Respiratory viral infection promotes the awakening and outgrowth of dormant metastatic breast cancer cells in lungs

doi:10.21203/rs.3.rs-4210090/v1

Download PDF

Biological Sciences - Article

Respiratory viral infection promotes the awakening and outgrowth of dormant metastatic breast cancer cells in lungs

https://doi.org/10.21203/rs.3.rs-4210090/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Breast cancer is the second most common cancer globally. Most deaths from breast cancer are due to metastatic disease which often follows long periods of clinical dormancy¹. Understanding the mechanisms that disrupt the quiescence of dormant disseminated cancer cells (DCC) is crucial for addressing metastatic progression. Infection with respiratory viruses (e.g. influenza or SARS-CoV-2) is common and triggers an inflammatory response locally and systemically^2,3. Here we show that influenza virus infection leads to loss of the pro-dormancy mesenchymal phenotype in breast DCC in the lung, causing DCC proliferation within days of infection, and a greater than 100-fold expansion of carcinoma cells into metastatic lesions within two weeks. Such DCC phenotypic change and expansion is interleukin-6 (IL-6)-dependent. We further show that CD4 T cells are required for the maintenance of pulmonary metastatic burden post-influenza virus infection, in part through attenuation of CD8 cell responses in the lungs. Single-cell RNA-seq analyses reveal DCC-dependent impairment of T-cell activation in the lungs of infected mice. SARS-CoV-2 infected mice also showed increased breast DCC expansion in lungs post-infection. Expanding our findings to human observational data, we observed that cancer survivors contracting a SARS-CoV-2 infection have substantially increased risks of lung metastatic progression and cancer-related death compared to cancer survivors who did not. These discoveries underscore the significant impact of respiratory viral infections on the resurgence of metastatic cancer, offering novel insights into the interconnection between infectious diseases and cancer metastasis.

Biological sciences/Cancer/Breast cancer

Biological sciences/Immunology/Tumour immunology/Immunosurveillance/Immunoediting

Biological sciences/Microbiology/Virology/Influenza virus

Biological sciences/Microbiology/Virology/SARS-CoV-2

Breast cancer is the most diagnosed cancer in women and is the second leading cause of cancer-associated deaths in the U.S.¹ After the initial remission, disseminated cancer cells (DCC) can stay dormant for years to decades⁴ before metastatic relapse, most commonly in lung, bone, and liver⁴. Both cell-intrinsic factors and the tumor microenvironment dictate if metastatic cells stay dormant or proliferate and form secondary tumors⁵. Importantly, perturbations of the tumor microenvironment such as with enhanced inflammation can be sufficient to increase metastasis⁵.

Viral respiratory infections are common: seasonal influenza affects over 1 billion people each year⁶ and, by March 2024, SARS-CoV-2 infection has already caused nearly 750 million cases of COVID-19⁷. Viral respiratory infections are typically associated with pulmonary inflammation, with a concomitant increase in pulmonary inflammatory cytokines such as IL-6 and interferons (IFN), and an expansion of immune cells, including neutrophils, macrophages, and T lymphocytes^2,3. Such inflammatory mechanisms, specifically involving IL-6/signal transducer and activator of transcription 3 (IL-6/STAT3)-signaling^8,9, neutrophils and neutrophil extracellular traps¹⁰, and the CD4⁺ cell/macrophage axis¹¹, have been identified as regulators of metastatic processes in cancer.

The observation that death rates from cancer rose in the first two years of the COVID-19 pandemic¹², not fully accounted for by COVID-19 deaths or delayed screening and treatment, prompts a critical hypothesis - that pulmonary viral infections, including SARS-CoV-2, increase cancer deaths by triggering the development of metastases from dormant cancer cells. We sought to test this hypothesis through a dual approach: examining the effects of viral respiratory infections (influenza virus and SARS-CoV-2) on breast cancer dormancy in mouse models and correlating SARS-CoV-2 infection among cancer survivors to metastatic progression and cancer mortality.

Mouse strains, influenza virus infection, and antibody treatments

Transgenic mouse models of breast cancer, using mammary tumor virus (MMTV) long terminal repeats, are widely used. In brief, MMTV-PyMT and MMTV-erbB2/neu/HER2 (MMTV-Her2) mice express the oncogenes Polyoma virus Middle T antigen (PyMT) and rat Erbb2 (encoding Her2), respectively, upstream of the MMTV promoter which confers expression in the mammary epithelium, as described elsewhere^10,13,14. The MMTV-PyMT transgene is congenic in the FvB background, and the MMTV-Her2 transgene is congenic in the FvB and C57Bl/6 backgrounds. MMTV-Her2 mice (FvB) were crossed with IL-6 KO mice as described^9,¹⁵.

Eight-week-old MMTV-PyMT and 12- to 14-week-old MMTV-Her2 mice were infected with 500 EIU Puerto Rico A/PR/8/34 H1N1 influenza A (IAV) through intranasal administration in 50 ml PBS. For viral administration, mice are anesthetized using 5% induction isoflurane and 2% maintenance, performed with a SomnoFlo Low-Flow electronic vaporizer machine in an induction chamber. After ensuring adequate anesthesia with slow and deep breathing, droplets of viral fluid are placed on the mouse’s nostrils. The mouse inhales the fluid through the nostrils. Once the fluid has been inhaled, the mouse is placed on a heating pad to recover.

For immune cell depletion experiments, mice were injected intraperitoneally with rat IgG as control (MP Biochemicals; cat# MPBio 0855951; Singapore), 100 mg anti-CD4 (Bio X cell, clone GK1.5; cat# BP003-1; Lebanon, NH) (Sup. Fig. 7), or 100 mg anti-CD8 (Bio X cell, clone2.43; cat#; Lebanon, NH) 1 day before IAV infection and every 6 days afterward, or 200 mg anti-Ly6G (Bio X cell, clone 1A8; cat#BP0075-1; Lebanon, NH) on the day of the flu infection, then 24 hours and every other day afterwards, until being euthanized. The University of Colorado Institutional Animal Care and Use Committee (IACUC) reviewed and approved all animal experiments, which were conducted in accordance with the NIH Guidelines for the Care and Use of Laboratory Animals.

SARS-CoV-2 MA10 propagation

Mouse-adapted SARS-CoV-2 MA10 (BEI Resources NR-55329) was propagated in Vero E6 cells (ATCC CRL-1586) as previously described¹⁶. Briefly, low passage Vero E6 monolayers were inoculated at an MOI of 0.01 with SARS-CoV-2 MA10. When Vero E6 monolayers exhibited 70-75% CPE (2-3 days post inoculation), supernatants were collected, clarified by centrifugation, supplemented with an additional 10% FBS, aliquoted and stored at -80°C. SARS-CoV-2 titers were determined by plaque assay on Vero-E6 cells. Vero-E6 cells were maintained at 37°C in Dulbecco’s Modified Eagle medium (DMEM, HyClone 11965-084) supplemented with 10% fetal bovine serum (FBS), 10 mM HEPES (pH 7.3) and 100 U/mL of penicillin-streptomycin.

SARS-CoV-2 MA10 infection of mice

C57BL/6J MMTV-Her2 mice at 14-19 weeks of age were anesthetized by intraperitoneal injection (i.p.) with a mixture of ketamine (80 mg/kg) and xylazine (7.5 mg/kg) in a volume of 100-200 uL. Fully anesthetized mice were inoculated intranasally (i.n.) with 10⁴ PFU of SARS-CoV-2 MA10 diluted in PBS supplemented with 1% bovine calf serum by administration of 25 uL of inoculum in each nostril for a total volume of 50 uL. Mouse weights were collected daily for 15 days, and mice inoculated with SARS-CoV-2 MA10 exhibited weight loss beginning at 2 dpi, with greatest loss achieved at 3-4 dpi as previously reported¹⁶. As controls, C57BL/6J MMTV-Her2 mice were mock inoculated with 50 uL of PBS/1% bovine calf serum.

