Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19.

Blood myeloid cells are known to be dysregulated in coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2. It is unknown whether the innate myeloid response differs with disease severity and whether markers of innate immunity discriminate high-risk patients. Thus, we performed high-dimensional flow cytometry and single-cell RNA sequencing of COVID-19 patient peripheral blood cells and detected disappearance of non-classical CD14LowCD16High monocytes, accumulation of HLA-DRLow classical monocytes (Human Leukocyte Antigen - DR isotype), and release of massive amounts of calprotectin (S100A8/S100A9) in severe cases. Immature CD10LowCD101-CXCR4+/- neutrophils with an immunosuppressive profile accumulated in the blood and lungs, suggesting emergency myelopoiesis. Finally, we show that calprotectin plasma level and a routine flow cytometry assay detecting decreased frequencies of non-classical monocytes could discriminate patients who develop a severe form of COVID-19, suggesting a predictive value that deserves prospective evaluation.

Multilayer spectrum of intratumoral heterogeneity in basal bladder cancer.

Basal/squamous (Ba/Sq) subtype represents an intrinsic and robust group in the consensus molecular classification of muscle-invasive bladder cancer (MIBC), with poor outcome and controversial chemosensitivity. We aimed to investigate the spectrum of intratumor heterogeneity (ITH) in the Ba/Sq subtype. First, we validated a 29-gene NanoString CodeSet to predict the Ba/Sq subtype for FFPE samples. We identified heterogeneous Ba/Sq tumors in a series of 331 MIBC FFPE samples using dual GATA3/KRT5/6 immunohistochemistry (IHC). Heterogeneous regions with distinct immunostaining patterns were studied separately for gene expression using the 29-gene CodeSet, for mutations by targeted next-generation sequencing, and for copy number alteration (CNA) by microarray hybridization. Among 83 Ba/Sq tumors identified by GATA3/KRT5/6 dual staining, 19 tumors showed heterogeneity at the IHC level. In one third of the 19 cases, regions from the same tumor were classified in different distinct molecular subtypes. The mutational and CNA profiles confirmed the same clonal origin for IHC heterogeneous regions with possible subclonal evolution. Overall, two patterns of intratumoral heterogeneity (ITH) were observed in Ba/Sq tumors: low ITH (regions with distinct immunostaining, but common molecular subtype and shared CNA) or high ITH (regions with distinct immunostaining, molecular subtype, and CNA). These results showed multilayer heterogeneity in Ba/Sq MIBC. In view of personalized medicine, this heterogeneity adds complexity and should be taken into account for sampling procedures used for diagnosis and treatment choice. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

A new efficient tool for non-invasive diagnosis of fetomaternal platelet antigen incompatibility.

Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is the consequence of platelet destruction by maternal alloantibodies against fetal human platelet antigens (HPA). This may result in intracranial haemorrhages (ICH) or even fetal death. Currently, fetal HPA genotyping is performed using invasive procedures. Here, we carried out a proof-of-concept study for non-invasive prenatal diagnosis of fetal platelet genotyping in four HPA systems (HPA-1, -3, -5 and-15) by droplet digital polymerase chain reaction (ddPCR) using cell-free DNA extracts from the plasma of 47 pregnant women with suspected, or history of, FNAIT. Results showed that 74% (35/47) of pregnant women presented incompatibility in at least one HPA system, and 38% (18/47) of cases presented HPA-1 incompatibility, including nine women with multiple incompatibilities. ICH occurred in one case of profound fetal thrombocytopenia with HPA-15 incompatibility, confirming the need for non-invasive prenatal genotyping in systems other than HPA-1. Fetal HPA genotypes predicted by ddPCR were confirmed in all FNAIT cases after amniocentesis or delivery. Fetal HPA genotyping on maternal plasma based on ddPCR is a fast, safe and reliable non-invasive method. This technique will be useful for the early identification of pregnancies at high risk of FNAIT requiring antenatal management to minimize the risk of fetal/neonatal haemorrhage.

Identifying clusters of health risk behaviors and their predictors in adult survivors of childhood cancer: A report from the French Childhood Cancer Survivor Study.

