Artificial intelligence for detection of prostate cancer in biopsies during active surveillance.

OBJECTIVES: To evaluate a cancer detecting artificial intelligence (AI) algorithm on serial biopsies in patients with prostate cancer on active surveillance (AS). PATIENTS AND METHODS: A total of 180 patients in the Prostate Cancer Research International Active Surveillance (PRIAS) cohort were prospectively monitored using pre-defined criteria. Diagnostic and re-biopsy slides from 2011 to 2020 (n = 4744) were scanned and analysed by an in-house AI-based cancer detection algorithm. The algorithm was analysed for sensitivity, specificity, and for accuracy to predict need for active treatment. Prognostic properties of cancer size, prostate-specific antigen (PSA) level and PSA density at diagnosis were evaluated. RESULTS: The sensitivity and specificity of the AI algorithm was 0.96 and 0.73, respectively, for correct detection of cancer areas. Original pathology report diagnosis was used as the reference method. The area of cancer estimated by the pathologists correlated highly with the AI detected cancer size (r = 0.83). By using the AI algorithm, 63% of the slides would not need to be read by a pathologist as they were classed as benign, at the risk of missing 0.55% slides containing cancer. Biopsy cancer content and PSA density at diagnosis were found to be prognostic of whether the patient stayed on AS or was discontinued for active treatment. CONCLUSION: The AI-based biopsy cancer detection algorithm could be used to reduce the pathologists' workload in an AS cohort. The detected cancer amount correlated well with the cancer length measured by the pathologist and the algorithm performed well in finding even small areas of cancer. To our knowledge, this is the first report on an AI-based algorithm in digital pathology used to detect cancer in a cohort of patients on AS.

Complete metabolic response with [18 F]fluorodeoxyglucose-positron emission tomography/computed tomography predicts survival following induction chemotherapy and radical cystectomy in clinically lymph node positive bladder cancer.

OBJECTIVE: To determine whether repeated [18 F]fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET-CT) scans can predict increased cancer-specific survival (CSS) after induction chemotherapy followed by radical cystectomy (RC). PATIENTS AND METHODS: Between 2007 and 2018, 86 patients with clinically lymph node (LN)-positive bladder cancer (T1-T4, N1-N3, M0-M1a) were included and underwent a repeated FDG-PET-CT during cisplatin-based induction chemotherapy. The 71 patients that had a response to chemotherapy underwent RC. Response to chemotherapy was evaluated in LNs through repeated FDG-PET-CT and stratified as partial response or complete response using three different methods: maximum standardised uptake value (SUVmax ), adapted Deauville criteria, and total lesion glycolysis (TLG). Progression-free survival (PFS) and CSS were analysed for all three methods by Cox regression analysis. RESULTS: After a median follow-up of 40 months, 15 of the 71 patients who underwent RC had died from bladder cancer. Using SUVmax and the adapted Deauville criteria, multivariable Cox regression analyses adjusting for age, clinical tumour stage and LN stage showed that complete response was associated with increased PFS (hazard ratio [HR] 3.42, 95% confidence interval [CI] 1.20-9.77) and CSS (HR 3.30, 95% CI 1.02-10.65). Using TLG, a complete response was also associated with increased PFS (HR 5.17, 95% CI 1.90-14.04) and CSS (HR 6.32, 95% CI 2.06-19.41). CONCLUSIONS: Complete metabolic response with FDG-PET-CT predicts survival after induction chemotherapy followed by RC in patients with LN-positive bladder cancer and comprises a novel tool in evaluating response to chemotherapy before surgery. This strategy has the potential to tailor treatment in individual patients by identifying significant response to chemotherapy, which motivates the administration of a full course of induction chemotherapy with a higher threshold for suspending treatment due to toxicity and side-effects.

The functional interlink between AR and MMP9/VEGF signaling axis is mediated through PIP5K1α/pAKT in prostate cancer.