Immunohistochemistry (IHC) and immunofluorescence (IF) staining

Lungs and mammary glands were collected and fixed in 10% neutral buffered formalin overnight, transferred to 70% ethanol the next day, and then embedded in paraffin. Tissue was sectioned (5 μm) and used for IHC and IF. Slides were deparaffinized in three incubations of 15 min in Histo-clear (Fisher Scientific, cat# 50-899-90147; Hampton, NH) then descending 10-min ethanol incubations: three at 100%, followed by 95%, and 70% followed by 10-min H₂O incubation. Heat-induced antigen retrieval was carried out for 10 min in a pressure cooker in citrate buffer (10 mM Citric Acid, pH 6.0). For IHC, samples were incubated in 1% H₂O₂ for 15 min to block endogenous peroxidase activity. Permeabilization was performed using 0.1% normal goat serum in 0.4% Triton-X 100 in PBS for 30 min. Sections were blocked for 1 h at room temperature (RT) with blocking solution (Abcam cat# AB64226; Cambridge, UK) containing M.O.M. blocking reagent (Vector Laboratories cat# MKB2213-1; Newark, CA), incubated with primary antibodies (Sup. Table. 3) at 4°C overnight in antibody diluent (Abcam cat# 64211; Cambridge, UK), then washed 3 times for 30 minutes each in 0.1% triton-X 100 in PBS. For IHC samples, sections were incubated in ImmPRESS HRP goat anti-rabbit or rat IgG polymer detection kit (Vector Laboratories cat# MP-7451/MP7404; Newark, CA) and ImmPACT DAB substrate, Peroxidase HRP (Vector Laboratories cat# SK4105; Newark, CA) according to the manufacturer’s instructions. IHC slides were mounted using micromount mounting medium (StatLab, cat# MMC0126; McKinney, Texas). For IF, sections were incubated with secondary antibodies for 1 h at RT in antibody diluent (Abcam cat# 64211; Cambridge, UK). Sections were then washed in 0.1% Triton-X 100 in PBS 3 times for 30 min each and were mounted using fluoroshield mounting media with DAPI (Abcam cat#104139). Immunofluorescence images were collected using a Zeiss Axiovert 200m fluorescence microscope. IHC images were collected using a Keyence BZ-X800 microscope. Sections staining, image capturing, and image analysis were done manually using ImageJ and were carried out by a researcher who was blinded to sample identities. Subsequent grouping and graphing were done by a different lab personnel unblinded after image analyses and quantification were completed.

Bronchoalveolar Lavage Fluid (BALF) processing

Bronchoalveolar lavage (BAL) was performed with 1 mL of PBS (ThermoFisher cat#14190-144; Waltham, MA) after mice were euthanized. BALF was collected and centrifuged at 500 x g for 5 min at 4ºC. Supernatant was flash frozen in liquid nitrogen and stored at -80ºC until analysis. Cells were resuspended in FACS buffer (PBS with 2% FBS and 2 mM EDTA) and were counted manually.

Flow cytometric analyses

Cells recovered from BALF were stained with antibodies (Extended Data Table. 3). Alternatively, whole lungs were harvested and digested using the method described elsewhere¹⁷. Briefly, lung digestion mix (1.5 mg/mL collagenase A (Sigma Aldrich cat# COLLA-RO; St. Louis, MO), 0.4 mg/mL deoxyribonuclease I (Worthington cat# LS002139; Lakewood, NJ), 10 mM HEPES pH 7.2, 5% FBS) was injected into the lungs through cannulae and were incubated at 37°C for 30 min. Digested lungs were passed through a 50 mm cell strainer and red blood cells were lysed using hemolytic buffer (150 mM NH₄Cl, 1 mM NaHCO₃, 1.1 mM Na₂EDTA). Single cells were resuspended in FACS buffer and stained with antibodies (Extended Data Table 3) for flow cytometry. Data were collected on the LSR II flow cytometer (BD Biosciences) and analyzed with FlowJo software v10.

Fixed single cell RNA-seq

Cells exhibiting >80% viability were fixed in a 4% formaldehyde solution and using the Chromium Next GEM Single Cell Fixed RNA Sample Preparation Kit (10X Genomics, Pleasanton, CA). The whole transcriptome probe pairs (10X Genomics) were added to the fixed single cell suspensions to hybridize to their complementary target RNA during an overnight incubation at 42°C. After hybridization, unbound probes were removed by washing. The fixed and probe-hybridized single cell suspensions were loaded onto a Chromium X (10X Genomics) microfluidics instrument to generate partitioned nanoliter-scale droplets in oil emulsion. The target is for each droplet to contain a barcoded gel bead, a single cell, and enzyme Master Mix (10X Genomics) for probe pair ligation and gel bead primer barcode extension. The droplets in oil emulsion were placed in a thermal cycler for 60 min at 25°C, 45 min at 60°C, and 20 min at 80°C. The single cell-barcoded, ligated probe products underwent library preparation using standard 10X Genomics protocols in preparation for Illumina next-generation sequencing. The gene expression library derived from single cell-barcoded, ligated probe product were sequenced as paired-end 150 bp reads on the Illumina NovaSeq 6000 (Illumina, San Diego, CA) at the University of Colorado Genomics Shared Resource (Aurora, CO, USA) at a target depth of 20,000 reads per cell for all samples.

Data Processing for single cell RNA-seq analysis

Single cell RNA-seq (scRNAseq) fastq files were processed using Cell Ranger software (version 7.1.0)¹⁸ from 10X genomics to assign reads to genes based on Cell Ranger’s Chromium mouse transcriptome probe set (version 1.0.1). The counts were analyzed using the Seurat R package¹⁹ and cells with less than 100 genes and genes that were seen in fewer than 10 cells were excluded. The R package scDblFinder²⁰ was used to identify and subsequently remove doublets from the data. Based on the distribution of UMIs, gene counts, and percentage of mitochondrial reads, data were filtered to remove cells with fewer than 200 and more than 7,500 UMIs or genes detected and mitochondrial reads greater than 2.5%. The data were then log transformed and scaled, regressing out cell cycle difference score, total UMI and % mitochondrial reads.

Principal Component Analysis (PCA) was performed using the top 2,000 variable genes. PCs (N=30) that captured most of the variation were then included in further data processing steps. Clusters were identified (at a resolution of 1.8) using the K-nearest neighbors’ algorithm. The top 100 enriched genes (with an adjusted P < 0.05 and higher average fold changes) within each cluster and within each sample were used for performing over-representation analysis (ORA)²¹ with gene sets from the MSigDB C8 collection²², PanglaoDB²³, MSigDB²⁴, GO biological processes²⁵ and Hallmark KEGG pathways²⁶ databases, annotated using enriched gene sets and expression of canonical cell type markers. Differentially expressed genes (DEGs) were identified using the Wilcoxon Rank Sum test within each of the cell types identified for the indicated comparisons. Gene set enrichment analysis (GSEA) was performed using the clusterProfiler R package (v 4.6.2) for the indicated comparisons and within cell types of interest. Benjamini-Hochberg method was used to calculate the adjusted P values. Raw and processed scRNAseq data will be deposited to the Gene Expression Omnibus.

Influenza virus RNA quantification

Whole lung tissue was homogenized, and RNA was isolated via TRIzol/chloroform extraction per the manufacturer’s protocol (ThermoFisher; Waltham, MA and MilliporeSigma; St. Louis, MO, respectively). RNA was reverse transcribed with an iScript cDNA synthesis Kit (Bio-Rad Laboratories, Inc., Hercules, CA) and the viral load was determined by qPCR for the PR8 acid polymerase (PA) gene compared to a standard curve of known PA copy numbers as previously described²⁷.

Quantification and statistical analyses (mouse models)

Statistical analyses were performed using Prism 10.2.1 software (GraphPad). Investigators were not blinded to allocation during virus (IAV or SARS-CoV-2) inoculation or antibody treatment. Quantification and image analysis were done in a blinded manner. N indicates number of mice per group. A minimum of 3 slides per mouse were used for image analysis. Total Her2⁺ cell counts (Fig. 1c, Fig. 2b), Her2⁺ cells, and Her2⁺ Ki67⁺ cells were counted manually using ImageJ. Three lung sections at least 50 µm apart per mouse were counted and summed. For other image quantifications, whole lung images were divided into fields using ImageJ’s grid function, 8-10 fields were selected at random per image and counted. For experiments with two groups, a two tailed student t-test was used; for experiments with more than one group, one-way ANOVA tests were used unless otherwise stated. Data were expressed as mean ± standard deviation (s.d.). P-values ≤ 0.05 were interpreted as evidence against the null hypothesis (i.e. no effect, no difference).

Human Observational Data

For the human follow-up studies, we selected SARS-CoV-2 infections as the driver virus as SARS-CoV-2 infection due to mandatory reporting during the first phases of the pandemic provides an opportunity to utilize real-world data to investigate the hypothesis that respiratory viral infections promote metastatic disease. We used two complementary datasets: 1) The UK Biobank, a population-based study including 502,356 adult volunteers aged 40 to 69 years at recruitment from 2006 to 2010^28,29. 2) Breast cancer patients/survivors from the Flatiron Health's nationwide electronic health record (EHR)-derived database ^30,31.

