Expression of HMB45, MelanA and SOX10 is rare in non-small cell lung cancer

Background: Non-small cell lung cancer (NSCLC) and melanoma are frequent entities in routine diagnostics. Whereas the differential diagnosis is usually straight forward based on histomorphology, it can be challenging in poorly differentiated tumors as melanoma may mimic various histological patterns. Distinction of the two entities is of outmost importance as both are treated differently. HMB45 and MelanA are recommended immunohistological markers for melanoma in this scenario. SOX10 has been described as an additional marker for melanoma. However, comprehensive large-scale data about the expression of melanoma markers in NSCLC tumor tissue specimen are lacking so far. Methods: Therefore, we analyzed the expression of these markers in 1085 NSCLC tumor tissue samples. Tissue microarrays of NSCLC cases were immunohistochemically stained for HMB45, MelanA, and SOX10. Positivity of a marker was defined as ≥1% positive tumor cells. Results: In 1027 NSCLC tumor tissue samples all melanoma as well as conventional immunohistochemical markers for NSCLC could be evaluated. HMB45, MelanA, and SOX10 were positive in 1 (< 1%), 0 (0%) and 5 (< 1%) cases. The HMB45 positive case showed co-expression of SOX10 and was classified as large cell carcinoma. Three out of five SOX10 positive cases were SqCC and one case was an adenosquamous carcinoma. Conclusions: Expression of HMB45, MelanA and SOX10 is evident but exceedingly rare in NSCLC cases. Together with conventional immunomarkers a respective marker panel allows a clear-cut differential diagnosis even in poorly differentiated tumors

WNT4 overexpression and secretion in thymic epithelial tumors drive an autocrine loop in tumor cells in vitro

BackgroundWNT4-driven non-canonical signaling is crucial for homeostasis and age-related involution of the thymus. Abnormal WNT signaling is important in many cancers, but the role of WNT signaling in thymic tumors is largely unknown.Materials & MethodsExpression and function of WNT4 and FZD6 were analyzed using qRT–PCR, Western blot, ELISA, in biopsies of non-neoplastic thymi (NT), thymoma and thymic carcinomas. ShRNA techniques and functional assays were used in primary thymic epithelial cells (pTECs) and TC cell line 1889c. Cells were conventionally (2D) grown and in three-dimensional (3D) spheroids.ResultsIn biopsy, WHO classified B3 thymomas and TCs showed increased WNT4 expression compared with NTs. During short-term 2D culture, WNT4 expression and secretion declined in neoplastic pTECs but not in 3D spheroids or medium supplemented with recombinant WNT4 cultures. Under the latter condition, the growth of pTECs was accompanied by increased expression of non-canonical targets RAC1 and JNK. Down-regulation of WNT4 by shRNA induced cell death in pTECs derived from B3 thymomas and led to decreased RAC1, but not JNK protein phosphorylation. Pharmacological inhibition of NF-κB decreased both RAC1 and JNK phosphorylation in neoplastic pTECs.ConclusionsLack of the age-related decline of non-canonical WNT4 expression in TETs and restoration of declining WNT4 expression through exogeneous WNT4 or 3D culture of pTECs hints at an oncogenic role of WNT4 in TETs and is compatible with the WNT4 autocrine loop model. Crosstalk between WNT4 and NF-κB signaling may present a promising target for combined interventions in TETs

Drug-microenvironment perturbations reveal resistance mechanisms and prognostic subgroups in CLL

The tumour microenvironment and genetic alterations collectively influence drug efficacy in cancer, but current evidence is limited and systematic analyses are lacking. Using chronic lymphocytic leukaemia (CLL) as a model disease, we investigated the influence of 17 microenvironmental stimuli on 12 drugs in 192 genetically characterised patient samples. Based on microenvironmental response, we identified four subgroups with distinct clinical outcomes beyond known prognostic markers. Response to multiple microenvironmental stimuli was amplified in trisomy 12 samples. Trisomy 12 was associated with a distinct epigenetic signature. Bromodomain inhibition reversed this epigenetic profile and could be used to target microenvironmental signalling in trisomy 12 CLL. We quantified the impact of microenvironmental stimuli on drug response and their dependence on genetic alterations, identifying interleukin 4 (IL4) and Toll-like receptor (TLR) stimulation as the strongest actuators of drug resistance. IL4 and TLR signalling activity was increased in CLL-infiltrated lymph nodes compared with healthy samples. High IL4 activity correlated with faster disease progression. The publicly available dataset can facilitate the investigation of cell-extrinsic mechanisms of drug resistance and disease progression

