Ultrarapid lytic granule release from CTLs activates Ca2+-dependent synaptic resistance pathways in melanoma cells.

Human cytotoxic T lymphocytes (CTLs) exhibit ultrarapid lytic granule secretion, but whether melanoma cells mobilize defense mechanisms with commensurate rapidity remains unknown. We used single-cell time-lapse microscopy to offer high spatiotemporal resolution analyses of subcellular events in melanoma cells upon CTL attack. Target cell perforation initiated an intracellular Ca2+ wave that propagated outward from the synapse within milliseconds and triggered lysosomal mobilization to the synapse, facilitating membrane repair and conferring resistance to CTL induced cytotoxicity. Inhibition of Ca2+ flux and silencing of synaptotagmin VII limited synaptic lysosomal exposure and enhanced cytotoxicity. Multiplexed immunohistochemistry of patient melanoma nodules combined with automated image analysis showed that melanoma cells facing CD8+ CTLs in the tumor periphery or peritumoral area exhibited significant lysosomal enrichment. Our results identified synaptic Ca2+ entry as the definitive trigger for lysosomal deployment to the synapse upon CTL attack and highlighted an unpredicted defensive topology of lysosome distribution in melanoma nodules.

Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning.

Histopathological diagnosis of lymphomas represents a challenge requiring either expertise or centralised review, and greatly depends on the technical process of tissue sections. Hence, we developed an innovative deep-learning framework, empowered with a certainty estimation level, designed for haematoxylin and eosin-stained slides analysis, with special focus on follicular lymphoma (FL) diagnosis. Whole-slide images of lymph nodes affected by FL or follicular hyperplasia were used for training, validating, and finally testing Bayesian neural networks (BNN). These BNN provide a diagnostic prediction coupled with an effective certainty estimation, and generate accurate diagnosis with an area under the curve reaching 0.99. Through its uncertainty estimation, our network is also able to detect unfamiliar data such as other small B cell lymphomas or technically heterogeneous cases from external centres. We demonstrate that machine-learning techniques are sensitive to the pre-processing of histopathology slides and require appropriate training to build universal tools to aid diagnosis.

Multiparametric analysis of CD8+ T cell compartment phenotype in chronic lymphocytic leukemia reveals a signature associated with progression toward therapy.

CD8+ T cells are frontline defenders against cancer and primary targets of current immunotherapies. In CLL, specific functional alterations have been described in circulating CD8+ T cells, yet a global view of the CD8+ T cell compartment phenotype and of its real impact on disease progression is presently elusive. We developed a multidimensional statistical analysis of CD8+ T cell phenotypic marker expression based on whole blood multi-color flow-cytometry. The analysis comprises both unsupervised statistics (hClust and PCA) and supervised classification methods (Random forest, Adaboost algorithm, Decision tree learning and logistic regression) and allows to cluster patients by comparing multiple phenotypic markers expressed by CD8+ T cells. Our results reveal a global CD8+ T cell phenotypic signature in CLL patients that is significantly modified when compared to healthy donors. We also uncover a CD8+ T cell signature characteristic of patients evolving toward therapy within 6 months after phenotyping. The unbiased, not predetermined and multimodal approach highlights a prominent role of the memory compartment in the prognostic signature. The analysis also reveals that imbalance of the central/effector memory compartment in CD8+ T cells can occur irrespectively of the elapsed time after diagnosis. Taken together our results indicate that, in CLL patients, CD8+ T cell phenotype is imprinted by disease clinical progression and reveal that CD8+ T cell memory compartment alteration is not only a hallmark of CLL disease but also a signature of disease evolution toward the need for therapy.