Study 1. Population-based analyses in the UK Biobank

We used data from the UK Biobank, a population-based study including 502,356 volunteers aged 40 to 69 years at recruitment from 2006 to 2010^28,29. Participants provided data on lifestyle, anthropometry, exposures, sociodemographic factors, medical history, and medications at baseline. Previous cancer diagnoses were obtained through (consented) linkage to the national cancer registry and confirmed SARS-CoV-2 test positivity status through linkage to national registers. Mortality data were obtained from the national death registries (NHS Digital, NHS Central Register, National Records of Scotland). We considered all-cause mortality, non-COVID-19 mortality (by excluding deaths with ICD codes U07.1 and U07.2³² or any death within one month of the latest recorded test positive result), and cancer mortality (considering cause of death with ICD codes listed in Extended Data Table 1).

As summarised in Figure 4a, we included 502,356 participants, excluding those who withdrew from the study. We excluded 65,374 participants due to missing data on covariates including sex, age, BMI, ethnicity, smoking status, alcohol consumption, education, employment status, and household income (N= 63,557); missing date on SARS-CoV-2 testing when the primary cause of death was COVID-19 (N=129); and missing cancer diagnosis date if the primary cause of death was cancer (N=1,688), leaving 436,982 participants. Of these, 91,518 had been diagnosed with cancer at the latest follow-up (1^st June 2022). Overall, 22,597 participants had died before the start of the COVID-19 pandemic (here defined as 1^st January 2020) (N=14,974) or were diagnosed with cancer after that date (N=9,543) and were therefore excluded. Participants diagnosed with multiple cancers before baseline were also excluded (N= 6,654).

Of the 60,347 participants with a cancer diagnosis before 1^st January 2020, a total of 3,462 had been reported to have tested positive for SARS-CoV-2. To ensure that test-positive and test-negative participants had similar cancer risk profiles, we adopted a non-parametric matching (without replacement) approach³³ to identify (up to) 10 test-negative participants for each test-positive participant. We first matched for the cancer diagnosis date (prior to 1^st January 2020) using generalised full matching³⁴. In a second step, we performed an exact matching based on cancer type and sex. We finally matched for age, ethnicity, smoking status, alcohol consumption, education, employment status, and household income using the nearest neighbour method, an algorithm based on propensity score matching. All the matching steps were without replacement. The resulting matched population included 28,855 participants: 3,400 test-positives matched to 25,455 test-negatives. Restricting this matched population to those who tested positive before vaccine rollout (here defined as 1^st December 2020) resulted in a total of 7,705 participants including 916 test positives and 6,789 matched test negatives.

Using test positivity as the predictor, we ran a series of unconditional logistic regression models for the three outcomes (all-cause, non-COVID-19, and cancer mortality). Models were adjusted for all matching factors to account for possible residual confounding. As a sensitivity analysis, we considered participants with cancer diagnosed at least five or ten years before the start of the COVID-19 pandemic in the UK by excluding the test-positive participants (and their matched test-negative controls) who were diagnosed with cancer between 1^st January 2015 and 31^st December 2019 (N=538 test positives and N=4,107 test negatives), and between 1^st January 2010 and 31^st December 2019, respectively (N=316 test positives and N=2,437 test negatives).

Data availability: This study used the UK Biobank resource under application number 69328 granting access to the corresponding UK Biobank genetic and phenotype data. The UK Biobank received ethical approval from the North West Multi-centre Research Ethics Committee (REC reference: 11/NW/0382) to obtain and disseminate participant data and samples (http://www.ukbiobank.ac.uk/ethics/).

Study 2. Flatiron - EHR-based Analyses

Data source

Flatiron Health's nationwide electronic health record (EHR)-derived database includes deidentified data from ~ 280 US cancer clinics (~ 800 sites of care). The database is longitudinal, comprising de-identified patient-level structured and unstructured data, curated via technology-enabled abstraction^30,31. The majority of patients in the database originate from community oncology settings; relative community/academic proportions may vary depending on study cohort. Institutional Review Board approval of the protocol was obtained prior to study conduct and included an informed consent waiver.

Included in our study were women aged at least 18 years old at the time of initial cancer diagnosis, and who had:

Early Breast Cancer. The cohort includes a probabilistic sample of patients with a diagnosis of Stage I - III Breast Cancer on or after January 1, 2011, including those who presented with non-metastatic disease but who subsequently developed recurrent or progressive disease, with at least two visits occurring on or after January 1, 2011 or -
Metastatic Breast Cancer. The cohort includes a probabilistic sample of patients diagnosed with Stage IV breast cancer on or after January 1, 2011, and those who presented with earlier stage breast cancer but who subsequently developed metastatic disease on or after January 1, 2011, and who had at least two clinic encounters evident in the database occurring on or after January 1, 2011.
Adult female patients ages 18+ years at the initial diagnosis

Real-world data source: The index date was defined as the date of the initial diagnosis of breast cancer. The COVID-19 status was defined positive if any COVID diagnosis (ICD codes B97.29, B97.21, J12.81, B34.2, U07.1) was made after the index date, and before the diagnosis of lung metastases or the last follow-up date. The start date of COVID-19 positivity status was the earliest COVID-19 diagnosis date. Baseline characteristics of gender, race, ethnicity, and age at index date were obtained from structured data.

Analyses: Baseline characteristics were summarized using descriptive statistics. Cause-specific analysis was conducted (death was censored). Univariable and multivariable Cox Proportional Hazard Models were used to evaluate the effect of COVID-19 diagnosis on the risk of metastasis to the lungs, in which COVID-19 diagnosis status was treated as a time varying covariate. The multivariable model adjusted for patient characteristics considered relevant, including age, race ethnicity, and gender. The unadjusted and adjusted hazard ratio with the corresponding 2-sided 95% confidence interval were reported. The two-sided likelihood ratio tests were conducted. The significant level was 0.05. Time to metastases to the lungs was defined as time from index date to date of metastases to the lungs. Patients without a date of pulmonary metastases were censored at the last confirmed activity date or death. Last confirmed activity was defined as the latest date of vitals record, medication administration, or reported laboratory tests/results. The statistical analyses were conducted using R version 4.1.0³⁵.

Influenza A virus infection awakens dormant breast cancer cells in lungs.

The metastatic process of cancer cells involves shedding, intra/extravasation, escaping immunosurveillance, establishment of dormancy at distant sites, and eventually awakening of dormant disseminated cancer cells (DCC)^36,37. To study the effects of influenza virus infection on awakening of dormant breast DCC in the lung, we used the MMTV-erbB2/neu/HER2 mouse model (hereafter MMTV-Her2) where mice overexpress rat Neu (Erbb2; paralog of human Her2) in epithelial mammary gland cells, a well-established mouse model of breast cancer metastatic dormancy^14,38. Her2⁺ early lesion cells in the mammary glands seed the lungs and other organs with DCCs within 12-14 weeks of life where they remain largely as dormant single cells for up to one year before progressing to overt metastatic disease³⁹. Thus, this model recapitulates the persistence of dormant DCCs in lungs and bone marrow in individuals who remain in remission for years to decades.

MMTV-Her2 mice (FVB background) were infected with a sublethal dose of influenza A virus (IAV) (Fig. 1a). Infected mice lose weight and recover by day 11-12 days post infection (dpi) (Extended Data Fig. 1a), and wild type (WT) and MMTV-Her2 mice elicit a similar inflammatory response, with increased cellularity of bronchoalveolar lavage (Extended Data Fig. 1b). The kinetics of viral clearance were similar between WT and MMTV-Her2 mice, where IAV RNA copies peaked around 6 dpi, with a 100-1000-fold reduction in viral load from 9-15 dpi (Extended Data Fig. 1c), suggesting that the presence of early DCCs in the lung does not curtail the initial virus-induced inflammatory response.

Lungs of MMTV-Her2 mice (FvB background) were harvested 3, 6, 9, 15, 28, and 60 dpi (Fig. 1a) and examined for the abundance of Her2⁺ cells (Fig. 1b, c) as reported³⁸. Consistent with previous work^38,40, we observed a small number of isolated DCC or small clusters (<10 cells) in the lungs prior to IAV infection. Strikingly, metastatic burden increased 100-1000-fold between 3 dpi to 15 dpi, the number of pulmonary Her2⁺ cells remained elevated even at 28 dpi and 60 dpi (Fig. 1b, c), and the large increase in metastatic burden was still evident at 9 months (Fig. 1d). IAV-mediated expansion of Her2⁺ DCC was similarly observed in the lungs of MMTV-Her2 mice in the C57BL/6J background at 15 dpi (Fig. 1e). Notably, the resultant expanded Her2⁺ cells exhibit a diffuse non-epithelial like architecture in the lungs, unlike the epithelial-like clusters and metastasis (>100 cells/cluster) of DCC observed in the lungs of MMTV-Her2 mice that are >10 months old (Extended Data Fig. 1d and ^38,40).

