Implementation of deep learning in liver pathology optimizes diagnosis of benign lesions and adenocarcinoma metastasis.

INTRODUCTION: Differentiation of histologically similar structures in the liver, including anatomical structures, benign bile duct lesions, or common types of liver metastases, can be challenging with conventional histological tissue sections alone. Accurate histopathological classification is paramount for the diagnosis and adequate treatment of the disease. Deep learning algorithms have been proposed for objective and consistent assessment of digital histopathological images. MATERIALS AND METHODS: In the present study, we trained and evaluated deep learning algorithms based on the EfficientNetV2 and ResNetRS architectures to discriminate between different histopathological classes. For the required dataset, specialized surgical pathologists annotated seven different histological classes, including different non-neoplastic anatomical structures, benign bile duct lesions, and liver metastases from colorectal and pancreatic adenocarcinoma in a large patient cohort. Annotation resulted in a total of 204.159 image patches, followed by discrimination analysis using our deep learning models. Model performance was evaluated on validation and test data using confusion matrices. RESULTS: Evaluation of the test set based on tiles and cases revealed overall highly satisfactory prediction capability of our algorithm for the different histological classes, resulting in a tile accuracy of 89% (38 413/43 059) and case accuracy of 94% (198/211). Importantly, the separation of metastasis versus benign lesions was certainly confident on case level, confirming the classification model performed with high diagnostic accuracy. Moreover, the whole curated raw data set is made publically available. CONCLUSIONS: Deep learning is a promising approach in surgical liver pathology supporting decision making in personalized medicine.

Corrigendum: Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections.

[This corrects the article DOI: 10.3389/fonc.2022.1022967.].

Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections.

Basal cell carcinoma (BCC), squamous cell carcinoma (SqCC) and melanoma are among the most common cancer types. Correct diagnosis based on histological evaluation after biopsy or excision is paramount for adequate therapy stratification. Deep learning on histological slides has been suggested to complement and improve routine diagnostics, but publicly available curated and annotated data and usable models trained to distinguish common skin tumors are rare and often lack heterogeneous non-tumor categories. A total of 16 classes from 386 cases were manually annotated on scanned histological slides, 129,364 100 x 100 µm (~395 x 395 px) image tiles were extracted and split into a training, validation and test set. An EfficientV2 neuronal network was trained and optimized to classify image categories. Cross entropy loss, balanced accuracy and Matthews correlation coefficient were used for model evaluation. Image and patient data were assessed with confusion matrices. Application of the model to an external set of whole slides facilitated localization of melanoma and non-tumor tissue. Automated differentiation of BCC, SqCC, melanoma, naevi and non-tumor tissue structures was possible, and a high diagnostic accuracy was achieved in the validation (98%) and test (97%) set. In summary, we provide a curated dataset including the most common neoplasms of the skin and various anatomical compartments to enable researchers to train, validate and improve deep learning models. Automated classification of skin tumors by deep learning techniques is possible with high accuracy, facilitates tumor localization and has the potential to support and improve routine diagnostics.

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.

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.

Deep Learning for the Classification of Non-Hodgkin Lymphoma on Histopathological Images.

The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued.

Role of Synaptophysin, Chromogranin and CD56 in adenocarcinoma and squamous cell carcinoma of the lung lacking morphological features of neuroendocrine differentiation: a retrospective large-scale study on 1170 tissue samples.

BACKGROUND: Synaptophysin, chromogranin and CD56 are recommended markers to identify pulmonary tumors with neuroendocrine differentiation. Whether the expression of these markers in pulmonary adenocarcinoma and pulmonary squamous cell carcinoma is a prognostic factor has been a matter of debate. Therefore, we investigated retrospectively a large cohort to expand the data on the role of synaptophysin, chromogranin and CD56 in non-small cell lung cancer lacking morphological features of neuroendocrine differentiation. METHODS: A cohort of 627 pulmonary adenocarcinomas (ADC) and 543 squamous cell carcinomas (SqCC) lacking morphological features of neuroendocrine differentiation was assembled and a tissue microarray was constructed. All cases were stained with synaptophysin, chromogranin and CD56. Positivity was defined as > 1% positive tumor cells. Data was correlated with clinico-pathological features including overall and disease free survival. RESULTS: 110 (18%) ADC and 80 (15%) SqCC were positive for either synaptophysin, chromogranin, CD56 or a combination. The most commonly positive single marker was synaptophysin. The least common positive marker was chromogranin. A combination of ≤2 neuroendocrine markers was positive in 2-3% of ADC and 0-1% of SqCC. There was no significant difference in overall survival in tumors with positivity for neuroendocrine markers neither in ADC (univariate: P = 0.4; hazard ratio [HR] = 0.867; multivariate: P = 0.5; HR = 0.876) nor in SqCC (univariate: P = 0.1; HR = 0.694; multivariate: P = 0.1, HR = 0.697). Likewise, there was no significant difference in disease free survival. CONCLUSIONS: We report on a cohort of 1170 cases that synaptophysin, chromogranin and CD56 are commonly expressed in ADC and SqCC and that their expression has no impact on survival, supporting the current best practice guidelines.

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

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.

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.

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

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.

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.

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.

Insulinoma-associated Protein 1 (INSM1) in Thoracic Tumors is Less Sensitive but More Specific Compared With Synaptophysin, Chromogranin A, and CD56.

