Distinct molecular profiles and shared drug vulnerabilities in pancreatic metastases of renal cell carcinoma.

Clear-cell renal cell carcinoma (ccRCC) is the most common origin of pancreatic metastases (PM). Distinct genomic aberrations, favorable prognosis, and clinical observations on high angiogenesis, and succeeding tyrosine kinase inhibitor (TKI) sensitivity have been reported in PM-ccRCC. However, no functional or single-cell studies have been conducted thus far. We recruited five PM-ccRCC patients and investigated the genomic, single-cell transcriptomic, and drug sensitivity profiles of their patient-derived cells (PDCs). The PM depicted both expected and novel genomic alterations. Further, the transcriptomics differed from both primary and metastatic ccRCC, with upregulations of the PI3K/mTOR and - supporting the clinical observations - angiogenesis pathways. Data integration at pathway level showed that transcriptomics explained drug sensitivities the best. Accordingly, PM-ccRCC PDCs shared sensitivity to many PI3K/mTOR inhibitors. Altogether, we show distinct genomic and transcriptomic signatures in PM-ccRCC, highlight the superiority of transcriptomics in interpreting drug sensitivities, and encourage the use of TKIs and PI3K/mTOR inhibitors in PM-ccRCC.

Image analysis Uncovers associations between immune landscape, collagen structure, and neoadjuvant chemotherapy in high-grade serous ovarian carcinomas.

Background: The changes in the tumor microenvironment of high-grade serous ovarian carcinomas following neoadjuvant chemotherapy are a complex area of study. Previous research underscores the importance of investigating the immune and collagen components within the tumor microenvironment for prognostic implications. Methods: In this study, we utilized computational pathology techniques with Hematoxylin and Eosin-stained images to quantitatively characterize the immune and collagen architecture within the tumor microenvironment of patients with high-grade serous ovarian carcinoma. Results: Our analysis of 12 pre- and post-neoadjuvant chemotherapy images revealed an increase in immune infiltrate, primarily within the epithelial region. Additionally, post-neoadjuvant chemotherapy images exhibited chaotic collagen architecture compared to pre-neoadjuvant chemotherapy images. Importantly, features extracted from post-neoadjuvant chemotherapy images showed associations with overall survival, potentially aiding in the selection of patients for immunotherapy trials. Conclusions: These findings offer critical insights into the changes in the tumor microenvironment of high-grade serous ovarian carcinomas following neoadjuvant chemotherapy and their potential implications for clinical outcomes.

Prostate Cancer Screening With PSA, Kallikrein Panel, and MRI: The ProScreen Randomized Trial.

Importance: Prostate-specific antigen (PSA) screening has potential to reduce prostate cancer mortality but frequently detects prostate cancer that is not clinically important. Objective: To describe rates of low-grade (grade group 1) and high-grade (grade groups 2-5) prostate cancer identified among men invited to participate in a prostate cancer screening protocol consisting of a PSA test, a 4-kallikrein panel, and a magnetic resonance imaging (MRI) scan. Design, Setting, and Participants: The ProScreen trial is a clinical trial conducted in Helsinki and Tampere, Finland, that randomized 61 193 men aged 50 through 63 years who were free of prostate cancer in a 1:3 ratio to either be invited or not be invited to undergo screening for prostate cancer between February 2018 and July 2020. Interventions: Participating men randomized to the intervention underwent PSA testing. Those with a PSA level of 3.0 ng/mL or higher underwent additional testing for high-grade prostate cancer with a 4-kallikrein panel risk score. Those with a kallikrein panel score of 7.5% or higher underwent an MRI of the prostate gland, followed by targeted biopsies for those with abnormal prostate gland MRI findings. Final data collection occurred through June 31, 2023. Main Outcomes and Measures: In descriptive exploratory analyses, the cumulative incidence of low-grade and high-grade prostate cancer after the first screening round were compared between the group invited to undergo prostate cancer screening and the control group. Results: Of 60 745 eligible men (mean [SD] age, 57.2 [4.0] years), 15 201 were randomized to be invited and 45 544 were randomized not to be invited to undergo prostate cancer screening. Of 15 201 eligible males invited to undergo screening, 7744 (51%) participated. Among them, 32 low-grade prostate cancers (cumulative incidence, 0.41%) and 128 high-grade prostate cancers (cumulative incidence, 1.65%) were detected, with 1 cancer grade group result missing. Among the 7457 invited men (49%) who refused participation, 7 low-grade prostate cancers (cumulative incidence, 0.1%) and 44 high-grade prostate cancers (cumulative incidence, 0.6%) were detected, with 7 cancer grade groups missing. For the entire invited screening group, 39 low-grade prostate cancers (cumulative incidence, 0.26%) and 172 high-grade prostate cancers (cumulative incidence, 1.13%) were detected. During a median follow-up of 3.2 years, in the group not invited to undergo screening, 65 low-grade prostate cancers (cumulative incidence, 0.14%) and 282 high-grade prostate cancers (cumulative incidence, 0.62%) were detected. The risk difference for the entire group randomized to the screening invitation vs the control group was 0.11% (95% CI, 0.03%-0.20%) for low-grade and 0.51% (95% CI, 0.33%-0.70%) for high-grade cancer. Conclusions and Relevance: In this preliminary descriptive report from an ongoing randomized clinical trial, 1 additional high-grade cancer per 196 men and 1 low-grade cancer per 909 men were detected among those randomized to be invited to undergo a single prostate cancer screening intervention compared with those not invited to undergo screening. These preliminary findings from a single round of screening should be interpreted cautiously, pending results of the study's primary mortality outcome. Trial Registration: ClinicalTrials.gov Identifier: NCT03423303.

