Qualitative Comparison Between Carrier-based and Classical Tissue Microarrays

Impact of T Cell Ratios on Survival in Pleural Mesothelioma: Insights from Tumor Microenvironment Analysis

Background: Immunotherapy has significantly improved overall survival in patients with pleural mesothelioma, yet this benefit does not extend to those with the epithelioid subtype. Tumor growth is believed to be influenced by the immune response. This study aimed to analyze the tumor microenvironment to gain a better understanding of its influence on tumor growth. Methods: The tumor immune cell infiltration of 188 patients with pleural mesothelioma was characterized by multiplex immunofluorescence staining for CD3+ cells (CD3+), CD4+ cells (CD3+/CD4+), CD8+ cells (CD3+/CD8+), Treg (CD3+/CD4+/CD8-/CD163-/Foxp3+), PD1 cells (PD1+), and T helper cells (CD3+/CD4+/CD8-/CD163-/FoxP3-). The distribution of specific immune cells was correlated with clinical parameters. Results: A total of 188 patients with pleural mesothelioma (135 epithelioid, 9 sarcomatoid, 44 biphasic subtypes) were analyzed. The median age was 64.8 years. Overall survival was significantly longer in the epithelioid subtype than in the non-epithelioid subtype (p = 0.016). The presence of PD-L1 expression had a negative effect on overall survival (p = 0.041). A high ratio of CD4+ cells to regulatory T cells was associated with a significantly longer overall survival of more than 12 months (p = 0.015). The ratio of CD4+ cells to regulatory T cells retained its significant effect on overall survival in the multivariate analysis. Conclusions: Distinct differences in the T cell immune infiltrates in mesothelioma are strongly associated with overall survival. The tumor microenvironment could therefore serve as a source of prognostic biomarkers.

Prognostic Impact of the Immune-Cell Infiltrate in N1-Positive Non-Small-Cell Lung Cancer

INTRODUCTION

The tumoral immune milieu plays a crucial role for the development of non-small-cell lung cancer (NSCLC) and may influence individual prognosis. We analyzed the predictive role of immune cell infiltrates after curative lung cancer surgery.

MATERIALS AND METHODS

The tumoral immune-cell infiltrate from 174 patients with pN1 NSCLC and adjuvant chemotherapy was characterized using immunofluorescence staining. The density and distribution of specific immune cells in tumor center (TU), invasive front (IF) and normal tissue (NORM) were correlated with clinical parameters and survival data.

RESULTS

Tumor specific survival (TSS) of all patients was 69.9% at 5 years. The density of tumor infiltrating lymphocytes (TIL) was higher in TU and IF than in NORM. High TIL density in TU (low vs. high: 62.0% vs. 86.7%; p = .011) and the presence of cytotoxic T-Lymphocytes (CTLs) in TU and IF were associated with improved TSS (positive vs. negative: 90.6% vs. 64.7% p = .024). High TIL-density correlated with programmed death-ligand 1 expression levels ≥50% (p < .001). Multivariate analysis identified accumulation of TIL (p = .016) and low Treg density (p = .003) in TU as negative prognostic predictors in squamous cell carcinoma (p = .025), whereas M1-like tumor- associated macrophages (p = .019) and high programmed death-ligand 1 status (p = .038) were associated with better survival in adenocarcinoma.

CONCLUSION

The assessment of specific intratumoral immune cells may serve as a prognostic predictor in pN1 NSCLC. However differences were observed related to adenocarcinoma or squamous cell carcinoma histology. Prospective assessment of the immune-cell infiltrate and further clarification of its prognostic relevance could assist patient selection for upcoming perioperative immunotherapies.

Canonical NF-κB Promotes Lung Epithelial Cell Tumour Growth by Downregulating the Metastasis Suppressor CD82 and Enhancing Epithelial-to-Mesenchymal Cell Transition

Simple Summary

Canonical NF-κB signalling pathway acts as a tumour promoter in several types of cancer including non-small cell lung cancer (NSCLC), but the mechanism(s) by which it contributes to NSCLC is still under investigation. We show here that NF-κB RelA/p65 is required for the tumour growth of human NSCLC cells grown in vivo as xenografts in immune-compromised mice. RNA-seq transcriptome profile analysis identified the metastasis suppressor CD82/KAI1/TSPAN27 as a canonical NF-κB target. Loss of CD82 correlated with malignancy. RelA/p65 stimulates cell migration and epithelial-to-mesenchymal cell transition (EMT), mediated, in part, by CD82/KAI1, through integrin-mediated signalling, thus, identifying a mechanism mediating NF-κB RelA/p65 lung tumour promoting function.

