A pipeline for evaluation of machine learning/AI models to quantify PD-L1 immunohistochemistry

Immunohistochemistry (IHC) is used to guide treatment decisions in multiple cancer types. For treatment with checkpoint inhibitors, 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 non-cancer 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 four-step pipeline for measuring TPS that includes the co-registration of H&E, 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 negative control (NC) images and evaluated the model performance by mean intersection over union (mIoU) against manual outlines. Only 14 cases were required to accomplish an 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 an 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 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.

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.

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.

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 α6β1. 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.

Organ-specific immunity: A tissue analysis framework for investigating local immune responses to SARS-CoV-2

Local immune activation at mucosal surfaces, mediated by mucosal lymphoid tissues, is vital for effective immune responses against pathogens. While pathogens like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread to multiple organs, patients with coronavirus disease 2019 (COVID-19) primarily experience inflammation and damage in their lungs. To investigate this apparent organ-specific immune response, we develop an analytical framework that recognizes the significance of mucosal lymphoid tissues. This framework combines histology, immunofluorescence, spatial transcript profiling, and mathematical modeling to identify cellular and gene expression differences between the lymphoid tissues of the lung and the gut and predict the determinants of those differences. Our findings indicate that mucosal lymphoid tissues are pivotal in organ-specific immune response to SARS-CoV-2, mediating local inflammation and tissue damage and contributing to immune dysfunction. The framework developed here has potential utility in the study of long COVID and may streamline biomarker discovery and treatment design for diseases with differential pathologies at the organ level.

ONECUT2 Activates Diverse Resistance Drivers of Androgen Receptor-Independent Heterogeneity in Prostate Cancer

Androgen receptor- (AR-) indifference is a mechanism of resistance to hormonal therapy in prostate cancer (PC). Here we demonstrate that the HOX/CUT transcription factor ONECUT2 (OC2) activates resistance through multiple drivers associated with adenocarcinoma, stem-like and neuroendocrine (NE) variants. Direct OC2 targets include the glucocorticoid receptor and the NE splicing factor SRRM4, among others. OC2 regulates gene expression by promoter binding, enhancement of chromatin accessibility, and formation of novel super-enhancers. OC2 also activates glucuronidation genes that irreversibly disable androgen, thereby evoking phenotypic heterogeneity indirectly by hormone depletion. 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. Our findings support enhanced efforts to therapeutically target this protein as a means of suppressing treatment-resistant disease.

Significant changes in macrophage and CD8 T cell densities in primary prostate tumors 2 weeks after SBRT

BACKGROUND

Radiotherapy impacts the local immune response to cancers. Prostate Stereotactic Body Radiotherapy (SBRT) is a highly focused method to deliver radiotherapy often used to treat prostate cancer. This is the first direct comparison of immune cells within prostate cancers before and after SBRT in patients.

METHODS

Prostate cancers before and 2 weeks after SBRT are interrogated by multiplex immune fluorescence targeting various T cells and macrophages markers and analyzed by cell and pixel density, as part of a clinical trial of SBRT neoadjuvant to radical prostatectomy.

RESULTS

Two weeks after SBRT, CD68, and CD163 macrophages are significantly increased while CD8 T cells are decreased. SBRT markedly alters the immune environment within prostate cancers.

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 PRAUC_NF1-high = 0.82+0.12, PRAUC_NF1-low = 0.62+0.25, and PRAUC_NF1-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.

Abstract 3835: Kindlin-2 complexes containing α6β1 integrin are responsive to hypoxia

The laminin-binding integrins are mechanosensory receptors critical for cell adhesion and structural organization that link the extracellular matrix (ECM) to the cytoskeleton. Integrin α6β1 is associated with prostate cancer (PCa) migration, invasion, metastasis, and decreased cancer-specific survival. Kindlin-2 (FERMT2) is a β1 integrin adaptor and mechanosensory focal adhesion (FA) protein that activates and clusters integrins in response to structural ECM alterations in the tumor microenvironment. Our goal was to determine if integrin-kindlin-2 adhesion complexes (kindlin-2:α6β1) were responsive to hypoxia, a physiologically relevant and altered microenvironment in PCa progression. Five different endpoints were tested including the biochemical analysis of kindlin-2 complexes, qRT-PCR, immunoblotting, immunocytochemistry, and electric cell impedance sensing (ECIS). Using DU145 prostate cancer cells grown under hypoxia (1% O2) for up to 16 hours, the results showed a reversible increase in kindlin-2:α6β1 complexes with maximal assembly within 4 hours and disassembly starting by 8 hours. Notably, kindlin-2:α6β1 complexes were found exclusively within membrane projections and were not observed within hypoxia-inducible paxillin (PXN)-containing FAs. The hypoxia induced kindlin-2:α6β1 complexes and classical FAs were dependent on kindlin-2 as determined by CRISPR-Cas9 heterozygous deletion of FERMT2. Protein co-localization of α6 integrin and PXN with kindlin-2 within membrane projections and FAs, respectively, was also induced under hypoxia. Further, non-invasive ECIS measurements in live cells confirmed functional cell-cell and cell-ECM dynamics driven by hypoxia and requiring kindlin-2. Our results indicate that the kindlin-2:α6β1 complexes are uniquely associated with FA-independent membrane projections induced by hypoxia, a tumor microenvironment associated with aggressive prostate cancer. The novel kindlin-2:α6β1 complexes may represent an actionable pharmacological target for blocking escape of organ confined disease and metastasis promoting steps of human prostate cancer.

(Partially supported by NIH grants CA P30 23074, DOD W81XWH-19-1-0455, and NCI R01 CA242226).

Citation Format: Daniel Hernandez-Cortes, Jaime M.C. Gard, Beatrice S. Knudsen, Noel A. Warfel, Anne E. Cress. Kindlin-2 complexes containing α6β1 integrin are responsive to hypoxia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3835.

The Movember Global Action Plan 1 (GAP1): Unique Prostate Cancer Tissue Microarray Resource

BACKGROUND

The need to better understand the molecular underpinnings of the heterogeneous outcomes of patients with prostate cancer is a pressing global problem and a key research priority for Movember. To address this, the Movember Global Action Plan 1 Unique tissue microarray (GAP1-UTMA) project constructed a set of unique and richly annotated tissue microarrays (TMA) from prostate cancer samples obtained from multiple institutions across several global locations.

METHODS

Three separate TMA sets were built that differ by purpose and disease state.

RESULTS

The intended use of TMA1 (Primary Matched LN) is to validate biomarkers that help determine which clinically localized prostate cancers with associated lymph node metastasis have a high risk of progression to lethal castration-resistant metastatic disease, and to compare molecular properties of high-risk index lesions within the prostate to regional lymph node metastases resected at the time of prostatectomy. TMA2 (Pre vs. Post ADT) was designed to address questions regarding risk of castration-resistant prostate cancer (CRPC) and response to suppression of the androgen receptor/androgen axis, and characterization of the castration-resistant phenotype. TMA3 (CRPC Met Heterogeneity)'s intended use is to assess the heterogeneity of molecular markers across different anatomic sites in lethal prostate cancer metastases.

CONCLUSIONS

The GAP1-UTMA project has succeeded in combining a large set of tissue specimens from 501 patients with prostate cancer with rich clinical annotation.

IMPACT

This resource is now available to the prostate cancer community as a tool for biomarker validation to address important unanswered clinical questions around disease progression and response to treatment.

Receptor-interacting protein kinase 2 (RIPK2) stabilizes c-Myc and is a therapeutic target in prostate cancer metastasis

Despite progress in prostate cancer (PC) therapeutics, distant metastasis remains a major cause of morbidity and mortality from PC. Thus, there is growing recognition that preventing or delaying PC metastasis holds great potential for substantially improving patient outcomes. Here we show receptor-interacting protein kinase 2 (RIPK2) is a clinically actionable target for inhibiting PC metastasis. RIPK2 is amplified/gained in ~65% of lethal metastatic castration-resistant PC. Its overexpression is associated with disease progression and poor prognosis, and its genetic knockout substantially reduces PC metastasis. Multi-level proteomics analyses reveal that RIPK2 strongly regulates the stability and activity of c-Myc (a driver of metastasis), largely via binding to and activating mitogen-activated protein kinase kinase 7 (MKK7), which we identify as a direct c-Myc-S62 kinase. RIPK2 inhibition by preclinical and clinical drugs inactivates the noncanonical RIPK2/MKK7/c-Myc pathway and effectively impairs PC metastatic outgrowth. These results support targeting RIPK2 signaling to extend metastasis-free and overall survival.