OBJECTIVE: Health risk behaviors (HRB) of childhood cancer survivors (CCS) are generally studied separately, despite the evidence suggesting that HRB are not independent. To our knowledge, few studies have examined HRB profiles in the former pediatric cancer patients. In this study, we identified HRB profiles and examined predictors engaging in unhealthy behaviors in CCS. METHODS: We used data from a French cohort of CCS that includes five-year survivors diagnosed between 1945 and 2000 and treated before reaching age 18, in five centers in France. A total of 2961 adult CCS answered a self-reported questionnaire pertaining to HRB. Latent class analysis was used to identify HRB profiles combining physical activity, smoking, cannabis use, and alcohol drinking. Multinomial logistic analyses examined predictors for engaging in unhealthy behaviors. RESULTS: Three HRB patterns emerged: "Low-risk" (n = 1846, 62.3%) included CCS who exhibited the highest frequency for usual physical activity and the lowest probabilities for current smoking or cannabis use, but most drank at least moderately; "Moderate-risk behaviors" (n = 291, 9.8%), and "High-risk behaviors" (n = 824, 27.8%) for CCS who exhibited the highest frequencies for current smoking, cannabis use, and heavy drinking. The multivariable regression revealed that male CCS, less educated or not married were significantly more likely to be in the high-risk behaviors group than the low-risk group. CONCLUSIONS: As CCS remain a vulnerable population, screening for HRB should be routinized in long-term follow-up care and interventions targeting multiple HRB simultaneously among survivors should be developed.

A benchmark study of scoring methods for non-coding mutations.

Motivation: Detailed knowledge of coding sequences has led to different candidate models for pathogenic variant prioritization. Several deleteriousness scores have been proposed for the non-coding part of the genome, but no large-scale comparison has been realized to date to assess their performance. Results: We compared the leading scoring tools (CADD, FATHMM-MKL, Funseq2 and GWAVA) and some recent competitors (DANN, SNP and SOM scores) for their ability to discriminate assumed pathogenic variants from assumed benign variants (using the ClinVar, COSMIC and 1000 genomes project databases). Using the ClinVar benchmark, CADD was the best tool for detecting the pathogenic variants that are mainly located in protein coding gene regions. Using the COSMIC benchmark, FATHMM-MKL, GWAVA and SOMliver outperformed the other tools for pathogenic variants that are typically located in lincRNAs, pseudogenes and other parts of the non-coding genome. However, all tools had low precision, which could potentially be improved by future non-coding genome feature discoveries. These results may have been influenced by the presence of potential benign variants in the COSMIC database. The development of a gold standard as consistent as ClinVar for these regions will be necessary to confirm our tool ranking. Availability and implementation: The Snakemake, C++ and R codes are freely available from https://github.com/Oncostat/BenchmarkNCVTools and supported on Linux. Contact: damien.drubay@gustaveroussy.fr or stefan.michiels@gustaveroussy.fr. Supplementary information: Supplementary data are available at Bioinformatics online.

A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events.

We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model reflects a different set of constraints acting either on the normal or tumor genome, and we identify the specific genome features that most contribute to these constraints. Importantly, we show that the somatic mutation model carries independent functional information that can be used to narrow down the non-coding regions that may be relevant to cancer progression. On this basis, we identify positions in non-coding RNAs and the non-coding parts of mRNAs that are both under purifying selection in the germline and protected from mutation in tumors, thus introducing a new strategy for future detection of cancer driver elements in the expressed non-coding genome.

Kidney cancer mortality and ionizing radiation among French and German uranium miners.

The investigation of potential adverse health effects of occupational exposures to ionizing radiation, on uranium miners, is an important area of research. Radon is a well-known carcinogen for lung, but the link between radiation exposure and other diseases remains controversial, particularly for kidney cancer. The aims of this study were therefore to perform external kidney cancer mortality analyses and to assess the relationship between occupational radiation exposure and kidney cancer mortality, using competing risks methodology, from two uranium miners cohorts. The French (n = 3,377) and German (n = 58,986) cohorts of uranium miners included 11 and 174 deaths from kidney cancer. For each cohort, the excess of kidney cancer mortality has been assessed by standardized mortality ratio (SMR) corrected for the probability of known causes of death. The associations between cumulative occupational radiation exposures (radon, external gamma radiation and long-lived radionuclides) or kidney equivalent doses and both the cause-specific hazard and the probability of occurrence of kidney cancer death have been estimated with Cox and Fine and Gray models adjusted to date of birth and considering the attained age as the timescale. No significant excess of kidney cancer mortality has been observed neither in the French cohort (SMR = 1.49, 95 % confidence interval [0.73; 2.67]) nor in the German cohort (SMR = 0.91 [0.77; 1.06]). Moreover, no significant association between kidney cancer mortality and any type of occupational radiation exposure or kidney equivalent dose has been observed. Future analyses based on further follow-up updates and/or large pooled cohorts should allow us to confirm or not the absence of association.

