Bronchial premalignant lesions (PMLs) are precursors of lung squamous cell carcinoma, but have variable outcome, and we lack tools to identify and treat PMLs at risk for progression to cancer. Here we report the identification of four molecular subtypes of PMLs with distinct differences in epithelial and immune processes based on RNA-Seq profiling of endobronchial biopsies from high-risk smokers. The Proliferative subtype is enriched with bronchial dysplasia and exhibits up-regulation of metabolic and cell cycle pathways. A Proliferative subtype-associated gene signature identifies subjects with Proliferative PMLs from normal-appearing uninvolved large airway brushings with high specificity. In progressive/persistent Proliferative lesions expression of interferon signaling and antigen processing/presentation pathways decrease and immunofluorescence indicates a depletion of innate and adaptive immune cells compared with regressive lesions. Molecular biomarkers measured in PMLs or the uninvolved airway can enhance histopathological grading and suggest immunoprevention strategies for intercepting the progression of PMLs to lung cancer. Bronchial premalignant lesions can potentially progress to lung squamous cell carcinoma. Here, the authors profile bronchial biopsies from high-risk smokers by RNA sequencing and identify four molecular subtypes of premalignant lesions and an immune molecular signature that associates with lesion progression. Exposure to cigarette smoke creates a field of injury throughout the entire respiratory tract by inducing a variety of genomic alterations that can lead to an at-risk airway where premalignant lesions (PMLs) and LCs develop. Lung squamous cell carcinoma (LUSC) arises in the epithelial layer of the bronchial airways and is often preceded by the development of PMLs through a stepwise histological progression from normal epithelium to hyperplasia, squamous metaplasia, dysplasia , carcinoma in situ (CIS), and finally to invasive and then metastatic LUSC3. In fact, the presence of high-grade persistent or progressive dysplasia (moderate or severe) is a marker of increased LC risk both at the lesion site (where they are the presumed precursors of squamous cell lung cancer) and elsewhere in the lung, although many dysplastic lesions do have varied outcomes5. Currently, however, we lack effective tools to identify PMLs at highest risk of progression to invasive carcinoma6. The development of markers of disease progression would identify patients at high-risk, suggest novel lung cancer chemoprevention agents, and provide molecular biomarkers for monitoring outcome in lung cancer prevention trials.Lung cancer (LC) is the leading cause of cancer death taking ~160,000 US lives each year, more than colorectal, pancreatic, breast, and prostate cancers combined. To decrease mortality, we need innovative strategies to intercept cancer development by diagnosing the disease at its earliest and potentially most curable stage. Development of LC risk biomarkers and interception strategies requires a detailed understanding of the earliest molecular alterations involved in lung carcinogenesis that occur in the respiratory epitheliumWe hypothesized that molecular characterization of bronchial endobronchial biopsies containing a mixture of epithelial and immune cells would allow us to identify transcriptomic alterations associated with high-grade histology and premalignant lesion progression. In this study, we used mRNA sequencing (mRNA-seq) to profile endobronchial biopsies and brushings obtained through serial bronchoscopies from high-risk smokers undergoing lung cancer screening by autofluorescence bronchoscopy and chest computed tomography (CT). Using the bronchial biopsies, we identified four molecular subtypes associated with clinical phenotypes and biological processes. One subtype (Proliferative subtype) is enriched with bronchial dysplasia, high basal cell and low ciliated cell signals, and expression of proliferation-associated pathways. Genes involved in interferon signaling and T-cell-mediated immunity were down regulated among progressive/persistent lesions within the Proliferative subtype compared with regressive lesions and these pathways correlated with decreases in both innate and adaptive immune cell types. Molecular classification of biopsies into a high-grade/progressive disease group may be used to stratify patients into prevention trials and to monitor efficacy of the treatment. The results also suggest that personalized lung cancer chemoprevention, targeting specific cancer-related pathways or the immune system may have potential therapeutic benefits.n = 29 patients, n = 191 biopsies, n = 91 brushes), and the Validation Cohort samples were obtained between 2012 and 2015 . The subjects are predominantly older smokers, many of which have a prior history of lung cancer, chronic obstructive pulmonary disease (COPD), and occupational exposures that confer a high-risk of developing lung cancer. Clinical characteristics reported at the baseline visit such as sex, age, smoking status (ever or never), pack-years, prior history of lung cancer, COPD status, and occupational exposures were not significantly different between the two cohorts  in Buffalo, NY. The Discovery Cohort samples were obtained from the Roswell subjects between 2010 and 2012 , gene modules were derived in the DC biopsies , the DC brushes , TCGA LUSC tumors10 , and tracheobronchial samples from mice treated with n-nitrosotris-(2-choroethyl)urea (NTCU) . DC biopsy gene modules that were highly correlated (absolute Pearson correlation coefficient r > 0.85) to at least one other non-DC biopsy module within each of the four data sets were selected. Genes in the selected modules were filtered by requiring that each gene was also present in at least one of the correlated non-DC biopsy modules, resulting in a set of nine gene modules that consisted of 3936 genes in total , Inflammatory , Secretory , and Normal-like  . The approach was chosen given that there is histological heterogeneity within biopsies and that pathological analyses were conducted using biopsies adjacent to biopsies profiled via mRNA-Seq. First, we sought to select a set of gene modules that are present across different LUSC–related data sets. Using weighted gene co-expression network analysis1%) Fig. , Table 3In order to characterize each molecular subtype, we first focused on identifying biological pathways enriched in each module, as the pattern of gene module expression defines the PML subtypes. Each gene module was found to be associated with distinct epithelial and immune biological processes Fig. , Data 1.12. Sample genomic smoking status, the subject from whom the sample was derived, and sample histology demonstrated significant associations with molecular subtype . The gene remained significantly up regulated in Proliferative normal/hyperplasia samples  and dysplasia samples , and these observations are supported by an increase in protein expression in representative samples    Fig. . In the PTPRC (CD45) consistent with enrichment of the LUSC–Secretory subtype , and this is also shown by a decrease in acetylated α-tubulin staining in representative histological samples , indicating enrichment of lesions with high-grade histology that tightly correlates with protein expression in representative histology samples   but is the most significantly increased in the Secretory subtype . In contrast, gene expression of SCGB1A1, a marker of club cells, is the lowest in the Proliferative subtype . The Normal-like subtype is supported by expression of all epithelial cell types and has the lowest expression of CD45  Fig. . In addi13 scores for each of the 9 modules (using the full set of genes for each module) between the predicted VC subtypes compared with the DC subtypes. The average behavior of PC1 across the subtypes was highly similar  in the Inflammatory subtype  consistent with enrichment of the LUSC–Secretory subtype  consistent with Module 6 . The Proliferative subtype has the highest expression of basal cell marker KRT5 , proliferation marker MKI67 , and cell cycle-associated Module 5  indicating enrichment of lesions expressing characteristics associated with high-grade histology. Gene expression of SCGB1A1, a marker of club cells, is the lowest in the Proliferative subtype . All epithelial cell type markers are expressed in the Normal-like subtype, and CD45 expression is decreased  and clinical and molecular phenotypes across the VC biopsies are analogous to those observed across the DC biopsies Fig.  and 3. I14; however, that study lacked biopsies and brushes from the same subjects. Above, in both the DC and the VC biopsies, the Proliferative subtype, represents a distinct subtype of PMLs enriched for dysplastic histology expressing metabolic and proliferative pathways. Biopsies classified as the Proliferative subtype may represent a group of PMLs that need close monitoring and intervention. As a result, we sought to explore whether or not we could predict the presence of Proliferative subtype biopsies using the brushes. The Proliferative subtype is defined by the behavior of Modules 4, 5, 6, and 7  versus regression (n = 15) among the Proliferative subtype DC biopsies, as these may be clinically relevant to identifying appropriate interception strategies. Using GSVA scores calculated across all the DC biopsies for each of the nine modules, we calculated which scores were statistically different between progressive/persistent versus regressive disease in the samples belonging to the Proliferative subtype  are more likely to progress to invasive carcinomaSP100, CIITA, CXCL10, SOCS1, GBP1, GBP4, B2M, TAP1, TAPBP, TRIM14, TRIM21, TRIM22, STAT1, PML, OAS2, OAS3, MX1, ADAR, ISG15, IFI35, IFIT3, IFI27, PSMB8, PSMB9, BST2, IRF1, IRF9, CD74, PSME1, PSME2, HLA-DQA1/DPA1/ DPB1/DRA/ DQB2/DRB1/ DQB1/DMA/DMB/DOA, HLA-A/B/C/E/F) and include the inhibitory receptor LAG3. As a result, we wanted to evaluate whether or not the presence or absence of innate or adaptive immune cells were associated with Module 9 expression within the Proliferative subtype. In an effort to deconvolute the potential presence of immune cell types, we generated GSVA scores using previously described immune cell signatures15 and scores for 64 different cell types using the xCell algorithm16, separately for both the DC and VC biopsies. We identified significant  associations between the cell type scores and Module 9 that were in common between the DC and VC biopsies , CD4 (n = 50 regions enumerated from n = 17 subjects), and CD8 T cells (n = 47 regions enumerated from n = 16 subjects) was performed (Supplementary Table p ≪ 0.001 in the concordant set). In contrast, staining of CD163 in combination with CD68, thought to be suggestive of M2 type macrophages, were decreased among the progressive/persistent lesions in the Proliferative subtype   in PMLs that progress/persist. Interestingly, among progressive/persistent lesions, the CD8 T cells had a distinct localization pattern , where CD8 T cells both lined and were embedded within