Abstract

Background: The development of non-small cell lung cancer (NSCLC) involves the progressive accumulation of genetic and epigenetic changes. These include somatic oncogenic KRAS and EGFR mutations and inactivating TP53 tumour suppressor mutations, leading to activation of canonical NF-κB. However, the mechanism(s) by which canonical NF-κB contributes to NSCLC is still under investigation. Methods: Human NSCLC cells were used to knock-down RelA/p65 (RelA/p65^KD) and investigate its impact on cell growth, and its mechanism of action by employing RNA-seq analysis, qPCR, immunoblotting, immunohistochemistry, immunofluorescence and functional assays. Results: RelA/p65^KD reduced the proliferation and tumour growth of human NSCLC cells grown in vivo as xenografts in immune-compromised mice. RNA-seq analysis identified canonical NF-κB targets mediating its tumour promoting function. RelA/p65^KD resulted in the upregulation of the metastasis suppressor CD82/KAI1/TSPAN27 and downregulation of the proto-oncogene ROS1, and LGR6 involved in Wnt/β-catenin signalling. Immunohistochemical and bioinformatics analysis of human NSCLC samples showed that CD82 loss correlated with malignancy. RelA/p65^KD suppressed cell migration and epithelial-to-mesenchymal cell transition (EMT), mediated, in part, by CD82/KAI1, through integrin-mediated signalling involving the mitogenic ERK, Akt1 and Rac1 proteins. Conclusions: Canonical NF-κB signalling promotes NSCLC, in part, by downregulating the metastasis suppressor CD82/KAI1 which inhibits cell migration, EMT and tumour growth.

Deep Learning in Pancreatic Tissue: Identification of Anatomical Structures, Pancreatic Intraepithelial Neoplasia, and Ductal Adenocarcinoma

Identification of pancreatic ductal adenocarcinoma (PDAC) and precursor lesions in histological tissue slides can be challenging and elaborate, especially due to tumor heterogeneity. Thus, supportive tools for the identification of anatomical and pathological tissue structures are desired. Deep learning methods recently emerged, which classify histological structures into image categories with high accuracy. However, to date, only a limited number of classes and patients have been included in histopathological studies. In this study, scanned histopathological tissue slides from tissue microarrays of PDAC patients (n = 201, image patches n = 81.165) were extracted and assigned to a training, validation, and test set. With these patches, we implemented a convolutional neuronal network, established quality control measures and a method to interpret the model, and implemented a workflow for whole tissue slides. An optimized EfficientNet algorithm achieved high accuracies that allowed automatically localizing and quantifying tissue categories including pancreatic intraepithelial neoplasia and PDAC in whole tissue slides. SmoothGrad heatmaps allowed explaining image classification results. This is the first study that utilizes deep learning for automatic identification of different anatomical tissue structures and diseases on histopathological images of pancreatic tissue specimens. The proposed approach is a valuable tool to support routine diagnostic review and pancreatic cancer research.

Prognostic Impact of PD-L1 Expression in pN1 NSCLC: A Retrospective Single-Center Analysis

Simple Summary

The analysis of prognostic biomarkers (e.g., PD-L1) helps to define treatment for lung cancer patients. To date, these markers have only been examined in metastatic or inoperable situations. We analyzed the PD-L1 expression-levels of tumors from 277 lung cancer patients that underwent curative intent surgery. PD-L1 was identified as a prognostic factor, depending on histologic subtype.

Abstract

The programmed death-ligand 1 (PD-L1) plays a crucial role in immunomodulatory treatment concepts for end-stage non-small cell lung cancer (NSCLC). To date, its prognostic significance in patients with curative surgical treatment but regional nodal metastases, reflecting tumor spread beyond the primary site, is unclear. We evaluated the prognostic impact of PD-L1 expression in a surgical cohort of 277 consecutive patients with pN1 NSCLC on a tissue microarray. Patients with PD-L1 staining (clone SP263) on >1% of tumor cells were defined as PD-L1 positive. Tumor-specific survival (TSS) of the entire cohort was 64% at five years. Low tumor stage (p < 0.0001) and adjuvant therapy (p = 0.036) were identified as independent positive prognostic factors in multivariate analysis for TSS. PD-L1 negative patients had a significantly better survival following adjuvant chemotherapy than PD-L1 positive patients. The benefit of adjuvant therapy diminished in patients with PD-L1 expression in more than 10% of tumor cells. Stratification towards histologic subtype identified PD-L1 as a significant positive predictive factor for TSS after adjuvant therapy in patients with adenocarcinoma, but not squamous cell carcinoma. Routine PD-L1 assessment in curative intent treatment may help to identify patients with a better prognosis. Further research is needed to elucidate the predictive value of PD-L1 in an adjuvant setting.

