ONECUT2 acts as a lineage plasticity driver in adenocarcinoma as well as neuroendocrine variants of prostate cancer.

Androgen receptor- (AR-) indifference is a mechanism of resistance to hormonal therapy in prostate cancer (PC). Here we demonstrate that ONECUT2 (OC2) activates resistance through multiple drivers associated with adenocarcinoma, stem-like and neuroendocrine (NE) variants. Direct OC2 gene targets include the glucocorticoid receptor (GR; NR3C1) and the NE splicing factor SRRM4, which are key drivers of lineage plasticity. Thus, OC2, despite its previously described NEPC driver function, can indirectly activate a portion of the AR cistrome through epigenetic activation of GR. Mechanisms by which OC2 regulates gene expression include promoter binding, enhancement of genome-wide chromatin accessibility, and super-enhancer reprogramming. Pharmacologic inhibition of OC2 suppresses lineage plasticity reprogramming induced by the AR signaling inhibitor enzalutamide. These results demonstrate that OC2 activation promotes a range of drug resistance mechanisms associated with treatment-emergent lineage variation in PC and support enhanced efforts to therapeutically target OC2 as a means of suppressing treatment-resistant disease.

A Pipeline for Evaluation of Machine Learning/Artificial Intelligence Models to Quantify Programmed Death Ligand 1 Immunohistochemistry.

Immunohistochemistry (IHC) is used to guide treatment decisions in multiple cancer types. For treatment with checkpoint inhibitors, programmed death ligand 1 (PD-L1) IHC is used as a companion diagnostic. However, the scoring of PD-L1 is complicated by its expression in cancer and immune cells. Separation of cancer and noncancer regions is needed to calculate tumor proportion scores (TPS) of PD-L1, which is based on the percentage of PD-L1-positive cancer cells. Evaluation of PD-L1 expression requires highly experienced pathologists and is often challenging and time-consuming. Here, we used a multi-institutional cohort of 77 lung cancer cases stained centrally with the PD-L1 22C3 clone. We developed a 4-step pipeline for measuring TPS that includes the coregistration of hematoxylin and eosin, PD-L1, and negative control (NC) digital slides for exclusion of necrosis, segmentation of cancer regions, and quantification of PD-L1+ cells. As cancer segmentation is a challenging step for TPS generation, we trained DeepLab V3 in the Visiopharm software package to outline cancer regions in PD-L1 and NC images and evaluated the model performance by mean intersection over union (mIoU) against manual outlines. Only 14 cases were required to accomplish a mIoU of 0.82 for cancer segmentation in hematoxylin-stained NC cases. For PD-L1-stained slides, a model trained on PD-L1 tiles augmented by registered NC tiles achieved a mIoU of 0.79. In segmented cancer regions from whole slide images, the digital TPS achieved an accuracy of 75% against the manual TPS scores from the pathology report. Major reasons for algorithmic inaccuracies include the inclusion of immune cells in cancer outlines and poor nuclear segmentation of cancer cells. Our transparent and stepwise approach and performance metrics can be applied to any IHC assay to provide pathologists with important insights on when to apply and how to evaluate commercial automated IHC scoring systems.

HistoEM: A Pathologist-Guided and Explainable Workflow Using Histogram Embedding for Gland Classification.

Pathologists have, over several decades, developed criteria for diagnosing and grading prostate cancer. However, this knowledge has not, so far, been included in the design of convolutional neural networks (CNN) for prostate cancer detection and grading. Further, it is not known whether the features learned by machine-learning algorithms coincide with diagnostic features used by pathologists. We propose a framework that enforces algorithms to learn the cellular and subcellular differences between benign and cancerous prostate glands in digital slides from hematoxylin and eosin-stained tissue sections. After accurate gland segmentation and exclusion of the stroma, the central component of the pipeline, named HistoEM, utilizes a histogram embedding of features from the latent space of the CNN encoder. Each gland is represented by 128 feature-wise histograms that provide the input into a second network for benign vs cancer classification of the whole gland. Cancer glands are further processed by a U-Net structured network to separate low-grade from high-grade cancer. Our model demonstrates similar performance compared with other state-of-the-art prostate cancer grading models with gland-level resolution. To understand the features learned by HistoEM, we first rank features based on the distance between benign and cancer histograms and visualize the tissue origins of the 2 most important features. A heatmap of pixel activation by each feature is generated using Grad-CAM and overlaid on nuclear segmentation outlines. We conclude that HistoEM, similar to pathologists, uses nuclear features for the detection of prostate cancer. Altogether, this novel approach can be broadly deployed to visualize computer-learned features in histopathology images.