the epithelium in areas where dysplasia is present  Fig. . In addient Fig. . The imm5. Currently, however, we lack effective tools to identify PMLs at highest risk of progression to invasive carcinoma6. The development of markers predictive of disease progression will be important in identifying patients at highest risk for LUSC development and in identifying biological pathways exploitable for LUSC chemoprevention. Towards this goal, we profile via mRNA-Seq bronchial brushes and endobronchial biopsies obtained from subjects undergoing longitudinal lung cancer screening by chest CT and autofluorescence bronchoscopy. We identify four transcriptionally distinct groups of biopsies, one of these we label Proliferative and find it to be associated with bronchial dysplasia. Patients with Proliferative PMLs can also be identified via gene expression measured from cells in the non-involved large airway epithelium. We further find that persistent/progressive Proliferative PMLs are characterized by decreased expression of genes involved in interferon signaling and antigen processing/presentation pathways. Consistent with these gene expression findings we find that progressive/persistent Proliferative PMLs are depleted for CD68+/CD163+ macrophages and CD8 T cells by immunofluorescence. Collectively, these data suggest both the potential to identify a subset of patients with progressive/persistent LUSC PMLs, who are at risk for developing invasive lung cancer, on the basis of airway gene expression; as well as the potential to decrease the risk for progression in these patients by augmenting the immune response associated with regression.LUSC is the second most common form of lung cancer. LUSC arises in the epithelial layer of the bronchial airways, and is often preceded by the development of lung squamous PMLs. The presence of dysplastic persistent and or progressive PMLs is a marker of increased risk for LUSC17. Altered protein levels of TP53, CCND1, CCNE1, BAX, and BCL2 have been associated with CIS or lung cancer occurrence independent of histological grade18. Telomere shortening and maintenance19 and loss of heterozygosity in regions frequently deleted in lung cancer  has been observed in early hyperplasia/metaplasia lesions22 and found to increase in frequency and size in higher-grade dysplasia. Genomic gains in loci containing SOX2, TP63, EGFR, MYC, CEP3, and CEP5 are also associated with progression of high-grade dysplasia23. Despite the numerous genomic alterations associated with PML histological grade and progression, we lack a comprehensive PML molecular classification system to complement pathologic examination. We pursued an unsupervised class discovery approach that led to the identification of four distinct molecular PML subtypes . The transcriptional patterns differentiating the PML subtypes are robust and a 22-gene panel identified in the DC can be used to distinguish between the molecular subtypes in an independent VC. Interestingly, whereas prior lung cancer history may influence airway gene expression and about two-thirds of the subjects have a prior history of lung cancer, we do not detect a significant association between lung cancer history and molecular subtype, and there is a similar diversity of molecular subtypes between biopsies collected from subjects with and without a lung cancer history. This observation is supported by prior work describing gene expression alterations that reflect the presence of bronchial dysplasia using airway brushings in a similar cohort14.Previous studies indicate a range of genomic alterations associated with bronchial dysplasia. Increased expression of EGFR and Ki67 staining of epithelial cells is associated with increasing histologic severity and subsequent histologic progression14, in PMLs adjacent to LUSC tumor24, and in smokers at high-risk for lung cancer25 as well as increases in proliferation  that have been utilized as an endpoint in lung cancer chemoprevention27. Identification of patients with Proliferative lesions may be useful to enrich lung cancer chemoprevention trials with high-risk subjects or to identify patients who would benefit from more frequent lung cancer screening. The Inflammatory subtype is predominated by PMLs from former smokers, but interestingly is not significantly enriched for dysplasia, despite similarly decreased expression of cilia-associated pathways, suggesting an abnormal epithelium. The Inflammatory subtype also shows increased expression of a gene module enriched for genes involved in inflammation and regulation of lymphocytes and leukocytes (Module 8). This gene module is also elevated in the Secretory subtype predominated by current smokers and increased expression of goblet cell markers. Interestingly, IL1B is part of this inflammation-related gene module, and inhibition of IL1B has recently been shown to reduce lung cancer incidence28.Among the molecular subtypes, the Proliferative subtype is enriched with dysplastic PMLs from current smokers and is characterized by up regulation of metabolic (OXPHOS/ETC/TCA) and cell cycle pathways and down regulation of cilia-associated pathways. Previous work indicates increases in metabolic pathways in the airways of subjects with dysplastic lesions34. In the current study, we have both normal-appearing bronchial brushes and endobronchial biopsies collected during the same procedure, allowing us to identify gene expression differences in brushings, which indicate the presence of Proliferative subtype PMLs. In both the discovery and VC, applying a Proliferative subtype classifier (based on PML biopsy gene expression) to gene expression data from the brushings resulted in Proliferative subtype predictions that were very specific (91%) but not sensitive (31–38%). Brushes classified as Proliferative have increased expression of cell cycle pathways and decreased expression of cilia-associated genes, suggesting that they are more similar to squamous metaplasia than normal epithelium. Potentially, a subset of patients may harbor widespread airway damage that serves as a marker for Proliferative bronchial dysplasia leading to modest sensitivity, but high specificity. In other cases, the area of damage that gives rise to these Proliferative PMLs may be more localized, and therefore difficult to detect by brushing, contributing to decreased sensitivity. These findings suggest that therapeutics to target changes throughout the entire airway epithelium may be necessary in some subjects, whereas, more site-specific ablation may be more effective in certain cases. Another possibility and area of future research, is that a Proliferative subtype brush is a predictor of incident LUSC.Our prior work has extensively studied gene expression alterations in normal-appearing airway epithelium by profiling cells obtained via brushing the mainstem bronchus during bronchoscopy+ macrophages and CD8+ T cells and the CD8+T cells had a distinct localization pattern. These lesions also contained greater numbers of CD4+T cells, and it will be important in future work to assess if these cells are T regulatory cells promoting an immune suppressive environment.The molecular profiling of PMLs and the identification of gene co-expression modules also provides an opportunity to identify the molecular determinants of subsequent PML progression. One of the nine gene co-expression modules used to define the molecular subtypes was significantly decreased between biopsies that progress/persist compared to biopsies that regress within the Proliferative subtype in both the discovery and VC. The module contains genes involved in interferon signaling and antigen processing and presentation, and its expression was correlated with the abundance innate and adaptive immune cells via computational prediction. By immunofluorescent staining of formalin-fixed paraformaldehyde-embedded (FFPE) biopsy sections we confirmed that the progressive/persistent Proliferative lesions with low Module 9 GSVA scores had fewer CD163+ cells and increased expression of genes involved in interferon (IFN)γ signaling is consistent with observations in oral squamous cell carcinoma PMLs where the presence of CD163+ macrophages with active IFNγ signaling is associated with better outcomes35. In addition, we observed fewer CD8+ T cells and lower expression of human leukocyte antigen (HLA) class I genes and B2M in progressive/persistent lesions within the Proliferative subtype. Disruptions in proper T-cell-mediated immunosurveillance have been described in several studies showing that impaired HLA class I antigen processing and presentation including down regulation or loss of B2M37 and interferon signaling38 in lung tumors affects response and acquired resistance to checkpoint inhibitors. Lung tumors lacking an HLA-I complex had lower cytotoxic CD8+ lymphocyte infiltration, and this was also associated with lower levels of PD-L1. In addition, studies have also suggested negative impacts on efficacy of checkpoint inhibitors as well as survival in patients with LC that have tumors with increased CD4+ T cells expressing T-regulatory markers , resulting in immunosuppressive state suggested to hinder the recruitment and effector functions of CD8+ T cells40. Future DNA sequencing data on the PMLs profiled here may indicate heterozygous or homozygous loss of B2M or mutations in other genes in the interferon and antigen processing and presentation pathways; however, even in the case of acquired resistance, mutations and copy number changes could not explain the down regulation of these pathways across all subjects, suggesting that other epigenetic alterations or signaling pathways may have a role. Unraveling the mechanisms of innate and adaptive immune downregulation in this subset of PMLs will be important to identifying potential immunoprevention therapies.The presence of tumor-associated macrophages with the polarized phenotypes (M1 as pro-inflammatory or M2 as anti-inflammatory) has been associated with lung cancer prognosis. The presence of predominantly M2 macrophages, marked by the expression of CD163, has been associated with worse survival. However, in the context of lung PMLs this relationship is not well studied. Our finding that regressive Proliferative PMLs have more CD163Our data suggest that there are subtype-specific transcriptomic alterations predictive of subsequent LUSC PML progression that are the result of a lack of infiltrating immune cells in the lesion microenvironment. These data suggest that biomarkers for determining PML subtype and assessing immune infiltration may have utility for the detection of aggressive PMLs that require more intensive clinical management and genes altered in these PMLs may serve as lung chemoprevention candidates. These biomarkers could either be measured directly in PML tissue or a surrogate tissue such as bronchial airway epithelium. A benefit of biomarkers predicting aggressive PML behavior measured in surrogate tissue is the potential that these biomarkers might also predict the behavior of PMLs not directly observed during bronchoscopy. Future