Mortality Risks Associated with Depression in Men with Prostate Cancer.

BACKGROUND: Men diagnosed with prostate cancer (PC) have an increased risk of depression; however, it is unclear to what extent depression affects long-term survival. A better understanding of such effects is needed to improve long-term care and outcomes for men with PC. OBJECTIVE: To determine the associations between major depression and mortality in a national cohort of men with PC. DESIGN, SETTING, AND PARTICIPANTS: A national cohort study was conducted of all 180 189 men diagnosed with PC in Sweden during 1998-2017. Subsequent diagnoses of major depression were ascertained from nationwide outpatient and inpatient records through 2018. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Deaths were identified from nationwide records through 2018. Cox regression was used to compute hazard ratios (HRs) for all-cause mortality associated with major depression, adjusting for sociodemographic factors and comorbidities. Subanalyses assessed differences by PC treatment during 2005-2017. PC-specific mortality was examined using competing risks models. RESULTS AND LIMITATIONS: In 1.3 million person-years of follow-up, 16 134 (9%) men with PC were diagnosed with major depression and 65 643 (36%) men died. After adjusting for sociodemographic factors and comorbidities, major depression was associated with significantly higher all-cause mortality in men with high-risk PC (HR, 1.50; 95% confidence interval [CI], 1.44-1.55) or low- or intermediate-risk PC (1.64; 1.56-1.71). These risks were elevated regardless of PC treatment or age at PC diagnosis, except for youngest men (<55 yr) in whom the risks were nonsignificant. Major depression was also associated with increased PC-specific mortality in men with either high-risk PC (HR, 1.35; 95% CI, 1.28-1.43) or low- or intermediate-risk PC (1.42; 1.27-1.59). This study was limited to Sweden and will need replication in other countries when feasible. CONCLUSIONS: In this national cohort of men with PC, major depression was associated with ∼50% higher all-cause mortality. Men with PC need timely detection and treatment of depression to support their long-term outcomes and survival. PATIENT SUMMARY: In this report, we examined the effects of depression on survival in men with prostate cancer. We found that among all men with prostate cancer, those who developed depression had a 50% higher risk of dying than those without depression. Men with prostate cancer need close monitoring for the detection and treatment of depression to improve their long-term health outcomes.

RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate.

Image registration can map the ground truth extent of prostate cancer from histopathology images onto MRI, facilitating the development of machine learning methods for early prostate cancer detection. Here, we present RAdiology PatHology Image Alignment (RAPHIA), an end-to-end pipeline for efficient and accurate registration of MRI and histopathology images. RAPHIA automates several time-consuming manual steps in existing approaches including prostate segmentation, estimation of the rotation angle and horizontal flipping in histopathology images, and estimation of MRI-histopathology slice correspondences. By utilizing deep learning registration networks, RAPHIA substantially reduces computational time. Furthermore, RAPHIA obviates the need for a multimodal image similarity metric by transferring histopathology image representations to MRI image representations and vice versa. With the assistance of RAPHIA, novice users achieved expert-level performance, and their mean error in estimating histopathology rotation angle was reduced by 51% (12 degrees vs 8 degrees), their mean accuracy of estimating histopathology flipping was increased by 5% (95.3% vs 100%), and their mean error in estimating MRI-histopathology slice correspondences was reduced by 45% (1.12 slices vs 0.62 slices). When compared to a recent conventional registration approach and a deep learning registration approach, RAPHIA achieved better mapping of histopathology cancer labels, with an improved mean Dice coefficient of cancer regions outlined on MRI and the deformed histopathology (0.44 vs 0.48 vs 0.50), and a reduced mean per-case processing time (51 vs 11 vs 4.5 min). The improved performance by RAPHIA allows efficient processing of large datasets for the development of machine learning models for prostate cancer detection on MRI. Our code is publicly available at: https://github.com/pimed/RAPHIA.

Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology.

Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on 2603 histological images of prostate tissue stained with hematoxylin and eosin. We analyzed various factors influencing tumor grade discordance between the vPatho system and six human pathologists. Our results demonstrated that vPatho achieved comparable performance in prostate cancer detection and tumor volume estimation, as reported in the literature. The concordance levels between vPatho and human pathologists were examined. Notably, moderate to substantial agreement was observed in identifying complementary histological features such as ductal, cribriform, nerve, blood vessel, and lymphocyte infiltration. However, concordance in tumor grading decreased when applied to prostatectomy specimens (κ = 0.44) compared to biopsy cores (κ = 0.70). Adjusting the decision threshold for the secondary Gleason pattern from 5 to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (κ from 0.44 to 0.64). Potential causes of grade discordance included the vertical extent of tumors toward the prostate boundary and the proportions of slides with prostate cancer. Gleason pattern 4 was particularly associated with this population. Notably, the grade according to vPatho was not specific to any of the six pathologists involved in routine clinical grading. In conclusion, our study highlights the potential utility of AI in developing a digital twin for a pathologist. This approach can help uncover limitations in AI adoption and the practical application of the current grading system for prostate cancer pathology.

Identification and characterization of intact glycopeptides in human urine.

Glycoproteins in urine have the potential to provide a rich class of informative molecules for studying human health and disease. Despite this promise, the urine glycoproteome has been largely uncharacterized. Here, we present the analysis of glycoproteins in human urine using LC-MS/MS-based intact glycopeptide analysis, providing both the identification of protein glycosites and characterization of the glycan composition at specific glycosites. Gene enrichment analysis reveals differences in biological processes, cellular components, and molecular functions in the urine glycoproteome versus the urine proteome, as well as differences based on the major glycan class observed on proteins. Meta-heterogeneity of glycosylation is examined on proteins to determine the variation in glycosylation across multiple sites of a given protein with specific examples of individual sites differing from the glycosylation trends in the overall protein. Taken together, this dataset represents a potentially valuable resource as a baseline characterization of glycoproteins in human urine for future urine glycoproteomics studies.

68Ga-RM2 PET-MRI versus MRI alone for evaluation of patients with biochemical recurrence of prostate cancer: a single-centre, single-arm, phase 2/3 imaging trial.

BACKGROUND: National Comprehensive Cancer Network guidelines include prostate-specific membrane antigen (PSMA)-targeted PET for detection of biochemical recurrence of prostate cancer. However, targeting a single tumour characteristic might not be sufficient to reflect the full extent of disease. Gastrin releasing peptide receptors (GRPR) have been shown to be overexpressed in prostate cancer. In this study, we aimed to evaluate the diagnostic performance of the GRPR-targeting radiopharmaceutical 68Ga-RM2 in patients with biochemical recurrence of prostate cancer. METHODS: This single-centre, single-arm, phase 2/3 trial was done at Stanford University (USA). Adult patients (aged ≥18 years) with biochemical recurrence of prostate cancer, a Karnofsky performance status of 50 or higher, increasing prostate-specific antigen concentration 0·2 ng/mL or more after prostatectomy or 2 ng/mL or more above nadir after radiotherapy, and non-contributory conventional imaging (negative CT or MRI, and bone scan) were eligible. All participants underwent 68Ga-RM2 PET-MRI. The primary outcome was the proportion of patients with PET-positive findings on 68Ga-RM2 PET-MRI compared with MRI alone after initial therapy, at a per-patient and per-lesion level. The primary outcome would be considered met if at least 30% of patients had one or more lesions detected by 68Ga-RM2 PET-MRI and the detection by 68Ga-RM2 PET-MRI was significantly greater than for MRI. Each PET scan was interpreted by three independent masked readers using a standardised evaluation criteria. This study is registered with ClinicalTrials.gov, NCT02624518, and is complete. FINDINGS: Between Dec 12, 2015, and July 27, 2021, 209 men were screened for eligibility, of whom 100 were included in analyses. Median follow-up was 49·3 months (IQR 36·7-59·2). The primary endpoint was met; 68Ga-RM2 PET-MRI was positive in 69 (69%) patients and MRI alone was positive in 40 (40%) patients (p<0·0001). In the per-lesion analysis 68Ga-RM2 PET-MRI showed significantly higher detection rates than MRI alone (143 vs 96 lesions; p<0·0001). No grade 1 or worse events were reported. INTERPRETATION: 68Ga-RM2 PET-MRI showed better diagnostic performance than MRI alone in patients with biochemical recurrence of prostate cancer. Further prospective comparative studies with PSMA-targeted PET are needed to gain a better understanding of GRPR and PSMA expression patterns in these patients. FUNDING: The US Department of Defense.

High expression of Trop2 is associated with aggressive localized prostate cancer and is a candidate urinary biomarker.

