The impact of radiomics libraries and gray level discretization on the discovery of immunotherapy biomarkers in NSCLC patients.

OBJECTIVE: The influence of radiomics pipeline and the grey-level discretization on the discovery of immunotherapy biomarkers is still a poorly understood topic. This study is aimed at identifying robust features by comparing two radiomics libraries and their association with clinical outcomes in non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs). METHODS: A retrospective cohort of 164 NSCLC patients administered with ICIs was used in this study. Radiomic features were extracted from the pre-treatment CT scans. Univariate models were used to assess the association of radiomics features with progression free survival (PFS), PD-L1 and CD8 cell counts. We also examined the impact of gray-level discretization on feature robustness by evaluating the association of features with clinical endpoints. RESULTS: We extracted 1224, 441 radiomic features using Pyradiomics and RaCat, respectively, out of which 75 were common between them. We showed that the directionality of association between features and clinical endpoints is specific to the radiomic library used. Overall, more Pyradiomics and RaCat features were statistically associated with PFS, and PD-L1, respectively. We found intensity-based features to be more agnostic to the gray-level discretization parameters. Among features that showed significant correlation with PFS with varying gray-level discretization parameters, 45% were intensity-based, compared to PD-L1, and CD8. CONCLUSIONS: This study highlights the heterogeneity of radiomics libraries and the gray level discretization parameters that will impact the feature selection and predictive model development. Importantly, our work highlights the significance of selecting features that are agnostic to radiomics libraries for clinical translation. ADVANCES IN KNOWLEDGE: Our study emphasizes the need to select stable CT-derived handcrafted features to build immunotherapy biomarkers, which is a necessary precursor for multi-institutional validation of imaging biomarkers.

CXCL13 Positive Cells Localization Predict Response to Anti-PD-1/PD-L1 in Pulmonary Non-Small Cell Carcinoma.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized non-small cell lung cancers (NSCLCs) treatment, but only 20-30% of patients benefit from these treatments. Currently, PD-L1 expression in tumor cells is the only clinically approved predictor of ICI response in lung cancer, but concerns arise due to its low negative and positive predictive value. Recent studies suggest that CXCL13+ T cells in the tumor microenvironment (TME) may be a good predictor of response. We aimed to assess if CXCL13+ cell localization within the TME can predict ICI response in advanced NSCLC patients. Methods: This retrospective study included 65 advanced NSCLC patients treated with Nivolumab/Pembrolizumab at IUCPQ or CHUM and for whom a pretreatment surgical specimen was available. Good responders were defined as having a complete radiologic response at 1 year, and bad responders were defined as showing cancer progression at 1 year. IHC staining for CXCL13 was carried out on a representative slide from a resection specimen, and CXCL13+ cell density was evaluated in tumor (T), invasive margin (IM), non-tumor (NT), and tertiary lymphoid structure (TLS) compartments. Cox models were used to analyze progression-free survival (PFS) and overall survival (OS) probability, while the Mann-Whitney test was used to compare CXCL13+ cell density between responders and non-responders. Results: We showed that CXCL13+ cell density localization within the TME is associated with ICI efficacy. An increased density of CXCL13+ cells across all compartments was associated with a poorer prognostic (OS; HR = 1.22; 95%CI = 1.04-1.42; p = 0.01, PFS; HR = 1.16; p = 0.02), or a better prognostic when colocalized within TLSs (PFS; HR = 0.84, p = 0.03). Conclusion: Our results support the role of CXCL13+ cells in advanced NSCLC patients, with favorable prognosis when localized within TLSs and unfavorable prognosis when present elsewhere. The concomitant proximity of CXCL13+ and CD20+ cells within TLSs may favor antigen presentation to T cells, thus enhancing the effect of PD-1/PD-L1 axis inhibition. Further validation is warranted to confirm the potential relevance of this biomarker in a clinical setting.

Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have emerged as one of the most promising first-line therapeutics in the management of non-small cell lung cancer (NSCLC). However, only a subset of these patients responds to ICIs, highlighting the clinical need to develop better predictive and prognostic biomarkers. This study will leverage pre-treatment imaging profiles to develop survival risk models for NSCLC patients treated with first-line immunotherapy. METHODS: Advanced NSCLC patients (n = 149) were retrospectively identified from two institutions who were treated with first-line ICIs. Radiomics features extracted from pretreatment imaging scans were used to build the predictive models for progression-free survival (PFS) and overall survival (OS). A compendium of five feature selection methods and seven machine learning approaches were utilized to build the survival risk models. The concordance index (C-index) was used to evaluate model performance. RESULTS: From our results, we found several combinations of machine learning algorithms and feature selection methods to achieve similar performance. K-nearest neighbourhood (KNN) with ReliefF (RL) feature selection was the best-performing model to predict PFS (C-index = 0.61 and 0.604 in discovery and validation cohorts), while XGBoost with Mutual Information (MI) feature selection was the best-performing model for OS (C-index = 0.7 and 0.655 in discovery and validation cohorts). CONCLUSION: The results of this study highlight the importance of implementing an appropriate feature selection method coupled with a machine learning strategy to develop robust survival models. With further validation of these models on external cohorts when available, this can have the potential to improve clinical decisions by systematically analyzing routine medical images.

The Cross-Scale Association between Pathomics and Radiomics Features in Immunotherapy-Treated NSCLC Patients: A Preliminary Study.

BACKGROUND: Recent advances in cancer biomarker development have led to a surge of distinct data modalities, such as medical imaging and histopathology. To develop predictive immunotherapy biomarkers, these modalities are leveraged independently, despite their orthogonality. This study aims to explore the cross-scale association between radiological scans and digitalized pathology images for immunotherapy-treated non-small cell lung cancer (NSCLC) patients. METHODS: This study involves 36 NSCLC patients who were treated with immunotherapy and for whom both radiology and pathology images were available. A total of 851 and 260 features were extracted from CT scans and cell density maps of histology images at different resolutions. We investigated the radiopathomics relationship and their association with clinical and biological endpoints. We used the Kolmogorov-Smirnov (KS) method to test the differences between the distributions of correlation coefficients with the two imaging modality features. Unsupervised clustering was done to identify which imaging modality captures poor and good survival patients. RESULTS: Our results demonstrated a significant correlation between cell density pathomics and radiomics features. Furthermore, we also found a varying distribution of correlation values between imaging-derived features and clinical endpoints. The KS test revealed that the two imaging feature distributions were different for PFS and CD8 counts, while similar for OS. In addition, clustering analysis resulted in significant differences in the two clusters generated from the radiomics and pathomics features with respect to patient survival and CD8 counts. CONCLUSION: The results of this study suggest a cross-scale association between CT scans and pathology H&E slides among ICI-treated patients. These relationships can be further explored to develop multimodal immunotherapy biomarkers to advance personalized lung cancer care.

Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study.

Background: Although the immune checkpoint inhibitors, nivolumab and pembrolizumab, were found to be promising in patients with advanced NSCLC, some of them either do not respond or have recurrence after an initial response. It is still unclear who will benefit from these therapies, and, hence, there is an unmet clinical need to build robust biomarkers. Methods: Patients with advanced NSCLC (N = 323) who were treated with pembrolizumab or nivolumab were retrospectively identified from two institutions. Radiomics features extracted from baseline pretreatment computed tomography scans along with the clinical variables were used to build the predictive models for overall survival (OS), progression-free survival (PFS), and programmed death-ligand 1 (PD-L1). To develop the imaging and integrative clinical-imaging predictive models, we used the XGBoost learning algorithm with ReliefF feature selection method and validated them in an independent cohort. The concordance index for OS, PFS, and area under the curve for PD-L1 was used to evaluate model performance. Results: We developed radiomics and the ensemble radiomics-clinical predictive models for OS, PFS, and PD-L1 expression. The concordance indices of the radiomics model were 0.60 and 0.61 for predicting OS and PFS and area under the curve was 0.61 for predicting PD-L1 in the validation cohort, respectively. The combined radiomics-clinical model resulted in higher performance with 0.65, 0.63, and 0.68 to predict OS, PFS, and PD-L1 in the validation cohort, respectively. Conclusions: We found that pretreatment computed tomography imaging along with clinical data can aid as predictive biomarkers for PD-L1 and survival end points. These imaging-driven approaches may prove useful to expand the therapeutic options for nonresponders and improve the selection of patients who would benefit from immune checkpoint inhibitors.

