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Background: Morphological features of cancer cell nuclei are routinely used to assess disease severity and prognosis, and cancer nuclear morphology has been linked to genomic alterations. Quantitative analyses of the nuclear features of cancer cells and other tumor-resident cell types, such as cancer-associated fibroblasts (CAFs), may reveal novel biomarkers for prognosis and treatment response. Here, we applied a pan-cancer nucleus detection and segmentation algorithm and a cell classification model to hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of breast cancer specimens, enabling the measurement of morphological features of nuclei of multiple cell types within a tumor. Methods: Convolutional Neural Network models for 1) nucleus detection and segmentation and 2) cell classification were deployed on H&E-stained WSIs from The Cancer Genome Atlas (TCGA) breast cancer dataset (primary surgical resections; N=890). Separate models were trained to segment regions of stromal subtypes, such as inflamed and fibroblastic stroma. Nuclear features (area, axis length, eccentricity, color, and texture) were computed and aggregated across each slide to summarize slide-level nuclear morphology for each cell type. Next-generation sequencing-based metrics of genomic instability (N=774) and gene expression (N=868) were acquired and paired with TCGA WSIs. Gene set enrichment analysis was performed using the Molecular Signatures Database. Spearman correlation compared nuclear features to genomic instability metrics. Linear regression was used to assess the relationship between nuclear features and bulk gene expression. Multivariable Cox regression with age and ordinal tumor stage as covariates was used to find association between overall survival (OS) and nuclear features. All reported results were significant (p< 0.05) when adjusted for false discovery rate via the Benjamini-Hochberg procedure. Results: Variation in cancer cell nuclear area, a quantitative metric related to pathologist-assessed nuclear pleomorphism, was calculated by the standard deviation of the nuclear area of cancer cells across a WSI. This feature was associated with genomic instability, as measured by aneuploidy score (r=0.448) and homologous recombination deficiency score (r=0.382), and reduced OS. In contrast, the variability in fibroblast and lymphocyte nuclear areas did not correlate with either metric of genomic instability (all r< 0.1, p>0.05). Furthermore, an association between variation in cancer cell nuclear area with the expression of cell cycle and proliferation pathway genes was observed, suggesting that increased nuclear size heterogeneity may indicate a more aggressive cancer phenotype. Features quantifying CAF nuclear morphology were also assessed, revealing that CAF nucleus shape (larger minor axis length) was associated with lower OS, as well as the expression of gene sets involved in extracellular matrix remodeling and degradation. Conclusions: The nuclear morphologies of breast cancer cells and CAFs reflect underlying genomic and transcriptomic properties of the tumor and correlates with patient outcome. The application of digital pathology analysis of breast cancer histopathology slides enables the integrative study of genomics, transcriptomics, tumor morphology, and overall survival to support research into disease biology research and biomarker discovery. Citation Format: John Abel, Christian Kirkup, Filip Kos, Ylaine Gerardin, Sandhya Srinivasan, Jacqueline Brosnan-Cashman, Ken Leidal, Sanjana Vasudevan, Deepta Rajan, Suyog Jain, Aaditya Prakash, Harshith Padigela, Jake Conway, Neel Patel, Benjamin Trotter, Limin Yu, Amaro Taylor-Weiner, Emma L. Krause, Matthew Bronnimann, Laura Chambre, Ben Glass, Chintan Parmar, Stephanie Hennek, Archit Khosla, Murray Resnick, Andrew H. Beck, Michael Montalto, Fedaa Najdawi, Michael G. Drage, Ilan Wapinski. AI-based quantitation of cancer cell and fibroblast nuclear morphology reflects transcriptomic heterogeneity and predicts survival in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-09-08.