Immunohistological expression of oestrogen receptor, progesterone receptor, mammaglobin, human epidermal growth factor receptor 2 and GATA-binding protein 3 in non-small-cell lung cancer

AIMS

Non-small-cell lung cancer (NSCLC) and breast cancer are common entities. Staining for oestrogen receptor (ER), progesterone receptor (PgR), mammaglobin (MAMG) and GATA-binding protein 3 (GATA3) is frequently performed to confirm a mammary origin in the appropriate diagnostic setting. However, comprehensive data on the immunohistological expression of these markers in NSCLC are limited. Therefore, the aim of this study was to analyse a large cohort of NSCLCs and correlate the staining results with clinicopathological variables.

METHODS AND RESULTS

A tissue microarray was stained for ER, PgR, MAMG, human epidermal growth factor receptor 2 (HER2), and GATA3, and included 636 adenocarcinomas (ADCs), 536 squamous cell carcinomas (SqCCs), 65 large-cell-carcinomas, 34 pleomorphic carcinomas, and 20 large-cell neuroendocrine carcinomas. HER2 status was determined for immunohistochemically positive cases with chromogenic in-situ hybridisation. Markers with a proportion of ≥5% positive cases in ADC and SqCC were considered for survival analysis. Among ADCs, 62 (10%), 17 (3%), one (<1%), seven (1%), and 49 (8%) cases were positive for ER, PgR, MAMG, HER2, and GATA3, respectively. Among SqCCs, 10 (2%), 14 (3%), two (<1%) and 109 (20%) cases were positive for ER, PgR, HER2, and GATA3, but none of the samples showed positivity for MAMG. ER positivity was associated with ADC, female sex, smaller tumour size, and lower clinical stage. None of the markers had an impact on survival.

CONCLUSION

We report on ER, PgR, MAMG, HER2 and GATA3 expression in a large cohort of NSCLCs. Interpretation of these markers in the differential diagnostic setting should be based on a multimarker panel.

Mass Spectrometry Imaging for Reliable and Fast Classification of Non-Small Cell Lung Cancer Subtypes

Simple Summary

Diagnostic subtyping of non-small cell lung cancer is paramount for therapy stratification. Our study shows that the subtyping into pulmonary adenocarcinoma and pulmonary squamous cell carcinoma by mass spectrometry imaging is rapid and accurate using limited tissue material.

Abstract

Subtyping of non-small cell lung cancer (NSCLC) is paramount for therapy stratification. In this study, we analyzed the largest NSCLC cohort by mass spectrometry imaging (MSI) to date. We sought to test different classification algorithms and to validate results obtained in smaller patient cohorts. Tissue microarrays (TMAs) from including adenocarcinoma (ADC, n = 499) and squamous cell carcinoma (SqCC, n = 440), were analyzed. Linear discriminant analysis, support vector machine, and random forest (RF) were applied using samples randomly assigned for training (66%) and validation (33%). The m/z species most relevant for the classification were identified by on-tissue tandem mass spectrometry and validated by immunohistochemistry (IHC). Measurements from multiple TMAs were comparable using standardized protocols. RF yielded the best classification results. The classification accuracy decreased after including less than six of the most relevant m/z species. The sensitivity and specificity of MSI in the validation cohort were 92.9% and 89.3%, comparable to IHC. The most important protein for the discrimination of both tumors was cytokeratin 5. We investigated the largest NSCLC cohort by MSI to date and found that the classification of NSCLC into ADC and SqCC is possible with high accuracy using a limited set of m/z species.