A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy. METHODS: This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort. RESULTS: We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively. CONCLUSIONS: We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.

Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors' response in non-small cell lung cancer: a multicenter cohort study.

Background: Recent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers. Methods: Radiomics features were extracted and compared from 642 advanced NSCLC patients on pre-ICI scans using established open-source PyRadiomics and a proprietary DeepRadiomics deep learning technology. The population was separated into two groups: a discovery cohort of 512 NSCLC patients from three academic centers and a validation cohort that included 130 NSCLC patients from a fourth center. We harmonized images to account for variations in reconstruction kernel, slice thicknesses, and device manufacturers. Multivariable models, evaluated using cross-validation, were used to estimate the predictive value of clinical variables, PD-L1 expression, and PyRadiomics or DeepRadiomics for progression-free survival at 6 months (PFS-6). Results: The best prognostic factor for PFS-6, excluding radiomics features, was obtained with the combination of Clinical + PD-L1 expression (AUC = 0.66 in the discovery and 0.62 in the validation cohort). Without image harmonization, combining Clinical + PyRadiomics or DeepRadiomics delivered an AUC = 0.69 and 0.69, respectively, in the discovery cohort, but dropped to 0.57 and 0.52, in the validation cohort. This lack of generalizability was consistent with observations in principal component analysis clustered by CT scan parameters. Subsequently, image harmonization eliminated these clusters. The combination of Clinical + DeepRadiomics reached an AUC = 0.67 and 0.63 in the discovery and validation cohort, respectively. Conversely, the combination of Clinical + PyRadiomics failed generalizability validations, with AUC = 0.66 and 0.59. Conclusion: We demonstrated that a risk prediction model combining Clinical + DeepRadiomics was generalizable following CT scan harmonization and machine learning generalization methods. These results had similar performances to routine oncology practice using Clinical + PD-L1. This study supports the strong potential of radiomics as a future non-invasive strategy to predict ICI response in advanced NSCLC.

Radiomics approaches to predict PD-L1 and PFS in advanced non-small cell lung patients treated with immunotherapy: a multi-institutional study.

With the increasing use of immune checkpoint inhibitors (ICIs), there is an urgent need to identify biomarkers to stratify responders and non-responders using programmed death-ligand (PD-L1) expression, and to predict patient-specific outcomes such as progression free survival (PFS). The current study is aimed to determine the feasibility of building imaging-based predictive biomarkers for PD-L1 and PFS through systematically evaluating a combination of several machine learning algorithms with different feature selection methods. A retrospective, multicenter study of 385 advanced NSCLC patients amenable to ICIs was undertaken in two academic centers. Radiomic features extracted from pretreatment CT scans were used to build predictive models for PD-L1 and PFS (short-term vs. long-term survivors). We first employed the LASSO methodology followed by five feature selection methods and seven machine learning approaches to build the predictors. From our analyses, we found several combinations of feature selection methods and machine learning algorithms to achieve a similar performance. Logistic regression with ReliefF feature selection (AUC = 0.64, 0.59 in discovery and validation cohorts) and SVM with Anova F-test feature selection (AUC = 0.64, 0.63 in discovery and validation datasets) were the best-performing models to predict PD-L1 and PFS. This study elucidates the application of suitable feature selection approaches and machine learning algorithms to predict clinical endpoints using radiomics features. Through this study, we identified a subset of algorithms that should be considered in future investigations for building robust and clinically relevant predictive models.

Single-cell spatial landscape of immunotherapy response reveals mechanisms of CXCL13 enhanced antitumor immunity.

