Background: The success and limitations of current immunotherapies have pushed research toward the development of alternative approaches and the possibility to manipulate other cytotoxic immune cells such as natural killer (NK) cells. Here, we targeted an intracellular inhibiting protein ‘cytokine inducible SH2-containing protein’ (CISH) in NK cells to evaluate the impact on their functions and antitumor properties. Methods: To further understand CISH functions in NK cells, we developed a conditional Cish-deficient mouse model in NK cells (Cish^fl/flNcr1^Ki/+ ). NK cells cytokine expression, signaling and cytotoxicity has been evaluated in vitro. Using intravenous injection of B16F10 melanoma cell line and EO711 triple negative breast cancer cell line, metastasis evaluation was performed. Then, orthotopic implantation of breast tumors was performed and tumor growth was followed using bioluminescence. Infiltration and phenotype of NK cells in the tumor was evaluated. Finally, we targeted CISH in human NK-92 or primary NK cells, using a technology combining the CRISPR(i)-dCas9 tool with a new lentiviral pseudotype. We then tested human NK cells functions. Results: In Cish^fl/flNcr1^Ki/+ mice, we detected no developmental or homeostatic difference in NK cells. Global gene expression of Cish^fl/flNcr1^Ki/+ NK cells compared with Cish^+/+Ncr1^Ki/+ NK cells revealed upregulation of pathways and genes associated with NK cell cycling and activation. We show that CISH does not only regulate interleukin-15 (IL-15) signaling pathways but also natural cytotoxicity receptors (NCR) pathways, triggering CISH protein expression. Primed Cish^fl/flNcr1^Ki/+ NK cells display increased activation upon NCR stimulation. Cish^fl/flNcr1^Ki/+ NK cells display lower activation thresholds and Cish^fl/flNcr1^Ki/+ mice are more resistant to tumor metastasis and to primary breast cancer growth. CISH deletion favors NK cell accumulation to the primary tumor, optimizes NK cell killing properties and decreases TIGIT immune checkpoint receptor expression, limiting NK cell exhaustion. Finally, using CRISPRi, we then targeted CISH in human NK-92 or primary NK cells. In human NK cells, CISH deletion also favors NCR signaling and antitumor functions. Conclusion: This study represents a crucial step in the mechanistic understanding and safety of Cish targeting to unleash NK cell antitumor function in solid tumors. Our results validate CISH as an emerging therapeutic target to enhance NK cell immunotherapy.

This multicenter retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from six manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The diagnostic performance of the device was compared with the ground truth established by the majority agreement of three U.S. board-certified radiologists. Furthermore, the DL algorithm’s time to notification was evaluated to demonstrate clinical effectiveness. The study included 1303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8–97.5%] and a specificity of 97.3% [95% CI: 96.2–98.1%]. The application classified positive cases by the AD type with an accuracy of 99.5% [95% CI: 98.9–99.8%] for type A and 97.5 [95% CI: 96.4–98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 s. This deep learning-based application demonstrated a strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.

Purpose: Vertebral compression fractures (VCFs) are the most prevalent skeletal manifestations of osteoporosis in cancer patients. Yet, they are frequently missed or not reported in routine clinical radiology, adversely impacting patient outcomes and quality of life. This study evaluates the diagnostic performance of a deep-learning (DL)-based application and its potential to reduce the miss rate of incidental VCFs in a high-risk cancer population. Materials and methods: We retrospectively analysed thoraco-abdomino-pelvic (TAP) CT scans from 1556 patients with stage IV cancer collected consecutively over a 4-month period (September–December 2023) in a tertiary cancer center. A DL-based application flagged cases positive for VCFs, which were subsequently reviewed by two expert radiologists for validation. Additionally, grade 3 fractures identified by the application were independently assessed by two expert interventional radiologists to determine their eligibility for vertebroplasty. Results: Of the 1556 cases, 501 were flagged as positive for VCF by the application, with 436 confirmed as true positives by expert review, yielding a positive predictive value (PPV) of 87%. Common causes of false positives included sclerotic vertebral metastases, scoliosis, and vertebrae misidentification. Notably, 83.5% (364/436) of true positive VCFs were absent from radiology reports, indicating a substantial non-report rate in routine practice. Ten grade 3 fractures were overlooked or not reported by radiologists. Among them, 9 were deemed suitable for vertebroplasty by expert interventional radiologists. Conclusion: This study underscores the potential of DL-based applications to improve the detection of VCFs. The analyzed tool can assist radiologists in detecting more incidental vertebral fractures in adult cancer patients, optimising timely treatment and reducing associated morbidity and economic burden. Moreover, it might enhance patient access to interventional treatments such as vertebroplasty. These findings highlight the transformative role that DL can play in optimising clinical management and outcomes for osteoporosis-related VCFs in cancer patients.

This multi-center retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from 6 manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The di-agnostic performance of the device was compared with the ground truth established by the majority agreement of three US board-certified radiologists. Furthermore, the DL-algorithm's time-to-notification was evaluated to demonstrate clinical effectiveness. The study included 1,303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8% – 97.5%] and a specificity of 97.3% [95% CI: 96.2% - 98.1%]. The application classified positive cases by AD types with an accuracy of 99.5% [95% CI: 98.9% – 99.8%] for type A and 97.5 [95% CI: 96.4% – 98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 seconds. This deep learning-based application demonstrated strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.

Cibler les protéines inhibitrices intracellulaires DOK1 et DOK2 pour améliorer l'immunothérapie des cellules T CD8+: Les stratégies d'immunothérapie anticancéreuse ciblant les cellules T CD8+ cytotoxiques sont très efficaces sur le plan thérapeutique. Cependant, la majorité des patients ne répondent pas aux immunothérapies disponibles. De nouvelles méthodes pour améliorer les thérapies du cancer basées sur les cellules T sont nécessaires.Le sujet de ce travail de thèse vise à élaborer un concept de ciblage de molécules inhibitrices intracellulaire de la signalisation lymphocytaire T comme une stratégie prometteuse en immunothérapie anticancéreuse. Pour preuve de concept, nous avons étudié dans le cadre de ce projet l’impact de la délétion des protéines inhibitrices de la signalisation du récepteur à l’antigène des cellules T (DOK1 et DOK2) sur le phénotype et les fonctions cytotoxiques des lymphocytes T CD8+.Nous avons montré que la déplétion de DOK1 et DOK2 n’affecte pas le développement, la prolifération, la mortalité, l’activation et la fonction cytotoxique des cellules T CD8+ naïves. Cependant, après la pré-stimulation in vitro, les cellules T CD8+ DOK1/DOK2 KO avaient un pourcentage plus élevé de cellules T à mémoire effectrice ainsi qu’une augmentation des niveaux de pAkt et pErk. Nous avons démontré que malgré le phénotype effecteur et signalisation plus forte les cellules T CD8+ DOK1/DOK2 KO n’avaient pas d’avantage cytotoxique contre la lignée de cellules tumorales. Dans l'ensemble, nous démontrons ici un nouvel aspect de la régulation négative par les protéines DOK1 et DOK2 dans les cellules T CD8+. En conclusion, les protéines DOK1 et DOK2 ont un rôle inhibiteur suite à des stimulations de longue durée des cellules T.