RIPK3 regulates IFN-β mRNA integrity through activation of PKR.

<p>Total RNA was extracted from IAV-infected BMD-Mφ (MOI 1) (A) or cells of the BAL (50 pfu) (B) and the expression of IFN-β mRNA was determined by qPCR. (C) Phosphorylation of PKR (green) was analyzed by immunofluorescence in WT and <i>Ripk3</i>^<i>-/-</i> BMD-Mφ at different time point post IAV-infection. Nuclei were stained with Hoechst (blue). The scale bars represent 10μm. (D) Phosphorylated and total forms of PKR and eIF2α in whole-cell lysates were analysed by immunoblotting. β-Actin was used as a loading control. One representative blot is shown (left panel). Densitometry analysis to quantify the ratio of phosphorylated PKR relative to total PKR (n = 4, right panel). (E) Cells were harvested from the BAL of infected (50 pfu) WT or RIPK3-deficient mice and levels of phosphorylated and total PKR were determined by western blot (top panel). Densitometry analysis to quantify the ratio of phosphorylated PKR relative to total PKR is shown in the bottom panel. (F) Difference in the expression of IFN-β mRNA between WT and <i>Ripk3</i>^<i>-/-</i> BMD-Mφ infected with IAV. Gene expression was analyzed by qPCR following cDNA generation using random hexamers (blue bars) or oligo(dT) primers (white bars). (G) Confocal images showing IFN-β production in IAV-infected BMD-Mφ. Cells were stained with a rabbit polyclonal antibody specific for IFN-β (red) as well as nuclear dye Hoechst (blue). (H) Percentage of cells positive for IFN-β per random field. The scale bars represent 50μm. (I) BMD-Mφ (1x10⁶ cells) from WT and <i>Ripk3</i>^<i>-/-</i> mice were adoptively transferred (i.t.) into naïve <i>Rag1</i>^<i>-/-</i> mice, which were then infected with 500 PFU of IAV 2h post-transfer. (J) Viral load was assessed 3 days after IAV-infection (n = 8, compilation of 2 experiments). (K) Wild Type and <i>Ripk3</i>^<i>-/-</i> mice were infected with 50 pfu of IAV. After 2 days, mice were intranasally administered PBS or 2000U of IFN-β. Viral load was determined at day 3 post-infection by standard plaque assay (n = 7–8 mice/group, compilation of 2 experiments). *p<0.05 **p <0.01, ****p <0.0001, ns = not significant.</p

RIPK3 enhances innate anti-viral immunity against Influenza A virus.

<p>Pulmonary infection by IAV triggers the recruitment of monocytes from the bone marrow that differentiate into macrophages. IAV encounters and infects those macrophages, where viral RNA activates the RIG-I/MAVS pathway, leading to production of the key anti-viral cytokine IFN-β. IAV-induced RIPK3 interaction with MAVS at the mitochondria and may represent an immune evasion strategy to decrease IFN-β production. In the absence of RIPK3, there is increased RIPK1/MAVS interactions, which enhance downstream signaling, resulting in higher TBK1/IRF3 activation and IFN-β mRNA levels. However, this mechanism is counteracted by the RIPK3-mediated activation of PKR. PKR stabilizes IFN-β mRNA through the poly(A) tail, leading to increased IFN-β protein production and, ultimately, host protection.</p

RIPK3-deficient BMD-Mφ are impaired in anti-viral immunity, independent of the necroptosis pathway.