We performed similar infection experiments in MMTV-PyMT transgenic mice that express polyoma virus middle-T oncoprotein in the mammary gland and also show early dissemination but a shorter-term dormancy in the lungs⁴⁰. MMTV-PyMT mice demonstrated an increased number of small tumor clusters in the lungs following influenza A virus infection (Fig. 1f). All together, these studies show that influenza virus infection can promote DCC expansion in multiple models of breast cancer DCC dormancy.

IAV-triggered IL-6 induces DCC awakening and phenotypic transitions

When we examined the proliferation of DCC in the lungs, we found a significant increase in the percentage of Her2⁺ cells expressing Ki67 (a marker of all cycle phases except G0) beginning at 3 dpi and peaking at 9 dpi (Fig. 2a, b). Although the fraction of Her2⁺ cells that express Ki67 decreased by 15 dpi, the total number of Her2⁺ cells expressing Ki67 remained highly elevated relative to baseline even 60 dpi given the overall increase in DCC burden in the lungs (Fig. 2b). These results indicate that, following IAV infection, DCC in the lungs experience a period of awakening leading to an increase in metastatic burden.

Dormant DCC in the Her2+ and PyMT models are characterized by a ZFP281-driven mesenchymal-like state and express genes such as vimentin, while loss of ZFP281 allows the DCC to adopt a more epithelial-like phenotype characterized by the expression of epithelial markers such as EpCAM and E-cadherin when exiting dormancy⁴⁰. Consistent with previous results, most dormant DCC present in uninfected lungs expressed vimentin and not EpCAM (Fig. 2c-f). The percentage of Her2⁺ cells expressing vimentin was not significantly affected early after infection (3-6 dpi). However, at 9 dpi the percentage of Her2⁺ cells expressing vimentin was decreased to ~50%, with a further decrease to <20% at 28 dpi (Fig. 2d). In contrast, early during IAV infection (3 dpi), a substantial fraction of Her2⁺ cells acquired EpCAM expression, associated with the awakening of DCC (Fig. 2e, f). This increase was largely transient: most Her2⁺ cells were EpCAM negative after 6 dpi, although the percentage of EpCAM⁺Her2⁺ cells remained elevated compared to uninfected lungs (Fig. 2f). Thus, while the loss of the mesenchymal marker is sustained after infection, there is a transient acquisition of a more epithelial phenotype, and the population remains mixed at subsequent timepoints. In all, these data indicate that viral infection is signaling to dormant DCC to attenuate the mesenchymal-like state and adopt a hybrid state that allows for dormant DCC awakening.

Inflammatory cytokines such as IL-6 and IL-1 are known to promote cancer malignancy and metastases^41-43. In addition, IL-6 produced during acute inflammation resulting from biopsy or chemotherapy contributes to the development of lung metastatic outgrowth of disseminated mammary tumor cells ^9,10. IL-6 (but much less IL-1β) is abundantly produced during IAV infection, in part due to the replication of the virus in lung epithelial cells⁴⁴. Similarly, we also detected high levels of IL-6 in bronchoalveolar lavage fluid (BALF) from wild-type and MMTV-Her2 mice following IAV infection, with very low levels of IL-1β (Extended Data Fig. 2a, b).

To determine whether IL-6 production triggered by IAV infection contributes to the awakening of dormant DCC, we used MMTV-Her2 mice crossed with IL-6 KO mice in the FVB background¹⁵. IL-6 KO:MMTV-Her2 mice were infected with IAV and harvested at 9 and 28 dpi (at this dose of IAV, all mice recovered without excessive weight loss). Prior to infection, there was no difference in the number of dormant Her2⁺ cells between IL-6 KO:MMTV-Her2 and MMTV-Her2 lungs (Fig. 2h, i), and these mice developed primary tumors requiring sacrifice with similar timing at older ages (Extended Data Fig. 3a). Thus, IL-6 is not required for primary tumor growth or for early cancer cell dissemination to the lungs. Strikingly, the number of Her2⁺ cells in lungs of IAV-infected IL-6 KO:MMTV-Her2 mice was drastically decreased compared with infected MMTV-Her2 mice at both 9 and 28 dpi (Fig. 2h, i), with substantial reductions in Ki67⁺Her2⁺ cells (Fig. 2j). Similarly, in the MMTV-PyMT mouse model of breast cancer metastasis, IAV-induced proliferation of PyMT⁺ small lesions and formation of micro-metastases in the lungs was dampened by IL-6 deficiency (Extended Data Fig. 3b, c). Staining for vimentin demonstrated that most Her2⁺ cells in lungs of IL-6 KO:MMTV-Her2 mice retain vimentin expression, supporting a dampened conversion from dormancy to awakening (Fig. 2k). These results indicate that IL-6 triggered by IAV infection plays a key role in mediating dormant DCC reawakening, proliferation, and phenotypic changes.

CD4 T cells are required for maintenance of expanded DCC post-IAV infection.

While IL-6 was essential for the awakening and the initial marked expansion of dormant DCC, minimal levels of IL-6 were detected in BALF of MMTV-Her2 mice 15 dpi (Extended Data Fig. 2a), suggesting the presence of other factors that promote survival post-expansion of DCC at later times post-infection. While recruitment of neutrophils to the lung occurs by 3 days post-infection with IAV, CD4 T cells, CD8 T cells, and B cells accumulate in the lung from around day 9 dpi in both WT and MMTV-Her2 mice (Extended Data Fig. 4a-d). Infection with IAV has also been shown to induce the formation in the lungs of inducible bronchus-associated lymphoid tissues (iBALT), lymphoid organizations that include primarily CD4 cells together with B cells. iBALT can be detected in the lungs long after the infection (up to 100 dpi)⁴⁵. Accordingly, we also detected these CD4 cell and B cell-enriched lymphoid organizations in the lung sections of WT and MMTV-Her2 mice 28 dpi (Fig. 3a; Extended Data Fig. 4f, g). As expected, B cells in these iBALT are also positive for the germinal center B cell marker GL7. In contrast to CD4 cells, very few CD8 cells were present in these lymphoid structures in either WT or MMTV-Her2 mice (Fig. 3a). Interestingly, co-staining of CD4 with Her2 revealed the selective presence of DCC in proximity to high-density clusters of CD4 cells. Regions lacking CD4 cells also lack Her2+ cells (Fig. 3b).

These results suggested that CD4 cells could be contributing to the maintenance of awakened DCC for longer periods post-IAV infection. We therefore examined the effect of depleting CD4 cells by administration of an anti-CD4 antibody to MMTV-Her2 mice starting the day prior to IAV infection (day -1) (Extended Data Fig. 5). Analysis of Her2⁺ cells in the lung sections 28 dpi revealed a drastic decrease of the number of DCC in anti-CD4-treated mice relative to control IgG-treated mice (Fig. 3c-e), demonstrating the contribution of CD4 cells to the maintenance of DCC post-awakening with influenza virus infection. In contrast, CD4 cell depletion (day -1) did not affect the numbers of Her2⁺ cells at 9 dpi (Extended Data Fig. 6a, b), consistent with the delayed accumulation of CD4 cells late during the infection (Extended Data Fig. 4b). Notably, the number of Her2⁺ cells in the lung was also significantly decreased when the depletion of CD4 cells was initiated 10 dpi (Fig. 3d), supporting the contribution of CD4 cells later during the infection. Together these data show that IL-6 (but not CD4 cells) contributes to the initial awakening and expansion of dormant DCC, but that later during the infection following the recruitment of T cells, CD4 cells are required for the maintenance of the awakened DCC.

Previous studies have shown how neutrophil extracellular traps produced during inflammation can awaken dormant cancer cells in the lungs⁴⁶. However, in contrast to CD4 cell depletion, the depletion of neutrophils with an anti-Ly6G antibody at the time of IAV infection did not alter the numbers of Her2⁺ cells in the lung (Extended Data Fig. 6c-e). Similarly, depletion of CD8 cells at the time of the infection had no effect on the presence of DCC (Fig. 3e), consistent with the paucity of CD8 cells in the lungs 28 dpi (Fig. 3a). Thus, maintenance of the awakened lung DCC following IAV infection is selectively dependent on the presence of CD4 cells.