OBJECTIVE: Recognition of neuroendocrine differentiation is important for tumor classification and treatment stratification. To detect and confirm neuroendocrine differentiation, a combination of morphology and immunohistochemistry is often required. In this regard, synaptophysin, chromogranin A, and CD56 are established immunohistochemical markers. Insulinoma-associated protein 1 (INSM1) has been suggested as a novel stand-alone marker with the potential to replace the current standard panel. In this study, we compared the sensitivity and specificity of INSM1 and established markers. MATERIALS AND METHODS: A cohort of 493 lung tumors including 112 typical, 39 atypical carcinoids, 77 large cell neuroendocrine carcinomas, 144 small cell lung cancers, 30 thoracic paragangliomas, 47 adenocarcinomas, and 44 squamous cell carcinomas were selected and tissue microarrays were constructed. Synaptophysin, chromogranin A, CD56, and INSM1 were stained on all cases and evaluated manually as well as with an analysis software. Positivity was defined as ≥1% stained tumor cells in at least 1 of 2 cores per patient. RESULTS: INSM1 was positive in 305 of 402 tumors with expected neuroendocrine differentiation (typical and atypical carcinoids, large cell neuroendocrine carcinomas, small cell lung cancers, and paraganglioma; sensitivity: 76%). INSM1 was negative in all but 1 of 91 analyzed non-neuroendocrine tumors (adenocarcinomas, squamous cell carcinomas; specificity: 99%). All conventional markers, as well as their combination, had a higher sensitivity (97%) and a lower specificity (78%) for neuroendocrine differentiation compared with INSM1. CONCLUSIONS: Although INSM1 might be a meaningful adjunct in the differential diagnosis of neuroendocrine neoplasias, a general uncritical vote for replacing the traditional markers by INSM1 may not be justified.

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.

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.

Combined Immunohistochemistry after Mass Spectrometry Imaging for Superior Spatial Information.

OBJECTIVE: Tissue slides analyzed by MS imaging (MSI) are stained by H&E (Haematoxylin and Eosin) to identify regions of interest. As it can be difficult to identify specific cells of interest by H&E alone, data analysis may be impaired. Immunohistochemistry (IHC) can highlight cells of interest but single or combined IHC on tissue sections analyzed by MSI have not been performed. METHODS: We performed MSI on bone marrow biopsies from patients with multiple myeloma and stained different antibodies (CD38, CD138, MUM1, kappa- and lambda). A combination of CK5/6/TTF1 and Napsin-A/p40 is stained after MSI on adenocarcinoma and squamous cell carcinoma of the lung. Staining intensities of p40 after MSI and on a serial section are quantified on a tissue microarray (n = 44) by digital analysis. RESULTS: Digital evaluation reveals weaker staining intensities after MSI as compared to serial sections. Staining quality and quantity after MSI enables to identify cells of interest. On the tissue microarray, one out of 44 tissue specimens shows no staining of p40 after MSI, but weak nuclear staining on a serial section. CONCLUSION: We demonstrated that single and double IHC staining is feasible on tissue sections previously analyzed by MSI, with decreased staining intensities.

Identification of MALDI Imaging Proteolytic Peptides Using LC-MS/MS-Based Biomarker Discovery Data: A Proof of Concept.

PURPOSE: Identification of proteolytic peptides from matrix-assisted laser desorption/ionization (MALDI) imaging remains a challenge. The low fragmentation yields obtained using in situ post source decay impairs identification. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an alternative to in situ MS/MS, but leads to multiple identification candidates for a given mass. The authors propose to use LC-MS/MS-based biomarker discovery results to reliably identify proteolytic peptides from MALDI imaging. EXPERIMENTAL DESIGN: The authors defined m/z values of interest for high grade squamous intraepithelial lesion (HSIL) by MALDI imaging. In parallel the authors used data from a biomarker discovery study to correlate m/z from MALDI imaging with masses of peptides identified by LC-MS/MS in HSIL. The authors neglected candidates that were not significantly more abundant in HSIL according to the biomarker discovery investigation. RESULTS: The authors assigned identifications to three m/z of interest. The number of possible identifiers for MALDI imaging m/z peaks using LC-MS/MS-based biomarker discovery studies was reduced by about tenfold compared using a single LC-MS/MS experiment. One peptide identification candidate was validated by immunohistochemistry. CONCLUSION AND CLINICAL RELEVANCE: This concept combines LC-MS/MS-based quantitative proteomics with MALDI imaging and allows reliable peptide identification. Public datasets from LC-MS/MS biomarker discovery experiments will be useful to identify MALDI imaging m/z peaks.

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.

Qualitative Comparison Between Carrier-based and Classical Tissue Microarrays.

Tissue microarrays (TMAs) are commonly used in biomarker research. To enhance the efficacy of TMAs and to avoid floating or folding of tissue cores, various improvements such as the application of carriers and melting techniques have been proposed. Compared with classical TMAs (cTMAs), carrier-based TMAs (cbTMAs) have been shown to have several advantages including sample handling and sectioning. Up to now, little is known about the efficacy and quality of cbTMAs compared with cTMAs. Thus, we set out to compare both types systematically. We constructed 5 spleen-based TMAs and 5 cTMAs with 10×10 different tissue types each. The total number of available cores, the number of folded cores, and the total core area was measured and evaluated by digital pathology. About 2% of cores got lost due to floating in both, cbTMAs and cTMAs, respectively. The remaining cores showed significant differences with regard to core integrity as about 1% of cbTMA cores and 9% of cTMA cores were folded (P<0.01). Folding or rolling was associated with specific tissue types. The size of the cores was smaller and less variable in cbTMAs (0.86±0.06 mm) compared with cTMAs (0.97±0.14 mm). The application of cbTMAs is an easy, inexpensive, and effective way to improve TMA-based research.