Mass Spectrometry Imaging Differentiates Chromophobe Renal Cell Carcinoma and Renal Oncocytoma with High Accuracy

Background: While subtyping of the majority of malignant chromophobe renal cell carcinoma (cRCC) and benign renal oncocytoma (rO) is possible on morphology alone, additional histochemical, immunohistochemical or molecular investigations are required in a subset of cases. As currently used histochemical and immunohistological stains as well as genetic aberrations show considerable overlap in both tumors, additional techniques are required for differential diagnostics. Mass spectrometry imaging (MSI) combining the detection of multiple peptides with information about their localization in tissue may be a suitable technology to overcome this diagnostic challenge.

Patients and Methods: Formalin-fixed paraffin embedded (FFPE) tissue specimens from cRCC (n=71) and rO (n=64) were analyzed by MSI. Data were classified by linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) algorithm with internal cross validation and visualized by t-distributed stochastic neighbor embedding (t-SNE). Most important variables for classification were identified and the classification algorithm was optimized.

Results: Applying different machine learning algorithms on all m/z peaks, classification accuracy between cRCC and rO was 85%, 82%, 84%, 77% and 64% for RF, SVM, KNN, CART and LDA. Under the assumption that a reduction of m/z peaks would lead to improved classification accuracy, m/z peaks were ranked based on their variable importance. Reduction to six most important m/z peaks resulted in improved accuracy of 89%, 85%, 85% and 85% for RF, SVM, KNN, and LDA and remained at the level of 77% for CART. t-SNE showed clear separation of cRCC and rO after algorithm improvement.

Conclusion: In summary, we acquired MSI data on FFPE tissue specimens of cRCC and rO, performed classification and detected most relevant biomarkers for the differential diagnosis of both diseases. MSI data might be a useful adjunct method in the differential diagnosis of cRCC and rO.

Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer

Reliable entity subtyping is paramount for therapy stratification in lung cancer. Morphological evaluation remains the basis for entity subtyping and directs the application of additional methods such as immunohistochemistry (IHC). The decision of whether to perform IHC for subtyping is subjective, and access to IHC is not available worldwide. Thus, the application of additional methods to support morphological entity subtyping is desirable. Therefore, the ability of convolutional neuronal networks (CNNs) to classify the most common lung cancer subtypes, pulmonary adenocarcinoma (ADC), pulmonary squamous cell carcinoma (SqCC), and small-cell lung cancer (SCLC), was evaluated. A cohort of 80 ADC, 80 SqCC, 80 SCLC, and 30 skeletal muscle specimens was assembled; slides were scanned; tumor areas were annotated; image patches were extracted; and cases were randomly assigned to a training, validation or test set. Multiple CNN architectures (VGG16, InceptionV3, and InceptionResNetV2) were trained and optimized to classify the four entities. A quality control (QC) metric was established. An optimized InceptionV3 CNN architecture yielded the highest classification accuracy and was used for the classification of the test set. Image patch and patient-based CNN classification results were 95% and 100% in the test set after the application of strict QC. Misclassified cases mainly included ADC and SqCC. The QC metric identified cases that needed further IHC for definite entity subtyping. The study highlights the potential and limitations of CNN image classification models for tumor differentiation.

Agreement of CK5/6, p40, and p63 immunoreactivity in non-small cell lung cancer

Histological subtyping of non-small cell lung cancer (NSCLC) is of utmost importance for therapy stratification. Common immunohistochemical markers to identify squamous lineage are CK5/6, p40, and p63. Although p40 is considered the gold standard by current guidelines, the agreement of all three markers is an important aspect for tumours more difficult to classify. A total of 1244 NSCLC including 569 squamous cell carcinomas (SqCC) and 675 adenocarcinomas were assembled on a tissue microarray and stained with CK5/6, p40, p63, TTF-1, and Napsin-A. Sensitivity and specificity for squamous lineage markers as well as agreement of CK5/6, p40 and p63 were calculated. Sensitivity of CK5/6, p40, and p63 for SqCC was 93%, 94%, and 94% and specificity was 98%, 97%, and 84%, respectively. Positivity for two of these markers was found in at least in 90% of SqCC. Highest agreement was observed for p40 and p63 (Cohen's kappa 0.80). We report a similar sensitivity of CK5/6, p40, and p63, but a decreased specificity of p63 as compared to CK5/6 and p40 for the identification of squamous lineage. Our results support the use of either CK5/6 or p40 over p63 in the routine diagnostic setting.

MALDI Imaging for Proteomic Painting of Heterogeneous Tissue Structures

PURPOSE

To present matrix-assisted laser desorption/ionization (MALDI) imaging as a powerful method to highlight various tissue compartments.

