One billion people worldwide get flu every year, including patients with non–small cell lung cancer (NSCLC). However, the impact of acute influenza A virus (IAV) infection on the composition of the tumor microenvironment (TME) and the clinical outcome of patients with NSCLC is largely unknown. We set out to understand how IAV load impacts cancer growth and modifies cellular and molecular players in the TME. Herein, we report that IAV can infect both tumor and immune cells, resulting in a long-term protumoral effect in tumor-bearing mice. Mechanistically, IAV impaired tumor-specific T-cell responses, led to the exhaustion of memory CD8+ T cells and induced PD-L1 expression on tumor cells. IAV infection modulated the transcriptomic profile of the TME, fine-tuning it toward immunosuppression, carcinogenesis, and lipid and drug metabolism. Consistent with these data, the transcriptional module induced by IAV infection in tumor cells in tumor-bearing mice was also found in human patients with lung adenocarcinoma and correlated with poor overall survival. In conclusion, we found that IAV infection worsened lung tumor progression by reprogramming the TME toward a more aggressive state.

The tumor microenvironment (TME) is a complex ecosystem comprising tumor cells, fibroblasts, endothelial, and immune cells which strongly influences clinical outcome and response to treatments (1–3). In the past decade, the central role of the lung TME in tumor control or progression has been underlined (4, 5). In non–small cell lung cancer (NSCLC), the most common subtype of lung cancer, the study of the immune landscape shows robust infiltration of T lymphocytes coexpressing immune checkpoints, B lymphocytes, natural killer (NK) cells, macrophages, and neutrophils (5–9). In addition, the TME is highly heterogeneous between patients, but how the individual TME is shaped and evolves during tumor progression is not completely understood. Several factors influence the TME, such as specific tumor mutations and tumor mutational burden (10, 11), epigenetic modifications (12), tumor cell metabolism (13), chronic inflammation, such as in the context of chronic obstructive pulmonary disease (COPD), and the lung microbiome (14–17). However, the effect of acute respiratory viral infections has barely been investigated so far. Patients with NSCLC may experience influenza A virus (IAV) infection during the course of their cancer, but the impact of acute IAV on lung tumor progression and antitumor immune responses is largely unexplored.

IAV infection occurs worldwide in a seasonal cycle. It causes acute respiratory disease and results in 3 to 5 million cases of severe illness (18). Elderly and immunocompromised individuals, such as those with lung cancer, are more likely to be infected and to develop severe respiratory disorders (19–21). Among the potential mechanisms, IAV is a single-stranded RNA virus that infects airways epithelial cells and is intracellularly recognized by RNA sensors, notably Toll-like receptor 7 (TLR7; ref. 22). We have previously shown that TLR7 is highly expressed by malignant cells in patients with NSCLC and confers a poor clinical outcome (23). Stimulation of tumor cells with TLR7 synthetic agonists induces NFκB activation and Bcl-2 expression, increased tumor progression and resistance to chemotherapy in murine tumor models (24, 25). In this study, we sought to analyze the impact of IAV infection on the long-term progression of NSCLC using subcutaneous, orthotopic, and genetically induced tumor models. We found that acute IAV infection of lung tumor–bearing mice increased tumor progression, induced tumor nodules of a higher grade, and favored immunosuppression. RNA sequencing (RNA-seq) of the tumor-bearing lungs from noninfected and IAV-infected mice revealed a distinct transcriptomic profile suggesting a specific TME induced by viral infection. Finally, we show that in patients, expression of this signature in epithelial cells predicted poor overall survival in the lung adenocarcinoma (LUAD) subtype of NSCLC.

Cell lines

KP cells (LUAD subtype expressing luciferase), derived from tumor-bearing lungs of KrasLSL-G12D/+Trp53flox/flox mice, were kindly provided by Dr. Laurie Menger (Institut Curie, Paris, France). KP cells were cultured in RPMI1640 medium (Gibco, catalog no. 61870) supplemented with 10% heat-inactivated FCS (Eurobio, catalog no. CVFSVF00-01) and 1% penicilin/streptomycin (Gibco, catalog no. 15140; the cells were not cultured for more than 15 passages). Lewis lung carcinoma (LLC) cells (ATCC CRL-1642; gift from Vincenzo di Bartolo in 2015, Institut Pasteur, Paris, France) were stably transfected with firefly luciferase. LLC cells were cultured in DMEM + Glutamax (Gibco, catalog no. 61965) with 10% heat-inactivated FCS, 1% penicillin/streptomycin, and 1% hygromycin B (Thermo Scientific Chemicals, catalog no. J60681; the cells were not cultured for more than 15 passages). All cell lines were routinely tested for Mycoplasma with a kit (Ozyme, catalog no. LT07-710) and used only if negative. No specific authentication of the cell lines was performed.

Tumor induction in mice

Subcutaneous tumor models

B6(C)/Rj-Tyrc/c mice (Janvier Labs) were subcutaneously injected with 105 KP cells or 2 × 104 LLC cells in their right flanks. Tumor progression was measured every 2 to 3 days by bioluminescence using IVIS Lumina II In vivo Imaging System for KP cells and by caliper for LLC cells. To measure tumors from KP cells, mice were injected intraperitoneally with d-Luciferin (XenoLight d-Luciferin-K+ Salt, PerkinElmer, catalog no. 122799; 150 mg Luciferin/kg body weight). Ten minutes after d-Luciferin injection, the bioluminescent signal was acquired during 5 minutes using the IVIS Lumina II system (Perkin Elmer). During the acquisition procedure, mice were anesthetized with 2.5% isoflurane (XGI-8 Gas anesthesia system, Perkin Elmer) for 10 minutes. Data were analyzed with Living image software by defining a region of interest on the thoracic area of each mouse and extracting the corresponding total flux. For LLC cells, tumor growth was monitored via repeated measurements of the tumor size using a digital caliper. Tumor volume was calculated using the following formula: tumor size (mm3) = (length × width × height)/8 × 4/3 × π.

