Programmed death-ligand 1 (PD-L1) expression in primary gastric adenocarcinoma and matched metastases.

BACKGROUND Combination of immunotherapy and chemotherapy is recommended for first line treatment of gastric adenocarcinoma (GC) patients with locally advanced unresectable disease or metastatic disease. However, data regarding the concordance rate between PD-L1 combined positive score (CPS) in primary GC and matched regional lymph node metastasis (LNmet) or matched distant metastasis (Dmet) is limited. METHODS Tissue microarray sections from primary resected GC, LNmet and Dmet were immunohistochemically stained with anti-PD-L1 (clone SP263). PD-L1 expression was scored separately in tumour cells and immune cells and compared between matched primary GC, LNmet and/or Dmet. CPS was calculated and results for CPS cut-offs 1 and 5 were compared between matched samples. RESULTS 275 PD-L1 stained GC were analysed. 189 primary GC had matched LNmet. CPS cut-off 1 concordance rate between primary GC and LNmet was 77%. 23 primary GC had matched Dmet but no matched LNmet, CPS cut-off 1 concordance rate was 70%. 63 primary GC had both matched LNmet and matched Dmet, CPS cut-off 1 concordance rate of 67%. CPS cut-off 5 results were similar. The proportion of PD-L1 positive tumour cells increased from primary GC (26%) to LNmet (42%) and was highest in Dmet (75%). CONCLUSION Our study showed up to 33% discordance of PD-L1 CPS between primary GC and LNmet and/or Dmet suggesting that multiple biopsies of primary GC and metastatic sites might need to be tested before considering treatment options. Moreover, this is the first study that seems to suggest that tumour cells acquire PD-L1 expression during disease progression

Immunophenotypes of pancreatic ductal adenocarcinoma: Meta-analysis of transcriptional subtypes.

Pancreatic ductal adenocarcinoma (PDAC) is the most common malignancy of the pancreas and has one of the highest mortality rates of any cancer type with a 5-year survival rate of <5%. Recent studies of PDAC have provided several transcriptomic classifications based on separate analyses of individual patient cohorts. There is a need to provide a unified transcriptomic PDAC classification driven by therapeutically relevant biologic rationale to inform future treatment strategies. Here, we used an integrative meta-analysis of 353 patients from four different studies to derive a PDAC classification based on immunologic parameters. This consensus clustering approach indicated transcriptomic signatures based on immune infiltrate classified as adaptive, innate and immune-exclusion subtypes. This reveals the existence of microenvironmental interpatient heterogeneity within PDAC and could serve to drive novel therapeutic strategies in PDAC including immune modulation approaches to treating this disease.This study was supported by the NIHR Oxford Biomedical Research Centre. CY is supported by a UK Medical Research Council Research Grant (MR/P02646X/1). MLD is funded by Wellcome Trust grant 100262Z/12/Z. SS is funded by a NIHR Academic Clinical Lecturership

Mixed gastric carcinomas show similar chromosomal aberrations in both their diffuse and glandular components

Gastric cancer is one of the most frequent malignancies in the world. Nonetheless, the knowledge of the molecular events involved in the development of gastric carcinoma is far from complete. One of the hallmarks of gastric cancer is chromosomal instability resulting in abnormal DNA copy number changes throughout the genome. Mixed gastric carcinomas constitute a rare histological entity, containing the two main histological phenotypes (diffuse and intestinal). Very little is known about the underlying mechanisms of phenotypic divergence in these mixed tumours. To the best of our knowledge only E-Cadherin mutations were implicated so far in the divergence of these tumours and nothing is known about the involvement of chromosome copy number changes in the two divergent histological components. In this study, we compared the DNA copy number changes, in the two different components (diffuse and intestinal) of mixed gastric carcinomas by microarray - comparative genomic hybridisation (array CGH). The analysis of 12 mixed gastric carcinomas showed no significant differences in array CGH profiles between the diffuse and intestinal components of mixed carcinomas. This supports the idea that the phenotypic divergence within mixed gastric carcinomas is not caused by DNA chromosomal aberrations.Portuguese Foundation for Science and Technology (FCT), grant POCTI/CBO/ 41179/2001 and by Dutch Cancer Society grant – KWF2004-305

Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning.

