Thioredoxin 1 (Trx1) is associated with poor prognosis in clear cell renal cell carcinoma (ccRCC): an example for the crucial role of redox signaling in ccRCC

Purpose Thioredoxins are major regulatory proteins of oxidative signaling. Trx1 is the most prominent thioredoxin and, therefore, the current study sought to evaluate the prognostic role of Trx1 in ccRCC. Methods and patients A tissue micro-array (TMA) study was carried out to evaluate the association of Trx1 with clinicopathological features and survival outcome. Data from the Cancer Genome Atlas (TCGA) were evaluated for the association of characteristics in the Trx1 gene with clinicopathological features and survival outcome. Results In the TMA, patients with ccRCC that had high Trx1 levels had lower T stages (p < 0.001), less often distant metastases (p = 0.018), lower nuclear grades (p < 0.001), and less often tumor necrosis (p = 0.037) or sarcomatoid features (p = 0.008). Patients with a combined score of ≥ 10 had better DSS than patients with a low combined score of < 10 (HR 95% CI 0.62 (0.39–0.98)). Interestingly, the survival outcome is compartment specific: ccRCC patients whose tumors had exclusively Trx1 expression in the cytoplasm had the worst survival outcome (HR 3.1; 95% CI 1.2–8.0). Genomic data from the TCGA demonstrated that patients with ccRCCs that had Trx1 losses had more advanced clinicopathological features and worse survival outcome in disease specific (p < 0.001), overall (p = 0.001), and progression free survival (p = 0.001) when compared to patients with ccRCCs without copy number variations (CNV) or gains. Conclusion The current study suggests a possible role of Trx1 in the tumor biology of ccRCC and thus, the current study strongly advises in depth investigations of redox signaling pathways in ccRCC.

Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images

Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry labelling to confirm the diagnosis, which delays molecular analysis and utilises precious sample. Therefore, we tested the capacity of convolutional neural networks (CNNs) to classify NSCLC based on pathologic HES diagnostic biopsies. The model was estimated with a learning cohort of 132 NSCLC patients and validated on an external validation cohort of 65 NSCLC patients. Based on image patches, a CNN using InceptionV3 architecture was trained and optimized to classify NSCLC between squamous and non-squamous subtypes. Accuracies of 0.99, 0.87, 0.85, 0.85 was reached in the training, validation and test sets and in the external validation cohort. At the patient level, the CNN model showed a capacity to predict the tumour histology with accuracy of 0.73 and 0.78 in the learning and external validation cohorts respectively. Selecting tumour area using virtual tissue micro-array improved prediction, with accuracy of 0.82 in the external validation cohort. This study underlines the capacity of CNN to predict NSCLC subtype with good accuracy and to be applied to small pathologic samples without annotation.

Co-Expression of Nogo-A in Dopaminergic Neurons of the Human Substantia Nigra Pars Compacta Is Reduced in Parkinson’s Disease

Parkinson’s disease is mainly characterized by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta. Together with the small number, the high vulnerability of the dopaminergic neurons is a major pathogenic culprit of Parkinson’s disease. Our previous findings of a higher survival of dopaminergic neurons in the substantia nigra co-expressing Nogo-A in an animal model of Parkinson’s disease suggested that Nogo-A may be associated with dopaminergic neurons resilience against Parkinson’s disease neurodegeneration. In the present study, we have addressed the expression of Nogo-A in the dopaminergic neurons in the substantia nigra in postmortem specimens of diseased and non-diseased subjects of different ages. For this purpose, in a collaborative effort we developed a tissue micro array (TMA) that allows for simultaneous staining of many samples in a single run. Interestingly, and in contrast to the observations gathered during normal aging and in the animal model of Parkinson’s disease, increasing age was significantly associated with a lower co-expression of Nogo-A in nigral dopaminergic neurons of patients with Parkinson’s disease. In sum, while Nogo-A expression in dopaminergic neurons is higher with increasing age, the opposite is the case in Parkinson’s disease. These observations suggest that Nogo-A might play a substantial role in the vulnerability of dopaminergic neurons in Parkinson’s disease.

New Artificial Intelligence Score and Immune Infiltrates as Prognostic Factors in Colorectal Cancer With Brain Metastases