Currently, no effective targeted therapeutics exists for treatment of metastatic prostate cancer (PCa). Given that matrix metalloproteinases 9 (MMP9) and its associated vascular endothelial growth factor (VEGF) are critical for tumor vascularization and invasion under castration-resistant condition, it is therefore of great importance to define the functional association and interplay between androgen receptor (AR) and MMP9 and their associated key survival and invasion pathways in PCa cells. Here, we found that there was a significant correlation between MMP9 and AR protein expression in primary and metastatic PCa tissues, and a trend that high level of MMP9 expression was associated with poor prognosis. We demonstrated that constitutive activation of AR increased expression of MMP9 and VEGF/VEGF receptors. We further showed that AR exerts its effect on MMP9/VEGF signaling axis through PIP5K1α/AKT. We showed that MMP9 physically interacted with PIP5K1α via formation of protein-protein complexes. Furthermore, elevated expression of MMP9 enhanced ability of AR to activate its target gene cyclin A1. The elevated sequential activation of AR/PIP5K1α/AKT/MMP9/VEGF signaling axis contributed to increased invasiveness and growth of metastatic tumors. Conversely, treatment with PIP5K1α inhibitor significantly suppressed invasiveness of PCa cells expressing constitutively activated AR, this was coincident with its inhibitory effect of this inhibitor on AR/MMP9/VEGF pathways. Our results suggest that AR and MMP9-associated network proteins may be effectively targeted by blocking PIP5K1α/AKT pathways using PIP5K1α inhibitor in metastatic PCa.

Development and validation of a 25-Gene Panel urine test for prostate cancer diagnosis and potential treatment follow-up.

BACKGROUND: Heterogeneity of prostate cancer (PCa) contributes to inaccurate cancer screening and diagnosis, unnecessary biopsies, and overtreatment. We intended to develop non-invasive urine tests for accurate PCa diagnosis to avoid unnecessary biopsies. METHODS: Using a machine learning program, we identified a 25-Gene Panel classifier for distinguishing PCa and benign prostate. A non-invasive test using pre-biopsy urine samples collected without digital rectal examination (DRE) was used to measure gene expression of the panel using cDNA preamplification followed by real-time qRT-PCR. The 25-Gene Panel urine test was validated in independent multi-center retrospective and prospective studies. The diagnostic performance of the test was assessed against the pathological diagnosis from biopsy by discriminant analysis. Uni- and multivariate logistic regression analysis was performed to assess its diagnostic improvement over PSA and risk factors. In addition, the 25-Gene Panel urine test was used to identify clinically significant PCa. Furthermore, the 25-Gene Panel urine test was assessed in a subset of patients to examine if cancer was detected after prostatectomy. RESULTS: The 25-Gene Panel urine test accurately detected cancer and benign prostate with AUC of 0.946 (95% CI 0.963-0.929) in the retrospective cohort (n = 614), AUC of 0.901 (0.929-0.873) in the prospective cohort (n = 396), and AUC of 0.936 (0.956-0.916) in the large combination cohort (n = 1010). It greatly improved diagnostic accuracy over PSA and risk factors (p < 0.0001). When it was combined with PSA, the AUC increased to 0.961 (0.980-0.942). Importantly, the 25-Gene Panel urine test was able to accurately identify clinically significant and insignificant PCa with AUC of 0.928 (95% CI 0.947-0.909) in the combination cohort (n = 727). In addition, it was able to show the absence of cancer after prostatectomy with high accuracy. CONCLUSIONS: The 25-Gene Panel urine test is the first highly accurate and non-invasive liquid biopsy method without DRE for PCa diagnosis. In clinical practice, it may be used for identifying patients in need of biopsy for cancer diagnosis and patients with clinically significant cancer for immediate treatment, and potentially assisting cancer treatment follow-up.

FDG-PET/CT for lymph node staging prior to radical cystectomy.