studies are needed to address the specific mechanism of impaired immunosurveillance in progressive/persistent lesions in the Proliferative subtype including single-cell sequencing, high coverage DNA sequencing, characterization of neoepitope burden, assessment of epigenetic alterations, and comprehensive characterization of the identified immune populations. Overall, our data support the potential for immunoprevention strategies to intercept the progression of PMLs to lung cancer.41. Eligibility for screening includes either a previous history of aerodigestive cancer and no disease at the time of enrollment or age > 50, a current or previous history of smoking for a minimum exposure of 20 pack-years and at least one additional risk factor including moderate COPD (defined as forced expiratory volume (FEV1) < 70%), confirmed asbestos related lung disease or a strong family history of lung cancer (at least 1–2 first-degree relatives). All research specimens were stored in RNA Allprotect (Qiagen) and stored at −80°C.Endobronchial biopsies and brushings were obtained from high-risk subjects undergoing lung cancer screening at ~1-year intervals by white light and autofluorescence bronchoscopy and computed tomography at Roswell. The bronchoscopy included visualization of the vocal cords, trachea, main carina, and orifices of the sub-segmental bronchi visible without causing trauma to the bronchial wall. All abnormal and suspicious areas are biopsied twice and the lung anatomic location is recorded . mRNA-seq was subsequently performed on a VC of samples comprising of endobronchial biopsies and brushes collected between 2012 and 2015 . Biopsy progression/regression was defined for each biopsy based on the histology of the biopsy and the worst histology recorded for the same lung anatomic location in the future. Histology changes between normal, hyperplasia, and metaplasia were classified as “normal stable”, decreases in histological dysplasia grade or changes from dysplastic histology to normal/hyperplasia/metaplasia were classified as “regressive”, lack of future histological data was classified as “unknown”, and everything else was classified as “progressive/persistent.” The Institutional Review Boards at Boston University Medical Center and Roswell approved the study and all subjects provided written informed consent.Subjects were selected that had biopsies collected in repeat locations via serial bronchoscopies; however, after RNA isolation, samples from three subjects had a single biopsy and one subject had a single brushing. mRNA-seq was performed on a DC of samples comprising of endobronchial biopsies and brushes collected between 2010 and 2012 . Sample relatedness within a patient was confirmed using Peddy software44. Samples with a high-rate of heterozygosity (>3 standard deviations above the median) or samples with low relatedness to samples from the same patient (>3 standard deviations below the median) were removed from further analyses . Samples were subsequently divided into the discovery and VC (as outlined above) and by tissue type (biopsy or brush). Subsequent sample and gene filtering was conducted separately on each set as follows: first, EdgeR45 was used to compute normalized data  and genes were excluded that either had an interquartile range equal to zero or a sum across samples equal or <1. Samples were excluded based on values > 2 standard deviations from the mean for more than one of the following criteria: (1) mean Pearson correlation with all other samples calculated across all filtered genes (2) the 1st or 2nd principal components calculated using the filtered gene expression matrix (3) transcript integrity number . After sample filtering, gene filtering was recomputed as described above on the final set of high-quality samples. The data are available from NCBI’s Gene Expression Omnibus using the accession GSE109743.Total RNA was extracted from endobronchial biopsies and bronchial brushings using miRNeasy Mini Kit or AllPrep DNA/RNA/miRNA Universal Kit (Qiagen). Sequencing libraries were prepared from total RNA samples using Illumina TruSeq RNA Kit v2 and multiplexed in groups of four using Illumina TruSeq Paired-End Cluster Kit. Each sample was sequenced on the Illumina HiSeq 2500 to generate paired-end 100-nucleotide reads. Demultiplexing and creation of FASTQ files were performed using Illumina CASAVA 1.8.2 or BaseSpace. Samples were aligned using hg19 and 2-pass STARn = 190 samples, n = 16653 genes) and brushes  were used to derive the molecular subtypes. Two additional RNA-Seq data sets were used during the derivation of the molecular subtypes: the TCGA squamous cell carcinoma (LUSC) tumors10  and a dataset of tracheobronchial samples from mice treated with NTCU . The mice develop lesions that are histologically and molecularly comparable to human lesions and that progress to LUSC and the samples represent a range of histology  . The mouse data are available from NCBI’s Gene Expression Omnibus using the accession GSE111091. Sample and gene filtering from the TCGA LUSC tumors and the mouse tissue were processed as described in the Supplementary Materials and Methods.The DC biopsies  and batch (Illumina flow cell) were used as input for WGCNA for the biopsy and brush data sets. For the mouse dataset, residual gene expression values adjusting for RNA quality (median TIN), mouse strain, and sample type (laser capture microdissected versus whole tissue) were used as input for WGCNA. For TCGA LUSC tumor samples, residual gene expression values adjusting for plate were used as input for WGCNA. Gene sets were created for each co-expression module for each dataset and then combined to create a compendium of gene sets. For each gene set in the compendium, the first principal component (PC1) was calculated across each z-score normalized dataset. For each data set, a matrix of absolute Pearson correlation coefficients based on PC1 values calculated for each gene sets in the compendium was computed and thresholds were set as follows: r > 0.85 was set to 1 and r ≤ 0.85 set to 0. The four matrices were subsequently summed, and gene sets derived from biopsy co-expression modules that were correlated to another non-biopsy-derived gene set across all data sets were retained (n = 9 modules retained). The genes defining the retained biopsy modules were required to be present in the biopsy module and at least in one of the correlated gene sets.WGCNAn = 3936) that was used to define the molecular subtypes in the biopsy data. The residual gene expression values across the reduced set of genes for the discovery biopsies were used as input for consensus clustering46. Consensus clustering was performed setting k (number of groups) to 10, the number of iterations to 1000, the subsampling to 80%, the clustering algorithm to partitioning around medoids, and the distance metric to Pearson correlation. The optimal value for k was 4 based on the relative change in area under the cumulative distribution function calculated based on the consensus matrix for each k.The filtering process above yielded a reduced set of genes (n = 3936) were used to derive a molecular subtype predictor. First, squared Pearson correlation coefficients were determined between each gene and the module eigengenes (PC1 for each of the nine modules). Genes were retained as part of a module if the squared correlation coefficient was the highest for the module in which it was assigned. The average squared Pearson correlation coefficient of the retained genes to the module eigengene was computed, and the number of genes chosen from each module for the predictor was inversely proportional to this metric. Second, genes with the highest squared correlation coefficients to the module eigengene were chosen to represent the module in the predictor. The 22 genes resulting from this analysis across the DC biopsy data were used to train a nearest centroid predictor using the pamr package with a threshold of zero and predict the molecular subtype across the VC biopsies. Prior to predicting the molecular subtype of these test sets, the training and test sets were combat47 adjusted and z-score normalized across combined training and test data. Using the methods described above we derived molecular subtypes using consensus clustering across the VC biopsies and compared these to the predicted subtypes.The DC biopsies across the module genes  with a particular molecular subtype versus all other samples (two-level factor) using a linear mixed effects model with patient as a random effect (using the ‘duplicateCorrelation’ function) via limma. In addition, biological pathways and transcription factors associated with each subtype were identified using GSEA49 and mSigDB50 gene sets using genes ranked by the t-statistic for their association with each subtype. The ranked lists were created using the limma51 and edgeR45 packages to identify differentially expressed genes associated with subtype membership. Each linear model used voom-transformed52 data and included membership in the subtype of interest, batch, and RNA quality (TIN) as covariates and patient as a random effect. Pathways enriched in the ranked lists (FDR < 0.05) were used to annotate the molecular subtypes. FDR values for individual genes were derived from this analysis or analogous models using only samples of normal/hyperplasia histology or dysplasia histology.The molecular subtypes in the DC biopsies were annotated according to the behavior of each gene module by calculating whether or not module GSVA14. Smoking status was determined at each time point for each subject by calculating the mean of the prediction scores ( >0 for current prediction and <0 for former/never prediction) across all biopsies and brushes sampled. The LUSC tumor subtype was determined as described previously11 across the genes predictive of the LUSC molecular subtype12. The ESTIMATE algorithm53 was used to infer relative epithelial, stromal, and immune cell content. Immune cell type specific signatures from Bindea et al.15 and epithelial cell type specific signatures from Dvorak et al.54 were used to generate GSVA13 scores across samples for each signature. In addition, residual gene expression values calculated using log RPKM values were inputted into the xCell16 to infer relative abundances of 64 different cell types. The above categorical phenotypes along with additional clinical variables such as biopsy histology, subject, previous lung cancer history, sex, and biopsy progression/regression status were associated with molecular subtype using two-sided Fisher’s exact test.For the DC and VC biopsies, residual gene expression values were used to predict smoking status, LUSC tumor subtype, and the relative abundance of epithelial and immune cells for each sample. Smoking status (current versus former/never) was predicted for each sample as described previouslyIn order to characterize the molecular alterations associated with lesion outcome, a linear mixed effects model was used to assess module GSVA score differences between progressive/persistent versus regressive