Interestingly, while only a low number of dispersed CD8 cells were present in the lungs 28 dpi in MMTV-Her2 mice, we found an increased accumulation of CD8 cells in the lungs of infected mice when CD4 cells were depleted (Fig. 3f). These results suggested that CD4 cells may repress the recruitment of CD8 cells to the lung, thus potentially compromising the immune surveillance against the awakened DCC. We therefore tested the effect of depleting both CD4 and CD8 cells on the maintenance of lung DCC following IAV infection. While CD4 cell depletion results in a marked reduction of Her2⁺ cells in the lungs of infected mice, the dual depletion of CD8 cells and CD4 cells partially restored the numbers of Her2⁺ cells in the lung (Fig. 3e). Thus, the effect of CD4 cells in the maintenance of awakened DCC in the lung post-IAV infection is in part mediated by repressing CD8 cell mediated immune responses.

We then examined whether the presence of awakened DCC in the lungs following IAV infection could reprogram the recruited T cells to a more suppressive or suppressed state by performing single cell RNA-seq (scRNAseq) of lungs of WT and MMTV-Her2 mice 9 and 15 dpi with IAV, at points where the accumulation of T cells in the lungs is high (Extended Data Fig. 7a-d). As expected, multiple immune cell types such as macrophages, natural killer (NK) cells, B cells, effector CD4 cells, CD4 IL7-receptor+ cells, effector CD8 cells, and proliferating effector CD8 cells were present in the lung at both 9- and 15 dpi (Extended Data Fig. 7b). We had two replicates for most conditions, which exhibited very similar gene expression patterns (Extended Data Fig. 7e, f). IAV infection induced type I and II interferon (IFN) responses across these cell types, as expected (Extended Data Fig. 8). We examined the phenotype of effector CD4 cells in WT and MMTV-Her2 mice. Interestingly, there was substantially increased expression of a selective subset of genes in effector CD4 cells from MMTV-Her2 mice relative to effector CD4 cells in WT mice. In particular, Tnfaip3, Zfp36l2, Dusp5, Dusp1, Klf6, Pdcd4, and Ctla4 were highly upregulated in effector CD4 cells from MMTV-Her2 mice relative to WT mice (Fig. 3g and Extended Data Resource 1). Tnfaip3 (which encodes the E3 ubiquitin ligase A20) suppresses anti-tumor activity of CD8 T cells⁴⁷, whilst Zfp36l2 (which regulates RNA stability through AU-rich elements) restrains CD8 cell activation and expansion^48,49. Dusp5 (which encodes a dual phosphatase) suppresses T cell proliferation and promotes their survival⁵⁰. Klf6 and Dusp1 are markers of central and resident memory T cells ^51,52. Ctla4 induces Pdcd4 in cytotoxic T cells, and Pdcd4 deficiency enhances their anti-tumor effector functions⁵³. In addition, comparing memory-like CD4⁺IL7R⁺ cells⁵⁴ from Her2⁺ versus WT mice at day 15 after IAV infection revealed similar changes in gene expression, with increased Klf6, Tnfaip3, Dusp1, Dusp10, and Pdcd4 in the infected Her2⁺ mice (Extended Data Fig. 8a). These data suggest that CD4 cells from infected MMTV-Her2 mice have more of a memory type phenotype than effector cells with increased survival and less effector function.

In addition, CD4 cell expression of genes important for T-cell activation, including Gadd45b and Slfn2^55-57, was reduced in CD4⁺ effector cells in infected Her2+ mice relative to WT mice (Fig. 3g and Extended Data Resource 1). Interestingly, the expression of a number of mitochondrial genes (e.g. mt-Atp6, mt-Nd1, mt-Co3, mt-Nd3) was greatly reduced in CD4 cells from MMTV-Her2 mice (Fig. 3g, Extended Data Fig. 8a and Extended Data Resource 1), indicating a reduced mitochondrial content in these cells. The reduced mitochondrial content could result from increased autophagy mediated by the upregulation of Tnfaip3, since Tnfaip3 deficiency has been shown to increase mitochondrial content in CD4 cells by promoting autophagy⁵⁸, or from reduced CD4 cell growth.

Pathway analysis further supports a compromised effector function of CD4 cells from MMTV-Her2 mice. Over-Representation Analysis (ORA) of scRNAseq comparing MMTV-Her2 and WT mice after IAV infection showed reduced interferon responses in MMTV-Her2 mice across multiple innate immune cell types and B cells, consistent with dampened increases in types I and II IFN (Extended Data Fig. 8c, d, 9a). Gene expression in CD4 cells was also strikingly different, with a significant suppression of multiple activation-associated pathways including IFN pathways, Cytokine-Cytokine Receptor Interaction, and Oxidative Phosphorylation in effector CD4 cells from infected MMTV-Her2 mice relative to infected WT mice by 15 dpi (Fig. 3h). Similarly, comparisons between CD8⁺ cells from WT and MMTV-Her2 mice after IAV infection showed a reduction in these pathways in the presence of DCC (Extended Data Fig. 8b, 10). Taken together, these results suggest that the presence of DCC impairs CD4 and CD8 cell activation in response to IAV infection, favoring tumor cells persistence.

Critically, GSEA comparing CD8⁺ T cells from MMTV-Her2 mice with IAV infection and CD4 depletion revealed significant activation of pathways involved in CD8⁺ cell activation 15 dpi such as IL2/STAT5 and MTORC1 signaling pathways, suggesting that CD8⁺ cells are more proliferative and activated when CD4 cells are depleted (Fig. 3i). Thus, the effect of CD4 cell depletion on eliminating DCC appears to be mediated by enhanced CD8 cell responses against DCC.

SARS-CoV-2 virus infection, DCC awakening and cancer-specific death in humans.

To determine whether SARS-CoV-2 infection of lungs can promote the reawakening of dormant DCC, we performed studies (analogous to those in Fig. 1 with IAV) using a mouse adapted SARS-CoV-2 that recognizes mouse ACE2, termed MA10, obtained by genetic modification of the spike gene⁵⁹ followed by serial passages in mice¹⁶. This SARS-CoV-2 strain induces a COVID-19 like disease in mice including acute lung injury characterized by impaired pulmonary function, diffuse alveolar damage and infiltration of immune cells¹⁶. Infection of MMTV-Her2 (C57BL6/J) mice with MA10-SARS-CoV-2 resulted in a striking increase in Her2⁺ cells by 28 dpi (Fig. 4a-b).

We utilized the COVID-19 pandemic, as a unique opportunity study the effect of pulmonary virus infections on cancer progression. In contrast to influenza, data on virus infections and resulting disease has been systematically collected in the first years of the pandemic. First, we analyzed data from the UK Biobank to determine whether a SARS-CoV-2 positive test result, among a population of cancer survivors, was associated with an increased risk of cancer-related mortality (Extended Data Fig. 11). We limited the analysis to subjects with a positive test prior to December 2020, to mitigate potential confounding factors from the deployment of vaccines and the increasing use of at-home COVID tests, as well as to provide an adequate follow-up period to assess mortality rates. Among our full study population including 7,705 participants with a cancer diagnosis before 1^st January 2020, we observed a total of 606 deaths (418 in the test negatives and 188 in the test positives), yielding an OR of 4.38 (95% CI 3.59−5.34) (Fig. 4c). When excluding the 95 deaths directly attributed to COVID-19, our analysis showed an increased mortality in COVID-19 test positive cases (OR of 2.38:95% CI 1.85−3.06). Using the 49 cancer-related deaths as outcome, we estimated a close to two-fold increase (OR 1.79 (95% CI 1.29−2.48)) in cancer mortality in participants with a prior cancer diagnosis who tested SARS-CoV-2 positive compared to those who tested negative. To focus on patients in inferred remission, we excluded participants whose latest cancer was diagnosed within five or ten years of the start of the COVID-19 pandemic in the UK. Analyses of cancer cases at least 5 years before a potential infection (i.e. diagnosed prior to January 1^st 2015; N=4,645, N deaths all-cause=276, N non-COVID deaths = 210, N cancer deaths=96) revealed an increased all-cause (OR= 6.68; 95% CI 5.03 −8.86), non-COVID-19 (OR= 3.59; 95% CI 2.55−5.06) and cancer mortality (OR=2.42; 95% CI 1.44−4.06) between test positives and test negatives (Fig. 4c). Analyses based on the participants diagnosed with cancer prior to January 1^st 2010 (N=2,753, N deaths all-cause=158, N deaths non-COVID-19, and N cancer-related deaths=52) showed an increase in (i) all-cause mortality (OR=6.79; 95% CI 4.67−9.87), (ii) non-COVID-19 mortality (OR=3.72; 95% CI 2.37-5.85), and (iii) cancer mortality (OR=2.29; 95% CI 1.13-4.63) in test positives compared to test negatives. Stratification of results based on primary tumor type and on metastatic disease was not possible due to insufficient numbers of observations. We can only infer that patients 5 or 10 years out from initial diagnosis of cancer are likely in remission (and thus any residual metastatic cancer cells are likely dormant). Recognizing these limitations, these data reveal a striking increased risk of death from cancer for cancer survivors who experience SARS-CoV-2 infection.