CellsFromSpace: a fast, accurate, and reference-free tool to deconvolve and annotate spatially distributed omics data.

Motivation: Spatial transcriptomics enables the analysis of cell crosstalk in healthy and diseased organs by capturing the transcriptomic profiles of millions of cells within their spatial contexts. However, spatial transcriptomics approaches also raise new computational challenges for the multidimensional data analysis associated with spatial coordinates. Results: In this context, we introduce a novel analytical framework called CellsFromSpace based on independent component analysis (ICA), which allows users to analyze various commercially available technologies without relying on a single-cell reference dataset. The ICA approach deployed in CellsFromSpace decomposes spatial transcriptomics data into interpretable components associated with distinct cell types or activities. ICA also enables noise or artifact reduction and subset analysis of cell types of interest through component selection. We demonstrate the flexibility and performance of CellsFromSpace using real-world samples to demonstrate ICA's ability to successfully identify spatially distributed cells as well as rare diffuse cells, and quantitatively deconvolute datasets from the Visium, Slide-seq, MERSCOPE, and CosMX technologies. Comparative analysis with a current alternative reference-free deconvolution tool also highlights CellsFromSpace's speed, scalability and accuracy in processing complex, even multisample datasets. CellsFromSpace also offers a user-friendly graphical interface enabling non-bioinformaticians to annotate and interpret components based on spatial distribution and contributor genes, and perform full downstream analysis. Availability and implementation: CellsFromSpace (CFS) is distributed as an R package available from github at https://github.com/gustaveroussy/CFS along with tutorials, examples, and detailed documentation.

Influence of microbiota-associated metabolic reprogramming on clinical outcome in patients with melanoma from the randomized adjuvant dendritic cell-based MIND-DC trial.

Tumor immunosurveillance plays a major role in melanoma, prompting the development of immunotherapy strategies. The gut microbiota composition, influencing peripheral and tumoral immune tonus, earned its credentials among predictors of survival in melanoma. The MIND-DC phase III trial (NCT02993315) randomized (2:1 ratio) 148 patients with stage IIIB/C melanoma to adjuvant treatment with autologous natural dendritic cell (nDC) or placebo (PL). Overall, 144 patients collected serum and stool samples before and after 2 bimonthly injections to perform metabolomics (MB) and metagenomics (MG) as prespecified exploratory analysis. Clinical outcomes are reported separately. Here we show that different microbes were associated with prognosis, with the health-related Faecalibacterium prausnitzii standing out as the main beneficial taxon for no recurrence at 2 years (p = 0.008 at baseline, nDC arm). Therapy coincided with major MB perturbations (acylcarnitines, carboxylic and fatty acids). Despite randomization, nDC arm exhibited MG and MB bias at baseline: relative under-representation of F. prausnitzii, and perturbations of primary biliary acids (BA). F. prausnitzii anticorrelated with BA, medium- and long-chain acylcarnitines. Combined, these MG and MB biomarkers markedly determined prognosis. Altogether, the host-microbial interaction may play a role in localized melanoma. We value systematic MG and MB profiling in randomized trials to avoid baseline differences attributed to host-microbe interactions.

Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options. The current standard of care in nonmetastatic settings is neoadjuvant chemotherapy (NACT), but treatment efficacy varies substantially across patients. This heterogeneity is still poorly understood, partly due to the paucity of curated TNBC data. Here we investigate the use of machine learning (ML) leveraging whole-slide images and clinical information to predict, at diagnosis, the histological response to NACT for early TNBC women patients. To overcome the biases of small-scale studies while respecting data privacy, we conducted a multicentric TNBC study using federated learning, in which patient data remain secured behind hospitals' firewalls. We show that local ML models relying on whole-slide images can predict response to NACT but that collaborative training of ML models further improves performance, on par with the best current approaches in which ML models are trained using time-consuming expert annotations. Our ML model is interpretable and is sensitive to specific histological patterns. This proof of concept study, in which federated learning is applied to real-world datasets, paves the way for future biomarker discovery using unprecedentedly large datasets.

