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Background Network-connected medical devices have rapidly proliferated in the wake of recent global catalysts, leaving clinical laboratories and healthcare organizations vulnerable to malicious actors seeking to ransom sensitive healthcare information. As organizations become increasingly dependent on integrated systems and data-driven patient care operations, a sudden cyberattack and the associated downtime can have a devastating impact on patient care and the institution as a whole. Cybersecurity, information security, and information assurance principles are, therefore, vital for clinical laboratories to fully prepare for what has now become inevitable, future cyberattacks. Content This review aims to provide a basic understanding of cybersecurity, information security, and information assurance principles as they relate to healthcare and the clinical laboratories. Common cybersecurity risks and threats are defined in addition to current proactive and reactive cybersecurity controls. Information assurance strategies are reviewed, including traditional castle-and-moat and zero-trust security models. Finally, ways in which clinical laboratories can prepare for an eventual cyberattack with extended downtime are discussed. Summary The future of healthcare is intimately tied to technology, interoperability, and data to deliver the highest quality of patient care. Understanding cybersecurity and information assurance is just the first preparative step for clinical laboratories as they ensure the protection of patient data and the continuity of their operations.

Purpose: PD-L1 expression in the pretreatment tumor microenvironment enriches for response to anti-PD-1/PD-L1 therapies. The purpose of this study was to quantitatively compare the performance of five monoclonal anti-PD-L1 antibodies used in recent landmark publications.Experimental Design: PD-L1 IHC was performed on 34 formalin-fixed paraffin-embedded archival melanoma samples using the 5H1, SP142, 28-8, 22C3, and SP263 clones. The percentage of total cells (including melanocytes and immune cells) demonstrating cell surface PD-L1 staining, as well as intensity measurements/H-scores, were assessed for each melanoma specimen using a computer-assisted platform. Staining properties were compared between antibodies.Results: Strong correlations were observed between the percentage of PD-L1(+) cells across all clones studied (R2 = 0.81–0.96). When present, discordant results were attributable to geographic heterogeneity of the melanoma tissue section rather than differences in PD-L1 antibody staining characteristics. PD-L1 intensity/H-scores strongly correlated with percentage of PD-L1(+) cells (R2 > 0.78, all clones).Conclusions: The 5H1, SP142, 28-8, 22C3, and SP263 clones all demonstrated similar performance characteristics when used in a standardized IHC assay on melanoma specimens. Reported differences in PD-L1 IHC assays using these antibodies are thus most likely due to assay characteristics beyond the antibody itself. Our findings also argue against the inclusion of an intensity/H-score in chromogenic PD-L1 IHC assays. Clin Cancer Res; 23(16); 4938–44. ©2017 AACR.

Supplemental Figures S1 Figure S2, Figure S3 and Table S1 and Table S2

Supplementary Figure S1: Quantitative volumetric image analysis.