Clinical trials in nonalcoholic steatohepatitis (NASH) require histologic scoring for assessment of inclusion criteria and endpoints. However, guidelines for scoring key features have led to variability in interpretation, impacting clinical trial outcomes. We developed an artificial intelligence (AI)-based measurement (AIM) tool for scoring NASH histology (AIM-NASH). AIM-NASH predictions for NASH Clinical Research Network (CRN) grades of necroinflammation and stages of fibrosis aligned with expert consensus scores and were reproducible. Continuous scores produced by AIM-NASH for key histological features of NASH correlated with mean pathologist scores and with noninvasive biomarkers and strongly predicted patient outcomes. In a retrospective analysis of the ATLAS trial, previously unmet pathological endpoints were met when scored by the AIM-NASH algorithm alone. Overall, these results suggest that AIM-NASH may assist pathologists in histologic review of NASH clinical trials, reducing inter-rater variability on trial outcomes and offering a more sensitive and reproducible measure of patient therapeutic response.

Risk stratification of gastrointestinal stromal tumors (GISTs) is based on experience with tumors of the stomach, small bowel, and rectum, which are far more common than GISTs of other sites. In this study from 47 institutions, we analyzed GISTs of the esophagus (n = 102), colon (n = 136), and appendix (n = 27) for their size, mitotic rate, morphology, and outcome to determine which criteria predict their behavior. Esophageal GISTs were small (median: 2.5 cm) with spindle cell morphology and a low mitotic rate (mean: 3.6/5 mm2). Twelve (12%) tumors progressed, including 11 with a mitotic rate >5/5 mm2 and one large (6.8 cm) GIST with a mitotic rate of 2/5 mm2. Colonic GISTs were smaller (median: 1.4 cm) and presented with abdominal pain or bleeding in 29% of cases. Most (92%) were composed of spindle cells with a mean mitotic rate of 4.6/5 mm2. Sixteen (12%) tumors progressed: 14 had mitotic rates >5/5 mm2, and two were >5.0 cm with a mitotic rate 5/5 mm2) and size >5.0 cm. These findings support the use of size and mitotic rate for prognostication of GISTs in these locations, similar to tumors of the stomach, small bowel, and rectum.

BACKGROUND Microscopic inflammation has been shown to be an important indicator of disease activity in ulcerative colitis (UC). However, manual histologic scoring is semi-quantitative and subject to interobserver variation, and AI-based solutions often lack interpretability. Here we report two distinct quantitative approaches to predict disease activity scores and histological remission using AI-powered digital pathology. Both the random forest classifier (RFC) and graph neural network (GNN) further provide explainability and biological insight by identifying histological features informing model predictions. METHODS Convolutional neural networks (CNNs) were developed using >162k annotations on 820 WSI of H&E-stained colorectal biopsies for pixel-level identification of tissue regions (e.g. crypt abscesses, erosion/ulceration) and cell types (e.g. neutrophils, plasma cells). All WSI were scored by 5 board-certified pathologists using the Nancy Histological Index (NHI) to establish consensus ground truth. A rich, quantitative set of human interpretable features that capture CNN predictions of the tissue region and cell type across each WSI was extracted and used to train a RFC to predict slide-level NHI score. To test the hypothesis that tissue region spatial relationships and cellular composition can inform AI-based predictions of disease activity, a separate GNN was trained, using nodes defined by spatially-resolved CNN model-generated outputs, to predict NHI score. The RFC and GNN also predicted histologic remission (NHI RESULTS The RFC and GNN both predicted histologic remission with high accuracy (weighted kappa 0.87 and 0.85, respectively). Both models also identified histologic features relevant to disease activity predictions. Some features are well established, e.g. infiltrated epithelium or neutrophil cell features distinguish cases with histologic remission. The models also identified features beyond those assessed by the NHI, e.g. area proportion of basal plasmacytosis associated with predictions of NHI 2 and 3. Other features not previously implicated in UC disease activity were also identified, e.g. intraepithelial lymphocytes differentiate cases with NHI 3. CONCLUSIONS We report quantitative and interpretable AI-powered approaches for UC histological assessment. CNN identification of UC histology was used as input to two distinct disease activity classifiers that showed strong concordance with consensus pathologist scoring. Both approaches provide interpretable features that explain model predictions and that may be used to inform biomarker selection and clinical development efforts.

