Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) may benefit from personalised treatment, requiring biomarkers that characterize the tumour and predict treatment response. We integrate pre-treatment CT radiomics and whole-transcriptome data from a multicentre retrospective cohort of 206 patients with locally advanced HNSCC treated with primary radiochemotherapy to classify tumour molecular subtypes based on radiomics, develop surrogate radiomics signatures for gene-based signatures related to different biological tumour characteristics and evaluate the potential of combining radiomics features with full-transcriptome data for the prediction of loco-regional control (LRC). Using end-to-end machine-learning, we developed and validated a model to classify tumours of the atypical subtype  based on CT imaging, observed that CT-based radiomics models have limited value as surrogates for six selected gene signatures (AUC < 0.60), and showed that combining a radiomics signature with a transcriptomics signature consisting of two metagenes representing the hedgehog pathway and E2F transcriptional targets improves the prognostic value for LRC compared to both individual sources . These results underline the potential of multi-omics analyses to generate reliable biomarkers for future application in personalized oncology. Depending on the tumour site and staging, treatment may combine surgery, radiotherapy, and chemotherapy2. Locally advanced cases show a heterogeneous treatment response with 5-year overall survival (OS) of approximately 50%1. In the era of precision medicine, novel concepts for personalized treatment are developed, including combined treatment modalities or biomarker-guided radiotherapy dose prescription3. For this aim, novel biomarkers that reflect the heterogeneity of the tumours are required. An integration of independent omics layers, i.e. multi-omics, may hold particular value to identify accurate biomarkers4.Head and neck squamous cell carcinoma (HNSCC) is the 6th most common tumour entity worldwide5, providing insight into the etiological heterogeneity of the tumours6. The atypical, basal, classical, and mesenchymal subtypes have been respectively related to a strong HPV+ gene signature lacking EGFR amplification6, hypoxia and gene signatures of basal cells from the human airway epithelium7, tobacco intake5, and acquisition of stemness and migration8. In order to assess treatment response, different biological tumour characteristics have been considered and corresponding biomarkers were developed, for example based on cancer stem cell markers9, genes associated to proliferation and DNA repair10, radioresistance12, immune response13, hypoxia14, or epithelial–mesenchymal transition15. These biomarkers are commonly determined from biopsies or surgical specimens of the tumour. Radiological imaging is an additional source of information and may hold the potential to supplement molecular biomarkers for increased prognostic performance or to replace them, e.g., to avoid invasive biopsies or enable tumour assessment at additional time points.Four HNSCC molecular subtypes have been identified by gene expression studies16. The relationship between radiomics features and underlying biological mechanisms are not well understood17. Radiogenomics (imaging genomics) has emerged as a field whose objective is the association of genomics and molecular alterations within a tumour with quantitative features derived from a radiomics analysis and their integration. For HNSCC, CT texture features have been related to cell-cycle mutations18 and classification models for five sites of methylation as well as NSD1 mutation have been developed using CT-based radiomics19.Radiomics analyses perform a quantitative characterisation of medical images to derive image biomarkers, employing machine learning algorithms for diagnosis or the prognosis of treatment outcomeHere, we further explored the link between macroscopic radiomics features and the underlying tumour biology by analysing a well characterised multicentre cohort of patients with locally advanced HNSCC treated with primary radiochemotherapy (RCTx). Based on paired whole-transcriptome microarray and CT imaging data, we assessed (1) if radiomic features could represent the four molecular HNSCC subtypes, (2) if radiomics features could predict gene-based classifiers related to different biological tumour characteristics such as hypoxia, immune processes, epithelial–mesenchymal transition, and radiosensitivity, and (3) if the prognostic value of CT-based radiomics and whole-transcriptome data could be enhanced by their integration.Whole-transcriptome and pre-treatment CT data were obtained from a retrospective, multicentric cohort of 206 patients presenting with histopathologically confirmed, locally advanced HNSCC who were treated with primary RCTx. This cohort was split into a discovery cohort n = 122) and a validation cohort (n = 84) to assess if radiomics can (1) identify the four molecular subtypes, (2) predict biological processes represented by different gene signatures, and (3) be integrated with full-transcriptome data for an improved prediction of LRC. Figure 22 and a Clinical characteristics of both patient cohorts are presented in Table 5: atypical (15.6%), basal (18.5%), classical (8.3%), and mesenchymal (11.2%). The remaining patients could not be classified due to either low correlation to all subtypes or similar correlations between two or more subtypes (46.4%). Association of molecular subtypes with OS and LRC is shown in Fig. Patients were classified into one of the four HNSCC molecular subtypes through correlation with published centroid datahymal 11.%. The rehymal 11.%. The ressical 8.%, and mesubtypes 6.4%. AssPre-treatment CT-derived radiomics features were computed, clustered, and used to train four one-versus-all (OVA) logistic regression models to distinguish between subtypes through an end-to-end modelling pipeline. Selected features and their scores across cross-validation folds of the discovery cohort are displayed in Supplementary Table The selected radiomics features for the presented atypical model were the morphological feature morph_vol_dens_aabb (IBSI: PBX1) and texture feature szm_glnu (IBSI: JNSA), being, respectively, the fraction of a rectangular box around the tumour volume that is occupied by the tumour and the non-uniformity of the size of similar grey level zones. Table Finally, to assess whether our model only predicts the HPV status, we assessed the observed features based on HPV16-DNA− tumours only. In a new OVA atypical model excluding HPV16-DNA+ tumours, the previously identified features retained their performance with an AUC of 0.70 [0.53–0.84] in discovery and 0.74 [0.56–0.89] in validation, as well as good calibration 15, with the aim to predict the classes using radiomics models. The selected features and their scores across cross-validation folds of the discovery cohort are displayed in Supplementary Table Patients were classified into two classes for each of six different gene signatures: a 7-gene signature related to DNA repair and progressionNone of the classifiers could successfully represent the gene signatures by a radiomics model in validation, as for all models, the lower end of the AUC’s 95% CI included the value 0.50 . Scores and hyperparameters for the selected features are shown in Supplementary Table A radiomics signature was obtained based on a previously implemented workflow21. Metagenes representing the activation of gene sets related to hallmarks of cancer were derived from MsigDB22. On the development cohort, these metagenes were assessed for association with LRC in a feature-selection procedure. The gene set representing hedgehog signalling was selected alongside the gene set for E2F transcriptional targets. These gene sets consist of 35 and 200 genes presented in Supplementary Tables 23, chemoresistance23, radioresistance24, and proliferation25 for the hedgehog metagene. The E2F transcriptional targets metagene contains genes associated with progression in advanced stages26, worse outcome27, or chemoresistance28. In prognostic modelling for LRC, the two metagenes achieved a median C-Index of 0.59 [0.51–0.67] on the discovery cohort and 0.59 [0.49–0.69] in validation. Patient stratification was significant in the discovery cohort (p = 0.022) and in validation (p = 0.023) as shown in Supplementary Fig. To identify relevant characteristics from whole transcriptome data, Gene Set Variation Analysis (GSVA) was appliedFinally, the two metagenes were combined with the radiomics signature to train a Cox model on the entire discovery cohort. Compared to the two individual models presented before, this integrated model yielded a moderately improved median C-Index of 0.65 0.56–0.74] for the discovery cohort and of 0.63 [0.55–0.73] for validation. Moreover, using a risk threshold of 1.502, a significant difference in LRC between the two risk groups was visible in both discovery (p < 0.001) and validation (p = 0.008), as can be observed from Supplementary Fig.  for the To ascertain the role of the log_stat_p90 feature, we trained another model using the GTV and the metagenes. The performance on the discovery cohort was 0.63 0.55–0.72] and 0.62 [0.53–0.72] in validation. Stratification was significant in discovery (p < 0.001), but only showed a trend in