Expected impact of MRI-targeted biopsy interreader variability among uropathologists on ProScreen prostate cancer screening trial: a pre-trial validation study.

PURPOSE: Prostate cancer (PCa) histology, particularly the Gleason score, is an independent prognostic predictor in PCa. Little is known about the inter-reader variability in grading of targeted prostate biopsy based on magnetic resonance imaging (MRI). The aim of this study was to assess inter-reader variability in Gleason grading of MRI-targeted biopsy among uropathologists and its potential impact on a population-based randomized PCa screening trial (ProScreen). METHODS: From June 2014 to May 2018, 100 men with clinically suspected PCa were retrospectively selected. All men underwent prostate MRI and 86 underwent targeted prostate of the prostate. Six pathologists individually reviewed the pathology slides of the prostate biopsies. The five-tier ISUP (The International Society of Urological Pathology) grade grouping (GG) system was used. Fleiss' weighted kappa (κ) and Model-based kappa for associations were computed to estimate the combined agreement between individual pathologists. RESULTS: GG reporting of targeted prostate was highly consistent among the trial pathologists. Inter-reader agreement for cancer (GG1-5) vs. benign was excellent (Model-based kappa 0.90, Fleiss' kappa κ = 0.90) and for clinically significant prostate cancer (csPCa) (GG2-5 vs. GG0 vs. GG1), it was good (Model-based kappa 0.70, Fleiss' kappa κ 0.67). CONCLUSIONS: Inter-reader agreement in grading of MRI-targeted biopsy was good to excellent, while it was fair to moderate for MRI in the same cohort, as previously shown. Importantly, there was wide consensus by pathologists in assigning the contemporary GG on MRI-targeted biopsy suggesting high reproducibility of pathology reporting in the ProScreen trial.

Protocol liver biopsy predicts graft survival after liver transplantation.

BACKGROUND: The use of protocol liver biopsy to monitor liver allograft status remains controversial. There is limited data from modern transplantation populations that includes protocol biopsies to evaluate its value in predicting clinical outcomes. METHODS: All protocol liver biopsies were identified from 875 patients who underwent liver transplantation at Helsinki University Hospital between 2000 and 2019. Each histologic component was analyzed for its ability to predict long-term outcomes, especially graft survival. We determined the frequency of significant biopsy findings based on the Banff working group definition. Liver function tests (LFTs) and clinical markers were evaluated for their ability to predict significant biopsy findings. RESULTS: In total, 867 protocol liver biopsies were analyzed. Significant findings were identified in 20.1% of the biopsies. In the first protocol biopsy, steatohepatitis (hazard ratio [HR] 3.504, p = .03) and moderate or severe congestion (HR 3.338, p = .04) predicted graft loss. The presence of cholangitis (HR 2.563, p = .04), necrosis (HR 7.635, p < .001), mild congestion (HR 4.291, p = .009), and significant biopsy finding (HR 2.540, p = .02) predicted inferior death-censored graft survival. While the degree of elevation of LFTs was positively associated with significant biopsy findings, the discrimination was poor (AUC .572-.622). Combined LFTs and clinical risk factors remained suboptimal for discriminating significant biopsy findings (AUC .696). CONCLUSIONS: Our findings support the use of protocol liver biopsies after liver transplantation since they frequently revealed changes associated with long-term outcomes, even when LFTs were normal.