Loss of CDCP1 triggers FAK activation in detached prostate cancer cells

A major metastasis suppressing mechanism is the rapid apoptotic death of cancer cells upon detachment from extracellular matrix, a process called anoikis. Focal adhesion kinase (PTK2/FAK) is a key enzyme involved in evasion of anoikis. We show that loss of the Cub-domain containing protein-1 (CDCP1), paradoxically stimulates FAK activation in the detached state of prostate cancer cells. In CDCP1^low DU145 and PC3 prostate cancer cells, detachment-activation of FAK occurs through local production of PI(4,5)P₂. PI(4,5)P₂ is generated by the PIP5K1c-201 splicing isoform of PIP5K1c, which contains a unique SRC phosphorylation site. In the detached state, reduced expression of CDCP1 and an alternative CDCP1-independent SRC activation mechanism triggers PIP5K1c-pY644 phosphorylation by SRC. This causes a switch of Talin binding from β1-integrin to PIP5K1c-pY644 and leads to activation of PIP5K1c-FAK. Reduced CDCP1 expression also inactivates CDK5, a negative regulator of PIP5K1c. Furthermore, immersion of prostate cancer cells in 10% human plasma or fetal bovine serum is required for activation of PIP5K1c-FAK. The PIP5K1c induced detachment-activation of FAK in preclinical models sensitizes CDCP1^low prostate cancer cells to FAK inhibitors. In patients, CDCP1^High versus CDCP1^low circulating tumor cells differ in expression of AR-v7, ONECUT2 and HOXB13 oncogenes and TMPRSS2 and display intra-patient heterogeneity of FAK-pY397 expression. Taken together, CDCP1^low and CDCP1^high detached prostate cancer cells activate distinct cytoplasmic kinase complexes and targetable transcription factors, which has important therapeutic implications.

Abstract LB256: Dynamic kindlin-2 complexes containing a laminin-binding integrin are responsive to hypoxia

The laminin-binding integrins are mechanosensory receptors critical for cell adhesion and structural organization that link the extracellular matrix (ECM) to the cytoskeleton. Integrin α6β1 is associated with prostate cancer (PCa) invasion, metastasis and decreased cancer-specific survival. Kindlins are integrin adaptors and mechanosensory focal adhesion (FA) proteins that activate and cluster integrins in response to structural ECM alterations in the tumor microenvironment. Hypoxia is a well-known inducer of tumor migration and invasion, results in ECM remodeling, and is physiologically relevant for PCa. The study's objective was to determine if integrin-kindlin-2 adhesion complexes were responsive to hypoxia and the dynamic distribution of integrin-kindlin-2 complexes using immunofluorescence microscopy (IFM). The methods included a combination of biochemical analysis of kindlin-2 adhesion complexes, qRT-PCR, immunoblotting, and immunocytochemistry. DU145 cells were analyzed in HEPES-containing media with 10% FBS under hypoxic conditions (1% O2) continuously exposed for 4, 8, 12, and 16 hours. The results showed that hypoxia increased the mRNA expression of CAIX, a well-known HIF-1α target gene, within 4 hours, resulting in 7-fold maximum expression. Similarly, VEGF-A mRNA was responsive to hypoxia as expected. In contrast, HIF-1α, α6 integrin, and kindlin-2 mRNA levels remained unchanged as compared to normoxia. Strikingly, we found that kindlin-2-containing adhesion complexes increased under hypoxia conditions as compared to normoxia and detected by immunoprecipitation, the complexes contained α6β1 integrin. It is important to note that the constitutive levels of either α6 integrin or kindlin-2 were not altered by hypoxia. Using IFM under normoxic conditions, we confirmed the location of kindlin-2 within phosphorylated (Y31) paxillin (pPXN)-containing FAs and within extended plasma membrane domains exclusive of pPXN. However, under hypoxic conditions, an increased reorganization of pPXN-containing kindlin-2 complexes occurred within 4 hours, was continuously changing up to 16 hours, with increasing fibrillar forms of FAs. In addition, kindlin-2 was observed as increased in plasma membrane protrusions devoid pPXN. The dynamic nature of the hypoxia-driven FAs was observed by a time course analysis and indicated the apparent assembly and disassembly of the structures during 16-hours of PCa cells exposed to hypoxia. We conclude that kindlin-2-integrin complexes are responsive to hypoxia and contain the unexpected α6β1 integrin in the membrane exclusive of FAs.

(Partially supported by NIH grants CA P30 23074, DOD W81XWH-19-1-0455, and NCI R01 CA242226).

Citation Format: Daniel Hernandez-Cortes, Beatrice S. Knudsen, Noel A. Warfel, Anne E. Cress. Dynamic kindlin-2 complexes containing a laminin-binding integrin are responsive to hypoxia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB256.

Imaging of PD-L1 in single cancer cells by SERS-based hyperspectral analysis

We developed a hyperspectral imaging tool based on surface-enhanced Raman spectroscopy (SERS) probes to determine the expression level and visualize the distribution of PD-L1 in individual cells. Electron-microscopic analysis of PD-L1 antibody - gold nanorod conjugates demonstrated binding the cell surface and internalization into endosomal vesicles. Stimulation of cells with IFN-γ or metformin was used to confirm the ability of SERS probes to report treatment-induced changes. The multivariate curve resolution-alternating least squares (MCR-ALS) analysis of spectra provided a greater signal-noise ratio than single peak mapping. However, single peak mapping allowed a systematic subtraction of background and the removal of non-specific binding and endocytic SERS signals. The mean or maximum peak height in the cell or the mean peak height in the area of specific PD-L1 positive pixels was used to estimate the PD-L1 expression levels in single cells. The PD-L1 levels were significantly up-regulated by IFN-γ and inhibited by metformin in human lung cancer cells from the A549 cell line. In conclusion, the method of analyzing hyperspectral SERS imaging data together with systematic and comprehensive removal of non-specific signals allows SERS imaging to be a quantitative tool in the detection of the cancer biomarker, PD-L1.

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.

Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images

BACKGROUND

Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of the unique colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel.

METHODS

Six different colored chromogens were utilized to label T-cells (CD3, CD4, CD8), B-cells (CD20), macrophages (CD16), and tumor cells (K17) in formalin-fixed paraffin-embedded (FFPE) PDAC tissue sections. We leveraged pathologist annotations to develop complementary deep learning-based methods: (1) ColorAE is a deep autoencoder which segments stained objects based on color; (2) U-Net is a convolutional neural network (CNN) trained to segment cells based on color, texture and shape; and ensemble methods that employ both ColorAE and U-Net, collectively referred to as (3) ColorAE:U-Net. We assessed the performance of our methods using: structural similarity and DICE score to evaluate segmentation results of ColorAE against traditional color deconvolution; F1 score, sensitivity, positive predictive value, and DICE score to evaluate the predictions from ColorAE, U-Net, and ColorAE:U-Net ensemble methods against pathologist-generated ground truth. We then used prediction results for spatial analysis (nearest neighbor).

RESULTS

We observed that (1) the performance of ColorAE is comparable to traditional color deconvolution for single-stain IHC images (note: traditional color deconvolution cannot be used for mIHC); (2) ColorAE and U-Net are complementary methods that detect 6 different classes of cells with comparable performance; (3) combinations of ColorAE and U-Net into ensemble methods outperform using either ColorAE and U-Net alone; and (4) ColorAE:U-Net ensemble methods can be employed for detailed analysis of the tumor microenvironment (TME). We developed a suite of scalable deep learning methods to analyze 6 distinctly labeled cell populations in mIHC WSIs. We evaluated our methods and found that they reliably detected and classified cells in the PDAC tumor microenvironment. We also present a use case, wherein we apply the ColorAE:U-Net ensemble method across 3 mIHC WSIs and use the predictions to quantify all stained cell populations and perform nearest neighbor spatial analysis. Thus, we provide proof of concept that these methods can be employed to quantitatively describe the spatial distribution immune cells within the tumor microenvironment. These complementary deep learning methods are readily deployable for use in clinical research studies.