BACKGROUND: Immunotherapy has revolutionized clinical outcomes for patients suffering from lung cancer, yet relatively few patients sustain long-term durable responses. Recent studies have demonstrated that the tumor immune microenvironment fosters tumorous heterogeneity and mediates both disease progression and response to immune checkpoint inhibitors (ICI). As such, there is an unmet need to elucidate the spatially defined single-cell landscape of the lung cancer microenvironment to understand the mechanisms of disease progression and identify biomarkers of response to ICI. METHODS: Here, in this study, we applied imaging mass cytometry to characterize the tumor and immunological landscape of immunotherapy response in non-small cell lung cancer by describing activated cell states, cellular interactions and neighborhoods associated with improved efficacy. We functionally validated our findings using preclinical mouse models of cancer treated with anti-programmed cell death protein-1 (PD-1) immune checkpoint blockade. RESULTS: We resolved 114,524 single cells in 27 patients treated with ICI, enabling spatial resolution of immune lineages and activation states with distinct clinical outcomes. We demonstrated that CXCL13 expression is associated with ICI efficacy in patients, and that recombinant CXCL13 potentiates anti-PD-1 response in vivo in association with increased antigen experienced T cell subsets and reduced CCR2+ monocytes. DISCUSSION: Our results provide a high-resolution molecular resource and illustrate the importance of major immune lineages as well as their functional substates in understanding the role of the tumor immune microenvironment in response to ICIs.

Hypertriglyceridemic Waist: A Simple Marker of High-Risk Atherosclerosis Features Associated With Excess Visceral Adiposity/Ectopic Fat.

BACKGROUND: Subclinical atherosclerosis identification remains challenging; abdominal visceral adiposity may improve risk stratification beyond traditional cardiovascular risk factors. Hypertriglyceridemic waist, a visceral adiposity marker combining elevated triglycerides (≥2 mmol/L) and waist circumference (≥90 cm), has been related to carotid atherosclerosis, although associations with high-risk features, including lipid-rich necrotic core (LRNC), remain unknown. We tested the hypothesis that hypertriglyceridemic waist is an independent marker of high-risk atherosclerosis features. METHODS AND RESULTS: In this cross-sectional study including 467 white men (mean age, 45.9±14.8 years; range 19.4-77.6 years), carotid atherosclerosis characteristics were examined by magnetic resonance imaging and associations with hypertriglyceridemic waist and benefits beyond Framingham Risk Score (FRS) and Pathobiological Determinants of Atherosclerosis in Youth (PDAY) were determined. Subclinical carotid atherosclerosis was present in 61.9% of participants, whereas 50.1% had LRNC. Hypertriglyceridemic waist was associated with carotid maximum wall thickness (P=0.014), wall volume (P=0.025), normalized wall index (P=0.004), and Carotid Atherosclerosis Score (derived from wall thickness and LRNC; P=0.049). Hypertriglyceridemic waist was associated with carotid LRNC volume beyond FRS (P=0.037) or PDAY (P=0.015), contrary to waist circumference alone (both P>0.05). Although 69.7% and 62.0% of participants with carotid atherosclerosis and/or LRNC were not high-risk by FRS or PDAY, respectively, hypertriglyceridemic waist correctly reclassified 9.7% and 4.5% of them, respectively. Combining hypertriglyceridemic waist with FRS (net reclassification improvement=0.17; P<0.001) or PDAY (net reclassification improvement=0.05; P=0.003) was superior to each score alone in identifying individuals with carotid atherosclerosis and/or LRNC. CONCLUSIONS: Hypertriglyceridemic waist is an independent marker of carotid high-risk atherosclerosis features in men, improving on FRS and PDAY risk score.

Dissecting host cell death programs in the pathogenesis of influenza.

Influenza A virus (IAV) is a pulmonary pathogen, responsible for significant yearly morbidity and mortality. Due to the absence of highly effective antiviral therapies and vaccine, as well as the constant threat of an emerging pandemic strain, there is considerable need to better understand the host-pathogen interactions and the factors that dictate a protective versus detrimental immune response to IAV. Even though evidence of IAV-induced cell death in human pulmonary epithelial and immune cells has been observed for almost a century, very little is known about the consequences of cell death on viral pathogenesis. Recent study indicates that both the type of cell death program and its kinetics have major implications on host defense and survival. In this review, we discuss advances in our understanding of cell death programs during influenza virus infection, in hopes of fostering new areas of investigation for targeted clinical intervention.