EXPERIMENTAL DESIGN

Formalin-fixed paraffin-embedded (FFPE) tissue of a uterine cervix, a pancreas, a duodenum, a teratoma, and a breast cancer tissue microarray (TMA) are analyzed by MALDI imaging and by immunohistochemistry (IHC). Peptide images are visualized and analyzed using FlexImaging and SCiLS Lab software. Different histological compartments are compared by hierarchical cluster analysis.

RESULTS

MALDI imaging highlights tissue compartments comparable to IHC. In cervical tissue, normal epithelium can be discerned from intraepithelial neoplasia. In pancreatic and duodenal tissues, m/z signals from lymph follicles, vessels, duodenal mucosa, normal pancreas, and smooth muscle structures can be visualized. In teratoma, specific m/z signals to discriminate squamous epithelium, sebaceous glands, and soft tissue are detected. Additionally, tumor tissue can be discerned from the surrounding stroma in small tissue cores of TMAs. Proteomic data acquisition of complex tissue compartments in FFPE tissue requires less than 1 h with recent mass spectrometers.

CONCLUSION AND CLINICAL RELEVANCE

The simultaneous characterization of morphological and proteomic features in the same tissue section adds proteomic information for histopathological diagnostics, which relies at present on conventional hematoxylin and eosin staining, histochemical, IHC and molecular methods.

In MALDI-Mass Spectrometry Imaging on Formalin-Fixed Paraffin-Embedded Tissue Specimen Section Thickness Significantly Influences m/z Peak Intensity

BACKGROUND

In matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) standardized sample preparation is important to obtain reliable results. Herein, the impact of section thickness in formalin-fixed paraffin embedded (FFPE) tissue microarrays (TMA) on spectral intensities is investigated.

PATIENTS AND METHODS

TMAs consisting of ten different tissues represented by duplicates of ten patients (n = 200 cores) are cut at 1, 3, and 5 μm. MSI analysis is performed and mean intensities of all evaluable cores are extracted. Measurements are merged and mean m/z intensities are compared.

RESULTS

Visual inspection of spectral intensities between 1, 3, and 5 μm reveals generally higher intensities in thinner tissue sections. Specifically, higher intensities are observed in the vast majority of peaks (98.6%, p < 0.01) in 1 μm compared with 5 μm sections. Note that 28.4% and 2.1% of m/z values exhibit a at least two- and threefold intensity difference (p < 0.01) in 1 μm compared to 5 μm sections, respectively.

CONCLUSION

A section thickness of 1 μm results in higher spectral intensities compared with 5 μm. The results highlight the importance of standardized protocols in light of recent efforts to identify clinically relevant biomarkers using MSI. The use of TMAs for comparative analysis seems advantageous, as section thickness displays less variability.

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.

Role of conventional immunomarkers, HNF4-α and SATB2, in the differential diagnosis of pulmonary and colorectal adenocarcinomas

AIMS

Pulmonary (ADC) and colorectal (CRC) adenocarcinomas are frequent entities in pathological routine diagnostics. Whereas the differential diagnosis is usually straightforward based on histomorphology, it can be challenging in small biopsies. In general, CDX-2, CK20, Napsin-A and TTF-1 are recommended immunohistological markers in this scenario. Hepatocyte nuclear factor 4 alpha (HNF4-α) and special AT-rich sequence-binding protein 2 (SATB2) were described recently as promising additional markers, but comprehensive large-scale data are lacking so far. Therefore, we analysed the expression of these six markers in 1021 non-small-cell lung cancers (NSCLC), including 472 ADC as well as in 80 pulmonary metastases of CRC.

METHODS AND RESULTS

Tissue microarrays of NSCLC and pulmonary metastases of CRC were stained for CDX-2, CK20, HNF4-α, Napsin-A, SATB2 and TTF-1 and staining results were correlated with clinicopathological variables. ADC exhibited expression of CDX-2, CK20, HNF4-α, Napsin-A, SATB2 and TTF-1 in nine (2%), 21 (4%), 17 (4%), 345 (73%), 35 (7%) and 408 (86%) samples, while 80 CRC were positive in 79 (99%), 74 (93%), 77 (96%), no (0%), 78 (98%) and five (6%) cases, respectively.

CONCLUSIONS

In addition to conventional immunomarkers, HNF4-α and particularly SATB2 may be helpful in the differential diagnosis of pulmonary ADC and metastases of CRC.