The intraprostatic immune environment after stereotactic body radiotherapy is dominated by myeloid cells

BACKGROUND:

Hundreds of ongoing clinical trials combine radiation therapy, mostly delivered as stereotactic body radiotherapy (SBRT), with immune checkpoint blockade. However, our understanding of the effect of radiotherapy on the intratumoral immune balance is inadequate, hindering the optimal design of trials that combine radiation therapy with immunotherapy. Our objective was to characterize the intratumoral immune balance of the malignant prostate after SBRT in patients.

METHODS:

16 patients with high-risk, non-metastatic prostate cancer at comparable Gleason Grade disease underwent radical prostatectomy with (n=9) or without (n=7) neoadjuvant SBRT delivered in 3 fractions of 8 Gy over 5 days completed 2 weeks before surgery. Freshly resected prostate specimens were processed to obtain single-cell suspensions, and immune-phenotyped for major lymphoid and myeloid cell subsets by staining with 2 separate 14-antibody panels and multicolor flow cytometry analysis.

RESULTS:

Malignant prostates two weeks after SBRT had an immune infiltrate dominated by myeloid cells, whereas malignant prostates without preoperative treatment were more lymphoid-biased (myeloid CD45⁺ cells 48.4 ± 19.7% vs 25.4 ± 7.0%; adjusted p value=0.11; and CD45⁺ lymphocytes 51.6 ± 19.7% vs 74.5 ± 7.0%; p=0.11; CD3⁺ T cells 35.2 ± 23.8% vs 60.9 ± 9.7%; p=0.12; mean±SD).

CONCLUSION:

SBRT drives a significant lymphoid to myeloid shift in the prostate tumor immune infiltrate. This may be of interest when combining SBRT with immunotherapies, particularly in prostate cancer.

Phase 1 Trial of Stereotactic Body Radiation Therapy Neoadjuvant to Radical Prostatectomy for Patients With High-Risk Prostate Cancer

PURPOSE

This study aimed to evaluate the feasibility and safety of prostate stereotactic body radiation therapy (SBRT) neoadjuvant to radical prostatectomy (RP) in a phase 1 trial. The primary endpoint was treatment completion rate without severe acute surgical complications. Secondary endpoints included patient-reported quality of life and physician-reported toxicities.

METHODS AND MATERIALS

Patients with nonmetastatic high-risk or locally advanced prostate cancer received 24 Gy in 3 fractions to the prostate and seminal vesicles over 5 days, completed 2 weeks before RP. Patients with pN1 disease were treated after multidisciplinary discussion and shared decision making. Patient-reported quality of life (International Prostate Symptom Score and Expanded Prostate Cancer Index Composite 26-item version questionnaires) and physician-reported toxicity (Common Terminology Criteria for Adverse Events, version 4.03) were assessed before SBRT, immediately before surgery, and at 3-month intervals for 1 year.

RESULTS

Twelve patients were enrolled, and 11 completed treatment (1 patient had advanced disease on prostate-specific membrane antigen positron emission tomography after enrollment but before treatment). There were no significant surgical complications. After RP, 2 patients underwent additional radiation therapy to nodes with androgen suppression for pN1 disease. Median follow-up after completion of treatment was 20.1 months, with 9 of 11 patients having a follow-up period of >12 months. Two patients had biochemical recurrence (prostate-specific antigen ≥0.05) within the first 12 months, with an additional 2 patients found to have biochemical recurrence after the 12-month period. The highest Common Terminology Criteria for Adverse Events genitourinary grades were 0, 1, 2, and 3 (n = 1, 4, 4, and 2, respectively), and the highest gastrointestinal grades were 0, 1, and 2 (n = 9, 1, and 1, respectively). At 12 months, incontinence was the only grade ≥2 toxicity. One and 2 of 9 patients had grade 2 and 3 incontinence, respectively. On the Expanded Prostate Cancer Index Composite (26-item version), the mean/median changes in scores from baseline to 12 months were -32.8/-31.1 for urinary incontinence, -1.6/-6.2 for urinary irritative/obstructive, -2.1/0 for bowel, -34.4/-37.5 for sexual function, and -10.6/-2.5 for hormonal. The mean/median change in International Prostate Symptom Score from baseline to 12 months was 0.5/0.5.

CONCLUSIONS

RP after neoadjuvant SBRT appears to be feasible and safe at the dose tested. The severity of urinary incontinence may be higher than RP alone.

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.

Centrosome loss results in an unstable genome and malignant prostate tumors

Localized, nonindolent prostate cancer (PCa) is characterized by large-scale genomic rearrangements, aneuploidy, chromothripsis, and other forms of chromosomal instability (CIN), yet how this occurs remains unclear. A well-established mechanism of CIN is the overproduction of centrosomes, which promotes tumorigenesis in various mouse models. Therefore, we developed a single-cell assay for quantifying centrosomes in human prostate tissue. Surprisingly, centrosome loss-which has not been described in human cancer-was associated with PCa progression. By chemically or genetically inducing centrosome loss in nontumorigenic prostate epithelial cells, mitotic errors ensued, producing aneuploid, and multinucleated cells. Strikingly, transient or chronic centrosome loss transformed prostate epithelial cells, which produced highly proliferative and poorly differentiated malignant tumors in mice. Our findings suggest that centrosome loss could create a cellular crisis with oncogenic potential in prostate epithelial cells.

A Circulating Tumor Cell-RNA Assay for Assessment of Androgen Receptor Signaling Inhibitor Sensitivity in Metastatic Castration-Resistant Prostate Cancer

Rationale: Our objective was to develop a circulating tumor cell (CTC)-RNA assay for characterizing clinically relevant RNA signatures for the assessment of androgen receptor signaling inhibitor (ARSI) sensitivity in metastatic castration-resistant prostate cancer (mCRPC) patients. Methods: We developed the NanoVelcro CTC-RNA assay by combining the Thermoresponsive (TR)-NanoVelcro CTC purification system with the NanoString nCounter platform for cellular purification and RNA analysis. Based on the well-validated, tissue-based Prostate Cancer Classification System (PCS), we focus on the most aggressive and ARSI-resistant PCS subtype, i.e., PCS1, for CTC analysis. We applied a rigorous bioinformatic process to develop the CTC-PCS1 panel that consists of prostate cancer (PCa) CTC-specific RNA signature with minimal expression in background white blood cells (WBCs). We validated the NanoVelcro CTC-RNA assay and the CTC-PCS1 panel with well-characterized PCa cell lines to demonstrate the sensitivity and dynamic range of the assay, as well as the specificity of the PCS1 Z score (the likelihood estimate of the PCS1 subtype) for identifying PCS1 subtype and ARSI resistance. We then selected 31 blood samples from 23 PCa patients receiving ARSIs to test in our assay. The PCS1 Z scores of each sample were computed and compared with ARSI treatment sensitivity. Results: The validation studies using PCa cell line samples showed that the NanoVelcro CTC-RNA assay can detect the RNA transcripts in the CTC-PCS1 panel with high sensitivity and linearity in the dynamic range of 5-100 cells. We also showed that the genes in CTC-PCS1 panel are highly expressed in PCa cell lines and lowly expressed in background WBCs. Using the artificial CTC samples simulating the blood sample conditions, we further demonstrated that the CTC-PCS1 panel is highly specific in identifying PCS1-like samples, and the high PCS1 Z score is associated with ARSI resistance samples. In patient bloods, ARSI-resistant samples (ARSI-R, n=14) had significantly higher PCS1 Z scores as compared with ARSI-sensitive samples (ARSI-S, n=17) (Rank-sum test, P=0.003). In the analysis of 8 patients who were initially sensitive to ARSI (ARSI-S) and later developed resistance (ARSI-R), we found that the PCS1 Z score increased from the time of ARSI-S to the time of ARSI-R (Pairwise T-test, P=0.016). Conclusions: Using our new methodology, we developed a first-in-class CTC-RNA assay and demonstrated the feasibility of transforming clinically-relevant tissue-based RNA profiling such as PCS into CTC tests. This approach allows for detecting RNA expression relevant to clinical drug resistance in a non-invasive fashion, which can facilitate patient-specific treatment selection and early detection of drug resistance, a goal in precision oncology.

Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images

Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head and a grading network head. Compared with a single-task model, our multi-task model can provide complementary contextual information, which contributes to better performance. Our model is achieved a state-of-the-art performance in epithelial cells detection and Gleason grading tasks simultaneously. Using fivefold cross-validation, our model is achieved an epithelial cells detection accuracy of 99.07% with an average area under the curve of 0.998. As for Gleason grading, our model is obtained a mean intersection over union of 79.56% and an overall pixel accuracy of 89.40%.

Integrin α6β4E variant is associated with actin and CD9 structures and modifies the biophysical properties of cell-cell and cell-extracellular matrix interactions

Integrin α6β4 is an essential, dynamic adhesion receptor for laminin 332 found on epithelial cells, required for formation of strong cell–extracellular matrix (ECM) adhesion and induced migration, and coordinated by regions of the β4C cytoplasmic domain. β4E, a unique splice variant of β4 expressed in normal tissue, contains a cytoplasmic domain of 231 amino acids with a unique sequence of 114 amino acids instead of β4C’s canonical 1089 amino acids. We determined the distribution of α6β4E within normal human glandular epithelium and its regulation and effect on cellular biophysical properties. Canonical α6β4C expressed in all basal cells, as expected, while α6β4E expressed within a subset of luminal cells. α6β4E expression was induced by three-dimensional culture conditions, activated Src, was reversible, and was stabilized by bortezomib, a proteasome inhibitor. α6β4C expressed in all cells during induced migration, whereas α6β4E was restricted to a subset of cells with increased kinetics of cell–cell and cell–ECM resistance properties. Interestingly, α6β4E presented in “ringlike” patterns measuring ∼1.75 × 0.72 microns and containing actin and CD9 at cell–ECM locations. In contrast, α6β4C expressed only within hemidesmosome-like structures containing BP180. Integrin α6β4E is an inducible adhesion isoform in normal epithelial cells that can alter biophysical properties of cell–cell and cell–ECM interactions.

Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides

During the diagnostic workup of lung adenocarcinomas (LAC), pathologists evaluate distinct histological tumor growth patterns. The percentage of each pattern on multiple slides bears prognostic significance. To assist with the quantification of growth patterns, we constructed a pipeline equipped with a convolutional neural network (CNN) and soft-voting as the decision function to recognize solid, micropapillary, acinar, and cribriform growth patterns, and non-tumor areas. Slides of primary LAC were obtained from Cedars-Sinai Medical Center (CSMC), the Military Institute of Medicine in Warsaw and the TCGA portal. Several CNN models trained with 19,924 image tiles extracted from 78 slides (MIMW and CSMC) were evaluated on 128 test slides from the three sites by F1-score and accuracy using manual tumor annotations by pathologist. The best CNN yielded F1-scores of 0.91 (solid), 0.76 (micropapillary), 0.74 (acinar), 0.6 (cribriform), and 0.96 (non-tumor) respectively. The overall accuracy of distinguishing the five tissue classes was 89.24%. Slide-based accuracy in the CSMC set (88.5%) was significantly better (p < 2.3E-4) than the accuracy in the MIMW (84.2%) and TCGA (84%) sets due to superior slide quality. Our model can work side-by-side with a pathologist to accurately quantify the percentages of growth patterns in tumors with mixed LAC patterns.

A method of quantifying centrosomes at the single-cell level in human normal and cancer tissue

Centrosome abnormalities are emerging hallmarks of cancer. The overproduction of centrosomes (known as centrosome amplification) has been reported in a variety of cancers and is currently being explored as a promising target for therapy. However, to understand different types of centrosome abnormalities and their impact on centrosome function during tumor progression, as well as to identify tumor subtypes that would respond to the targeting of a centrosome abnormality, a reliable method for accurately quantifying centrosomes in human tissue samples is needed. Here, we established a method of quantifying centrosomes at a single-cell level in different types of human tissue samples. We tested multiple anti-centriole and pericentriolar-material antibodies to identify bona fide centrosomes and multiplexed these with cell border markers to identify individual cells within the tissue. High-resolution microscopy was used to generate multiple Z-section images, allowing us to acquire whole cell volumes in which to scan for centrosomes. The normal cells within the tissue serve as internal positive controls. Our method provides a simple, accurate way to distinguish alterations in centrosome numbers at the level of single cells.

Spatial Mapping of Myeloid Cells and Macrophages by Multiplexed Tissue Staining

An array of phenotypically diverse myeloid cells and macrophages (MC&M) resides in the tumor microenvironment, requiring multiplexed detection systems for visualization. Here we report an automated, multiplexed staining approach, named PLEXODY, that consists of five MC&M-related fluorescently-tagged antibodies (anti - CD68, - CD163, - CD206, - CD11b, and - CD11c), and three chromogenic antibodies, reactive with high- and low-molecular weight cytokeratins and CD3, highlighting tumor regions, benign glands and T cells. The staining prototype and image analysis methods which include a pixel/area-based quantification were developed using tissues from inflamed colon and tonsil and revealed a unique tissue-specific composition of 14 MC&M-associated pixel classes. As a proof-of-principle, PLEXODY was applied to three cases of pancreatic, prostate and renal cancers. Across digital images from these cancer types we observed 10 MC&M-associated pixel classes at frequencies greater than 3%. Cases revealed higher frequencies of single positive compared to multi-color pixels and a high abundance of CD68+/CD163+ and CD68+/CD163+/CD206+ pixels. Significantly more CD68+ and CD163+ vs. CD11b+ and CD11c+ pixels were in direct contact with tumor cells and T cells. While the greatest percentage (~70%) of CD68+ and CD163+ pixels was 0–20 microns away from tumor and T cell borders, CD11b+ and CD11c+ pixels were detected up to 240 microns away from tumor/T cell masks. Together, these data demonstrate significant differences in densities and spatial organization of MC&M-associated pixel classes, but surprising similarities between the three cancer types.

Emerin Deregulation Links Nuclear Shape Instability to Metastatic Potential

Abnormalities in nuclear shape are a well-known feature of cancer, but their contribution to malignant progression remains poorly understood. Here, we show that depletion of the cytoskeletal regulator, Diaphanous-related formin 3 (DIAPH3), or the nuclear membrane-associated proteins, lamin A/C, in prostate and breast cancer cells, induces nuclear shape instability, with a corresponding gain in malignant properties, including secretion of extracellular vesicles that contain genomic material. This transformation is characterized by a reduction and/or mislocalization of the inner nuclear membrane protein, emerin. Consistent with this, depletion of emerin evokes nuclear shape instability and promotes metastasis. By visualizing emerin localization, evidence for nuclear shape instability was observed in cultured tumor cells, in experimental models of prostate cancer, in human prostate cancer tissues, and in circulating tumor cells from patients with metastatic disease. Quantitation of emerin mislocalization discriminated cancer from benign tissue and correlated with disease progression in a prostate cancer cohort. Taken together, these results identify emerin as a mediator of nuclear shape stability in cancer and show that destabilization of emerin can promote metastasis.Significance: This study identifies a novel mechanism integrating the control of nuclear structure with the metastatic phenotype, and our inclusion of two types of human specimens (cancer tissues and circulating tumor cells) demonstrates direct relevance to human cancer.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/21/6086/F1.large.jpg Cancer Res; 78(21); 6086-97. ©2018 AACR.