Distinguishing indolent from clinically significant localized prostate cancer is a major clinical challenge and influences clinical decision-making between treatment and active surveillance. The development of novel predictive biomarkers will help with risk stratification, and clinical decision-making, leading to a decrease in over or under-treatment of patients with prostate cancer. Here, we report that Trop2 is a prognostic tissue biomarker for clinically significant prostate cancer by utilizing the Canary Prostate Cancer Tissue Microarray (CPCTA) cohort composed of over 1100 patients from a multi-institutional study. We demonstrate that elevated Trop2 expression is correlated with worse clinical features including Gleason score, age, and pre-operative PSA levels. More importantly, we demonstrate that elevated Trop2 expression at radical prostatectomy predicts worse overall survival in men undergoing radical prostatectomy. Additionally, we detect shed Trop2 in urine from men with clinically significant prostate cancer. Our study identifies Trop2 as a novel tissue prognostic biomarker and a candidate non-invasive marker for prostate cancer.

UCHL1 is a potential molecular indicator and therapeutic target for neuroendocrine carcinomas.

Neuroendocrine carcinomas, such as neuroendocrine prostate cancer and small-cell lung cancer, commonly have a poor prognosis and limited therapeutic options. We report that ubiquitin carboxy-terminal hydrolase L1 (UCHL1), a deubiquitinating enzyme, is elevated in tissues and plasma from patients with neuroendocrine carcinomas. Loss of UCHL1 decreases tumor growth and inhibits metastasis of these malignancies. UCHL1 maintains neuroendocrine differentiation and promotes cancer progression by regulating nucleoporin, POM121, and p53. UCHL1 binds, deubiquitinates, and stabilizes POM121 to regulate POM121-associated nuclear transport of E2F1 and c-MYC. Treatment with the UCHL1 inhibitor LDN-57444 slows tumor growth and metastasis across neuroendocrine carcinomas. The combination of UCHL1 inhibitors with cisplatin, the standard of care used for neuroendocrine carcinomas, significantly delays tumor growth in pre-clinical settings. Our study reveals mechanisms of UCHL1 function in regulating the progression of neuroendocrine carcinomas and identifies UCHL1 as a therapeutic target and potential molecular indicator for diagnosing and monitoring treatment responses in these malignancies.

Spatial transcriptomics identifies candidate stromal drivers of benign prostatic hyperplasia.

Benign prostatic hyperplasia (BPH) is the nodular proliferation of the prostate transition zone in older men, leading to urinary storage and voiding problems that can be recalcitrant to therapy. Decades ago, John McNeal proposed that BPH originates with the "reawakening" of embryonic inductive activity by adult prostate stroma, which spurs new ductal proliferation and branching morphogenesis. Here, by laser microdissection and transcriptional profiling of the BPH stroma adjacent to hyperplastic branching ducts, we identified secreted factors likely mediating stromal induction of prostate glandular epithelium and coinciding processes. The top stromal factors were insulin-like growth factor 1 (IGF1) and CXC chemokine ligand 13 (CXCL13), which we verified by RNA in situ hybridization to be coexpressed in BPH fibroblasts, along with their cognate receptors (IGF1R and CXCR5) on adjacent epithelium. In contrast, IGF1 but not CXCL13 was expressed in human embryonic prostate stroma. Finally, we demonstrated that IGF1 is necessary for the generation of BPH-1 cell spheroids and patient-derived BPH cell organoids in 3D culture. Our findings partially support historic speculations on the etiology of BPH and provide what we believe to be new molecular targets for rational therapies directed against the underlying processes driving BPH.

Risks of Depression, Anxiety, and Suicide in Partners of Men with Prostate Cancer: A National Cohort Study.