Orthotopic tumor model

B6(C)/Rj-Tyrc/c mice were intravenously injected with 5 × 104 KP cells in their tail vein. Tumor progression was measured weekly by measuring bioluminescence. We choose to use B6(C)/Rj-Tyrc/c mice because, being albinos, they are more suitable for IVIS imaging.

Transgenic mice

LUAD were induced in 8–12 weeks old female KrasLSL-G12D/+Trp53flox/flox mice (referred to as KP mice, kindly provided by Dr. Tyler Jacks, Massachusetts Institute of Technology, Cambridge, MA) by intratracheal administration of 2 × 104 lentiviral (LTV) particles as described previously (26). Lentivirus was generated from Cre recombinase-expressing plasmids in Dr. Olivier Lantz's laboratory to express the MHC class I OVA epitopes fused to the C-terminal end of the luciferase protein (27). In vivo tumor progression was measured weekly by measuring bioluminescence using IVIS Lumina II In Vivo Imaging System (Perkin Elmer).

All mice were housed in specific pathogen free conditions in full accordance with FELASA recommendations. All procedures were performed in compliance with European Union Directive 63/2010 and received approval from the Charles Darwin Ethics Committee for animal experimentation (APAFIS 18589 - 201901211717868).

IAV infection of tumor-bearing mice

For the subcutaneous lung tumor models, when tumors were appreciable (10–13 days after cells injection), mice were randomly divided into the different groups: the groups of IAV-infected mice were intratumorally infected either with the Influenza A/Scotland/20/74 (H3N2 strain) virus or the A/PR/8/34 (H1N1 strain; 150 or 1,000 pfu in 100 μL PBS). Noninfected control mice were injected with PBS.

For the orthotopic lung tumor model, when tumors were detectable (total flux around 1 × 105 photons/second), mice were randomly divided into two groups, one intranasally infected with H3N2 IAV (150 pfu in 40 μL PBS) and the other one with PBS.

For the genetic lung tumor model, 11 weeks after LTV inoculation, KP mice were infected with H3N2 IAV by intranasal inoculation (150 pfu in 40 μL PBS). Control mice received PBS alone. IAV infection was confirmed by measuring the body weight loss of the mice daily after viral infection and quantifying anti-IAV immunoglobulins. Mice were euthanized 16 weeks after LTV delivery.

Both IAV strains, originally obtained from Professor S. Van der Werf (Institut Pasteur, Paris, France) were purified as described previously (28). Tumor growth was followed up by IVIS Lumina II In Vivo Imaging System (Perkin Elmer).

IHC

Mice were perfused intracardially with PBS followed by 4% paraformaldehyde (Chem Cruz, catalog no. sc-281692) at room temperature before dissection. Lung tissue was embedded in paraffin using a Leica ASP300/Histocore Arcadia H instrument. Histologic 3 to 5 μm sections were processed by cutting 20 slides to perform hematoxylin (Dako, catalog no. 53309) and eosin (Abcam, catalog no. ab246824; H&E) staining. Three slides for each were used for the quantification (using the first slide, the 10th, and the 20th). All slides were scanned with the NanoZoomer 2.0HT (Hamamatsu) and analyzed by NDP.view2 viewing software (Hamamatsu). We counted the number of tumor nodes and identified the grade of the tumors in H&E sections, based on the tissue histology, as described previously (26) and with the help of a pathologist, who validated our quantification. Finally, the surface area of the tumor and the tissue were quantified with NDP.view2 viewing software (Hamamatsu).

For immunofluorescence staining of murine and human tumors (see below), antigen retrieval was done at 97°C for 30 minutes at pH 9 using a PT-Link instrument (Dako). Primary antibody staining was carried out for 15 minutes at room temperature, followed by the use of polymers for 10 minutes at room temperature. Antibody detection was performed with Opal technology. A list of the antibodies and other reagents used in the immunofluorescence staining can be found in Supplementary Table S1. All sections were stained with DAPI (1 μg/mL, Invitrogen, catalog no. D21490) for 5 minutes and mounted in Prolong Diamond antifade mounting (Invitrogen, catalog no. P36961). Slides were scanned using Axioscan (Zeiss) and the analysis and quantification were done with Halo software version 3.5 (Indica Labs).

Anti-influenza antibody quantification by ELISA

Blood collected from anesthetized mice was centrifuged at 10,000 rpm for 10 minutes at 4°C. Purified IAV was coated at 1 × 106 pfu/mL overnight at 4°C in 96 microtiter plates (Greiner). After washing, blocking was performed with 2% BSA and 0.05% Tween-20 in PBS for 2 hours at room temperature. Serial dilutions of plasma were prepared in 0.1% BSA, 0.05% Tween and PBS buffer, and incubated for 1.5 hours at 37°C. Mouse horseradish peroxidase (HRP)–conjugated IgG (Southern Biotech, catalog no. 1030-05) or mouse HRP–conjugated IgM (Millipore, catalog no. AP500P) diluted at 1:10,000 were used as detection antibodies for 30 minutes at 37°C. Peroxidase activity was revealed using sigmaFAST OPD (Sigma, catalog no. P9187). The colorimetric reaction was stopped using H2SO4 and plates were read at 492 nm on the SPARK 10M reader (Tecan). IgG and IgM concentrations were determined using a standard curve of mouse purified total IgG (Sigma, catalog no. PP54) or IgM (Sigma, catalog no. PP50).

