Predict Prostate Cancer Aggressiveness Research Articles

Integrating Proteomic Analysis and Machine Learning to Predict Prostate Cancer Aggressiveness

Prostate cancer (PCa) poses a significant challenge because of the difficulty in identifying aggressive tumors, leading to overtreatment and missed personalized therapies. Although only 8% of cases progress beyond the prostate, the accurate prediction of aggressiveness remains crucial. Thus, this study focused on studying retinoblastoma phosphorylated at Serine 249 (Phospho-Rb S249), N-cadherin, β-catenin, and E-cadherin as biomarkers for identifying aggressive PCa using a logistic regression model and a classification and regression tree (CART). Using immunohistochemistry (IHC), we targeted the expression of these biomarkers in PCa tissues and correlated their expression with clinicopathological data of the tumor. The results showed a negative correlation between E-cadherin and β-catenin with aggressive tumor behavior, whereas Phospho-Rb S249 and N-cadherin positively correlated with increased tumor aggressiveness. Furthermore, patients were stratified based on Gleason scores and E-cadherin staining patterns to evaluate their capability for early identification of aggressive PCa. Our findings suggest that the classification tree is the most effective method for measuring the utility of these biomarkers in clinical practice, incorporating β-catenin, tumor grade, and Gleason grade as relevant determinants for identifying patients with Gleason scores ≥ 4 + 3. This study could potentially benefit patients with aggressive PCa by enabling early disease detection and closer monitoring.

Comparison of serum prostate specific antigen (PSA), soluble E-cadherin (sE-cad), and inosine monophosphate dehydrogenase-2 (IMPDH-2) as aggressive prostate cancer predictors

BackgroundThis study aimed to compare serum prostate specific antigen (PSA), Soluble E-cadherin (sE-cad), and Inosine Monophosphate Dehydrogenase-2 in predicting prostate cancer aggressiveness by determining their correlations with Gleason score and International Society of Urological Pathology (ISUP) Grade Groups.MethodsThis was a hospital-based descriptive quantitative cross-sectional study whereby we enrolled 48 newly diagnosed prostate adenocarcinoma patients in the study. Their serum was analysed for PSA, sE-cad, and IMPDH-2. Pearson correlation coefficient was used to test the correlation between the serum sE-cad and Gleason score while Spearman rho correlation coefficient was used for PSA and IMPDH-2. The correlation coefficient (r) was graded as very weak (< 0.3), weak (0.3–0.4), moderate (0.5–0.6), or strong (≥ 0.7), while the magnitude was determined by calculating the coefficient of determination for the respective analysis (R2). The correlation between the biomarkers and the ISUP Grade groups was determined using the Kendall tau correlation coefficient (τ). All levels of statistical significance were set at p < 0.05.ResultsThe mean age of the subjects was 69.4 years. The Means of serum PSA, sE-cad, and IMPDH-2 were 47.2 ng/ml, 136.5 ng/ml, and 89.8 pg/ml respectively. Serum PSA weakly correlated with both Gleason score (r = 0.3, p = 0.04) and ISUP grade groups (τ = 0.3, p = 0.02). The magnitude was 0.097. Similarly, serum sE-cad correlated weakly with both Gleason scores (r = 0.4, p = 0.01), and ISUP Grade Groups (τ = 0.3, p = 0.005). The magnitude was 0.134. However, serum IMPDH-2 neither correlated with Gleason score (r = 0.03, p = 0.86) nor ISUP Grade Groups (τ = 0.004, p = 0.97). Serum sE-cad did not outperform both IMPDH-2 (p = 0.91) or PSA (p = 0.23) in predicting the Gleason score.ConclusionsSerum sE-cad best predicted aggressive prostate cancer but did not statistically outperform serum PSA or IMPDH-2. Hence, neither of the three are reliable predictors of aggressive prostate cancer.

Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study

PurposeThe Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa.Material and MethodsA total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic.ResultsThe 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively.ConclusionsMRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.

Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study

PurposeTo develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment.Material and methodsA total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists. The radiomics models for predicting csPCa were built using 4 machine-learning algorithms. The PI-RADS were adjusted by the radiomics model. The relationship between the Rad-score and ISUP was evaluated by Spearman analysis.ResultsThe radiomics models made using the random forest algorithm yielded areas under the receiver operating characteristic curves (AUCs) of 0.874, 0.876, and 0.893 in an internal testing cohort and external testing cohorts, respectively. The AUC of the adjusted_PI-RADS was improved, and the specificity was improved at a slight sacrifice of sensitivity. The participant-level correlation showed that the Rad-score was positively correlated with ISUP in all testing cohorts (r > 0.600 and p < 0.0001).ConclusionsThis radiomics model resulted as a powerful, non-invasive auxiliary tool for accurately predicting prostate cancer aggressiveness. The radiomics model could reduce unnecessary biopsies and help improve the diagnostic performance of radiologists’ PI-RADS. Yet, prospective studies are still needed to validate the radiomics models further.Critical relevance statementThe radiomics model with MRI may help to accurately screen out clinically significant prostate cancer, thereby assisting physicians in making individualized treatment plans.Key points• The diagnostic performance of the radiomics model using the Random Forest algorithm is comparable to the Prostate Imaging Reporting and Data System (PI-RADS) obtained by radiologists.• The performance of the adjusted Prostate Imaging Reporting and Data System (PI-RADS) was improved, which implied that the radiomics model could be a potential radiological assessment tool.• The radiomics model lowered the percentage of equivocal cases. Moreover, the Rad-scores can be used to characterize prostate cancer aggressiveness.Graphical

Development and Optimization of a Subtraction-Normalized Immunocyte Profiling Signature for Prostate Cancer Active Surveillance Risk Stratification.

Less invasive decision support tools are desperately needed to identify occult high-risk disease in men with prostate cancer (PCa) on active surveillance (AS). For a variety of reasons, many men on AS with low- or intermediate-risk disease forgo the necessary repeat surveillance biopsies needed to identify potentially higher-risk PCa. Here, we describe the development of a blood-based immunocyte transcriptomic signature to identify men harboring occult aggressive PCa. We then validate it on a biopsy-positive population with the goal of identifying men who should not be on AS and confirm those men with indolent disease who can safely remain on AS. This model uses subtraction-normalized immunocyte transcriptomic profiles to risk-stratify men with PCa who could be candidates for AS. Men were eligible for enrollment in the study if they were determined by their physician to have a risk profile that warranted prostate biopsy. Both training (n = 1017) and validation cohort (n = 1198) populations had blood samples drawn coincident to their prostate biopsy. Purified CD2+ and CD14+ immune cells were obtained from peripheral blood mononuclear cells, and RNA was extracted and sequenced. To avoid overfitting and unnecessary complexity, a regularized regression model was built on the training cohort to predict PCa aggressiveness based on the National Comprehensive Cancer Network PCa guidelines. This model was then validated on an independent cohort of biopsy-positive men only, using National Comprehensive Cancer Network unfavorable intermediate risk and worse as an aggressiveness outcome, identifying patients who were not appropriate for AS. The best final model for the AS setting was obtained by combining an immunocyte transcriptomic profile based on 2 cell types with PSA density and age, reaching an AUC of 0.73 (95% CI: 0.69-0.77). The model significantly outperforms (P < .001) PSA density as a biomarker, which has an AUC of 0.69 (95% CI: 0.65-0.73). This model yields an individualized patient risk score with 90% negative predictive value and 50% positive predictive value. While further validation in an intended-use cohort is needed, the immunocyte transcriptomic model offers a promising tool for risk stratification of individual patients who are being considered for AS.

Abstract B096: Cigarette smoking and prostate cancer aggressiveness among African and European American men

Abstract Background: Smoking is a modifiable lifestyle factor linked to prostate cancer (PCa) risk, but its association with PCa aggressiveness is unclear. Further, African-American men are likelier to smoke and be at risk for PCa. Therefore, we examined racial disparities in the association between cigarette smoking and PCa aggressiveness. Methods: Data for this study came from the North Carolina-Louisiana Prostate Cancer Project, a registry-based cohort study of incident PCa cases from 2004-2009 (n = 1,457). We defined PCa aggressiveness as high (Gleason score ≥ 8, Gleason score = 7 and TNM T3-T4, or prostate-specific antigen (PSA) score &amp;gt; 20 ng/mL) or low (Gleason score &amp;lt; 7, TNM T1-T2, and PSA &amp;lt; 10 ng/mL). We classified cigarette smoking as current, former, or never. Our analyses used multivariable logistic regression to estimate adjusted odds ratios (OR) and 95% confidence intervals (CI). Results: One-third (35%) of PCa cases were high-aggressive. Half (48%) of the study sample was African-American. Current smoking, but not former smoking, was associated with high-aggressive PCa compared to never (OR = 2.2; 95% CI [1.5, 3.3]). African-Americans who currently smoked had higher odds of high-aggressive PCa than those who never smoked (OR = 3.2; 95% CI [1.9, 5.4]), as did those who formerly smoked (OR = 1.8; 95% CI [1.2, 2.3]). However, smoking did not significantly predict PCa aggressiveness in European-Americans. Conclusion: Cigarette smoking is associated with PCa aggressiveness, an association moderated by race. Future research should investigate the biological and social effects of smoking and PCa aggressiveness between racial groups. Citation Format: Edgar T. Ellis, Brian J. Fairman, Shelbie D. Stahr, L. Joseph Su, Ping-Ching Hsu. Cigarette smoking and prostate cancer aggressiveness among African and European American men [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B096.

