Validation of a whole slide image management system for metabolic-associated steatohepatitis for clinical trials.

The gold standard for enrollment and endpoint assessment in metabolic dysfunction-associated steatosis clinical trials is histologic assessment of a liver biopsy performed on glass slides. However, obtaining the evaluations from several expert pathologists on glass is challenging, as shipping the slides around the country or around the world is time-consuming and comes with the hazards of slide breakage. This study demonstrated that pathologic assessment of disease activity in steatohepatitis, performed using digital images on the AISight whole slide image management system, yields results that are comparable to those obtained using glass slides. The accuracy of scoring for steatohepatitis (nonalcoholic fatty liver disease activity score ≥4 with ≥1 for each feature and absence of atypical features suggestive of other liver disease) performed on the system was evaluated against scoring conducted on glass slides. Both methods were assessed for overall percent agreement with a consensus "ground truth" score (defined as the median score of a panel of three pathologists' glass slides). Each case was also read by three different pathologists, once on glass and once digitally with a minimum 2-week washout period between the modalities. It was demonstrated that the average agreement across three pathologists of digital scoring with ground truth was noninferior to the average agreement of glass scoring with ground truth [noninferiority margin: -0.05; difference: -0.001; 95% CI: (-0.027, 0.026); and p < 0.0001]. For each pathologist, there was a similar average agreement of digital and glass reads with glass ground truth (pathologist A, 0.843 and 0.849; pathologist B, 0.633 and 0.605; and pathologist C, 0.755 and 0.780). Here, we demonstrate that the accuracy of digital reads for steatohepatitis using digital images is equivalent to glass reads in the context of a clinical trial for scoring using the Clinical Research Network scoring system.

AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseases.

Clinical trials in metabolic dysfunction-associated steatohepatitis (MASH, formerly known as nonalcoholic steatohepatitis) require histologic scoring for assessment of inclusion criteria and endpoints. However, variability in interpretation has impacted clinical trial outcomes. We developed an artificial intelligence-based measurement (AIM) tool for scoring MASH histology (AIM-MASH). AIM-MASH predictions for MASH Clinical Research Network necroinflammation grades and fibrosis stages were reproducible (κ = 1) and aligned with expert pathologist consensus scores (κ = 0.62-0.74). The AIM-MASH versus consensus agreements were comparable to average pathologists for MASH Clinical Research Network scores (82% versus 81%) and fibrosis (97% versus 96%). Continuous scores produced by AIM-MASH for key histological features of MASH correlated with mean pathologist scores and noninvasive biomarkers and strongly predicted progression-free survival in patients with stage 3 (P < 0.0001) and stage 4 (P = 0.03) fibrosis. In a retrospective analysis of the ATLAS trial (NCT03449446), responders receiving study treatment showed a greater continuous change in fibrosis compared with placebo (P = 0.02). Overall, these results suggest that AIM-MASH may assist pathologists in histologic review of MASH clinical trials, reducing inter-rater variability on trial outcomes and offering a more sensitive and reproducible measure of patient responses.

Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies.

This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.

Utility of pathologist panels for achieving consensus in NASH histologic scoring in clinical trials: Data from a phase 3 study.

BACKGROUND: Liver histopathologic assessment is the accepted surrogate endpoint in NASH trials; however, the scoring of NASH Clinical Research Network (CRN) histologic parameters is limited by intraobserver and interobserver variability. We designed a consensus panel approach to minimize variability when using this scoring system. We assessed agreement between readers, estimated linear weighted kappas between 2 panels, compared them with published pairwise kappa estimates, and addressed how agreement or disagreement might impact the precision and validity of the surrogate efficacy endpoint in NASH trials. METHODS: Two panels, each comprising 3 liver fellowship-trained pathologists who underwent NASH histology training, independently evaluated scanned whole slide images, scoring fibrosis, inflammation, hepatocyte ballooning, and steatosis from baseline and month 18 biopsies for 100 patients from the precirrhotic NASH study REGENERATE. The consensus score for each parameter was defined as agreement by ≥2 pathologists. If consensus was not reached, all 3 pathologists read the slide jointly to achieve a consensus score. RESULTS: Between the 2 panels, the consensus was 97%-99% for steatosis, 91%-93% for fibrosis, 88%-92% for hepatocyte ballooning, and 84%-91% for inflammation. Linear weighted kappa scores between panels were similar to published NASH CRN values. CONCLUSIONS: A panel of 3 trained pathologists independently scoring 4 NASH CRN histology parameters produced high consensus rates. Interpanel kappa values were comparable to NASH CRN metrics, supporting the accuracy and reproducibility of this method. The high concordance for fibrosis scoring was reassuring, as fibrosis is predictive of liver-specific outcomes and all-cause mortality.