Flow cytometry of in vitro IAV-infected KP cells

In vitro IAV-treated (KP cells: 0.01 or 0.05 multiplicity of infection (MOI) for 24, 48, and 72 hours; LLC cells: 1 or 2 MOI for 24, 48, and 72 hours) and nontreated cells were detached using trypsin (Gibco, catalog no. 25300-054) and washed in RPMI1640 with 10% heat-inactivated FCS (complete medium). Fc receptors were blocked using purified anti-mouse CD16/CD32 (1:50, BD Pharmingen). Cells were stained with LIVE/DEAD Fixable Yellow dead cell staining kit (1:100, Invitrogen, catalog no. L32250), anti-PD-L1 and anti-MHC class-I (H-2Kb; information regarding the antibodies can be found in Supplementary Table S2). Cells were washed, fixed, and permeabilized using the intracellular fixation and permeabilization buffer set (eBioscience, catalog no. 88-8823-88), according to the manufacturer's guidelines. Intracellular staining was performed using an anti-IAV nucleoprotein (NP; Supplementary Table S2). Cells were analyzed using a BD Fortessa X20 flow cytometer (BD Biosciences). Data were studied using Kaluza Analysis software, version 1.3 (Beckman Coulter).

Flow cytometry analyses of lungs from KP mice

Mouse lungs were harvested in RPMI1640 medium (Gibco) into gentleMacs C tubes (Miltenyi Biotec) and enzymatically digested with the Miltenyi Biotec tumor dissociation kit (catalog no. 130-096-730) by using the gentleMACS octo dissociator at 37°C, according to manufacturer's instructions. Tumor adjacent normal tissue was excluded at maximum so that only tumor tissue was included in these analyses. Tumor-bearing lung pieces were manually filtered through a 70 μm cell strainer. Erythrocytes were lysed in ammonium-chloride-potassium (ACK) buffer for 5 minutes and the reaction was halted with RPMI1640 medium. Approximately 2 million cells were transferred into a 96-well plate for flow cytometry staining. Fc receptors were blocked with anti-mouse CD16/CD32 (1:100, BD Pharmingen) along with Zombie Aqua Fixable Viability dye (BioLegend) for 10 minutes at room temperature. Cells were washed with PBS followed with specific antibody staining diluted in PBS for 15 minutes at room temperature. All antibodies used are listed in Supplementary Table S2. Mouse MHC class I tetramers [H-2K(b) chicken OVA 257-264 SIINFEKL-PE tetramer and H-2D(b) Influenza A PA (polymerase acidic protein) 224–233 SSLENFRAYV-BV421 tetramer] were kindly provided by the NIH Tetramer Core Facility. Tetramer staining was performed in PBS at room temperature for 15 minutes prior to staining of cell surface proteins. The anti-CD8 clone KT15 (at 1:200 dilution) was used in concert with tetramer staining to avoid antibody competition with the CD8 molecule for MHC class I molecule binding. After staining, cells were resuspended in PBS 4% paraformaldehyde for 10 minutes, washed twice in PBS and analyzed using a BD Fortessa X20 flow cytometer (BD Biosciences). Absolute cell counts were determined by using CountBright Absolute Counting Beads (Invitrogen, catalog no. C36950) following the manufacturer's recommendations. Results were plotted and analyzed using the FlowJo 10 software.

Unsupervised analysis of the flow cytometry data was performed using RStudio software 1.3.959 and the Excyted pipeline (https://github.com/maximemeylan/Excyted; ref. 29). T cells and NK and B cells panels were analyzed by the unsupervised strategy. Intensity values of events gated from live cells were normalized with the Logicle transformation. Uniform Manifold Approximation and Projection (UMAP) calculation and unsupervised clustering was performed with 10,000 events for each sample using k = 30 and Rphenograph (Leland McInnes arXiv:1802.03426, https://github.com/i-cyto/Rphenograph). Further reclustering in the T cells panel was carried out by rerunning the Excyted pipeline on selected CD3-positive clusters using k = 100.

Taqman assay for viral sensors and IFN-stimulated genes

RNA was extracted using the RNeasy Mini Kit (Qiagen, catalog no. 74136) from KP cells infected with 0.01 or 0.05 MOI of IAV for 24, 48, and 72 hours. Noninfected cells were used as control. The quality and quantity of extracted RNA were tested by using Agilent RNA 6000 Nano Kit (Agilent Technologies, catalog no. 5067-1511) and Agilent Bioanalyzer according to the manufacturers’ guidelines. Reverse transcription of RNA to cDNA was performed using 1 μg of total RNA using high-capacity cDNA Reverse transcription kit (Applied Biosystems, catalog no. 10400745) and Thermocycler Eppendorf nexus Mastercycler. For individual gene expression analysis, qPCR reactions were carried out by using FastStart Universal Probe Master Mix (Roche, catalog no. 4913949001) and inventoried TaqMan probes (FAM-MGB dye) specific for murine Tlr7 (Thermo Fisher Scientific, catalog no. Mm00446590_m1), Tlr3 (Thermo Fisher Scientific, catalog no. Mm01207404_m1), Ddx58 (Thermo Fisher Scientific, catalog no. Mm01216852_m1), Ifih1 (Thermo Fisher Scientific, catalog no. Mm00459183_m1), and Gapdh (Thermo Fisher Scientific, catalog no. Mm99999915_g1), according to manufacturer guidelines. These experiments were performed in triplicates. Gene expression was determined by fold change (2−∆∆Ct) comparing infected and noninfected samples.

