Role of nuclear bodies in apoptosis signalling

AbstractPromyelocytic leukemia nuclear bodies (PML NBs) are dynamic macromolecular multiprotein complexes that recruit and release a plethora of proteins. A considerable number of PML NB components play vital roles in apoptosis, senescence regulation and tumour suppression. The molecular basis by which PML NBs control these cellular responses is still just beginning to be understood. In addition to PML itself, numerous further tumour suppressors including transcriptional regulator p53, acetyl transferase CBP (CREB binding protein) and protein kinase HIPK2 (homeodomain interacting protein kinase 2) are recruited to PML NBs in response to genotoxic stress or oncogenic transformation and drive the senescence and apoptosis response by regulating p53 activity. Moreover, in response to death-receptor activation, PML NBs may act as nuclear depots that release apoptotic factors, such as the FLASH (FLICE-associated huge) protein, to amplify the death signal. PML NBs are also associated with other nuclear domains including Cajal bodies and nucleoli and share apoptotic regulators with these domains, implying crosstalk between NBs in apoptosis regulation. In conclusion, PML NBs appear to regulate cell death decisions through different, pathway-specific molecular mechanisms

Evaluating Deep Learning-based Melanoma Classification using Immunohistochemistry and Routine Histology: A Three Center Study

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine

FLASH Knockdown Sensitizes Cells To Fas-Mediated Apoptosis via Down-Regulation of the Anti-Apoptotic Proteins, MCL-1 and Cflip Short

FLASH (FLICE-associated huge protein or CASP8AP2) is a large multifunctional protein that is involved in many cellular processes associated with cell death and survival. It has been reported to promote apoptosis, but we show here that depletion of FLASH in HT1080 cells by siRNA interference can also accelerate the process. As shown previously, depletion of FLASH halts growth by down-regulating histone biosynthesis and arrests the cell cycle in S-phase. FLASH knockdown followed by stimulating the cells with Fas ligand or anti-Fas antibodies was found to be associated with a more rapid cleavage of PARP, accelerated activation of caspase-8 and the executioner caspase-3 and rapid progression to cellular disintegration. As is the case for most anti-apoptotic proteins, FLASH was degraded soon after the onset of apoptosis. Depletion of FLASH also resulted in the reduced intracellular levels of the anti-apoptotic proteins, MCL-1 and the short isoform of cFLIP. FLASH knockdown in HT1080 mutant cells defective in p53 did not significantly accelerate Fas mediated apoptosis indicating that the effect was dependent on functional p53. Collectively, these results suggest that under some circumstances, FLASH suppresses apoptosis

Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists: Supplementary Material

The study was designed to compare the performance of classifiers based on image analyses by convolutionnal neural networks (CNNs) with that of 18 expert dermatopathologists in a binary classification task for pigmented skin lesions. Mendeley Supplementary Figure 1 shows a schematic representation of the testing approach. The CNNs were trained by cross-testing. Each iteration consists of five folds. Orange rectangles represent the folds used for testing and blue rectangles represent folds for training. For each iteration a CNN is trained and tested on the respective folds. Each trained CNN has a performance that is determined on the fold. To calculate the overall performance, the sum of all 5 performances is taken. This procedure was repeated 3 times and the results were combined to generate an ensemble. Mendeley Supplementary Figure 2 depicts the whole slide image (WSI) analysis. Tissue sections on whole slide images (top) were divided into tiles (middle). The CNN (a pre-trained ResNeXt50) assigned a malignancy score to every individual tile (bottom). Red tiles were classified as melanoma, blue tiles as nevus. Scores for all tiles on one image were averaged to a final malignancy score for the complete slide. Supplementary Table 1 shows the characteristics of the pigmented skin lesions included in the test set. Supplementary Table 2 provides an analysis of statistical differences between the performance of pathologists and CNN classifiers

Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists: Supplementary Material

The study was designed to compare the performance of classifiers based on image analyses by convolutionnal neural networks (CNNs) with that of 18 expert dermatopathologists in a binary classification task for pigmented skin lesions. Mendeley Supplementary Figure 1 shows a schematic representation of the testing approach. The CNNs were trained by cross-testing. Each iteration consists of five folds. Orange rectangles represent the folds used for testing and blue rectangles represent folds for training. For each iteration a CNN is trained and tested on the respective folds. Each trained CNN has a performance that is determined on the fold. To calculate the overall performance, the sum of all 5 performances is taken. This procedure was repeated 3 times and the results were combined to generate an ensemble. Mendeley Supplementary Figure 2 depicts the whole slide image (WSI) analysis. Tissue sections on whole slide images (top) were divided into tiles (middle). The CNN (a pre-trained ResNeXt50) assigned a malignancy score to every individual tile (bottom). Red tiles were classified as melanoma, blue tiles as nevus. Scores for all tiles on one image were averaged to a final malignancy score for the complete slide. Supplementary Table 1 shows the characteristics of the pigmented skin lesions included in the test set. Supplementary Table 2 provides an analysis of statistical differences between the performance of pathologists and CNN classifiers

Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study.

Pathologists routinely use immunohistochemical (IHC)-stained tissue slides against MelanA in addition to hematoxylin and eosin (H&E)-stained slides to improve their accuracy in diagnosing melanomas. The use of diagnostic Deep Learning (DL)-based support systems for automated examination of tissue morphology and cellular composition has been well studied in standard H&E-stained tissue slides. In contrast, there are few studies that analyze IHC slides using DL. Therefore, we investigated the separate and joint performance of ResNets trained on MelanA and corresponding H&E-stained slides. The MelanA classifier achieved an area under receiver operating characteristics curve (AUROC) of 0.82 and 0.74 on out of distribution (OOD)-datasets, similar to the H&E-based benchmark classification of 0.81 and 0.75, respectively. A combined classifier using MelanA and H&E achieved AUROCs of 0.85 and 0.81 on the OOD datasets. DL MelanA-based assistance systems show the same performance as the benchmark H&E classification and may be improved by multi stain classification to assist pathologists in their clinical routine