BACKGROUND: A diagnosis of prostate cancer (PC) may cause psychosocial distress not only in a man but also his intimate partner. However, long-term risks of depression, anxiety, or suicide in partners of men with PC are largely unknown. METHODS: A national cohort study was conducted of 121,530 partners of men diagnosed with PC during 1998-2017 and 1,093,304 population-based controls in Sweden. Major depression, anxiety disorder, and suicide death were ascertained through 2018. Cox regression was used to compute hazard ratios (HRs) while adjusting for sociodemographic factors. RESULTS: Partners of men with high-risk PC had increased risks of major depression (adjusted HR, 1.34; 95% CI, 1.30-1.39) and anxiety disorder (1.25; 1.20-1.30), which remained elevated ≥10 years later. Suicide death was increased in partners of men with distant metastases (adjusted HR, 2.38; 95% CI, 1.08-5.22) but not other high-risk PC (1.14; 0.70-1.88). Among partners of men with high-risk PC, risks of major depression and anxiety disorder were highest among those aged ≥80 years (adjusted HR, 1.73; 95% CI, 1.53-1.96; and 1.70; 1.47-1.96, respectively), whereas suicide death was highest among those aged <60 years (7.55; 2.20-25.89). In contrast, partners of men with low- or intermediate-risk PC had modestly or no increased risks of these outcomes. CONCLUSIONS: In this large cohort, partners of men with high-risk PC had increased risks of major depression and anxiety disorder, which persisted for ≥10 years. Suicide death was increased 2-fold in partners of men with distant metastases. Partners as well as men with PC need psychosocial support and close follow-up for psychosocial distress.

Patient and physician perspectives on treatments for low-risk prostate cancer: a qualitative study.

BACKGROUND: Patients diagnosed with low-risk prostate cancer (PCa) are confronted with a difficult decision regarding whether to undergo definitive treatment or to pursue an active surveillance protocol. This is potentially further complicated by the possibility that patients and physicians may place different value on factors that influence this decision. We conducted a qualitative investigation to better understand patient and physician perceptions of factors influencing treatment decisions for low-risk PCa. METHODS: Semi-structured interviews were conducted among 43 racially and ethnically diverse patients diagnosed with low-risk PCa, who were identified through a population-based cancer registry, and 15 physicians who were selected to represent a variety of practice settings in the Greater San Francisco Bay Area. RESULTS: Patients and physicians both described several key individual (e.g., clinical) and interpersonal (e.g., healthcare communications) factors as important for treatment decision-making. Overall, physicians' perceptions largely mirrored patients' perceptions. First, we observed differences in treatment preferences by age and stage of life. At older ages, there was a preference for less invasive options. However, at younger ages, we found varying opinions among both patients and physicians. Second, patients and physicians both described concerns about side effects including physical functioning and non-physical considerations. Third, we observed differences in expectations and the level of difficulty for clinical conversations based on information needs and resources between patients and physicians. Finally, we discovered that patients and physicians perceived patients' prior knowledge and the support of family/friends as facilitators of clinical conversations. CONCLUSIONS: Our study suggests that the gap between patient and physician perceptions on the influence of clinical and communication factors on treatment decision-making is not large. The consensus we observed points to the importance of developing relevant clinical communication roadmaps as well as high quality and accessible patient education materials.

ACAA2 is a novel molecular indicator for cancers with neuroendocrine phenotype.

BACKGROUND: Neuroendocrine phenotype is commonly associated with therapy resistance and poor prognoses in small-cell neuroendocrine cancers (SCNCs), such as neuroendocrine prostate cancer (NEPC) and small-cell lung cancer (SCLC). Expression levels of current neuroendocrine markers exhibit high case-by-case variability, so multiple markers are used in combination to identify SCNCs. Here, we report that ACAA2 is elevated in SCNCs and is a potential molecular indicator for SCNCs. METHODS: ACAA2 expressions in tumour xenografts, tissue microarrays (TMAs), and patient tissues from prostate and lung cancers were analysed via immunohistochemistry. ACAA2 mRNA levels in lung and prostate cancer (PC) patients were assessed in published datasets. RESULTS: ACAA2 protein and mRNA levels were elevated in SCNCs relative to non-SCNCs. Medium/high ACAA2 intensity was observed in 78% of NEPC PDXs samples (N = 27) relative to 33% of adeno-CRPC (N = 86), 2% of localised PC (N = 50), and 0% of benign prostate specimens (N = 101). ACAA2 was also elevated in lung cancer patient tissues with neuroendocrine phenotype. 83% of lung carcinoid tissues (N = 12) and 90% of SCLC tissues (N = 10) exhibited medium/high intensity relative to 40% of lung adenocarcinoma (N = 15). CONCLUSION: ACAA2 expression is elevated in aggressive SCNCs such as NEPC and SCLC, suggesting it is a potential molecular indicator for SCNCs.

Identification of age- and immune-related gene signatures for clinical outcome prediction in lung adenocarcinoma.