Background: Neoadjuvant treatment of breast cancer has been shown to potentially reduce the extent and morbidity of subsequent surgery. Response to neoadjuvant therapy may also be prognostic; complete pathologic response (pCR) following neoadjuvant treatment is associated with improved long-term outcomes. pCR, defined as the absence of residual invasive cancer, is determined by evaluation of H&E-stained breast resections and regional lymph nodes following neoadjuvant treatment; however, pathologist assessment is subject to intra- and inter-reader variability. Here we report machine learning (ML)-based models to identify tissue regions and cell types in the tumor microenvironment (TME) of H&E-stained breast cancer specimens. Model predictions were used to derive tumor bed area, a key component of the residual cancer burden score (RCB) used to assess neoadjuvant-treatment pathological response. Methods: Convolutional neural network (CNN) models were trained using digitized H&E-stained whole slide images (WSIs) of 2700 neoadjuvant-treated breast cancer specimens (resections and biopsies) from 4 sources, and an additional 1100 breast cancer primary resections from TCGA. 229,901 pathologist annotations were used to train CNN models to segment tissue regions (cancer epithelium, stroma, diffuse inflammatory infiltrate, ductal carcinoma in situ, lymph nodes and necrosis) and cell types (cancer epithelial cells, fibroblasts, lymphocytes, macrophages, foamy macrophages and plasma cells) at single-pixel resolution. These tissue region segmentations were then used to derive tumor bed area using a convex hull algorithm. Each model was evaluated by board certified pathologists for performance. Model predictions of tumor bed area were evaluated in comparison to mean measurements from 3 pathologists for each of 22 held-out test slides. To further assess cell model performance, 5 pathologists exhaustively annotated 120 frames (300 x 300 pixels) on test samples from a dataset not used in model development (N=536; resections and biopsies) to produce consensus ground truth cell labels. Model predictions were compared with pathologist annotations in these frames using Pearson correlation, precision, recall, and F1 metrics. Only those classes with greater than 50 consensus cells identified were evaluated. Results: CNN predictions of tissue and cell classes within H&E breast cancer WSIs showed concordance with manual pathologist consensus labels. The weighted average Pearson correlation (across the relevant cell types) between the model and consensus was 0.75, comparable to the correlation of 0.81 between pathologists and consensus. Classification metrics for each cell class are reported in Table 1. Reduced performance of the model relative to the average pathologist performance may be due to heterogeneous slide characteristics and infrequency of some cell types in the data. For prediction of tumor bed area, CNN model predictions showed moderate correlation with pathologist consensus (Pearson r=0.65, 95% CI: 0.38-0.81). Conclusions: CNN model classification of cell types and tissue regions across entire H&E breast cancer WSIs shows concordance with pathologist consensus. Model predictions of tumor bed area also show concordance with pathologist assessment and can be used to derive the RCB score. These models can be reproducibly applied to quantify diverse histological features in large datasets, potentially enabling improved standardization and efficiency of pathologist evaluation of the breast cancer TME and neoadjuvant response. Classification Metrics for Individual Cell Classes Citation Format: Christian Kirkup, Sanjana Vasudevan, Filip Kos, Benjamin Trotter, Murray Resnick, Andrew H. Beck, Michael Montalto, Ilan Wapinski, Ben Glass, Mary Lin, Stephanie Hennek, Archit Khosla, Michael G. Drage, Laura Chambre. Machine learning-based characterization of the breast cancer tumor microenvironment for assessment of neoadjuvant-treatment response [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-08.

