixed paraffin-embedded tissue samples collected within the clinical diagnostics. According to the Finnish Biobank Act, these samples can be coupled with patients' clinical follow-up data and the data retrieved from national health registries. We collected a nationwide pulmonary carcinoid tumour series from Finnish biobanks to study prognostic factors as well as to explore how the number of tumours found in the Finnish biobanks corresponds to the number of tumours registered by the Finnish Cancer Registry (FCR). Finnish biobanks identified 88% of the tumours registered by the FCR and were able to deliver 63%. The main reasons for lacking samples were paucity of resected primary tumour tissue, incompatible primary diagnosis, and the absence of tissue blocks in the archives. The main bottleneck in the sample application process was retrieving patient data. Altogether, we received 224 tumour samples with appropriate patient data and identified six prognostic factors for shorter disease-specific survival: age over 56 years at the time of diagnosis, tumour size over 2.5 cm, atypical histology, Ki-67 proliferation index higher than 2.5%, hilar/mediastinal lymph node involvement at the time of diagnosis, and the presence of metastatic disease. In conclusion, the Finnish biobank infrastructure offers excellent opportunities for tissue-based research. However, to be able to develop the biobank operations further, involving more medical knowledge in the sample and data acquisition process is a necessity. Also, when working with tissue samples collected over decades, histological expertise is essential for re-evaluation and re-classification of the samples.

Prostate Cancer Risk Stratification in Men With a Clinical Suspicion of Prostate Cancer Using a Unique Biparametric MRI and Expression of 11 Genes in Apparently Benign Tissue: Evaluation Using Machine-Learning Techniques.

BACKGROUND: Accurate risk stratification of men with a clinical suspicion of prostate cancer (cSPCa) remains challenging despite the increasing use of MRI. PURPOSE: To evaluate the diagnostic accuracy of a unique biparametric MRI protocol (IMPROD bpMRI) combined with clinical and molecular markers in men with cSPCa. STUDY TYPE: Prospective single-institutional clinical trial (NCT01864135). SUBJECTS: Eighty men with cSPCa. FIELD STRENGTH/SEQUENCE: 3T, surface array coils. Two T2 -weighted and three diffusion-weighted imaging (DWI) acquisitions: 1) b-values 0, 100, 200, 300, 500 s/mm2 ; 2) b-values 0,1500 s/mm2 ; 3) b-values 0, 2000 s/mm2 . ASSESSMENT: IMPROD bpMRI examinations were qualitatively (IMPROD bpMRI Likert score) and quantitatively (DWI-based Gleason grade score) prospectively reported. Men with IMPROD bpMRI Likert 3-5 had two targeted biopsies followed by 12-core systematic biopsies (SB); those with IMPROD bpMRI Likert 1-2 had only SB. Additionally, 2-core from normal-appearing prostate areas were obtained for the mRNA expression of ACSM1, AMACR, CACNA1D, DLX1, PCA3, PLA2G7, RHOU, SPINK1, SPON2, TMPRSS2-ERG, and TDRD1 measured by quantitative reverse-transcription polymerase chain reaction. STATISTICAL TESTS: Univariate and multivariate analysis using regularized least-squares, feature selection and tournament leave-pair-out cross-validation (TLPOCV), as well as 10 random splits of the data in training-testing sets, were used to evaluate the mRNA, clinical and IMPROD bpMRI parameters in detecting clinically significant prostate cancer (SPCa) defined as Gleason score ≥ 3 + 4. The evaluation metric was the area under the curve (AUC). RESULTS: IMPROD bpMRI Likert demonstrated the highest TLPOCV AUC of 0.92. The tested clinical variables had AUC 0.56-0.73, while the mRNA and additional IMPROD bpMRI parameters had AUC 0.50-0.67 and 0.65-0.89 respectively. The combination of clinical and mRNA biomarkers produced TLPOCV AUC of 0.87, the highest TLPOCV performance without including IMPROD bpMRI Likert. DATA CONCLUSION: The qualitative IMPROD bpMRI Likert score demonstrated the highest accuracy for SPCa detection compared with the tested clinical variables and mRNA biomarkers. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1540-1553.