BACKGROUND: The understanding of the factors causing decreased overall survival (OS) in older patients compared to younger patients in lung adenocarcinoma (LUAD) remains. METHODS: Gene expression profiles of LUAD were obtained from publicly available databases by Kaplan-Meier analysis was performed to determine whether age was associated with patient OS. The immune cell composition in the tumor microenvironment (TME) was evaluated using CIBERSORT. The fraction of stromal and immune cells in tumor samples were also using assessed using multiple tools including ESTIMATE, EPIC, and TIMER. Differentially expressed genes (DEGs) from the RNA-Seq data that were associated with age and immune cell composition were identified using the R package DEGseq. A 22-gene signature composed of DEGs associated with age and immune cell composition that predicted OS were constructed using Least Absolute Shrinkage and Selection Operator (LASSO). RESULTS: In The Cancer Genome Atlas (TCGA)-LUAD dataset, we found that younger patients (≤70) had a significant better OS compared to older patients (>70). In addition, older patients had significantly higher expression of immune checkpoint proteins including inhibitory T cell receptors and their ligands. Moreover, analyses using multiple bioinformatics tools showed increased immune infiltration, including CD4+ T cells, in older patients compared to younger patients. We identified a panel of genes differentially expressed between patients >70 years compared to those ≤70 years, as well as between patients with high or low immune scores and selected 84 common genes to construct a prognostic gene signature. A risk score calculated based on 22 genes selected by LASSO predicted 1, 3, and 5-year OS, with an area under the curve (AUC) of 0.72, 0.72, 0.69, receptively, in TCGA-LUAD dataset and an independent validation dataset available from the European Genome-phenome Archive (EGA). CONCLUSION: Our results demonstrate that age contributes to OS of LUAD patients atleast in part through its association with immune infiltration in the TME.

Proteomics analysis of urine and catheter-associated biofilms in spinal cord injury patients.

After spinal cord injury (SCI), use chronic urinary catheters for bladder management is common, making these patients especially vulnerable to catheter-associated complications. Chronic catheterization is associated with bacterial colonization and frequent catheter-associated urinary tract infections (CAUTI). One determinant of infection success and treatment resistance is production of catheter-associated biofilms, composed of microorganisms and host- and microbial-derived components. To better understand the biofilm microenvironment, we performed proteomics analysis of catheter-associated biofilms and paired urine samples from four people with SCI with chronic indwelling urinary catheters. We developed a novel method for the removal of adhered cellular components on catheters that contained both human and microbial homologous proteins. Proteins from seven microbial species were identified including: Escherichia coli, Klebsiella species (spp), Enterococcus spp, Proteus mirabilis, Pseudomonas spp, Staphylococcus spp, and Candida spp. Peptides identified from catheter biofilms were assigned to 4,820 unique proteins, with 61% of proteins assigned to the biofilm-associated microorganisms, while the remainder were human-derived. Contrastingly, in urine, only 51% were assigned to biofilm-associated microorganisms and 4,554 proteins were identified as a human-derived. Of the proteins assigned to microorganisms in the biofilm and paired urine, Enterococcus, Candida spp, and P. mirabilis had greater associations with the biofilm phase, whereas E. coli and Klebsiella had greater associations with the urine phase, thus demonstrating a significant difference between the urine and adhered microbial communities. The microbial proteins that differed significantly between the biofilm and paired urine samples mapped to pathways associated with amino acid synthesis, likely related to adaptation to high urea concentrations in the urine, and growth and protein synthesis in bacteria in the biofilm. Human proteins demonstrated enrichment for immune response in the catheter-associated biofilm. Proteomic analysis of catheter-associated biofilms and paired urine samples has the potential to provide detailed information on host and bacterial responses to chronic indwelling urinary catheters and could be useful for understanding complications of chronic indwelling catheters including CAUTIs, urinary stones, and catheter blockages.

Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning.

PURPOSE: Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes. MATERIALS AND METHODS: Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues. RESULTS: From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC. CONCLUSION: Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.

Risks of alcohol and drug use disorders in prostate cancer survivors: a national cohort study.