Supplementary Figure S1-S3 legends

Purpose: To evaluate safety and characterize anticancer efficacy of hepatic hypoxia-activated intra-arterial therapy (HAIAT) with evofosfamide in a rabbit model.Experimental Design: VX2-tumor-bearing rabbits were assigned to 4 intra-arterial therapy (IAT) groups (n = 7/group): (i) saline (control); (ii) evofosfamide (Evo); (iii) doxorubicin–lipiodol emulsion followed by embolization with 100–300 μm beads (conventional, cTACE); or (iv) cTACE and evofosfamide (cTACE + Evo). Blood samples were collected pre-IAT and 1, 2, 7, and 14 days post-IAT. A semiquantitative scoring system assessed hepatocellular damage. Tumor volumes were segmented on multidetector CT (baseline, 7/14 days post-IAT). Pathologic tumor necrosis was quantified using manual segmentation on whole-slide images. Hypoxic fraction (HF) and compartment (HC) were determined by pimonidazole staining. Tumor DNA damage, apoptosis, cell proliferation, endogenous hypoxia, and metabolism were quantified (γ-H2AX, Annexin V, caspase-3, Ki-67, HIF1α, VEGF, GAPDH, MCT4, and LDH).Results: cTACE + Evo showed a similar profile of liver enzymes elevation and pathologic scores compared with cTACE. Neither hematologic nor renal toxicity were observed. Animals treated with cTACE + Evo demonstrated smaller tumor volumes, lower tumor growth rates, and higher necrotic fractions compared with cTACE. cTACE + Evo resulted in a marked reduction in the HF and HC. Correlation was observed between decreases in HF or HC and tumor necrosis. cTACE + Evo promoted antitumor effects as evidenced by increased expression of γ-H2AX, apoptotic biomarkers, and decreased cell proliferation. Increased HIF1α/VEGF expression and tumor glycolysis supported HAIAT.Conclusions: HAIAT achieved a promising step towards the locoregional targeting of tumor hypoxia. The favorable toxicity profile and enhanced anticancer effects of evofosfamide in combination with cTACE pave the way towards clinical trials in patients with liver cancer. Clin Cancer Res; 23(2); 536–48. ©2016 AACR.

Supplementary Figures 4-8 from DNA Hypomethylation Arises Later in Prostate Cancer Progression than CpG Island Hypermethylation and Contributes to Metastatic Tumor Heterogeneity

Hypomethylation of CpG dinucleotides in genomic DNA was one of the first somatic epigenetic alterations discovered in human cancers. DNA hypomethylation is postulated to occur very early in almost all human cancers, perhaps facilitating genetic instability and cancer initiation and progression. We therefore examined the nature, extent, and timing of DNA hypomethylation changes in human prostate cancer. Contrary to the prevailing view that global DNA hypomethylation changes occur extremely early in all human cancers, we show that reductions in 5meC content in the genome occur very late in prostate cancer progression, appearing at a significant extent only at the stage of metastatic disease. Furthermore, we found that, whereas some LINE1 promoter hypomethylation does occur in primary prostate cancers compared with normal tissues, this LINE1 hypomethylation is significantly more pronounced in metastatic prostate cancer. Next, we carried out a tiered gene expression microarray and bisulfite genomic sequencing–based approach to identify genes that are silenced by CpG island methylation in normal prostate cells but become overexpressed in prostate cancer cells as a result of CpG island hypomethylation. Through this analysis, we show that a class of cancer testis antigen genes undergoes CpG island hypomethylation and overexpression in primary prostate cancers, but more so in metastatic prostate cancers. Finally, we show that DNA hypomethylation patterns are quite heterogeneous across different metastatic sites within the same patients. These findings provide evidence that DNA hypomethylation changes occur later in prostate carcinogenesis than the CpG island hypermethylation changes and occur heterogeneously during prostate cancer progression and metastatic dissemination. [Cancer Res 2008;68(21):8954–67]

Supplementary Materials, Figures 1-3 from DNA Hypomethylation Arises Later in Prostate Cancer Progression than CpG Island Hypermethylation and Contributes to Metastatic Tumor Heterogeneity

Supplementary Tables 1-4 from DNA Hypomethylation Arises Later in Prostate Cancer Progression than CpG Island Hypermethylation and Contributes to Metastatic Tumor Heterogeneity