Pulmonary cancers across different histotypes share hybrid tuft cell/ionocyte-like molecular features and potentially druggable vulnerabilities

Tuft cells are chemosensory epithelial cells in the respiratory tract and several other organs. Recent studies revealed tuft cell-like gene expression signatures in some pulmonary adenocarcinomas, squamous cell carcinomas (SQCC), small cell carcinomas (SCLC), and large cell neuroendocrine carcinomas (LCNEC). Identification of their similarities could inform shared druggable vulnerabilities. Clinicopathological features of tuft cell-like (tcl) subsets in various lung cancer histotypes were studied in two independent tumor cohorts using immunohistochemistry (n = 674 and 70). Findings were confirmed, and additional characteristics were explored using public datasets (RNA seq and immunohistochemical data) (n = 555). Drug susceptibilities of tuft cell-like SCLC cell lines were also investigated. By immunohistochemistry, 10–20% of SCLC and LCNEC, and approximately 2% of SQCC expressed POU2F3, the master regulator of tuft cells. These tuft cell-like tumors exhibited “lineage ambiguity” as they co-expressed NCAM1, a marker for neuroendocrine differentiation, and KRT5, a marker for squamous differentiation. In addition, tuft cell-like tumors co-expressed BCL2 and KIT, and tuft cell-like SCLC and LCNEC, but not SQCC, also highly expressed MYC. Data from public datasets confirmed these features and revealed that tuft cell-like SCLC and LCNEC co-clustered on hierarchical clustering. Furthermore, only tuft cell-like subsets among pulmonary cancers significantly expressed FOXI1, the master regulator of ionocytes, suggesting their bidirectional but immature differentiation status. Clinically, tuft cell-like SCLC and LCNEC had a similar prognosis. Experimentally, tuft cell-like SCLC cell lines were susceptible to PARP and BCL2 co-inhibition, indicating synergistic effects. Taken together, pulmonary tuft cell-like cancers maintain histotype-related clinicopathologic characteristics despite overlapping unique molecular features. From a therapeutic perspective, identification of tuft cell-like LCNECs might be crucial given their close kinship with tuft cell-like SCLC

Programmed cell death ligand 1 (PD-L1, CD274) in cholangiocarcinoma – correlation with clinicopathological data and comparison of antibodies

Background: Cholangiocarcinoma (CCA) may arise in the intra- or extrahepatic biliary tract and is associated with a poor prognosis. Despite recent advances, to date there is still no established targeted therapeutic approach available. Non-surgical therapeutic agents are urgently needed, as most patients are non-eligible to surgical resection. Anti-PD-L1 therapy prevents cancer cells from evading the immune system and has emerged as a new treatment option in several cancer entities. Recently, PD-L1 expression has been analyzed in comparably small CCA patient cohorts. However, a systematic validation of different PD-L1 antibodies has not been performed in CCA so far. Methods: We stained a tissue microarray consisting of 170 patients, including 72 intrahepatic cholangiocarcinomas (iCCAs), 57 perihilar cholangiocarcinomas (pCCAs) and 41 distal cholangiocarcinomas (dCCAs) by immunohistochemistry and evaluated PD-L1 positivity in tumor and stromal cells. We analyzed three different PD-L1 antibodies (clones 28–8, SP142, and SP263) that are frequently used and recommended for predictive diagnostic testing in other cancer types. Results: For PD-L1 antibody clone SP263, 5% of iCCAs, 4% of pCCAs and 3% of dCCAs exhibited PD-L1 expression on tumor cells, thereby showing the highest frequencies of PD-L1 positivity. Accordingly, highest PD-L1 positivity rates of stromal cells with 31% in iCCA, 40% in pCCA and 61% in dCCA were detected for clone SP263. Agreement of PD-L1 positivity in tumor cells was moderate for clone 28–8 and SP263 (κ = 0.44) and poor between 28-8 and SP142 (κ = 0.13), as well as  SP142 and SP263 (κ = 0.11), respectively. Statistical analyses of PD-L1 expression (clone SP263) on tumor cells with clinicopathological data revealed a positive correlation with shortened overall survival in CCA patients. Conclusions: Selection of appropriate PD-L1 antibodies and careful evaluation of immunohistochemical staining patterns have a significant impact on PD-L1 testing in CCA. Clinical trials are necessary to investigate the putative beneficial effects of PD-L1 targeted immunotherapy in CCA patients

Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition)