IMPROD biparametric MRI in men with a clinical suspicion of prostate cancer (IMPROD Trial): Sensitivity for prostate cancer detection in correlation with whole-mount prostatectomy sections and implications for focal therapy.

BACKGROUND: Prostate MRI is increasingly being used in men with a clinical suspicion of prostate cancer (PCa). However, development and validation of methods for focal therapy planning are still lagging. PURPOSE: To evaluate the diagnostic accuracy on lesion, region-of-interest (ROI), and voxel level of IMPROD biparametric prostate MRI (bpMRI) for PCa detection in men with a clinical suspicion of PCa who subsequently underwent radical prostatectomy. STUDY TYPE: Prospective single-institution clinical trial (NCT01864135). POPULATION: Sixty-four men who underwent radical prostatectomy after IMPROD bpMRI performed in prebiopsy settings. FIELD STRENGTH/SEQUENCE: IMPROD bpMRI consisted of T2 -weighted imaging (T2 w) and three separate diffusion-weighted imaging acquisitions with an average acquisition time of 15 minutes. ASSESSMENT: The diagnostic accuracy of prospectively reported manual cancer delineations and regions increased with 3D dilation were evaluated on the voxel level (volume of 1.17 mm3 , 1 mm3 , 125 mm3 ) as well as the 36 ROI level. Only PCa lesions with a diameter ≥ 5 mm or any Gleason Grade 4 were analyzed. All data and protocols are freely available at: http://petiv.utu.fi/improd STATISTICAL TESTS: Sensitivity, specificity, accuracy. RESULTS: In total, 99 PCa lesions were identified. Forty (40%, 40/99) had a Gleason score (GS) of >3 + 4. Twenty-eight PCa lesions (28%, 28/99) were missed by IMPROD bpMRI, three (7.5%, 3/40) with GS >3 + 4. 3D dilation of manual cancer delineations in all directions by ~10-12 mm (corresponding to the Hausdorff distance) was needed to achieve sensitivity approaching 100% on a voxel level. DATA CONCLUSION: IMPROD bpMRI had a high sensitivity on lesion level for PCa with GS >3 + 4. Increasing 3D lesion delineations by ~10-12 mm (corresponding to the Hausdorff distance) was needed to achieve high sensitivity on the voxel level. Such information may help in planning ablation therapies. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1641-1650.

Somatostatin Receptor Expression Is Associated With Metastasis and Patient Outcome in Pulmonary Carcinoid Tumors.

CONTEXT: Pulmonary carcinoids (PCs) belong to neuroendocrine tumors that often overexpress somatostatin receptors (SSTRs). This overexpression provides a molecular basis for tumor imaging and treatment with somatostatin analogs. OBJECTIVE: To evaluate SSTR1 to SSTR5 distribution in a large set of PC tumors and to investigate whether the expression is associated with clinicopathological and outcome data. DESIGN, SETTING, AND PATIENTS: This retrospective study was conducted at Helsinki University Hospital and University of Helsinki. It included 178 PC tumors coupled with patients' clinical data retrieved through Finnish biobanks. After histological reclassification, tissue specimens were processed into next-generation tissue microarray format and stained immunohistochemically with monoclonal SSTR1 to SSTR5 antibodies. MAIN OUTCOME MEASURE: SSTR1 to SSTR5 expression in PC tumors. RESULTS: Expression of SSTR1 to SSTR5 was detected in 52%, 75%, 56%, 16%, and 32% of the tumors, respectively. Membrane-bound staining was observed for all receptors. SSTR2 negativity and SSTR4 positivity was associated with lymph node involvement at the time of surgery (P = 0.014 and P = 0.017, respectively) and with distant metastasis (P = 0.027 and P = 0.015, respectively). SSTR3 and SSTR4 expression was associated with increased risk of shorter survival [P = 0.046, hazard ratio (HR) 4.703, 95% CI 1.027 to 21.533; and P = 0.013, HR 6.64, 95% CI 1.48 to 29.64, respectively], whereas expression of SSTR1 and SSTR2 was associated with improved outcome (P = 0.021, HR 0.167, 95% CI 0.037 to 0.765; and P = 0.022, HR 0.08, 95% CI 0.01 to 0.70, respectively). CONCLUSION: SSTR1 to SSTR5 expression is observed in PCs. As SSTR expression is associated with the tumor's metastatic potential and patient outcome, these receptors may offer the possibility for individualized prognosis estimation.