BACKGROUND Computational pathology uses deep learning (DL) to extract biomarkers from routine pathology slides. Large multicentric datasets improve performance, but such datasets are scarce for gastric cancer. This limitation could be overcome by Swarm Learning (SL). METHODS Here, we report the results of a multicentric retrospective study of SL for prediction of molecular biomarkers in gastric cancer. We collected tissue samples with known microsatellite instability (MSI) and Epstein-Barr Virus (EBV) status from four patient cohorts from Switzerland, Germany, the UK and the USA, storing each dataset on a physically separate computer. RESULTS On an external validation cohort, the SL-based classifier reached an area under the receiver operating curve (AUROC) of 0.8092 (± 0.0132) for MSI prediction and 0.8372 (± 0.0179) for EBV prediction. The centralized model, which was trained on all datasets on a single computer, reached a similar performance. CONCLUSIONS Our findings demonstrate the feasibility of SL-based molecular biomarkers in gastric cancer. In the future, SL could be used for collaborative training and, thus, improve the performance of these biomarkers. This may ultimately result in clinical-grade performance and generalizability

Five biopsy specimens from the proximal part of the tumor reliably determine HER2 protein expression status in gastric cancer

Background: National guidelines recommend trastuzumab for treatment of patients with metastatic HER2-positive gastric cancer (GC). There is currently no guideline indicating the number of biopsy specimens and the location from which they should be obtained to reliably determine the human epidermal growth factor receptor 2 (HER2) status in GC. The aim of this pilot study was (a) to quantify HER2-positive tumor cells in different tumor regions to assess the spatial heterogeneity of HER2 expression and (b) to establish the required number of biopsy specimens and the location from which they should be obtained within the tumor to achieve concordance between HER2 expression status in the biopsy specimens and the resection specimen. Methods: HER2 expression was quantified in six different regions of 24 HER2-positive GC and in six virtual biopsy specimens from different luminal regions. Intratumoral regional heterogeneity and concordance between HER2 status in the biopsy specimens and the resection specimen were analyzed. Results: HER2-positive cells were more frequent in the luminal tumor surface compared with deeper layers (p < 0.001). GCs with differentiated histological features were more commonly HER2 positive (p < 0.001). Assessment of HER2 expression status in five biopsy specimens was sufficient to achieve 100 % concordance between the biopsy specimens and the resection specimen. Conclusions: This is the first study to suggest preferential HER2 positivity at the luminal surface in GC and to establish a minimum number of biopsy specimens needed to obtain a biopsy HER2 result which is identical to that from the whole tumor. Our study suggests that HER2 testing in five tumor-containing endoscopic biopsy specimens from the proximal (oral) part of the tumor is advisable. The results from this pilot study require validation in a prospective study

Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study

Aim Gastric cancer (GC) is a tumor entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesized that GC primary tissue contains information that is predictive of lymph node status and patient prognosis and that this information can be extracted using Deep Learning (DL). Methods Using three patient cohorts comprising 1146 patients, we trained and validated a DL system to predict lymph node status directly from hematoxylin-and-eosin stained GC tissue sections. We investigated the concordance between the DL-based prediction from the primary tumor slides (aiN score) and the histopathological lymph node status (pN). Furthermore, we assessed the prognostic value of the aiN score alone and when combined with the pN status. Results The aiN score predicted the pN status reaching Area Under the Receiver Operating Characteristic curves (AUROCs) of 0.71 in the training cohort and 0.69 and 0.65 in the two test cohorts. In a multivariate Cox analysis, the aiN score was an independent predictor of patient survival with Hazard Ratios (HR) of 1.5 in the training cohort and of 1.3 and 2.2 in the two test cohorts. A combination of the aiN score and the pN status prognostically stratified patients by survival with p-values <0.05 in log-rank tests. Conclusion GC primary tumor tissue contains additional prognostic information that is accessible using the aiN score. In combination with the pN status, this can be used for personalized management of gastric cancer patients after prospective validation

Low levels of IgM antibodies recognizing oxidation-specific epitopes are associated with human non-alcoholic fatty liver disease