Artificial Intelligence-Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 Study.

INTRODUCTION: Pathologic response (PathR) by histopathologic assessment of resected specimens may be an early clinical end point associated with long-term outcomes with neoadjuvant therapy. Digital pathology may improve the efficiency and precision of PathR assessment. LCMC3 (NCT02927301) evaluated neoadjuvant atezolizumab in patients with resectable NSCLC and reported a 20% major PathR rate. METHODS: We determined PathR in primary tumor resection specimens using guidelines-based visual techniques and developed a convolutional neural network model using the same criteria to digitally measure the percent viable tumor on whole-slide images. Concordance was evaluated between visual determination of percent viable tumor (n = 151) performed by one of the 47 local pathologists and three central pathologists. RESULTS: For concordance among visual determination of percent viable tumor, the interclass correlation coefficient was 0.87 (95% confidence interval [CI]: 0.84-0.90). Agreement for visually assessed 10% or less viable tumor (major PathR [MPR]) in the primary tumor was 92.1% (Fleiss kappa = 0.83). Digitally assessed percent viable tumor (n = 136) correlated with visual assessment (Pearson r = 0.73; digital/visual slope = 0.28). Digitally assessed MPR predicted visually assessed MPR with outstanding discrimination (area under receiver operating characteristic curve, 0.98) and was associated with longer disease-free survival (hazard ratio [HR] = 0.30; 95% CI: 0.09-0.97, p = 0.033) and overall survival (HR = 0.14, 95% CI: 0.02-1.06, p = 0.027) versus no MPR. Digitally assessed PathR strongly correlated with visual measurements. CONCLUSIONS: Artificial intelligence-powered digital pathology exhibits promise in assisting pathologic assessments in neoadjuvant NSCLC clinical trials. The development of artificial intelligence-powered approaches in clinical settings may aid pathologists in clinical operations, including routine PathR assessments, and subsequently support improved patient care and long-term outcomes.

AI-based histologic scoring enables automated and reproducible assessment of enrollment criteria and endpoints in NASH clinical trials.

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.

Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASH.

Nonalcoholic steatohepatitis (NASH) is the most common chronic liver disease globally and a leading cause for liver transplantation in the US. Its pathogenesis remains imprecisely defined. We combined two high-resolution modalities to tissue samples from NASH clinical trials, machine learning (ML)-based quantification of histological features and transcriptomics, to identify genes that are associated with disease progression and clinical events. A histopathology-driven 5-gene expression signature predicted disease progression and clinical events in patients with NASH with F3 (pre-cirrhotic) and F4 (cirrhotic) fibrosis. Notably, the Notch signaling pathway and genes implicated in liver-related diseases were enriched in this expression signature. In a validation cohort where pharmacologic intervention improved disease histology, multiple Notch signaling components were suppressed.

Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology.

Ulcerative colitis is a chronic inflammatory bowel disease that is characterized by a relapsing and remitting course. Assessment of disease activity critically informs treatment decisions. In addition to endoscopic remission, histologic remission is emerging as a treatment target and a key factor in the evaluation of disease activity and therapeutic efficacy. However, manual pathologist evaluation is semiquantitative and limited in granularity. Machine learning approaches are increasingly being developed to aid pathologists in accurate and reproducible scoring of histology, enabling precise quantitation of clinically relevant features. Here, we report the development and validation of convolutional neural network models that quantify histologic features pertinent to ulcerative colitis disease activity, directly from hematoxylin and eosin-stained whole slide images. Tissue and cell model predictions were used to generate quantitative human-interpretable features to fully characterize the histology samples. Tissue and cell predictions showed comparable agreement to pathologist annotations, and the extracted slide-level human-interpretable features demonstrated strong correlations with disease severity and pathologist-assigned Nancy histological index scores. Moreover, using a random forest classifier based on 13 human-interpretable features derived from the tissue and cell models, we were able to accurately predict Nancy histological index scores, with a weighted kappa (κ = 0.91) and Spearman correlation (⍴ = 0.89, P < .001) when compared with pathologist consensus Nancy histological index scores. We were also able to predict histologic remission, based on the absence of neutrophil extravasation, with a high accuracy of 0.97. This work demonstrates the potential of computer vision to enable a standardized and robust assessment of ulcerative colitis histopathology for translational research and improved evaluation of disease activity and prognosis.