Interactions of SNPs in Folate Metabolism Related Genes on Prostate Cancer Aggressiveness in European Americans and African Americans.

Studies showed that folate and related single nucleotide polymorphisms (SNPs) could predict prostate cancer (PCa) risk. However, little is known about the interactions of folate-related SNPs associated with PCa aggressiveness. The study's objective is to evaluate SNP-SNP interactions among the DHFR 19-bp polymorphism and 10 SNPs in folate metabolism and the one-carbon metabolism pathway associated with PCa aggressiveness. We evaluated 1294 PCa patients, including 690 European Americans (EAs) and 604 African Americans (AAs). Both individual SNP effects and pairwise SNP-SNP interactions were analyzed. None of the 11 individual polymorphisms were significant for EAs and AAs. Three SNP-SNP interaction pairs can predict PCa aggressiveness with a medium to large effect size. For the EA PCa patients, the interaction between rs1801133 (MTHFR) and rs2236225 (MTHFD1), and rs1801131 (MTHFR) and rs7587117 (SLC4A5) were significantly associated with aggressive PCa. For the AA PCa patients, the interaction of DHFR-19bp polymorphism and rs4652 (LGALS3) was significantly associated with aggressive PCa. These SNP-SNP interactions in the folate metabolism-related genes have a larger impact than SNP individual effects on tumor aggressiveness for EA and AA PCa patients. These findings can provide valuable information for potential biological mechanisms of PCa aggressiveness.

Difficulties and Recommendations for AI-Based Prediction of Prostate Cancer Aggressiveness in Digital Pathology

Prostate cancer is among the most common cancers in men with around 1.4 million new cases each year world-wide. A vital part in the diagnosis of prostate cancer is the evaluation of its severity using biopsies and histopathology. Recent progress in artificial intelligence-based image analysis has led to a flurry of algorithms for the automated analysis of prostate cancer histopathological data focusing on the detection of cancerous areas, the grading of cancer severity, and patient outcome. Some of these approaches have reached human expert-level performance and digital models trained directly on patient outcomes might surpass human performance in the future. Although these results hold great promise for the future usage of digital pathology in clinical settings, several bottlenecks remain to be addressed. Especially the robustness, reliability and trustworthiness of predictions must be guaranteed across a wide range of variation in protocols and instrumentation. While human experts are relatively robust to technical and biological variation in biopsies, artificial intelligence-based systems tend to struggle with differences in staining intensity, color, scanner type, and image resolution, impeding the clinical usage of digital models. In this work we highlight salient problems and minimal requirements of computational pathology for future use in clinical settings, while focusing on prostate cancer as a use case. In particular, we highlight data and model problems and solutions that include data variability, dataset size, and data annotations, as well as model robustness to data heterogeneity, model prediction confidence, and the explainability of model decisions. While model and data requirements for successful computational pathology in clinics will be highlighted, legal, ethical, and deployment requirements will not be addressed in this review. In summary, we provide a short overview of the field, salient problems, and potential solutions to harvest the full potential of digital pathology for prostate cancer in clinical practice.

A0488 - Liquid biopsy-based exo-oncomiRNAs can predict prostate cancer aggressiveness

A0488 - Liquid biopsy-based exo-oncomiRNAs can predict prostate cancer aggressiveness

Performance of apparent diffusion coefficient values and ratios for the prediction of prostate cancer aggressiveness across different MRI acquisition settings.