Pancreatic intraepithelial neoplasia (PanIN) is a precursor to pancreatic cancer and represents a critical opportunity for cancer interception. However, the number, size, shape, and connectivity of PanINs in human pancreatic tissue samples are largely unknown. In this study, we quantitatively assessed human PanINs using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution. Using a cohort of 38 large slabs of grossly normal human pancreas from surgical resection specimens, we identified striking multifocality of PanINs, with a mean burden of 13 spatially separate PanINs per cm3of sampled tissue. Extrapolating this burden to the entire pancreas suggested a median of approximately 1000 PanINs in an entire pancreas. In order to better understand the clonal relationships within and between PanINs, we developed a pipeline for CODA-guided multi-region genomic analysis of PanINs, including targeted and whole exome sequencing. Multi-region assessment of 37 PanINs from eight additional human pancreatic tissue slabs revealed that almost all PanINs contained hotspot mutations in the oncogeneKRAS, but no gene other thanKRASwas altered in more than 20% of the analyzed PanINs. PanINs contained a mean of 13 somatic mutations per region when analyzed by whole exome sequencing. The majority of analyzed PanINs originated from independent clonal events, with distinct somatic mutation profiles between PanINs in the same tissue slab. A subset of the analyzed PanINs contained multipleKRASmutations, suggesting a polyclonal origin even in PanINs that are contiguous by rigorous 3D assessment. This study leverages a novel 3D genomic mapping approach to describe, for the first time, the spatial and genetic multifocality of human PanINs, providing important insights into the initiation and progression of pancreatic neoplasia.

Background Eponyms are commonly used in medicine, but there are no specific studies of the use of eponyms in clinical chemistry. Methods Clinical chemistry eponyms were manually collected from books, review articles and journal articles from 1847 through 2020. Eponym usage was examined by searching titles and abstracts in PubMed. Custom Python scripts were used to first permute eponyms into multiple forms, and then to search PubMed using Biopython. The eponyms identified in PubMed were further focused on 2 clinical chemistry journals Clinica Chimica Acta [CCA] and Clinical Chemistry [CCJ]. Results The manual collection identified >300 eponyms in clinical chemistry. The Biopython search of PubMed identified a subset of 97 unique eponyms in 33,232 articles. PubMed identified 26 eponyms used in 130 CCA articles; whereas a full-text search identified 1187 articles. In comparison, PubMed identified 36 eponyms used in 158 CCJ articles; whereas a full-text CCJ search identified 708 articles. PubMed shows that the journals CCA and CCJ had a peak number of eponym citations in 1977 followed by a steady decline. Conclusions Eponyms have been frequently used in clinical chemistry with 97 eponyms in common use in PubMed. Overall, the use of clinical chemistry eponyms appears to be declining.

Despite technological advances in the analysis of digital images for medical consultations, many health information systems lack the ability to correlate textual descriptions of image findings linked to the actual images. Images and reports often reside in separate silos in the medical record throughout the process of image viewing, report authoring, and report consumption. Forward-thinking centers and early adopters have created interactive reports with multimedia elements and embedded hyperlinks in reports that connect the narrative text with the related source images and measurements. Most of these solutions rely on proprietary single-vendor systems for viewing and reporting in the absence of any encompassing industry standards to facilitate interoperability with the electronic health record (EHR) and other systems. International standards have enabled the digitization of image acquisition, storage, viewing, and structured reporting. These provide the foundation to discuss enhanced reporting. Lessons learned in the digital transformation of radiology and pathology can serve as a basis for interactive multimedia reporting (IMR) across image-centric medical specialties. This paper describes the standard-based infrastructure and communications to fulfill recently defined clinical requirements through a consensus from an international workgroup of multidisciplinary medical specialists, informaticists, and industry participants. These efforts have led toward the development of an Integrating the Healthcare Enterprise (IHE) profile that will serve as a foundation for interoperable interactive multimedia reporting.