Somatic mutations in collagens are associated with a distinct tumor environment and overall survival in gastric cancer.

BACKGROUND: Gastric cancer is a heterogeneous disease with poorly understood genetic and microenvironmental factors. Mutations in collagen genes are associated with genetic diseases that compromise tissue integrity, but their role in tumor progression has not been extensively reported. Aberrant collagen expression has been long associated with malignant tumor growth, invasion, chemoresistance, and patient outcomes. We hypothesized that somatic mutations in collagens could functionally alter the tumor extracellular matrix. METHODS: We used publicly available datasets including The Tumor Cancer Genome Atlas (TCGA) to interrogate somatic mutations in collagens in stomach adenocarcinomas. To demonstrate that collagens were significantly mutated above background mutation rates, we used a moderated Kolmogorov-Smirnov test along with combination analysis with a bootstrap approach to define the background accounting for mutation rates. Association between mutations and clinicopathological features was evaluated by Fisher or chi-squared tests. Association with overall survival was assessed by Kaplan-Meier and the Cox-Proportional Hazards Model. Gene Set Enrichment Analysis was used to interrogate pathways. Immunohistochemistry and in situ hybridization tested expression of COL7A1 in stomach tumors. RESULTS: In stomach adenocarcinomas, we identified individual collagen genes and sets of collagen genes harboring somatic mutations at a high frequency compared to background in both microsatellite stable, and microsatellite instable tumors in TCGA. Many of the missense mutations resemble the same types of loss of function mutations in collagenopathies that disrupt tissue formation and destabilize cells providing guidance to interpret the somatic mutations. We identified combinations of somatic mutations in collagens associated with overall survival, with a distinctive tumor microenvironment marked by lower matrisome expression and immune cell signatures. Truncation mutations were strongly associated with improved outcomes suggesting that loss of expression of secreted collagens impact tumor progression and treatment response. Germline collagenopathy variants guided interpretation of impactful somatic mutations on tumors. CONCLUSIONS: These observations highlight that many collagens, expressed in non-physiologically relevant conditions in tumors, harbor impactful somatic mutations in tumors, suggesting new approaches for classification and therapy development in stomach cancer. In sum, these findings demonstrate how classification of tumors by collagen mutations identified strong links between specific genotypes and the tumor environment.

Trastuzumab with trimodality treatment for oesophageal adenocarcinoma with HER2 overexpression (NRG Oncology/RTOG 1010): a multicentre, randomised, phase 3 trial.

BACKGROUND: 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. METHODS: 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/m2 intravenously over 1 h) and carboplatin (area under the curve 2, intravenously over 30-60 min) for 6 weeks with radiotherapy 50·4 Gy in 28 fractions (chemoradiotherapy) followed by surgery, with or without intravenous trastuzumab (4 mg/kg in week one, 2 mg/kg per week for 5 weeks during chemoradiotherapy, 6 mg/kg once presurgery, and 6 mg/kg every 3 weeks for 13 treatments starting 21-56 days after surgery). The primary endpoint, disease-free survival, was defined as the time from randomisation to death or first of locoregional disease persistence or recurrence, distant metastases, or second primary malignancy. Analyses were done by modified intention to treat. This study is registered with Clinicaltrials.gov, NCT01196390; it is now closed and in follow-up. FINDINGS: 606 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). INTERPRETATION: 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. FUNDING: National Cancer Institute and Genentech.

A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis.