BACKGROUND: 18F-Fluorodeoxyglucose positron emission combined with computed tomography (FDG-PET/CT) has been proposed to improve preoperative staging in patients with bladder cancer subjected to radical cystectomy (RC). OBJECTIVE: Our aim was to assess the accuracy of FDG-PET/CT for lymph node staging ascertained at the multidisciplinary tumour board compared to lymph node status in the surgical lymphadenectomy specimen obtained at RC, and to explore potential factors associated with false-positive FDG-PET/CT results. DESIGN, SETTING AND PARTICIPANTS: Consecutive patients with bladder cancer undergoing RC with extended lymph node dissection between 2011 and 2019 without preoperative chemotherapy in a tertial referral cystectomy unit were included in the study. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES: Sensitivity, specificity, positive and negative predictive values and likelihood ratios were calculated. Potential factors investigated for association with false-positive FDG-PET/CT were; bacteriuria within four weeks prior to FDG-PET/CT, Bacillus Calmette-Guerin (BCG) treatment within 12 months prior to FDG-PET/CT and transurethral resection of bladder tumour (TURB) within four weeks prior to FDG-PET/CT. RESULTS: Among 157 patients included for analysis, 44 (28%) were clinically node positive according to FDG-PET/CT. The sensitivity and specificity for detection of lymph node metastasis were 50% and 84%, respectively, and the corresponding positive predictive and negative predictive values were 61% and 76%. Positive and negative likelihood ratios were 3.0 and 0.6, respectively. No association was found between bacteriuria, previous BCG treatment or TURB within 28 days and false-positive FDG-PET/CT results. CONCLUSIONS: Preoperative FDG-PET/CT prior to RC had a clinically meaningful high specificity (84%) but lower sensitivity (50%) for detection of lymph node metastases compared to lymph node status in an extended pelvic lymphadenectomy template. We could not identify any factors associated with false-positive FDG-PET/CT outcomes.

Urodrill - a novel MRI-guided endoscopic biopsy technique to sample and molecularly classify muscle-invasive bladder cancer without fractionating the specimen during transurethral resection.

The current diagnostic pathway for patients with muscle-invasive bladder cancer (MIBC), which involves with computed tomography urography, cystoscopy, and transurethral resection of the bladder (TURB) to histologically confirm MIBC, delays definitive treatment. The Vesical Imaging-Reporting and Data System (VI-RADS) has been suggested for MIBC identification using magnetic resonance imaging (MRI), but a recent randomized trial reported misclassification in one-third of patients. We investigated a new endoscopic biopsy device (Urodrill) for histological confirmation of MIBC and assessment of molecular subtype by gene expression in patients with VI-RADS 4 and 5 lesions on MRI. In ten patients, Urodrill biopsies were guided by MR images to the muscle-invasive portion of the tumor via a flexible cystoscope under general anesthesia. During the same session, conventional TURB was subsequently performed. A Urodrill sample was successfully obtained in nine of ten patients. MIBC was verified in six of nine patients, and seven of nine samples contained detrusor muscle. In seven of eight patients for whom a Urodrill biopsy sample was subjected to RNA sequencing, single-sample molecular classification according to the Lund taxonomy was feasible. No complications related to the biopsy device occurred. A randomized trial comparing this new diagnostic pathway for patients with VI-RADS 4 and 5 lesions and the current standard (TURB) is warranted. Patient summary: We report on a novel biopsy device for patients with muscle-invasive bladder cancer that facilitates histology analysis and molecular characterization of tumor samples.

Assessing the accuracy of [18F]PSMA-1007 PET/CT for primary staging of lymph node metastases in intermediate- and high-risk prostate cancer patients.