Very Long-Term Complete Remission Can Be Achieved in Men With High-Risk Localized Prostate Cancer and a Very High PSA Value: An Analysis of the GETUG 12 Phase 3 Trial.

INTRODUCTION: Serum prostate specific antigen (PSA) is a well-known prognostic parameter in men with prostate cancer. The treatment of men with very high PSA values and apparently no detectable metastases is not fully established. PATIENTS AND METHODS: Ancillary analysis from the GETUG 12 phase 3 trial. Patients with non-metastatic high-risk prostate cancer by bone and computerized tomography (CT) scan were randomly assigned to receive androgen deprivation therapy (ADT) and docetaxel plus estramustine or ADT alone. Relapse-free survival (RFS), clinical RFS, metastases-free survival (MFS), overall survival (OS), and prostate cancer-specific survival (PCSS) were estimated using the Kaplan-Meier method for different levels of PSA (50 ng/mL, 75 ng/mL, and 100 ng/mL). The relationship between PSA and outcomes was studied using residual-based approaches and spline functions. RESULTS: The median follow-up was 12 years (range: 0-15.3). Baseline PSA (<50 ng/mL, n = 328; ≥50ng/mL, n = 85) was associated with improved RFS (P = .0005), cRFS (P = .0024), and MFS (P = .0068). The 12-year RFS rate was 46.33% (CI 40.59-51.86), 33.59% (CI 22.55-44.97), and 11.76% (1.96-31.20) in men with PSA values <50 ng/mL (n = 328), 50-100 ng/mL (n = 68), and ≥100 ng/mL (n = 17), respectively. Exploratory analyses revealed no deviation from the linear relationship assumption between PSA and the log hazard of events. CONCLUSIONS: Men with apparently localized prostate cancer and a high baseline PSA value have a reasonable chance of being long-term disease-free when treated with curative intent combining systemic and local therapy.

Complex Disease Individual Molecular Characterization Using Infinite Sparse Graphical Independent Component Analysis.

Identifying individual mechanisms involved in complex diseases, such as cancer, is essential for precision medicine. Their characterization is particularly challenging due to the unknown relationships of high-dimensional omics data and their inter-patient heterogeneity. We propose to model individual gene expression as a combination of unobserved molecular mechanisms (molecular components) that may differ between the individuals. Considering a baseline molecular profile common to all individuals, these molecular components may represent molecular pathways differing from the population background. We defined an infinite sparse graphical independent component analysis (isgICA) to identify these molecular components. This model relies on double sparseness: the source matrix sparseness defines the subset of genes involved in each molecular component, whereas the weight matrix sparseness identifies the subset of molecular components associated with each patient. As the number of molecular components is unknown but likely high, we simultaneously inferred it and the weight matrix sparseness using the beta-Bernoulli process (BBP). We simulated data from a double sparse ICA with 10/30 components with specific sparseness structures for 100/500 individuals and 500/1000/5000 genes with different noise variance levels to evaluate the reconstruction of the latent structures by our model. For all simulations, the isgICA was able to reconstruct with higher accuracy than 2 state-of-the-art methods (ica and fastICA) the number of components, the weight and source matrix sparsenesses (correlation simulated/estimated >.8). Applying our model to the expression of 1063 genes of 614 breast cancer patients, the isgICA identified 22 components. According to the source matrix, 7 of these 22 components seemed to be specifically related to 3 known molecular pathways with a prognostic effect in early breast cancer (immune system, proliferation, and stroma invasion). This proposed algorithm provides an insight into individual molecular heterogeneity to better understand complex disease mechanisms.

A non-parametric Bayesian joint model for latent individual molecular profiles and survival in oncology.