BACKGROUND AND AIMS: The hepatic venous pressure gradient (HVPG) is the standard for estimating portal pressure but requires expertise for interpretation. We hypothesized that HVPG could be extrapolated from liver histology using a machine learning (ML) algorithm. APPROACH AND RESULTS: Patients with NASH with compensated cirrhosis from a phase 2b trial were included. HVPG and biopsies from baseline and weeks 48 and 96 were reviewed centrally, and biopsies evaluated with a convolutional neural network (PathAI, Boston, MA). Using trichrome-stained biopsies in the training set (n = 130), an ML model was developed to recognize fibrosis patterns associated with HVPG, and the resultant ML HVPG score was validated in a held-out test set (n = 88). Associations between the ML HVPG score with measured HVPG and liver-related events, and performance of the ML HVPG score for clinically significant portal hypertension (CSPH) (HVPG ≥ 10 mm Hg), were determined. The ML-HVPG score was more strongly correlated with HVPG than hepatic collagen by morphometry (ρ = 0.47 vs. ρ = 0.28; P < 0.001). The ML HVPG score differentiated patients with normal (0-5 mm Hg) and elevated (5.5-9.5 mm Hg) HVPG and CSPH (median: 1.51 vs. 1.93 vs. 2.60; all P < 0.05). The areas under receiver operating characteristic curve (AUROCs) (95% CI) of the ML-HVPG score for CSPH were 0.85 (0.80, 0.90) and 0.76 (0.68, 0.85) in the training and test sets, respectively. Discrimination of the ML-HVPG score for CSPH improved with the addition of a ML parameter for nodularity, Enhanced Liver Fibrosis, platelets, aspartate aminotransferase (AST), and bilirubin (AUROC in test set: 0.85; 95% CI: 0.78, 0.92). Although baseline ML-HVPG score was not prognostic, changes were predictive of clinical events (HR: 2.13; 95% CI: 1.26, 3.59) and associated with hemodynamic response and fibrosis improvement. CONCLUSIONS: An ML model based on trichrome-stained liver biopsy slides can predict CSPH in patients with NASH with cirrhosis.

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.

Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601-0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to 'black-box' methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment.

A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH.

BACKGROUND AND AIMS: Manual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in NASH, but is limited by variability in interpretation and insensitivity to change. Thus, there is a critical need for improved tools to assess liver pathology in order to risk stratify NASH patients and monitor treatment response. APPROACH AND RESULTS: Here, we describe a machine learning (ML)-based approach to liver histology assessment, which accurately characterizes disease severity and heterogeneity, and sensitively quantifies treatment response in NASH. We use samples from three randomized controlled trials to build and then validate deep convolutional neural networks to measure key histological features in NASH, including steatosis, inflammation, hepatocellular ballooning, and fibrosis. The ML-based predictions showed strong correlations with expert pathologists and were prognostic of progression to cirrhosis and liver-related clinical events. We developed a heterogeneity-sensitive metric of fibrosis response, the Deep Learning Treatment Assessment Liver Fibrosis score, which measured antifibrotic treatment effects that went undetected by manual pathological staging and was concordant with histological disease progression. CONCLUSIONS: Our ML method has shown reproducibility and sensitivity and was prognostic for disease progression, demonstrating the power of ML to advance our understanding of disease heterogeneity in NASH, risk stratify affected patients, and facilitate the development of therapies.

A Multi-institutional Study of Peritoneal Recurrence Following Resection of Low-grade Appendiceal Mucinous Neoplasms.

BACKGROUND: 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. OBJECTIVE: The goal of this study was to evaluate the risk of peritoneal recurrence in patients with localized LAMNs. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Combination Therapies Including Cilofexor and Firsocostat for Bridging Fibrosis and Cirrhosis Attributable to NASH.

BACKGROUND AND AIMS: Advanced fibrosis attributable to NASH is a leading cause of end-stage liver disease. APPROACH AND RESULTS: In this phase 2b trial, 392 patients with bridging fibrosis or compensated cirrhosis (F3-F4) were randomized to receive placebo, selonsertib 18 mg, cilofexor 30 mg, or firsocostat 20 mg, alone or in two-drug combinations, once-daily for 48 weeks. The primary endpoint was a ≥1-stage improvement in fibrosis without worsening of NASH between baseline and 48 weeks based on central pathologist review. Exploratory endpoints included changes in NAFLD Activity Score (NAS), liver histology assessed using a machine learning (ML) approach, liver biochemistry, and noninvasive markers. The majority had cirrhosis (56%) and NAS ≥5 (83%). The primary endpoint was achieved in 11% of placebo-treated patients versus cilofexor/firsocostat (21%; P = 0.17), cilofexor/selonsertib (19%; P = 0.26), firsocostat/selonsertib (15%; P = 0.62), firsocostat (12%; P = 0.94), and cilofexor (12%; P = 0.96). Changes in hepatic collagen by morphometry were not significant, but cilofexor/firsocostat led to a significant decrease in ML NASH CRN fibrosis score (P = 0.040) and a shift in biopsy area from F3-F4 to ≤F2 fibrosis patterns. Compared to placebo, significantly higher proportions of cilofexor/firsocostat patients had a ≥2-point NAS reduction; reductions in steatosis, lobular inflammation, and ballooning; and significant improvements in alanine aminotransferase (ALT), aspartate aminotransferase (AST), bilirubin, bile acids, cytokeratin-18, insulin, estimated glomerular filtration rate, ELF score, and liver stiffness by transient elastography (all P ≤ 0.05). Pruritus occurred in 20%-29% of cilofexor versus 15% of placebo-treated patients. CONCLUSIONS: In patients with bridging fibrosis and cirrhosis, 48 weeks of cilofexor/firsocostat was well tolerated, led to improvements in NASH activity, and may have an antifibrotic effect. This combination offers potential for fibrosis regression with longer-term therapy in patients with advanced fibrosis attributable to NASH.