An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies

Automated Gleason grading is an important preliminary step for quantitative histopathological feature extraction. Different from the traditional task of classifying small pre-selected homogeneous regions, semantic segmentation provides pixel-wise Gleason predictions across an entire slide. Deep learning-based segmentation models can automatically learn visual semantics from data, which alleviates the need for feature engineering. However, performance of deep learning models is limited by the scarcity of large-scale fully annotated datasets, which can be both expensive and time-consuming to create. One way to address this problem is to leverage external weakly labeled datasets to augment models trained on the limited data. In this paper, we developed an expectation maximization-based approach constrained by an approximated prior distribution in order to extract useful representations from a large number of weakly labeled images generated from low-magnification annotations. This method was utilized to improve the performance of a model trained on a limited fully annotated dataset. Our semi-supervised approach trained with 135 fully annotated and 1800 weakly annotated tiles achieved a mean Jaccard Index of 49.5% on an independent test set, which was 14% higher than the initial model trained only on the fully annotated dataset.

Effect of Preanalytic Variables on an Automated PTEN Immunohistochemistry Assay for Prostate Cancer

Context.—

Phosphatase and tensin homolog (PTEN) is a promising prognostic and potentially predictive biomarker in prostate cancer.

Objective.—

To assess the effects of preanalytic variables on an analytically validated and fully automated PTEN immunohistochemistry assay.

Design.—

PTEN immunohistochemistry was performed on Ventana immunostaining systems. In benign prostate tissues, immunostaining intensity across variable conditions was assessed by digital image analysis. In prostate tumor tissues, immunostaining was scored visually.

Results.—

Delay of fixation for 4 hours or longer at room temperature or 48 hours or longer at 4°C and duration of formalin fixation did not significantly alter immunostaining intensity. Intensity of staining was highest in 10% formalin compared with other fixatives. Tumor tissues with PTEN loss processed using protocols from 11 academic institutions were all evaluable and scored identically. PTEN immunostaining of needle biopsies where tissue blocks had been stored for less than 10 years was more frequently scored as nonevaluable compared with blocks that had been stored for 10 years or longer. This effect was less evident for radical prostatectomy specimens, where low rates of nonevaluable staining were seen for 23 years or more of storage. Storage of unstained slides for 5 years at room temperature prior to immunostaining resulted in equivalent scoring compared with freshly cut slides. Machine-to-machine variability assessed across 3 Ventana platforms and 2 institutions was negligible in 12 tumors, and platform-to-platform variability was also minor comparing Ventana and Leica instruments across 77 tumors (κ = 0.926).

Conclusions.—

Automated PTEN immunostaining is robust to most preanalytic variables in the prostate and may be performed on prostate tumor tissues subjected to a wide range of preanalytic conditions. These data may help guide assay development if PTEN becomes a key predictive biomarker.

Abstract A049: Centrosome loss and chromosomal instability in prostate tumor progression

Background: Chromosomal instability is a hallmark of cancer. Early genomic events in tumor progression include copy number variations such as tandem duplications and deletions that involve at least &gt;100kB stretches of DNA. Although single-driver mutations are remarkably infrequent in most cancers (e.g., accounting for only 2-3% of human epithelial cancers), approximately 20% of prostate tumors are reported to display chromothripsis. Errors in mitotic chromosome segregation produce micronuclei that are susceptible to chromothripsis, and this can occur due to changes in centrosome numbers. Centrosomes are organelles that nucleate microtubule growth and, thus, determine the number of mitotic spindle poles. Normally, mitotic cells contain two centrosomes that guide formation of a bipolar spindle, ensuring that daughter cells divide within the epithelial plane and inherit an equal complement of the genome. Centrioles are the duplicating elements of centrosomes, and their loss or overduplication (known as “amplification”) promotes spindle assembly defects that lead to chromothripsis. Precise control of centriole copy number is vital in order to maintain genomic stability and normal tissue homeostasis.

Methods: We developed an assay for the detection and quantitation of centrosome numbers in FFPE normal and cancer tissue and tissue culture cell lines. In addition, a 3D culture model of an immortalized nontumorigenic prostate epithelial cell line (RWPE-1) was used to characterize the progression of prostate spheroids from normal gland to high-grade prostatic intraepithelial neoplasia (HG-PIN) type structures in vitro. Genomic instability was detected by polyploidy (flow and metaphase analysis), SKY (Roswell Park Cancer Institute Cytogenetics SKY core laboratory), and micronuclei formation. Chromosome fragility was assessed by time-lapse microscopy.

Results: Human prostate cancer has a readily detectable centrosome dysfunction that occurs early in the disease progression. Whereas triple-negative breast cancer specimens contained centrosome amplification as expected, surprisingly, centrosome loss was detected in early-stage prostate cancer (Gleason stage 3+3, 3+4 and 4+4) tissue specimens and in AR-positive cell lines (LnCAP, VCaP). We note that centrosome amplification was observed in AR-negative cell lines (DU145, PC3 and H660). Using TCGA data, twelve centriolar genes were either amplified, deleted, or mutated in prostate adenocarcinoma and metastatic disease. In contrast, all twelve centriolar genes were highly amplified in neuroendocrine prostate cancer.

Using centrinone, a small-molecule inhibitor of Polo-like kinase 4, we blocked centriole duplication in nontumorigenic prostate cells (RWPE-1). Centrinone treatment resulted in significant genomic instability (polyploidy, copy number variation across all chromosomes, and micronuclei formation) and mitotic errors (increased mitotic index, prolonged mitosis, and increased chromosome fragility). Additionally, centrosome loss produced prostate cancer-specific phenotypes (erg overexpression, TMPRSS2 amplification, and invasive budding).

Conclusions: The loss of centrosomes during early stages of prostate cancer development may account for the increased chromosomal instability manifested as polyploidy and copy number variations. Whether alterations in centrosome numbers occur in high-grade prostate cancer or metastatic lesions is unknown. Centrosomes may be promising biomarkers for advancing disease but also rational targets for personalized therapy in prostate cancer. (Supported in part by NIH grants CA 23074, CA 159406 and NIGMS R01GFM110166.)

Citation Format: Mengdie Wang, Beatrice S. Knudsen, Gregory C. Rogers, Anne E. Cress. Centrosome loss and chromosomal instability in prostate tumor progression [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr A049.

A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors

We introduce and validate a new precision oncology framework for the systematic prioritization of drugs targeting mechanistic tumor dependencies in individual patients. Compounds are prioritized on the basis of their ability to invert the concerted activity of master regulator proteins that mechanistically regulate tumor cell state, as assessed from systematic drug perturbation assays. We validated the approach on a cohort of 212 gastroenteropancreatic neuroendocrine tumors (GEP-NETs), a rare malignancy originating in the pancreas and gastrointestinal tract. The analysis identified several master regulator proteins, including key regulators of neuroendocrine lineage progenitor state and immunoevasion, whose role as critical tumor dependencies was experimentally confirmed. Transcriptome analysis of GEP-NET-derived cells, perturbed with a library of 107 compounds, identified the HDAC class I inhibitor entinostat as a potent inhibitor of master regulator activity for 42% of metastatic GEP-NET patients, abrogating tumor growth in vivo. This approach may thus complement current efforts in precision oncology.

Abstract 3042: Nuclear morphology predicts prostate cancer metastasis at diagnosis

Background: Prostate needle biopsies (PNBX) that are obtained during the initial diagnostic workup are often the only source of tumor tissue that is available to determine the severity of prostate cancer (PC). Interestingly, tumor grades and pathologic features in PNBXs of men with lethal metastatic disease (M1 stage) are indistinguishable from those of high-grade nonmetastatic tumors of patients who do not progress after treatment (M0 stage). We hypothesize that the morphology of tumor nuclei can be used as a source of biomarker development to distinguish M1 tumors from high-grade localized M0 tumors.

Methods: Our study consists of a cohort of 85 high-grade M0 and 78 M1 cases, within a biorepository of 2150 patients at the Greater Los Angeles Veterans Affairs hospital. PNBX slides were digitized at 40X and pathologists annotated all cancer foci. These annotated regions were divided into smaller image tiles (dimensions) and fed into our digital image analysis pipeline for nuclear segmentation and feature extraction. We applied two distinct feature extraction methods to capture morphologic information within tumor nuclei. The 64 Handcrafted (HC) features describe nuclear properties such as shape, area, and chromatin conformation. The 62 Autoencoder (AE) features are abstract descriptors generated by a deep learning algorithm, which learns to redraw the nuclei. We built 2 machine learning models using AE or/and HC features for classification of M1 versus M0 cases. We divided our patients into 3 groups: training, testing and validation. A 7-fold cross-validation was performed in the training and testing sets and the area under the curve of the receiver operating characteristic (ROC) was calculated in the validation set.