Trastuzumab is a monoclonal antibody against HER2 (also known as ERBB2). The primary objective of the NRG Oncology/RTOG-1010 trial was to establish whether trastuzumab improves disease-free survival when combined with trimodality treatment (paclitaxel plus carboplatin and radiotherapy, followed by surgery) for patients with untreated HER2-overexpressing oesophageal adenocarcinoma.NRG Oncology/RTOG-1010 was an open label, randomised, phase 3 trial for which patients were accrued from 111 NRG-affiliated institutions in the USA. Eligible patients were adults (aged ≥18 years) with newly diagnosed pathologically confirmed oesophageal adenocarcinoma, American Joint Committee on Cancer 7th edition T1N1-2 or T2-3N0-2 stage disease, and a Zubrod performance status of 0-2. Patients were stratified by adenopathy (no vs yes [coeliac absent] vs yes [coeliac present ≤2 cm]) and randomly assigned (1:1) to receive weekly intravenous paclitaxel (50 mg/m606 patients were entered for HER2 assessment from Dec 30, 2010 to Nov 10, 2015, and 203 eligible patients who were HER2-positive were enrolled and randomly assigned to chemoradiotherapy plus trastuzumab (n=102) or chemoradiotherapy alone (n=101). Median duration of follow-up was 2·8 years (IQR 1·4-5·7). Median disease-free survival was 19·6 months (95% CI 13·5-26·2) with chemoradiotherapy plus trastuzumab compared with 14·2 months (10·5-23·0) for chemoradiotherapy alone (hazard ratio 0·99 [95% CI 0·71-1·39], log-rank p=0·97). Grade 3 treatment-related adverse events occurred in 41 (43%) of 95 patients in the chemoradiotherapy plus trastuzumab group versus 52 (54%) of 96 in the chemoradiotherapy group and grade 4 events occurred in 20 (21%) versus 21 (22%). The most common grade 3 or worse treatment-related adverse events for both groups were haematological (53 [56%] of 95 patients in the chemoradiotherapy plus trastuzumab group vs 55 [57%] of 96 patients in the chemotherapy group) or gastrointestinal disorders (28 [29%] vs 20 [21 %]). 34 (36%) of 95 patients in the chemoradiotherapy plus trastuzumab group and 27 (28%) of 96 patients in the chemoradiotherapy only group had treatment-related serious adverse events. There were eight treatment-related deaths: five (5%) of 95 patients in the chemoradiotherapy plus trastuzumab group (bronchopleural fistula, oesophageal anastomotic leak, lung infection, sudden death, and death not otherwise specified), and three (3%) of 96 in the chemoradiotherapy group (two multiorgan failure and one sepsis).The addition of trastuzumab to neoadjuvant chemoradiotherapy for HER2-overexpressing oesophageal cancer was not effective. Trastuzumab did not lead to increased toxicities, suggesting that future studies combining it with or using other agents targeting HER2 in oesophageal cancer are warranted.National Cancer Institute and Genentech.