lesions within each molecular subtype with patient as a random effect via limma. We estimated differences in the immune cell content  between progressive/persistent versus regressive lesions in the Proliferative subtype via a linear mixed effects model correcting for epithelial cell content  and patient as a random effect. We focused on cell types that were significantly different (FDR < 0.05) between progressive/persistent versus regressive lesions in the Proliferative subtype in both the DC and VC.We wanted to quantify the predictive performance of the brush with regards to the presence of a biopsy of the Proliferative subtype. A subset of the 22-gene molecular subtype predictor was used to predict the presence or absence of the Proliferative subtype across the DC and VC brushes and biopsies. Specifically, we used eight genes (out of the 22) that corresponded to modules 4 through 7 (significantly up  or down regulated in the Proliferative subtype) to classify samples as Proliferative or not using the same methodology described above for the molecular subtype predictor. Sensitivity and specificity performance metrics were calculated based on the ability of a Proliferative subtype prediction in the DC or VC brushes to indicate the presence of at least one biopsy of the Proliferative subtype. In order to further understand the Proliferative subtype predictions in the brushes, we analyzed the behavior of the modules that define the Proliferative subtype in the DC biopsies (based on methods above) across the DC and VC brushes.Standard formalin fixation and embedding techniques were employed at Roswell where five-micron sections were cut from the FFPE samples used for the routine pathological evaluation at Roswell (Supplementary Table Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Dataset 1Raw Image File 1 for Figure 1ERaw Image File 2 for Figure 1ERaw Image File 3 for Figure 1ERaw Image File 4 for Figure 1ERaw Image File 5 for Figure 1ERaw Image File 6 for Figure 1ERaw Image File 7 for Figure 1ERaw Image File 8 for Figure 1ERaw Image File 9 for Figure 1ERaw Image File 10 for Figure 1ERaw Image File 11 for Figure 1ERaw Image File 12 for Figure 1ERaw Image File 13 for Figure 1ERaw Image File 14 for Figure 1ERaw Image File 15 for Figure 1ERaw Image File 16 for Figure 1ERaw Image File 17 for Figure 4DRaw Image File 18 for Figure 4DRaw Image File 19 for Figure 4DRaw Image File 20 for Figure 4DRaw Image File 21 for Figure 4DRaw Image File 22 or Figure 4DRaw Image File 23 or Figure 4DRaw Image File 24 or Figure 4DRaw Image File 25 or Figure 4DRaw Image File 26 or Figure 4DRaw Image File 27 or Figure 4DRaw Image File 28 or Figure 4DReporting SummarySource Data


Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a wide spectrum of clinical manifestations and degrees of severity. Few genomic biomarkers for SLE have been validated and employed to inform clinical classifications and decisions. To discover and assess the gene-expression based SLE predictors in published studies, we performed a meta-analysis using our established signature database and a data similarity-driven strategy. From 13 training data sets on SLE gene-expression studies, we identified a SLE meta-signature (SLEmetaSig100) containing 100 concordant genes that are involved in DNA sensors and the IFN signaling pathway. We rigorously examined SLEmetaSig100 with both retrospective and prospective validation in two independent data sets. Using unsupervised clustering, we retrospectively elucidated that SLEmetaSig100 could classify clinical samples into two groups that correlated with SLE disease status and disease activities. More importantly, SLEmetaSig100 enabled personalized stratification demonstrating its ability to prospectively predict SLE disease at the individual patient level. To evaluate the performance of SLEmetaSig100 in predicting SLE, we predicted 1,171 testing samples to be either non-SLE or SLE with positive predictive value (97–99%), specificity (85%-84%), and sensitivity (60–84%). Our study suggests that SLEmetaSig100 has enhanced predictive value to facilitate current SLE clinical classification and provides personalized disease activity monitoring. Systemic lupus erythematosus (SLE) is a chronic systemic autoimmune disease that predominantly affects young women. It is characterized by heterogeneous clinical manifestations with varying degrees of severity punctuated by alternating phases of remission and flares . PatientGiven its wide variety of relapsing-remitting symptoms, lupus is typically difficult to diagnose. To complicate matters, there is no laboratory test that can definitively identify the illness. As a result, it can take years for a diagnosis to be made. Disease activity (DA) is also difficult to measure. One method is using any of six validated composite scores, one of which is the SLE Disease Activity Index (SLEDAI) . BecauseThe use of gene expression microarrays in clinical research has led to the establishment of biomarker signatures. SLE patients display unique blood transcriptional signatures linked to type I interferon (IFN) and granulocytes –7. PreliThe value of this approach to discover stable disease markers has been questioned . One comThese limitations can be overcome by combining related but independent studies into a meta-analysis forming a larger sample size with lower false discovery rates. We have developed and utilized a robust meta-analysis approach called EXALT(EXpression AnaLysis Tool) for gene expression profile studies from thousands of Gene Expression Omnibus (GEO) and published breast cancer datasets –13. A geThe present work describes the implementation of our unique EXALT approach for the meta-analysis of blood microarray transcriptional profiles on SLE. To this end, we aimed to identify a meta-signature (SLEmetaSig100) that correlates SLE status and DA from thirteen training data sets. We then validated the SLEmetaSig100 in two independent test data sets to determine its correlation with SLE and DA and prospective predictive value of SLE disease at the individual patient level. This enabled patient stratification based on a personalized transcriptional immunomonitoring signature correlating with DA in each patient.Subjects were recruited by the individual studies. Clinical diagnosis and demographic characteristics of anonymized SLE patients were confirmed and summarized in the previously published studies . EXALT iWhole blood PBMC or T cell subset samples from the training data sets  were grouped by their clinical attributes and study designs. Based on the existing sample group description in the published studies, each data set had at least two groups of samples including normal healthy controls and various SLE related phenotypes, and/or molecular markers such as a lupus flare, low or high disease grade activities, TLR and IFN gene expression levels . Two or One of the signatures (PMID: 16777955) in the training set  was deriWe used EXALT in an iterative manner (iterative EXALT)  to conduIn the cluster, each of the thirteen signatures comprised several hundred genes with various overlapping signature genes. In order to identify a recurrent and concordant gene expression pattern in the SLE signature cluster, all signature genes were assembled together to form a synthetic signature (SLEmetaSig). The top 100 genes (SLEmetaSig100), as determined by ranking their frequency of recurrence and gene expression profile concordance, were identified using the method previously described .We constructed a centroid-based reference signature associated with known SLE status and the SLEmetaSig100 signature values from the thirteen training datasets using the method described before .Two GEO datasets (GSE65391 and GSE11909) were used as validation data sets to test the predictive ability of SLEmetaSig100. The 1,171 testing SLEmetaSig100 signatures were made from 92 healthy subjects and 1,079 SLE samples (211 patients) [By performing a Spearman’s rank correlation between the reference signature and individual gene-expression profiles in test datasets, we were able to determine SLE status of individual patients from the two test data sets.For each testing sample, a Spearman rank correlation value between the reference SLEmetaSig100 profile and the test sample profile was calculated. The sample is considered to be ‘SLE’ if the correlation value with the reference was equal to or above the predefined threshold value (0.3). The samMann-Whitney U test to examine the difference in mean numbers SLE subjects between two SLEmetaSig100 classified groups. We used Fisher’s Exact Test to compare prospective prediction rates of SLEmetaSig100 in two test data sets.An EXALT built-in statistical approach was used to assess signature similarity of training data sets. To evaluate of SLEmetaSig100 retrospective classification, we used Hierarchical clustering and Spearman's rank correlation were performed and visualized using the TIGR MeV . UnsuperProspective SLE prediction in test data sets was compared with actual clinical diagnosis. The primary predictive endpoint was SLE diagnosis or SLE disease activity (DA) for the validation cohort. The predictive performance was assessed using the derived positive predictive value (PPV), the negative predictive value (NPV), sensitivity, and specificity.Receiver operating characteristic (ROC) analysis was performed to determine the sensitivity and specificity of SLEmetaSig100 predictions and the area under the ROC curve. ROC analyses were performed using R, version 3.3.3.In the past two decades, a large number of gene-expression studies have been reported and deposited in public domain including GEO, PubMed, and EXALT signature database . EXALT mTo avoid the weaknesses of single study-derived signatures and to better utilize the available gene expression data from independent studies, we have developed a meta-analysis strategy called EXALT. EXALT is essentially a database containing thousands of gene expression signatures extracted from published studies that enables signature comparisons . We haveSome of these 167 gene signatures are biologically related to SLE disease status. There are inherent limitations for any individual profiling study such as small sample size relative to the large number of potential gene probes, limitations of technological platforms, sample variation, and bioinformatics or statistical method bias. To overcome these problems, we implemented a meta-analysis approach (iterative EXALT) that combines individual transcriptional profiling signatures to deduce a common transcriptional signature across studies (SLEmetaSig100).This conserved profile (SLEmetaSig100) was derived from 1,869 patient samples from thirteen individual SLE studies . The exphttp://www.genome.jp/kegg) and its analysis tool[To determine pathways that SLEmetaSig100 may be involved in, we used KEGG Pathway database  and constructed an innate immune DNA-sensor model of SLEmetaSig100. A cartoon depicting genes and their relationship in this model is shown in e.g. IFN-β expression [DNA sensor genes such as cyclic GMP-AMP Synthase (MB21D1 