In this study, we assessed the performance of apparent diffusion coefficient (ADC) and diffusion-weighted imaging (DWI) metrics and their ratios across different magnetic resonance imaging (MRI) acquisition settings, with or without an endorectal coil (ERC), for the evaluation of prostate cancer (PCa) aggressiveness using whole-mount specimens as a reference. We retrospectively reviewed the data of prostate carcinoma patients with a Gleason score (GS) of 3+4 or higher who underwent prostate MRI using a 3T unit at our institution. They were divided into two groups based on the use of ERC for MRI acquisition, and patients who underwent prostate MRI with an ERC constituted the ERC (n = 55) data set, while the remaining patients accounted for the non-ERC data set (n = 41). DWI was performed with b-values of 50, 500, 1000, and 1,400 s/mm2, and ADC maps were automatically calculated. Additionally, computed DWI (cDWI) was performed with a b-value of 2000 s/mm2. Six ADC and two cDWI parameters were evaluated. In the ERC data set, receiver operating characteristic (ROC) curves were plotted for each metric to determine the best cutoff threshold values for differentiating GS 3+4 PCa from that with a higher GS. The performance of these cutoff values was assessed in non-ERC dataset. The diagnostic accuracies and area under the curves (AUCs) of the metrics were compared using Fisher's exact test and De Long's method, respectively. Among all metrics, the ADCmean-ratio yielded the highest AUC, 0.84, for differing GS 3+4 PCa from that with a higher GS. The best threshold cutoff values of ADCmean-ratio (£0.51) for discriminating GS 3+4 PCa from that with a higher GS classified 48 patients out of 55 with an accuracy of 87.27%. However, there was no significant difference between each metric in terms of accuracy and AUC (p = 0.163 and 0.214). Similarly, in the non-ERC data set, the ADCmean-ratio provided the highest diagnostic accuracy (82.92%) by classifying 34 patients out of 41. However, Fisher's exact test yielded no significant difference between DWI and ADC metrics in terms of diagnostic accuracy in non-ERC data (p = 0.561). The mean ADC ratio of the tumor to the normal prostate showed the highest accuracy and AUC in differentiating GS 3+4 PCa and PCa with a higher GS across different MRI acquisition settings; however, the performance of different ADC and DWI metrics did not differ significantly.

Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI.

Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.

Can we use Ki67 expression to predict prostate cancer aggressiveness?

specialists have an urge for biomarkers that can discriminate indolent prostate cancer from aggressive tumors. Ki67 is a proliferation marker, and its expression is associated with the aggressiveness of several cancers. analyze the expression of Ki67 in prostate cancer samples correlating with the aggressiveness of the disease. Ki67 mRNA levels were determined utilizing data from a TCGA cohort (Tumor(n)=492 and control(n)=52). The protein expression was determined on 94 biopsies from patients by immunohistochemical assay. in mRNA, the Ki67 upregulation is associated with cancer tissue (p<0.0001) and worst disease-free survival (p=0.035). The protein upregulation is associated with increase of the ISUP score (p<0.0001), cancer stage (p=0.05), biochemical recurrence (p=0.0006) and metastasis (p<0.0001). We also show a positive correlation between Ki67 expression and ISUP score (r=0.5112, p<0.0001) and disease risk stratification (r=0.3388, p=0.0009). Ki67 expression is a factor independently associated with biochemical recurrence (p=0.002) and metastasis (p<0.0001). Finally, the patients with high Ki67expression shows better survival regarding biochemical recurrence (p=0.008) and metastasis (p=0.056). Patients with high Ki67 expression are 2.62 times more likely to develop biochemical recurrence (p=0.036). Ki67 upregulation is associated with prostate cancer aggressiveness.

Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics.

Simple SummaryThe use of radiomics has been studied to predict Gleason Score from bi-parametric prostate MRI examinations. However, different combinations of type of input data (whole prostate gland/lesion features), sampling strategy, feature selection method and machine learning algorithm can be used. The impact of such choices was investigated and it was found that features extracted from the whole prostate gland were more stable to segmentation differences and produced better models (higher performance and less overfitting). This result suggests that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion.Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts have been made in the past to use radiomics coupled with machine learning to predict prostate cancer aggressiveness from clinical images, showing promising results. Thus, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from bpMRI examinations, to predict biological aggressiveness; 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data, sampling strategy, feature selection method and machine learning algorithm. On a cohort of 281 lesions from 183 patients, it was found that (1) radiomic features extracted from the lesion volume of interest were less stable to segmentation than the equivalent extraction from the whole gland volume of interest; and (2) radiomic features extracted from the whole gland volume of interest produced higher performance and less overfitted classifiers than radiomic features extracted from the lesions volumes of interest. This result suggests that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion.