PURPOSE We focus on the problem of scarcity of annotated training data for nucleus recognition in Ki-67 immunohistochemistry (IHC)–stained pancreatic neuroendocrine tumor (NET) images. We hypothesize that deep learning–based domain adaptation is helpful for nucleus recognition when image annotations are unavailable in target data sets. METHODS We considered 2 different institutional pancreatic NET data sets: one (ie, source) containing 38 cases with 114 annotated images and the other (ie, target) containing 72 cases with 20 annotated images. The gold standards were manually annotated by 1 pathologist. We developed a novel deep learning–based domain adaptation framework to count different types of nuclei (ie, immunopositive tumor, immunonegative tumor, nontumor nuclei). We compared the proposed method with several recent fully supervised deep learning models, such as fully convolutional network-8s (FCN-8s), U-Net, fully convolutional regression network (FCRN) A, FCRNB, and fully residual convolutional network (FRCN). We also evaluated the proposed method by learning with a mixture of converted source images and real target annotations. RESULTS Our method achieved an F1 score of 81.3% and 62.3% for nucleus detection and classification in the target data set, respectively. Our method outperformed FCN-8s (53.6% and 43.6% for nucleus detection and classification, respectively), U-Net (61.1% and 47.6%), FCRNA (63.4% and 55.8%), and FCRNB (68.2% and 60.6%) in terms of F1 score and was competitive with FRCN (81.7% and 70.7%). In addition, learning with a mixture of converted source images and only a small set of real target labels could further boost the performance. CONCLUSION This study demonstrates that deep learning–based domain adaptation is helpful for nucleus recognition in Ki-67 IHC stained images when target data annotations are not available. It would improve the applicability of deep learning models designed for downstream supervised learning tasks on different data sets.

ObjectivesHistologic findings after endoscopic resection using submucosal lifting agents Eleview and ORISE gel are described.MethodsFour cases were identified based on the histologic presence of ORISE gel. Cases were selected to illustrate the histologic appearance of the lifting agent immediately after injection (day 0) and after an interval of approximately 2 months.ResultsImmediately after injection, the gel had an appearance similar to acellular mucin on H&E stain and showed mucicarmine positivity but was negative for periodic acid-Schiff stain and Alcian blue. At 2 months, the appearance changed drastically and was characterized by a hard, homogenous eosinophilic quality and elicited a robust foreign body-type giant cell reaction; we have proposed the name lifting agent granuloma for this histologic appearance. The aged material may be mistaken for amyloid or a pulse (legume) granuloma; however, the material was negative on Congo red stain and had a different clinical history and distribution in the tissue from those of a pulse granuloma.ConclusionsIt is important to take note of the histologic appearance of these new submucosal lifting agents over a varying time interval, as outlined here, so that they are readily recognized and not mistaken for other entities.

Objective: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example, in Ki67 immunohistochemistry stained images. Although many automated methods have been proposed, most use a multi-stage processing pipeline to categorize nuclei, leading to cumbersome, low-throughput, and error-prone assessments. To address this issue, we propose a novel deep fully convolutional network for single-stage nucleus recognition. Methods: Instead of conducting direct pixel-wise classification, we formulate nucleus identification as a deep structured regression model. For each input image, it produces multiple proximity maps, each of which corresponds to one nucleus category and exhibits strong responses in central regions of the nuclei. In addition, by taking into consideration the nucleus distribution in histopathology images, we further introduce an auxiliary task, region of interest (ROI) extraction, to assist and boost the nucleus quantification with weak ROI annotation. The proposed network can be learned in an end-to-end, pixel-to-pixel manner for simultaneous nucleus detection and classification. Results: We have evaluated this network on a pancreatic neuroendocrine tumor Ki67 image dataset, and the experiments demonstrate that our method outperforms recent state-of-the-art approaches. Conclusion: We present a new, pixel-to-pixel deep neural network with two sibling branches for effective nucleus recognition and observe that learning with another relevant task, ROI extraction, can further boost individual nucleus localization and classification. Significance: Our method provides a clean, single-stage nucleus recognition pipeline for histopathology image analysis, especially a new perspective for Ki67 image quantification, which would potentially benefit individual object quantification in whole-slide images.

