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  • 1
    Online Resource
    Online Resource
    Immunohistological Expression of SOX-10 in Triple-Negative Breast Cancer: A Descriptive Analysis of 113 Samples
    MDPI AG ; 2020
    In:  International Journal of Molecular Sciences Vol. 21, No. 17 ( 2020-09-03), p. 6407-
    Details
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 21, No. 17 ( 2020-09-03), p. 6407-
    Abstract: Background: SRY-related HMG-box 10 (SOX-10) is commonly expressed in triple negative breast cancer (TNBC). However, data on the biological significance of SOX-10 expression is limited. Therefore, we investigated immunhistological SOX-10 expression in TNBC and correlated the results with genetic alterations and clinical data. Methods: A tissue microarray including 113 TNBC cases was stained by SOX-10. Immunohistological data of AR, BCL2, CD117, p53 and Vimentin was available from a previous study. Semiconductor-based panel sequencing data including commonly altered breast cancer genes was also available from a previous investigation. SOX-10 expression was correlated with clinicopathological, immunohistochemical and genetic data. Results: SOX-10 was significantly associated with CD117 and Vimentin, but not with AR expression. An association of SOX-10 with BCL2, EGFR or p53 staining was not observed. SOX-10-positive tumors harbored more often TP53 mutations but less frequent mutations of PIK3CA or alterations of the PIK3K pathway. SOX-10 expression had no prognostic impact either on disease-free, distant disease-free, or overall survival. Conclusions: While there might be a value of SOX-10 as a differential diagnostic marker to identify metastases of TNBC, its biological role remains to be investigated.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    URL: Article
    DOI: 10.3390/ijms21176407
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2019364-6
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  • 2
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    Imaging Mass Spectrometry for the Classification of Melanoma Based on BRAF/NRAS Mutational Status
    MDPI AG ; 2023
    In:  International Journal of Molecular Sciences Vol. 24, No. 6 ( 2023-03-07), p. 5110-
    Details
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 24, No. 6 ( 2023-03-07), p. 5110-
    Abstract: Mutations of the oncogenes v-raf murine sarcoma viral oncogene homolog B1 (BRAF) and neuroblastoma RAS viral oncogene homolog (NRAS) are the most frequent genetic alterations in melanoma and are mutually exclusive. BRAF V600 mutations are predictive for response to the two BRAF inhibitors vemurafenib and dabrafenib and the mitogen-activated protein kinase kinase (MEK) inhibitor trametinib. However, inter- and intra-tumoral heterogeneity and the development of acquired resistance to BRAF inhibitors have important clinical implications. Here, we investigated and compared the molecular profile of BRAF and NRAS mutated and wildtype melanoma patients’ tissue samples using imaging mass spectrometry-based proteomic technology, to identify specific molecular signatures associated with the respective tumors. SCiLSLab and R-statistical software were used to classify peptide profiles using linear discriminant analysis and support vector machine models optimized with two internal cross-validation methods (leave-one-out, k-fold). Classification models showed molecular differences between BRAF and NRAS mutated melanoma, and identification of both was possible with an accuracy of 87–89% and 76–79%, depending on the respective classification method applied. In addition, differential expression of some predictive proteins, such as histones or glyceraldehyde-3-phosphate-dehydrogenase, correlated with BRAF or NRAS mutation status. Overall, these findings provide a new molecular method to classify melanoma patients carrying BRAF and NRAS mutations and help provide a broader view of the molecular characteristics of these patients that may help understand the signaling pathways and interactions involving the altered genes.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    URL: Article
    DOI: 10.3390/ijms24065110
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2019364-6
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  • 3
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    Insulinoma-associated Protein 1 (INSM1) in Thoracic Tumors is Less Sensitive but More Specific Compared With Synaptophysin, Chromogranin A, and CD56
    Ovid Technologies (Wolters Kluwer Health) ; 2020
    In:  Applied Immunohistochemistry & Molecular Morphology Vol. 28, No. 3 ( 2020-03), p. 237-242
    Details
    In: Applied Immunohistochemistry & Molecular Morphology, Ovid Technologies (Wolters Kluwer Health), Vol. 28, No. 3 ( 2020-03), p. 237-242
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1541-2016
    URL: Article
    DOI: 10.1097/PAI.0000000000000715
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2020
    detail.hit.zdb_id: 2052398-1
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  • 4
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    Targeted Feature Extraction in MALDI Mass Spectrometry Imaging to Discriminate Proteomic Profiles of Breast and Ovarian Cancer
    Wiley ; 2019
    In:  PROTEOMICS – Clinical Applications Vol. 13, No. 1 ( 2019-01)
    Details
    In: PROTEOMICS – Clinical Applications, Wiley, Vol. 13, No. 1 ( 2019-01)
    Abstract: To develop a mass spectrometry imaging (MSI) based workflow for extracting m/z values related to putative protein biomarkers and using these for reliable tumor classification. Experimental design Given a list of putative breast and ovarian cancer biomarker proteins, a set of related m/z values are extracted from heterogeneous MSI datasets derived from formalin‐fixed paraffin‐embedded tissue material. Based on these features, a linear discriminant analysis classification model is trained to discriminate the two tumor types. Results It is shown that the discriminative power of classification models based on the extracted features is increased compared to the automatic training approach, especially when classifiers are applied to spectral data acquired under different conditions (instrument, preparation, laboratory). Conclusions and clinical relevance Robust classification models not confounded by technical variation between MSI measurements are obtained. This supports the assumption that the classification of the respective tumor types is based on biological rather than technical differences, and that the selected features are related to the proteomic profiles of the tumor types under consideration.