MRI grading for the prediction of prostate cancer aggressiveness

ObjectivesTo evaluate the value of multiparametric MRI (mpMRI) for the prediction of prostate cancer (PCA) aggressiveness.MethodsIn this single center cohort study, consecutive patients with histologically confirmed PCA were retrospectively enrolled. Four different ISUP grade groups (1, 2, 3, 4–5) were defined and fifty patients per group were included. Several clinical (age, PSA, PSAD, percentage of PCA infiltration) and mpMRI parameters (ADC value, signal increase on high b-value images, diameter, extraprostatic extension [EPE], cross-zonal growth) were evaluated and correlated within the four groups. Based on combined descriptors, MRI grading groups (mG1–mG3) were defined to predict PCA aggressiveness.ResultsIn total, 200 patients (mean age 68 years, median PSA value 8.1 ng/ml) were analyzed. Between the four groups, statistically significant differences could be shown for age, PSA, PSAD, and for MRI parameters cross-zonal growth, high b-value signal increase, EPE, and ADC (p < 0.01). All examined parameters revealed a significant correlation with the histopathologic biopsy ISUP grade groups (p < 0.01), except PCA diameter (p = 0.09). A mixed linear model demonstrated the strongest prediction of the respective ISUP grade group for the MRI grading system (p < 0.01) compared to single parameters.ConclusionsMpMRI yields relevant pre-biopsy information about PCA aggressiveness. A combination of quantitative and qualitative parameters (MRI grading groups) provided the best prediction of the biopsy ISUP grade group and may improve clinical pathway and treatment planning, adding useful information beyond PI-RADS assessment category. Due to the high prevalence of higher grade PCA in patients within mG3, an early re-biopsy seems indicated in cases of negative or post-biopsy low-grade PCA.Key Points• MpMRI yields relevant pre-biopsy information about prostate cancer aggressiveness.• MRI grading in addition to PI-RADS classification seems to be helpful for a size independent early prediction of clinically significant PCA.• MRI grading groups may help urologists in clinical pathway and treatment planning, especially when to consider an early re-biopsy.

Tissue- and Liquid-Based Biomarkers in Prostate Cancer Precision Medicine

Worldwide, prostate cancer (PC) is the second-most-frequently diagnosed male cancer and the fifth-most-common cause of all cancer-related deaths. Suspicion of PC in a patient is largely based upon clinical signs and the use of prostate-specific antigen (PSA) levels. Although PSA levels have been criticised for a lack of specificity, leading to PC over-diagnosis, it is still the most commonly used biomarker in PC management. Unfortunately, PC is extremely heterogeneous, and it can be difficult to stratify patients whose tumours are unlikely to progress from those that are aggressive and require treatment intensification. Although PC-specific biomarker research has previously focused on disease diagnosis, there is an unmet clinical need for novel prognostic, predictive and treatment response biomarkers that can be used to provide a precision medicine approach to PC management. In particular, the identification of biomarkers at the time of screening/diagnosis that can provide an indication of disease aggressiveness is perhaps the greatest current unmet clinical need in PC management. Largely through advances in genomic and proteomic techniques, exciting pre-clinical and clinical research is continuing to identify potential tissue, blood and urine-based PC-specific biomarkers that may in the future supplement or replace current standard practices. In this review, we describe how PC-specific biomarker research is progressing, including the evolution of PSA-based tests and those novel assays that have gained clinical approval. We also describe alternative diagnostic biomarkers to PSA, in addition to biomarkers that can predict PC aggressiveness and biomarkers that can predict response to certain therapies. We believe that novel biomarker research has the potential to make significant improvements to the clinical management of this disease in the near future.