RIPK3 interacts with MAVS to regulate type I IFN-mediated immunity to Influenza A virus infection.

The type I interferon pathway plays a critical role in both host defense and tolerance against viral infection and thus requires refined regulatory mechanisms. RIPK3-mediated necroptosis has been shown to be involved in anti-viral immunity. However, the exact role of RIPK3 in immunity to Influenza A Virus (IAV) is poorly understood. In line with others, we, herein, show that Ripk3-/- mice are highly susceptible to IAV infection, exhibiting elevated pulmonary viral load and heightened morbidity and mortality. Unexpectedly, this susceptibility was linked to an inability of RIKP3-deficient macrophages (Mφ) to produce type I IFN in the lungs of infected mice. In Mφ infected with IAV in vitro, we found that RIPK3 regulates type I IFN both transcriptionally, by interacting with MAVS and limiting RIPK1 interaction with MAVS, and post-transcriptionally, by activating protein kinase R (PKR)-a critical regulator of IFN-ß mRNA stability. Collectively, our findings indicate a novel role for RIPK3 in regulating Mφ-mediated type I IFN anti-viral immunity, independent of its conventional role in necroptosis.

The energy sensor AMPK regulates T cell metabolic adaptation and effector responses in vivo.

Naive T cells undergo metabolic reprogramming to support the increased energetic and biosynthetic demands of effector T cell function. However, how nutrient availability influences T cell metabolism and function remains poorly understood. Here we report plasticity in effector T cell metabolism in response to changing nutrient availability. Activated T cells were found to possess a glucose-sensitive metabolic checkpoint controlled by the energy sensor AMP-activated protein kinase (AMPK) that regulated mRNA translation and glutamine-dependent mitochondrial metabolism to maintain T cell bioenergetics and viability. T cells lacking AMPKα1 displayed reduced mitochondrial bioenergetics and cellular ATP in response to glucose limitation in vitro or pathogenic challenge in vivo. Finally, we demonstrated that AMPKα1 is essential for T helper 1 (Th1) and Th17 cell development and primary T cell responses to viral and bacterial infections in vivo. Our data highlight AMPK-dependent regulation of metabolic homeostasis as a key regulator of T cell-mediated adaptive immunity.

Targeting eicosanoid pathways in the development of novel anti-influenza drugs.

The constant new emergence of life-threatening human respiratory viral pathogens presents new challenges to clinicians who are left with no available therapeutic interventions. Highly pathogenic strains of influenza A virus (IAV) share an enhanced capacity to propagate to the lower airways and paralyze alveolar macrophage antiviral capacity in order to replicate efficiently and cause pathologic inflammation. Following a century of using NSAIDs for the management of influenza symptoms, a number of studies have interrogated their function in the host response to IAV infection. We herein provide an overview of these studies as well as further insight of how pathogenic IAV hijacks the microsomal prostaglandin E synthase-1-dependent prostaglandin E2 pathway in order to evade host type I interferon-mediated antiviral immunity. We also reflect on the potential beneficial action of microsomal prostaglandin E synthase-1 inhibitory compounds in the treatment of IAV infections and potentially other RNA viruses.

NLRX1 prevents mitochondrial induced apoptosis and enhances macrophage antiviral immunity by interacting with influenza virus PB1-F2 protein.

