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- Mercan, Caner, et al.
(författare)
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Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer
- 2022
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Ingår i: npj Breast Cancer. - : Nature Portfolio. - 2374-4677. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- To guide the choice of treatment, every new breast cancer is assessed for aggressiveness (i.e., graded) by an experienced histopathologist. Typically, this tumor grade consists of three components, one of which is the nuclear pleomorphism score (the extent of abnormalities in the overall appearance of tumor nuclei). The degree of nuclear pleomorphism is subjectively classified from 1 to 3, where a score of 1 most closely resembles epithelial cells of normal breast epithelium and 3 shows the greatest abnormalities. Establishing numerical criteria for grading nuclear pleomorphism is challenging, and inter-observer agreement is poor. Therefore, we studied the use of deep learning to develop fully automated nuclear pleomorphism scoring in breast cancer. The reference standard used for training the algorithm consisted of the collective knowledge of an international panel of 10 pathologists on a curated set of regions of interest covering the entire spectrum of tumor morphology in breast cancer. To fully exploit the information provided by the pathologists, a first-of-its-kind deep regression model was trained to yield a continuous scoring rather than limiting the pleomorphism scoring to the standard three-tiered system. Our approach preserves the continuum of nuclear pleomorphism without necessitating a large data set with explicit annotations of tumor nuclei. Once translated to the traditional system, our approach achieves top pathologist-level performance in multiple experiments on regions of interest and whole-slide images, compared to a panel of 10 and 4 pathologists, respectively.
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- Oliveira, Guilherme, et al.
(författare)
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EWSR1 rearrangement is a frequent event in papillary thyroid carcinoma and in carcinoma of the thyroid with Ewing family tumor elements (CEFTE).
- 2017
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Ingår i: Virchows Archiv. - : Springer. - 0945-6317 .- 1432-2307. ; 470:5, s. 517-525
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Tidskriftsartikel (refereegranskat)abstract
- Carcinomas of the thyroid with Ewing family tumor element (CEFTEs) are small-cell thyroid tumors with epithelial differentiation that disclose p63 expression and EWSR1-FLI1 rearrangement, carry a favorable prognosis and may co-exist with papillary thyroid carcinoma (PTC) foci. Two histogenetic hypotheses have been advanced regarding the origin of CEFTEs: arising in PTCs or in solid cell nests (SCN). A total of 3 CEFTEs, 54 PTCs, and 10 SCNs were reviewed, and fluorescence in situ hybridization (FISH) technique was performed in all cases to search for the presence of EWSR1 rearrangements. The three CEFTEs disclosed the EWSR1-FLI1 rearrangement both in the small cell and in the PTC component. Out of the 54 PTC cases, 28 (51.9%) were positive, 20 (37.0%) were negative, and 6 (11.1%) were inconclusive for EWSR1 rearrangement; in two of the positive PTC cases, the EWSR1-FLI1 rearrangement was detected. Classic PTC disclosed more often the EWSR1 rearrangement than other PTC variants (p = 0.031). PTCs with EWSR1 rearrangement disclosed a lower percentage of nuclei with EWSR1 polysomy than those without EWSR1 rearrangement (p = 0.001). Out of the 10 SCNs, 7 (70.0%) were negative and 3 (30.0%) were inconclusive for the EWSR1 rearrangement. Monosomic nuclei were more frequent (mean of 44.3%) in SCNs than in PTCs (p < 0.001). The presence of the EWSR1-FLI1 rearrangement in PTC component of all studied CEFTEs and the existence of the EWSR1 rearrangement in some PTCs favor the origin of CEFTE from PTC. The high frequency of EWSR1 rearrangements in PTC may represent a new diagnostic marker of these tumors.
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- Persson, Nina, et al.
(författare)
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Epitope mapping of a new anti-Tn antibody detecting gastric cancer cells
- 2017
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Ingår i: Glycobiology. - : Oxford University Press (OUP). - 0959-6658 .- 1460-2423. ; 27:7, s. 635-645
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Tidskriftsartikel (refereegranskat)abstract
- Here, we introduce a novel scFv antibody, G2-D11, specific for two adjacent Tn-antigens (GalNAc- Ser/Thr) binding equally to three dimeric forms of the epitope, Ser-Thr, Thr-Thr and Thr-Ser. Compared to other anti-Tn reagents, the binding of G2-D11 is minimally influenced by the peptide structure, which indicates a high degree of carbohydrate epitope dominance and a low influence from the protein backbone. With a high affinity (KDapp = 1.3 × 10-8 M) and no cross-reactivity to either sialyl-Tn epitope or blood group A antigens, scFv G2-D11 is an excellent candidate for a well-defined anti-Tn-antigen reagent. Detailed immunohistochemical evaluation of tissue sections from a cohort of 80 patients with gastric carcinoma showed in all cases positive tumor cells. The observed staining was localized to the cytoplasm and in some cases to the membrane, whereas the surrounding tissue was completely negative demonstrating the usefulness of the novel Tn-antigen binding antibody.
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- Pocas, Juliana, et al.
(författare)
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Syndecan-4 is a maestro of gastric cancer cell invasion and communication that underscores poor survival
- 2023
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences (PNAS). - 0027-8424 .- 1091-6490. ; 120:20
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Tidskriftsartikel (refereegranskat)abstract
- Gastric cancer is a dominating cause of cancer-associated mortality with limited therapeutic options. Here, we show that syndecan-4 (SDC4), a transmembrane pro-teoglycan, is highly expressed in intestinal subtype gastric tumors and that this sig -nature associates with patient poor survival. Further, we mechanistically demonstrate that SDC4 is a master regulator of gastric cancer cell motility and invasion. We also find that SDC4 decorated with heparan sulfate is efficiently sorted in extracellular vesicles (EVs). Interestingly, SDC4 in EVs regulates gastric cancer cell-derived EV organ distribution, uptake, and functional effects in recipient cells. Specifically, we show that SDC4 knockout disrupts the tropism of EVs for the common gastric cancer metastatic sites. Our findings set the basis for the molecular implications of SDC4 expression in gastric cancer cells and provide broader perspectives on the development of therapeutic strategies targeting the glycan-EV axis to limit tumor progression.
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- Swiderska-Chadaj, Zaneta, et al.
(författare)
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Learning to detect lymphocytes in immunohistochemistry with deep learning
- 2019
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Ingår i: Medical Image Analysis. - : ELSEVIER. - 1361-8415 .- 1361-8423. ; 58
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Tidskriftsartikel (refereegranskat)abstract
- The immune system is of critical importance in the development of cancer. The evasion of destruction by the immune system is one of the emerging hallmarks of cancer. We have built a dataset of 171,166 manually annotated CD3(+) and CD8(+) cells, which we used to train deep learning algorithms for automatic detection of lymphocytes in histopathology images to better quantify immune response. Moreover, we investigate the effectiveness of four deep learning based methods when different subcompartments of the whole-slide image are considered: normal tissue areas, areas with immune cell clusters, and areas containing artifacts. We have compared the proposed methods in breast, colon and prostate cancer tissue slides collected from nine different medical centers. Finally, we report the results of an observer study on lymphocyte quantification, which involved four pathologists from different medical centers, and compare their performance with the automatic detection. The results give insights on the applicability of the proposed methods for clinical use. U-Net obtained the highest performance with an F1-score of 0.78 and the highest agreement with manual evaluation (kappa = 0.72), whereas the average pathologists agreement with reference standard was kappa = 0.64. The test set and the automatic evaluation procedure are publicly available at lyon19.grand-challenge.org. (C) 2019 Elsevier B.V. All rights reserved.
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