Results: Model 1 utilizes processed features derived from patient-level distributions and generalized linear model. The estimated AUC for predicting M1 stage is 0.79 and 0.74 for HC and AE features, respectively. Model 2 uses the average of each features obtained from dominant group of nuclei within individual tiles and neural network models. Nuclei groups were assigned by unsupervised clustering method. For this model, the AUC of predicting M1 stage is 0.77 and 0.75 for HC and AE features, respectively.

Conclusion: By applying two distinct feature extraction methods and two approaches to summarize features, we obtain similar prediction accuracies. These results demonstrate that quantitative nuclear features contain information to classify M1 and M0 cases, which cannot be classified based on tumor grade or pathologic features.

Citation Format: Fangjin Huang, Nathan Ing, Eric N. Miller, Hootan Salemi, Michael S. Lewis, Isla P. Garraway, Arkadiusz Gertych, Beatrice S. Knudsen. Nuclear morphology predicts prostate cancer metastasis at diagnosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3042.

Abstract 1576: Clonal diversity revealed by morphoproteomic and copy number profiles of single prostate cancer cells at diagnosis

Tumor heterogeneity is prevalent in both treatment-naïve and end-stage metastatic castration-resistant prostate cancer (PCa), and may contribute to the broad range of clinical presentation, treatment response, and disease progression. To characterize molecular heterogeneity associated with de novo metastatic PCa, multiplatform single cell profiling was performed using High Definition Single Cell Analysis (HD-SCA). HD-SCA enabled morphoproteomic and morphogenomic profiling of single cells from touch preparations of tissue cores (prostate and bone marrow biopsies) as well as liquid samples (peripheral blood and bone marrow aspirate). Morphology, nuclear features, copy number alterations, and protein expression were analyzed. Tumor cells isolated from prostate tissue touch preparation (PTTP) and bone marrow touch preparation (BMTP) as well as metastatic tumor cells (MTCs) isolated from bone marrow aspirate were characterized by morphology and cytokeratin expression. Although peripheral blood was examined, circulating tumor cells were not definitively observed. Targeted proteomics of PTTP, BMTP, and MTCs revealed cell lineage and luminal prostate epithelial differentiation associated with PCa, including co-expression of EpCAM, PSA, and PSMA. Androgen receptor expression was highest in MTCs. Hallmark PCa copy number alterations, including PTEN and ETV6 deletions and NCOA2 amplification, were observed in cells within the primary tumor and bone marrow biopsy samples. Genomic landscape of MTCs revealed to be a mix of both primary and bone metastatic tissue. This multiplatform analysis of single cells reveals several clonal origins of metastatic PCa in a newly diagnosed, untreated patient with polymetastatic disease. This case demonstrates that real-time molecular profiling of cells collected through prostate and bone marrow biopsies is feasible and has the potential to elucidate the origin and evolution of metastatic tumor cells. Altogether, biological and genomic data obtained through longitudinal biopsies can be used to reveal the properties of PCa and can impact clinical management.

Citation Format: Paymaneh D. Malihi, Michael Morikado, Lisa Welter, Anders Carlsson, Carmen Ruiz Velasco, Anand Kolatkar, Mariam Rodriguez-Lee, James Hicks, Peter Kuhn, Sandy T. Liu, Eric T. Miller, Radu M. Cadaneanu, Beatrice S. Knudsen, Michael S. Lewis, Isla P. Garraway. Clonal diversity revealed by morphoproteomic and copy number profiles of single prostate cancer cells at diagnosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1576.

Novel Regulation of Integrin Trafficking by Rab11-FIP5 in Aggressive Prostate Cancer

The laminin-binding integrins, α3β1 and α6β1, are needed for tumor metastasis and their surface expression is regulated by endocytic recycling. β1 integrins share the Rab11 recycling machinery, but the trafficking of α3β1 and α6β1 are distinct by an unknown mechanism. Using a mouse PDX tumor model containing human metastatic prostate cancer, Rab11 family interacting protein 5 (Rab11-FIP5) was identified as a lead candidate for α6β1 trafficking. Rab11-FIP5 and its membrane-binding domain were required for α6β1 recycling, without affecting the other laminin-binding integrin (i.e., α3β1) or unrelated membrane receptors like CD44, transferrin receptor, or E-cadherin. Depletion of Rab11-FIP5 resulted in the intracellular accumulation of α6β1 in the Rab11 recycling compartment, loss of cell migration on laminin, and an unexpected loss of α6β1 recycling in cell-cell locations. Taken together, these data demonstrate that α6β1 is distinct from α3β1 via Rab11-FIP5 recycling and recycles in an unexpected cell-cell location.Implications: Rab11-FIP5-dependent α6β1 integrin recycling may be selectively targeted to limit migration of prostate cancer cells into laminin-rich tissues. Mol Cancer Res; 16(8); 1319-31. ©2018 AACR.

A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies

Gleason grading of histological images is important in risk assessment and treatment planning for prostate cancer patients. Much research has been done in classifying small homogeneous cancer regions within histological images. However, semi-supervised methods published to date depend on pre-selected regions and cannot be easily extended to an image of heterogeneous tissue composition. In this paper, we propose a multi-scale U-Net model to classify images at the pixel-level using 224 histological image tiles from radical prostatectomies of 20 patients. Our model was evaluated by a patient-based 10-fold cross validation, and achieved a mean Jaccard index of 65.8% across 4 classes (stroma, Gleason 3, Gleason 4 and benign glands), and 75.5% for 3 classes (stroma, benign glands, prostate cancer), outperforming other methods.

A Non-integrating Lentiviral Approach Overcomes Cas9-Induced Immune Rejection to Establish an Immunocompetent Metastatic Renal Cancer Model

The CRISPR-based technology has revolutionized genome editing in recent years. This technique allows for gene knockout and evaluation of function in cell lines in a manner that is far easier and more accessible than anything previously available. Unfortunately, the ability to extend these studies to in vivo syngeneic murine cell line implantation is limited by an immune response against cells transduced to stably express Cas9. In this study, we demonstrate that a non-integrating lentiviral vector approach can overcome this immune rejection and allow for the growth of transduced cells in an immunocompetent host. This technique enables the establishment of a von Hippel-Lindau (VHL) gene knockout RENCA cell line in BALB/c mice, generating an improved model of immunocompetent, metastatic renal cell carcinoma (RCC).

Clonal diversity revealed by morphoproteomic and copy number profiles of single prostate cancer cells at diagnosis

Tumor heterogeneity is prevalent in both treatment-naïve and end-stage metastatic castration-resistant prostate cancer (PCa), and may contribute to the broad range of clinical presentation, treatment response, and disease progression. To characterize molecular heterogeneity associated with de novo metastatic PCa, multiplatform single cell profiling was performed using high definition single cell analysis (HD-SCA). HD-SCA enabled morphoproteomic and morphogenomic profiling of single cells from touch preparations of tissue cores (prostate and bone marrow biopsies) as well as liquid samples (peripheral blood and bone marrow aspirate). Morphology, nuclear features, copy number alterations, and protein expression were analyzed. Tumor cells isolated from prostate tissue touch preparation (PTTP) and bone marrow touch preparation (BMTP) as well as metastatic tumor cells (MTCs) isolated from bone marrow aspirate were characterized by morphology and cytokeratin expression. Although peripheral blood was examined, circulating tumor cells were not definitively observed. Targeted proteomics of PTTP, BMTP, and MTCs revealed cell lineage and luminal prostate epithelial differentiation associated with PCa, including co-expression of EpCAM, PSA, and PSMA. Androgen receptor expression was highest in MTCs. Hallmark PCa copy number alterations, including PTEN and ETV6 deletions and NCOA2 amplification, were observed in cells within the primary tumor and bone marrow biopsy samples. Genomic landscape of MTCs revealed to be a mix of both primary and bone metastatic tissue. This multiplatform analysis of single cells reveals several clonal origins of metastatic PCa in a newly diagnosed, untreated patient with polymetastatic disease. This case demonstrates that real-time molecular profiling of cells collected through prostate and bone marrow biopsies is feasible and has the potential to elucidate the origin and evolution of metastatic tumor cells. Altogether, biological and genomic data obtained through longitudinal biopsies can be used to reveal the properties of PCa and can impact clinical management.