Diagnostic and evidential static image, video clip, and sound multimedia are captured during routine clinical care in cardiology, dermatology, ophthalmology, pathology, physiatry, radiation oncology, radiology, endoscopic procedural specialties, and other medical disciplines. Providers typically describe the multimedia findings in contemporaneous electronic health record clinical notes or associate a textual interpretative report. Visual communication aids commonly used to connect, synthesize, and supplement multimedia and descriptive text outside medicine remain technically challenging to integrate into patient care. Such beneficial interactive elements may include hyperlinks between text, multimedia elements, alphanumeric and geometric annotations, tables, graphs, timelines, diagrams, anatomic maps, and hyperlinks to external educational references that patients or provider consumers may find valuable. This HIMSS-SIIM Enterprise Imaging Community workgroup white paper outlines the current and desired clinical future state of interactive multimedia reporting (IMR). The workgroup adopted a consensus definition of IMR as “interactive medical documentation that combines clinical images, videos, sound, imaging metadata, and/or image annotations with text, typographic emphases, tables, graphs, event timelines, anatomic maps, hyperlinks, and/or educational resources to optimize communication between medical professionals, and between medical professionals and their patients.” This white paper also serves as a precursor for future efforts toward solving technical issues impeding routine interactive multimedia report creation and ingestion into electronic health records.

Artificial intelligence has been applied to histopathology for decades, but the recent increase in interest is attributable to well-publicized successes in the application of deep-learning techniques, such as convolutional neural networks, for image analysis. Recently, generative adversarial networks (GANs) have provided a method for performing image-to-image translation tasks on histopathology images, including image segmentation. In this issue of the JCI, Koyuncu et al. applied GANs to whole-slide images of p16-positive oropharyngeal squamous cell carcinoma (OPSCC) to automate the calculation of a multinucleation index (MuNI) for prognostication in p16-positive OPSCC. Multivariable analysis showed that the MuNI was prognostic for disease-free survival, overall survival, and metastasis-free survival. These results are promising, as they present a prognostic method for p16-positive OPSCC and highlight methods for using deep learning to measure image biomarkers from histopathologic samples in an inherently explainable manner.

Advances in artificial intelligence (AI), especially in deep learning, improve pathological image analysis in basic, translational, and clinical research and in routine clinical practice. Deep learning is currently the dominant technique among the best solutions for many tasks in digital pathology. This chapter provides a general overview of different applications of deep learning in pathological image analysis, such as image classification, object detection, image segmentation, stain normalization, and image superresolution. It summarizes deep learning achievements and identifies the contributions in specific tasks. In addition, it discusses open challenges and potential directions of deep learning-based pathological image computing and presents barriers to clinical adoption of AI in digital pathology.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest forms of cancer. Accumulating evidence indicates the tumor microenvironment is highly associated with tumorigenesis through regulation of cellular physiology, signaling systems, and gene expression profiles of cancer cells. Yet the mechanisms by which the microenvironment evolves from normal pancreas architecture to precursor lesions and invasive cancer is poorly understood. Obtaining high-content and high-resolution information from a complex tumor microenvironment in large volumetric landscapes represents a key challenge in the field of cancer biology. To address this challenge, we established a novel method to reconstruct three-dimensional (3D) centimeter-scale tissues containing billions of cells from serially sectioned histological samples, utilizing deep learning approaches to recognize eight distinct tissue subtypes from hematoxylin and eosin stained sections at micrometer and single-cell resolution. Using samples from a range of normal, precancerous, and invasive pancreatic cancer tissue, we map in 3D modes of cancer invasion in the tumor microenvironment, and emphasize the need for further 3D quantification of biological systems.

Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by using deep neural networks. However, training supervised deep models such as convolutional neural networks (CNNs) usually requires sufficient annotated image data, which is prohibitively expensive or unavailable in some applications. Additionally, when applying a CNN to new datasets, it is common to annotate individual cells/nuclei in those target datasets for model re-learning, leading to inefficient and low-throughput image analysis. To tackle these problems, we present a bidirectional, adversarial domain adaptation method for nucleus detection on cross-modality microscopy image data. Specifically, the method learns a deep regression model for individual nucleus detection with both source-to-target and target-to-source image translation. In addition, we explicitly extend this unsupervised domain adaptation method to a semi-supervised learning situation and further boost the nucleus detection performance. We evaluate the proposed method on three cross-modality microscopy image datasets, which cover a wide variety of microscopy imaging protocols or modalities, and obtain a significant improvement in nucleus detection compared to reference baseline approaches. In addition, our semi-supervised method is very competitive with recent fully supervised learning models trained with all real target training labels.