Biophysical phenotype mixtures reveal advantages for tumor muscle invasion in vivo.

Bladder, colon, gastric, prostate, and uterine cancers originate in organs surrounded by laminin-coated smooth muscle. In human prostate cancer, tumors that are organ confined, without extracapsular extension through muscle, have an overall cancer survival rate of up to 97% compared with 32% for metastatic disease. Our previous work modeling extracapsular extension reported the blocking of tumor invasion by mutation of a laminin-binding integrin called α6ß1. Expression of the α6AA mutant resulted in a biophysical switch from cell-ECM (extracellular matrix) to cell-cell adhesion with drug sensitivity properties and an inability to invade muscle. Here we used different admixtures of α6AA and α6WT cells to test the cell heterogeneity requirements for muscle invasion. Time-lapse video microscopy revealed that tumor mixtures self-assembled into invasive networks in vitro, whereas α6AA cells assembled only as cohesive clusters. Invasion of α6AA cells into and through live muscle occurred using a 1:1 mixture of α6AA and α6WT cells. Electric cell-substrate impedance sensing measurements revealed that compared with α6AA cells, invasion-competent α6WT cells were 2.5-fold faster at closing a cell-ECM or cell-cell wound, respectively. Cell-ECM rebuilding kinetics show that an increased response occurred in mixtures since the response was eightfold greater compared with populations containing only one cell type. A synthetic cell adhesion cyclic peptide called MTI-101 completely blocked electric cell-substrate impedance sensing cell-ECM wound recovery that persisted in vitro up to 20 h after the wound. Treatment of tumor-bearing animals with 10 mg/kg MTI-101 weekly resulted in a fourfold decrease of muscle invasion by tumor and a decrease of the depth of invasion into muscle comparable to the α6AA cells. Taken together, these data suggest that mixed biophysical phenotypes of tumor cells within a population can provide functional advantages for tumor invasion into and through muscle that can be potentially inhibited by a synthetic cell adhesion molecule.

Automating Ground Truth Annotations for Gland Segmentation Through Immunohistochemistry.

Microscopic evaluation of glands in the colon is of utmost importance in the diagnosis of inflammatory bowel disease and cancer. When properly trained, deep learning pipelines can provide a systematic, reproducible, and quantitative assessment of disease-related changes in glandular tissue architecture. The training and testing of deep learning models require large amounts of manual annotations, which are difficult, time-consuming, and expensive to obtain. Here, we propose a method for automated generation of ground truth in digital hematoxylin and eosin (H&E)-stained slides using immunohistochemistry (IHC) labels. The image processing pipeline generates annotations of glands in H&E histopathology images from colon biopsy specimens by transfer of gland masks from KRT8/18, CDX2, or EPCAM IHC. The IHC gland outlines are transferred to coregistered H&E images for training of deep learning models. We compared the performance of the deep learning models to that of manual annotations using an internal held-out set of biopsy specimens as well as 2 public data sets. Our results show that EPCAM IHC provides gland outlines that closely match manual gland annotations (Dice = 0.89) and are resilient to damage by inflammation. In addition, we propose a simple data sampling technique that allows models trained on data from several sources to be adapted to a new data source using just a few newly annotated samples. The best performing models achieved average Dice scores of 0.902 and 0.89 on Gland Segmentation and Colorectal Adenocarcinoma Gland colon cancer public data sets, respectively, when trained with only 10% of annotated cases from either public cohort. Altogether, the performances of our models indicate that automated annotations using cell type-specific IHC markers can safely replace manual annotations. Automated IHC labels from single-institution cohorts can be combined with small numbers of hand-annotated cases from multi-institutional cohorts to train models that generalize well to diverse data sources.

Mutant POLQ and POLZ/REV3L DNA polymerases may contribute to the favorable survival of patients with tumors with POLE mutations outside the exonuclease domain.