KLK3 SNP\u2013SNP interactions for prediction of prostate cancer aggressiveness

Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP–SNP interactions in the four pathways (the angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways) associated with PCa aggressiveness, we tested 8587 SNPs for 20,729 cases from the PCa consortium. We identified 3 KLK3 SNPs, and 1083 (P < 3.5 × 10–9) and 3145 (P < 1 × 10–5) SNP–SNP interaction pairs significantly associated with PCa aggressiveness. These SNP pairs associated with PCa aggressiveness were more significant than each of their constituent SNP individual effects. The majority (98.6%) of the 3145 pairs involved KLK3. The 3 most common gene–gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH13, and KLK3-TGFBR3. Predictions from the SNP interaction-based polygenic risk score based on 24 SNP pairs are promising. The prevalence of PCa aggressiveness was 49.8%, 21.9%, and 7.0% for the PCa cases from our cohort with the top 1%, middle 50%, and bottom 1% risk profiles. Potential biological functions of the identified KLK3 SNP–SNP interactions were supported by gene expression and protein–protein interaction results. Our findings suggest KLK3 SNP interactions may play an important role in PCa aggressiveness.

Advanced Imaging Analysis in Prostate MRI: Building a Radiomic Signature to Predict Tumor Aggressiveness.

Radiomics allows the extraction quantitative features from imaging, as imaging biomarkers of disease. The objective of this exploratory study is to implement a reproducible radiomic-pipeline for the extraction of a magnetic resonance imaging (MRI) signature for prostate cancer (PCa) aggressiveness. One hundred and two consecutive patients performing preoperative prostate multiparametric magnetic resonance imaging (mpMRI) and radical prostatectomy were enrolled. Multiparametric images, including T2-weighted (T2w), diffusion-weighted and dynamic contrast-enhanced images, were acquired at 1.5 T. Ninety-three imaging features (Ifs) were extracted from segmentation of index lesion. Ifs were ranked based on a stability rank and redundant Ifs were excluded. Using unsupervised hierarchical clustering, patients were grouped on the basis of similar radiomic patterns, whose association with Gleason Grade Group (GGG), extracapsular extension (ECE), and nodal involvement (pN) was tested. Signatures composed by IFs from T2w-images and Apparent Diffusion Coefficient (ADC) maps were tested for the prediction of GGG, ECE, and pN. T2w radiomic pattern was associated with pN, ECE, and GGG (p = 0.027, 0.05, 0.03) and ADC radiomic pattern was associated with GGG (p = 0.004). The best performance was reached by the signature combing IFs from multiparametric images (0.88, 0.89, and 0.84 accuracy for GGG, pN, and ECE). A reliable multiparametric MRI radiomic signature was extracted, potentially able to predict PCa aggressiveness, to be further validated on an independent sample.

Liquid Biopsy-Based Exo-oncomiRNAs Can Predict Prostate Cancer Aggressiveness.

Liquid biopsy-based biomarkers, including microRNAs packaged within extracellular vesicles, are promising tools for patient management. The cytokine tumor necrosis factor-like weak inducer of apoptosis (TWEAK) is related to PCa progression and is found in the semen of patients with PCa. TWEAK can induce the transfer of exo-oncomiRNAs from tumor cells to body fluids, and this process might have utility in non-invasive PCa prognosis. We investigated TWEAK-regulated exo-microRNAs in semen and in post-digital rectal examination urine from patients with different degrees of PCa aggressiveness. We first identified 14 exo-oncomiRNAs regulated by TWEAK in PCa cells in vitro, and subsequently validated those using liquid biopsies from 97 patients with PCa. Exo-oncomiR-221-3p, -222-3p and -31-5p were significantly higher in the semen of high-risk patients than in low-risk peers, whereas exo-oncomiR-193-3p and -423-5p were significantly lower in paired samples of post-digital rectal examination urine. A panel of semen biomarkers comprising exo-oncomiR-221-3p, -222-3p and TWEAK was designed that could correctly classify 87.5% of patients with aggressive PCa, with 85.7% specificity and 76.9% sensitivity with an area under the curve of 0.857. We additionally found that TWEAK modulated two exo-oncomiR-221-3p targets, TCF12 and NLK. Overall, we show that liquid biopsy detection of TWEAK-regulated exo-oncomiRNAs can improve PCa prognosis prediction.