Quantitative biomarkers are key prognostic and predictive factors in the diagnosis and treatment of cancer. In the clinical laboratory, the majority of biomarker quantitation is still performed manually, but digital image analysis (DIA) methods have been steadily growing and account for around 25% of all quantitative immunohistochemistry (IHC) testing performed today. Quantitative DIA is primarily employed in the analysis of breast cancer IHC biomarkers, including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2/neu; more recently clinical applications have expanded to include human epidermal growth factor receptor 2/neu in gastroesophageal adenocarcinomas and Ki-67 in both breast cancer and gastrointestinal and pancreatic neuroendocrine tumors. Evidence in the literature suggests that DIA has significant benefits over manual quantitation of IHC biomarkers, such as increased objectivity, accuracy, and reproducibility. Despite this fact, a number of barriers to the adoption of DIA in the clinical laboratory persist. These include difficulties in integrating DIA into clinical workflows, lack of standards for integrating DIA software with laboratory information systems and digital pathology systems, costs of implementing DIA, inadequate reimbursement relative to those costs, and other factors. These barriers to adoption may be overcome with international standards such as Digital Imaging and Communications in Medicine (DICOM), increased adoption of routine digital pathology workflows, the application of artificial intelligence to DIA, and the emergence of new clinical applications for DIA.

Histologic findings after endoscopic resection using submucosal lifting agents Eleview and ORISE gel are described.Four cases were identified based on the histologic presence of ORISE gel. Cases were selected to illustrate the histologic appearance of the lifting agent immediately after injection (day 0) and after an interval of approximately 2 months.Immediately after injection, the gel had an appearance similar to acellular mucin on HE stain and showed mucicarmine positivity but was negative for periodic acid-Schiff stain and Alcian blue. At 2 months, the appearance changed drastically and was characterized by a hard, homogenous eosinophilic quality and elicited a robust foreign body-type giant cell reaction; we have proposed the name lifting agent granuloma for this histologic appearance. The aged material may be mistaken for amyloid or a pulse (legume) granuloma; however, the material was negative on Congo red stain and had a different clinical history and distribution in the tissue from those of a pulse granuloma.It is important to take note of the histologic appearance of these new submucosal lifting agents over a varying time interval, as outlined here, so that they are readily recognized and not mistaken for other entities.

April 12, 2017 marked a significant day in the evolution of digital pathology in the United States, when the US Food and Drug Administration announced its approval of the Philips IntelliSite Pathology Solution for primary diagnosis in surgical pathology. Although this event is expected to facilitate more widespread adoption of whole slide imaging for clinical applications in the United States, it also raises a number of questions as to the means by which pathologists might choose to incorporate this technology into their clinical practice. This article from the College of American Pathologists Digital Pathology Committee reviews frequently asked questions on this topic and provides answers based on currently available information.