BACKGROUND: Mutations in the exonuclease domain of POLE, a DNA polymerase associated with DNA replication and repair, lead to cancers with ultra-high mutation rates. Most studies focus on intestinal and uterine cancers with POLE mutations. These cancers exhibit a significant immune cell infiltrate and favorable prognosis. We questioned whether loss of function of other DNA polymerases can cooperate to POLE to generate the ultramutator phenotype. METHODS: We used cases and data from 15 cancer types in The Cancer Genome Atlas to investigate mutation frequencies of 14 different DNA polymerases. We tested whether tumor mutation burden, patient outcome (disease-free survival) and immune cell infiltration measured by ESTIMATE can be attributed to mutations in POLQ and POLZ/REV3L. RESULTS: Thirty six percent of colorectal, stomach and endometrial cancers with POLE mutations carried additional mutations in POLQ (E/Q), POLZ/REV3L (E/Z) or both DNA polymerases (E/Z/Q). The mutation burden in these tumors was significantly greater compared to POLE-only (E) mutant tumors (p < 0.001). In addition, E/Q, E/Z, and E/Q/Z mutant tumors possessed an increased frequency of mutations in the POLE exonuclease domain (p = 0.013). Colorectal, stomach and endometrial E/Q, E/Z, and E/Q/Z mutant tumors within TCGA demonstrated 100% disease-free survival, even if the POLE mutations occurred outside the exonuclease domain (p = 0.003). However, immune scores in these tumors were related to microsatellite instability (MSI) and not POLE mutation status. This suggests that the host immune response may not be the sole mechanism for prolonged disease-free survival of ultramutated tumors in this cohort. CONCLUSION: Results in this study demonstrate that mutations in POLQ and REV3L in POLE mutant tumors should undergo further investigation to determine whether POLQ and REV3L mutations contribute to the ultramutator phenotype and favorable outcome of patients with POLE mutant tumors.

Chromosomal instability in untreated primary prostate cancer as an indicator of metastatic potential.

BACKGROUND: Metastatic prostate cancer (PC) is highly lethal. The ability to identify primary tumors capable of dissemination is an unmet need in the quest to understand lethal biology and improve patient outcomes. Previous studies have linked chromosomal instability (CIN), which generates aneuploidy following chromosomal missegregation during mitosis, to PC progression. Evidence of CIN includes broad copy number alterations (CNAs) spanning > 300 base pairs of DNA, which may also be measured via RNA expression signatures associated with CNA frequency. Signatures of CIN in metastatic PC, however, have not been interrogated or well defined. We examined a published 70-gene CIN signature (CIN70) in untreated and castration-resistant prostate cancer (CRPC) cohorts from The Cancer Genome Atlas (TCGA) and previously published reports. We also performed transcriptome and CNA analysis in a unique cohort of untreated primary tumors collected from diagnostic prostate needle biopsies (PNBX) of localized (M0) and metastatic (M1) cases to determine if CIN was linked to clinical stage and outcome. METHODS: PNBX were collected from 99 patients treated in the VA Greater Los Angeles (GLA-VA) Healthcare System between 2000 and 2016. Total RNA was extracted from high-grade cancer areas in PNBX cores, followed by RNA sequencing and/or copy number analysis using OncoScan. Multivariate logistic regression analyses permitted calculation of odds ratios for CIN status (high versus low) in an expanded GLA-VA PNBX cohort (n = 121). RESULTS: The CIN70 signature was significantly enriched in primary tumors and CRPC metastases from M1 PC cases. An intersection of gene signatures comprised of differentially expressed genes (DEGs) generated through comparison of M1 versus M0 PNBX and primary CRPC tumors versus metastases revealed a 157-gene "metastasis" signature that was further distilled to 7-genes (PC-CIN) regulating centrosomes, chromosomal segregation, and mitotic spindle assembly. High PC-CIN scores correlated with CRPC, PC-death and all-cause mortality in the expanded GLA-VA PNBX cohort. Interestingly, approximately 1/3 of M1 PNBX cases exhibited low CIN, illuminating differential pathways of lethal PC progression. CONCLUSIONS: Measuring CIN in PNBX by transcriptome profiling is feasible, and the PC-CIN signature may identify patients with a high risk of lethal progression at the time of diagnosis.

A basal cell defect promotes budding of prostatic intraepithelial neoplasia.