The development of prognostic molecular signatures considering the inter-patient heterogeneity is a key challenge for the precision medicine. We propose a joint model of this heterogeneity and the patient survival, assuming that tumor expression results from a mixture of a subset of independent signatures. We deconvolute the omics data using a non-parametric independent component analysis with a double sparseness structure for the source and the weight matrices, corresponding to the gene-component and individual-component associations, respectively. In a simulation study, our approach identified the correct number of components and reconstructed with high accuracy the weight ([Formula: see text]0.85) and the source ([Formula: see text]0.75) matrices sparseness. The selection rate of components with high-to-moderate prognostic impacts was close to 95%, while the weak impacts were selected with a frequency close to the observed false positive rate ([Formula: see text]25%). When applied to the expression of 1063 genes from 614 breast cancer patients, our model identified 15 components, including six associated to patient survival, and related to three known prognostic pathways in early breast cancer (i.e. immune system, proliferation, and stromal invasion). The proposed algorithm provides a new insight into the individual molecular heterogeneity that is associated with patient prognosis to better understand the complex tumor mechanisms.

Tumor infiltrating lymphocyte stratification of prognostic staging of early-stage triple negative breast cancer.

The importance of integrating biomarkers into the TNM staging has been emphasized in the 8th Edition of the American Joint Committee on Cancer (AJCC) Staging system. In a pooled analysis of 2148 TNBC-patients in the adjuvant setting, TILs are found to strongly up and downstage traditional pathological-staging in the Pathological and Clinical Prognostic Stage Groups from the AJJC 8th edition Cancer Staging System. This suggest that clinical and research studies on TNBC should take TILs into account in addition to stage, as for example patients with stage II TNBC and high TILs have a better outcome than patients with stage I and low TILs.

Immune Infiltrate and Tumor Microenvironment Transcriptional Programs Stratify Pediatric Osteosarcoma into Prognostic Groups at Diagnosis.

The outcomes of adolescents/young adults with osteosarcoma have not improved in decades. The chaotic karyotype of this rare tumor has precluded the identification of prognostic biomarkers and patient stratification. We reasoned that transcriptomic studies should overcome this genetic complexity. RNA sequencing (RNA-seq) of 79 osteosarcoma diagnostic biopsies identified stable independent components that recapitulate the tumor and microenvironment cell composition. Unsupervised classification of the independent components stratified this cohort into favorable (G1) and unfavorable (G2) prognostic tumors in terms of overall survival. Multivariate survival analysis ranked this stratification as the most influential variable. Functional characterization associated G1 tumors with innate immunity and G2 tumors with angiogenic, osteoclastic, and adipogenic activities as well as PPARγ pathway upregulation. A focused gene signature that predicted G1/G2 tumors from RNA-seq data was developed and validated within an independent cohort of 82 osteosarcomas. This signature was further validated with a custom NanoString panel in 96 additional osteosarcomas. This study thus proposes new biomarkers to detect high-risk patients and new therapeutic options for osteosarcoma. SIGNIFICANCE: These findings indicate that the osteosarcoma microenvironment composition is a major feature to identify hard-to-treat patient tumors at diagnosis and define the biological pathways and potential actionable targets associated with these tumors.

Dynamics of circulating calprotectin accurately predict the outcome of moderate COVID-19 patients.

BACKGROUND: Severe COVID-19 is associated with a high circulating level of calprotectin, the S100A8/S100A9 alarmin heterodimer. Baseline calprotectin amount measured in peripheral blood at diagnosis correlates with disease severity. The optimal use of this biomarker along COVID-19 course remains to be delineated. METHODS: We focused on patients with a WHO-defined moderate COVID-19 requiring hospitalization in a medical ward. We collected plasma and serum from three independent cohorts (N = 626 patients) and measured calprotectin amount at admission. We performed longitudinal measures of calprotectin in 457 of these patients (1461 samples) and used a joint latent class mixture model in which classes were defined by age, body mass index and comorbidities to identify calprotectin trajectories predicting the risk of transfer into an intensive care unit or death. FINDINGS: After adjustment for age, sex, body mass index and comorbidities, the predictive value of baseline calprotectin in patients with moderate COVID19 could be refined by serial monitoring of the biomarker. We discriminated three calprotectin trajectories associated with low, moderate, and high risk of poor outcome, and we designed an algorithm available as online software (https://calpla.gustaveroussy.fr:8443/) to monitor the probability of a poor outcome in individual patients with moderate COVID-19. INTERPRETATION: These results emphasize the clinical interest of serial monitoring of calprotectin amount in the peripheral blood to anticipate the risk of poor outcomes in patients with moderate COVID-19 hospitalized in a standard care unit. FUNDING: The study received support (research grants) from ThermoFisher immunodiagnostics (France) and Gustave Roussy Foundation.