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

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

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

Risk of Clinically Significant Prostate Cancer after a Nonsuspicious Prostate MRI-A Comparison with the General Population.

BACKGROUND: We compare the risk of clinically significant (csPCa; ISUP Grade Group ≥ 2) and insignificant prostate cancer (isPCa; ISUP Grade Group 1) in men with a nonsuspicious prostate MRI (nMRI; PI-RADS ≤ 2) with the general population, and assess the value of PSA density (PSAD) in stratification. METHODS: In this retrospective population-based cohort study we identified 1,682 50-79-year-old men, who underwent nMRI at HUS (2016-2019). We compared their age-standardized incidence rates (IR) of csPCa and the odds of isPCa to a local age- and sex-matched general population (n = 230,458) during a six-year follow-up. Comparisons were performed by calculating incidence rate ratios (IRR) and ORs with 95% confidence intervals (CI). We repeated the comparison for the 920 men with nMRI and PSAD < 0.15 ng/mL/cm3. RESULTS: Compared with the general population, the IR of csPCa was significantly higher after nMRI [1,852 vs. 552 per 100,000 person-years; IRR 3.4 (95% CI, 2.8-4.1)]. However, the IR was substantially lower if PSAD was low [778 per 100,000 person-years; IRR 1.4 (95% CI, 0.9-2.0)]. ORs for isPCa were 2.4 (95% CI, 1.7-3.5) for all men with nMRI and 5.0 (95% CI, 2.8-9.1) if PSAD was low. CONCLUSIONS: Compared with the general population, the risk of csPCa is not negligible after nMRI. However, men with nMRI and PSAD <0.15 ng/mL/cm3 have worse harm-benefit balance than men in the general population. IMPACT: Prostate biopsies for men with nMRI should be reserved for cases indicated by additional risk stratification. See related In the Spotlight, p. 641.

MRI-Targeted Prostate Biopsy Introduces Grade Inflation and Overtreatment.

Purpose: The use of MRI-targeted biopsies has led to lower detection of Gleason Grade Group 1 (GG1) prostate cancer and increased detection of GG2 disease. Although this finding is generally attributed to improved sensitivity and specificity of MRI for aggressive cancers, it might also be explained by grade inflation. Our objective was to determine the likelihood of definitive treatment and risk of post-treatment recurrence for patients with GG2 cancer diagnosed using targeted biopsies relative to men with GG1 cancer diagnosed using systematic biopsies. Methods: We performed a retrospective study on a large tertiary centre registry (HUS Acamedic Datalake) to retrieve data on prostate cancer diagnosis, treatment, and cancer recurrence. We included patients with either GG1 with systematic biopsies (3317 men) or GG2 with targeted biopsies (554 men) from 1993 to 2019. We assessed the risk of curative treatment and recurrence after treatment. Kaplan-Meier survival curves were computed to assess treatment- and recurrence-free survival. Cox proportional hazards regression analysis was performed to assess the risk of posttreatment recurrence. Results: Patients with systematic biopsy detected GG1 cancer had a significantly longer median time-to-treatment (31 months) than those with targeted biopsy detected GG2 cancer (4 months, p<0.0001). The risk of recurrence after curative treatment was similar between groups with the upper bound of 95% CI, excluding an important difference (HR: 0.94, 95% CI [0.71-1.25], p=0.7). Conclusion: GG2 cancers detected by MRI-targeted biopsy are treated more aggressively than GG1 cancers detected by systematic biopsy, despite having similar oncologic risk. To prevent further overtreatment related to the MRI pathway, treatment guidelines from the pre-MRI era need to be updated to consider changes in the diagnostic pathway.

Engineering the future of 3D pathology.

In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets.

