Impact of Angiogenesis- and Hypoxia-Associated Polymorphisms on Tumor Recurrence in Patients with Hepatocellular Carcinoma Undergoing Surgical Resection

Simple Summary: Hepatocellular carcinoma remains a leading cause of cancer-related death and the most common primary hepatic malignancy in the Western hemisphere. Previous research found that angiogenesis-related cytokines and elevated levels of interleukin 8 and vascular endothelial growth factor (VEGF) shorten the expected time of survival. Moreover, factors of tumor angiogenesis- and hypoxia-driven signaling pathways are already associated with worse outcome in disease-free survival in several tumor entities. Our study investigates the prognosis of hepatocellular carcinoma patients based on a selection of ten different single-nucleotide polymorphisms from angiogenesis, carcinogenesis, and hypoxia pathways. Our study with 127 patients found supporting evidence that polymorphisms in angiogenesis-associated pathways corelate with disease-free survival and clinical outcome in patients with hepatocellular carcinoma. Abstract: Tumor angiogenesis plays a pivotal role in hepatocellular carcinoma (HCC) biology. Identifying molecular prognostic markers is critical to further improve treatment selection in these patients. The present study analyzed a subset of 10 germline polymorphisms involved in tumor angiogenesis pathways and their impact on prognosis in HCC patients undergoing partial hepatectomy in a curative intent. Formalin-fixed paraffin-embedded (FFPE) tissues were obtained from 127 HCC patients at a German primary care hospital. Genomic DNA was extracted, and genotyping was carried out using polymerase chain reaction (PCR)-restriction fragment length polymorphism-based protocols. Polymorphisms in interleukin-8 (IL-8) (rs4073; p = 0.047, log-rank test) and vascular endothelial growth factor (VEGF C + 936T) (rs3025039; p = 0.045, log-rank test) were significantly associated with disease-free survival (DFS). After adjusting for covariates in the multivariable model, IL-8 T-251A (rs4073) (adjusted p = 0.010) and a combination of "high-expression" variants of rs4073 and rs3025039 (adjusted p = 0.034) remained significantly associated with DFS. High-expression variants of IL-8 T-251A may serve as an independent molecular marker of prognosis in patients undergoing surgical resection for HCC. Assessment of the patients' individual genetic risks may help to identify patient subgroups at high risk for recurrence following curative-intent surgery

Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning

Background and 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 cheaper than molecular assays. But clinical application of this technology requires high performance and multisite validation, which have not yet been performed. Methods: We collected hematoxylin and eosin-stained slides, and findings from molecular analyses for MSI and dMMR, from 8836 colorectal tumors (of all stages) included in the MSIDETECT 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=6406 specimens) and validated in an external cohort (n=771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC). Results: The deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound 0.91, upper bound 0.93) and an AUPRC of 0.63 (range, 0.59–0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC curve of 0.95 (range, 0.92–0.96) without image-preprocessing and an AUROC curve of 0.96 (range, 0.93–0.98) after color normalization. Conclusions: We developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using hematoxylin and eosin-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens

Genetic polymorphisms in interleukin-1β (rs1143634) and interleukin-8 (rs4073) are associated with survival after resection of intrahepatic cholangiocarcinoma

Abstract Intrahepatic cholangiocarcinoma (iCCA) is a rare, understudied primary hepatic malignancy with dismal outcomes. Aiming to identify prognostically relevant single-nucleotide polymorphisms, we analyzed 11 genetic variants with a role in tumor-promoting inflammation (VEGF, EGF, EGFR, IL-1B, IL-6, CXCL8 (IL-8), IL-10, CXCR1, HIF1A and PTGS2 (COX-2) genes) and their association with disease-free (DFS) and overall survival (OS) in patients undergoing curative-intent surgery for iCCA. Genomic DNA was isolated from 112 patients (64 female, 48 male) with iCCA. Germline polymorphisms were analyzed with polymerase chain reaction-restriction fragment length polymorphism protocols. The IL-1B +3954 C/C (73/112, hazard ratio (HR) = 1.735, p = 0.012) and the IL-8 -251 T/A or A/A (53/112 and 16/112, HR = 2.001 and 1.1777, p = 0.026) genotypes were associated with shorter OS in univariable and multivariable analysis. The IL-1B +3954 polymorphism was also associated with shorter DFS (HR = 1.983, p = 0.012), but this effect was not sustained in the multivariable model. A genetic risk model of 0, 1 and 2 unfavorable alleles was established and confirmed in multivariable analysis. This study supports the prognostic role of the IL-1B C+3954T and the IL-8 T-251A variant as outcome markers in iCCA patients, identifying patient subgroups at higher risk for dismal clinical outcomes

Pure high-grade papillary urothelial bladder cancer: a luminal-like subgroup with potential for targeted therapy