or cGAS), IFI16, and Toll-like receptors (TLRs) are upstream stimulators in Toll-like receptor signaling pathway , NF-kapppression . IFN genpression . SLE canpression . Thus, iTo further study the characteristics of SLEmetaSig100, we compared SLEmetaSig100 with some other known SLE signatures . Unlike When signature genes were analyzed by an overlapping analysis, we found that there were very few common genes between SLEmetaSig100 and others. For example, only one common gene (1%) was found between SLEmetaSig100 and the other two signatures  in the dendrograms based on differential gene expression patterns. The associated sample phenotypes, either healthy (white bar) or SLE (black bars), were also grouped in each dataset. We observed that healthy samples were enriched in the left group of both testing data sets . The resMore specifically, in the SLE dominant sample groups, there was only one  normal sample in GSE65391 and noneSLE is a heterogeneous disease that cannot be diagnosed by a single symptom or lab test. Personalized prediction of SLE status by comparing a test sample profile to a reference SLEmetaSig100 signature may provide a new method to facilitate clinical diagnosis.We leveraged SLEmetaSig100 to determine whether it can distinguish individual SLE patients from normal healthy subjects and to uncover associated disease activity (SLEDAI) when clinical SLE information is masked in test data sets.To evaluate the ability of SLEmetaSig100 to predict SLE, we divided 1,171 testing samples to be either predicted healthy or predicted SLE. Those results were then compared to their actual clinic diagnoses. The derived positive predictive value (PPV), the negative predictive value (NPV), sensitivity, and specificity were then calculated and compared accordingly. While assessing the SLEmetaSig100 prospective prediction results with the actual clinic diagnoses, we found that SLEmetaSig100 could significantly correctly predict SLE in two independent cohorts . The ROC results are comparable displaying areas under the curve (AUC) of 0.89 (GSE65391) and 0.85 (GSE11909), respectively, indicating that SLEmetaSig100 has reliable performance in heterogeneous patient populations. When SLEmetaSig100 prediction was implemented with centroid model, the GSE65391 test resulted in a specificity of 85% and a sensitivity of 84%. The GSE11909 test displayed a specificity of 84% and a sensitivity of 60% . These samples were classified into three DA group scores: S1 (SLEDAI: 0–2), S2 (SLEDAI:3–7), or S3 (SLEDAI > 7). From this, we constructed three DA reference signatures . Using the same SLE prediction process, we computed three correlation scores between each test sample and three reference DA signatures. The predicted SLE DA group for each sample was determined by the highest correlation score of the three correlations calculated. We then compared the average actual DA group scores  and published studies that enable signature comparisons. We previously used this robust meta-analysis of gene expression profiles from hundreds of breast cancer datasets –13. By uin silico method (EXALT) to identify and validate the meta-signature (SLEmetaSig100) based on fifteen available published studies. We demonstrated that heterogeneous signatures from thirteen training datasets containing 1,869 samples from heterogeneous whole blood PBMC or T cells could be systematically organized by their common data elements  and assembled into a new signature data type called a meta-signature  . These cBesides the overexpressed IFN-inducible genes, SLEmetaSig100 also contains DNA sensor genes as described in the innate immunity DNA-sensing model  such as Other DNA sensor genes in the SLEmetaSig100 signature are key enzymes involved in breakdown of DNA including nucleases such as DNASE1, DNASE1lL3, TREX1, and TREX2. Importantly, a loss-of-function variant of DNASE1L3 causes a familial form of SLE. Mutations in TREX1 are associated with familial chilblain lupus and are also associated with the inflammatory disorder Aicardi-Goutieres syndrome. The SLEmetasig100 emphasizes the importance of including DNA processing pathways, which may capture the contributions of proteostasis and ER stress to SLE pathogenesis.Lupus nephritis is a frequently seen complication in patients with SLE and is known to significantly reduce the survival of SLE patients. A hallmark of lupus nephritis is the renal inflammation caused by deposition of autoimmune complexes to kidney glomeruli. There are four SLEmetaSig100 genes  in NF-kappa B signaling pathway  that havPrevious SLE signatures have been essentially equivalent in correlation to SLE DA except for the INFγ signature . HoweverOur approach may provide a new SLE biomarker for clinical diagnosis, classification and monitoring. Previous signatures have correlated with DA at the cohort level (retrospective stratification), such as IFN or plasmablast signatures ; howeverA limitation of the current study is that we only tested SLEmetSig100 in samples of healthy and SLE subjects. Heterogeneous cell types with limited clinical attributes and follow-up information could hamper the training process and interpretation of our meta-analyses. There is no test data set available showing the correlation between the SLEmetaSig100 profile and a manifestation of SLE or another autoimmune disease. Treatment status was not accessible in the meta-data of the training and testing sets. Therefore, it may be possible that the SLE patients with signatures that were most similar to healthy controls were responding well to therapy. The capacity for SLEmetasig100 to predict early treatment response will be an important future application as well as its ability to distinguish SLE from other autoimmune disorders that may overlap in clinical presentation.Future studies on SLEmetaSig100 with consecutive blood sampling from the same patient would allow us to better measure SLEmetSig100’s performance by tracking disease activity and response to treatment over time. More importantly, a more controlled training set would allow us to improve our meta-signature’s predictive ability to distinguish SLE profiles from those of other autoimmune disorders.In summary, our finding supports the potential application of SLEmetSig100 as a promising biomarkers in clinical practice with an acceptable specificity and sensitivity. Biomarkers that can prospectively predict occurrence and frequency of flares will be of great clinical value in clinical practice . The datS1 FigSLEmetaSig100 includes eight Toll-like receptors (TLR) genes . Most of TLRs are up-regulated  while two TLRs (TLR3 and TLR9) are down-regulated in SLE disease conditions.(TIF)Click here for additional data file.S2 FigNF-kappa B signaling pathway. There are four up-regulated SLEmetaSig100 genes  and one down-regulated gene (NFKB1) in the NF-kappa B signaling pathway that are also present in the TLR signaling pathway as expected. As a negative regulator protein, NFKB1 is controlled by various mechanisms of post-translational modification and subcellular compartmentalization as well as by interactions with other cofactors or co-repressors.(TIF)Click here for additional data file.S3 FigThe network of cytokine-cytokine receptor Interaction contains 18 SLEmetaSig100 genes . Most cytokine genes are up-regulated  like IFN(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


Tenofovir (TFV) treatment of female reproductive tract (FRT) cells results in differential accumulation of intracellular Tenofovir diphosphate (TFV-DP) in different cell types, with greater concentrations in epithelial cells (100-fold) and fibroblasts (10-fold) than in CD4+ T cells. The possibility that TFV-DP accumulation and retention in epithelial cells and fibroblasts may alter TFV availability and protection of CD4+ T cells against HIV infection, prompted us to evaluate TFV and/or Tenofovir alafenamide (TAF) release from FRT cells. Endometrial, endocervical and ectocervical polarized epithelial cells and fibroblasts were pre-loaded with TFV or TAF, and secretions tested for their ability to inhibit HIV infection of activated blood CD4+ T cells. Epithelial cell basolateral secretions , but not apical secretions, suppressed HIV infection of CD4+ T cells, as did secretions from pre-loaded fibroblasts from each site. Intracellular TFV-DP levels in epithelial cells following preloading with TFV or TAF correlated directly with ARV protection of CD4+ T cells from HIV infection. When added apically to epithelial cells, TFV/TAF was released basolaterally, in part through Multidrug Resistant Protein transporters, taken up by fibroblasts and released into secretions to partially protect CD4+ T cells. These findings demonstrate that epithelial cells and fibroblasts release TFV/TAF for use by CD4+ T cells and suggest that the tissue environment plays a major role in the sustained protection against HIV infection. In endemic areas like Sub-Sharan Africa however, women are at disproportionate increased risk for HIV acquisition compared to men, and HIV is the main cause of death for reproductive age women2. Sexual transmission is the main route for HIV acquisition in women, therefore, preventive strategies in women need to be effective in the female reproductive tract (FRT). The immune system in the FRT has the dual role of protecting against infections while allowing pregnancy to occur3. To this end, immune cells in the FRT are tightly regulated by sex hormones and the tissue environment, which control immune cell distribution and function10.Half of the people infected with HIV worldwide are women11 was shown in several trials to protect against HIV-1 infection in heterosexual men and women14. In contrast, only one trial (CAPRISA 004) using topical TFV applied in the vagina has shown significant protection against HIV acquisition in women, while several other trials involving only women, using topical or oral PrEP  have shown no protective effect17. Beyond compliance, the success or failure of ARVs depends on effective concentrations of ARVs being achieved and maintained in those tissue cells (CD4+ T cells and macrophages) susceptible to HIV-1 infection.Central to the strategy of preventing the sexual transmission of HIV to women is the use of pre-exposure prophylaxis (PrEP), in which antiretrovirals (ARVs) such as Tenofovir (TFV) are delivered topically into the vagina or taken orally as tenofovir disoproxil fumarate and emtricitabine . Oral PrEP21. TAF, due to its neutral charge, readily diffuses into the cell, although transporters may also be involved in cell entry22. Thus TAF achieves similar protection against HIV infection at concentrations ~300 fold lower than TFV7. Intracellular TAF is readily converted to TFV via the actions of Cathepsin A. Once in the cell, TFV is converted into TFV-diphosphate (TFV-DP) through two sequential phosphorylation reactions23. It is TFV-DP, the active metabolite of TFV and TAF, which interferes with viral replication.TFV and its prodrug tenofovir alafenamide (TAF) are HIV nucleoside analog reverse transcriptase inhibitors that act via their integration