BACKGROUND: Prostate cancer (PC) survivors may potentially use substances to cope with psychological distress or poorly controlled physical symptoms. Little is known, however, about the long-term risks of alcohol use disorder (AUD) or drug use disorders in men with PC. METHODS: A national cohort study was conducted in Sweden of 180 189 men diagnosed with PC between 1998 and 2017 and 1 801 890 age-matched population-based control men. AUD and drug use disorders were ascertained from nationwide records through 2018. Cox regression was used to compute hazard ratios (HRs) while adjusting for sociodemographic factors and prior psychiatric disorders. Subanalyses examined differences by PC treatment from 2005 to 2017. RESULTS: Men with high-risk PC had increased risks of both AUD (adjusted HR = 1.44, 95% confidence interval [CI] = 1.33 to 1.57) and drug use disorders (adjusted HR = 1.93, 95% CI = 1.67 to 2.24). Their AUD risk was highest in the first year and was no longer significantly elevated 5 years after PC diagnosis, whereas their drug use disorders risk remained elevated 10 years after PC diagnosis (adjusted HR = 2.26, 95% CI = 1.45 to 3.52), particularly opioid use disorder (adjusted HR = 3.07, 95% CI = 1.61 to 5.84). Those treated only with androgen-deprivation therapy had the highest risks of AUD (adjusted HR = 1.91, 95% CI = 1.62 to 2.25) and drug use disorders (adjusted HR = 2.23, 95% CI = 1.70 to 2.92). Low- or intermediate-risk PC was associated with modestly increased risks of AUD (adjusted HR = 1.38, 95% CI = 1.30 to 1.46) and drug use disorders (adjusted HR = 1.19, 95% CI = 1.06 to 1.34). CONCLUSIONS: In this large cohort, men with PC had significantly increased risks of both AUD and drug use disorders, especially those with high-risk PC and treated only with androgen-deprivation therapy. PC survivors need long-term psychosocial support and timely detection and treatment of AUD and drug use disorders.

Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The purpose of the study was to establish the clinical application of an in-house developed artificial intelligence (AI) image analysis workflow for automated detection of PTEN loss on digital images for identifying patients at risk of early recurrence and metastasis. Postsurgical tissue microarray sections from the Canary Foundation (n = 1264) stained with anti-PTEN antibody were evaluated independently by pathologist conventional visual scoring (cPTEN) and an automated AI-based image analysis pipeline (AI-PTEN). The relationship of PTEN evaluation methods with cancer recurrence and metastasis was analyzed using multivariable Cox proportional hazard and decision curve models. Both cPTEN scoring by the pathologist and quantification of PTEN loss by AI (high-risk AI-qPTEN) were significantly associated with shorter metastasis-free survival (MFS) in univariable analysis (cPTEN hazard ratio [HR], 1.54; CI, 1.07-2.21; P = .019; AI-qPTEN HR, 2.55; CI, 1.83-3.56; P < .001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter MFS (HR, 2.17; CI, 1.49-3.17; P < .001) and recurrence-free survival (HR, 1.36; CI, 1.06-1.75; P = .016) when adjusting for relevant postsurgical clinical nomogram (Cancer of the Prostate Risk Assessment [CAPRA] postsurgical score [CAPRA-S]), whereas cPTEN does not show a statistically significant association (HR, 1.33; CI, 0.89-2; P = .2 and HR, 1.26; CI, 0.99-1.62; P = .063, respectively) when adjusting for CAPRA-S risk stratification. More importantly, AI-qPTEN was associated with shorter MFS in patients with favorable pathological stage and negative surgical margins (HR, 2.72; CI, 1.46-5.06; P = .002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy postsurgery. This study demonstrates the clinical value of an affordable and fully automated AI-powered PTEN assessment for evaluating the risk of developing metastasis or disease recurrence after radical prostatectomy. Adding the AI-qPTEN assessment workflow to clinical variables may affect postoperative surveillance or management options, particularly in low-risk patients.

Long-term Risks of Depression and Suicide Among Men with Prostate Cancer: A National Cohort Study.