Incidence of brain metastases has increased in patients with colorectal cancer (CRC) as their survival has improved. CD3 T-cells and, lately, DGMate (DiGital tuMor pArameTErs) score, have been identified as prognostic factors in locally advanced CRC. Until now, there is no data concerning the prognostic value of these markers in patients with CRC-derived brain metastases. All consecutive patients with CRC-derived brain metastases diagnosed between 2000 and 2017 were retrospectively included. Staining for CD3, CD8, PD-1, PD-L1 and DGMate analyses were performed using tissue micro-array from primary tumors and, if available, brain metastases. All in all, 83 patients were included with 80 primary tumor samples and 37 brain metastases samples available. CD3 and CD8 T-cell infiltration was higher in primary tumors compared to brain metastases. We observed a significant higher DGMate score in rectal tumors compared to colon tumors (p=0.03). We also noted a trend of higher CD3 T-cell infiltration in primary tumors when brain metastases were both supra and subtentorial compared to brain metastases that were only subtentorial or supratentorial (p=0.36 and p=0.03, respectively). No correlation was found between CD3 or CD8 infiltration or DGMate score in primary tumors or brain metastases and overall survival (OS) in the overall population. In patients with rectal tumors, a high DGMate score in brain metastases was associated with longer OS (13.4 ± 6.1 months versus 6.1 ± 1.4 months, p=0.02). High CD3 T-cell infiltration in brain metastases was associated with lower OS in patients with supratentorial brain metastases (9.8 ± 3.3 months versus 16.7 ± 5.9 months, p=0.03). PD-L1 overexpression was rare, both in primary tumors and brain metastases, but PD-L1 positive primary tumors were associated with worse OS (p=0.01). In contrast to breast and lung cancer derived brain metastases, CD3 and CD8 infiltration and DGMate score are not major prognostic factors in patients with CRC-derived brain metastases.

Connections between prolactin and ovarian cancer

Ovarian cancer (OC) is characterized by a high morbidity and mortality, highlighting a great need for a better understanding of biological mechanisms that affect OC progression and improving its early detection methods. This study investigates effects of prolactin (PRL) on ovarian cancer cells, analyzes PRL receptors (PRLR) in tissue micro arrays and relates PRLR expression to survival of ovarian cancer. A database, composed of transcript profiles from OC, was searched for PRLR expression and results were put in relation to survival. Expression of PRLR in OC tissue sections and OC cell lines SKOV3, OV2008 and OVSAHO was assessed using immunohistochemistry, western blots and quantitative real-time PCR. The biological function of PRLR was evaluated by proliferation, colony formation and wound healing assays. Levels of PRLR mRNA are related to survival; in epithelial OC a high PRLR mRNA expression is related to a shorter survival. Analysis of a tissue micro array consisting of 84 OC showed that 72% were positive for PRLR immuno-staining. PRLR staining tended to be higher in OC of high grade tumors compared to lower grades. PRLR mRNA and protein can further be detected in OC cell lines. Moreover, in vitro treatment with PRL significantly activated the JAK/STAT pathway. PRLR expression is associated with OC survivals. PRL and its receptor may play an onco-modulatory role and promote tumor aggressiveness in OC. Alternatively, increased PRLR levels may form a base for the development of PRLR antagonist or PRLR antagonist-drug conjugate to increase selective uptake of anti-cancer drugs.

Sulfation of O-glycans on mucin-type proteins from serous ovarian epithelial tumors

Despite that sulfated O-linked glycans are abundant on ovarian cancer (OC) glycoproteins, their regulation during cancer development and involvement in cancer pathogenesis remain unexplored. We characterized O-glycans carrying sulfation on galactose residues and compared their expression to defined sulfotransferases regulated during OC development. Desialylated sulfated oligosaccharides were released from acidic glycoproteins in the cyst fluid from one patient with a benign serous cyst and one patient with serous OC. Oligosaccharides characterized by LC-MSn were identified as core 1 and core 2 O-glycans up to the size of decamers, and with 1-4 sulfates linked to GlcNAc residues and to C-3 and/or C-6 of Gal. To study the specificity of the potential ovarian sulfotransferases involved, Gal3ST2 (Gal-3S)-, Gal3ST4 (Gal-3S)-, and CHST1 (Gal-6S)-encoding expression plasmids were transfected individually into CHO cells also expressing the P-selectin glycoprotein ligand-1/mouse immunoglobulin G2b (PSGL-1/mIg G2b) fusion protein and the human core 2 transferase (GCNT1). Characterization of the PSGL-1/mIg G2b O-glycans showed that Gal3ST2 preferentially sulfated Gal on the C-6 branch of core 2 structures and Gal3ST4 preferred Gal on the C-3 branch independently if core-1 or-2. CHST1 sulfated Gal residues on both the C-3 (core 1/2) and C-6 branches of core 2 structures. Using serous ovarian tissue micro array, Gal3ST2 was found to be decreased in tissue classified as malignant compared to tissues classified as benign or borderline, with the lowest expression in poorly differentiated malignant tissue. Neither Gal3ST4 nor CHST1 were differentially expressed in benign, borderline or malignant tissue, and there was no correlation between expression level and differentiation stage. The data displays a complex sulfation pattern of O-glycans on OC glycoproteins and that aggressiveness of the cancer is associated with a decreased expression of the Gal3ST2 transferase.