Purpose Non-invasive high-grade (HG) bladder cancer is a heterogeneous disease that is characterized insufficiently. First-line Bacillus Calmette-Guerin instillation fails in a substantial amount of cases and alternative bladder-preserving treatments are limited, underlining the need to promote a further molecular understanding of non-invasive HG lesions. Here, we characterized pure HG papillary urothelial bladder cancer (pure pTa HG), a potential subgroup of non-invasive HG bladder carcinomas, with regard to molecular subtype affiliation and potential for targeted therapy. Methods An immunohistochemistry panel comprising luminal (KRT20, ERBB2, ESR2, GATA3) and basal (KRT5/6, KRT14) markers as well as p53 and FGFR3 was used to analyze molecular subtype affiliations of 78 pure pTa HG/papillary pT1(a) HG samples. In 66 of these, ERBB2 fluorescence in situ hybridization was performed. Additionally, targeted sequencing (31 genes) of 19 pTa HG cases was conducted, focusing on known therapeutic targets or those described to predict response to targeted therapies noted in registered clinical trials or that are already approved. Results We found that pure pTa HG/papillary pT1(a) HG lesions were characterized by a luminal-like phenotype associated with frequent (58% of samples) moderate to high ERBB2 protein expression, rare FGFR3 alterations on genomic and protein levels, and a high frequency (89% of samples) of chromatin-modifying gene alterations. Of note, 95% of pTa HG/papillary pT1 HG cases harbored at least one potential druggable genomic alteration. Conclusions Our data should help guiding the selection of targeted therapies for investigation in future clinical trials and, additionally, may provide a basis for prospective mechanistic studies of pTa HG pathogenesis

Intraoperative Transfusion of Fresh Frozen Plasma Predicts Morbidity Following Partial Liver Resection for Hepatocellular Carcinoma

Background!#!The reduction of perioperative morbidity is a main surgical goal in patients undergoing partial hepatectomy for hepatocellular carcinoma (HCC). Here, we investigated clinical determinants of perioperative morbidity in a European cohort of patients undergoing surgical resection for HCC.!##!Methods!#!A total 136 patients who underwent partial hepatectomy for HCC between 2011 and 2017 at our institution were included in this analysis. The associations between major surgical complications (Clavien-Dindo ≥ 3) and overall morbidity (Clavien-Dindo ≥ 1) with clinical variables were assessed using univariate and multivariable binary logistic regression analysis.!##!Results!#!Multivariable analysis identified the Child-Pugh-Score (CPS, HR = 3.23; p = 0.040), operative time (HR = 5.63; p = 0.003), and intraoperatively administered fresh frozen plasma (FFP, HR = 5.62; p = 0.001) as independent prognostic markers of major surgical complications, while only FFP (HR = 6.52; p = 0.001) was associated with morbidity in the multivariable analysis. The transfusion of FFP was not associated with perioperative liver functions tests.!##!Conclusions!#!The intraoperative administration of FFP is an important independent predictor of perioperative morbidity in patients undergoing partial hepatectomy for HCC

An international multi-institutional validation study of the algorithm for prostate cancer detection and Gleason grading

Abstract Pathologic examination of prostate biopsies is time consuming due to the large number of slides per case. In this retrospective study, we validate a deep learning-based classifier for prostate cancer (PCA) detection and Gleason grading (AI tool) in biopsy samples. Five external cohorts of patients with multifocal prostate biopsy were analyzed from high-volume pathology institutes. A total of 5922 H&E sections representing 7473 biopsy cores from 423 patient cases (digitized using three scanners) were assessed concerning tumor detection. Two tumor-bearing datasets (core n = 227 and 159) were graded by an international group of pathologists including expert urologic pathologists (n = 11) to validate the Gleason grading classifier. The sensitivity, specificity, and NPV for the detection of tumor-bearing biopsies was in a range of 0.971–1.000, 0.875–0.976, and 0.988–1.000, respectively, across the different test cohorts. In several biopsy slides tumor tissue was correctly detected by the AI tool that was initially missed by pathologists. Most false positive misclassifications represented lesions suspicious for carcinoma or cancer mimickers. The quadratically weighted kappa levels for Gleason grading agreement for single pathologists was 0.62–0.80 (0.77 for AI tool) and 0.64–0.76 (0.72 for AI tool) for the two grading datasets, respectively. In cases where consensus for grading was reached among pathologists, kappa levels for AI tool were 0.903 and 0.855. The PCA detection classifier showed high accuracy for PCA detection in biopsy cases during external validation, independent of the institute and scanner used. High levels of agreement for Gleason grading were indistinguishable between experienced genitourinary pathologists and the AI tool

Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study

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 learning-based classifiers to detect microsatellite instability and EBV status from routine histology slides. METHODS: In 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. FINDINGS: Across 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