We utilized the Flatiron Health database with 36,845 female breast cancer patients with complete information to determine whether women with a primary diagnosis of breast cancer experienced an increased risk of progression to metastatic disease in the lungs following COVID-19 (Extended Data Table 2A; Extended Data Fig. 12). Critically, female breast cancer patients who experienced COVID-19 after their initial diagnosis exhibited a hazard ratio (HR) of 1.44 (p=0.043) for subsequent diagnosis of metastatic breast cancer in the lungs (Figure 4d and Extended Data Table 2b-e). These data indicate that COVID-19 increases the risk of progression to lung metastasis for female breast cancer patients.

Our results indicate that respiratory virus infections promote the awakening and expansion of dormant cancer cells that had seeded the lungs before viral infection. This proceeds through two phases. First, an IL-6 dependent switch of DCC from a mesenchymal phenotype to a hybrid state that promotes expansion. Second, this expansion is followed by a significant cessation of proliferation and the establishment of CD4 cell niches that inhibit DCC elimination in part through suppression of CD8 cells (Fig. 5). We further reveal how the presence of Her2⁺ tumor cells results in a suppressive phenotype for CD4+ cells, and that depletion of CD4⁺ cells leads to elimination of influenza virus infection expanded DCC, dependent on CD8⁺ cells. We further show that a mouse-adapted SARS-CoV-2 similarly leads to DCC expansion in the lung.

Since early in the pandemic, the research community has pondered how COVID-19 might influence cancer pathogenesis^60-63. The results of the UK-biobank analyses show that individuals with a prior diagnosis of cancer are at increased risk of dying of cancer after having tested positive for SARS-CoV-2. The cause of the increased cancer mortality is not known, but a re-activation of dormant cancer cells may play a role, which is bolstered by the significant increased risk of cancer deaths for individuals who were 5 or 10 years out from their cancer diagnoses. Greater specificity was obtained for analyses of the Flatiron Health database, demonstrating a substantial increase in the risk of progression to metastatic lung disease for women previously diagnosed with breast cancer who experienced COVID-19. Together with our mouse models, these results reveal the substantial risk for cancer survivors who experience COVID-19.

FDA-approved strategies for managing severe COVID-19 include the inhibition of IL-6⁶⁴ or downstream (through JAK1/2⁶⁵) signaling, and modern cancer therapies frequently leverage checkpoint inhibitors to reinvigorate adaptive immunity in patients with cancer, raising the prospect of interventions that could reduce the risk of respiratory virus infection-induced metastatic cancer progression. Of course, such an intervention would need to be safe, or even beneficial to patients with a virus infection (as IL-6/JAK inhibitors can be), to justify application for the likely millions of cancer survivors who experience respiratory virus infections.

In all, our studies should have significant implications for understanding how infections could impact the risks of cancer recurrence, and inform public and medical policy on how to limit the increased risks for lung metastases resulting from COVID-19 or other respiratory virus infections.

Funding and Acknowledgement

We gratefully acknowledge funding from grant 1 I01 BX004495 to JD from the Veteran’s Administration; the Courtenay C. and Lucy Patten Davis Endowed Chair in Lung Cancer Research to JD, and an Across the Finish Line grant from University of Colorado School of Medicine to JD, MR and TEM. JMB is supported by R01AG081226, R01AI173305, and P30 AG067988. This work was also supported by National Institute of Health (NIH) /National Cancer Institute (NCI) awards CA109182 and CA013330, the Samuel Waxman Cancer Research Foundation Tumor Dormancy Program to JAAG, who is also a Samuel Waxman Cancer Research Foundation Investigator. MP is supported by a CRI Irvington Fellowship. SBC was supported by grant AWD-232405-SC from the Colorado Cancer League, BJJ was supported by T32 NIGMS/NIH 5T32GM141742-02, and ANC is supported by a NIAMS/NIH predoctoral fellowship (T32AR079114). RV and MC-H acknowledge support from the European Union H2020 EXPANSE project (Horizon 2020 grant No 874627 to RV). FG acknowledges funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/X020258/1), funded by the UK Medical Research Council (MRC). This UK funded award is carried out in the frame of the Global Health EDCTP3 Joint Undertaking. HM is supported by the UK National Institute for Health Research’s Comprehensive Biomedical Centre at University College London Hospitals. The authors thank the Biostatistics and Bioinformatics, Genomics, and Flow Cytometry Shared Resources supported by National Cancer Institute grant P30CA046934 to the University of Colorado Cancer Center, and the Rocky Mountain Regional VAMC Flow Core, and the Gates Institute Histology Core at the University of Colorado. This work was supported by the Alpine HPC system, which is jointly funded by the University of Colorado Boulder, the University of Colorado Anschutz, Colorado State University, and the National Science Foundation (award 2201538). Images were created with Biorender.com.

We also gratefully acknowledge Giselle Geno, Wendy Wang and Mengru Wang from Flatiron Health and Jessica Figura (University of Colorado) for guiding our studies with this database. We thank Charles Swanton (Crick Institute, London) for connecting us with the team that performed the UK Biobank analyses, and Daniel Merrick and Paul Jedlicka for advice on evaluation of DCC pathology.

Author Contribution Statement

S.B.C., B.J.J. – Conceptualization of the study. Designed, executed, analyzed experiments. Compiled data and graphed figures. Initial drafting and editing of the manuscript.

J.H., D.G. – Analyzed Flatiron Health data of breast cancer patients and generated relevant figures. Editing of manuscript.

R.V., M.C-H., F.G., H.M. – Analyzed UK Biobank data of cancer patients and generated relevant figures. Editing of manuscript.

B.J.J., M.B., V.S., A.G. – Processing and analyzing single cell RNA seq (scRNAseq) data, and generated relevant figures. Editing of manuscript.

B.D. – Executed mouse experiments involving SARS-CoV-2 MA10 infection.

F.V.P. – Analyzed experiments pertaining to MMTV-PyMT and assisted in tissue processing for flow cytometry.

V.Z. – Executed all influenza PR8 infections and intraperitoneal injections of antibodies.

W.E.S. – Executed and analyzed MMTV-PyMT experiments.

A.N.C., J.M.B. – Processed influenza-infected lungs and calculated influenza viral load.

M.P. – Provided protocols and expertise in immunofluorescent stains.

A.B. – Comparisons of scRNAseq data from the mouse model with scRNAseq data from patients with COVID-19.

S.B.B. – Comparisons of scRNAseq data from the mouse model with scRNAseq data from patients with COVID-19.

A. G., J.C. – Supervised scRNAseq data processing and analyses. Editing of manuscript.

T.E.M. Provided expertise on and supervised SARS-CoV-2 MA10 related work, and provided SARS-CoV-2 virus. Editing of manuscript.

J.A.A-G. Directed experimental design, and provided mouse strains and protocols. Editing of manuscript. Supervised the studies.

M.R. – Conceptualization, design and analysis of experiments. Writing and editing of the manuscript. Supervised the studies.

J.D. – Overall lead of studies. Initial conceptualization of studies. Designed and analyzed experiments. Writing and editing of the manuscript. Supervised the studies.

Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2020. CA Cancer J Clin 70, 7-30 (2020). https://doi.org:10.3322/caac.21590
Newton, A. H., Cardani, A. & Braciale, T. J. The host immune response in respiratory virus infection: balancing virus clearance and immunopathology. Semin Immunopathol 38, 471-482 (2016). https://doi.org:10.1007/s00281-016-0558-0
Tan, L. Y., Komarasamy, T. V. & Rmt Balasubramaniam, V. Hyperinflammatory Immune Response and COVID-19: A Double Edged Sword. Front Immunol 12, 742941 (2021). https://doi.org:10.3389/fimmu.2021.742941
Soni, A. et al. Breast cancer subtypes predispose the site of distant metastases. Am J Clin Pathol 143, 471-478 (2015). https://doi.org:10.1309/AJCPYO5FSV3UPEXS
Phan, T. G. & Croucher, P. I. The dormant cancer cell life cycle. Nat Rev Cancer 20, 398-411 (2020). https://doi.org:10.1038/s41568-020-0263-0
Organization), W. W. H. Influenza (Seasonal), <https://www.who.int/news-room/fact-sheets/detail/influenza-(seasonal)> (
World Health Organization. WHO Coronavirus (COVID-19) Dashboard, <https://covid19.who.int/> (2022).
Manore, S. G., Doheny, D. L., Wong, G. L. & Lo, H. W. IL-6/JAK/STAT3 Signaling in Breast Cancer Metastasis: Biology and Treatment. Front Oncol 12, 866014 (2022). https://doi.org:10.3389/fonc.2022.866014
Ganesan, R. et al. Taxane chemotherapy induces stromal injury that leads to breast cancer dormancy escape. PLoS Biol 21, e3002275 (2023). https://doi.org:10.1371/journal.pbio.3002275
Hobson, J. et al. Acute inflammation induced by the biopsy of mouse mammary tumors promotes the development of metastasis. Breast cancer research and treatment 139, 391-401 (2013). https://doi.org:10.1007/s10549-013-2575-1
DeNardo, D. G. et al. CD4(+) T cells regulate pulmonary metastasis of mammary carcinomas by enhancing protumor properties of macrophages. Cancer Cell 16, 91-102 (2009). https://doi.org:10.1016/j.ccr.2009.06.018
Fedeli, U., Barbiellini Amidei, C., Han, X. & Jemal, A. Changes in cancer-related mortality during the COVID-19 pandemic in the United States. JNCI: Journal of the National Cancer Institute 116, 167-169 (2023). https://doi.org:10.1093/jnci/djad191
Guy, C. T., Cardiff, R. D. & Muller, W. J. Induction of mammary tumors by expression of polyomavirus middle T oncogene: a transgenic mouse model for metastatic disease. Mol Cell Biol 12, 954-961 (1992). https://doi.org:10.1128/mcb.12.3.954-961.1992
Guy, C. T. et al. Expression of the neu protooncogene in the mammary epithelium of transgenic mice induces metastatic disease. Proc Natl Acad Sci U S A 89, 10578-10582 (1992). https://doi.org:10.1073/pnas.89.22.10578
Poli, V. et al. Interleukin-6 deficient mice are protected from bone loss caused by estrogen depletion. The EMBO journal 13, 1189-1196 (1994). https://doi.org:10.1002/j.1460-2075.1994.tb06368.x
Leist, S. R. et al. A Mouse-Adapted SARS-CoV-2 Induces Acute Lung Injury and Mortality in Standard Laboratory Mice. Cell 183, 1070-1085 e1012 (2020). https://doi.org:10.1016/j.cell.2020.09.050
Yu, Y. R. et al. A Protocol for the Comprehensive Flow Cytometric Analysis of Immune Cells in Normal and Inflamed Murine Non-Lymphoid Tissues. PLoS One 11, e0150606 (2016). https://doi.org:10.1371/journal.pone.0150606
Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun 8, 14049 (2017). https://doi.org:10.1038/ncomms14049
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573-3587 e3529 (2021). https://doi.org:10.1016/j.cell.2021.04.048
Germain, P. L., Lun, A., Garcia Meixide, C., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Res 10, 979 (2021). https://doi.org:10.12688/f1000research.73600.2
Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb) 2, 100141 (2021). https://doi.org:10.1016/j.xinn.2021.100141
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545-15550 (2005). https://doi.org:10.1073/pnas.0506580102
Franzen, O., Gan, L. M. & Bjorkegren, J. L. M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database (Oxford) 2019 (2019). https://doi.org:10.1093/database/baz046
Castanza, A. S. et al. Extending support for mouse data in the Molecular Signatures Database (MSigDB). Nature methods 20, 1619-1620 (2023). https://doi.org:10.1038/s41592-023-02014-7
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739-1740 (2011). https://doi.org:10.1093/bioinformatics/btr260
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell systems 1, 417-425 (2015). https://doi.org:10.1016/j.cels.2015.12.004
Jelley-Gibbs, D. M. et al. Persistent depots of influenza antigen fail to induce a cytotoxic CD8 T cell response. J Immunol 178, 7563-7570 (2007). https://doi.org:10.4049/jimmunol.178.12.7563
Batty, G. D., Gale, C. R., Kivimaki, M., Deary, I. J. & Bell, S. Comparison of risk factor associations in UK Biobank against representative, general population based studies with conventional response rates: prospective cohort study and individual participant meta-analysis. BMJ 368, m131 (2020). https://doi.org:10.1136/bmj.m131
Allen, N. E. et al. Prospective study design and data analysis in UK Biobank. Sci Transl Med 16, eadf4428 (2024). https://doi.org:10.1126/scitranslmed.adf4428
Ma, X., Long, L., Moon, S., Adamson, B. J. S. & Baxi, S. S. Comparison of Population Characteristics in Real-World Clinical Oncology Databases in the US: Flatiron Health, SEER, and NPCR. medRxiv.org (2023). https://doi.org:https://doi.org/10.1101/2020.03.16.20037143
Birnbaum, B. et al. Model-assisted cohort selection with bias analysis for generating large-scale cohorts from the EHR for oncology research. arXiv.org (2020). https://doi.org: https://doi.org/10.48550/arXiv.2001.09765
Elliott, J. et al. COVID-19 mortality in the UK Biobank cohort: revisiting and evaluating risk factors. Eur J Epidemiol 36, 299-309 (2021). https://doi.org:10.1007/s10654-021-00722-y
Ho, D., Imai, K., King, G. & Stuart, E. A. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. Journal of Statistical Software 42 (2011). https://doi.org:10.18637/jss.v042.i08
Savje, F., Higgins, M. J. & Sekhon, J. S. Generalized Full Matching. Political Analysis 29, 423-447 (2021).
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, <https://www.R-project.org> (2021).
Aguirre-Ghiso, J. A. How dormant cancer persists and reawakens. Science 361, 1314-1315 (2018). https://doi.org:10.1126/science.aav0191
Pommier, A. et al. Unresolved endoplasmic reticulum stress engenders immune-resistant, latent pancreatic cancer metastases. Science 360 (2018). https://doi.org:10.1126/science.aao4908
Harper, K. L. et al. Mechanism of early dissemination and metastasis in Her2(+) mammary cancer. Nature 540, 588-592 (2016). https://doi.org:10.1038/nature20609
Husemann, Y. et al. Systemic spread is an early step in breast cancer. Cancer Cell 13, 58-68 (2008). https://doi.org:10.1016/j.ccr.2007.12.003
Nobre, A. R. et al. ZFP281 drives a mesenchymal-like dormancy program in early disseminated breast cancer cells that prevents metastatic outgrowth in the lung. Nat Cancer 3, 1165-1180 (2022). https://doi.org:10.1038/s43018-022-00424-8
Raskova, M. et al. The Role of IL-6 in Cancer Cell Invasiveness and Metastasis-Overview and Therapeutic Opportunities. Cells 11 (2022). https://doi.org:10.3390/cells11223698
Gelfo, V. et al. Roles of IL-1 in Cancer: From Tumor Progression to Resistance to Targeted Therapies. Int J Mol Sci 21 (2020). https://doi.org:10.3390/ijms21176009
Werner-Klein, M. et al. Interleukin-6 trans-signaling is a candidate mechanism to drive progression of human DCCs during clinical latency. Nat Commun 11, 4977 (2020). https://doi.org:10.1038/s41467-020-18701-4
Dienz, O. et al. Essential role of IL-6 in protection against H1N1 influenza virus by promoting neutrophil survival in the lung. Mucosal Immunol 5, 258-266 (2012). https://doi.org:10.1038/mi.2012.2
Tan, H. X. et al. Inducible Bronchus-Associated Lymphoid Tissues (iBALT) Serve as Sites of B Cell Selection and Maturation Following Influenza Infection in Mice. Frontiers in immunology 10, 611 (2019). https://doi.org:10.3389/fimmu.2019.00611
Albrengues, J. et al. Neutrophil extracellular traps produced during inflammation awaken dormant cancer cells in mice. Science 361 (2018). https://doi.org:10.1126/science.aao4227
Giordano, M. et al. The tumor necrosis factor alpha-induced protein 3 (TNFAIP3, A20) imposes a brake on antitumor activity of CD8 T cells. Proc Natl Acad Sci U S A 111, 11115-11120 (2014). https://doi.org:10.1073/pnas.1406259111
Cook, M. E. et al. The ZFP36 family of RNA binding proteins regulates homeostatic and autoreactive T cell responses. Sci Immunol 7, eabo0981 (2022). https://doi.org:10.1126/sciimmunol.abo0981
Moore, M. J. et al. ZFP36 RNA-binding proteins restrain T cell activation and anti-viral immunity. Elife 7 (2018). https://doi.org:10.7554/eLife.33057
Kutty, R. G. et al. Dual Specificity Phosphatase 5 Is Essential for T Cell Survival. PLoS One 11, e0167246 (2016). https://doi.org:10.1371/journal.pone.0167246
Andreatta, M. et al. A CD4(+) T cell reference map delineates subtype-specific adaptation during acute and chronic viral infections. Elife 11 (2022). https://doi.org:10.7554/eLife.76339
Smith, N. P. et al. Resident memory T cell development is associated with AP-1 transcription factor upregulation across anatomical niches. bioRxiv (2023). https://doi.org:10.1101/2023.09.29.560006
Lingel, H. et al. CTLA-4-mediated posttranslational modifications direct cytotoxic T-lymphocyte differentiation. Cell death and differentiation 24, 1739-1749 (2017). https://doi.org:10.1038/cdd.2017.102
McDonald, P. W. et al. IL-7 signalling represses Bcl-6 and the TFH gene program. Nat Commun 7, 10285 (2016). https://doi.org:10.1038/ncomms10285
Honey, K. Keeping CD4+ T cells under control. Nature Reviews Immunology 6, 6-7 (2006). https://doi.org:10.1038/nri1770
Ju, S. et al. Gadd45b and Gadd45g are important for anti-tumor immune responses. European journal of immunology 39, 3010-3018 (2009). https://doi.org:https://doi.org/10.1002/eji.200839154
Yue, T. et al. SLFN2 protection of tRNAs from stress-induced cleavage is essential for T cell–mediated immunity. Science 372, eaba4220 (2021). https://doi.org:doi:10.1126/science.aba4220
Matsuzawa, Y. et al. TNFAIP3 promotes survival of CD4 T cells by restricting MTOR and promoting autophagy. Autophagy 11, 1052-1062 (2015). https://doi.org:10.1080/15548627.2015.1055439
Dinnon, K. H., 3rd et al. A mouse-adapted model of SARS-CoV-2 to test COVID-19 countermeasures. Nature 586, 560-566 (2020). https://doi.org:10.1038/s41586-020-2708-8
Francescangeli, F. et al. COVID-19-Induced Modifications in the Tumor Microenvironment: Do They Affect Cancer Reawakening and Metastatic Relapse? Front Oncol 10, 592891 (2020). https://doi.org:10.3389/fonc.2020.592891
Rahimmanesh, I. et al. Cancer Occurrence as the Upcoming Complications of COVID-19. Front Mol Biosci 8, 813175 (2021). https://doi.org:10.3389/fmolb.2021.813175
du Plessis, M., Fourie, C., Riedemann, J., de Villiers, W. J. S. & Engelbrecht, A. M. Cancer and Covid-19: Collectively catastrophic. Cytokine Growth Factor Rev 63, 78-89 (2022). https://doi.org:10.1016/j.cytogfr.2021.10.005
Derosa, L. et al. The immuno-oncological challenge of COVID-19. Nature Cancer 1, 946-964 (2020). https://doi.org:10.1038/s43018-020-00122-3
COVID Treatment Guidelines - NIH, <https://www.covid19treatmentguidelines.nih.gov/therapies/immunomodulators/interleukin-6-inhibitors/> (
Rubin, R. Baricitinib Is First Approved COVID-19 Immunomodulatory Treatment. JAMA 327, 2281-2281 (2022). https://doi.org:10.1001/jama.2022.9846