Abstract 41: Proteomic stratification of HPV(+) oropharyngeal squamous cell carcinoma identifies targets in insulin receptor signaling within nonresponders

Objective: The global rise of HPV(+) oropharyngeal squamous cell carcinoma (OPSCC) has generated considerable interest underlying its etiology and management. Relative to HPV(-) OPSCC, HPV(+) patients are younger and more responsive to chemoradiation, though non-responders within this sub-class of OPSCC indicate divergent molecular pathophysiology, and continue to challenge clinicians. Rather than an adaptive approach to what many consider distinct disease processes, treatment remain the same for all OPSCC. Improved molecular stratification would greatly enhance the clinician's ability to precisely tailor treatment without jeopardizing outcomes. Here, two HPV(+) OPSCC cohorts delineated based on treatment response are compared via a hybrid Data Dependent Acquisition/Data Independent Acquisition (DDA/DIA) approach via mass spectrometry, to detect low-abundance proteins and highlight pathways not detected through genomic platforms alone.

Methods: Fourteen HPV(+) OPSCC patients who underwent definitive chemoradiation were stratified as clinical responders (n=6) or nonresponders (n=8). Responders were defined as having no recurrence for at least three years after treatment. Formalin-fixed paraffin tissue (FFPE) slides of tissue at diagnosis were confirmed to be p16(+) OPSCC by an independent pathologist, microdissected to achieve &gt;80% tumor purity, and subjected to heat and pressure cycling via barocycler to enhance protein yield and identification. Equal mass of protein was digested and run on a Sciex TripleTOF mass spectrometer. Peptides identified with &lt;1% Protein FDR from DDA and DIA modes were combined into a high confidence assay library comprised of 1603 proteins, which were subsequently quantified using the openSWATH workflow. Identified proteins underwent normalization and statistical analysis using mapDIA and Ingenuity Pathway Analysis.

Results: Collectively, 1440 proteins were identified and quantified. Of those, 212 were found to be significantly different between responders and nonresponders (FDR&lt;0.01 and fold change greater than ± 1.25). Responders showed relative enrichment in targets of the insulin receptor (INSR) (p=2.86 x 10-14) whose expression pattern predicted inhibition of this pathway (z-score=-3.52). Targets linked to insulin-like growth factor 1 (IGF1R) indicated this related pathway was also inhibited (z-score=-2.60). IGF1R and INSR are both known to have oncogenic functions and increase cell growth, proliferation, and survival, with the biofunction “proliferation of cells” found to be inhibited in the responders (z-score=-1.54). Overall, tumor sub-types from non-responder patients demonstrated molecular profiles consistent with elevated growth, survival, and proliferation signaling.

Conclusions: We demonstrate the feasibility of a modified barocycler approach to obtain robust proteomic data from FFPE tissues at a comparable rate to fresh frozen tissues. We further identify via a hybrid mass spectrometry approach targets of insulin receptor signaling that may be a driver of pathogenesis at the proteomic level. Such targets may stratify HPV(+) OPSCC patients at diagnosis and help tailor the intensity of therapy to mitigate toxicities based on recurrence risk.

Citation Format: Aaron Robinson, Sarah J. Parker, Shiao L. Stephen, Beatrice S. Knudsen, Zachary S. Zumsteg, Jennifer E. Van Eyk, Allen S. Ho. Proteomic stratification of HPV(+) oropharyngeal squamous cell carcinoma identifies targets in insulin receptor signaling within nonresponders [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; April 23-25, 2017; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(23_Suppl):Abstract nr 41.

A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome

Gene expression signatures are commonly used as predictive biomarkers, but do not capture structural features within the tissue architecture. Here we apply a 2-step machine learning framework for quantitative imaging of tumor vasculature to derive a spatially informed, prognostic gene signature. The trained algorithms classify endothelial cells and generate a vascular area mask (VAM) in H&E micrographs of clear cell renal cell carcinoma (ccRCC) cases from The Cancer Genome Atlas (TCGA). Quantification of VAMs led to the discovery of 9 vascular features (9VF) that predicted disease-free-survival in a discovery cohort (n = 64, HR = 2.3). Correlation analysis and information gain identified a 14 gene expression signature related to the 9VF's. Two generalized linear models with elastic net regularization (14VF and 14GT), based on the 14 genes, separated independent cohorts of up to 301 cases into good and poor disease-free survival groups (14VF HR = 2.4, 14GT HR = 3.33). For the first time, we successfully applied digital image analysis and targeted machine learning to develop prognostic, morphology-based, gene expression signatures from the vascular architecture. This novel morphogenomic approach has the potential to improve previous methods for biomarker development.

Data integration from pathology slides for quantitative imaging of multiple cell types within the tumor immune cell infiltrate

Background

Immune cell infiltrates (ICI) of tumors are scored by pathologists around tumor glands. To obtain a better understanding of the immune infiltrate, individual immune cell types, their activation states and location relative to tumor cells need to be determined. This process requires precise identification of the tumor area and enumeration of immune cell subtypes separately in the stroma and inside tumor nests. Such measurements can be accomplished by a multiplex format using immunohistochemistry (IHC).

Method

We developed a pipeline that combines immunohistochemistry (IHC) and digital image analysis. One slide was stained with pan-cytokeratin and CD45 and the other slide with CD8, CD4 and CD68. The tumor mask generated through pan-cytokeratin staining was transferred from one slide to the other using affine image co-registration. Bland-Altman plots and Pearson correlation were used to investigate differences between densities and counts of immune cell underneath the transferred versus manually annotated tumor masks. One-way ANOVA was used to compare the mask transfer error for tissues with solid and glandular tumor architecture.

Results

The overlap between manual and transferred tumor masks ranged from 20%–90% across all cases. The error of transferring the mask was 2- to 4-fold greater in tumor regions with glandular compared to solid growth pattern (p < 10⁻⁶). Analyzing data from a single slide, the Pearson correlation coefficients of cell type densities outside and inside tumor regions were highest for CD4 + T-cells (r = 0.8), CD8 + T-cells (r = 0.68) or CD68+ macrophages (r = 0.79). The correlation coefficient for CD45+ T- and B-cells was only 0.45. The transfer of the mask generated an error in the measurement of intra- and extra- tumoral CD68+, CD8+ or CD4+ counts (p < 10⁻¹⁰).

Conclusions

In summary, we developed a general method to integrate data from IHC stained slides into a single dataset. Because of the transfer error between slides, we recommend applying the antibody for demarcation of the tumor on the same slide as the ICI antibodies.

Electronic supplementary material

The online version of this article (10.1186/s13000-017-0658-8) contains supplementary material, which is available to authorized users.

Abstract 5795: Loss of CDCP1 in patient prostate cancer metastasis leads to uncoupling of beta-1 integrin from its cytoplasmic signaling through FAK

The Cub-Domain Containing Protein - 1, CDCP1, is a transmembrane glycoprotein, which is able to sequester Src and PKCδ into unique microdomains within the plasma membrane. CDCP1 can either promote or suppress tumor metastasis, dependent on the cancer type and experimental system. How the loss of CDCP1 leads to tumor metastasis is not well understood. We demonstrated for the first time in patients with prostate cancer (PCA), a significant reduction of CDCP1 expression in circulating cancer cells (CTC) and tumor metastasis relative to primary tumors and concordant analytical results. To investigate how the loss of CDCP1 facilitates PCA metastasis, we determined the consequences of CDCP1 loss in non-adherent cancer cells, which provide an experimental model system of CTCs. In this system, CDCP1 silenced cells exhibit 3-fold higher proliferation, 4-fold greater anchorage independent growth with colonies exceeding 5 microns in diameter, and a 2-fold reduction in migration ability. In 10% human plasma, these cells up-regulate p-FAK, p-SRC, p-AKT and p-MAPK expression, and at the same time loose expression of activated β1-integrin. The loss of inside-out activation of β1-integrin occurs through prevention of TALIN phosphorylation. Upon loss of CDCP1, Talin is no longer phosphorylated by CDK5 and this causes the disassembly of the β1 integrin - Talin complex. In addition, β1-integrin dissociates from CDK5 and from the CDK5-regulatory subunit, p35, but remains bound to p-FAK. We determined the pathway by which the loss of CDCP1 arrests CDK5 kinase activity. Upon loss of CDCP1, SRC phosphorylates p35. This generates a binding site for the C2 domain of PKCδ and phosphorylation of CDK5-T77 by PKCδ. The subsequent dissociation of the regulatory subunit abolishes the activity of CDK5. We show that both SRC inhibition and silencing of PKCδ reestablish the inside-out activation of β1-integrin. Altogether we discovered a new mechanism of regulation of CDK5 in prostate cancer cells, which leads to the uncoupling of β1-integrin and FAK. The potential biological and clinical consequences of this mechanism are (1) a switch from cell-matrix to cell-cell adhesion, (2) increased sensitivity of CTCs to FAK inhibitors and (3) improved adaptation and survival of cancer cells in the circulation and at the metastatic sites.