TaqMan gene expression array was performed using the TaqMan Array Mouse Immune Panel (Applied Biosystems, catalog no. 4367786) following recommendations of the manufacturer, for the study of IFN-stimulated genes’ expression. The plates were read on a 7900HT Fast Real-Time PCR System (Thermo Fisher Scientific).

Bulk RNA-seq

Primary human lung tumors were obtained after resection surgery at Cochin Hospital (Paris). Written informed consent was obtained from the 8 patients before participation. The patients included in this study signed an informed consent form prior to inclusion. The research was approved (CPP Ile-de-France II no. 2012-06-12) by the medical ethics board at Cochin Hospital and conducted according to the recommendations in the Helsinki Declaration. Tumors were cut in two pieces of approximately 5 mm3: one part was infected with IAV (closed to MOI 1) for 24 hours, while the other part was not infected and served as control.

Total RNA was extracted from human tumors and tumor-bearing lungs from mice infected or not with IAV. RNA was extracted using the Maxwell 16 LEV simply RNA tissue kit (Promega, catalog no. AS120) and was quantified using the fluorometric qubit RNA assay (Life Technologies, catalog no. Q32852). RNA integrity (RIN) was determined with the Agilent 2100 bioanalyzer (Agilent Technologies). RNA-seq was performed at the GENOM'IC platform (Cochin Institute, Paris). To construct the libraries, 1 μg of high-quality total RNA (RIN ≥ 8 for murine tumors, RIN ≥ 5 for human tumors) was processed using TruSeq Stranded mRNA kit (Illumina, catalog no. 20020594). Briefly, after purification of poly-A containing mRNA molecules, these were fragmented and reverse transcribed using random primers. Replacement of dTTP by dUTP during the second strand synthesis permitted high strand specificity. Addition of a single A base to the cDNA was followed by ligation of Illumina adapters. Libraries were quantified by qPCR using the KAPA library quantification kit for Illumina libraries (Kapa Biosystems, catalog no. 5067-4626) and library profiles were assessed with the DNA high sensitivity labchip kit on an Agilent bioanalyzer. Libraries were sequenced on an Illumina NextSeq 500 system using 75 base-lengths read V2 chemistry in a paired-end mode. After sequencing, a primary analysis based on AOZAN software (ENS, Paris) was applied to demultiplex and control the quality of the raw data (based on FastQC modules, version 0.11.5).

Statistical and bioinformatics analyses

Comparisons of tumor growth were analyzed by using the two-way ANOVA with Sidak correction for multiple comparisons. Correlation between IAV infection and the expression of viral sensors, PD-L1 and MHC-I was calculated by Spearman correlation. Differences in cytokine expression between IAV-infected and noninfected KP cells were studied by linear regression with Benjamini–Hochberg correction for multiple testing. The percentage of tumor nodules with different grades per total lung lobule was compared between groups with the χ2 test. Data obtained from immunofluorescent staining were compared with the Mann–Whitney U test. Differences in IgM and IgG anti-IAV were studied with the ordinary one-way ANOVA with Sidak correction for multiple comparisons. Data obtained from flow cytometry and studied by the supervised gating strategy were analyzed by multiple Mann–Whitney tests with Benjamini–Hochberg correction, except for data concerning regulatory T cells (Treg), B cells activation, and myeloid subsets (one-way ANOVA) as well as PD-1 expression in OVA-tetramer+ as compared with IAV-tetramer+ CD8+ T cells (Mann–Whitney U test). Differences in clusters found by the unsupervised analysis of flow cytometry data were studied by the Mann–Whitney U test. All these statistical analyses were performed using GraphPad Prism software (version 9.2.0).

Differential gene expression analysis from RNA-seq data (mouse and human tumors) was performed using Galaxy Software (ARTBio platform, IBPS, Paris, France). Fastq files obtained were aligned using the HiSAT2 algorithm (on MM10 murine and on GRCH38 human reference genomes) and quality control of the alignment was performed with the RSeQC package. Reads were counted using htseq-count and the analysis of differential expression was performed with the DESeq2 package and Biomart (mouse GRC38p6). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) Biological processes were interrogated and gene set enrichment analysis (GSEA) was done using the R package GSEABase. These analyses were performed using R version 4.1.3.

The Cancer Genome Atlas LUAD analysis

For The Cancer Genome Atlas (TCGA) analysis, gene expression and clinical data from TCGA LUAD cohort (30) were obtained from the Genomics Data Portal (https://portal.gdc.cancer.gov) for n = 473 tumors. Fragments per kilobase million (FPKM) data were normalized and set to logarithmic scale. For the REVISE signature definition, human orthologs of genes overexpressed in infected compared with noninfected mice were obtained using the R package biomaRt. The Pearson correlation matrix of the expression of these genes in TCGA LUAD cohort was examined and clustered using hierarchical clustering with Euclidian distance and Ward criterion. Five groups were identified from the clustering, each with strong intragroup correlation and low correlation between groups. These groups were labeled 1 to 5. The impact on survival was estimated using Kaplan–Meier curves and log-rank test.

In all statistical studies, *, P < 0.05; **, P < 0.01; ***, P < 0.001; and ****, P < 0.0001.

OncoSg analysis

For oncoSg analysis, gene expression (log RNA-seq V2 RSEM) and clinical data (31) were obtained from cBioPortal (https://www.cbioportal.org) for n = 169 samples. The impact on survival was estimated using Kaplan–Meier curves and log-rank test with the R package survival.