Yes there is potential Competing Interest. HM has consulted Astra Zeneca relating to the use of monoclonal antibodies in the prevention and treatment of SARS-CoV-2 infection. J.A.A-G is a scientific co-founder, scientific advisory board member and equity owner in HiberCell and receives financial compensation as a consultant for HiberCell, a Mount Sinai spin-off company focused on the research and development of therapeutics that prevent or delay the recurrence of cancer. J.A.A-G is also a consultant for Astrin Biosciences, Inc and chief mission officer of Samuel Waxman Cancer Research Foundation.

ExtendedDataResource1scRNAseq.xlsx
Extended Data Resource 1
Figuressubmission.pdf
Extended Data Figure 1. Weight change of MMTV-Her2 and WT mice post-IAV infection (a). Total cell counts in BALF 3, 6, 9, 15dpi (b). IAV lung viral load at 3, 6, 9, 15dpi or for vehicle control mice (c). Lungs of an 18-week-old mouse 28dpi with IAV and a naïve 9-month-old mouse stain with Her2 (green) (d). Extended Data Figure 2. Concentrations of IL-6 and IL-1b in BAL vehicle treated or 3, 6, 9, 15dpi with IAV (a, b). Extended Data Figure 3. Kaplan-Meier curve for time to sacrifice due to primary tumor burden of MMTV-Her2 and IL-6 KO/MMTV-Her2 mice (a). Mice were sacrificed when primary tumors reached 2 cm in diameter; there was not a significant difference between the two groups. Quantification of PyMT⁺ lesions (≤5 cells) with at least one Ki67⁺PyMT⁺ cell in lungs 21 dpi with IAV (b). PyMT⁺ micrometastases (defined by lesions with an area < 0.03 mm²) were quantified (c); the WT groups are the same as in Fig. 1g, and are included to allow comparison with the IL-6 KO. Extended Data Figure 4. Counts of immune cells in bronchoalveolar lavage. Numbers of neutrophils, CD4, CD8, and B cells in BAL for mice treated with PBS or 3, 6, 9, 15dpi with IAV (a-d). Immunofluorescent stain of Her2 (green), GL7 (magenta) (f), and B220 (magenta) (g) in lungs of PBS-treated or 28dpi with IAV MMTV-Her2, and WT mice (f). Extended Data Figure 5. Flow cytometric analysis of CD3⁺, CD4⁺ and CD8⁺ cells in lungs and spleens of WT mice 6 days post injection with 100mg aCD4 antibody clone GK1.5. Extended Data Figure 6. Quantification of Her2⁺ and Ki67⁺/Her2⁺ cells in lungs of MMTV-Her2 and CD4-depleted MMTV-Her2 mice 9dpi with IAV or uninfected (PBS) controls (a, b). For c-e, mice were injected with anti-Ly6G to deplete neutrophils or IgG control. Quantification of Her2⁺ cells 28dpi with IAV is shown in (c). Immunofluorescent stains for myeloperoxidase (magenta) (d; 6 dpi) and Her2 (e; 28dpi). Representative images show neutrophil (myeloperoxidase⁺) depletion (d) and maintenance of Her2⁺ cells despite this depletion (e). Extended Data Figure 7. scRNAseq of lungs. Quality control of the scRNAseq data set: distribution of UMIs, genes and mitochondria within each sample pre-filtering (a) and post-filtering (b) with dashed lines indicating the filtering thresholds. UMAP plot labelled by cell type (c). Dot plot showing the expression of canonical marker genes used to identify the cell types (d). UMAP plots for CD4-depleted MMTV-Her2+IAV (e) and MMTV-Her2+IAV (f) mice at 9 and 15 dpi with the replicates within each group highlighted (demonstrating reproducible patterns). Extended Data Figure 8. Immune pathways identified as significant in influenza-infected mice. Heatmap of top 20 differentially expressed genes comparing CD4⁺IL7R⁺ T cells from MMTV-Her2+IAV versus WT+IAV mice at 15 dpi (a). GSEA enrichment plot comparing the effector CD8+ T cells in MMTV-Her2+IAV versus WT+IAV mice at 15 dpi (b). Heatmaps displaying -log10(FDR) from ORA in CD4+ effector T cells comparing the indicated samples for interferon α and γ response enrichment at 9 and 15 dpi (c). Similar ORA heatmap comparing the indicated samples across all cell types for interferon α and γ response enrichment at 15 dpi (d). Extended Data Figure 9. Concentrations of IFN-a, IFN-b, and IFN-g in BAL at the indicated times post-IAV infection (a). GSEA analyses comparing effector CD8⁺ T cells in MMTV-Her2 versus WT mice 15dpi with IAV (b). Extended Data Figure 10. Flow cytometric analyses of immune cells from the lungs of MMTV-Her2 and WT mice 6dpi with IAV. Percentages of CD44⁺CD4⁺ and FoxP3⁺CD4⁺ (relative to all CD4 cells), and CD44⁺CD8⁺, and GzmB⁺CD8⁺ cells (relative to all CD8 cells) 6dpi are graphed (a). Gating strategies of CD4 and CD8 cells from mouse lungs (b). Extended Data Figure 11. Summary representation of the selection, exclusion, and matching criteria implemented for UK Biobank. Extended Data Figure 12. Summary representation of the selection, exclusion, and matching criteria implemented for the Flatiron Health Database.
ExtendedDataTables.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Respiratory viral infection promotes the awakening and outgrowth of dormant metastatic breast cancer cells in lungs

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Results

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1