Semiquantitative immunohistochemistry (IHC) is commonly used in combination with fluorescence in situ hybridization (FISH) to detect HER2 amplification in gastroesophageal adenocarcinomas. Most laboratories apply these tests in a sequential algorithm, using IHC as a frontline test and reserving FISH for IHC-equivocal cases. To gain a better understanding of the concordance of IHC and FISH results at our institution, we identified all gastroesophageal adenocarcinomas at our institution tested for HER2 (n=125). Matched IHC and FISH were available for 116 cases (94%). Cases consisted of adenocarcinoma of the distal esophagus (22%), gastroesophageal junction (24%), stomach (43%), and metastatic sites (12%). A total of 88 cases (70%) were biopsies, whereas 37 cases (30%) were resections. Overall, 15 cases (13%) were HER2 positive (IHC 3+ and/or FISH amplified). A total of 60 cases (52%) were IHC score 0; none of these were HER2 amplified by FISH. A total of 30 cases (26%) were IHC 1+; 5 (17%) of these cases were HER2 amplified by FISH. A total of 20 cases (17%) were IHC 2+; 4 (20%) of these cases were HER2 amplified by FISH. A total of 6 cases were IHC score 3+; all of these were HER2 amplified by FISH. Although there was a high overall concordance between IHC and FISH results (96%), a subset (17%) of IHC-negative cases (score 1+) were HER2 amplified as evaluated by FISH, representing 33% of all HER2 amplified cases. This suggests that the common practice of limited FISH testing to IHC 2+ cases will miss a significant number of HER2 amplified cases.

We currently live in an age of massive personal data creation through activities such as social media, online banking, and healthcare. In healthcare, modern concepts of patient privacy were codified in the 1996 Health Insurance Portability and Accountability Act (HIPAA).6 In 2009, the Health Information Technology for Economic and Clinical Health Act was passed to encourage the adoption of electronic medical records. These laws regulate the privacy of patients' clinical laboratory data. With the generation and collection of large sets of clinical laboratory data, there is a privacy risk. However, this risk is balanced by the use of clinical laboratory data for biomarker discovery and the measurement of clinical outcomes. Recently proposed initiatives such as the Cancer Moonshot and Precision Medicine Initiative (now called “All of Us”) will require the information of millions of patients, including both conventional laboratory and genomic data. In this Q&A, 4 experts explore the current state of data warehouses and health exchanges with a focus on the clinical laboratory. Can you provide an example of a data warehouses or health information exchanges? Toby C. Cornish: Health Data Compass, which went live in 2015, is the enterprise health data warehouse for the University of Colorado (UC) School of Medicine and UCHealth. It is a joint venture by the UC School of Medicine, University of Colorado Medicine, UCHealth, and Children's Hospital Colorado. Laboratory data were added to the data warehouse in July 2016 and initially included over 369 million patient laboratory test orders and results from UCHealth and over 124 million patient laboratory test orders and results from Children's Hospital Colorado. Health Data Compass does not yet contain genomic data, but it is expected to begin housing single-nucleotide polymorphism results from the Colorado Center for Personalized Medicine's Biobank project when that data become available. UCHealth participates …

Purpose: To evaluate safety and characterize anticancer efficacy of hepatic hypoxia-activated intra-arterial therapy (HAIAT) with evofosfamide in a rabbit model. Experimental Design: VX2-tumor-bearing rabbits were assigned to 4 intra-arterial therapy (IAT) groups (n = 7/group): (i) saline (control); (ii) evofosfamide (Evo); (iii) doxorubicin–lipiodol emulsion followed by embolization with 100–300 μm beads (conventional, cTACE); or (iv) cTACE and evofosfamide (cTACE + Evo). Blood samples were collected pre-IAT and 1, 2, 7, and 14 days post-IAT. A semiquantitative scoring system assessed hepatocellular damage. Tumor volumes were segmented on multidetector CT (baseline, 7/14 days post-IAT). Pathologic tumor necrosis was quantified using manual segmentation on whole-slide images. Hypoxic fraction (HF) and compartment (HC) were determined by pimonidazole staining. Tumor DNA damage, apoptosis, cell proliferation, endogenous hypoxia, and metabolism were quantified (γ-H2AX, Annexin V, caspase-3, Ki-67, HIF1α, VEGF, GAPDH, MCT4, and LDH). Results: cTACE + Evo showed a similar profile of liver enzymes elevation and pathologic scores compared with cTACE. Neither hematologic nor renal toxicity were observed. Animals treated with cTACE + Evo demonstrated smaller tumor volumes, lower tumor growth rates, and higher necrotic fractions compared with cTACE. cTACE + Evo resulted in a marked reduction in the HF and HC. Correlation was observed between decreases in HF or HC and tumor necrosis. cTACE + Evo promoted antitumor effects as evidenced by increased expression of γ-H2AX, apoptotic biomarkers, and decreased cell proliferation. Increased HIF1α/VEGF expression and tumor glycolysis supported HAIAT. Conclusions: HAIAT achieved a promising step towards the locoregional targeting of tumor hypoxia. The favorable toxicity profile and enhanced anticancer effects of evofosfamide in combination with cTACE pave the way towards clinical trials in patients with liver cancer. Clin Cancer Res; 23(2); 536–48. ©2016 AACR.

