BACKGROUND & AIMS: Microsatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and less expensively than molecular assays. However, clinical application of this technology requires high performance and multisite validation, which have not yet been performed. METHODS: We collected H&E-stained slides and findings from molecular analyses for MSI and dMMR from 8836 colorectal tumors (of all stages) included in the MSI-DETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (N ¼ 6406 specimens) and validated in an external cohort (n ¼ 771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve

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ObjectiveAlpha-1 antitrypsin deficiency (AATD) is a common, potentially lethal inborn disorder caused by mutations in alpha-1 antitrypsin (AAT). Homozygosity for the ‘Pi*Z’ variant of AAT (Pi*ZZ genotype) causes lung and liver disease, whereas heterozygous ‘Pi*Z’ carriage (Pi*MZ genotype) predisposes to gallstones and liver fibrosis. The clinical significance of the more common ‘Pi*S’ variant remains largely undefined and no robust data exist on the prevalence of liver tumours in AATD.DesignBaseline phenotypes of AATD individuals and non-carriers were analysed in 482 380 participants in the UK Biobank. 1104 participants of a multinational cohort (586 Pi*ZZ, 239 Pi*SZ, 279 non-carriers) underwent a comprehensive clinical assessment. Associations were adjusted for age, sex, body mass index, diabetes and alcohol consumption.ResultsAmong UK Biobank participants, Pi*ZZ individuals displayed the highest liver enzyme values, the highest occurrence of liver fibrosis/cirrhosis (adjusted OR (aOR)=21.7 (8.8–53.7)) and primary liver cancer (aOR=44.5 (10.8–183.6)). Subjects with Pi*MZ genotype had slightly elevated liver enzymes and moderately increased odds for liver fibrosis/cirrhosis (aOR=1.7 (1.2–2.2)) and cholelithiasis (aOR=1.3 (1.2–1.4)). Individuals with homozygous Pi*S mutation (Pi*SS genotype) harboured minimally elevated alanine aminotransferase values, but no other hepatobiliary abnormalities. Pi*SZ participants displayed higher liver enzymes, more frequent liver fibrosis/cirrhosis (aOR=3.1 (1.1–8.2)) and primary liver cancer (aOR=6.6 (1.6–26.9)). The higher fibrosis burden was confirmed in a multinational cohort. Male sex, age ≥50 years, obesity and the presence of diabetes were associated with significant liver fibrosis.ConclusionOur study defines the hepatobiliary phenotype of individuals with the most relevant AATD genotypes including their predisposition to liver tumours, thereby allowing evidence-based advice and individualised hepatological surveillance.

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Background Response to immunotherapy in gastric cancer is associated with microsatellite instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We therefore aimed to develop and validate deep learningbased classifiers to detect microsatellite instability and EBV status from routine histology slides. MethodsIn this retrospective, multicentre study, we collected tissue samples from ten cohorts of patients with gastric cancer from seven countries (South Korea, Switzerland, Japan, Italy, Germany, the UK and the USA). We trained a deep learning-based classifier to detect microsatellite instability and EBV positivity from digitised, haematoxylin and eosin stained resection slides without annotating tumour containing regions. The performance of the classifier was assessed by within-cohort cross-validation in all ten cohorts and by external validation, for which we split the cohorts into a five-cohort training dataset and a five-cohort test dataset. We measured the area under the receiver operating curve (AUROC) for detection of microsatellite instability and EBV status. Microsatellite instability and EBV status were determined to be detectable if the lower bound of the 95% CI for the AUROC was above 0•5. FindingsAcross the ten cohorts, our analysis included 2823 patients with known microsatellite instability status and 2685 patients with known EBV status. In the within-cohort cross-validation, the deep learning-based classifier could detect microsatellite instability status in nine of ten cohorts, with AUROCs ranging from 0•597 (95% CI 0•522-0•737) to 0•836 (0•795-0•880) and EBV status in five of eight cohorts, with AUROCs ranging from 0•819 (0•752-0•841) to 0•897 (0•513-0•966). Training a classifier on the pooled training dataset and testing it on the five remaining cohorts resulted in high classification performance with AUROCs ranging from 0•723 (95% CI 0•676-0•794) to 0•863 (0•747-0•969) for detection of microsatellite instability and from 0•672 (0•403-0•989) to 0•859 (0•823-0•919) for detection of EBV status. Interpretation Classifiers became increasingly robust when trained on pooled cohorts. After prospective validation, this deep learning-based tissue classification system could be used as an inexpensive predictive biomarker for immunotherapy in gastric cancer. Funding German Cancer Aid and German Federal Ministry of Health.

Urachal cancer (UrC) is a rare but aggressive malignancy often diagnosed in advanced stages requiring systemic treatment. Although cytotoxic chemotherapy is of limited effectiveness, prospective clinical studies can hardly be conducted. Targeted therapeutic treatment approaches and potentially immunotherapy based on a biological rationale may provide an alternative strategy. We therefore subjected 70 urachal adenocarcinomas to targeted next-generation sequencing, conducted in situ and immunohistochemical analyses (including PD-L1 and DNA mismatch repair proteins [MMR]) and evaluated the microsatellite instability (MSI) status. The analytical findings were correlated with clinicopathological and outcome data and Kaplan-Meier and univariable/multivariable Cox regression analyses were performed. The patients had a mean age of 50 years, 66% were male and a 5-year overall survival (OS) of 58% and recurrence-free survival (RFS) of 45% was detected. Sequence variations were observed in TP53 (66%), KRAS (21%), BRAF (4%), PIK3CA (4%), FGFR1 (1%), MET (1%), NRAS (1%), and PDGFRA (1%). Gene amplifications were found in EGFR (5%), ERBB2 (2%), and MET (2%). We detected no evidence of MMR-deficiency (MMR-d)/MSI-high (MSI-h), whereas 10 of 63 cases (16%) expressed PD-L1. Therefore, anti-PD-1/PD-L1 immunotherapy approaches might be tested in UrC. Importantly, we found aberrations in intracellular signal transduction pathways (RAS/RAF/PI3K) in 31% of UrCs with potential implications for anti-EGFR therapy. Less frequent potentially actionable genetic alterations were additionally detected in ERBB2 (HER2), MET, FGFR1, and PDGFRA. The molecular profile strengthens the notion that UrC is a distinct entity on the genomic level with closer resemblance to colorectal than to bladder cancer.

alpha-1 antitrypsin deficiency-related liver disease of the European Reference Network (ERN) "Rare Liver" and the European Association for the Study of the Liver (EASL) registry group "Alpha-1 Liver,