Basal cells in a simple secretory epithelium adhere to the extracellular matrix (ECM), providing contextual cues for ordered repopulation of the luminal cell layer. Early high-grade prostatic intraepithelial neoplasia (HG-PIN) tissue has enlarged nuclei and nucleoli, luminal layer expansion and genomic instability. Additional HG-PIN markers include loss of α6ß4 integrin or its ligand laminin-332, and budding of tumor clusters into laminin-511-rich stroma. We modeled the invasive budding phenotype by reducing expression of α6ß4 integrin in spheroids formed from two normal human stable isogenic prostate epithelial cell lines (RWPE-1 and PrEC 11220). These normal cells continuously spun in culture, forming multicellular spheroids containing an outer laminin-332 layer, basal cells (expressing α6ß4 integrin, high-molecular-weight cytokeratin and p63, also known as TP63) and luminal cells that secrete PSA (also known as KLK3). Basal cells were optimally positioned relative to the laminin-332 layer as determined by spindle orientation. ß4-integrin-defective spheroids contained a discontinuous laminin-332 layer corresponding to regions of abnormal budding. This 3D model can be readily used to study mechanisms that disrupt laminin-332 continuity, for example, defects in the essential adhesion receptor (ß4 integrin), laminin-332 or abnormal luminal expansion during HG-PIN progression.

Clinical protein science in translational medicine targeting malignant melanoma.

Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Malignant melanoma is a highly aggressive skin cancer and metastases rapidly extend to the regional lymph nodes (stage 3) and to distal organs (stage 4). Targeted oncotherapy is one of the standard treatment for progressive stage 4 melanoma, and BRAF inhibitors (e.g. vemurafenib, dabrafenib) combined with MEK inhibitor (e.g. trametinib) can effectively counter BRAFV600E-mutated melanomas. Compared to conventional chemotherapy, targeted BRAFV600E inhibition achieves a significantly higher response rate. After a period of cancer control, however, most responsive patients develop resistance to the therapy and lethal progression. The many underlying factors potentially causing resistance to BRAF inhibitors have been extensively studied. Nevertheless, the remaining unsolved clinical questions necessitate alternative research approaches to address the molecular mechanisms underlying metastatic and treatment-resistant melanoma. In broader terms, proteomics can address clinical questions far beyond the reach of genomics, by measuring, i.e. the relative abundance of protein products, post-translational modifications (PTMs), protein localisation, turnover, protein interactions and protein function. More specifically, proteomic analysis of body fluids and tissues in a given medical and clinical setting can aid in the identification of cancer biomarkers and novel therapeutic targets. Achieving this goal requires the development of a robust and reproducible clinical proteomic platform that encompasses automated biobanking of patient samples, tissue sectioning and histological examination, efficient protein extraction, enzymatic digestion, mass spectrometry-based quantitative protein analysis by label-free or labelling technologies and/or enrichment of peptides with specific PTMs. By combining data from, e.g. phosphoproteomics and acetylomics, the protein expression profiles of different melanoma stages can provide a solid framework for understanding the biology and progression of the disease. When complemented by proteogenomics, customised protein sequence databases generated from patient-specific genomic and transcriptomic data aid in interpreting clinical proteomic biomarker data to provide a deeper and more comprehensive molecular characterisation of cellular functions underlying disease progression. In parallel to a streamlined, patient-centric, clinical proteomic pipeline, mass spectrometry-based imaging can aid in interrogating the spatial distribution of drugs and drug metabolites within tissues at single-cell resolution. These developments are an important advancement in studying drug action and efficacy in vivo and will aid in the development of more effective and safer strategies for the treatment of melanoma. A collaborative effort of gargantuan proportions between academia and healthcare professionals has led to the initiation, establishment and development of a cutting-edge cancer research centre with a specialisation in melanoma and lung cancer. The primary research focus of the European Cancer Moonshot Lund Center is to understand the impact that drugs have on cancer at an individualised and personalised level. Simultaneously, the centre increases awareness of the relentless battle against cancer and attracts global interest in the exceptional research performed at the centre.

The Cohesive Metastasis Phenotype in Human Prostate Cancer.