    Type of Medium: Online Resource
    ISSN: 1862-8346 , 1862-8354
    URL: Issue
    URL: Article
    DOI: 10.1002/prca.v13.1
    DOI: 10.1002/prca.201700168
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 2317130-3
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  • 5
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    Combined Immunohistochemistry after Mass Spectrometry Imaging for Superior Spatial Information
    Wiley ; 2019
    In:  PROTEOMICS – Clinical Applications Vol. 13, No. 1 ( 2019-01)
    Details
    In: PROTEOMICS – Clinical Applications, Wiley, Vol. 13, No. 1 ( 2019-01)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1862-8346 , 1862-8354
    URL: Issue
    URL: Article
    DOI: 10.1002/prca.v13.1
    DOI: 10.1002/prca.201800035
    Language: English
    Publisher: Wiley
    Publication Date: 2019
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  • 6
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    Identification of MALDI Imaging Proteolytic Peptides Using LC‐MS/MS‐Based Biomarker Discovery Data: A Proof of Concept
    Wiley ; 2019
    In:  PROTEOMICS – Clinical Applications Vol. 13, No. 1 ( 2019-01)
    Details
    In: PROTEOMICS – Clinical Applications, Wiley, Vol. 13, No. 1 ( 2019-01)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1862-8346 , 1862-8354
    URL: Issue
    URL: Article
    DOI: 10.1002/prca.v13.1
    DOI: 10.1002/prca.201800158
    Language: English
    Publisher: Wiley
    Publication Date: 2019
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  • 7
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    MALDI Imaging for Proteomic Painting of Heterogeneous Tissue Structures
    Wiley ; 2019
    In:  PROTEOMICS – Clinical Applications Vol. 13, No. 1 ( 2019-01)
    Details
    In: PROTEOMICS – Clinical Applications, Wiley, Vol. 13, No. 1 ( 2019-01)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1862-8346 , 1862-8354
    URL: Issue
    URL: Article
    DOI: 10.1002/prca.v13.1
    DOI: 10.1002/prca.201800045
    Language: English
    Publisher: Wiley
    Publication Date: 2019
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  • 8
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    Online Resource
    Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI
    MDPI AG ; 2022
    In:  Cancers Vol. 14, No. 24 ( 2022-12-14), p. 6181-
    Details
    In: Cancers, MDPI AG, Vol. 14, No. 24 ( 2022-12-14), p. 6181-
    Abstract: Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment plan. This task, however, may still present challenges in clinical routine. We propose a two-modality, AI-based classification algorithm to detect and subtype tumor areas, which combines information from matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) data and digital microscopy whole slide images (WSIs) of lung tissue sections. The method consists of first detecting areas with high tumor cell content by performing a segmentation of the hematoxylin and eosin-stained (H & E-stained) WSIs, and subsequently classifying the tumor areas based on the corresponding MALDI MSI data. We trained the algorithm on six tissue microarrays (TMAs) with tumor samples from N = 232 patients and used 14 additional whole sections for validation and model selection. Classification accuracy was evaluated on a test dataset with another 16 whole sections. The algorithm accurately detected and classified tumor areas, yielding a test accuracy of 94.7% on spectrum level, and correctly classified 15 of 16 test sections. When an additional quality control criterion was introduced, a 100% test accuracy was achieved on sections that passed the quality control (14 of 16). The presented method provides a step further towards the inclusion of AI and MALDI MSI data into clinical routine and has the potential to reduce the pathologist’s work load. A careful analysis of the results revealed specific challenges to be considered when training neural networks on data from lung cancer tissue.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers14246181
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527080-1
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  • 9
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    Deep Learning for the Classification of Small-Cell and Non-Small-Cell Lung Cancer
    MDPI AG ; 2020
    In:  Cancers Vol. 12, No. 6 ( 2020-06-17), p. 1604-
    Details
    In: Cancers, MDPI AG, Vol. 12, No. 6 ( 2020-06-17), p. 1604-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers12061604
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 10
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    Mass Spectrometry Imaging for Reliable and Fast Classification of Non-Small Cell Lung Cancer Subtypes
    MDPI AG ; 2020
    In:  Cancers Vol. 12, No. 9 ( 2020-09-21), p. 2704-
    Details
    In: Cancers, MDPI AG, Vol. 12, No. 9 ( 2020-09-21), p. 2704-
    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.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers12092704
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2527080-1
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