Citation Format: Sara G. Pollan, Fangjin Huang, Joshua M. Lang, Jamie M. Sperger, Kavita Shah, Beatrice S. Knudsen. Loss of CDCP1 in patient prostate cancer metastasis leads to uncoupling of beta-1 integrin from its cytoplasmic signaling through FAK [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5795. doi:10.1158/1538-7445.AM2017-5795

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.

Impact of carbohydrate restriction in the context of obesity on prostate tumor growth in the Hi-Myc transgenic mouse model

Introduction

Previously, we showed that carbohydrate restriction with calorie restriction slowed tumor growth in xenograft mouse prostate cancer models. Herein, we examined the impact of carbohydrate restriction without calorie restriction on tumor development within the context of diet-induced obesity in the Hi-Myc transgenic mouse model of prostate cancer.

Methods

Mice were randomized at 5 weeks of age to ad libitum Western diet (WD; 40% fat, 42% carbohydrate; n=39) or ad libitum no carbohydrate ketogenic diet (NCKD; 82% fat, 1% carbohydrate; n=44). At age 3 or 6 months, mice were sacrificed, prostates weighed and prostate histology, proliferation, apoptosis, and macrophage infiltration evaluated by H&E, Ki67, TUNEL and F4/80 staining, respectively. Body composition was assessed by DEXA, serum cytokines measured using multiplex, and Akt/mTOR signaling assessed by Western.

Results

Caloric intake was higher in the NCKD group, resulting in elevated body weights at 6 months of age, relative to the WD group (45g vs. 38g; p=0.008). Despite elevated body weights, serum MCP-1 and IL-1α levels were lower in NCKD versus WD mice (p=0.046 and p=0.118, respectively), and macrophage infiltration was reduced in prostates of NCKD versus WD mice (p=0.028). Relative Akt phosphorylation and phospho-S6 ribosomal protein levels were reduced in prostates of NCKD versus WD mice. However, while mice randomized to NCKD had smaller prostates after adjustment for body weight at 3 and 6 months (p=0.004 and p=0.002, respectively), NCKD mice had higher rates of adenocarcinoma at 6 months compared to WD mice (100% vs. 80%, p=0.04).

Conclusions

Despite higher caloric intake and elevated body weights, carbohydrate restriction lowered serum MCP-1 levels, reduced prostate macrophage infiltration, reduced prostate weight, but failed to slow adenocarcinoma development. Together, these data suggest that although carbohydrate restriction within the context of obesity may reduce obesity-associated systemic inflammation, it is not sufficient to counteract obesity-associated tumor growth.

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 (k_actual ) 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 k_actual 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 α6β4 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 α6β1 integrin. Silencing of α6 integrin expression however, had no significant effect on the k_actual 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.

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 α6β4 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 α6β4 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 α6β4 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. β4-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 (β4 integrin), laminin-332 or abnormal luminal expansion during HG-PIN progression.

Impact of treatment on progression to castration-resistance, metastases, and death in men with localized high-grade prostate cancer

Men with high‐grade prostate cancer (HGPC) are at greatest risk of disease progression. Clinical risk factors associated with castration‐resistant prostate cancer (CRPC), metastases, and prostate cancer‐specific mortality (PCSM) were identified in a contemporary HGPC cohort. Clinical data was collected from men diagnosed with Gleason sum (GS) ≥8 at the Greater Los Angeles Veterans Affairs (GLA‐VA) Healthcare System between 2000 and 2013. Multivariable competing risks regression analyses assessed progression to CRPC, metastases, and PCSM within three treatment strata. The cumulative incidence of disease progression was calculated at 2, 5, and 10‐year time points. Review of 2149 prostate cancer cases yielded 322 with HGPC. Median survival times for cancer‐specific and overall mortality were significantly shorter in men treated with primary androgen deprivation therapy (ADT) (P = 0.0002 and P < 0.0001). Multivariable analyses revealed that clinical stage N1, GS 10, and treatment with primary ADT were significantly associated with increased risk of CRPC, metastases, and PCSM. Significant differences in these outcomes were not observed in men treated with radical prostatectomy (RP) when compared to those treated with radiation therapy combined with short‐term ADT (XRT‐ADT). Ten‐year event rates of progression to CRPC, metastases, and PCSM, for men treated with primary ADT, were 45.5%, 25.4%, and 25.1%, respectively. In conclusion, GS 10 and lymph node involvement, as well as primary ADT treatment in men with HGPC was associated with increased risk of CRPC, metastases, and PCSM. Curative‐intent treatment with RP or XRT‐ADT is associated with reduced progression rates and death in men with HGPC.

Keratin 13 Is Enriched in Prostate Tubule-Initiating Cells and May Identify Primary Prostate Tumors that Metastasize to the Bone

Background

Benign human prostate tubule-initiating cells (TIC) and aggressive prostate cancer display common traits, including tolerance of low androgen levels, resistance to apoptosis, and microenvironment interactions that drive epithelial budding and outgrowth. TIC can be distinguished from epithelial and stromal cells that comprise prostate tissue via cell sorting based upon Epcam, CD44, and CD49f antigenic profiles. Fetal prostate epithelial cells (FC) possess a similar antigenic profile to adult TIC and are capable of inducing tubule formation. To identify the TIC niche in human prostate tissue, differential keratin (KRT) expression was evaluated.

Results

Gene expression data generated from Affymetrix Gene Chip human U133 Plus 2.0 array of sorted adult and fetal epithelial cells revealed KRT13 to be significantly enriched in FC and TIC compared to basal cells (BC) and luminal cells (LC) (p<0.001). Enriched KRT13 expression was confirmed by RT-PCR and cytospin immunostaining. Immunohistochemical analysis of KRT13 expression revealed rare KRT13⁺ epithelia throughout prostatic ducts/acini in adult tissue specimens and differentiated tubules in 24-week recombinant grafts, In contrast, abundant KRT13 expression was observed in developing ducts/acini in fetal prostate and cord-like structures composing 8-week recombinant grafts. Immunostaining of a prostate tissue microarray revealed KRT13⁺ tumor foci in approximately 9% of cases, and this subset displayed significantly shorter time to recurrence (p = 0.031), metastases (p = 0.032), and decreased overall survival (p = 0.004). Diagnostic prostate needle biopsies (PNBX) from untreated patients with concurrent bone metastases (clinical stage M1) displayed KRT13⁺ tumor foci, as did bone metastatic foci.

Conclusions

The expression profile of KRT13 in benign fetal and adult prostate tissue and in recombinant grafts, as well as the frequency of KRT13 expression in primary and metastatic prostate cancer indicates that it may be a marker of a stem/progenitor-like cell state that is co-opted in aggressive tumor cells. KRT13 is enriched in benign stem-like cells that display androgen-resistance, apoptosis-resistance, and branching morphogenesis properties. Collectively our data demonstrate that KRT13 expression is associated with poor prognosis at multiple stages of disease progression and may represent an important biomarker of adverse outcome in patients with prostate cancer.