A critical barrier for the successful prevention and treatment of recurrent prostate cancer is detection and eradication of metastatic and therapy-resistant disease. Despite the fall in diagnoses and mortality, the reported incidence of metastatic disease has increased 72% since 2004. Prostate cancer arises in cohesive groups as intraepithelial neoplasia, migrates through muscle and leaves the gland via perineural invasion for hematogenous dissemination. Current technological advances have shown cohesive-clusters of tumor (also known as microemboli) within the circulation. Circulating tumor cell (CTC) profiles are indicative of disseminated prostate cancer, and disseminated tumor cells (DTC) are found in cohesive-clusters, a phenotypic characteristic of both radiation- and drug-resistant tumors. Recent reports in cell biology and informatics, coupled with mass spectrometry, indicate that the integrin adhesome network provides an explanation for the biophysical ability of cohesive-clusters of tumor cells to invade thorough muscle and nerve microenvironments while maintaining adhesion-dependent therapeutic resistance. Targeting cohesive-clusters takes advantage of the known ability of extracellular matrix (ECM) adhesion to promote tumor cell survival and represents an approach that has the potential to avoid the progression to drug- and radiotherapy-resistance. In the following review we will examine the evidence for development and dissemination of cohesive-clusters in metastatic prostate cancer.

Characterization of Laminin Binding Integrin Internalization in Prostate Cancer Cells.

Laminin binding integrins α6 (CD49f) and α3 (CD49c) are persistently but differentially expressed in prostate cancer (PCa). Integrin internalization is an important determinant of their cell surface expression and function. Using flow cytometry, and first order kinetic modeling, we quantitated the intrinsic internalization rates of integrin subunits in a single cycle of internalization. In PCa cell line DU145, α6 integrin internalized with a rate constant (kactual ) of 3.25 min-1 , threefold faster than α3 integrin (1.0 min-1 ), 1.5-fold faster than the vitronectin binding αv integrin (CD51) (2.2 min-1 ), and significantly slower than the unrelated transferrin receptor (CD71) (15 min-1 ). Silencing of α3 integrin protein expression in DU145, PC3, and PC3B1 cells resulted in up to a 1.71-fold increase in kactual for α6 integrin. The internalized α6 integrin was targeted to early endosomes but not to lamp1 vesicles. Depletion of α3 integrin expression resulted in redistribution of α6ß4 integrin to an observed cell-cell staining pattern that is consistent with a suprabasal distribution observed in epidermis and early PIN lesions in PCa. Depletion of α3 integrin increased cell migration by 1.8-fold, which was dependent on α6ß1 integrin. Silencing of α6 integrin expression however, had no significant effect on the kactual of α3 integrin or its distribution in early endosomes. These results indicate that α3 and α6 integrins have significantly different internalization kinetics and that coordination exists between them for internalization. J. Cell. Biochem. 118: 1038-1049, 2017. © 2016 Wiley Periodicals, Inc.

High-throughput sequencing of two populations of extracellular vesicles provides an mRNA signature that can be detected in the circulation of breast cancer patients.

Extracellular vesicles (EVs) contain a wide range of RNA types with a reported prevalence of non-coding RNA. To date a comprehensive characterization of the protein coding transcripts in EVs is still lacking. We performed RNA-Sequencing (RNA-Seq) of 2 EV populations and identified a small fraction of transcripts that were expressed at significantly different levels in large oncosomes and exosomes, suggesting they may mediate specialized functions. However, these 2 EV populations exhibited a common mRNA signature that, in comparison to their donor cells, was significantly enriched in mRNAs encoding E2F transcriptional targets and histone proteins. These mRNAs are primarily expressed in the S-phase of the cell cycle, suggesting that they may be packaged into EVs during S-phase. In silico analysis using subcellular compartment transcriptome data from the ENCODE cell line compendium revealed that EV mRNAs originate from a cytoplasmic RNA pool. The EV signature was independently identified in plasma of patients with breast cancer by RNA-Seq. Furthermore, several transcripts differentially expressed in EVs from patients versus controls mirrored differential expression between normal and breast cancer tissues. Altogether, this largest high-throughput profiling of EV mRNA demonstrates that EVs carry tumor-specific alterations and can be interrogated as a source of cancer-derived cargo.

A bitter pill for type 2 diabetes? The activation of bitter taste receptor TAS2R38 can stimulate GLP-1 release from enteroendocrine L-cells.