validation (p = 0.074) compared to the model with log_stat_p90. Calibration was successful as well in both discovery (p = 0.73) and validation (p = 0.98). Kaplan Meier curves and calibration plots for the model are shown in Supplementary Fig. 5–0.72 anWithin the present study, we investigated the relationship between macroscopic radiomics features and gene-based information on biological tumour characteristics within locally advanced HNSCC. Using CT-derived radiomics, whole-transcriptome data, and machine learning techniques, we (1) developed and validated a model to differentiate the atypical subtype from the other subtypes, (2) observed that radiomics models had limited value as surrogates for six selected gene signatures, and (3) demonstrated the added value of combining full-transcriptome data with a radiomics signature for the prediction of LRC. Overall, this study comprises one of the most extensive multi-omics analyses combining imaging and transcriptome data for patients with locally advanced HNSCC so far.29 and of different imaging modalities, e.g. CT and PET30, has been shown to improve prognostic value. Radiogenomics within HNSCC so far focused mostly on developing and validating signatures for HPV status prediction: Bogowicz et al. derived a CT radiomics signature with a validation AUC of 0.80 on patients with definitive radiochemotherapy31, showing that HPV+ tumours appear more homogeneous on CT. Some HNSCC studies dealt with the association of radiomics texture features to somatic mutations, e.g. Zwirner et al. correlated somatic mutations of FAT1 to decreased intra-tumour heterogeneity18. Huang et al.19 predicted the four molecular subtypes using patients with pre-treatment CT from The Cancer Genome Atlas (TCGA), with the atypical subtype displaying an AUC of 0.65, but was not restricted to locally advanced HNSCC patients and the subtype models were not validated.Several studies have combined radiomics signatures with other types of data: the combination with clinical data32. In our study, the proportion of HPV16-DNA positive tumours within the atypical group was significantly higher than for the other subtypes. These tumours were visually more homogeneous than the other subtypes as reflected by the selected radiomics features. The selected morphological and texture features volume density  and grey level non-uniformity (size zone matrix) represent the volume fraction that the tumour volume has within a corresponding bounding box and the non-uniformity of the grey levels in the size zone matrix, respectively. A higher volume density  would represent a more spatially regular, box-like volume of the tumour. A higher grey level non-uniformity (size zone matrix) would indicate a more inhomogeneous tumour in grey levels with bigger spatial patterns as more zones have grey levels that predominate over others. To confirm that our radiomics model does not only predict the HPV status, it was retrained on the HPV- subpopulation, achieving a similarly good performance. Atypical tumours are described as lacking epithelial growth factor receptor (EGFR) amplification19, a prominent oncogene in HNSCC, therefore possibly making EGFR-targeted therapies such as Cetuximab less effective. They are also described as being more radiosensitive than the other subtypes, even when they are HPV−33. A model predicting the atypical subtype, especially for HPV− tumours, may thus be clinically relevant. Radiomics models to identify the other three subtypes  could not be successfully validated. This might indicate that the three remaining subtypes are more similar from a CT radiomics point of view compared to the atypical subtype.Atypical HNSCC was shown to be associated to HPV positivity34 or partially35. These studies showed low to moderate correlations of radiomics features with gene-based hypoxia markers36. MRI has been used to study hypoxia in glioblastoma37 and prostate cancer38, suggesting that other imaging modalities might be more appropriate for this task.Within this study, non-contrast-enhanced CT-based radiomics showed limited capabilities to predict the classification of the 6 selected gene signatures related to different molecular tumour processes within HNSCC, as our radiomics models could not be validated. This might be due to the gene signatures reflecting microscopic processes that are not readily observable at the macroscopic image level where CT-based radiomics operates. While studies have been conducted to predict hypoxia using radiomics within HNSCC, they employed functional imaging e.g. PET, either totally21 metagenes representing the activation of the hedgehog pathway and E2F transcriptional targets genes from MsigDB22, achieving significant stratification and good model calibration. Higher values of the hedgehog metagene were related to a higher risk of a loco-regional recurrence, which is in agreement with the literature, where higher expression of this pathway in HNSCC has been related to worse prognosis25. Its inhibition through antagonists such as cyclopamine or vismodegib has been shown to suppress proliferation39, stromal repopulation after radiotherapy through GLI1 expression24, and to aid in sensitization to radiotherapy through GLI1 and IAP40. Transcription factors encoded by the E2F transcriptional target genes are related to DNA repair and cell-cycle regulation. E2F transcriptional targets expression has been used to partition HNSCC into subtypes41 with HPV+ tumours tending to show high values of E2F-regulated genes, while HPV− tumours tended to show lower values, which might relate to the observation that higher values of the E2F transcriptional targets metagene were associated with a lower risk of LRC in our model.As the CT-based radiomics features showed low correlations to the considered gene signatures, the combination of such non-redundant data may be useful to improve biomarkers for the prognosis of treatment outcome. Here, we observed an improvement in discrimination through the combination of a developed CT-based radiomics signature with two GSVA-derived42. Repeated biopsies at different locations within the tumour, however, were not available. Only pre-treatment CT imaging data were available for radiomics analysis, while other imaging modalities, for example PET or MRI, may show stronger associations to the underlying tumour biology. For the molecular subtype classification task, the limited amount of discovery subjects to train the models made us consider an OVA approach instead of a potentially more clinically-useful multiclass classifier. Furthermore, the limited number of classical-type tumours within the validation cohort prevented the assessment of the OVA model performance due to wide confidence intervals. The observed differences in clinical parameters between the discovery and validation cohort may impact the validation of the developed models and while image and batch normalisation were performed, metadata missingness may mask confounding effects for the radiomics models like the tube voltage, reconstruction kernels or exposure time.This retrospective study has limitations. Transcriptome data originate from single biopsies of the tumours, which likely do not reflect the heterogeneous transcriptome within locally advanced tumourswww.clinicaltrials.gov, NCT02059668), before potential application in an interventional clinical trial for personalised treatment of locally advanced HNSCC.In conclusion, we have evaluated three different aspects of the relationship between macroscopic CT-based radiomics and whole-transcriptome data in patients with HNSCC: (1) The atypical molecular subtype could be well classified by a radiomics approach, (2) representing gene signatures by radiomics features was not successful, and (3) a combination of a radiomics signature with two transcriptome metagenes led to an improved prediction of LRC. These results underline the potential of multi-omics analyses to generate reliable biomarkers for application in personalized oncology. In the future, we aim to validate our findings within the prospective HNPrädBio study of the DKTK-ROG  and a validation cohort (n = 84) according to these studies. Patients of the discovery cohort were treated at one of six partner sites of the German Cancer Consortium—Radiation Oncology Group (DKTK-ROG) between 2006 and 20109. In the validation cohort, 39 patients were treated at the University Hospital Dresden (UKD) between 2002 and 201443 and 44 patients were treated within a prospective imaging trial (NCT00180180) at the UKD between 2006 and 201345. Inclusion and exclusion criteria were presented previously9. Ethical approval for multicentre retrospective analyses of clinical, imaging and biological data was obtained from the Ethics Committees of all DKTK partner sites. The endpoints LRC and OS were considered and calculated from the first day of RCTx until the day of the corresponding event or censoring.The present multicentric retrospective study was conducted on 206 patients for whom treatment-planning CT imaging and whole-transcriptome data of a tumour biopsy were available. All patients were diagnosed with histopathologically confirmed locally advanced HNSCC and underwent primary RCTx with curative intent. Radiation dose  was prescribed to the tumour region consisting of the tumour primary and grossly involved lymph nodes. Concomitant cisplatin (95.5%) or mitomycin