GSE131907 single-cell RNA-seq analysis

For single-cell RNA-seq analysis, gene expression [log2 transcripts per kilobase million (TPM)] and metadata from cohort GSE131907 (32) were downloaded from the Gene Expression Omnibus portal. Only cells from lung tumor samples (Sample_Origin “tLung”, n = 11 samples) were kept. Data visualization and analysis was performed using the R package Seurat v4 (33).

COPD cohort analysis

Data were obtained from cohort GSE124180 (34) Gene counts and metadata were downloaded from the Gene Expression Omnibus portal and transformed into TPM values. Data visualization and analysis was performed using R.

Data availability statement

The data generated in this study are available at the European Nucleotide Archive study, accession number: PRJEB59490, or from the corresponding author upon reasonable request.

IAV infection enhances tumor growth in subcutaneous, orthotopic, and genetically-induced autochthonous LUAD

In an attempt to characterize the molecular modifications induced after IAV infection, KP and LLC cells were in vitro infected (H3N2 strain). We observed productive infection as revealed by viral NP expression, which increased between 24 and 72 hours after infection (Fig. 1A; Supplementary Fig. S1A and S1B), concomitant with upregulation of the viral sensors interferon induced with helicase C domain 1 (Ifih1), TLR3, TLR7, and DExD/H-box helicase 58 (Ddx58), in addition to CD274 and MHC-I (Fig. 1A). We also found that many genes encoding molecules involved in immune response regulation including the cytokines IL1α, TNFα, IL6, and IL13, the chemokines CXCL10 and CXCL11, and Nos2 or HMOX1 were highly increased by IAV in tumor cells, suggesting that IAV may influence the TME composition (Fig. 1B). In addition, IAV infection did not alter cell viability of LLC cells and reduced KP cell viability by 40% 72 hours after infection (Supplementary Fig. S1A and S1B).

We analyzed the effect of IAV infection in subcutaneous and orthotopic murine models of lung carcinoma cells injection. KP and LLC cells were first subcutaneously injected in B6(C)/Rj-Tyrc/c mice and intratumoral IAV infection (150 and 1,000 pfu/mice) was performed. Increased tumor growth was induced by IAV infection as compared with noninfected subcutaneous tumors both in the KP (Fig. 1C and D) and in the LLC tumor model, whatever the strain of IAV and the dose of virus injected (Supplementary Fig. S2). For the orthotopic model, KP cells were inoculated in mice by intravenous injection. As soon as lung tumors were detected around 13 days after cells injection, mice were intranasally infected with IAV (150 pfu/mice). Again, IAV infection significantly increased the growth of already established tumors (Fig. 1E and F).

We then studied the effect of intranasal IAV infection on lung tumor progression in a mouse model of genetically-induced autochthonous LUAD (the most common histologic subtype of NSCLC) driven by the expression of oncogenic KrasG12D and loss of p53 (KrasLSL-G12D/+Trp53flox/flox, KP mice; ref. 26). Tumor progression was monitored weekly by bioluminescence imaging and when 50% of mice developed detectable tumors (total flux <2 × 105 photons/second, week 11 after LTV inoculation), mice were intranasally administered with a sublethal dose of IAV or PBS (experimental design in Fig. 2A). Non–LTV-inoculated mice, all of which did not develop tumors, served as controls. IAV infection in tumor-bearing mice resulted in significantly larger tumors at 5 weeks after infection (Fig. 2B and C) and reduced survival (Fig. 2D) as compared with noninfected tumor-bearing mice. IAV infection resulted in a comparable reduced body weight in all infected mice with or without tumors (loss of 15%–20%, 10 days after infection; Fig. 2E). Furthermore, both control and tumor-bearing mice had detectable anti-IAV IgM and IgG titers in the serum following viral infection when sacrificed (Fig. 2F), which further confirmed that all mice were successfully infected. Histologic characterization of the lung tumors (performed as in ref. 26) revealed higher tumor grade in IAV-infected lungs compared with the noninfected ones (Fig. 2G). Viral hemagglutinin (HA) protein was detected in both epithelial pan cytokeratin (panCK+) tumor cells (median of 4.6% HA+ cells) and CD45+ immune cells (median of 2.2% HA+ cells; Fig. 2H and I), showing that IAV infects both tumor and immune cells and persists long term in the lungs (up to 5 weeks after infection). In addition, viral infection induced high PD-L1 expression in panCK+ malignant cells (from 2.8% to 32%, P = 0.009; Fig. 2J and K), consistent with the in vitro upregulation of PD-L1 after IAV infection in KP cells (Fig. 1A).

These results demonstrate that acute IAV infection of tumor and immune cells in the TME accelerates lung tumor growth.

IAV infection affects tumor-associated CD8+ T cells

To understand the underlying causes of the accelerated tumor growth upon IAV infection, we performed a comprehensive immune cell phenotyping analysis of the lungs of KP tumor-bearing and nontumor-bearing mice by flow cytometry, at sacrifice (Supplementary Fig. S3A–S3D describes the gating strategies). Although the distributions of CD4+ and CD8+ T cells, NK cells, B cells, and myeloid cells were similar between the groups (Supplementary Fig. S4A), the T-cell compartment was markedly affected by IAV infection. In particular, IAV infection led to an increase in effector and effector memory CD8+ T-cell populations in lungs both with and without tumors, concomitant with a decrease of naïve cells (Fig. 3A; Supplementary Fig. S4B). CD8+ T cells harboring a tissue-resident memory phenotype (TRM CD8+ T) were enriched in infected lungs as compared with the noninfected ones (Fig. 3B; Supplementary Fig. S4C). In contrast, no notable changes were observed in the tumor-infiltrating effector CD4+, TRM CD4+, and regulatory CD4+ T cell (Tregs) compartments (Supplementary Fig. S4D–S4F).