Novel COL1A1 Mutation c.3290G>T Associated With Severe Form of Osteogenesis Imperfecta in a Fetus.

Osteogenesis imperfecta is a genetically and clinically heterogenous group of skeletal dysplasias characterized by bone fragility. Its severity ranges from nearly asymptomatic individuals to perinatal lethality. The majority of cases are caused by mutations in either the COL1A1 or the COL1A2 gene coding for alpha 1 and alpha 2 chains of collagen type 1, respectively, and a large number of pathogenic variants of these genes has been identified. We describe a novel COL1A1 mutation associated with prenatally diagnosed severe form of osteogenesis imperfecta.

PLA2G7 associates with hormone receptor negativity in clinical breast cancer samples and regulates epithelial-mesenchymal transition in cultured breast cancer cells.

Breast cancer is the leading cause of cancer-related deaths in women due to distinct cancer subtypes associated with early recurrence and aggressive metastatic progression. High lipoprotein-associated phospholipase A2 (PLA2G7) expression has previously been associated with aggressive disease and metastasis in prostate cancer. Here, we explore the expression pattern and functional role of PLA2G7 in breast cancer. First, a bioinformatic analysis of genome-wide gene expression data from 970 breast samples was carried out to evaluate the expression pattern of PLA2G7 mRNA in breast cancer. Second, the expression profile of PLA2G7 was studied in 1042 breast cancer samples including 89 matched lymph node metastasis samples using immunohistochemistry. Third, the effect of PLA2G7 silencing on genome-wide gene expression profile was studied and validated in cultured breast cancer cells expressing PLA2G7 at high level. Last, the expression pattern of PLA2G7 mRNA was investigated in 24 nonmalignant tissue samples and 65 primary and 7 metastatic tumour samples derived from various organs using qRT-PCR. The results from clinical breast cancer samples indicated that PLA2G7 is overexpressed in a subset of breast cancer samples compared to its expression in benign breast tissue samples and that high PLA2G7 expression associated with hormone receptor negativity as well as with poor prognosis in a subset of breast cancer samples. In vitro functional studies highlighted the putative role of PLA2G7 in the regulation of epithelial-mesenchymal transition (EMT)-related signalling pathways, vimentin and E-cadherin protein expression as well as cell migration in cultured breast cancer cells. Furthermore, supporting the findings in breast and prostate cancer, high PLA2G7 mRNA expression was associated with metastatic cancer in four additional organs of origin. In conclusion, our results indicate that PLA2G7 is highly expressed in a subset of metastatic and aggressive breast cancers and in metastatic samples of various tissues of origin and promotes EMT and migration in cultured breast cancer cells.

Patient-specific pharmacokinetic parameter estimation on dynamic contrast-enhanced MRI of prostate: Preliminary evaluation of a novel AIF-free estimation method.