BACKGROUND: A diagnosis of prostate cancer (PC) may cause psychosocial distress that worsens quality of life; however, long-term mental health outcomes are unclear. OBJECTIVE: To determine the long-term risks of major depression and death by suicide in a large population-based cohort. DESIGN, SETTING, AND PARTICIPANTS: This was a national cohort study of 180 189 men diagnosed with PC during 1998-2017 and 1 801 890 age-matched, population-based, control men in Sweden. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Major depression and death by suicide were ascertained from nationwide outpatient, inpatient, and death records up to 2018. Cox regression was used to compute hazard ratios (HRs) adjusted for sociodemographic factors and comorbidities. Subanalyses assessed differences by PC treatment during 2005-2017. RESULTS AND LIMITATIONS: Men diagnosed with high-risk PC had higher relative rates of major depression (adjusted HR [aHR] 1.82, 95% confidence interval [CI] 1.75-1.89) and death by suicide (aHR 2.43, 95% CI 2.01-2.95). These associations persisted for ≥10 yr after PC diagnosis. The relative increase in major depression was lower among those treated with radiation (aHR 1.44, 95% CI 1.31-1.57) or surgery (aHR 1.60, 95% CI 1.31-1.95) in comparison to androgen deprivation therapy (ADT) alone (aHR 2.02, 95% CI 1.89-2.16), whereas the relative rate of suicide death was higher only among those treated solely with ADT (aHR 2.83, 95% CI 1.80-4.43). By contrast, men with low- or intermediate-risk PC had a modestly higher relative rate of major depression (aHR 1.19, 95% CI 1.16-1.23) and higher relative rate of suicide death at 3-12 mo after PC diagnosis (aHR 1.88, 95% CI 1.11-3.18) but not across the entire follow-up period (aHR 1.02, 95% CI 0.84-1.25). This study was limited to Sweden and will need replication in other populations. CONCLUSIONS: In this large cohort, high-risk PC was associated with substantially higher relative rates of major depression and death by suicide, which persisted for ≥10 yr after PC diagnosis. PC survivors need close follow-up for timely detection and treatment of psychosocial distress. PATIENT SUMMARY: In a large Swedish population, men with aggressive prostate cancer had higher long-term relative rates of depression and suicide.

NUSAP1 Binds ILF2 to Modulate R-Loop Accumulation and DNA Damage in Prostate Cancer.

Increased expression of NUSAP1 has been identified as a robust prognostic biomarker in prostate cancer and other malignancies. We have previously shown that NUSAP1 is positively regulated by E2F1 and promotes cancer invasion and metastasis. To further understand the biological function of NUSAP1, we used affinity purification and mass spectrometry proteomic analysis to identify NUSAP1 interactors. We identified 85 unique proteins in the NUSAP1 interactome, including ILF2, DHX9, and other RNA-binding proteins. Using proteomic approaches, we uncovered a function for NUSAP1 in maintaining R-loops and in DNA damage response through its interaction with ILF2. Co-immunoprecipitation and colocalization using confocal microscopy verified the interactions of NUSAP1 with ILF2 and DHX9, and RNA/DNA hybrids. We showed that the microtubule and charged helical domains of NUSAP1 were necessary for the protein-protein interactions. Depletion of ILF2 alone further increased camptothecin-induced R-loop accumulation and DNA damage, and NUSAP1 depletion abolished this effect. In human prostate adenocarcinoma, NUSAP1 and ILF2 mRNA expression levels are positively correlated, elevated, and associated with poor clinical outcomes. Our study identifies a novel role for NUSAP1 in regulating R-loop formation and accumulation in response to DNA damage through its interactions with ILF2 and hence provides a potential therapeutic target.

PlexusNet: A neural network architectural concept for medical image classification.

State-of-the-art (SOTA) convolutional neural network models have been widely adapted in medical imaging and applied to address different clinical problems. However, the complexity and scale of such models may not be justified in medical imaging and subject to the available resource budget. Further increasing the number of representative feature maps for the classification task decreases the model explainability. The current data normalization practice is fixed prior to model development and discounting the specification of the data domain. Acknowledging these issues, the current work proposed a new scalable model family called PlexusNet; the block architecture and model scaling by the network's depth, width, and branch regulate PlexusNet's architecture. The efficient computation costs outlined the dimensions of PlexusNet scaling and design. PlexusNet includes a new learnable data normalization algorithm for better data generalization. We applied a simple yet effective neural architecture search to design PlexusNet tailored to five clinical classification problems that achieve a performance noninferior to the SOTA models ResNet-18 and EfficientNet B0/1. It also does so with lower parameter capacity and representative feature maps in ten-fold ranges than the smallest SOTA models with comparable performance. The visualization of representative features revealed distinguishable clusters associated with categories based on latent features generated by PlexusNet. The package and source code are at https://github.com/oeminaga/PlexusNet.git.