C (4.5%) were applied in combination with 5-fluorouracil. Included patients were part of several previously published retrospective studies46. HPV16-DNA status was obtained as described previously9. CTs and contours of the gross tumour volume (GTV) were collected for radiomics analyses. The GTV was defined and segmented within the CT as the visible tumour primary and grossly involved nodes as specified in previous studies9.Formalin-fixed paraffin-embedded (FFPE) blocks of the primary tumour biopsies were collected centrally at the DKTK partner site Dresden for extraction of total RNA for whole-transcriptome analysis as described previouslyEthical approval for the multicentre retrospective analyses of clinical, imaging and biological data was obtained from the Ethics Committee at the Technische Universität Dresden, Germany (EK397102014) and from the Ethics Committees of all DKTK partner sites. The requirement for individual informed consent was waived owing to the retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations.46, using the Affymetrics Human Transcriptome Array (HTA) 2.0 . Quality control procedures were performed on the microarray probe-level intensity files using Transcriptome Analysis Console (TAC) . Signal data were normalised using the Signal Space Transformation alongside with the Robust Multiarray Average method (SST-RMA)47. ComBat normalisation48 was subsequently performed to correct for systematic variability within our data arising from different experimental conditions between the different contributing studies45. Signal intensities were then filtered for coding genes that had a gene annotation, resulting in 25328 gene expression features.Whole-transcriptome analysis was conducted as described in a previous study3 using cubic splines to compensate for differing voxel spacing and slice thickness between centres. Intensity values for the CT volumes were restricted to the range between −150 and 180 Hounsfield Units (HU) as the GTVs included air cavities and bone regions that had to be excluded. A set of Laplacian of Gaussian (LoG) filters with 5 different kernel widths  were applied individually to the base image and averaged to a single image to quantify characteristics such as edges (sharp transitions in image intensities) or blobs (gross image details).Patients received a CT scan for treatment planning prior to radiotherapy. Acquisition and reconstruction parameters are summarized in Supplementary Table 49 using the publicly-available MIRP Python package50. Feature computation parameters are reported in Supplementary Table From the base image and the LoG-transformed image, a set of 18 statistical, 2 local-intensity based, 29 morphological, 37 intensity-histogram-based, and 137 texture-based features were extracted from the GTV leading to 446 features per patient. The entire image pre-processing pipeline was implemented according to the Image Biomarker Standardisation Initiative (IBSI)51. The images were rotated  and the size of the GTV segmentation altered  in the discovery cohort, producing 20 images per patient from which to analyse feature stability through the intraclass correlation coefficient (ICC). Features for which the lower bound of the 95% confidence interval (CI) of the ICC fell below 0.7552 were considered unstable and subsequently dropped for both cohorts. Morphological and texture features from the LoG response map were dropped.Image-augmentation strategies were employed to filter non-robust features53. PCA analysis of the statistical, intensity histogram-based, and texture features was performed and consensus clustering of the PCA features conducted to identify a reference cluster of patients and outlier patients using ConsensusClusterPlus54, with parameters shown in Supplementary Table To correct for possible bias in features affected by differences in scanner types, convolution kernels and other parameters that might differ between centres, we performed a non-parametric ComBat correction with a reference batch5. Gene expression profiles were median-centered for each patient and the Pearson correlation to the published centroid data of each subtype was calculated and interpreted as reported previously55. Patients whose largest correlation coefficient was smaller than 0.2 or whose difference between the two highest coefficients was 0.2 or less were dropped, not being classified as any subtype or having an ambiguous classification between two subtypes, respectively. After assigning the subtypes, the Kaplan–Meier estimator was used to assess their association with the endpoints LRC and OS.In order to classify the tumours into one of the four molecular subtypes, 838 gene expression features were selected according to previous studies56 on a logistic regression model on each fold. The final model was then trained on the whole discovery cohort using the highest-ranking features across CV folds and with the median number of features across CV folds. Models were subsequently validated on the validation cohort. This process was repeated four times, each with a different subtype as the positive class and the other three aggregated together as the negative class. This end-to-end pipeline was implemented using the Familiar package57.To develop radiomics-based subtype-classification models, radiomics features were clustered by hierarchical clustering with complete linkage within the discovery cohort to reduce redundancy. The distance metric was defined as 1 − |ρ| (Spearman correlation coefficient ρ) with the distance metric threshold to form the clusters being 0.3. Cluster representatives were features that had the highest within-cluster average Spearman correlation. Radiomics representatives in the discovery cohort were then transformed through a Yeo–Johnson transform and standardised through a z-transform and transformation parameters were transferred to the validation cohort. Afterwards, feature selection was performed based on minimum redundancy maximum relevance (MRMR) within 33 repetitions of threefold cross-validation of the discovery cohort. Features were aggregated across cross-validation folds and ranked based on the enhanced Borda score. Features across CV folds were determined based on Bayesian hyperparameter optimisation applying a sequential model-based optimisation algorithmA systematic search across PubMed and Google Scholar was conducted to identify validated gene signatures in patients with locally advanced HNSCC treated with primary radiochemotherapy to subsequently explore the capabilities of CT-based radiomics to produce surrogates of such signatures.58. Chosen signatures were a 7-gene signature associated with proliferation and DNA-repair10, an 11-gene signature for radioresistance11, a 12-gene signature for immune processes13, a 15-gene hypoxia-associated signature14, and a 42-gene signature for epithelial–mesenchymal transition15. A well-known 31-gene radiosensitivity signature identified in a meta-analysis NCI-60 cell lines was also included12.Out of the 21 found studies and signatures, one gene signature was selected for each of the following mechanisms: DNA-repair, radioresistance, immune processes, hypoxia and epithelial–mesenchymal transition. In case more than one signature was found per mechanism, the signature defined on the cohort with the highest number of patients was includedTo derive radiomics surrogates, binary classes were created within the discovery cohort using k-means clustering on the gene expressions of each signature. Class assignment within the validation cohort was conducted by computing the Euclidean distance between the data points in the validation cohort and each class centroid from the discovery cohort, assigning the class represented by the closest centroid. Surrogate radiomics signatures were then created similarly as for molecular subtype classification.59 and was also significantly associated with LRC in our discovery cohort  was employed through the GSVA R packageGSVA metagenes were filtered based on a Google Scholar search to discard pathways not known to be related to HNSCC . For univariate association of clinical features with the LRC and OS endpoints, missing values were imputed by their median value for numerical variables and by their mode for categorical variables except for alcohol consumption and smoking status. These two features were transformed into two binary features, respectively representing positivity and non-availability. The following categorical variables were binarized: cT stage (0 for cT < 4 and 1 for cT = 4), cN stage (0 for cN < 2 and 1 cN ≥ 2), Grading (0 for Grading ≤ 2 and 1 for Grading > 2) and UICC stage .LRC and OS time-to-event endpoints were calculated from the first day of RCTx to the day of event or censoring. They were compared between patient subgroups using the log-rank test. Clinical categorical variables and subtype proportions were compared between discovery and validation cohorts through the Subtype classification of radiomics models was assessed through the AUC and the f1 score due to imbalance within the classes. Reference values , indicating the expected value for a random model that maximises the f1 score, are provided per subtype and cohort 60, and by assessing fit and slope of the linear fit.Proportional hazards assumption of the Cox models was assessed through the Supplementary Information 1.Supplementary Information 2.