into nascent viral DNA to prevent transcription of the viral RNA into viral DNA, a key early step in the HIV lifecycle. TFV and TAF, differ in their ability to enter cells. TFV with its inherent negative charge is poorly taken up by cells and is dependent on limited diffusion as well as energy dependent transporters24. We found that concentrations of TFV-DP were 100-fold higher in epithelial cells and 10-fold higher in fibroblasts when compared to CD4+ T cells and macrophages. In other studies, the distribution of TFV-DP was analyzed using combined confocal Raman spectroscopy (CRS) and optical coherence tomography (OCT) to measure the distribution of TFV in intact porcine vaginal tissues26. Measured with sub-100-micron spatial resolution, the concentration of TFV following topical application was greatest in the epithelium and rapidly diminished deeper in the stroma. Taken together, these findings indicate a cell-specific distribution of TFV-DP in the reproductive tract and demonstrate that tissue biopsy concentrations may not reflect the physiologically-relevant concentrations of an ARV needed to prevent the sexual transmission of HIV. The recognition that ARVs are not uniformly distributed between cells in the reproductive tract emphasizes the need to understand the role of the tissue environment in modulating protection and susceptibility to HIV infection.Previous studies by us evaluated the intracellular concentrations of TFV-DP (the active form of TFV) in purified immune and non-immune cells from the upper and lower human FRT29. In contrast, following oral administration, TFV reaches the subepithelial stroma of the reproductive tract through the blood stream30. Important differences have been described in TFV-DP vaginal levels after topical and oral administration of TFV32. Whether epithelial cells and fibroblasts act as a sink for TFV-DP to compromise protection, or release TFV slowly after initial uptake to maintain protection in the genital mucosa is unknown.When TFV is applied topically into the vagina, it first enters epithelial cells at the apical  surface after which it is released basolaterally into the subepithelial  compartment, possibly through several mechanisms, including passive diffusion and/ or transporters, such as breast cancer resistant protein (BCRP) and P-glycoprotein (P-gp) or Multidrug Resistant Proteins (MRP)Using polarized primary epithelial cells and fibroblasts from the endometrium (EM), endocervix (CX) and ectocervix (ECX), we examined whether TFV- and/or TAF-derived intracellular accumulation of TFV-DP enhances or inhibits TFV-mediated protection against HIV by interfering with availability to CD4+ T cells. When epithelial cells were pre-loaded with TFV or TAF, we found that both ARVs were released basolaterally for at least 3 days at concentrations that provide partial protection of CD4+ T cells from HIV infection, and that ARV release was partially due to Multidrug Resistant Protein (MRP) transporters. These findings demonstrate that epithelial cells and fibroblasts contribute to mucosal protection against HIV throughout the FRT by acting as an ARV reservoir that extends partial protection following ARV administration.All human subject work was carried out with the approval of the Dartmouth College Institutional Review Board. Approval to use tissues was previously obtained from the Committee for the Protection of Human Subjects (CPHS), and with written informed consent obtained from the patient before surgery. All samples were anonymized, and all investigations were conducted according to the principles expressed in the Declaration of Helsinki.Human FRT tissues were obtained immediately following surgery from women who had undergone hysterectomies at Dartmouth-Hitchcock Medical Center . Tissues from the endometrium (EM), endocervix (CX) and ectocervix (ECX) were collected from patients with benign conditions such as fibroids and prolapse (age from 26 to 61 years old). Tissue samples were distal from the sites of pathology and were without pathological lesions as determined by a pathologist. Blood donors were anonymous, no information regarding age or hormonal status was available and only female donors were used in this study.8. Purified blood CD4+ T cells were activated in vitro using X-vivo 15 media  with Phenol Red plus Phytohemagglutinin  and IL-2  for 24 hr as described previously8. Activated CD4+ T cells were plated at a density of 1 × 105 cells per well in round-bottom 96-well culture plates  in 0.1 ml of Immune cell media consisting of X-vivo 15 Media supplemented with Phenol Red and 10% human AB serum  prior to treatment.Blood from women was obtained from our IRB-approved Blood Donor Program at Dartmouth-Hitchcock Medical Center. CD4+ T cells were purified with the CD4+ T cell isolation kit (Miltenyi Biotech) from frozen peripheral blood mononuclear cells (PBMC) as described before3  and processed as previously described34. Tissues were then minced under sterile conditions into 1-2 mm fragments and digested at 37 °C for 1 hr using a mixture containing : 0.05% collagenase type IV  and 0.01% DNase  in HBSS . Type IV collagenase was selected based on preliminary studies to ensure non-cleavage of surface markers6. After digestion, cells were dispersed through a 250-µm nylon mesh screen , washed, and resuspended in complete media consisting of DMEM/F12 medium without Phenol Red, supplemented with 10 mM HEPES , 100 µg/ml primocin , 2 mM L-glutamine, 2.5% heat-inactivated defined fetal Bovine Serum (FBS) (both from Thermo Scientific Hyclone) and 2.5% NuSerum . Epithelial cell sheets were separated from stromal cells by filtration through a 20-µm mesh filter . Epithelial cell sheets were retained on the filter, while stromal cells passed through.Tissues were rinsed with HBSS  supplemented with Phenol Red, 100 U/ml penicillin, 100 µg/ml streptomycin , and 0.35 mg/ml NaCO34. To establish a cell culture system of polarized human FRT epithelial cells with both apical and basolateral compartments, FRT epithelial cells were cultured in Matrigel matrix (BD Biosciences) coated Falcon cell culture inserts in 24-well companion culture plates (Fisher Scientific). Apical and basolateral compartments contained 300 and 500 µl of complete medium, respectively, which was changed every 2 days. Tight junction formation of epithelial cell monolayers from EM and CX was assessed by periodically measuring transepithelial resistance (TER) using an EVOM electrode and Voltammeter , as described previously35. To keep the culture conditions similar, the same procedure was followed for culturing squamous ECX epithelial cells, which do not polarize.Epithelial cell sheets were recovered by rinsing and backwashing the filter with complete medium, centrifuged at 500 g for 5 min and analyzed for cell number and viability as previously described36. Briefly, following removal of epithelial sheets, the flow-through containing stromal fibroblasts and immune cells was collected, centrifuged at 500 × g for 10 min, resuspended, cell number and viability determined. Freshly isolated stromal fibroblasts and immune cells were incubated in complete medium in a T75 cell culture flask . Media was replaced every 48 hr to remove non-adherent cells. Once cells reached confluence, they were trypsinized and plated at a concentration of 1 × 105 cells/well in 24-well culture plates (Fisher Scientific) for at least 48 hr prior to treatment.Stromal fibroblasts were isolated as previously described37. TAF was kindly supplied by Gilead Sciences Inc.  and was dissolved in PBS at 10 mM, sterilely filtered (0.2 um) and the concentration checked by absorbance using a molar extinction coefficient of 11690 at 260 nm8. Subsequent dilutions of TFV and TAF were made in complete media to the appropriate working concentrations.TFV in powder form was obtained from AIDS Research and Reference Reagent Program . A stock concentration of TFV 5 mg/ml was prepared by adding 1 ml of PBS to 5 mg of TFV powder, before being diluted in stripped media to the appropriate working concentration24. Intracellular TFV-DP concentrations were measured by liquid chromatography with tandem mass spectrometry (LC-MS/MS) and normalized values to fmol/million cells based on the number of cells per sample32.TFV or TAF were added to polarized epithelial cells both apically (lumen) and basolaterally (tissue) compartments for 24 hr. For some experiments, TFV and TAF were added only apically as indicated in the Results. After treatment, cells were washed, harvested and lysed in 300 µl of 70% methanol, and stored immediately at −80 °C prior to TFV-DP evaluation as previously described24, was based on the initial clinical trial (CAPRISA 004) which used topical TFV applied in the vagina and reported significant protection against HIV acquisition in women when taken between 24 hr prior to sexual intercourse to 24 hr after sex15. Following incubation, cells were washed in media 3 times to remove extracellular TFV or TAF, after which epithelial cell inserts were transferred into new wells and fresh media was added to both the apical and basolateral compartments. Conditioned media (CM) were recovered from apical and basolateral compartments of cell inserts after 24 hr. Similarly, TFV or TAF was added to stromal fibroblasts grown to confluence in 24-well plates for 24 hr, after which they were washed repeatedly prior to the addition of fresh media to collect CM at 24 hr. For epithelial cell time course experiments, media in each compartment was replaced at 24 hr intervals , CM was collected at each time point. To evaluate the interaction between epithelial cells and fibroblasts, polarized epithelial cell inserts were transferred to 24-well plates containing confluent subject-paired fibroblast cultures. TFV or TAF were added to polarized epithelial cells apically for 24 hr, after which basolateral CM was collected. At the end of this incubation, cell inserts and incubation chambers +/− fibroblasts were washed thoroughly both apically and basolaterally so that both epithelial cells and fibroblasts were free of any extracellular TFV or TAF. Cells were then divided in to 2 separate groups consisting of EC inserts transferred to new wells (no fibroblasts), and fibroblasts in the lower chamber alone (no EC inserts). In each of these combinations, fresh media lacking ARVs was added and cells incubated for an additional 24 hr after which conditioned media (containing released intracellular ARVs) was recovered and analyzed for anti-HIV activity.TFV or TAF were added to polarized epithelial cells both apically (lumen) and basolaterally (tissue) compartments for 24 hr, or only apically, as indicated. The time of measurement of TFV-DP used in this study and in our previous researchueous One Solution cell proliferation assay  and trypan blue staining  as described before8. All samples were assayed blind without any information provided as to cell origin and treatment.To evaluate the role of MRPs in ARV movement, epithelial cells were incubated with TFV or TAF for 20 hr, followed by the addition of human MRP-specific inhibitor