BACKGROUND: [18F]PSMA-1007 is a promising tracer for integrated positron emission tomography and computed tomography (PET/CT). OBJECTIVE: Our aim was to assess the diagnostic accuracy of [18F]PSMA-1007 PET/CT for primary staging of lymph node metastasis before robotic-assisted laparoscopy (RALP) with extended lymph node dissection (ePLND). DESIGN, SETTING AND PARTICIPANTS: The study was a retrospective cohort in a tertiary referral center. Men with prostate cancer that underwent surgical treatment for intermediate- or high-risk prostate cancer between May 2019 and August 2021 were included. INTERVENTIONS: [18F]PSMA-1007 PET/CT for initial staging followed by RALP and ePLND. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES: Sensitivity and specificity were calculated both for the entire cohort and for patients with lymph node metastasis ≥ 3 mm. Positive (PPV) and negative (NPV) predictive values were calculated. RESULTS AND LIMITATIONS: Among 104 patients included in the analyses, 26 patients had lymph node metastasis based on pathology reporting and metastases were ≥ 3 mm in size in 13 of the cases (50%). In the entire cohort, the sensitivity and specificity of [18F]PSMA-1007 were 26.9% (95% confidence interval (CI); 11.6-47.8) and 96.2% (95% CI; 89.2-99.2), respectively. The sensitivity and specificity of [18F]PSMA-1007 to detect a lymph node metastasis ≥ 3 mm on PET/CT were 53.8% (95% CI; 25.1-80.8) and 96.7% (95% CI; 90.7-99.3), respectively. PPV was 70% and NPV 93.6%. CONCLUSIONS: In primary staging of intermediate- and high-risk prostate cancer, [18F]PSMA-1007 PET/CT is highly specific for prediction of lymph node metastases, but the sensitivity for detection of metastases smaller than 3 mm is limited. Based on our results, [18F]PSMA-1007 PET/CT cannot completely replace ePLND. This study investigated the use of an imaging method based on a prostate antigen-specific radiopharmaceutical tracer to detect lymph node prostate cancer metastasis. We found that it is unreliable to discover small metastasis.

K-RAS Associated Gene-Mutation-Based Algorithm for Prediction of Treatment Response of Patients with Subtypes of Breast Cancer and Especially Triple-Negative Cancer.

Purpose: There is an urgent need for developing new biomarker tools to accurately predict treatment response of breast cancer, especially the deadly triple-negative breast cancer. We aimed to develop gene-mutation-based machine learning (ML) algorithms as biomarker classifiers to predict treatment response of first-line chemotherapy with high precision. Methods: Random Forest ML was applied to screen the algorithms of various combinations of gene mutation profiles of primary tumors at diagnosis using a TCGA Cohort (n = 399) with up to 150 months follow-up as a training set and validated in a MSK Cohort (n = 807) with up to 220 months follow-up. Subtypes of breast cancer including triple-negative and luminal A (ER+, PR+ and HER2−) were also assessed. The predictive performance of the candidate algorithms as classifiers was further assessed using logistic regression, Kaplan−Meier progression-free survival (PFS) plot, and univariate/multivariate Cox proportional hazard regression analyses. Results: A novel algorithm termed the 12-Gene Algorithm based on mutation profiles of KRAS, PIK3CA, MAP3K1, MAP2K4, PTEN, TP53, CDH1, GATA3, KMT2C, ARID1A, RunX1, and ESR1, was identified. The performance of this algorithm to distinguish non-progressed (responder) vs. progressed (non-responder) to treatment in the TCGA Cohort as determined using AUC was 0.96 (95% CI 0.94−0.98). It predicted progression-free survival (PFS) with hazard ratio (HR) of 21.6 (95% CI 11.3−41.5) (p < 0.0001) in all patients. The algorithm predicted PFS in the triple-negative subgroup with HR of 19.3 (95% CI 3.7−101.3) (n = 42, p = 0.000). The 12-Gene Algorithm was validated in the MSK Cohort with a similar AUC of 0.97 (95% CI 0.96−0.98) to distinguish responder vs. non-responder patients, and had a HR of 18.6 (95% CI 4.4−79.2) to predict PFS in the triple-negative subgroup (n = 75, p < 0.0001). Conclusions: The novel 12-Gene algorithm based on multitude gene-mutation profiles identified through ML has a potential to predict breast cancer treatment response to therapies, especially in triple-negative subgroups patients, which may assist personalized therapies and reduce mortality.

Gene-Mutation-Based Algorithm for Prediction of Treatment Response in Colorectal Cancer Patients.