Context: Inferring gene regulatory networks (GRN) from high-throughput gene expression data is a challenging task for which different strategies have been developed. Nevertheless, no ever-winning method exists, and each method has its advantages, intrinsic biases, and application domains. Thus, in order to analyze a dataset, users should be able to test different techniques and choose the most appropriate one. This step can be particularly difficult and time consuming, since most methods’ implementations are made available independently, possibly in different programming languages. The implementation of an open-source library containing different inference methods within a common framework is expected to be a valuable toolkit for the systems biology community. Results: In this work, we introduce GReNaDIne (Gene Regulatory Network Data-driven Inference), a Python package that implements 18 machine learning data-driven gene regulatory network inference methods. It also includes eight generalist preprocessing techniques, suitable for both RNA-seq and microarray dataset analysis, as well as four normalization techniques dedicated to RNA-seq. In addition, this package implements the possibility to combine the results of different inference tools to form robust and efficient ensembles. This package has been successfully assessed under the DREAM5 challenge benchmark dataset. The open-source GReNaDIne Python package is made freely available in a dedicated GitLab repository, as well as in the official third-party software repository PyPI Python Package Index. The latest documentation on the GReNaDIne library is also available at Read the Docs, an open-source software documentation hosting platform. Contribution: The GReNaDIne tool represents a technological contribution to the field of systems biology. This package can be used to infer gene regulatory networks from high-throughput gene expression data using different algorithms within the same framework. In order to analyze their datasets, users can apply a battery of preprocessing and postprocessing tools and choose the most adapted inference method from the GReNaDIne library and even combine the output of different methods to obtain more robust results. The results format provided by GReNaDIne is compatible with well-known complementary refinement tools such as PYSCENIC. The expression of numerous genes needs to be tightly regulated to control key biological processes, such as cell division and growth, tissue and organ differentiation, organisms’ development, and morphogenesis, as well as the response and adaptation to different environments ,2,3,4. TAmong the different families of methods, GRN inference approaches based on the data-driven paradigm are among the most popular due to the fact of their ease of use, computational efficiency, and accuracy .Early data-driven approaches aimed at inferring co-expression networks, assuming that the regulatory interactions between genes can be measured using correlation statistics  or inforMore recent data-driven techniques identify the regulators of a target gene as the subset of transcription factors, whose expressions contribute the most to the prediction of the expression level of the TG. These methods use a feature importance scoring procedure, training regression or classification algorithms to predict the expressions of TGs from those of their regulatory TFs ,12,13. UAccording to , each inhttps://gitlab.com/bf2i/grenadine, accessed on 3 November 2022), as well as in Python Package Index (PyPi), the official third-party software repository for Python . The latest documentation of the GReNaDIne library is also available in an open-source software documentation hosting platform called Read the Docs .GReNaDIne is an open-source software that consists of three separate modules  allowingAs a preliminary step, through the first module, GReNaDIne aims at normalizing and standardizing the datasets. This first module integrates widely used RNA-seq normalization techniques to cope with library size biases (reads per million (RPM)), gene length biases (reads per kilobase (RPK)), or both problems simultaneously (reads per kilobase million (RPKM) and transcripts per kilobase million (TPM)). In addition, GReNaDIne includes a wrapper to use the DESeq2  normalizZ-score-full: the distribution of scores obtained by a given method is standardized by means of a general z-score normalization, where each score is standardized by subtracting the mean of the distribution from all observed scores and then dividing by the standard deviation of the distribution; (ii) Z-score-TG: the distribution of scores obtained by a given method is standardized by means of a TG-centric z-score normalization, where each score is standardized by subtracting the mean of the distribution from scores of all regulatory links entering the corresponding TG, and then dividing by the standard deviation of this distribution; (iii) Z-score-TF: the distribution of scores obtained by a given method is standardized by means of a TF-centric z-score normalization, where each score is standardized by subtracting the mean of the distribution from scores of all regulatory links leaving the corresponding TF, and then dividing by the standard deviation of this distribution; (iv) Rank-full: each score is replaced by its corresponding rank in the distribution from all observed scores (1 representing the highest score); (v) Rank-TG: each score is replaced by its corresponding rank in the distribution of scores from all regulatory links entering the corresponding TG only; (vi) Rank-TF: each score is replaced by its corresponding rank in the distribution of scores from all regulatory links leaving the corresponding TF only.Data-driven GRN inference methods score all possible regulatory links between TFs and TGs, based on their gene expression. Traditional GRN inference methods assume that the regulatory relationships between TFs and TGs can be inferred by measuring the correlation or the MI between their respective gene expression levels: GReNaDIne includes four co-expression methods based on the widely used Pearson correlation coefficient (PR), Spearman correlation coefficient (SR), Kendall tau statistic (KT), as well as a method termed context likelihood of relatedness (CLR) relying on a MI statistic. Moreover, GReNaDIne also includes 14 GRN inference methods based on well-known machine learning classification and regression algorithms. These methods aim at training a model  to predict the expression level of each TG from those of a set of TFs. Then, the importance of each TF to the prediction task is computed as a feature importance score. These scores are directly used as proxies to quantify the regulatory relation between each TF and its TGs. Regressors are directly trained on continuous gene expression data, while classifiers require the TG expression to be previously discretized. To do so, the discretization methods implemented in the first module can be used, and, in practice, the K-means discretization technique with five discrete levels of expression  was applied successfully in . GReNaDIMus musculus single-cell RNA-seq dataset.Finally, the inference output format of GReNaDIne is compatible with the multinetwork PYSCENIC Python library . Once a After scoring all possible regulatory links between TFs and TGs, a classic procedure consists of selecting a subset of regulatory links with high scores to define putative GRNs. GReNaDIne includes functions that allow ranking of the possible regulatory links according to their scores, as well as functions that select the top-k links of the dataset and the top-k links involving a particular TF or TG. This module also includes some unsupervised tools that allow to compare the results obtained using different methods, based on principal component analysis and Jaccard index. Finally, the third GReNaDIne module includes some methods that allow to compute standard evaluation measures for binary classification , the area under the receiver operating characteristic curve , permitting to assess the predicted GRNs when gold standard datasets describing the validated regulatory links are available, such as in the DREAM5 benchmark. The methods implemented in this module are inspired by those described and used in . In thisEscherichia coli, Saccharomyces cerevisiae, and Staphylococcus aureus, and a synthetic gold standard dataset. GRN inference has been evaluated as a binary classification task, which consists of predicting the presence of true regulatory links from gene expression, as described in https://www.synapse.org/#!Synapse:syn2820440/wiki/71024, accessed on 3 November 2022. The experiments presented in this article were executed on an Intel(R) Xeon(R) 2.40 GHz CPU, running Debian GNU/Linux 10, with a 120 Go RAM capacity.The DREAM5 evaluation framework  was usedS. aureus, E. coli, and S. cerevisiae) but exhibited a quality loss for the synthetic dataset. The methods based on correlation and MI led to comparable results for the real organisms’ datasets, while they exhibited poorer results for the synthetic dataset. These results are coherent with the conclusions presented in [The GRN inference methods implemented in GReNaDIne provided results that were comparable