PURPOSE: To develop and evaluate a prostate-based method (PBM) for estimating pharmacokinetic parameters on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) by leveraging inherent differences in pharmacokinetic characteristics between the peripheral zone (PZ) and transition zone (TZ). MATERIALS AND METHODS: This retrospective study, approved by the Institutional Review Board, included 40 patients who underwent a multiparametric 3T MRI examination and subsequent radical prostatectomy. A two-step PBM for estimating pharmacokinetic parameters exploited the inherent differences in pharmacokinetic characteristics associated with the TZ and PZ. First, the reference region model was implemented to estimate ratios of Ktrans between normal TZ and PZ. Subsequently, the reference region model was leveraged again to estimate values for Ktrans and ve for every prostate voxel. The parameters of PBM were compared with those estimated using an arterial input function (AIF) derived from the femoral arteries. The ability of the parameters to differentiate prostate cancer (PCa) from benign tissue was evaluated on a voxel and lesion level. Additionally, the effect of temporal downsampling of the DCE MRI data was assessed. RESULTS: Significant differences (P < 0.05) in PBM Ktrans between PCa lesions and benign tissue were found in 26/27 patients with TZ lesions and in 33/38 patients with PZ lesions; significant differences in AIF-based Ktrans occurred in 26/27 and 30/38 patients, respectively. The 75th and 100th percentiles of Ktrans and ve estimated using PBM positively correlated with lesion size (P < 0.05). CONCLUSION: Pharmacokinetic parameters estimated via PBM outperformed AIF-based parameters in PCa detection. J. Magn. Reson. Imaging 2016;44:1405-1414.

High-throughput transcriptomic and RNAi analysis identifies AIM1, ERGIC1, TMED3 and TPX2 as potential drug targets in prostate cancer.

Prostate cancer is a heterogeneous group of diseases and there is a need for more efficient and targeted methods of treatment. In this study, the potential of gene expression data and RNA interference technique were combined to advance future personalized prostate cancer therapeutics. To distinguish the most promising in vivo prevalidated prostate cancer drug targets, a bioinformatic analysis was carried out using genome-wide gene expression data from 9873 human tissue samples. In total, 295 genes were selected for further functional studies in cultured prostate cancer cells due to their high mRNA expression in prostate, prostate cancer or in metastatic prostate cancer samples. Second, RNAi based cell viability assay was performed in VCaP and LNCaP prostate cancer cells. Based on the siRNA results, gene expression patterns in human tissues and novelty, endoplasmic reticulum function associated targets AIM1, ERGIC1 and TMED3, as well as mitosis regulating TPX2 were selected for further validation. AIM1, ERGIC1, and TPX2 were shown to be highly expressed especially in prostate cancer tissues, and high mRNA expression of ERGIC1 and TMED3 associated with AR and ERG oncogene expression. ERGIC1 silencing specifically regulated the proliferation of ERG oncogene positive prostate cancer cells and inhibited ERG mRNA expression in these cells, indicating that it is a potent drug target in ERG positive subgroup of prostate cancers. TPX2 expression associated with PSA failure and TPX2 silencing reduced PSA expression, indicating that TPX2 regulates androgen receptor mediated signaling. In conclusion, the combinatorial usage of microarray and RNAi techniques yielded in a large number of potential novel biomarkers and therapeutic targets, for future development of targeted and personalized approaches for prostate cancer management.

Integrative genomic, transcriptomic, and RNAi analysis indicates a potential oncogenic role for FAM110B in castration-resistant prostate cancer.

BACKGROUND: Castration-resistant prostate cancer (CRPC) represents a therapeutic challenge for current medications. METHODS: In order to explore the molecular mechanisms involved in CRPC progression and to identify new therapeutic targets, we analyzed a unique sample set of 11 CRPCs and 7 advanced tumors by array-CGH and gene expression microarrays. The genome-wide DNA and RNA data were integrated to identify genes whose overexpression was driven by their amplification. To assess the functional role of these genes, their expression was analyzed in a transcriptional data set of 329 clinical prostate cancers and the corresponding gene products were silenced using RNA interference in prostate cancer cells. RESULTS: Six recurrent genetic targets were identified in the CRPCs; ATP1B1, AR, FAM110B, LAS1L, MYC, and YIPF6. In addition to AR and MYC, FAM110B emerged as a potential key gene involved in CRPC progression in a subset of the tumors. FAM110B was able to regulate AR signaling in prostate cancer cells and FAM110B itself was regulated by androgens. FAM110B siRNA inhibited the growth of prostate cancer cells in vitro, and this effect was substantially enhanced in androgen deficient conditions. Ectopic FAM110B expression in non-cancerous epithelial prostate cells induced aneuploidy and impaired antigen presentation. CONCLUSIONS: The DNA/RNA gene outlier detection combined with siRNA cell proliferation assay identified FAM110B as a potential growth promoting key gene for CRPC. FAM110B appears to have a key role in the androgen signaling and progression of CRPC impacting multiple cancer hallmarks and therefore highlighting a potential therapeutic target.