Purpose: Despite the high mortality of metastatic colorectal cancer (mCRC), no new biomarker tools are available for predicting treatment response. We developed gene-mutation-based algorithms as a biomarker classifier to predict treatment response with better precision than the current predictive factors. Methods: Random forest machine learning (ML) was applied to identify the candidate algorithms using the MSK Cohort (n = 471) as a training set and validated in the TCGA Cohort (n = 221). Logistic regression, progression-free survival (PFS), and univariate/multivariate Cox proportional hazard analyses were performed and the performance of the candidate algorithms was compared with the established risk parameters. Results: A novel 7-Gene Algorithm based on mutation profiles of seven KRAS-associated genes was identified. The algorithm was able to distinguish non-progressed (responder) vs. progressed (non-responder) patients with AUC of 0.97 and had predictive power for PFS with a hazard ratio (HR) of 16.9 (p < 0.001) in the MSK cohort. The predictive power of this algorithm for PFS was more pronounced in mCRC (HR = 16.9, p < 0.001, n = 388). Similarly, in the TCGA validation cohort, the algorithm had AUC of 0.98 and a significant predictive power for PFS (p < 0.001). Conclusion: The novel 7-Gene Algorithm can be further developed as a biomarker model for prediction of treatment response in mCRC patients to improve personalized therapies.

FcγRIIIa receptor interacts with androgen receptor and PIP5K1α to promote growth and metastasis of prostate cancer.

Low-affinity immunoglobulin gamma Fc region receptor III-A (FcγRIIIa) is a cell surface protein that belongs to a family of Fc receptors that facilitate the protective function of the immune system against pathogens. However, the role of FcγRIIIa in prostate cancer (PCa) progression remained unknown. In this study, we found that FcγRIIIa expression was present in PCa cells and its level was significantly higher in metastatic lesions than in primary tumors from the PCa cohort (P = 0.006). PCa patients with an elevated level of FcγRIIIa expression had poorer biochemical recurrence (BCR)-free survival compared with those with lower FcγRIIIa expression, suggesting that FcγRIIIa is of clinical importance in PCa. We demonstrated that overexpression of FcγRIIIa increased the proliferative ability of PCa cell line C4-2 cells, which was accompanied by the upregulation of androgen receptor (AR) and phosphatidylinositol-4-phosphate 5-kinase alpha (PIP5Kα), which are the key players in controlling PCa progression. Conversely, targeted inhibition of FcγRIIIa via siRNA-mediated knockdown or using its inhibitory antibody suppressed growth of xenograft PC-3 and PC-3M prostate tumors and reduced distant metastasis in xenograft mouse models. We further showed that elevated expression of AR enhanced FcγRIIIa expression, whereas inhibition of AR activity using enzalutamide led to a significant downregulation of FcγRIIIa protein expression. Similarly, inhibition of PIP5K1α decreased FcγRIIIa expression in PCa cells. FcγRIIIa physically interacted with PIP5K1α and AR via formation of protein-protein complexes, suggesting that FcγRIIIa is functionally associated with AR and PIP5K1α in PCa cells. Our study identified FcγRIIIa as an important factor in promoting PCa growth and invasion. Further, the elevated activation of FcγRIII and AR and PIP5K1α pathways may cooperatively promote PCa growth and invasion. Thus, FcγRIIIa may serve as a potential new target for improved treatment of metastatic and castration-resistant PCa.

Tumor detection of 18F-PSMA-1007 in the prostate gland in patients with prostate cancer using prostatectomy specimens as reference method.