or outperformed those obtained by the 35 DREAM5 competitors, as well as those obtained by the robust community approach that combined all participants’ results  Figure . Interesented in , which sFor most of the inference methods, applying preprocessing techniques beforehand tended to have, on average, a rather small impact in terms of inference quality, considering both the AUROC and AUPR scores . HoweverIn addition, the ensembles of the GReNaDIne predictors described in , containThese results illustrate the functionalities made available in the GReNaDIne library and support the value of this package for the systems biology community interested in data-driven GRN inference.Project name: GReNaDIne—Gene Regulatory Network Data-driven Inference.https://gitlab.com/bf2i/grenadine, accessed on 3 November 2022.Project home page: https://pypi.org/project/GReNaDIne/, accessed on 3 November 2022.PyPi repository web page: https://grenadine.readthedocs.io/en/latest/, accessed on 3 November 2022.Project documentation: Programming language: Python 3.License: GNU GPLv3.https://www.ncbi.nlm.nih.gov/gds, accessed on 3 November 2022). In addition, users can rely on ad hoc gene annotations or publicly available ones  in order to run any of the 18 GReNaDIne methods and perform gene regulatory networks inference in their preferred biological system. Users can thus rely on this toolbox to choose the most adapted method to their dataset, within the same framework, and even combining the output of different methods to obtain robust and accurate results.The GReNaDIne toolbox represents a major methodological contribution to the field of systems biology. In addition to the classical co-expression network analysis, GReNaDIne also includes some more recent and complex GRN inference strategies, which rely on functional annotation of genes to identify transcription factors (TFs), infer directed links between TFs and their target genes (TGs), and predict the expression of TGs based on the expression of their TFs. The GReNaDIne package contains a battery of preprocessing tools that users can apply to treat their own experimental gene expression datasets or publicly available ones (e.g., NCBI Gene Expression Omnibus,


Molecular classification has improved diagnosis and treatment for patients with malignant gliomas. However, classification has relied on individual assays that are both costly and slow, leading to frequent delays in treatment. Here, we propose the use of DNA methylation, as an emerging clinical diagnostic platform, to classify gliomas based on major genomic alterations and provide insight into subtype characteristics. We show that using machine learning models, DNA methylation signatures can accurately predict somatic alterations and show improvement over existing classifiers. The established Unified Diagnostic Pipeline (UniD) we develop is rapid and cost-effective for genomic alterations and gene expression subtypes diagnostic at early clinical phase and improves over individual assays currently in clinical use. The significant relationship between genetic alteration and epigenetic signature indicates broad applicability of our approach to other malignancies. No clinical assay currently exists to classify glioma tumours based on gene expression. Here, the authors develop a DNA methylation-based classifier, Unified Diagnostic Pipeline (UniD) that identifies genomic alterations and gene expression subtypes of gliomas. DNA methylation is a stable feature and reflects both inter- and intra-tumor heterogeneity which has been used to classify different types of tumors5. For example, the recently published DNA methylation-based histopathological classification of central nervous system (CNS) tumors6 (Unsupervised CNS Classification) has challenged conventional histologic classification and tumor grading. This unsupervised CNS classification used the unsupervised learning approach to identify CNS tumor classes with distinct DNA methylation profiles. The established random forest-based classifier can classify CNS tumor into one of those histopathological classes based on the tumor DNA methylation profile.Epigenetics play a crucial role in cancer7. These devastating tumors have been subjected to comprehensive, molecular profiling, particularly by The Cancer Genome Atlas (TCGA). Previous studies have identified some key molecular features in gliomas which play critical roles in glioma initiation, progression, diagnosis, and treatment. For example, isocitrate dehydrogenase (IDH) mutation is positively associated with younger age and longer survival time. Chromosome 1p/19q co-deletion (chr1p19q codel) is prognostic for improved survival and predictive of response to chemotherapy8. IDH mutation and chr1p19q codel are part of the current WHO diagnosis criteria for gliomas. Telomerase reverse transcriptase promoter (TERTp) mutations and alpha thalassemia/mental retardation syndrome X-linked (ATRX) mutations are mutually exclusive alterations in gliomas and both are functionally correlated with telomere length maintenance9. Telomerase inhibitory therapies, heterochromatin silence-mechanism targeted therapies, and G4-destabilizing therapies are promising therapeutic targets for gliomas with ATRX or TERTp mutation11. The O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status12 is a prognostic and alkylating chemotherapy-predictive biomarker. It can be predicted using an established methylation array-based algorithm MGMT-STP2713. At the transcriptional level, glioblastoma (GBM) has been classified into three subtypes based on characteristic gene expression signatures called classical (CL), proneural (PN), and mesenchymal (MES)14. CL GBMs are characterized by epidermal growth factor receptor (EGFR) amplification while MES GBMs are enriched for neurofibromin 1 (NF1) deletion and mutations14.Infiltrating gliomas, including WHO grade II-IV gliomas, are the most common and lethal primary brain tumorsATRX, and TERTp status, often rely on next generation sequencing (NGS). Fluorescence in situ hybridization (FISH) or loss of heterozygosity (LOH) analysis are usually applied to obtain chr1p19q status. Methylation-specific PCR (MS-PCR) or pyrosequencing assays are usually utilized15 to obtain MGMT promoter status. No clinical assay currently exists to classify tumors by gene expression. This is because the most commonly available analyte following glioma resection is formalin-fixed and paraffin-embedded (FFPE) tissue. FFPE-derived RNA is highly degraded and chemically modified, therefore, its transcriptional sequencing quality is low and can be biased by artifacts and technical variance16. In summary, the cost, time, and tissue requirements for these individual assays frequently result in delayed or incomplete molecular diagnosis, leading to suboptimal treatment and ineligibility for clinical trials. Indeed, there is an urgent need to develop a rapid, cost-effective assay that requires minimal amounts of FFPE tissue for patients with infiltrating gliomas.All somatic alterations described above are essential for diagnosis, treatment decision-making, and patient prognosis. However, individual assays are usually required to obtain each of these somatic alterations. Detection of somatic mutations, such as IDH, In our study, we develop a DNA-methylation-based classifier for gliomas, which we validate using an independent cohort. The developed models can be easily applied to all infiltrating gliomas, including both low-grade gliomas and GBM. Exploration of DNA methylation-based misclassified cases provides valuable ideas for future research directions and demonstrates potential superiority of the methylation approach over existing individual assays.TERTp, and ATRX mutation status, chr1p19q codel status, and gene expression subtype of infiltrating gliomas. The above molecular features can be separated into two categories in terms of their status: binary class, including IDH, TERTp, and ATRX mutation or wild type, and chr1p19q codel or intact; and gene expression subtypes, including CL, PN, or MES. Separate classifiers were developed for prediction of each of the binary classes  and for prediction of gene expression subtype, using a rigorous machine learning approach. The binary genomic alteration classifiers were trained and validated on a large cohort of both low-grade and high-grade glioma samples from TCGA, while the gene expression subtype classifier was trained on TCGA glioblastoma samples only, since these subtypes were originally described using high-grade glioma datasets. The performance of all classifiers was validated with an independent cohort (NOA-04) from a multicenter phase III randomized trial conducted by the German Neurooncology Working Group (NOA) of the German Cancer Society, which includes both low-grade and high-grade gliomas17.We aimed to develop a DNA methylation-based classifier which accurately determines IDH, TERTp, and ATRX mutation, and chr1p19q codel status, respectively, with AUC of 1.0, 1.0, 0.9952 and 0.9974 respectively. For gene expression subtype prediction, GBM samples with HM450K and HM27K data were processed as described in Methods. The final random forest model was refitted with training (n = 212) and development (n = 72) sets and achieved