15-Hydroxyprostaglandin dehydrogenase associates with poor prognosis in breast cancer, induces epithelial-mesenchymal transition, and promotes cell migration in cultured breast cancer cells.

Breast cancer is the most frequent cancer and the leading cause of cancer-related deaths in women worldwide. The prognosis of breast cancer is tightly correlated with the degree of spread beyond the primary tumour. Arachidonic acid (AA) and prostaglandin E(2) (PGE(2)) are known to regulate tumour metastasis enabling epithelial-mesenchymal transition (EMT). However, the detailed role of 15-hydroxyprostaglandin dehydrogenase (HPGD), the key enzyme degrading prostaglandin E(2) , remains unclear in breast cancer. Here, we show that HPGD mRNA is overexpressed in a subset of clinical breast cancers compared to normal breast tissue samples and that high HPGD mRNA expression associates with poor prognosis. Immunohistochemical staining of primary breast cancer and lymph node metastasis tissue samples confirmed high HPGD protein expression in 20% of the samples, as well as associated HPGD expression with aggressive characteristics, such as increased risk of disease relapse and shorter disease-free survival. Results from cultured cells indicated abundant HPGD expression in highly metastatic breast cancer cells, and impairment of HPGD expression using RNA interference led to a significant decrease in transforming growth factor-ß signalling, in cellular arachidonic acid levels as well as in cell migration. Furthermore, gene expression microarray analysis followed by quantitative RT-PCR validation showed that HPGD silencing decreased aryl hydrocarbon receptor signalling and induced mesenchymal-epithelial transition. In conclusion, our results indicate that HPGD is highly expressed in metastatic and aggressive breast cancer and promotes EMT and migration in breast cancer cells.

Phospholipase PLA2G7, associated with aggressive prostate cancer, promotes prostate cancer cell migration and invasion and is inhibited by statins.

Prostate cancer is the second leading cause of cancer mortality in men in developed countries. Due to the heterogeneous nature of the disease, design of novel personalized treatments is required to achieve efficient therapeutic responses. We have recently identified phospholipase 2 group VII (PLA2G7) as a potential drug target especially in ERG oncogene positive prostate cancers. Here, the expression profile of PLA2G7 was studied in 1137 prostate cancer and 409 adjacent non-malignant prostate tissues using immunohistochemistry to validate its biomarker potential and putative association with disease progression. In order to reveal the molecular alterations induced by PLA2G7 impairment, lipidomic and gene expression profiling was performed in response to PLA2G7 silencing in cultured prostate cancer cells. Moreover, the antineoplastic effect of statins combined with PLA2G7 impairment was studied in prostate cancer cells to evaluate the potential of repositioning of in vivo compatible drugs developed for other indications towards anti-cancer purposes. The results indicated that PLA2G7 is a cancer-selective biomarker in 50 % of prostate cancers and associates with aggressive disease. The alterations induced by PLA2G7 silencing highlighted the potential of PLA2G7 inhibition as an anti-proliferative, pro-apoptotic and anti-migratorial therapeutic approach in prostate cancer. Moreover, the anti-proliferative effect of PLA2G7 silencing was potentiated by lipid-lowering statins in prostate cancer cells. Taken together, our results support the potential of PLA2G7 as a biomarker and a drug target in prostate cancer and present a rationale for combining PLA2G7 inhibition with the use of statins in prostate cancer management.

GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets.

BACKGROUND: Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type ('outlier genes'), a hallmark of potential oncogenes. METHODOLOGY: A new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target. CONCLUSIONS/SIGNIFICANCE: Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1).