Prostate-specific membrane antigen (PSMA) radiopharmaceuticals used with positron emission tomography/computed tomography (PET-CT) are a promising tool for managing patients with prostate cancer. This study aimed to determine the accuracy of 18F-PSMA-1007 PET-CT for detecting tumors in the prostate gland using radical prostatectomy (RP) specimens as a reference method and to determine whether a correlation exists between 18F-PSMA-1007 uptake and the International Society of Urological Pathology (ISUP) grade and prostate specific antigen (PSA) levels at diagnosis. Methods: Thirty-nine patients referred for 18F-PSMA-1007 PET-CT for initial staging and who underwent RP within four months were retrospectively included. Uptake of 18F-PSMA-1007 indicative of cancer was assessed and maximum standardized uptake values (SUVmax) and total lesion uptake (TLU) were calculated for the index tumor. Histopathology was assessed from RP specimens. True positive, false negative, and false positive lesions were calculated. Results: In 94.9% of patients, the index tumor was correctly identified with PET. SUVmax was significantly higher in the tumors vs normal prostate tissue, but no significant differences were found between different ISUP grades and SUVmax There was a poor correlation between PSA at diagnosis and SUVmax (r=0.23) and moderate agreement between PSA at diagnosis and TLU (r=0.67). When all tumors (also non-index tumors) were considered, many small tumors (approx. 1-2 mm) were not detected with PET. Conclusion: 18F-PSMA-1007 PET-CT performs well in correctly identifying the index tumor in patients with intermediate to high-risk prostate cancer. Approximately 5% of the index tumors were missed by PET, which agrees with previous studies.

Targeted inhibition of ERα signaling and PIP5K1α/Akt pathways in castration-resistant prostate cancer.

Selective ERα modulator, tamoxifen, is well tolerated in a heavily pretreated castration-resistant prostate cancer (PCa) patient cohort. However, its targeted gene network and whether expression of intratumor ERα due to androgen deprivation therapy (ADT) may play a role in PCa progression is unknown. In this study, we examined the inhibitory effect of tamoxifen on castration-resistant PCa in vitro and in vivo. We found that tamoxifen is a potent compound that induced a high degree of apoptosis and significantly suppressed growth of xenograft tumors in mice, at a degree comparable to ISA-2011B, an inhibitor of PIP5K1α that acts upstream of PI3K/AKT survival signaling pathway. Moreover, depletion of tumor-associated macrophages using clodronate in combination with tamoxifen increased inhibitory effect of tamoxifen on aggressive prostate tumors. We showed that both tamoxifen and ISA-2011B exert their on-target effects on prostate cancer cells by targeting cyclin D1 and PIP5K1α/AKT network and the interlinked estrogen signaling. Combination treatment using tamoxifen together with ISA-2011B resulted in tumor regression and had superior inhibitory effect compared with that of tamoxifen or ISA-2011B alone. We have identified sets of genes that are specifically targeted by tamoxifen, ISA-2011B or combination of both agents by RNA-seq. We discovered that alterations in unique gene signatures, in particular estrogen-related marker genes are associated with poor patient disease-free survival. We further showed that ERα interacted with PIP5K1α through formation of protein complexes in the nucleus, suggesting a functional link. Our finding is the first to suggest a new therapeutic potential to inhibit or utilize the mechanisms related to ERα, PIP5K1α/AKT network, and MMP9/VEGF signaling axis, providing a strategy to treat castration-resistant ER-positive subtype of prostate cancer tumors with metastatic potential.

An Artificial Intelligence-based Support Tool for Automation and Standardisation of Gleason Grading in Prostate Biopsies.