Elastin in the Tumor Microenvironment.

Elastic fibers are found in the extracellular matrix (ECM) of tissues requiring resilience and depend on elasticity. Elastin and its degradation products have multiple roles in the oncologic process. In many malignancies, the remodeled ECM expresses high levels of the elastin protein which may have either positive or negative effects on tumor growth. Elastin cross-linking with other ECM components and the enzymes governing this process all have effects on tumorigenesis. Elastases, and specifically neutrophil elastase, are key drivers of invasion and metastasis and therefore are important targets for inhibition. Elastin degradation leads to the generation of bioactive fragments and elastin-derived peptides that further modulate tumor growth and spread. Interestingly, elastin-like peptides (ELP) and elastin-derived peptides (EDP) may also be utilized as nano-carriers to combat tumor growth. EDPs drive tumor development in a variety of ways, and specifically targeting EDPs and their binding proteins are major objectives for ongoing and future anti-cancer therapies. Research on both the direct anti-cancer activity and the drug delivery capabilities of ELPs is another area likely to result in novel therapeutic agents in the near future.

Smooth muscle tumors of the gastrointestinal tract: an analysis of prognostic features in 407 cases.

Smooth muscle tumors represent the second most common mural mesenchymal neoplasm in the gastrointestinal tract, but established criteria for prognostic assessment of these tumors are lacking. A large cohort of surgically resected intramural gastrointestinal smooth muscle tumors from 31 institutions was analyzed to identify potential prognostic features. Pathologic features were assessed by expert gastrointestinal and/or soft tissue pathologists at each center. Immunohistochemical confirmation was required. A total of 407 cases from the esophagus (n = 97, 24%), stomach (n = 180, 44%), small bowel (n = 74, 18%), and colorectum (n = 56, 14%) were identified. Patients ranged in age from 19 to 92 years (mean 55 years), with a slight female predominance (57%). Mean tumor size was 5.4 cm, with the largest tumor measuring 29 cm. Disease progression following surgery, defined as local recurrence, metastasis, or disease-related death, occurred in 56 patients (14%). Colorectal tumors were most likely to progress, followed by small bowel and gastric tumors. None of the esophageal tumors in this series progressed. Receiver operator characteristic analysis identified optimal cutoffs of 9.8 cm and 3 mitoses/5 mm2 for discriminating between progressive and non-progressive tumors. Histologic features strongly associated with progression by univariate analysis included moderate-to-severe atypia, high cellularity, abnormal differentiation (defined as differentiation not closely resembling that of normal smooth muscle), tumor necrosis, mucosal ulceration, lamina propria involvement, and serosal involvement (P < 0.0001 for all features). Age, sex, and margin status were not significantly associated with progression (P = 0.23, 0.82, and 0.07, respectively). A risk assessment table was created based on tumor site, size, and mitotic count, and Kaplan-Meier plots of progression-free survival for each subgroup revealed progression-based tiers. Based on our findings, it appears that nonesophageal gastrointestinal smooth muscle tumors measuring >10 cm and/or showing ≥3 mitoses/5 mm2 may behave aggressively, and therefore close clinical follow-up is recommended in these cases.

Expression of Indoleamine 2, 3-dioxygenase 1 (IDO1) and Tryptophanyl-tRNA Synthetase (WARS) in Gastric Cancer Molecular Subtypes.