Preoperative prostate health index predicts adverse pathology and Gleason score upgrading after radical prostatectomy for prostate cancer

BackgroundWe aimed to explore the utility of prostate specific antigen (PSA) isoform [− 2] proPSA and its derivatives for prediction of pathological outcome after radical prostatectomy (RP).MethodsPreoperative blood samples were prospectively and consecutivelyanalyzed from 472 patients treated with RP for clinically localized prostate cancerat four medical centers. Measured parameters were PSA, free PSA (fPSA), fPSA/PSA ratio, [− 2] proPSA (p2PSA), p2PSA/fPSA ratio and Prostate Health Index (PHI)(p2PSA/fPSA)*√PSA]. Logistic regression models were fitted to determine the accuracy of markers for prediction of pathological Gleason score (GS) ≥7, Gleason score upgrading, extracapsular extension of the tumor (pT3) and the presence of positive surgical margin (PSM). The accuracy of predictive models was compared using area under the receiver operating curve (AUC).ResultsOf 472 patients undergoing RP, 339 (72%) were found to have pathologic GS ≥ 7, out of them 178 (53%) experienced an upgrade from their preoperative GS = 6. The findings of pT3 and PSM were present in 132 (28%) and 133 (28%) cases, respectively. At univariable analysis of all the preoperative parameters, PHI was the most accurate predictor of pathological GS ≥7 (OR 1.02, 95% CI 1.01–1.03, p<0.001), GS upgrading (OR 1.02, 95% CI 1.01–1.03, p<0.003), pT3 disease (OR 1.01, 95% CI 1.00–1.02, p<0.007) and the presence of PSM (OR 1.01, 95% CI 1.00–1.02, p<0.002). Adding of PHI into the base multivariable model increased significantly the accuracy for prediction of pathological GS by 4.4% to AUC = 66.6 (p = 0.015) and GS upgrading by 5.0% to AUC = 65.9 (p = 0.025), respectively.ConclusionsPreoperative PHI levels may contribute significantly to prediction of prostate cancer aggressiveness and expansion of the tumor detected at final pathology.

Abstract 282: MicroRNAs located at chromosome 8p21-22 region as novel factors underlying racial disparities in prostate cancer aggressiveness

Abstract The mechanistic basis of racial disparities in prostate cancer (PCa) aggressiveness between African American (AA) and Caucasian (CA) men are not yet fully understood. Further, molecular biomarkers to predict aggressive disease at its onset have been studied primarily in CAs, the relevance of these biomarkers to AAs are largely unknown. We propose a novel, paradigm-shifting hypothesis that expression of microRNA (miRNA) genes-miR-3622a and miR-383-located within frequently deleted chr8p21-22 region constitute important molecular factors contributing to racial disparities in PCa aggressiveness in AA vs CA men. Chromosome 8p deletions are a frequent alteration of PCa genome, with a common region of loss of heterozygosity at the chr8p21-22 locus. We previously showed that this region is associated with a set of tumor suppressive miRNAs (miR-3622a/b, -383, -4288) that are downregulated in PCa with important causal roles in tumor progression, recurrence and metastasis. Chr8p deletions increase significantly with tumor grade and are associated with poor prognosis. These deletions are well studied in CA men. However, there have been conflicting reports on frequency of chr8p deletions in AA men. In view of these conflicting studies and in light of our paradigm-shifting studies establishing the link between chr8p deletions and loss of miRNA genes in this region, we examined the expression of these miRNAs in AA vs CA clinical PCa tissues. We found that miR-3622a and miR-383 expression is significantly lower in AA men as compared to age, tumor stage and grade matched CAs. To examine if these miRNAs constitute causal factors contributing to PCa racial disparities, we overexpressed miR-3622a/miR-383/control miR in AA and CA PCa cell lines followed by functional assays. We found that these miRNAs exert more prominent effects on cellular proliferation and apoptosis in AA cell lines. To understand molecular mechanisms contributing to disparate functional effects of these miRNAs in AA vs CA cell lines, we examined potential miRNA target genes and found that miR-3622a regulates PCa stem cell marker, CD44 while miR-383 represses LEF1 and ZEB1 in a race-specific manner. Further, we determined the mechanistic basis of differential expression of miR-3622a in AAs vs CAs by examining copy number alterations (CNAs) and methylation at this locus using PCa clinical samples. Our analyses showed that while this locus is genomically altered at a lower frequency in AAs, significantly increased frequency of miRNA promoter hypermethylation was found in AAs as compared to CAs. In conclusion, our data suggests that miRNAs located in frequently deleted chr8p21-22 region constitute important causal factors contributing to racial disparities in PCa aggressiveness and clinical outcomes and are potential biomarkers to predict PCa aggressiveness and tumor recurrence in AA men. Citation Format: Divya Bhagirath, Nikhil Patel, Santu Ghosh, Roni Bollag, Sharanjot Saini. MicroRNAs located at chromosome 8p21-22 region as novel factors underlying racial disparities in prostate cancer aggressiveness [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 282.