We then evaluated the exhaustion state of CD8+ T cells by characterizing the expression of negative immune checkpoints. Unsupervised analysis of the flow cytometry data from tumor-bearing mice with a T-cell marker panel using the Excyted pipeline (https://github.com/maximemeylan/Excyted; ref. 29), which performs automated cell gating and clustering, showed 16 T-cell clusters (Fig. 3C), based on the expression of the markers included in the panel (Fig. 3D). In-depth analysis of each cluster showed that five CD8+ T-cell clusters (clusters 2, 3, 6, 12, and 13) were differentially represented in infected and noninfected tumors (Fig. 3E). Among them, cluster 12, which was significantly overrepresented in IAV-infected tumors, corresponded to exhausted TRM CD8+ T cells expressing PD-1, TIGIT, LAG-3, CD39, CD69, and CD103 (Fig. 3E). This result was further confirmed using a classical gating strategy. The expression of PD-1, CTLA-4, TIGIT, LAG-3, or TIM-3 was significantly increased in TRM CD8+ T cells in tumor-bearing lungs following IAV infection, PD-1 being the highest expressed in IAV-infected tumor-bearing and control mice (Fig. 3F; Supplementary Fig. S4G). However, IAV did not induce expression of the five immune checkpoints in either the overall CD8+ T-cell compartment (Supplementary Fig. S4H) or the non-TRM CD8+ T cells (Supplementary Fig. S4I).

Because malignant cells in the KP mice express the SIINFEKL OVA peptide (27), we used specific MHC class I tetramers to analyze the specific T-cell responses against tumor (OVA tetramer) or viral (IAV tetramer) antigens. IAV-specific CD8+ T cells were strongly increased in number in IAV-infected tumors (Fig. 3G). In contrast, OVA-specific total CD8+ T cells were diminished in number in infected as compared with noninfected lung tumors (Fig. 3H). Furthermore, IAV infection also led to increased PD-1 expression in these OVA tumor-specific CD8+ T cells (from 17% to 32%, P = 0.048; Fig. 3I). Importantly, PD-1 expression was significantly higher in tumor-specific than in IAV-specific CD8+ T cells (Fig. 3J).

These data show that IAV infection promoted a decrease in the number of tumor-specific CD8+ T cells in the lungs, and that these cells are strongly exhausted, which suggests an impaired antitumor response in IAV-infected mice.

IAV induces minor changes in the NK-cell and B-cell compartments and no changes in myeloid cells

Unsupervised analysis of T, NK, and B cells from the lungs of KP tumor-bearing mice revealed 18 clusters (Supplementary Fig. S5A), which were separated in four groups of cells (B cells, CD11b+ memory B cells, T cells, and NK cells), as evidenced by the expression of the markers used in the panel (Supplementary Fig. S5B). Four clusters were significantly enriched (T-cell clusters 1 and 2, B-cell cluster 3, and CD11b+ memory B-cell cluster 4) and three excluded (NK-cell cluster 6, B-cell cluster 11, and CD11b+ memory B-cell cluster 13) in tumors of IAV-infected mice as compared with the noninfected ones (Supplementary Fig. S5C). Because unsupervised analysis of NK cells and B cells revealed distinct profiles between the different groups of mice, we further examined B-cell subsets by classical flow cytometry evaluation. Viral infection did not modify B-cell compartments based on the expression of IgM and IgD (Supplementary Fig. S5D), nor B-cell activation (Supplementary Fig. S5E). In addition, no significant impact of viral infection could be observed on myeloid-cell subsets (Supplementary Fig. S5F). Altogether, these results show modifications in B-cell and NK-cell clusters.

IAV infection induces a distinct transcriptional profile in tumor-bearing lungs

To decipher the mechanisms involved in the accelerated lung tumor growth we observed upon IAV infection, we analyzed the transcriptional profile of KP tumor-bearing and nontumor-bearing lungs from IAV-infected and noninfected mice, by RNA-seq analysis at sacrifice point. The four groups of mice were delineated in four well-defined clusters in a principal component analysis (Fig. 4A). IAV infection led to the upregulation of 273 genes and downregulation of 12 genes, as represented in the volcano plot (Fig. 4B).

Enrichment analyses on the 273 upregulated genes were carried out using GO and KEGG pathways. They revealed enrichment of pathways related to immune response (Fig. 4C), antiviral response, carcinogenesis, lipid metabolism, and drug metabolism (Fig. 4D). To further annotate the biological functions enriched in the 273 upregulated genes, we performed GSEAs. Here again, we observed upregulation of pathways involved in response to viruses (Fig. 4E), chemical carcinogenesis (Fig. 4F), drug metabolism (Fig. 4GJ), and cAMP signaling pathway (Fig. 4K). We performed an additional differential gene expression analysis between the four groups of mice by pairwise comparison. The Venn diagram showed 52 genes (Supplementary Table S3) that were specifically overexpressed only in IAV-infected tumors as compared with the other groups (Fig. 4L). Altogether, our data show that IAV infection induces a distinct transcriptional signature that distinguishes infected from noninfected tumors. The overexpressed genes are associated with antiviral response, lipid metabolism, and drug metabolism, as well as an immune response–related profile.