a prediction accuracy of 72.2% (52/72) in the test set.For binary genetic alterations, all predictive models achieved high prediction accuracy as shown in Fig. TERTp, ATRX, and chr1p19q codel with the Glioblastoma-CpG island Methylator Phenotype (G-CIMP) signature18, we found no significant overlap among these five probe signatures  and chromosome 14 (7.2%). Interestingly, probes in the TERTp mutation, chromosome 1p/19q co-deletion, and gene expression subtype prediction models were all enriched in chromosome 18   and chromosome 21 (8.8%) while probes in the 3.08% andCFLAR) gene had four probes mapped and nuclear receptor subfamily 4, group A, member 1 (NR4A1) had three probes mapped. Applying those 65 genes to the DAVID19 (version 6.7) for functional annotation, the top GOs were regulation of apoptosis , regulation of programmed cell death , and regulation of cell death  , potassium voltage-gated channel interacting protein 3 (KCNIP3), death effector domain containing 2 (DEDD2), lectin, galactoside-binding, soluble, 1 (LGALS1), NR4A1, proline dehydrogenase 1 (PRODH), retinoic acid receptor, gamma (RARG), and erb-b2 receptor tyrosine kinase 2 (ERBB2). Those GOs were not significant after p value adjustment due to the small gene set.Among the 100 probes in the IDH predictive signature, 45% (45/100) were located on the promoter region   (Supplementary Table ISM1) with ten probes. The second most frequently mapped gene was atlastin GTPase 3 (ATL3) with seven probes. For gene functional annotation, the top significant GOs were all related to regulation of transcription  (Supplementary Table FBXO6) has five probes mapped and cathepsin F (CTSF) gene was mapped four times. Using the DAVID gene functional annotation, genes were significantly enriched in development related GOs, including embryonic development and neuron development  and phosphodiesterase 7B (PDE7B). The top GO enriched for those overlapped genes was cell-cell signaling (Supplementary Data For the ATL3 and fibroblast growth factor receptor 2 (FGFR2). The top two GOs were regulation of cellular protein metabolic process  and BMP signaling pathway . However, no GOs were significant after Benjamini p value adjustment  and 5ʹ-UTR regions 25.8%)  were formed based on the DNA-seq based ATRX status, methyl-based ATRX status, and single nucleotide variation (SNV) information  and set 4 : the enriched mutations shifted from “frameshift indels, in frame indels, and splice site” to “intron, missense and nonsense” which may not lead to ATRX loss of function. More importantly, no significant differences in ATRX gene expression were observed among set2, set3, and set5, which are all methyl-based ATRX mutant samples, and no difference between set1 and set4, which are all methyl-based ATRX wild type samples. Interestingly, when the methylation results were discordant, even when the sequencing results were in an agreement, a significant difference in expression was observed  except for one probe  were categorized by methyl-based and transc-based gene expression subtypes  by PCR-seq; for ATRX mutation, 92.7% (89/96) by immunohistochemistry (IHC); and for chr1p19q status, 88.89% (88/99) by MLPA and 95.65% (110/115) by HM450K-derived CNV profiles  by PCR-seq and 99.10% (114/115) by unsupervised clustering analysis of the HM450k DNA methylation profile; for les Fig. . In termMGMT promoter methylation status, and mutation and CNV status of critical genes. It is clear that the almost all IDH1/IDH2 mutations and most TP53 and ATRX mutations matched with the methyl-based PN subtype. EGFR amplifications are hardly observed in PN subtype. These observations follow the known characteristics of the PN subtype and support the methyl-based classification.In TCGA LGG samples, gene expression subtypes predicted by methyl-based and transc-based algorithms showed large differences in the classification results of the PN subtype Fig. : 422/486The key difference between the UniD and Unsupervised CNS classification is that UniD aims to predict each infiltrating glioma’s key molecular features based on the DNA methylation values of selected loci for each molecular feature, while the Unsupervised CNS classification aims to classify each CNS tumor into one histopathological class based on its overall DNA methylation profile. Accordingly, the Unsupervised CNS classification is an unsupervised learning-based model that has been developed to include all CNS tumors, while UniD is a supervised learning-based model which focuses only on gliomas. The objectives of these two classifiers are different, but it is informative to compare the results of these two classifiers.n = 644) were classified into nine groups based on the UniD predicted molecular features status. These groups and their Unsupervised CNS classification-based classes are summarized. in Fig. ATRX and TERTp, suggesting that alternative mechanisms may exist to maintain their telomere length. Discordant samples between the two classification systems are described in Fig. P values of the log-rank test between any two groups are shown in Fig. ATRX, TERTp, or chr1p19q status and IDH wildtype glioma (Grp7 and Grp8) showed significantly worse survival compared to IDH mutant gliomas. Moreover, Grp7 demonstrated poorer survival compared with Grp8, whose samples harbored TERTp mutations, indicating the negative prognostic significance of TERTp mutation in the absence of IDH or ATRX mutation. However, the lack of stratification based on CDKN2A deletion status, a recently identified prognostic factor21, may limit the interpretation of these results. Grp7  also included a significantly higher percentage (P < 0.01) of tumors with MGMT promoter methylation compared to Grp8 .Gliomas  may be deactivated by some other mechanisms while ATRX mutations at the DNA sequencing level in set4  do not affect the function of the protein; however, this conclusion may be limited due to the small number of cases in each set. When we compared the overall classification based on mutation status of IDH, ATRX, and TERTp, we found that the methyl-based annotations provided more precise genetic characterizations than DNA-seq annotations  has been used to derive the CNV profile from HM450K methylation data and was applied with the R package conumeeNF1-silenced gliomas were shown to be more sensitive to radiation than temozolomide treatment22. MES gliomas have been associated with poor survival and with advanced patient age14. Several studies have demonstrated that PN tumors will shift to MES subtype at recurrence and the master regulators of the MES may serve as therapeutic targets24. This study demonstrates that UniD provides gene expression subtype determination in a clinical setting and facilitates the use of this classification for therapeutic development.Currently, there is no clinical assay of transcription-based gene expression subtype determination and as a result the clinical significance of this component of molecular classification has been largely ignored. For example, PN gliomas are more sensitive to chemotherapy and radiation treatment and 6 has shown great value in standardizing and clarifying brain tumor diagnosis by reducing the inter-observer variance and classifying tumors previously unclassified by histology. However, this classification focused on comprehensive CNS tumor classification based on WHO entities and not focused on genomic alterations of specific tumor types, particularly gliomas. Our comparison of Unsupervised CNS Classification and genomic alterations shows that it may not be able to properly identify the specific genomic alteration subclasses studied in this report.The Unsupervised CNS ClassificationOur classifier demonstrates that DNA methylation signatures accurately predict somatic genomic alterations in human gliomas, emphasizing the extensive and significant relationship between cancer epigenetic signatures and somatic genomic alterations. Given that all predictors are based on a single experimental platform, the Infinium methylation BeadChip arrays, the classifier lends itself to the clinical diagnostic setting. The array’s cost, processing time, and tissue requirements are significantly less than individual sequencing, IHC, and copy number assays  which are currently used clinically. Moreover, the Infinium array is suitable for FFPE samples and can be used for clinical diagnostic tissues.13, InfiniumPurify25 (which estimates tumor purity), and ChAMP-CNV26, that allow for further unification of glioma biomarkers into a single assay. Lastly, the successful development of this DNA methylation-based, infiltrating glioma-specific classifier highlights that methylation-based tumor classification systems can be easily developed for other tumor types, not only for genomic alteration-based classification, but further grading and prognosis, such as for breast3 or lung cancer27.Besides DNA methylation levels, many array-based bioinformatics tools have been developed, such as MGMT-STP27This study was performed in accordance with the guidelines and policies and with approval of the NYU Langone Institutional Review Board (IRB). Based on our institution’s policy, our research which uses only non-identifiable data is not considered as research involving human subjects, therefore