The third edition of Flow Cytometry Guidelines provides the key aspects to consider when performing flow cytometry experiments and includes comprehensive sections describing phenotypes and functional assays of all major human and murine immune cell subsets. Notably, the Guidelines contain helpful tables highlighting phenotypes and key differences between human and murine cells. Another useful feature of this edition is the flow cytometry analysis of clinical samples with examples of flow cytometry applications in the context of autoimmune diseases, cancers as well as acute and chronic infectious diseases. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid. All sections are written and peer‐reviewed by leading flow cytometry experts and immunologists, making this edition an essential and state‐of‐the‐art handbook for basic and clinical researchers.DFG, 389687267, Kompartimentalisierung, Aufrechterhaltung und Reaktivierung humaner Gedächtnis-T-Lymphozyten aus Knochenmark und peripherem BlutDFG, 80750187, SFB 841: Leberentzündungen: Infektion, Immunregulation und KonsequenzenEC/H2020/800924/EU/International Cancer Research Fellowships - 2/iCARE-2DFG, 252623821, Die Rolle von follikulären T-Helferzellen in T-Helferzell-Differenzierung, Funktion und PlastizitätDFG, 390873048, EXC 2151: ImmunoSensation2 - the immune sensory syste

Identification of Gastritis Subtypes by Convolutional Neuronal Networks on Histological Images of Antrum and Corpus Biopsies

Background: Gastritis is a prevalent disease and commonly classified into autoimmune (A), bacterial (B), and chemical (C) type gastritis. While the former two subtypes are associated with an increased risk of developing gastric intestinal adenocarcinoma, the latter subtype is not. In this study, we evaluated the capability to classify common gastritis subtypes using convolutional neuronal networks on a small dataset of antrum and corpus biopsies. Methods: 1230 representative 500 × 500 µm images of 135 patients with type A, type B, and type C gastritis were extracted from scanned histological slides. Patients were allocated randomly into a training set (60%), a validation set (20%), and a test set (20%). One classifier for antrum and one classifier for corpus were trained and optimized. After optimization, the test set was analyzed using a joint result from both classifiers. Results: Overall accuracy in the test set was 84% and was particularly high for type B gastritis with a sensitivity of 100% and a specificity of 93%. Conclusions: Classification of gastritis subtypes is possible using convolutional neural networks on a small dataset of histopathological images of antrum and corpus biopsies. Deep learning strategies to support routine diagnostic pathology merit further evaluation

Unsupervised Segmentation in NSCLC: How to Map the Output of Unsupervised Segmentation to Meaningful Histological Labels by Linear Combination?

Background: Segmentation is, in many Pathomics projects, an initial step. Usually, in supervised settings, well-annotated and large datasets are required. Regarding the rarity of such datasets, unsupervised learning concepts appear to be a potential solution. Against this background, we tested for a small dataset on lung cancer tissue microarrays (TMA) if a model (i) first can be in a previously published unsupervised setting and (ii) secondly can be modified and retrained to produce meaningful labels, and (iii) we finally compared this approach to standard segmentation models. Methods: (ad i) First, a convolutional neuronal network (CNN) segmentation model is trained in an unsupervised fashion, as recently described by Kanezaki et al. (ad ii) Second, the model is modified by adding a remapping block and is retrained on an annotated dataset in a supervised setting. (ad iii) Third, the segmentation results are compared to standard segmentation models trained on the same dataset. Results: (ad i–ii) By adding an additional mapping-block layer and by retraining, models previously trained in an unsupervised manner can produce meaningful labels. (ad iii) The segmentation quality is inferior to standard segmentation models trained on the same dataset. Conclusions: Unsupervised training in combination with subsequent supervised training offers for histological images here no benefit

Detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) including Variant Analysis by Mass Spectrometry in Placental Tissue

Among neonates, tested positive for SARS-CoV-2, the majority of infections occur through postpartum transmission. Only few reports describe intrauterine or intrapartum SARS-CoV-2 infections in newborns. To understand the route of transmission, detection of the virus or virus nucleic acid in the placenta and amniotic tissue are of special interest. Current methods to detect SARS-CoV-2 in placental tissue are immunohistochemistry, electron microscopy, in-situ hybridization, polymerase chain reaction (PCR) and next-generation sequencing. Recently, we described an alternative method for the detection of viral ribonucleic acid (RNA), by combination of reverse transcriptase-PCR and mass spectrometry (MS) in oropharyngeal and oral swabs. In this report, we could detect SARS-CoV-2 in formal-fixed and paraffin-embedded (FFPE) placental and amniotic tissue by multiplex RT-PCR MS. Additionally, we could identify the British variant (B.1.1.7) of the virus in this tissue by the same methodology. Combination of RT-PCR with MS is a fast and easy method to detect SARS-CoV-2 viral RNA, including specific variants in FFPE tissue