IAV infection induces a distinct transcriptional profile in ex vivo cultures of primary human lung tumors

To characterize the effect of IAV infection in human lung tumors, we infected ex vivo eight primary tumor pieces (seven adenocarcinoma, one squamous cell carcinoma) obtained immediately after surgery at the Cochin Hospital (Paris, France) with IAV (MOI around 1). Noninfected tumor pieces served as control. We detected viral infection of tumor cells by HA staining of cytokeratin expressing tumor cells (Supplementary Fig. S6A). We then examined the transcriptional profile by RNA-seq analysis 24 hours after infection. We found 27 overexpressed and 17 downregulated genes (log2 fold change>1 and P < 0.05; Supplementary Fig. S6B; Supplementary Table S4) associated with immune response processes (Supplementary Fig. S6C) and the immune antiviral response (Supplementary Fig. S6D). cGMP-PKG, PPAR, and Ras signaling pathways, influenza A pathway and TLR signaling pathways were also enriched in KEGG analysis (Supplementary Fig. S6E). These results demonstrate that IAV infection modulates the human lung TME, affecting both tumor-associated features (e.g., lipid metabolism and oncogenesis) and the immune response, at early stages of the infection.

The REVISE signature identifies patients with LUAD with poor survival

Because IAV infection induced a specific gene expression signature in lung tumor–bearing mice, we analyzed the cohort of human patients with LUAD from TCGA (30) to identify such a signature and study its impact on patients’ clinical outcome. Of the 273 genes upregulated in the mouse model, we identified 217 human orthologs with expression values in TCGA dataset. A correlation heat map of these genes revealed five modules, labeled 1 to 5 (Fig. 5A; Table 1). These subsignatures were identified by a strong internal correlation and by their low correlation between each other. To estimate which cell populations expressed these modules, we analyzed a single-cell RNA-seq dataset of LUAD from GSE131907 (32). We selected only lung tumor samples. Our cell type annotation based on the data provided by the original authors is shown in Fig. 5B. We observed the expression pattern of the full signature of 217 genes and of each module (Fig. 5C and D). Only module 1 was specific of epithelial cells. We named this gene signature module REVISE (Respiratory Virus Infection Signature in Epithelium; Table 1).

We further investigated the clinical relevance of the REVISE signature in human LUAD. Given the distribution of the REVISE expression in TCGA LUAD cohort, we chose the third quartile as cutoff between high and low expression (Fig. 5E). A high expression of the REVISE signature was found to be associated with a poor prognosis in TCGA LUAD cohort (Fig. 5F). To validate this, we obtained data from an independent cohort of human LUAD, OncoSg (31). We similarly separated the cohort into high and low expression of the REVISE signature (Fig. 5G) and found that there was a trend toward a significantly worse prognosis for patients with tumors having a high REVISE signature expression (Fig. 5H). Finally, to estimate whether the expression of the REVISE signature stems from previous infections of the lung, we analyzed RNA-seq data from large airways epithelium in patients with or without COPD (Supplementary Fig. S7). No significantly different expression of the REVISE signature was observed between patients experiencing COPD and the control cases, which could indicate that the expression of the REVISE signature in the tumor is related to tumor-specific mechanisms and is not due to preexisting inflammation like COPD.

Patients with lung cancer frequently exhibit a compromised immune response that makes them prone to microbial infection. Reciprocally, the consequences of viral infections and the ensuing response on lung cancer progression remains ambiguous. Several studies and clinical case reports have suggested that vaccination against IAV, yellow fever virus and even, SARS-CoV-2, leads to tumor regression (35–37). It has also been shown in murine models, that IAV infection prior to lung tumor initiation enables better control of tumor progression because of the expression of abnormal self-antigens before tumor challenge, which induce immune memory and cancer immunosurveillance (38, 39). On the other hand, recent studies have shown that SARS-CoV-2 infection leads to a worse outcome in patients with cancer (40), and epidemiologic studies evidenced an increased risk of developing lung cancer due to cumulative exposure to IAV (41, 42). In line with our observations herein that acute IAV infection leads to tumor progression, Kohlhapp and colleagues demonstrated that lung IAV infection of mice harboring distal melanoma enhances tumor growth and reduces survival (43). We found that tumor cells are infected with IAV and respond with specific transcriptional programs. Indeed, responses to live virus infection and vaccination can be distinct with vaccinations bringing additional adjuvant-induced responses. T cells specific to viral epitopes have also been found in tumors and, upon specific reactivation can lead to tumor growth control by creating an inflammatory environment within the tumor (44). We found that, upon acute IAV infection, the expansion of virus-specific T cells was accompanied by an exhaustion of tumor antigen-specific T cells. Together, the response of tumor cells to virus infection and the expansion of virus-specific T cells to acute IAV infection led to a lower antitumor immune response and an increase in tumor growth.

In conclusion, differences observed between studies could be argued because of the different mouse tumor models and viral concentrations used in each of them. In this respect, the KP mouse model used in the current study, harboring KrasG12D mutation with p53 deletion, finely resembles human lung cancer, and IAV infection may mimic its impact on human patients. In addition, this model is suitable to study the impact of IAV infection, because the Kras mutation itself is unlikely to directly affect the immune cells in the TME. Indeed, in a previous study, we found no difference in immune cell densities (mature dendritic and CD8+ T cells) between mutated and wild-type KRAS (10).