an IRB approval is not required.A total of 129 samples from the TCGA-GBM dataset and 516 samples from the TCGA-LGG dataset were used to train the classifier. Clinical characteristics of the patients from whom the samples were derived are listed in Supplementary Table TERTp, and ATRX mutation, and chr1p19 codel, raw data (IDAT files) of infiltrating gliomas profiled on Infinium HumanMethylation 450K BeadChip arrays  were subjected to sample level and probe level quality control  array: (1) remove probes not mapped to the autosomal chromosomes; (2) remove probes with single nucleotide polymorphisms (SNPs) within 10 bases of the targeted CpG site (snp-hit)28; (3) remove probes whose sequences align non-specifically  (multi-hit)28; (4) remove probes not available on the current Infinium MethylationEPIC BeadChip array  in order to accommodate the application of this assay on the EPIC array. Then in a sample-wise fashion, probes with beadcount less than or equal to 3 and probes not significantly detected compared to the background  were set as missing values. Probes with more than 10% missing values across the samples were deleted. Samples with more than 5% missing values across all probes were deleted due to bad quality. The remaining missing values were imputed using the k-nearest neighbor (KNN) algorithm.For all binary genomic alterations, including IDH, TERTp mutation, ATRX mutation and 1p/19q co-deletion prediction, respectively.Ultimately, 1513, 2325, 2112, and 1279 probes were selected in the variable selection step for prediction of IDH mutation, 29. Approximately 21,000 probes overlapped between the HM450K and HM27K platforms based on probe ID. However, the majority of overlapping probes from HM450K were Infinium II type probes while those from HM27K were mostly Infinium I probes, resulting in a likely batch effect when combining datasets. Batch adjustment was required to ensure the data were comparable between platforms. Several adjustment methods have been published, such as: BMIQ30 (R package: ChAMP), SWAN31 (R package: lumi), and PBC32 (R package: wateRmelon). Acute myeloid leukemia (AML) DNA methylation data from TCGA was used for evaluating adjustment methods. All 194 AML samples were available on both HM27K and HM450K platforms. There are three steps to evaluate these methods, briefly described as below  or HM450K were included. GBM samples without gene expression data (Agilent 244 K) were excluded. TCGA level 3 data were directly used for samples with HM27K data. Probes used in the HM450K array included both Infinium I and Infinium II types, while probes used in the HM27K assay included only Infinium I type. The β-value derived from Infinium II probes has a smaller dynamic range and lower sensitivity compared to Infinium I probes33 (CNMF) method. These two cluster results were compared and the concordance between these two platforms was high. This initial classification was used as the “gold standard.”Step 1: 194 AML samples were clustered independently using the data from HM27K and HM450K. For each assay, probes were sorted by median absolute deviation (MAD) and the top 1000 and top 2000 probes were clustered with consensus non-negative matrix factorizationStep 2: Three datasets that consisted of admixtures of data from the HM27K and HM450K datasets were simulated. Dataset 1 consisted of 25% samples coming from the HM27K dataset and 75% samples from the HM450K dataset. Dataset 2 consisted of 50% of samples from HM27K and 50% from HM450K. Dataset 3 consisted of 75% samples from HM27K and 25% from HM450K. CNMF clustering methods were applied on each of the admixture datasets. The clustering results from each admixture datasets were compared with the “gold standard.”Step 3: For each adjustment method, the admixture process was repeated and a fourth dataset containing only HM450K data was created. CNMF was applied to each admixture dataset and the membership for each sample was obtained. Membership indicated the subgroup which each sample belonged to, for example, the first AML sample may belong to subgroup 1 while the second AML sample belongs to subgroup 2. The membership of the classification was compared with gold standard as was done for the unadjusted datasets.https://digitalcommons.library.tmc.edu/dissertations/AAI1597033/.After the sample membership comparison, BMIQ showed the best results and was chosen as the final method for our data analysis. For more details and results, please refer to 28, SNP-hit28, located on chromosome X or Y, and with ≥5% missing values in the dataset. Then the retained 450K probes were intersected with 27K probes. Only probes existing in both platforms were kept for the following analysis. Retained probes belonging to the following categories were deleted: (1) with missing values in Just as for the binary genomic alteration prediction, for the gene expression subtype prediction, HM450K probes belonging to the following categories were deleted: missing from the EPIC platform, multi-hitded Fig. .After probe selection was completed, there remained 129 HM450K platform samples with 407067 probes and 287 HM27K platform samples with 23578 probes. After data integration, 416 samples with 20720 probes were available. After probe filters, 9519 probes were kept for correlation evaluation. We only kept the samples with gene expression information available, which led to 1263 probes and 356 samples as the final data set available. Of these, 212 samples in the training set were evaluated for their probe importance, 985 probes were assigned zero importance and then were excluded from the analysis, leaving 278 probes.34: whole genome or exome sequencing (DNA-seq) for IDH and ATRX mutation; targeted sequencing or whole genome sequencing (DNA-seq) for TERTp mutation; and Affymetrix SNP6 array (SNP6) copy number variation (CNV) for chr1p19q codel. HM450K data were used as the independent input variables and represented as M-values35. Using IDH mutation as an example: samples were randomly apportioned into the training (60%), the development (20%), and the test (20%) sets, stratified by IDH mutation status. Variable selection and hyper-parameter tuning were applied within the training set using Elastic Net Regularization36. There are two parameters we need to specify: alpha (α) and lambda (λ).For gene alterations within binary classes, TCGA annotations were used as the reference36) was set from 0.1 to 1, using 0.1 as a step. For each alpha value, 200 lambda values were randomly generated. Among the 200 lambda values, the best lambda value was picked out based on prediction accuracy. For each alpha and lambda value combination, 5-fold cross-validation (CV) was applied in the training dataset. For each fold among the cross validation, a set of probes was selected to build the regularized linear model with non-zero coefficient. By summarizing the selection results among 5 folds per CV, the percentage for each probe was calculated. Therefore, for each alpha value, we obtained a set of probes with their selection percentage. Then we combined the selected probe sets among ten alpha values and calculate their overall selection percentage. Probes were ranked by their selection percentage from high to low.For the training set, the parameter alpha . DNA methylation HM450K data were available for all tumor samples. Most of the tumors were characterized for genomic alterations and these data served as the reference standard for comparison: targeted resequencing of the amplified mutational hotspot (PCR-seq) for IDH (n = 108) and TERTp mutation (n = 99); multiplex ligation-dependent probe amplification (MLPA) for 1p19q codel (n = 99); and immunohistochemistry (IHC) for ATRX mutation (n = 96). In addition, IDH mutation status was also determined by unsupervised clustering with the HM450K data and chr1p19q codel status was also obtained by reviewing the CNV profiles derived from HM450K data (n = 115) (R package conumee)20. The methyl-based binary genetic alterations were predicted using build predicted models for all samples and compared to reference standards. The MGMT promoter methylation status obtained by methylation-specific PCR (MSP)15 were compared with MGMT-STP27 prediction13. Note that the genomic alteration reference in the external validation set may be different from the training/development/test set. For example, chr1p19q codel status in the external validation set was annotated using either multiplex ligation-dependent probe amplification (MLPA) or direct CNV determination using HM450k data, while chr1p19q code status in the training/development/test set were determined using CNV derived from SNP6. Similar model building strategies were applied to TERTp and ATRX mutation and chr1p19q codel del Fig.  and a pr14. Because reference samples might be heterogeneous and contain multiple subtypes, the probability of belonging to each subtype  was calculated for every single sample using the formula shown below. For example, the probability that a given sample belongs to the CL subtype (Probc) was calculated as:Samples were then randomly sampled into training (60%), development (20%), and test (20%) sets, stratified by gene expression subtype. For gene expression subtype prediction, the model building reference