Non-small cell lung cancer (NSCLC) has a 5-y survival rate of ∼16%, with most deaths associated with uncontrolled metastasis. We screened for stem cell identity-related genes preferentially expressed in a panel of cell lines with high versus low metastatic potential, derived from NSCLC tumors of Kras LA1/+ ;P53 R172HΔG/+ (KP) mice. The Musashi-2 (MSI2) protein, a regulator of mRNA translation, was consistently elevated in metastasis-competent cell lines. MSI2 was overexpressed in 123 human NSCLC tumor specimens versus normal lung, whereas higher expression was associated with disease progression in an independent set of matched normal/primary tumor/lymph node specimens. Depletion of MSI2 in multiple independent metastatic murine and human NSCLC cell lines reduced invasion and metastatic potential, independent of an effect on proliferation. MSI2 depletion significantly induced expression of proteins associated with epithelial identity, including tight junction proteins [claudin 3 (CLDN3), claudin 5 (CLDN5), and claudin 7 (CLDN7)] and down-regulated direct translational targets associated with epithelial-mesenchymal transition, including the TGF-β receptor 1 (TGFβR1), the small mothers against decapentaplegic homolog 3 (SMAD3), and the zinc finger proteins SNAI1 (SNAIL) and SNAI2 (SLUG). Overexpression of TGFβRI reversed the loss of invasion associated with MSI2 depletion, whereas overexpression of CLDN7 inhibited MSI2-dependent invasion. Unexpectedly, MSI2 depletion reduced E-cadherin expression, reflecting a mixed epithelialmesenchymal phenotype. Based on this work, we propose that MSI2 provides essential support for TGFβR1/SMAD3 signaling and contributes to invasive adenocarcinoma of the lung and may serve as a predictive biomarker of NSCLC aggressiveness.on-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths in the world (1). Approximately 7% of individuals born in the United States in 2013 will ultimately be diagnosed with lung cancer, and 160,000 die from this disease each year (1). The 5-y survival rate for lung cancer is around 16% of diagnosed cases (2). Much of the lethality of lung cancer is due to frequent diagnosis of the malignancy at the metastatic stage, when fundamental changes in tumor biology cause the disease to be refractory to many treatments. A better understanding of the biological processes that promote NSCLC metastasis promises to further improve clinical care of the patients. Kras LA1/+ ;P53 R172HΔG/+ (KP) mice provide a useful and wellvalidated model for the study of NSCLC metastasis. These mice combine a mutant p53 allele (p53 R175HΔG) with an activating KrasG12D allele (Kras LA1 ) (3), leading to development of adenocarcinomas resembling human NSCLC, which are often characterized by mutation of KRAS (∼30%) (4) and loss of TP53 (∼60%) (5). Many of the KP tumors metastasize to sites commonly seen in NSCLC patients (3). These features make the KP murine model a useful tool with which to evaluate factors that underlie NSCLC metastasis. Among the pathways activated...

Conflicting reports regarding whether high tumor-associated neutrophils (TAN) are associated with outcomes in colorectal cancer (CRC) exist. Previous investigators have counted TAN using non-neutrophil-specific immunohistochemistry (IHC) stains. We examined whether TAN levels as determined by multi-field manual counting would predict prognosis. IRB approval was obtained and two pathologists, blinded to stage/outcome, counted TAN in 20 high power fields (HPF) per specimen. TAN score was defined as the mean of these counts. High TAN was defined as at or greater than the median score for that stage. Demographics, tumor characteristics, and overall survival (OS) were obtained from the records and examined for association with TAN score. IHC for arginase expression was performed in a subset of samples. 221 patients were included. Stage II patients with high TAN scores had an OS of 232 months as compared to those with low TAN (OS = 85 months, p = 0.03). The survival benefit persisted in multivariable analysis (HR 0.48, CI 0.25–0.91, p = 0.026) controlling for age and sex. Women had increased survival as compared to men, and there were no significant prognostic associations with TAN count in stage III/IV patients, although there were only 12 stage IV patients. Arginase staining did not provide additional information. Stage II colorectal cancer patients with high TAN live nearly 3 times longer than those with low TAN. Women with stage II disease and high TAN counts appear to be driving the survival benefit seen in the stage II patients and have increased overall survival in all stages.

Context.-Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched.Objective.-To investigate the use of DL for nonneoplastic gastric biopsies.Design.-Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion.Results.-For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] ¼ 99.7%) and H pylori (AUC ¼ 100%), and 40% for reactive gastropathy (AUC ¼ 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/ specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%).Conclusions.-A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis.

Context.— Pathology studies using convolutional neural networks (CNNs) have focused on neoplasms, while studies in inflammatory pathology are rare. We previously demonstrated a CNN differentiates reactive gastropathy, Helicobacter pylori gastritis (HPG), and normal gastric mucosa. Objective.— To determine whether a CNN can differentiate the following 2 gastric inflammatory patterns: autoimmune gastritis (AG) and HPG. Design.— Gold standard diagnoses were blindly established by 2 gastrointestinal (GI) pathologists. One hundred eighty-seven cases were scanned for analysis by HALO-AI. All levels and tissue fragments per slide were included for analysis. The cases were randomized, 112 (60%; 60 HPG, 52 AG) in the training set and 75 (40%; 40 HPG, 35 AG) in the test set. A HALO-AI correct area distribution (AD) cutoff of 50% or more was required to credit the CNN with the correct diagnosis. The test set was blindly reviewed by pathologists with different levels of GI pathology expertise as follows: 2 GI pathologists, 2 general surgical pathologists, and 2 residents. Each pathologist rendered their preferred diagnosis, HPG or AG. Results.— At the HALO-AI AD percentage cutoff of 50% or more, the CNN results were 100% concordant with the gold standard diagnoses. On average, autoimmune gastritis cases had 84.7% HALO-AI autoimmune gastritis AD and HP cases had 87.3% HALO-AI HP AD. The GI pathologists, general anatomic pathologists, and residents were on average, 100%, 86%, and 57% concordant with the gold standard diagnoses, respectively. Conclusions.— A CNN can distinguish between cases of HPG and autoimmune gastritis with accuracy equal to GI pathologists.

The importance of the immune microenvironment in ovarian cancer progression, metastasis, and response to therapies has become increasingly clear, especially with the new emphasis on immunotherapies. In order to leverage the power of patient-derived xenograft (PDX) models within a humanized immune microenvironment, three ovarian cancer PDX were grown in humanized NBSGW mice engrafted with human CD34+ cord blood-derived hematopoietic stem cells. Analysis of cytokine levels in the ascites fluid and identification of infiltrating immune cells in the tumors demonstrated that these humanized PDX (huPDX) established an immune tumor microenvironment similar to what has been reported for ovarian cancer patients. The lack of human myeloid cell differentiation has been a major setback for humanized mouse models, but our analysis shows that PDX engraftment increases the human myeloid population in the peripheral blood. Analysis of cytokines within the ascites fluid of huPDX revealed high levels of human M-CSF, a key myeloid differentiation factor as well as other elevated cytokines that have previously been identified in ovarian cancer patient ascites fluid including those involved in immune cell differentiation and recruitment. Human tumor-associated macrophages and tumor-infiltrating lymphocytes were detected within the tumors of humanized mice, demonstrating immune cell recruitment to tumors. Comparison of the three huPDX revealed certain differences in cytokine signatures and in the extent of immune cell recruitment. Our studies show that huNBSGW PDX models reconstitute important aspects of the ovarian cancer immune tumor microenvironment, which may recommend these models for preclinical therapeutic trials.