AIMS: Developments in genomic pathology have led to novel molecular classification schemes in gastric cancers. Two of these new subtypes, Epstein-Barr virus (EBV)-associated and microsatellite instability-high (MSI-H), are associated with a dominant T-cell-mediated immune response. The roles of the immune modulators, indoleamine 2, 3-dioxygenase 1 (IDO1) and tryptophanyl-tRNA synthetase (WARS), have not been investigated in the context of this classification. METHODS AND RESULTS: Using in situ hybridization and immunohistochemistry we subclassified 421 primary gastric adenocarcinomas into 5 subtypes, EBV-associated, epithelial to mesenchymal transition, MSI-H, p53-aberrant, and p53-wildtype tumors. Tumor-infiltrative lymphocytes were counted and protein expression of IDO1 and WARS was graded on tissue microarrays of these 421 tumors. High tumor-infiltrative lymphocytes as well as high expression of both IDO1 and WARS was found in EBV and MSI-H tumors. The prognostic effects of IDO1 and WARS expression were tumor subtype dependent. Although high expression levels of IDO1 and WARS were associated with poor prognosis in p53-aberrant, p53-wildtype, and all cancers combined, WARS expression was associated with better prognosis in MSI tumors. CONCLUSIONS: The immunomodulators, IDO1 and WARs, are upregulated and have prognostic significance in EBV-associated and MSI-H tumors. Novel therapies targeting these proteins should be considered in the treatment of these patients.

Stromal ColXα1 expression correlates with tumor-infiltrating lymphocytes and predicts adjuvant therapy outcome in ER-positive/HER2-positive breast cancer.

BACKGROUND: The breast cancer microenvironment contributes to tumor progression and response to chemotherapy. Previously, we reported that increased stromal Type X collagen α1 (ColXα1) and low TILs correlated with poor pathologic response to neoadjuvant therapy in estrogen receptor and HER2-positive (ER+/HER2+) breast cancer. Here, we investigate the relationship of ColXα1 and long-term outcome of ER+/HER2+ breast cancer patients in an adjuvant setting. METHODS: A total of 164 cases with at least 5-year follow-up were included. Immunohistochemistry for ColXα1 was performed on whole tumor sections. Associations between ColXα1expression, clinical pathological features, and outcomes were analyzed. RESULTS: ColXα1 expression was directly proportional to the amount of tumor associated stroma (p = 0.024) and inversely proportional to TILs. Increased ColXα1 was significantly associated with shorter disease free survival and overall survival by univariate analysis. In multivariate analysis, OS was lower in ColXα1 expressing (HR = 2.1; 95% CI = 1.2-3.9) tumors of older patients (> = 58 years) (HR = 5.3; 95% CI = 1.7-17) with higher stage (HR = 2.6; 95% CI = 1.3-5.2). Similarly, DFS was lower in ColXα1 expressing (HR = 1.8; 95% CI = 1.6-5.7) tumors of older patients (HR = 3.2; 95% CI = 1.3-7.8) with higher stage (HR = 2.7; 95% CI = 1.6-5.7) and low TILs. In low PR+ tumors, higher ColXα1 expression was associated with poorer prognosis. CONCLUSION: ColXα1 expression is associated with poor disease free survival and overall survival in ER+/HER2+ breast cancer. This study provides further support for the prognostic utility of ColXα1 as a breast cancer associated stromal factor that predicts response to chemotherapy.

Cytokeratin 7-negative and GATA binding protein 3-negative breast cancers: Clinicopathological features and prognostic significance.

BACKGROUND: Cytokeratin 7 (CK7) and GATA binding protein 3 (GATA3) are considered as immunohistochemical hallmarks of breast cancers; however, there are breast tumors lacking these markers. Clinicopathological characterization of CK7 negative breast cancer has not been addressed previously and similar studies on GATA3 negative tumors are limited. METHODS: This study included 196 consecutive cases of Nottingham Grade 3 breast cancers with 159 cases of Grade 1 and Grade 2 tumors for comparison. CK7 and GATA3 expression was correlated with patient's age, histological type, pathological grade and stage, hormone receptor status, molecular subtype and overall survival. RESULTS: CK7 negativity was seen in 13% of Grade 3, 9% of Grade 2, and 2% of Grade 1 cases (P = 0.0457). Similarly, 28% of Grade 3, 5% of Grade 2 and 2% of Grade 1 cases were GATA3 negative (P < 0.0001). CK7 negative tumors did not show association with other clinicopathological parameters. GATA3 negative tumors were enriched in the basal-like molecular subgroup and were associated with negative estrogen receptor (ER) and negative progesterone receptor (PR) statuses. Both CK7 and GATA3 expression showed no association with overall survival in patients with Grade 3 tumor. CONCLUSIONS: This is the first study to characterize CK7 negative breast tumors in the context of clinicopathology. Profiling the CK7 negative and GATA3 negative breast cancers helps to understand the biology of these specific tumor subgroups and may aid in their diagnosis.