Histomic and transcriptomic features of MRI-visible and invisible clinically significant prostate cancers are associated with prognosis.

Magnetic resonance imaging (MRI) is increasingly used to triage patients for prostate biopsy. However, 9% to 24% of clinically significant (cs) prostate cancers (PCas) are not visible in MRI. We aimed to identify histomic and transcriptomic determinants of MRI visibility and their association to metastasis, and PCa-specific death (PCSD). We studied 45 radical prostatectomy-treated patients with csPCa (grade group [GG]2-3), including 30 with MRI-visible and 15 with MRI-invisible lesions, and 18 men without PCa. First, histological composition was quantified. Next, transcriptomic profiling was performed using NanoString technology. MRI visibility-associated differentially expressed genes (DEGs) and Reactome pathways were identified. MRI visibility was classified using publicly available genes in MSK-IMPACT and Decipher, Oncotype DX, and Prolaris. Finally, DEGs and clinical parameters were used to classify metastasis and PCSD in an external cohort, which included 76 patients with metastatic GG2-4 PCa, and 84 baseline-matched controls without progression. Luminal area was lower in MRI-visible than invisible lesions and low luminal area was associated with short metastasis-free and PCa-specific survival. We identified 67 DEGs, eight of which were associated with survival. Cell division, inflammation and transcriptional regulation pathways were upregulated in MRI-visible csPCas. Genes in Decipher, Oncotype DX and MSK-IMPACT performed well in classifying MRI visibility (AUC = 0.86-0.94). DEGs improved classification of metastasis (AUC = 0.69) and PCSD (AUC = 0.68) over clinical parameters. Our data reveals that MRI-visible csPCas harbor more aggressive histomic and transcriptomic features than MRI-invisible csPCas. Thus, targeted biopsy of visible lesions may be sufficient for risk stratification in patients with a positive MRI.

Visual Assessment of 2-Dimensional Levels Within 3-Dimensional Pathology Data Sets of Prostate Needle Biopsies Reveals Substantial Spatial Heterogeneity.

Prostate cancer prognostication largely relies on visual assessment of a few thinly sectioned biopsy specimens under a microscope to assign a Gleason grade group (GG). Unfortunately, the assigned GG is not always associated with a patient's outcome in part because of the limited sampling of spatially heterogeneous tumors achieved by 2-dimensional histopathology. In this study, open-top light-sheet microscopy was used to obtain 3-dimensional pathology data sets that were assessed by 4 human readers. Intrabiopsy variability was assessed by asking readers to perform Gleason grading of 5 different levels per biopsy for a total of 20 core needle biopsies (ie, 100 total images). Intrabiopsy variability (Cohen κ) was calculated as the worst pairwise agreement in GG between individual levels within each biopsy and found to be 0.34, 0.34, 0.38, and 0.43 for the 4 pathologists. These preliminary results reveal that even within a 1-mm-diameter needle core, GG based on 2-dimensional images can vary dramatically depending on the location within a biopsy being analyzed. We believe that morphologic assessment of whole biopsies in 3 dimension has the potential to enable more reliable and consistent tumor grading.

The impact of imputation quality on machine learning classifiers for datasets with missing values.

BACKGROUND: Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier's performance. METHODS: We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data. RESULTS: The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised. CONCLUSIONS: It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable.

Many artificial intelligence (AI) methods aim to classify samples of data into groups, e.g., patients with disease vs. those without. This often requires datasets to be complete, i.e., that all data has been collected for all samples. However, in clinical practice this is often not the case and some data can be missing. One solution is to 'complete' the dataset using a technique called imputation to replace those missing values. However, assessing how well the imputation method performs is challenging. In this work, we demonstrate why people should care about imputation, develop a new method for assessing imputation quality, and demonstrate that if we build AI models on poorly imputed data, the model can give different results to those we would hope for. Our findings may improve the utility and quality of AI models in the clinic.

Immunologic Characterization and T cell Receptor Repertoires of Expanded Tumor-infiltrating Lymphocytes in Patients with Renal Cell Carcinoma.