MK571  for 4 hr. MK571 was added in 250 µl of complete media to both the apical and basolateral compartments and was present in the fresh media following cell rinsing and incubation. CM was collected from apical and basolateral compartments of cell inserts after 24 hr. CM was centrifuged at 10,000 g and stored at −80 °C until HIV infection assay. Untreated control cells were donor-matched to treated cells and processed in parallel at the same time. Cell viability was tested after treatment using the CellTiter 96 AQ8. Stock for HIV-BaL (R5) was obtained through the AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH, from Dr. Suzanne Gartner, Dr. Mikulas Popovic and Dr. Robert Gallo38. Briefly, activated blood CD4+ T cells were incubated with apical and basolateral epithelial cell or fibroblast CM for 24 hr and then washed with PBS prior to HIV infection. After washing, cells were incubated with HIV-BaL for 2 hr at an MOI of 0.1 and then washed to remove residual virus. Fresh 0.2 ml IL-2 supplemented immune cell media was added to each well and cells were incubated for 5 days, with half of the media from each well collected and replaced with fresh media on day 3. Released p24 in the culture media on day 5 was measured by p24 enzyme-linked immunosorbent assay  following the manufacturer’s recommendations.Activated blood CD4+ T cells were infected as previously described with minor modifications36. Total RNA was isolated from epithelial cells using RNeasy reagent  and QIAshredder columns according to the manufacturer’s recommendations (Qiagen), and purified by elution through RNeasy columns (Qiagen) with on-column DNase digestion using the RNase-Free DNase set (Qiagen). For each specimen, 400 ng of total RNA was reverse-transcribed using the iScript complementary DNA (cDNA) synthesis kit  according to the manufacturer’s recommendations. Relative mRNA expression levels of genes of interest were measured using the 5′ fluorogenic nuclease assay in real-time quantitative PCR using TaqMan chemistry on the ABI 7300 Prism real-time PCR instrument . The 5 members from human multidrug resistance-associated protein family   and β-actin (4333762 F) primer/MGB probe sets were obtained from Applied Biosystems assays-on-demand. The 4 epithelial cell tight junction genes analyzed included TJP1 (Zona Occluden 1) (ID nos. Hs01551861_m1), OCLN (Occludin) (ID nos. Hs00170162_m1), CLDN2 (ID nos. Hs00252666_s1) and 4 (ID nos. Hs00533616_s1) (Claudin 2 and 4). PCR was conducted using the following cycle parameters: 12 min at 95 °C for one cycle, followed by 40 cycles of 20 seconds at 95 °C and 1 min at 60 °C. Analysis was conducted using the sequence detection software supplied with the ABI 7300. The software calculates the threshold cycle (Ct) for each reaction and this was used to quantify the amount of starting template in the reaction. The Ct values for each set of duplicate reactions were averaged for all subsequent calculations. A difference in Ct values (ΔCt) was calculated for each gene by taking the mean Ct of each gene of interest and subtracting the mean Ct for the housekeeping gene β-actin for each cDNA sample. The relative expression level of each gene was calculated using the formula 2−ΔCt.Real-time reverse transcription-polymerase chain reaction (RT-PCR) was done with a two-step protocol as described previouslyData analysis was performed using the GraphPad Prism 5.0 . A two-sided P value < 0.05 was considered statistically significant. Comparison of three or more groups was performed applying Kruskal-Wallis test for non-matched samples or Friedman test for matched samples, followed by Dunns-post test for multiple comparison correction. Comparison studies of HIV infections in the absence vs presence of inhibitor MK571 or accumulation of TFV-DP from apical and basolateral treatment and time course studies were analyzed using two-way ANOVA with Bonferroni post-test for multiple comparison correction.22. EM epithelial cells were treated with different doses of TFV or TAF for 24 hr and intracellular levels TFV-DP were determined7. Using this data as a standard curve, we then calculated TFV and TAF doses that would result in equal amounts of intracellular TFV-DP to compare both drugs side by side. As seen in Fig. 7, we predetermined the dose for each ARV that would give the same intracellular concentration of TFV-DP. Incubation with TFV at 3 concentrations  and TAF  resulted in equivalent amounts of intracellular TFV-DP. Exposure for 24 hr was chosen based on our previous publications24 and the initial clinical trial (CAPRISA 004) that reported significant protection against HIV acquisition in women that applied topical TFV in the vagina 24 hr prior to sexual intercourse39. The findings presented here provide the foundation for comparisons in the following sections that measure the effectiveness of ARVs released into secretions for their ability to protect target cells from HIV infection.In order to compare the protective effects of TFV and TAF, studies were undertaken to ensure that comparable concentrations of TFV-DP were present in polarized Endometrial (EM) epithelial cells following incubation with each ARV. These studies were carried out based on the recognition that TAF is administered clinically at lower doses than TFV and that intracellular conversion of TAF into TFV-DP is much more efficient than TFVin vitro HIV infection as detailed in methods. As shown in Fig. Based on our findings in Fig. To determine whether protection by basolateral secretions persisted beyond 24 hr, CM was collected and replaced with fresh media daily for 3 days. As seen in Fig. Recognizing that cell integrity is essential, we measured transepithelial resistance (TER) both prior to the addition of TFV and following its addition and wash out both apically and basolaterally for 3 days in culture. As seen in Fig. Recognizing that ARVs following preloading are preferentially released basolaterally, we investigated whether cell uptake of TFV and TAF also exhibited polarity. As seen in Fig. Overall, these findings indicate that epithelial cells preloaded with either TFV or TAF gradually release ARVs basolaterally in a way that confers partial tissue protection of CD4+ T cells that persists for days.The possibility that the release pattern of ARVs was unique to endometrial epithelial cells prompted us to ask if epithelial cells from other sites in the FRT might share a similar pattern of ARV release. Purified endocervical (CX) and ectocervical (ECX) epithelial cells were grown to confluence, incubated with TFV or TAF for 24 hr and washed to remove excess ARVs after which CM was collected for 24 hr. When cells were preloaded with TFV or TAF, basolateral CM from CX Fig.  and ECX 24. To determine whether fibroblast secretions from the EM, CX and ECX were capable of protecting CD4+ T cells from HIV infection, fibroblasts grown to confluence in 24 well plates were incubated with either TFV (3277 μM) or TAF (10 μM) for 24 hr. Following repeated washes, cells were incubated for an additional 24 hr in fresh media prior to collection of CM for analysis. As shown in Fig. Our previous studies indicated that in addition to epithelial cells, stromal fibroblasts from the FRT also concentrate intracellular TFV-DP approximately 10 fold higher than FRT CD4+ T cells32. To mimic the movement of ARVs from the luminal surface, polarized epithelial cells were treated apically with TFV or TAF followed by basolateral CM collection after 24 hr incubation. Following multiple washes of epithelial cells, cells were incubated with fresh media for an additional 24 hr after which CM was collected. As seen in Fig. In topical Pre-exposure prophylaxis studies, TFV is deposited in the vagina at the epithelial cell surface after which it moves upstream into the ECX, CX and EM and into the underlying stroma containing CD4+ T cellsin situ, both epithelial cells and fibroblasts release ARVs that contribute to the protection of CD4+ T cells in stromal tissues of the FRT.To further mimic the interactions of ARVs with the cells present below the epithelium, we apically treated polarized epithelial cells with TFV or TAF for 24 hr during which stromal fibroblasts were present beneath but not in contact with epithelial cells Fig. . We foun40. To determine whether primary FRT epithelial cells contain MRPs, polarized epithelial cells from the EM, CX and ECX were analyzed for the presence of MRP1, MRP3, MRP4, MRP5 and MRP6. As seen in Fig. Our finding of preferential basolateral release by epithelial cells of ARVs at concentrations capable of protecting underlying cells from infection, suggested that ARV release may in part be mediated through active transport. MRP1 and MRP3 have been reported to be selectively expressed in the basolateral surface of intestinal epithelial cells and mediate TFV transport outside the cell41. MK571 was present in wash media and during the following 24 hr when cells were placed in fresh media. If MRPs are responsible for basolateral transport of TFV, we would expect that after MRP blockade less TFV would be released into the CM resulting in increased infection of CD4+ T cells by HIV. As see in Fig. Based on these findings, we hypothesized that MRP transporters might in part be responsible for the preferential basolateral release of TFV and TAF from polarized epithelial cells. To test this, EM epithelial cells were incubated with TFV or TAF for 20 hr after which the human MRP-specific inhibitor MK571 was added to apical and basolateral compartments for 4 hrTo confirm that MK571 was biologically active, we measured intracellular TFV-DP levels in epithelial cells incubated with TFV and TAF for 24 hr in the presence of MK571. As seen in Fig. Overall, our studies demonstrate that following incubation with ARVs, EM, CX and ECX epithelial cells and fibroblasts retain and subsequently release TFV or TAF that provide downstream protection of CD4+ T cells from HIV acquisition. ARV release by polarized epithelial cells is selective towards the subepithelial  compartment, where it can be taken up by underlying fibroblasts. Together, both epithelial cells and fibroblasts gradually release ARVs to protect CD4+ T cells. Following apical TFV or TAF administration, there is complete protection from HIV infection for the first 24 hr. However, after drug removal, epithelial cells and fibroblasts act as tissue reservoirs that slowly release TFV or TAF, conferring partial protection that persists for at least three days. These findings highlight a previously un-recognized role of the mucosal environment in protection against HIV acquisition by modulating availability of ARVs to HIV-target cells.24. These findings prompted us to ask whether epithelial cells and fibroblast accumulation of ARVs extends protection by acting as a reservoir, or compromises it by reducing drug availability to HIV target cells in the FRT. A major finding in the present study is that by gradually releasing TFV and TAF at concentrations sufficient to provide protection of CD4+ T cells from HIV infection, epithelial cells and fibroblasts extend