<p>(A-F, I-J) BMD-Mφ from WT and <i>Ripk3</i>^<i>-/-</i> mice were generated and infected with IAV at MOI 1. Total active type I IFN (α and β) (A) and IFN- β (B) was assessed in the supernatants. (C) The relative levels of viral NS1 mRNA were determined via qPCR. (D) BMD-Mφ from WT and <i>Ripk3</i>^<i>-/-</i> mice were infected with IAV and the level of viral protein NP was analyzed by flow cytometry. Zebra plots (left panel) are representative of the 24h time-point and numbers adjacent to the gates indicate percent of NP⁺ Mφ as quantified in the right panel. Level of total active type I IFN in cell culture supernatants (E) and viral load (F) in BMD-Mφ from WT mice treated, or not, with the selective RIPK3 inhibitor GSK‘843 (10μM) and infected with IAV. (G-H) Human monocyte-derived Mφ treated, or not, with the selective RIPK3 inhibitor GSK‘843 (10μM) were infected with IAV H3N2. Levels of active type I IFN (G) and viral load (H) were assessed in culture supernatants 24h after infection. (I) BMD-Mφ from WT and <i>Ripk3</i>^<i>-/-</i> mice were generated and treated with various combinations of zVAD-FMK (zVAD, 25μM) and necrostatin-1 (Nec-1, 10μM) for 1h and then were infected with IAV. Necroptosis was assessed by lactate dehydrogenase (LDH) assay after 24h of IAV infection. (J) LDH was measured in BMD-Mφ cell culture supernatants following IAV infection at various time points. Data are representative of the mean ± SEM of triplicate wells and are representative of at least 3 experiments. *p<0.05, **p<0.001, ***p<0.001, ****p<0.0001</p

RIPK3 restricts early viral replication and prevents excessive inflammation, morbidity, and mortality during IAV infection.

<p>(A-J) WT and <i>Ripk3</i>^<i>-/-</i> mice were infected with a sublethal dose (50 pfu) of IAV and morbidity, as a percentage of original weight (A), and survival (B) were assessed. Pulmonary viral loads (C) and total active type I IFN (α and β) via B16-blue reporter cells in the bronchoalveolar lavage (BAL) (D) or lung parenchyma (E) were measured at various times post-infection. (F-G) At 3 days post-infection lungs and BAL from WT and <i>Ripk3</i>^<i>-/-</i> mice were collected and cells were intracellularly stained for IAV NP protein. (F) Percentage of NP⁺ non-leukocytes and leukocytes in the lung. (G) Representative histogram (left panel) of NP protein levels in Mφ (CD45.2⁺ F4/80⁺ CD19^- cells) of the BAL and the frequency of NP⁺ Mφ (right panel). (H) Number of alveolar Mφ (AM), interstitial Mφ (IM), dendritic cells (DC), neutrophils (Neutro), Gr1⁺ inflammatory monocytes (Inflam Mono), and Gr1^- resident monocytes (Res Mono) present in the BAL at day 3 post-infection. (I) Micrographs of H&E-stained lung sections prepared prior to and 6 days after IAV infection. At low power, inflammation is absent in both Wild Type and <i>Ripk3</i>^<i>-/-</i> (day 0). At high power, the inflammatory infiltrate is composed of lymphocytes, histiocytes and neutrophils within the alveolar space (solid arrow) and bronchiolar lumen (dotted arrow), shown at 6 days post-infection. Scale bar represents 1mm (low magnification) and 50μm (higher magnification). Using flexivent, total respiratory resistance (J) of uninfected or IAV-infected mice was measured following methacholine challenge at day 6 post-infection. Data are represented as mean ± SEM. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001 between genotypes as indicated, in J, † indicate significant differences over baseline parameter readings of the same genotype. Except in A and B (as indicated), n = 4–8 animals per group per time point.</p

Image_3_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.jpeg

BackgroundRecent 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.MethodsRadiomics 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).ResultsThe 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.ConclusionWe 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.</p

Table_2_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.pdf

BackgroundRecent 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.MethodsRadiomics 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).ResultsThe 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.ConclusionWe 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.</p

Image_4_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.jpeg

BackgroundRecent 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.MethodsRadiomics 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).ResultsThe 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.ConclusionWe 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.</p

Image_1_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.jpeg

BackgroundRecent 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.MethodsRadiomics 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).ResultsThe 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.ConclusionWe 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.</p

Image_5_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.jpeg

BackgroundRecent 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.MethodsRadiomics 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).ResultsThe 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.ConclusionWe 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.</p

Table_1_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.pdf

BackgroundRecent 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.MethodsRadiomics 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).ResultsThe 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.ConclusionWe 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.</p