Unconventional human CD61 pairing with CD103 promotes TCR signaling and antigen-specific T cell cytotoxicity.

Cancer remains one of the leading causes of mortality worldwide, leading to increased interest in utilizing immunotherapy strategies for better cancer treatments. In the past decade, CD103+ T cells have been associated with better clinical prognosis in patients with cancer. However, the specific immune mechanisms contributing toward CD103-mediated protective immunity remain unclear. Here, we show an unexpected and transient CD61 expression, which is paired with CD103 at the synaptic microclusters of T cells. CD61 colocalization with the T cell antigen receptor further modulates downstream T cell antigen receptor signaling, improving antitumor cytotoxicity and promoting physiological control of tumor growth. Clinically, the presence of CD61+ tumor-infiltrating T lymphocytes is associated with improved clinical outcomes, mediated through enhanced effector functions and phenotype with limited evidence of cellular exhaustion. In conclusion, this study identified an unconventional and transient CD61 expression and pairing with CD103 on human immune cells, which potentiates a new target for immune-based cellular therapies.

Glutamine deprivation alters the origin and function of cancer cell exosomes.

Exosomes are secreted extracellular vesicles carrying diverse molecular cargos, which can modulate recipient cell behaviour. They are thought to derive from intraluminal vesicles formed in late endosomal multivesicular bodies (MVBs). An alternate exosome formation mechanism, which is conserved from fly to human, is described here, with exosomes carrying unique cargos, including the GTPase Rab11, generated in Rab11-positive recycling endosomal MVBs. Release of Rab11-positive exosomes from cancer cells is increased relative to late endosomal exosomes by reducing growth regulatory Akt/mechanistic Target of Rapamycin Complex 1 (mTORC1) signalling or depleting the key metabolic substrate glutamine, which diverts membrane flux through recycling endosomes. Vesicles produced under these conditions promote tumour cell proliferation and turnover and modulate blood vessel networks in xenograft mouse models in vivo. Their growth-promoting activity, which is also observed in vitro, is Rab11a-dependent, involves ERK-MAPK-signalling and is inhibited by antibodies against amphiregulin, an EGFR ligand concentrated on these vesicles. Therefore, glutamine depletion or mTORC1 inhibition stimulates release from Rab11a compartments of exosomes with pro-tumorigenic functions, which we propose promote stress-induced tumour adaptation.

Automated quality assessment of large digitised histology cohorts by artificial intelligence.

Research using whole slide images (WSIs) of histopathology slides has increased exponentially over recent years. Glass slides from retrospective cohorts, some with patient follow-up data are digitised for the development and validation of artificial intelligence (AI) tools. Such resources, therefore, become very important, with the need to ensure that their quality is of the standard necessary for downstream AI development. However, manual quality control of large cohorts of WSIs by visual assessment is unfeasible, and whilst quality control AI algorithms exist, these focus on bespoke aspects of image quality, e.g. focus, or use traditional machine-learning methods, which are unable to classify the range of potential image artefacts that should be considered. In this study, we have trained and validated a multi-task deep neural network to automate the process of quality control of a large retrospective cohort of prostate cases from which glass slides have been scanned several years after production, to determine both the usability of the images at the diagnostic level (considered in this study to be the minimal standard for research) and the common image artefacts present. Using a two-layer approach, quality overlays of WSIs were generated from a quality assessment (QA) undertaken at patch-level at [Formula: see text] magnification. From these quality overlays the slide-level quality scores were predicted and then compared to those generated by three specialist urological pathologists, with a Pearson correlation of 0.89 for overall 'usability' (at a diagnostic level), and 0.87 and 0.82 for focus and H&E staining quality scores respectively. To demonstrate its wider potential utility, we subsequently applied our QA pipeline to the TCGA prostate cancer cohort and to a colorectal cancer cohort, for comparison. Our model, designated as PathProfiler, indicates comparable predicted usability of images from the cohorts assessed (86-90% of WSIs predicted to be usable), and perhaps more significantly is able to predict WSIs that could benefit from an intervention such as re-scanning or re-staining for quality improvement. We have shown in this study that AI can be used to automate the process of quality control of large retrospective WSI cohorts to maximise their utility for research.

The Potential of Artificial Intelligence to Detect Lymphovascular Invasion in Testicular Cancer.

Testicular cancer is the most common cancer in men aged from 15 to 34 years. Lymphovascular invasion refers to the presence of tumours within endothelial-lined lymphatic or vascular channels, and has been shown to have prognostic significance in testicular germ cell tumours. In non-seminomatous tumours, lymphovascular invasion is the most powerful prognostic factor for stage 1 disease. For the pathologist, searching multiple slides for lymphovascular invasion can be highly time-consuming. The aim of this retrospective study was to develop and assess an artificial intelligence algorithm that can identify areas suspicious for lymphovascular invasion in histological digital whole slide images. Areas of possible lymphovascular invasion were annotated in a total of 184 whole slide images of haematoxylin and eosin (H&E) stained tissue from 19 patients with testicular germ cell tumours, including a mixture of seminoma and non-seminomatous cases. Following consensus review by specialist uropathologists, we trained a deep learning classifier for automatic segmentation of areas suspicious for lymphovascular invasion. The classifier identified 34 areas within a validation set of 118 whole slide images from 10 patients, each of which was reviewed by three expert pathologists to form a majority consensus. The precision was 0.68 for areas which were considered to be appropriate to flag, and 0.56 for areas considered to be definite lymphovascular invasion. An artificial intelligence tool which highlights areas of possible lymphovascular invasion to reporting pathologists, who then make a final judgement on its presence or absence, has been demonstrated as feasible in this proof-of-concept study. Further development is required before clinical deployment.

Self-Maintaining CD103+ Cancer-Specific T Cells Are Highly Energetic with Rapid Cytotoxic and Effector Responses.

Enrichment of CD103+ tumor-infiltrating T lymphocytes (TIL) is associated with improved outcomes in patients. However, the characteristics of human CD103+ cytotoxic CD8+ T cells (CTL) and their role in tumor control remain unclear. We investigated the features and antitumor mechanisms of CD103+ CTLs by assessing T-cell receptor (TCR)-matched CD103+ and CD103- cancer-specific CTL immunity in vitro and its immunophenotype ex vivo Interestingly, we found that differentiated CD103+ cancer-specific CTLs expressed the active form of TGFß1 to continually self-regulate CD103 expression, without relying on external TGFß1-producing cells. The presence of CD103 on CTLs improved TCR antigen sensitivity, which enabled faster cancer recognition and rapid antitumor cytotoxicity. These CD103+ CTLs had elevated energetic potential and faster migration capacity. However, they had increased inhibitory receptor coexpression and elevated T-cell apoptosis following prolonged cancer exposure. Our data provide fundamental insights into the properties of matured human CD103+ cancer-specific CTLs, which could have important implications for future designs of tissue-localized cancer immunotherapy strategies.