Longitudinal analysis of DC subsets in patients with ovarian cancer: Implications for immunotherapy.

The use of circulating cDC1 to generate anti-cancer vaccines is among the most promising approaches to overcome the limited immunogenicity and clinical efficacy of monocyte-derived DC. However, the recurrent lymphopenia and the reduction of DC numbers and functionality in patients with cancer may represent an important limitation of such approach. In patients with ovarian cancer (OvC) that had received chemotherapy, we previously showed that cDC1 frequency and function were reduced. We recruited healthy donors (HD, n=7) and patients with OvC at diagnosis and undergoing interval debulking surgery (IDS, n=6), primary debulking surgery (PDS, n=6) or at relapse (n=8). We characterized longitudinally phenotypic and functional properties of peripheral DC subsets by multiparametric flow cytometry. We show that the frequency of cDC1 and the total CD141+ DC capacity to take up antigen are not reduced at the diagnosis, while their TLR3 responsiveness is partially impaired in comparison with HD. Chemotherapy causes cDC1 depletion and increase in cDC2 frequency, but mainly in patients belonging to the PDS group, while in the IDS group both total lymphocytes and cDC1 are preserved. The capacity of total CD141 <sup>+</sup> DC and cDC2 to take up antigen is not impacted by chemotherapy, while the activation capacity upon Poly(I:C) (TLR3L) stimulation is further decreased. Our study provides new information about the impact of chemotherapy on the immune system of patients with OvC and sheds a new light on the importance of considering timing with respect to chemotherapy when designing new vaccination strategies that aim at withdrawing or targeting specific DC subsets

Les lymphocytes B circulants de patients atteints de sclerodermie systemique secretent de l'interleukine 6 et du TGF-[beta] et participent a l'activation des fibroblastes

International audienc

Shared acute phase traits in effector and memory human CD8 T cells.

CD8 T cells have multiple functional properties that mediate acute phase and long-term immune protection. Several effector and memory CD8 T cell subsets have been described with diverse functionalities and marker profiles. In contrast to the many comprehensive mouse studies, most human studies lack samples from the acute infection phase, a major reason why current knowledge of human T cell subsets and differentiation remains incomplete, particularly with regard to the T cell heterogeneity early during the immune response. Here we analysed the human CD8 T cell response to yellow fever vaccination as the best-known model to study the human immune response to acute viral infection. We performed flow cytometry on 21 markers conventionally used in mice and in humans to describe differentiation, activation, cycling, and so-called effector functions. We found clearly distinct 'acute traits' at the peak of the response that are shared amongst all non-naïve antigen-specific subsets, including memory-differentiated cells. These acute traits were low BCL-2 and high KI67, CD38, HLA-DR, as well as increased Granzyme B and Perforin, previously attributed only to effector cells at the peak of the response. Furthermore, analysis of chromatin accessibility at the single cell level revealed that memory- and effector-differentiated cells clustered together specifically in the acute phase. Altogether, we demonstrate 'acute traits' across differentiation subsets, and point out the need to discriminate the differentiation states when studying human CD8 T cells that undergo an acute response

A differential process mining analysis of COVID-19 management for cancer patients

During the acute phase of the COVID-19 pandemic, hospitals faced a challenge to manage patients, especially those with other comorbidities and medical needs, such as cancer patients. Here, we use Process Mining to analyze real-world therapeutic pathways in a cohort of 1182 cancer patients of the Lausanne University Hospital following COVID-19 infection. The algorithm builds trees representing sequences of coarse-grained events such as Home, Hospitalization, Intensive Care and Death. The same trees can also show probability of death or time-to-event statistics in each node. We introduce a new tool, called Differential Process Mining, which enables comparison of two patient strata in each node of the tree, in terms of hits and death rate, together with a statistical significance test. We thus compare management of COVID-19 patients with an active cancer in the first vs. second COVID-19 waves to quantify hospital adaptation to the pandemic. We also compare patients having undergone systemic therapy within 1 year to the rest of the cohort to understand the impact of an active cancer and/or its treatment on COVID-19 outcome. This study demonstrates the value of Process Mining to analyze complex event-based real-world data and generate hypotheses on hospital resource management or on clinical patient care

A differential process mining analysis of COVID-19 management for cancer patients.

During the acute phase of the COVID-19 pandemic, hospitals faced a challenge to manage patients, especially those with other comorbidities and medical needs, such as cancer patients. Here, we use Process Mining to analyze real-world therapeutic pathways in a cohort of 1182 cancer patients of the Lausanne University Hospital following COVID-19 infection. The algorithm builds trees representing sequences of coarse-grained events such as Home, Hospitalization, Intensive Care and Death. The same trees can also show probability of death or time-to-event statistics in each node. We introduce a new tool, called Differential Process Mining, which enables comparison of two patient strata in each node of the tree, in terms of hits and death rate, together with a statistical significance test. We thus compare management of COVID-19 patients with an active cancer in the first vs. second COVID-19 waves to quantify hospital adaptation to the pandemic. We also compare patients having undergone systemic therapy within 1 year to the rest of the cohort to understand the impact of an active cancer and/or its treatment on COVID-19 outcome. This study demonstrates the value of Process Mining to analyze complex event-based real-world data and generate hypotheses on hospital resource management or on clinical patient care

Immunodynamics of explanted human tumors for immuno-oncology

International audienceDecision making in immuno-oncology is pivotal to adapt therapy to the tumor microenvironment (TME) of the patient among the numerous options of monoclonal antibodies or small molecules. Predicting the best combinatorial regimen remains an unmet medical need. Here, we report a multiplex functional and dynamic immuno-assay based on the capacity of the TME to respond to ex vivo stimulation with twelve immunomodulators including immune checkpoint inhibitors (ICI) in 43 human primary tumors. This "in sitro" (in situ/in vitro) assay has the potential to predict unresponsiveness to anti-PD-1 mAbs, and to detect the most appropriate and personalized combinatorial regimen. Prospective clinical trials are awaited to validate this in sitro assay