Background: In lung cancer, the KEAP1/NRF2 pathway modulates an anti-tumor response by regulating cellular metabolism and inflammatory processes. Approximately 19% of lung adenocarcinoma (LUAD) tumors have a mutation in KEAP1. Clinically, KEAP1-mutated LUAD has poor prognosis, and there is need for rapid and accurate patient genotyping to inform treatment decisions. Here, we describe machine learning (ML) models that can predict KEAP1 mutation status from tissue histology. Methods: ML models, pre-trained to identify and quantify areas of tissue (cancer epithelium, cancer stroma, and necrosis), counts of cancer, fibroblast, and immune cells (lymphocytes, macrophages, plasma cells) in non-small cell lung cancer (NSCLC), were applied to 208 hematoxylin and eosin (H&E)-stained whole slide images (WSI) of LUAD from The Cancer Genome Atlas (TCGA) without further training. Genomic analyses indicated that 17% (N=35) of these cases are KEAP1MUT. Human Interpretable Features (HIFs), based on histology predictions, are automatically extracted from the model and provide a quantitative description of the tumor microenvironment of each WSI. Associations between HIFs and KEAP1MUT were determined by univariate analysis followed by false discovery rate (FDR) correction. Hierarchical clustering using cross correlation and combining p-values for HIF groups using an Empirical Brown’s method identified correlations between HIFs. Confounding factor influence was accounted for after positive associations were identified. Independent validation of associations between KEAP1MUT and HIFs was performed using TCGA transcriptomic data to correlate specific mutations with mRNA expression of relevant markers. Results: ML-models generated 4,443 HIFs from the TCGA LUAD WSI, which were reduced to 2,684 HIFs after removal of outlier HIFs, exclusion of HIFs that are degenerate, have missing features, or are of an absolute value. KEAP1MUT was significantly associated with 193 HIFs in univariate analyses (p Conclusions: ML model quantification of TME histological features can generate HIFs that correlate with the KEAP1MUT status of a LUAD biopsy. These results exemplify how ML-powered digital pathology could predict molecular markers directly from standard H&E biopsy slides. Citation Format: Robert Egger, Martin Ieong, Murray Resnick, Amaro Taylor-Weiner, Victoria Mountain, Ilan Wapinski, Michael Montalto, Andrew Beck, Josie Hayes, Benjamin Glass. Machine learning models identify histological features that can predict KEAP1 mutations in lung adenocarcinoma [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 449.

Introduction: Important immunotherapy drugs targeting PD-L1 are approved for first and second line treatment for various stages of NSCLC. Reproducible and precise evaluation of PD-L1 expression is essential to accurately evaluate patients’ eligibility for treatment and for enrollment in clinical trials. Current guidelines rely on pathologists to interpret tumor samples, which is challenging in part because different PD-L1 assays have distinct scoring criteria. As a result, determining eligibility by manual assessment can be inconsistent and inaccurate, leading to untreated patients. To support pathologist quantification of PD-L1 in clinical trials, PathAI has developed scanner-and antibody-agnostic machine learning (ML) models, AI-based histologist measurement of PD-L1 in NSCLC (AIM-PD-L1-NSCLC), for the quantification of PD-L1 expression in NSCLC using four PD-L1 immunohistochemistry (IHC) clones. Methods: AIM-PD-L1-NSCLC was trained using convolutional neural networks to identify and quantify PD-L1-positive cells in digitized whole slide images (WSI) of tissue samples. Models were developed using over 5,000 diverse clinical biopsies and resections, including primary and metastatic adenocarcinoma and squamous cell carcinoma samples collected from 10 clinical trials and from two clinical laboratories, each stained for PD-L1 with one of four IHC clones: SP263 (N=1,320), SP142 (N=1,829) (both Ventana Medical Systems Inc., Tucson, AZ), 28-8 (N=1,331), or 22C3 (N=843) (both Agilent Technologies, Santa Clara, USA). Slides were digitized using Aperio, Philips, and Ventana scanners, and WSI were split into training (N=3,818) and test (N=1,505) datasets. The training dataset was annotated by board certified pathologists (313,770 annotations) to label tissue regions and cells. Human Interpretable features representing the number of tumor cells were automatically extracted from the model and a slide level Tumor Proportion Score (TPS) calculated as the proportion of PD-L1+ cancer cells divided by total cancer cells in tumor regions. Model predicted slide level TPS were compared with the median TPS of five pathologists’ scores using intraclass correlation coefficient (ICC) statistics. Results: There was high concordance between ML model-predicted and median pathologists’ slide level TPS for all PD-L1 clones (ICC 0.93 (95% CI 0.90-0.94), and for each individual clone: 22C3 ICC 0.93 (95% CI 0.89-0.96); SP142 ICC 0.88 (95% CI 0.79-0.93); SP263 ICC 0.96 (95% CI 0.93-0.97; 28-8 ICC 0.90 (95% CI 0.85-0.93). Conclusions: AIM PD-L1 NSCLC is highly concordant with the gold standard pathologist consensus score across four PD-L1 clones in a large diverse dataset. This model could support patient enrollment and stratification in prospective clinical trials, as well as quality control of staining and pathology drift. Citation Format: Michael Griffin, Mevlana Gemici, Ashar Javed, Nishant Agrawal, Murray Resnick, Limin Yu, Sara Hoffman, Victoria Mountain, Jamie Harisiades, Megan Rothney, Benjamin Glass, Ilan Wapinski, Andrew Beck, Eric Walk. AIM PD-L1-NSCLC: Artificial intelligence-powered PD-L1 quantification for accurate prediction of tumor proportion score in diverse, multi-stain clinical tissue samples [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 471.