Mechanistically, IAV HA protein persisted in malignant cells and in immune cells 5 weeks after infection suggesting sustained responses in the TME. IAV considerably impaired tumor-specific T-cell responses. TRM CD8+ T cells were notably increased in the lung tumors following IAV infection, but, crucially, they were exhausted and unable to support an antitumor immune response. IAV infection induced the expression of checkpoint molecules, including PD-1, on tumor-specific TRMs, as well as a strong expression of PD-L1 in the lung epithelial tumor cells. PD-1 expression was significantly higher in tumor-specific TRMs as compared with IAV-specific TRM CD8+ T cells, suggesting that the antigen specificity of the TRMs drove their exhaustion. Echoing our results, Kohlhapp and colleagues showed that upon IAV lung infection, distal melanoma-specific CD8+ T cells migrated to the lungs and expressed high levels of PD-1. Although the main target in their study were not TRM but circulating T cells, PD-1 therapeutic blockade inhibited tumor growth in IAV-infected mice suggesting that PD-1 expression might not be related to TRM cells alone (43). In addition, although to a lesser extent as compared with PD-1, the expression of CTLA-4, TIGIT, LAG-3, and TIM-3 was also stimulated by IAV in our study and their involvement may be interesting to study further in our mouse model. The effect of IAV infection on T-cell exhaustion or T-cell dysfunction has already been described in humans (45) and mice (46) in responses to different viral strains (47) and may be due to the infection of antigen-presenting cells, which impairs their ability to cross-present antigens to CD8+ T cells (48).

Regarding the NK-cell and B-cell compartments, our unsupervised analysis revealed significant differences in seven clusters in the tumors of IAV-infected and noninfected mice. Among them, B-cell and CD11b+ memory B-cell clusters were indistinctly overrepresented and underrepresented. The markers included in the panel were not sufficient to more deeply analyze the specific characteristics of each of these clusters. The current study has implications for the evaluation of the B-cell compartment in future studies.

Using RNA-seq, we identified genes overexpressed in IAV-infected tumor-bearing mice. Strikingly, some of these genes were also upregulated when primary human lung tumors were infected ex vivo with IAV. Despite the different kinetics of the experiments (RNA-seq experiments were performed 5 weeks and 24 hours after infection for in vivo mouse models and ex vivo primary human tumors, respectively), the induction of IFIT1, OASL, and DUOXA2 was found in both cases, highlighting their importance in the process from an early timepoint. These three genes are well known for their antiviral role against respiratory viruses (49–51), although their implication in lung cancer has not been deeply studied yet. It will be interesting to assess the implication of these genes in lung cancer.

The lack of data on IAV infection experienced by patients from the public cohorts LUAD TCGA and OncoSg cohort prevented us from confirming its direct implication in the accelerated lung tumor progression. However, based on the murine signature, we could translate our data from murine experiments to human. The analysis of differentially expressed genes in the cohort of 273 patients with LUAD from TCGA database revealed a novel signature (REVISE), expressed by epithelial cells, that is likely predictive of an inflammatory gene signature that supports tumor growth, and that could stem from IAV or other previous infections, which may also affect the TME and tumor progression. Of clinical interest, the high expression of the REVISE signature was significantly associated with a poorer overall survival. We propose that the REVISE signature may be used to identify patients with poor clinical outcome.

In summary, our findings indicate a protumoral role of IAV infection in established lung tumors and highlight the impact of the virus on the TME and may explain the epidemiologic data showing increased cancer-specific deaths following IAV infection (52).

I. Garmendia reports grants from CARPEM (Cancer Research for Personalized Medicine), Postdoctoral Fellowship during the conduct of the study. A. Darbois-Delahousse reports grants from Institut National du Cancer during the conduct of the study. L.T. Roumenina reports personal fees from CommitBio; grants from Roche, CSL Behring, and Novartis outside the submitted work. K. Leroy reports personal fees and nonfinancial support from Roche and AstraZeneca; personal fees from Janssen, Amgen, and GSK outside the submitted work. D. Damotte reports grants from AstraZeneca outside the submitted work. M. Alifano reports personal fees from AstraZeneca, Roche, MSD, and IPSEN outside the submitted work. O. Lantz reports grants from Institut national du cancer during the conduct of the study; grants and personal fees from Biomunex and personal fees from Saber Sa outside the submitted work. No disclosures were reported by the other authors.

I. Garmendia: Conceptualization, formal analysis, investigation, methodology, writing–original draft. A. Varthaman: Conceptualization, formal analysis, investigation, methodology, writing–original draft. S. Marmier: Conceptualization, formal analysis, investigation, visualization, methodology, writing–original draft. M. Angrini: Investigation. I. Matchoua: Data curation, software, formal analysis. A. Darbois-Delahousse: Investigation. N. Josseaume: Investigation. P.-E. Foy: Investigation. L.T. Roumenina: Writing–review and editing. N. Naouar: Data curation, software, formal analysis. M. Meylan: Resources, data curation, formal analysis. S. Sibéril: Writing–review and editing. J. Russick: Validation, writing–review and editing. P.-E. Joubert: Writing–review and editing. K. Leroy: Investigation, writing–review and editing. D. Damotte: Formal analysis, writing–review and editing. A. Mansuet-Lupo: Formal analysis, investigation. M. Wislez: Validation, writing–review and editing. M. Alifano: Writing–review and editing. L. Menger: Methodology. I. Garcia-Verdugo: Resources. J. Sallenave: Resources. O. Lantz: Resources, writing–review and editing. F. Petitprez: Conceptualization, data curation, software, formal analysis, writing–review and editing. I. Cremer: Conceptualization, supervision, funding acquisition, methodology, writing–original draft, writing–review and editing.

We thank the Cochin Hospital for contributing to the tissue collection. We also thank the Center of Histology Imaging and Cytometry (CHIC) and the Center for Animal Experiment (CEF) facilities of the Centre de Recherche des Cordeliers.

This work was supported by the Institut National de la Sante et de la Recherche Medicale (INSERM), Sorbonne Universite, Universite de Paris, the LabEx Immuno-Oncology, the Institut National du Cancer (2016-PLBIO) and foundation ARC.

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Note: Supplementary data for this article are available at Cancer Immunology Research Online (http://cancerimmunolres.aacrjournals.org/).

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Supplementary data