To subvert host immunity, influenza A virus (IAV) induces early apoptosis in innate immune cells by disrupting mitochondria membrane potential via its polymerase basic protein 1-frame 2 (PB1-F2) accessory protein. Whether immune cells have mechanisms to counteract PB1-F2-mediated apoptosis is currently unknown. Herein, we define that the host mitochondrial protein nucleotide-binding oligomerization domain-like receptor (NLR)X1 binds to viral protein PB1-F2, preventing IAV-induced macrophage apoptosis and promoting both macrophage survival and type I IFN signaling. We initially observed that Nlrx1-deficient mice infected with IAV exhibited increased pulmonary viral replication, as well as enhanced inflammatory-associated pulmonary dysfunction and morbidity. Analysis of the lungs of IAV-infected mice revealed markedly enhanced leukocyte recruitment but impaired production of type I IFN in Nlrx1(-/-) mice. Impaired type I IFN production and enhanced viral replication was recapitulated in Nlrx1(-/-) macrophages and was associated with increased mitochondrial mediated apoptosis. Through gain- and loss-of-function strategies for protein interaction, we identified that NLRX1 directly bound PB1-F2 in the mitochondria of macrophages. Using a recombinant virus lacking PB1-F2, we confirmed that deletion of PB1-F2 abrogated NLRX1-dependent macrophage type I IFN production and apoptosis. Thus, our results demonstrate that NLRX1 acts as a mitochondrial sentinel protecting macrophages from PB1-F2-induced apoptosis and preserving their antiviral function. We further propose that NLRX1 is critical for macrophage immunity against IAV infection by sensing the extent of viral replication and maintaining a protective balance between antiviral immunity and excessive inflammation within the lungs.

Targeted prostaglandin E2 inhibition enhances antiviral immunity through induction of type I interferon and apoptosis in macrophages.

Aspirin gained tremendous popularity during the 1918 Spanish Influenza virus pandemic, 50 years prior to the demonstration of their inhibitory action on prostaglandins. Here, we show that during influenza A virus (IAV) infection, prostaglandin E2 (PGE2) was upregulated, which led to the inhibition of type I interferon (IFN) production and apoptosis in macrophages, thereby causing an increase in virus replication. This inhibitory role of PGE2 was not limited to innate immunity, because both antigen presentation and T cell mediated immunity were also suppressed. Targeted PGE2 suppression via genetic ablation of microsomal prostaglandin E-synthase 1 (mPGES-1) or by the pharmacological inhibition of PGE2 receptors EP2 and EP4 substantially improved survival against lethal IAV infection whereas PGE2 administration reversed this phenotype. These data demonstrate that the mPGES-1-PGE2 pathway is targeted by IAV to evade host type I IFN-dependent antiviral immunity. We propose that specific inhibition of PGE2 signaling might serve as a treatment for IAV.

Muramyl dipeptide induces NOD2-dependent Ly6C(high) monocyte recruitment to the lungs and protects against influenza virus infection.

Bacterial peptidoglycan-derived muramyl dipeptide (MDP) and derivatives have long-recognized antiviral properties but their mechanism of action remains unclear. In recent years, the pattern-recognition receptor NOD2 has been shown to mediate innate responses to MDP. Here, we show that MDP treatment of mice infected with Influenza A virus (IAV) significantly reduces mortality, viral load and pulmonary inflammation in a NOD2-dependent manner. Importantly, the induction of type I interferon (IFN) and CCL2 chemokine was markedly increased in the lungs following MDP treatment and correlated with a NOD2-dependent enhancement in circulating monocytes. Mechanistically, the protective effect of MDP could be explained by the NOD2-dependent transient increase in recruitment of Ly6C(high) "inflammatory" monocytes and, to a lesser extent, neutrophils to the lungs. Indeed, impairment in both Ly6C(high) monocyte recruitment and survival observed in infected Nod2-/- mice treated with MDP was recapitulated in mice deficient for the chemokine receptor CCR2 required for CCL2-mediated Ly6C(high) monocyte migration from the bone marrow into the lungs. MDP-induced pulmonary monocyte recruitment occurred normally in IAV-infected and MDP-treated Ips-1-/- mice. However, IPS-1 was required for improved survival upon MDP treatment. Finally, mycobacterial N-glycolyl MDP was more potent than N-acetyl MDP expressed by most bacteria at reducing viral burden while both forms of MDP restored pulmonary function following IAV challenge. Overall, our work sheds light on the antiviral mechanism of a clinically relevant bacterial-derived compound and identifies the NOD2 pathway as a potential therapeutic target against IAV.