The successful use of expanded tumor-infiltrating lymphocytes (TIL) in adoptive TIL therapies has been reported, but the effects of the TIL expansion, immunophenotype, function, and T cell receptor (TCR) repertoire of the infused products relative to the tumor microenvironment (TME) are not well understood. In this study, we analyzed the tumor samples (n = 58) from treatment-naïve patients with renal cell carcinoma (RCC), "pre-rapidly expanded" TILs (pre-REP TIL, n = 15) and "rapidly expanded" TILs (REP TIL, n = 25) according to a clinical-grade TIL production protocol, with single-cell RNA (scRNA)+TCRαß-seq (TCRαß sequencing), TCRß-sequencing (TCRß-seq), and flow cytometry. REP TILs encompassed a greater abundance of CD4+ than CD8+ T cells, with increased LAG-3 and low PD-1 expressions in both CD4+ and CD8+ T cell compartments compared with the pre-REP TIL and tumor T cells. The REP protocol preferentially expanded small clones of the CD4+ phenotype (CD4, IL7R, KLRB1) in the TME, indicating that the largest exhausted T cell clones in the tumor do not expand during the expansion protocol. In addition, by generating a catalog of RCC-associated TCR motifs from >1,000 scRNA+TCRαß-seq and TCRß-seq RCC, healthy and other cancer sample cohorts, we quantified the RCC-associated TCRs from the expansion protocol. Unlike the low-remaining amount of anti-viral TCRs throughout the expansion, the quantity of the RCC-associated TCRs was high in the tumors and pre-REP TILs but decreased in the REP TILs. Our results provide an in-depth understanding of the origin, phenotype, and TCR specificity of RCC TIL products, paving the way for a more rationalized production of TILs. Significance: TILs are a heterogenous group of immune cells that recognize and attack the tumor, thus are utilized in various clinical trials. In our study, we explored the TILs in patients with kidney cancer by expanding the TILs using a clinical-grade protocol, as well as observed their characteristics and ability to recognize the tumor using in-depth experimental and computational tools.

Phosphorylation of PACSIN2 at S313 Regulates Podocyte Architecture in Coordination with N-WASP.

Changes in the dynamic architecture of podocytes, the glomerular epithelial cells, lead to kidney dysfunction. Previous studies on protein kinase C and casein kinase 2 substrates in neurons 2 (PACSIN2), a known regulator of endocytosis and cytoskeletal organization, reveal a connection between PACSIN2 and kidney pathogenesis. Here, we show that the phosphorylation of PACSIN2 at serine 313 (S313) is increased in the glomeruli of rats with diabetic kidney disease. We found that phosphorylation at S313 is associated with kidney dysfunction and increased free fatty acids rather than with high glucose and diabetes alone. Phosphorylation of PACSIN2 emerged as a dynamic process that fine-tunes cell morphology and cytoskeletal arrangement, in cooperation with the regulator of the actin cytoskeleton, Neural Wiskott-Aldrich syndrome protein (N-WASP). PACSIN2 phosphorylation decreased N-WASP degradation while N-WASP inhibition triggered PACSIN2 phosphorylation at S313. Functionally, pS313-PACSIN2 regulated actin cytoskeleton rearrangement depending on the type of cell injury and the signaling pathways involved. Collectively, this study indicates that N-WASP induces phosphorylation of PACSIN2 at S313, which serves as a mechanism whereby cells regulate active actin-related processes. The dynamic phosphorylation of S313 is needed to regulate cytoskeletal reorganization.

Prognostic impact of kallikrein-related peptidase transcript levels in prostate cancer.

We aimed to study mRNA levels and prognostic impact of all 15 human kallikrein-related peptidases (KLKs) and their targets, proteinase-activated receptors (PARs), in surgically treated prostate cancer (PCa). Seventy-nine patients with localized grade group 2-4 PCas represented aggressive cases, based on metastatic progression during median follow-up of 11 years. Eighty-six patients with similar baseline characteristics, but no metastasis during follow-up, were assigned as controls. Transcript counts were detected with nCounter technology. KLK12 protein expression was investigated with immunohistochemistry. The effects of KLK12 and KLK15 were studied in LNCaP cells using RNA interference. KLK3, -2, -4, -11, -15, -10 and -12 mRNA, in decreasing order, were expressed over limit of detection (LOD). The expression of KLK2, -3, -4 and -15 was decreased and KLK12 increased in aggressive cancers, compared to controls (P < .05). Low KLK2, -3 and -15 expression was associated with short metastasis-free survival (P < .05) in Kaplan-Meier analysis. PAR1 and -2 were expressed over LOD, and PAR1 expression was higher, and PAR2 lower, in aggressive cases than controls. Together, KLKs and PARs improved classification of metastatic and lethal disease over grade, pathological stage and prostate-specific antigen combined, in random forest analyses. Strong KLK12 immunohistochemical staining was associated with short metastasis-free and PCa-specific survival in Kaplan-Meier analysis (P < .05). Knock-down of KLK15 reduced colony formation of LNCaP cells grown on Matrigel basement membrane preparation. These results support the involvement of several KLKs in PCa progression, highlighting, that they may serve as prognostic PCa biomarkers.