BACKGROUND Mast cells are of interest in prostate cancer because they possess both pro- and anti-tumorigenic properties and are present in the tumor microenvironment. We studied the association of mast cell count and densities with prostate cancer recurrence using tissue microarrays (TMAs) for 462 men who recurred (cases) and 462 controls that were matched to the cases nested in a cohort of radical prostatectomy patients. METHODS Dual-immunostaining for mast cell tryptase and epithelial cytokeratin-8 and whole slide image analysis were used to assess total mast cell number, mast cell density (mast cell number/tissue area), and mast cell number per epithelial or stromal area in TMA spots containing tumor (up to 4 per man). We used conditional logistic regression to estimate the odds ratio (OR) and 95% confidence interval of recurrence for the mean, minimum, and maximum mast cell parameters in tumor tissue among each man's TMA spots. RESULTS After taking into account matching factors of age, race, Gleason sum, and pathologic stage, higher minimum mast cell density in the tumor (comparing highest to lowest quartiles: OR = 0.58, 95% CI 0.40–0.86; P-trend = 0.004) was associated with a lower risk of recurrence. Patterns for mast cell number and ratio of mast cell number to epithelial or stromal area were similar to those for mast cell density. CONCLUSIONS Our results suggest that intratumoral mast cells may be protective against prostate cancer recurrence and could potentially serve as a prognostic biomarker after prostatectomy. Prostate © 2016 Wiley Periodicals, Inc.

Most cancer-associated deaths result from metastasis. However, it remains unknown whether the size, microenvironment or other features of a metastatic lesion dictate its behaviour or determine the efficacy of chemotherapy in the adjuvant (micrometastatic) setting. Here we delineate the natural history of metastasis in an autochthonous model of pancreatic ductal adenocarcinoma (PDAC), using lineage tracing to examine the evolution of disseminated cancer cells and their associated microenvironment. With increasing size, lesions shift from mesenchymal to epithelial histology, become hypovascular and accumulate a desmoplastic stroma, ultimately recapitulating the primary tumours from which they arose. Moreover, treatment with gemcitabine and nab-paclitaxel significantly reduces the overall number of metastases by inducing cell death in lesions of all sizes, challenging the paradigm that PDAC stroma imposes a critical barrier to drug delivery. These results illuminate the cellular dynamics of metastatic progression and suggest that adjuvant chemotherapy affords a survival benefit by directly targeting micrometastases., Metastatic colonization—the growth of single metastatic cells or small lesions into gross metastases—is poorly understood. Here, the authors characterize the natural history of metastatic growth and assess the impact of chemotherapy in an autochthonous model of pancreatic cancer.

Cytomegalovirus (CMV) causes significant morbidity and mortality in congenitally-infected children and immunocompromised hosts. Among healthy individuals, CMV is generally thought to cause mild, self-limited illness. CMV enterocolitis, in particular, is rarely considered among immunocompetent children presenting with diarrhea. We describe two cases of invasive CMV colitis in immunocompetent infants presenting with diarrhea, and review the literature to date on this topic. Although invasive CMV enterocolitis has been sporadically reported among immunocompetent children, it remains an under-recognized cause of infectious diarrhea in this population, and indications for antiviral therapy are lacking. We propose that CMV should be included in the differential diagnosis of intractable diarrhea in immunocompetent children.