BACKGROUND: Gleason grading is the standard diagnostic method for prostate cancer and is essential for determining prognosis and treatment. The dearth of expert pathologists, the inter- and intraobserver variability, as well as the labour intensity of Gleason grading all necessitate the development of a user-friendly tool for robust standardisation. OBJECTIVE: To develop an artificial intelligence (AI) algorithm, based on machine learning and convolutional neural networks, as a tool for improved standardisation in Gleason grading in prostate cancer biopsies. DESIGN, SETTING, AND PARTICIPANTS: A total of 698 prostate biopsy sections from 174 patients were used for training. The training sections were annotated by two senior consultant pathologists. The final algorithm was tested on 37 biopsy sections from 21 patients, with digitised slide images from two different scanners. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Correlation, sensitivity, and specificity parameters were calculated. RESULTS AND LIMITATIONS: The algorithm shows high accuracy in detecting cancer areas (sensitivity: 100%, specificity: 68%). Compared with the pathologists, the algorithm also performed well in detecting cancer areas (intraclass correlation coefficient [ICC]: 0.99) and assigning the Gleason patterns correctly: Gleason patterns 3 and 4 (ICC: 0.96 and 0.94, respectively), and to a lesser extent, Gleason pattern 5 (ICC: 0.82). Similar results were obtained using two different scanners. CONCLUSIONS: Our AI-based algorithm can reliably detect prostate cancer and quantify the Gleason patterns in core needle biopsies, with similar accuracy as pathologists. The results are reproducible on images from different scanners with a proven low level of intraobserver variability. We believe that this AI tool could be regarded as an efficient and interactive tool for pathologists. PATIENT SUMMARY: We developed a sensitive artificial intelligence tool for prostate biopsies, which detects and grades cancer with similar accuracy to pathologists. This tool holds promise to improve the diagnosis of prostate cancer.

Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients.

Objective: To avoid over-treatment of low-risk prostate cancer patients, it is important to identify clinically significant and insignificant cancer for treatment decision-making. However, no accurate test is currently available. Methods: To address this unmet medical need, we developed a novel gene classifier to distinguish clinically significant and insignificant cancer, which were classified based on the National Comprehensive Cancer Network risk stratification guidelines. A non-invasive urine test was developed using quantitative mRNA expression data of 24 genes in the classifier with an algorithm to stratify the clinical significance of the cancer. Two independent, multicenter, retrospective and prospective studies were conducted to assess the diagnostic performance of the 24-Gene Classifier and the current clinicopathological measures by univariate and multivariate logistic regression and discriminant analysis. In addition, assessments were performed in various Gleason grades/ISUP Grade Groups. Results: The results showed high diagnostic accuracy of the 24-Gene Classifier with an AUC of 0.917 (95% CI 0.892-0.942) in the retrospective cohort (n = 520), AUC of 0.959 (95% CI 0.935-0.983) in the prospective cohort (n = 207), and AUC of 0.930 (95% 0.912-CI 0.947) in the combination cohort (n = 727). Univariate and multivariate analysis showed that the 24-Gene Classifier was more accurate than cancer stage, Gleason score, and PSA, especially in the low/intermediate-grade/ISUP Grade Group 1-3 cancer subgroups. Conclusions: The 24-Gene Classifier urine test is an accurate and non-invasive liquid biopsy method for identifying clinically significant prostate cancer in newly diagnosed cancer patients. It has the potential to improve prostate cancer treatment decisions and active surveillance.

Establishment of Prostate Tumor Growth and Metastasis Is Supported by Bone Marrow Cells and Is Mediated by PIP5K1α Lipid Kinase.

Cancer cells facilitate growth and metastasis by using multiple signals from the cancer-associated microenvironment. However, it remains poorly understood whether prostate cancer (PCa) cells may recruit and utilize bone marrow cells for their growth and survival. Furthermore, the regulatory mechanisms underlying interactions between PCa cells and bone marrow cells are obscure. In this study, we isolated bone marrow cells that mainly constituted populations that were positive for CD11b and Gr1 antigens from xenograft PC-3 tumor tissues from athymic nu/nu mice. We found that the tumor-infiltrated cells alone were unable to form tumor spheroids, even with increased amounts and time. By contrast, the tumor-infiltrated cells together with PCa cells formed large numbers of tumor spheroids compared with PCa cells alone. We further utilized xenograft athymic nu/nu mice bearing bone metastatic lesions. We demonstrated that PCa cells were unable to survive and give rise to colony-forming units (CFUs) in media that were used for hematopoietic cell colony-formation unit (CFU) assays. By contrast, PC-3M cells survived when bone marrow cells were present and gave rise to CFUs. Our results showed that PCa cells required bone marrow cells to support their growth and survival and establish bone metastasis in the host environment. We showed that PCa cells that were treated with either siRNA for PIP5K1α or its specific inhibitor, ISA-2011B, were unable to survive and produce tumor spheroids, together with bone marrow cells. Given that the elevated expression of PIP5K1α was specific for PCa cells and was associated with the induced expression of VEGF receptor 2 in PCa cells, our findings suggest that cancer cells may utilize PIP5K1α-mediated receptor signaling to recruit growth factors and ligands from the bone marrow-derived cells. Taken together, our study suggests a new mechanism that enables PCa cells to gain proliferative and invasive advantages within their associated host microenvironment. Therapeutic interventions using PIP5K1α inhibitors may not only inhibit tumor invasion and metastasis but also enhance the host immune system.