The bitter taste receptor TAS2R38 is a G protein coupled receptor (GPCR) that has been found in many extra-oral locations like the gastrointestinal (GI) system, respiratory system, and brain, though its function at these locations is only beginning to be understood. To probe the receptor's potential metabolic role, immunohistochemistry of human ileum tissues was performed, which showed that the receptor was co-localized with glucagon-like peptide 1 (GLP-1) in L-cells. In a previous study, we had modeled the structure of this receptor for its many taste-variant haplotypes (Tan et al. 2011), including the taster haplotype PAV. The structure of this haplotype was then used in a virtual ligand screening pipeline using a collection of ∼2.5 million purchasable molecules from the ZINC database. Three compounds (Z7, Z3, Z1) were purchased from the top hits and tested along with PTU (known TAS2R38 agonist) in in vitro and in vivo assays. The dose-response study of the effect of PTU and Z7 on GLP-1 release using wild-type and TAS2R38 knockout HuTu-80 cells showed that the receptor TAS2R38 plays a major role in GLP-1 release due to these molecules. In vivo studies of PTU and the three compounds showed that they each increase GLP-1 release. PTU was also chemical linked to cellulose to slow its absorption and when tested in vivo, it showed an enhanced and prolonged GLP-1 release. These results suggest that the GI lumen location of TAS2R38 on the L-cell makes it a relatively safe drug target as systemic absorption is not needed for a TAS2R38 agonist drug to effect GLP-1 release.

Hypoxia after transarterial chemoembolization may trigger a progenitor cell phenotype in hepatocellular carcinoma.

AIMS: To test the hypothesis that the hypoxia marker carbonic anhydrase IX (CAIX), and the cholangiocytic/progenitor markers cytokeratin (CK) 19 and epithelial cell adhesion molecule (EpCAM), may be expressed in areas of hypoxia in hepatocellular carcinoma (HCC) after transarterial chemoembolization (TACE). METHODS AND RESULTS: Immunohistochemistry for CAIX, CK19 and EpCAM (BerEP4) was performed in 57 HCCs, including 40 residual/recurrent tumours adjacent to the TACE treatment site and 17 untreated tumours from the same 40 patients. CAIX was exxpressed in 19 of 40 residual/recurrent HCCs and in two of 17 untreated HCCs. The rate of CAIX immunoreactivity in the treated tumours was significantly higher than that in the non-treated tumours (47.5% versus 11.8%, P = 0.015). CK19 and EpCAM were expressed in six of 19 and in seven of 19 CAIX-positive TACE-treated HCCs, respectively, but were not expressed in CAIX-negative tumours or untreated tumours. There were significant associations between CK19 and CAIX immunoreactivity, and between EpCAM and CAIX immunoreactivity (P = 0.007 and P = 0.003, respectively). Double staining of CAIX and CK19 showed co-localization of both proteins in five of six cases. Three of seven EpCAM-positive tumours were also positive for CK19. CONCLUSIONS: Hypoxia may trigger the expression of proteins that are normally associated with cholangiocytic/progenitor cell differentiation, suggesting that TACE paradoxically causes an aggressive tumour phenotype.

Tumour cell survival mechanisms in lethal metastatic prostate cancer differ between bone and soft tissue metastases.

The complexity of survival mechanisms in cancer cells from patients remains poorly understood. To obtain a comprehensive picture of tumour cell survival in lethal prostate cancer metastases, we examined five survival proteins that operate within three survival pathways in a cohort of 185 lethal metastatic prostate metastases obtained from 44 patients. The expression levels of BCL-2, BCL-XL, MCL-1, cytoplasmic survivin, nuclear survivin, and stathmin were measured by immunohistochemistry in a tissue microarray. Simultaneous expression of three or more proteins occurred in 81% of lethal prostate cancer metastases and BCL-2, cytoplasmic survivin and MCL-1 were co-expressed in 71% of metastatic sites. An unsupervised cluster analysis separated bone and soft tissue metastases according to patterns of survival protein expression. BCL-2, cytoplasmic survivin and MCL-1 had significantly higher expression in bone metastases (p < 10(-5)), while nuclear survivin was significantly higher in soft tissue metastases (p = 3 × 10(-14)). BCL-XL overexpression in soft tissue metastases almost reached significance (p = 0.09), while stathmin expression did not (p = 0.28). In addition, the expression of MCL-1 was significantly higher in AR-positive tumours. Neuroendocrine differentiation was not associated with specific survival pathways. These studies show that bone and soft tissue metastases from the same patient differ significantly in expression of a panel of survival proteins and that with regard to survival protein expression, expression is associated with the metastatic site and not the patient. Altogether, this suggests that optimal therapeutic inhibition may require combinations of drugs that target both bone and soft tissue-specific survival pathways.

Improved Preservation of Mouse Intestinal Tissue Using a Formalin/Acetic Acid Fixative and Quantitative Histological Analysis Using QuPath.