Background: Lipid oxidation of membrane phospholipids is accompanied by the formation of oxidation-specific epitopes (OSE). These epitopes are recognized by specific antibodies and represent danger-associated molecular patterns that are generated during chronic inflammatory processes. In a murine model for hepatic inflammation during non-alcoholic fatty liver disease (NAFLD), increased antibody levels targeting OSE were found to be protective. Here, our aim was to determine an association between OSE-specific antibody titers and NAFLD in humans. Methods: IgM and IgG levels with specificity for various OSE were assessed in the plasma of patients with NAFLD (n = 71) and healthy controls (n = 68). Antibody titers were comprehensively analyzed in patients with NAFLD after classification by histological evaluation of liver biopsies. Statistical analysis was performed to determine significant correlations and odds ratios. To study the specificity for NAFLD, plasma antibody titers were measured in patients with hepatitis C (n = 40) and inflammatory bowel disease (n = 62). Results: IgM titers against OSE were lower in patients with NAFLD compared to controls. Further biopsy-based classification of patients with NAFLD did not show any difference in IgM levels. Plasma IgM titers towards the P1 mimotope demonstrated an inverse correlation with markers for obesity, systemic inflammation, and liver damage. In contrast, hepatitis C and increased disease activity during inflammatory bowel disease was not associated with reduced IgM titers. Conclusions: Our data highlight the importance of immune recognition of OSE by IgM antibodies in the pathophysiology of NAFLD

Comprehensive Mutation Analysis in Colorectal Flat Adenomas

Background: Flat adenomas are a subgroup of colorectal adenomas that have been associated with a distinct biology and a more aggressive clinical behavior compared to their polypoid counterparts. In the present study, we aimed to compare the mutation spectrum of 14 cancer genes, between these two phenotypes. Methods: A consecutive series of 106 flat and 93 polypoid adenomas was analyzed retrospectively for frequently occurring mutations in “hot spot” regions of KRAS, BRAF, PIK3CA and NRAS, as well as selected mutations in CTNNB1 (β-catenin), EGFR, FBXW7 (CDC4), PTEN, STK11, MAP2K4, SMAD4, PIK3R1 and PDGFRA using a high-throughput genotyping technique. Additionally, APC was analyzed using direct sequencing. Results: APC mutations were more frequent in polypoid adenomas compared to flat adenomas (48.5% versus 30.3%, respectively, p = 0.02). Mutations in KRAS, BRAF, NRAS, FBXW7 and CTNNB1 showed similar frequencies in both phenotypes. Between the different subtypes of flat adenomas (0-IIa, LST-F and LST-G) no differences were observed for any of the investigated genes. Conclusion: The lower APC mutation rate in flat adenomas compared to polypoid adenomas suggests that disruption of the Wnt-pathway may occur via different mechanisms in these two phenotypes. Furthermore, in contrast to previous observations our results in this large well-defined sample set indicate that there is no significant association between the different morphological phenotypes and mutations in key genes of the RAS-RAF-MAPK pathway

Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology.

Artificial intelligence (AI) can extract visual information from histopathological slides and yield biological insight and clinical biomarkers. Whole slide images are cut into thousands of tiles and classification problems are often weakly-supervised: the ground truth is only known for the slide, not for every single tile. In classical weakly-supervised analysis pipelines, all tiles inherit the slide label while in multiple-instance learning (MIL), only bags of tiles inherit the label. However, it is still unclear how these widely used but markedly different approaches perform relative to each other. We implemented and systematically compared six methods in six clinically relevant end-to-end prediction tasks using data from N=2980 patients for training with rigorous external validation. We tested three classical weakly-supervised approaches with convolutional neural networks and vision transformers (ViT) and three MIL-based approaches with and without an additional attention module. Our results empirically demonstrate that histological tumor subtyping of renal cell carcinoma is an easy task in which all approaches achieve an area under the receiver operating curve (AUROC) of above 0.9. In contrast, we report significant performance differences for clinically relevant tasks of mutation prediction in colorectal, gastric, and bladder cancer. In these mutation prediction tasks, classical weakly-supervised workflows outperformed MIL-based weakly-supervised methods for mutation prediction, which is surprising given their simplicity. This shows that new end-to-end image analysis pipelines in computational pathology should be compared to classical weakly-supervised methods. Also, these findings motivate the development of new methods which combine the elegant assumptions of MIL with the empirically observed higher performance of classical weakly-supervised approaches. We make all source codes publicly available at https://github.com/KatherLab/HIA, allowing easy application of all methods to any similar task