Background: PD-L1 expression evaluated by immunohistochemistry (IHC) is a well-established predictor of anti-PD-L1/PD-1 cancer immunotherapy (CIT). The Phase II LCMC3 (NCT02927301) study evaluated pre-operative treatment (tx) with atezolizumab (anti-PD-L1) in pts with untreated early stage resectable NSCLC, achieving a 20% major pathologic response (MPR) rate (primary efficacy pts, n=143). A digital PD-L1 scoring method was developed to assess PD-L1 expression as a potential predictive marker for MPR in squamous and non-squamous tumor samples from LCMC3. Methods: Manual scoring was used to determine PD-L1 status on pre-tx biopsy samples using the tumor proportion score (TPS) with a positive threshold of TPS≥50 (22C3). Binary results were correlated with MPR and stratified by squamous/non-squamous histology. A digital pathology workflow for automated PD-L1 scoring was developed to yield a precise continuous PD-L1 TPS. Deep convolutional neural networks trained using pathologist annotations were used to detect individual cells within the tumor and tumor microenvironment and quantify their PD-L1 expression. These cell type predictions were used to compute a digital PD-L1 TPS. LCMC3 pts with available digital and manual PD-L1 scores were then used to assess the role of PD-L1 expression in predicting MPR. Results: PD-L1 scores were available for pre-tx biopsies from 108 pts. No significant difference in scores was seen between histological subtypes. At cutoff (Oct 15, 2021), TPS≥50 was seen in 41 (non-squamous, n=26 [39%]; squamous, n=15 [36%]) of 108 pts and was associated with MPR in non-squamous (odds ratio [OR], 28.6; P Conclusions: These findings support using digitally assessed PD-L1 IHC as a centralized and standardized scoring system and suggest that tumor histological subtype could be an important factor in the utility of PD-L1 as a predictive biomarker for neoadjuvant CIT in early stage NSCLC. Citation Format: John Abel, Christopher Rivard, Filip Kos, Guillaume Chhor, Yi Liu, Jennifer Giltnane, Sara Hoffman, Murray Resnick, Cyrus Hedvat, Amaro Taylor-Weiner, Farah Khalil, Alan Nicholas, Gregory A. Fishbein, Lynette M. Sholl, Natasha Rekhtman, Stephanie Hennek, Ilan Wapinski, Ann Johnson, Michael Montalto, Katja Schulze, Bruce E. Johnson, David P. Carbone, Konstantin Shilo, Andrew H. Beck, Sanja Dacic, William D. Travis, Ignacio Wistuba. AI-powered and manual assessment of PD-L1 are comparable in predicting response to neoadjuvant atezolizumab in patients (pts) with resectable non-squamous, non-small cell lung cancer (NSCLC) [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 CT112.