The architecture and morphology of the intestinal tissue from mice or other small animals are difficult to preserve for histological and molecular analysis due to the fragile nature of this tissue. The intestinal mucosa consists of villi and crypts lined with epithelial cells. In between the epithelial folds extends the lamina propria, a loose connective tissue that contains blood and lymph vessels, fibroblasts, and immune cells. Underneath the mucosa are two layers of contractile smooth muscle and nerves. The tissue experiences significant changes during fixation, which can impair the reliability of histologic analysis. Poor-quality histologic sections are not suitable for quantitative image-based tissue analysis. This article offers a new fixative composed of neutral buffered formalin (NBF) and acetic acid, called FA. This fixative significantly improved the histology of mouse intestinal tissue compared to traditional NBF and enabled precise, reproducible histologic molecular analyses using QuPath software. Algorithmic training of QuPath allows for automated segmentation of intestinal compartments, which can be further interrogated for cellular composition and disease-related changes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Improved preservation of mouse intestinal tissue using a formalin/acetic acid fixative Support Protocol: Quantitative tissue analysis using QuPath.

ONECUT2 is a druggable driver of luminal to basal breast cancer plasticity.

PURPOSE: Tumor heterogeneity complicates patient treatment and can be due to transitioning of cancer cells across phenotypic cell states. This process is associated with the acquisition of independence from an oncogenic driver, such as the estrogen receptor (ER) in breast cancer (BC), resulting in tumor progression, therapeutic failure and metastatic spread. The transcription factor ONECUT2 (OC2) has been shown to be a master regulator protein of metastatic castration-resistant prostate cancer (mCRPC) tumors that promotes lineage plasticity to a drug-resistant neuroendocrine (NEPC) phenotype. Here, we investigate the role of OC2 in the dynamic conversion between different molecular subtypes in BC. METHODS: We analyze OC2 expression and clinical significance in BC using public databases and immunohistochemical staining. In vitro, we perform RNA-Seq, RT-qPCR and western-blot after OC2 enforced expression. We also assess cellular effects of OC2 silencing and inhibition with a drug-like small molecule in vitro and in vivo. RESULTS: OC2 is highly expressed in a substantial subset of hormone receptor negative human BC tumors and tamoxifen-resistant models, and is associated with poor clinical outcome, lymph node metastasis and heightened clinical stage. OC2 inhibits ER expression and activity, suppresses a gene expression program associated with luminal differentiation and activates a basal-like state at the gene expression level. We also show that OC2 is required for cell growth and survival in metastatic BC models and that it can be targeted with a small molecule inhibitor providing a novel therapeutic strategy for patients with OC2 active tumors. CONCLUSIONS: The transcription factor OC2 is a driver of BC heterogeneity and a potential drug target in distinct cell states within the breast tumors.

Neighborhood Deprivation, Race and Ethnicity, and Prostate Cancer Outcomes Across California Health Care Systems.