ARV-mediated protection by providing a reservoir that lengthens the effectiveness of TFV and TAF beyond their initial application. Recognizing that ARVs can be administered orally or locally in the FRT, other studies have reported that the route of administration  influences the time needed for TFV to reach protective levels within HIV-target cells31. Following a single oral dose of TFV, the concentration of TFV and TFV-DP in the gastrointestinal (GI) mucosa is 100-fold greater than the FRT, and requires 7–14 days to reach a protective concentration in the FRT32. A possible reason for this delay could be that orally administered ARVs must GI cross the mucosa prior to entering circulation. Our studies indicating that TFV accumulates in FRT epithelial cells and fibroblasts, suggest that if a comparable concentration occurs in the GI tract cells, it may delay ARV levels reaching effective concentrations in the FRT. This is particularly relevant for TAF which was undetectable in most FRT tissues following a single oral dose, while still being detectable in circulation42. However, by acting as a local ARV reservoir, GI epithelial cells would also extend ARV protection for GI CD4+ T cells, which are susceptible to HIV infection. Whether FRT epithelial cells, and those from other mucosal sites, can act as a reservoir for other ARVs being considered for PrEP, such as dapivirine, remains to be determined, but could be an important factor in modulating their efficacy.Previous studies using purified cells from the FRT to measure intracellular TFV-DP following incubation with TFV demonstrated that epithelial cells and fibroblast TFV-DP concentrations were 100- and 10-fold higher respectively than those found in CD4+ T cellsin vivo, epithelial cells and fibroblasts work in tandem, with each contributing to a sustained tissue concentration that provides extended protection of HIV target cells. Although we did not measure the concentrations of TFV or TAF being released, based on our previous in vitro studies8, we know that the concentrations of TFV and TAF that result in partial protection of activated blood CD4+ T cells, as seen in this study, are very low (1–5 μM and 5–10 nM for TFV and TAF respectively) and several logs below the detectable limits for measuring TFV and TAF24. Moreover, apical concentrations were below these concentrations (1–5 μM and 5–10 nM for TFV and TAF respectively), since we repeatedly found no evidence of protection. However, in vivo, tissue protection may be greater than predicted in our in vitro studies, because of the limited fluid volume within the interstitial space of the FRT, where concentrations of ARVs would be expected to be several folds higher than in our system. This conclusion is based on recent findings demonstrating that, whereas the stroma was thought to be dense connective tissue, the interstitium at mucosal sites is now recognized as a series of fluid-filled compartments43. Given the partial protection we observe with CM, we speculate that protection in vivo would be more complete, given the reduced interstitial volume and consequently higher concentration of secreted ARVs. Further studies are needed to obtain a more accurate profile of in vivo intracellular and interstitial concentrations of ARVs in FRT tissues, recognizing that even within the same tissue, drug distribution, and therefore efficacy, is likely to vary with cell type.An important finding is that ARVs released by epithelial cells can be taken up and re-released by underlying fibroblasts. This suggests that 45. To explore the underlying mechanisms responsible for basolateral release, we examined the expression of MRPs, which eliminate drugs from the cells, and found that FRT epithelial cells express MRP 1, 3, 4, 5 and 6. Chemical inhibition of MRP function resulted in a moderate but significant increase in intracellular TFV-DP levels in epithelial cells, and a decrease in CD4+ T cell protection by basolateral secretions from both TAF and TFV pre-loaded epithelial cells. These findings are consistent with increased TFV and TAF accumulation inside of epithelial cells when transporters are inhibited and suggest that MRPs are selectively expressed on the basolateral membrane of epithelial cells40. However, since protection was only partially reduced, this raises questions about the extent to which other transporters may also be involved in TFV and TAF efflux, since different families of transporters have been described in FRT cells, including BCRP and P-gp27. Another potential explanation for the partial effect seen in these studies is that initial levels of TFV and TAF in epithelial cells following preloading diffuse out and mask transporter function as ARVs exit the cell  decreases by 10–100 fold within 24 hr. When women have sex during this time, TFV concentrations further decrease in the vaginal lumen. These findings further support our conclusion that epithelial cells and fibroblasts serve as important sources of ARVs that are gradually released into underlying tissues to provide protection over a period of days. Second, it implies that TFV concentrations in CVL following topical or oral administration of TFV and TAF are not a good indicator of tissue concentrations and protection, and should not be used to monitor adherence or anti-HIV activity in clinical trials. Third, our findings offer another potential explanation for why TFV did not protect women with genital inflammation derived from STIs49, which probably increases target cell presence in the mucosa, and induces epithelial injury in the form of crypt abscesses, mucosal erosions and ulceration. Under these conditions immune cells would not be protected once the initial drug dose is washed out. Given our results, different factors that alter TFV-DP concentrations in epithelial cells and fibroblasts, such as inflammation49, sex hormones such as estradiol which is known to increase uterine epithelial cell intracellular TFV-DP concentrations24, or menopause which lowers concentrations50, may be more relevant for protection than previously anticipated.Our findings of preferential basolateral release of ARVs have important clinical implications. First, as shown by others53. Our previous studies have demonstrated that medroxyprogesterone acetate (MPA), a commonly used contraceptive in Sub-Saharan Africa associated with increased risk of HIV acquisition, inhibits the conversion of TFV to TFV-DP in activated blood CD4+ T cells at concentrations similar to that used in this study, thus leading to decreased protection against HIV infection8. Similarly, MPA inhibits conversion of TAF to TFV-DP in FRT CD4+ T cells8. Alterations in the vaginal microbiome are also linked to decreased ARV efficacy. Anaerobic bacteria such as Gardnerella vaginalis commonly seen in bacterial vaginosis, decreased the pool of vaginal TFV available for HIV target cells faster than Lactobacillus species51. Lastly inflammation, either due to the composition of vaginal gels54 or pre-existing sexually-transmitted infections53, can drive the recruitment of target cells to the mucosal surface bringing them into proximity of incoming HIV, which in turn can undermine the effectiveness of TFV49. Our studies add an additional component to the complexity of interactions that dictate ARV effectiveness. Our results of preferential basolateral release of ARVs suggest that TFV or TAF treatment would not protect against HIV infection events happening on the mucosal surface, induce under conditions of inflammation or STIs that attract target cells to the surface.Beyond dosage, the efficacy of an ARV is modulated by several factors including local inflammation, use of contraceptives, composition of the microbiome, and presence of sexually-transmitted infectionsex vivo treated tissue explants55, our study is novel in several aspects. First, ours is the first study to demonstrate that primary epithelial cells throughout the FRT accumulate TFV-DP inside the cells and, after the initial drug inoculum is cleared, gradually release TFV, and release it in sufficient amounts and in a sustained manner to protect CD4+ T cells from HIV infection. Second, we demonstrate for the first time that the same is true for TAF, and that the same effect can be achieved with much lower doses than TFV. Third, while previous studies have demonstrated the presence of TFV in the basolateral compartment, to the best of our knowledge, no formal comparison of basolateral and apical release has been performed until now. Lastly, our findings are unique in that they demonstrate the release of ARVs (TFV and TAF) by both columnar  and squamous (ECX) EC and underlying stromal fibroblasts from each of these sites.While there is evidence that TFV is released basolaterally by intestinal (Caco-2) and endometrial (HEC-1-A) epithelial cell lines, and that TFV is present in 24. Similarly, TAF enters cells and is sequentially converted to TFV, and then to TFV-DP20. As a part of this metabolic cycle, TFV-DP from TFV and TAF is converted back into TFV, which either leaves the cell or is metabolized to adenosine20. Our studies suggest that following washout, two distinct steps (diffusion and transport) regulate the efflux of TFV and possibly TAF and are involved in the protection of CD4+ T cells from HIV infection. We speculate that basolateral protection following ARV washout, is due to the gradual release of TFV and TAF that is retained inside the epithelial cells and fibroblasts as well as TFV-DP and TFV-MP until converted back to TFV. Upon efflux and uptake by CD4+ T cells, TFV is converted back to TFV-DP which exerts a protective effect by inhibiting viral replication. The gradual conversion of TFV-DP and release of TFV from epithelial cells and stromal fibroblasts steadily decreases over days following washout, and thus the protective effect against HIV infection also decreases. Also shown is the transfer of TFV from epithelial cells to stromal fibroblasts and the fibroblasts ability to gradually release TFV to protect CD4+ T cells from HIV infection. Further studies are needed to more fully define the mechanisms whereby epithelial cells and fibroblasts throughout the female reproductive tract function as reservoirs to extend protection of CD4+ T cells beyond the initial ARV tissue exposure.As seen in our summary Fig. , on enteIn conclusion, our results demonstrate that epithelial cells and underlying stromal fibroblasts from throughout the FRT readily take up TFV and TAF and produce TFV-DP. After initial drug exposure, degradation of TFV-DP back to TFV as well as any residual TFV and TAF can be gradually released to confer protection of CD4+ T cells from HIV infection for several days. Our findings demonstrate a previously unrecognized role of the mucosal environment in controlling ARV-mediated protection against HIV acquisition and suggest that factors that modify retention of ARVs, and intracellular concentrations of their active metabolites, such as TFV-DP, in epithelial cells and fibroblasts may be more relevant for protection than previously anticipated.Supplementary Information File