Targeted suppression of AR-V7 using PIP5K1α inhibitor overcomes enzalutamide resistance in prostate cancer cells.

One mechanism of resistance of prostate cancer (PCa) to enzalutamide (MDV3100) treatment is the increased expression of AR variants lacking the ligand binding-domain, the best characterized of which is AR-V7. We have previously reported that Phosphatidylinositol-4-phosphate 5-kinase alpha (PIP5Kα), is a lipid kinase that links to CDK1 and AR pathways. The discovery of PIP5Kα inhibitor highlight the potential of PIP5K1α as a drug target in PCa. In this study, we show that AR-V7 expression positively correlates with PIP5K1α in tumor specimens from PCa patients. Overexpression of AR-V7 increases PIP5K1α, promotes rapid growth of PCa in xenograft mice, whereas inhibition of PIP5K1α by its inhibitor ISA-2011B suppresses the growth and invasiveness of xenograft tumors overexpressing AR-V7. PIP5K1α is a key co-factor for both AR-V7 and AR, which are present as protein-protein complexes predominantly in the nucleus of PCa cells. In addition, PIP5K1α and CDK1 influence AR-V7 expression also through AKT-associated mechanism dependent on PTEN-status. ISA-2011B disrupts protein stabilization of AR-V7 which is dependent on PIP5K1α, leading to suppression of invasive growth of AR-V7-high tumors in xenograft mice. Our study suggests that combination of enzalutamide and PIP5K1α may have a significant impact on refining therapeutic strategies to circumvent resistance to antiandrogen therapies.

Cyclin A1 and P450 Aromatase Promote Metastatic Homing and Growth of Stem-like Prostate Cancer Cells in the Bone Marrow.

Bone metastasis is a leading cause of morbidity and mortality in prostate cancer. While cancer stem-like cells have been implicated as a cell of origin for prostate cancer metastasis, the pathways that enable metastatic development at distal sites remain largely unknown. In this study, we illuminate pathways relevant to bone metastasis in this disease. We observed that cyclin A1 (CCNA1) protein expression was relatively higher in prostate cancer metastatic lesions in lymph node, lung, and bone/bone marrow. In both primary and metastatic tissues, cyclin A1 expression was also correlated with aromatase (CYP19A1), a key enzyme that directly regulates the local balance of androgens to estrogens. Cyclin A1 overexpression in the stem-like ALDH(high) subpopulation of PC3M cells, one model of prostate cancer, enabled bone marrow integration and metastatic growth. Further, cells obtained from bone marrow metastatic lesions displayed self-renewal capability in colony-forming assays. In the bone marrow, cyclin A1 and aromatase enhanced local bone marrow-releasing factors, including androgen receptor, estrogen and matrix metalloproteinase MMP9 and promoted the metastatic growth of prostate cancer cells. Moreover, ALDH(high) tumor cells expressing elevated levels of aromatase stimulated tumor/host estrogen production and acquired a growth advantage in the presence of host bone marrow cells. Overall, these findings suggest that local production of steroids and MMPs in the bone marrow may provide a suitable microenvironment for ALDH(high) prostate cancer cells to establish metastatic growths, offering new approaches to therapeutically target bone metastases. Cancer Res; 76(8); 2453-64. ©2016 AACR.