OSCAR: Optimal subset cardinality regression using the L0-pseudonorm with applications to prognostic modelling of prostate cancer.

In many real-world applications, such as those based on electronic health records, prognostic prediction of patient survival is based on heterogeneous sets of clinical laboratory measurements. To address the trade-off between the predictive accuracy of a prognostic model and the costs related to its clinical implementation, we propose an optimized L0-pseudonorm approach to learn sparse solutions in multivariable regression. The model sparsity is maintained by restricting the number of nonzero coefficients in the model with a cardinality constraint, which makes the optimization problem NP-hard. In addition, we generalize the cardinality constraint for grouped feature selection, which makes it possible to identify key sets of predictors that may be measured together in a kit in clinical practice. We demonstrate the operation of our cardinality constraint-based feature subset selection method, named OSCAR, in the context of prognostic prediction of prostate cancer patients, where it enables one to determine the key explanatory predictors at different levels of model sparsity. We further explore how the model sparsity affects the model accuracy and implementation cost. Lastly, we demonstrate generalization of the presented methodology to high-dimensional transcriptomics data.

The roles of proteases in prostate cancer.

Since the proposition of the pro-invasive activity of proteolytic enzymes over 70 years ago, several roles for proteases in cancer progression have been established. About half of the 473 active human proteases are expressed in the prostate and many of the most well-characterized members of this enzyme family are regulated by androgens, hormones essential for development of prostate cancer. Most notably, several kallikrein-related peptidases, including KLK3 (prostate-specific antigen, PSA), the most well-known prostate cancer marker, and type II transmembrane serine proteases, such as TMPRSS2 and matriptase, have been extensively studied and found to promote prostate cancer progression. Recent findings also suggest a critical role for proteases in the development of advanced and aggressive castration-resistant prostate cancer (CRPC). Perhaps the most intriguing evidence for this role comes from studies showing that the protease-activated transmembrane proteins, Notch and CDCP1, are associated with the development of CRPC. Here, we review the roles of proteases in prostate cancer, with a special focus on their regulation by androgens.

PP2A methylesterase PME-1 suppresses anoikis and is associated with therapy relapse of PTEN-deficient prostate cancers.

While organ-confined prostate cancer (PCa) is mostly therapeutically manageable, metastatic progression of PCa remains an unmet clinical challenge. Resistance to anoikis, a form of cell death initiated by cell detachment from the surrounding extracellular matrix, is one of the cellular processes critical for PCa progression towards aggressive disease. Therefore, further understanding of anoikis regulation in PCa might provide therapeutic opportunities. Here, we discover that PCa tumours with concomitant inhibition of two tumour suppressor phosphatases, PP2A and PTEN, are particularly aggressive, having < 50% 5-year secondary-therapy-free patient survival. Functionally, overexpression of PME-1, a methylesterase for the catalytic PP2A-C subunit, inhibits anoikis in PTEN-deficient PCa cells. In vivo, PME-1 inhibition increased apoptosis in in ovo PCa tumour xenografts, and attenuated PCa cell survival in zebrafish circulation. Molecularly, PME-1-deficient PC3 cells display increased trimethylation at lysines 9 and 27 of histone H3 (H3K9me3 and H3K27me3), a phenotype known to correlate with increased apoptosis sensitivity. In summary, our results demonstrate that PME-1 supports anoikis resistance in PTEN-deficient PCa cells. Clinically, these results identify PME-1 as a candidate biomarker for a subset of particularly aggressive PTEN-deficient PCa.

Spatially resolved clonal copy number alterations in benign and malignant tissue.

Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.