Peritoneal dissemination of low-grade appendiceal mucinous neoplasms (LAMNs), sometimes referred to as pseudomyxoma peritonei, can result in significant morbidity and mortality. Little is known about the natural history of localized (non-disseminated) LAMNs.The goal of this study was to evaluate the risk of peritoneal recurrence in patients with localized LAMNs.We performed a multi-institutional retrospective review of patients with pathologically confirmed localized LAMNs. Baseline characteristics, pathology, and follow-up data were collected. The primary endpoint was the rate of peritoneal recurrence.We identified 217 patients with localized LAMNs. Median age was 59 years (11-95) and 131 (60%) patients were female. Surgical management included appendectomy for 124 (57.1%) patients, appendectomy with partial cecectomy for 26 (12.0%) patients, and colectomy for 67 (30.9%) patients. Pathology revealed perforation in 46 patients (37.7% of 122 patients with perforation status mentioned in the report), extra-appendiceal acellular mucin (EAM) in 49 (22.6%) patients, and extra-appendiceal neoplastic cells (EAC) in 13 (6.0%) patients. Median follow-up was 51.1 months (0-271). Seven (3.2%) patients developed a peritoneal recurrence, with a median time to recurrence of 14.4 months (2.5-47.0). Seven (15.2%) patients with histologic evidence of perforation had recurrence, versus no patients (0%) without perforation (p 0.001); five (10.2%) patients with EAM versus two (1.2%) patients without EAM (p = 0.007), and one (7.7%) patient with EAC versus six (2.9%) patients without EAC (p = 0.355) had recurrence.This multi-institutional study represents the largest reported series of patients with localized LAMNs. In the absence of perforation or extra-appendiceal mucin or cells, recurrence was extremely rare; however, patients with any of these pathologic findings require careful follow-up.

An 83-year-old male with a history of diabetes but with an otherwise intact immune system presented with melena. Upper endoscopy showed gastric and duodenal ulcers. Colonoscopy showed colonic ulcers. Biopsies revealed cytomegalovirus (CMV). Therapy with an antiviral such as ganciclovir should be considered even in an immunocompetent patient if male and over the age of 55, or if they have chronic diseases such as diabetes or chronic kidney disease.

Carcinoid tumors have been reported to occur in various locations, particularly in the gastrointestinal tract. The relationship between the development of carcinoids and ulcerative colitis has been an unclear and controversial one. The association of ulcerative colitis and the development of ileal-pouch carcinoids has not, however, been well documented. We report a case of carcinoid tumor arising in an ileoanal pouch and discuss its unique diagnostic and therapeutic considerations.

Colorectal cancer is the second leading cause of cancer related death in the United States. The majority of these deaths are due to metastasis, with the liver easily accounting as the most common site of deposit. While there are multiple steps in the CRC hepatic metastatic cascade, this review attempts to summarize the different processes involved, focusing on the most recent discoveries, as well as the associated effects in relation to prognosis.

The lysyl oxidase gene family members function as extracellular matrix modulating enzymes. We have found that another member of this family, lysyl oxidase related protein-1 (LOR-1), is highly expressed in metastatic breast cancer-derived cell lines but not in the nonmetastatic estrogen-dependent MCF-7 cells. Furthermore, LOR-1 expression in periductal tumor cells of breast carcinomas is significantly correlated with increased tumor malignancy. MCF-7 cells expressing recombinant LOR-1 formed estrogen-dependent tumors that developed much slower than tumors derived from empty vector-transfected MCF-7 cells. The cells of these LOR-1-expressing tumors were surrounded by a high concentration of dense collagen fibers, and the tumors contained many fibrotic foci. Induction of fibrosis in vivo by lysyl oxidase-like enzymes has never been observed before and suggests that LOR-1 may function as an autonomous inducer of fibrosis. The appearance of fibrotic foci in spontaneous breast cancer tumors is correlated with poor prognosis and metastasis, and we, therefore, examined the invasiveness of the LOR-1-expressing tumors. LOR-1-expressing MCF-7 cells invaded the pseudocapsules surrounding the tumors. In contrast, vector-transfected MCF-7 cells did not invade the pseudocapsules. This observation suggests that LOR-1 enhances the malignancy of the tumors. Furthermore, the LOR-1-expressing tumor cells invaded blood vessels, nerves, and muscles adjacent to the tumor, indicating that the LOR-1-expressing MCF-7 cells acquired metastatic properties. We conclude that LOR-1 promotes tumor fibrosis and tumor invasiveness simultaneously, which indicates that these two processes may be associated.