The Polarity and Specificity of Antiviral T Lymphocyte Responses Determine Susceptibility to SARS-CoV-2 Infection in Patients with Cancer and Healthy Individuals.

Vaccination against coronavirus disease 2019 (COVID-19) relies on the in-depth understanding of protective immune responses to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). We characterized the polarity and specificity of memory T cells directed against SARS-CoV-2 viral lysates and peptides to determine correlates with spontaneous, virus-elicited, or vaccine-induced protection against COVID-19 in disease-free and cancer-bearing individuals. A disbalance between type 1 and 2 cytokine release was associated with high susceptibility to COVID-19. Individuals susceptible to infection exhibited a specific deficit in the T helper 1/T cytotoxic 1 (Th1/Tc1) peptide repertoire affecting the receptor binding domain of the spike protein (S1-RBD), a hotspot of viral mutations. Current vaccines triggered Th1/Tc1 responses in only a fraction of all subject categories, more effectively against the original sequence of S1-RBD than that from viral variants. We speculate that the next generation of vaccines should elicit Th1/Tc1 T-cell responses against the S1-RBD domain of emerging viral variants. SIGNIFICANCE: This study prospectively analyzed virus-specific T-cell correlates of protection against COVID-19 in healthy and cancer-bearing individuals. A disbalance between Th1/Th2 recall responses conferred susceptibility to COVID-19 in both populations, coinciding with selective defects in Th1 recognition of the receptor binding domain of spike. See related commentary by McGary and Vardhana, p. 892. This article is highlighted in the In This Issue feature, p. 873.

Intestinal Akkermansia muciniphila predicts clinical response to PD-1 blockade in patients with advanced non-small-cell lung cancer.

Aside from PD-L1 expression, biomarkers of response to immune checkpoint inhibitors (ICIs) in non-small-cell lung cancer (NSCLC) are needed. In a previous retrospective analysis, we documented that fecal Akkermansia muciniphila (Akk) was associated with clinical benefit of ICI in patients with NSCLC or kidney cancer. In the current study, we performed shotgun-metagenomics-based microbiome profiling in a large cohort of patients with advanced NSCLC (n = 338) treated with first- or second-line ICIs to prospectively validate the predictive value of fecal Akk. Baseline stool Akk was associated with increased objective response rates and overall survival in multivariate analyses, independent of PD-L1 expression, antibiotics, and performance status. Intestinal Akk was accompanied by a richer commensalism, including Eubacterium hallii and Bifidobacterium adolescentis, and a more inflamed tumor microenvironment in a subset of patients. However, antibiotic use (20% of cases) coincided with a relative dominance of Akk above 4.8% accompanied with the genus Clostridium, both associated with resistance to ICI. Our study shows significant differences in relative abundance of Akk that may represent potential biomarkers to refine patient stratification in future studies.

Ketogenic diet and ketone bodies enhance the anticancer effects of PD-1 blockade.

Limited experimental evidence bridges nutrition and cancer immunosurveillance. Here, we show that ketogenic diet (KD) - or its principal ketone body, 3-hydroxybutyrate (3HB), most specifically in intermittent scheduling - induced T cell-dependent tumor growth retardation of aggressive tumor models. In conditions in which anti-PD-1 alone or in combination with anti-CTLA-4 failed to reduce tumor growth in mice receiving a standard diet, KD, or oral supplementation of 3HB reestablished therapeutic responses. Supplementation of KD with sucrose (which breaks ketogenesis, abolishing 3HB production) or with a pharmacological antagonist of the 3HB receptor GPR109A abolished the antitumor effects. Mechanistically, 3HB prevented the immune checkpoint blockade-linked upregulation of PD-L1 on myeloid cells, while favoring the expansion of CXCR3+ T cells. KD induced compositional changes of the gut microbiota, with distinct species such as Eisenbergiella massiliensis commonly emerging in mice and humans subjected to carbohydrate-low diet interventions and highly correlating with serum concentrations of 3HB. Altogether, these results demonstrate that KD induces a 3HB-mediated antineoplastic effect that relies on T cell-mediated cancer immunosurveillance.

Immunodynamics of explanted human tumors for immuno-oncology.

Decision 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.