Importance: Non-Hispanic Black (hereafter, Black) individuals experience worse prostate cancer outcomes due to socioeconomic and racial inequities of access to care. Few studies have empirically evaluated these disparities across different health care systems. Objective: To describe the racial and ethnic and neighborhood socioeconomic status (nSES) disparities among residents of the same communities who receive prostate cancer care in the US Department of Veterans Affairs (VA) health care system vs other settings. Design, Setting, and Participants: This cohort study obtained data from the VA Central Cancer Registry for veterans with prostate cancer who received care within the VA Greater Los Angeles Healthcare System (VA cohort) and from the California Cancer Registry (CCR) for nonveterans who received care outside the VA setting (CCR cohort). The cohorts consisted of all males with incident prostate cancer who were living within the same US Census tracts. These individuals received care between 2000 and 2018 and were followed up until death from any cause or censoring on December 31, 2018. Data analyses were conducted between September 2022 and December 2023. Exposures: Health care setting, self-identified race and ethnicity (SIRE), and nSES. Main Outcomes and Measures: The primary outcome was all-cause mortality (ACM). Cox proportional hazards regression models were used to estimate hazard ratios for associations of SIRE and nSES with prostate cancer outcomes in the VA and CCR cohorts. Results: Included in the analysis were 49 461 males with prostate cancer. Of these, 1881 males were in the VA cohort (mean [SD] age, 65.3 [7.7] years; 833 Black individuals [44.3%], 694 non-Hispanic White [hereafter, White] individuals [36.9%], and 354 individuals [18.8%] of other or unknown race). A total of 47 580 individuals were in the CCR cohort (mean [SD] age, 67.0 [9.6] years; 8183 Black individuals [17.2%], 26 206 White individuals [55.1%], and 13 191 individuals [27.8%] of other or unknown race). In the VA cohort, there were no racial disparities observed for metastasis, ACM, or prostate cancer-specific mortality (PCSM). However, in the CCR cohort, the racial disparities were observed for metastasis (adjusted odds ratio [AOR], 1.36; 95% CI, 1.22-1.52), ACM (adjusted hazard ratio [AHR], 1.13; 95% CI, 1.04-1.24), and PCSM (AHR, 1.15; 95% CI, 1.05-1.25). Heterogeneity was observed for the racial disparity in ACM in the VA vs CCR cohorts (AHR, 0.90 [95% CI, 0.76-1.06] vs 1.13 [95% CI, 1.04-1.24]; P = .01). No evidence of nSES disparities was observed for any prostate cancer outcomes in the VA cohort. However, in the CCR cohort, heterogeneity was observed for nSES disparities with ACM (AHR, 0.82; 95% CI, 0.80-0.84; P = .002) and PCSM (AHR, 0.86; 95% CI, 0.82-0.89; P = .007). Conclusions and Relevance: Results of this study suggest that racial and nSES disparities were wider among patients seeking care outside of the VA health care system. Health systems-related interventions that address access barriers may mitigate racial and socioeconomic disparities in prostate cancer.

Investigation of the relationship between prostate cancer and MSMB and NCOA4 genetic variants and protein expression.

Two genome-wide association studies (GWAS) identified the ß-microseminoprotein (MSMB) promoter SNP, rs10993994:C>T, as significantly associated with prostate cancer (PC) risk. Follow-up studies demonstrate that the variant allele directly affects expression of the MSMB-encoded protein, PSP94, and also suggest that it affects mRNA expression levels of an adjacent gene, NCOA4, which is involved in androgen receptor transactivation. In a population-based study of 1,323 cases and 1,268 age-matched controls, we found the NCOA4 SNP, rs7350420:T>C, was associated with a 15% reduction in PC risk, but the association was not significant after adjustment for the rs10993994:C>T genotype. Tumor tissue microarrays of 519 radical prostatectomy patients were used to measure PSP94 and NCOA4 protein expression. Taken together, these data confirm that the rs10993994:C>T variant allele is associated with decreased PSP94 expression, and the association is stronger in tumor compared to normal prostate tissue. No association was observed between rs10993994:C>T and NCOA4 expression, and only moderate associations were seen between two NCOA4 SNPs, rs10761618:T>C and rs7085433:G>A, and NCOA4 protein expression. These data indicate that the increase in PC risk associated with rs10993994:C>T is likely mediated by the variant's effect on PSP94 expression; however, this effect does not extend to NCOA4 in the data presented here.

Predicting IHC staining classes of NF1 using features in the hematoxylin channel.

Immunohistochemistry (IHC) highlights specific cell types in tissues and traditionally involves antibody staining together with a hematoxylin counterstain. The intensity and pattern of hematoxylin staining differs between cell types and reveals morphological characteristics of cells. Here, we propose that features in the hematoxylin stain can be used to predict IHC labels, such as Neurofibromin (encoded by the gene NF1). The dataset consists of 7.2 million cells from benign and kidney cancer cores in a tissue microarray. Morphology and hematoxylin (H&M) features defined within QuPath are subjected to a clustering analysis in CytoMap. H&M features are also used to train 4 different XGBoost models to predict high, low, and negative NF1 stain classes in benign renal tubules, clear cell (ccRCC), papillary (PRCC), and chromophobe (ChRCC) renal carcinoma. The prediction accuracies of NF1 staining classes in benign, ccRCC, ChRCC, and PRCC range between 70% and 90% with areas under the precision recall curve PRAUCNF1-high = 0.82+0.12, PRAUCNF1-low = 0.62+0.25, and PRAUCNF1-negative = 0.83+0.16. The most important feature for predicting the NF1 class involves the minimum cellular hematoxylin staining intensity. Together, these results demonstrate the feasibility to predict NF1 expression solely from features in hematoxylin staining using open source software. Since the hematoxylin features can be obtained from regular H&E and IHC slides, the proposed workflow has broad applicability.