Using a Convolutional Neural Network for Classification of Squamous and Non-Squamous Non-Small Cell Lung Cancer Based on Diagnostic Histopathology HES Images

Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry (IHC) labelling to confirm the diagnosis, which delays molecular analysis and utilises precious sample. Therefore, we tested the capacity of convolutional neural networks (CNNs) to classify NSCLC based on pathologic HES diagnostic biopsies. The model was estimated with a learning cohort of 132 NSCLC patients and validated on an external validation cohort of 65 NSCLC patients. Based on image patches, a CNN using InceptionV3 architecture was trained and optimized to classify NSCLC between squamous and non-squamous subtypes. Accuracies of 0.99, 0.85, 0.87, 0.85 was reached in the training, validation and test sets and in the external validation cohort. At the patient level, the CNN model showed a capacity to predict the tumour histology with accuracy of 0.73 and 0.78 in the learning and external validation cohorts respectively. Selecting tumour area using virtual Tissue Micro-Array (TMA) improved prediction, with accuracy of 0.79 in both learning and external validation cohorts. This study underlines the capacity of CNN to predict NSCLC subtype with good accuracy and to be applied to small pathologic samples without annotation.

Evaluation of SSTR2 Expression in SI-NETs and Relation to Overall Survival after PRRT

(1) Purpose: Small intestinal neuroendocrine tumors (SI-NETs) often present with distant metastases at diagnosis. Peptide receptor radionuclide therapy (PRRT) with radiolabeled somatostatin analogues is a systemic treatment that increases overall survival (OS) in SI-NET patients with stage IV disease. However, the treatment response after PRRT, which targets somatostatin receptor 2 (SSTR2), is variable and predictive factors have not been established. This exploratory study aims to evaluate if SSTR2 expression in SI-NETs could be used to predict OS after PRRT treatment. (2) Methods: Using a previously constructed Tissue Micro Array (TMA) we identified tissue samples from 42 patients that had received PRRT treatment during 2006–2017 at Sahlgrenska University hospital. Immunohistochemical expression of SSTR2, Ki-67 and neuroendocrine markers synaptophysin and Chromogranin A (CgA) were assessed. A retrospective estimation of 177Lu-DOTATATE uptake in 33 patients was performed. Data regarding OS and non-surgical treatment after PRRT were collected. Another subgroup of 34 patients with paired samples from 3 tumor sites (primary tumor, lymph node and liver metastases) was identified in the TMA. The SSTR2 expression was assessed in corresponding tissue samples (n = 102). (3) Results: The patients were grouped into Low SSTR2 or High SSTR2 groups based upon on levels of SSTR2 expression. There was no significant difference in 177Lu-DOTATATE uptake between the groups. The patients in the Low SSTR2 group had significantly longer OS after PRRT than the patients in the High SSTR2 group (p = 0.049). PRRT treated patients with low SSTR2 expression received less additional treatment compared with patients with high SSTR2 expression. SSTR2 expression did not vary between tumor sites but correlated within patients. (4) Conclusion: The results from the present study suggest that retrospective evaluation of SSTR2 expression in resected tumors cannot be used to predict OS after PRRT.

745 High throughput tissue imaging and phenotyping by streamlined 8-plex staining and analysis of complex TMA samples

ConclusionsIn this study, we highlighted the benefits of using a combination of well-characterized TMAs, a fast, optimized 8-plex mIHC protocol, and a detailed analysis pipeline to characterize the immune-response in a broad range of cancer types and samples, leading to a better understanding of the TME as well as a streamlined workflow for further translational studies.ResultsImmune cell counts and phenotypes were identified using automated analysis for cores within the tumor and within the tumor margin using a panel characterizing a range of immune cell populations, and compared across each tissue type. Deep phenotyping was performed for each core to identify unique profiles for each tissue type, with a workflow optimized for high-throughput analysis of rich-content TMAs.MethodsEach slide comprised 144 cores (1 mm) and included duplicate cores for each case (1 from invasive margin; 1 from tumor center) from 11 different tumor types including breast cancer (ER+, Her2+, TNBC), NSCLC (squamous, adenocarcinoma), SCLC, CRC, pancreatic, gastric, hepatic and esophageal cancers. TMA sections were stained using the UltiMapper I/O Immuno8 panel, which includes markers for CD3, CD4, CD8, FOXP3, CD68, PD-1, PD-L1, and a pan-CK/SOX10 cocktail as a tumor indicator. The stained TMAs were scanned at 20X magnification on a fluorescence whole slide scanner. To provide accurate marker colocalization data, marker images were aligned using the UltiStacker software, using the nuclear counterstain images as references from multiple rounds of imaging. Image analysis was performed using Visiopharm software and generated total and negative cell phenotype counts, cell density in tumor and stroma, as well as spatial interactions maps in each of the 288 cores in the TMA set.BackgroundMultiplex immunohistochemistry (mIHC) and associated data analysis methods are rapidly becoming invaluable tools to improve our understanding of the complex tumor micro-environment (TME) and accelerate the discovery of novel immunotherapy targets. These techniques can enable the accurate phenotyping of the immune response and checkpoint expression in the spatial context of the tumor. The goal of this study was to identify the populations of immune cells (T-cytotoxic, T-helper, T-reg, and macrophages), their functional status, as well as their interactions with the tumor, in a range of samples and indications using a carefully designed multi-tumor Tissue Micro-Array (TMA) set of 2 slides from TriStar.