of subtype and each subtype’s probability were calculated according to published algorithmsp value for each subtype was used. P valuec is the empirical p value for the CL subtype, and similarly for other subtypes.The permutation-based empirical 22. The R package Fselector (R package) built within the mlr package37 was utilized to calculate those metrics. Probes were ranked by each evaluation metric from high to low. Then the rank sum was added up for each probe. Probes were sorted by the rank sum from high to low. The top probes were those which showed the most importance in terms of their response variable.Probes were selected for model building based on entropy-based metrics, including information gain, gain ratio, and symmetrical uncertaintyWith the selected probes, 21 machine learning algorithms   signature18.For each binary genetic alteration, predictive signature probes were compared between the binary subgroups using the Wilcoxon rank sum test. The most significantly different probes were selected and applied to unsupervised clustering analysis. Predictive signatures of IDH, Chromosome enrichment. The number of probes located on each chromosome was summarized and normalized by the total number of probes available on the chromosome. The percentage for each chromosome was calculated by the normalized percent. A proportional test  was applied between the number of probes for each chromosome and the total number of probes available for each chromosome. (2) CpG island relationship enrichment. Probes were categorized into following six classes based on their distance to CpG islands: CpG island, N_shlef (2–4 kb from island), S_shlef, N_shore (1–2 kb from island), S_shore, and unknown. The first class in order was used if probes were annotated with multiple categories. Then a proportional test was applied between the number of probes for each category and the total number of probes available for each category. (3) Gene structure enrichment. Probes were categorized into one of the following seven classes based on their relationship to functional gene structure: TSS200, TSS1500, Body, 3ʹ-UTR, 5ʹ-UTR, 1st Exon, and unknown. For probes that can be mapped to multiple gene structures, only the first category was used. The number of probes in each category was compared with the total available probes in each category. (4) Mapping genes. Genes mapped by the probes were summarized for frequency by array annotation. If one probe mapped to multiple genes, then the first gene was counted. (5) Gene Ontology (GO) enrichment. Genes mapped by signature probes were used as input for GO analysis. This analysis was carried out with the web-based DAVID19 (version 6.7).Each signature probe set was mapped back to the reference genome and further compared for following categories. (1) ATRX mutation status, samples were regrouped by the DNA-seq and methylation-based (methyl-based) status. For misclassified samples, the single nucleotide variations (SNVs) called by MuTect239, VarScan40, MuSE41, and Somaticsniper42 were collected  and compared with the ATRX mutation status. To compare ATRX expression level between subgroups, the two-sample t test was applied. To clarify the sample genetic characteristics, samples were regrouped by IDH, ATRX, and TERTp mutation status obtained by DNA-seq and methyl-based prediction. The methylation level  of HM450K probes located on ATRX were compared between subgroups using the ANOVA test. For 1p19q codel misclassified samples, sample CNV profiles were derived from HM450K data using the R package conumee20.Binary genetic alterations status was compared between methylation-based model prediction and the reference standard. To investigate the misclassified samples of n = 72) were re-grouped by their transcriptional subtypes and methylation predicted subtypes. The correctly classified samples and misclassified samples were compared in terms of CNVs and gene expression for each transcriptional subtype using the Wilcoxon rank sum test.For gene expression subtype prediction, misclassified samples in the test set . DNA methylation HM450K data were available for all tumor samples. Most of the tumors were characterized for genomic alterations and these data served as the reference standard for comparison: targeted resequencing of the amplified mutational hotspot (PCR-seq) for IDH (n = 108) and TERTp mutation (n = 99); multiplex ligation-dependent probe amplification (MLPA) for 1p19q codel (n = 99); and immunohistochemistry (IHC) for ATRX mutation (n = 96). In addition, IDH mutation status was also determined by unsupervised clustering with the HM450K data and chr1p19q codel status was also obtained by reviewing the CNV profiles derived from HM450K data (n = 115) (R package conumee)20. The methyl-based binary genetic alterations were predicted using build predicted models for all samples and compared to reference standards. The MGMT promoter methylation status obtained by methylation-specific PCR (MSP)15 were compared with MGMT-STP27 prediction.For binary genetic alterations, the phase III clinical trial NOA-0414. Histopathological and genomic characteristics were compared between methyl-based and transc-based subtype determinations using χ-square or Fisher’s exact tests.To validate the gene expression subtype prediction, TCGA LGG gene expression subtypes were compared between DNA methyl-based prediction and gene expression profile determined (transc-based)Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


TP53 mutations, proximal molecular subtypes and myeloid cell recruitment. In squamous carcinomas myofibroblasts were not prognostic despite being transcriptomically equivalent. These findings have important implications for developing fibroblast-targeting strategies for cancer therapy.Fibroblasts are poorly characterised cells that variably impact tumour progression. Here, we use single cell RNA-sequencing, multiplexed immunohistochemistry and digital cytometry (CIBERSORTx) to identify and characterise three major fibroblast subpopulations in human non-small cell lung cancer: adventitial, alveolar and myofibroblasts. Alveolar and adventitial fibroblasts (enriched in control tissue samples) localise to discrete spatial niches in histologically normal lung tissue and indicate improved overall survival rates when present in lung adenocarcinomas (LUAD). Trajectory inference identifies three phases of control tissue fibroblast activation, leading to myofibroblast enrichment in tumour samples: initial upregulation of inflammatory cytokines, followed by stress-response signalling and ultimately increased expression of fibrillar collagens. Myofibroblasts correlate with poor overall survival rates in LUAD, associated with loss of epithelial differentiation,  Fibroblast heterogeneity is a prominent but poorly understood feature of solid tumours. Here three major fibroblast subpopulations in non-small cell lung cancer are identified and characterised through single cell RNA-sequencing, multiplexed immunohistochemistry and digital cytometry. Multiple studies have shown their positive influence on tumour progression: promoting metastasis, immune evasion and therapy resistance, making them an attractive therapeutic target. However, multiple CAF targeting strategies have failed in the clinic3, and it is increasingly clear that CAFs play an important but context-dependent role in cancer, with subpopulations capable of both promoting and suppressing tumour progression6. This suggests that a more nuanced understanding of fibroblast heterogeneity and biology is required if CAF targeting is to become an effective part of cancer therapy.Cancer-associated fibroblasts (CAFs) are a prominent component of solid tumours, associated with poor prognosis in a wide range of cancer types8. The role of these CAF subpopulations remains poorly understood and may depend on further sub-categorisations, as described in breast cancer7. A key area of contention is myoCAF’s role in tumour progression, with conflicting reports produced in different tumours. Multiple human tissue analyses have shown that α-smooth muscle actin (SMA)-positive myoCAFs are linked to poor prognosis in many cancer types1 and to immunotherapy resistance in non-small cell lung cancer (NSCLC)7. However, in pancreatic ductal adenocarcinoma (PDAC) models, these cells can suppress tumour progression9. Murine studies have also shown that stromal cells expressing iCAF markers increase chemoresistance, metastases and immune suppression11. However, iCAF’s influence on human tumour progression is yet to be robustly investigated. Importantly, myoCAF and iCAF are now recognised to be plastic cell populations interconvertible in vitro depending on the biochemical and mechanical features of the culture environment9.Two major CAF subpopulations have been described in several cancer types: myofibroblastic-CAFs (myoCAFs) and inflammatory-CAFs (iCAFs)15. However, this remains an evolving area of research with multiple discrepancies. For example, the degree of fibroblast heterogeneity (number of subpopulations identified) is commonly confounded across studies due to varying resolution of sub-clustering and whether mural cells (vascular smooth muscle cells and pericytes) are excluded prior to analysis16. Additionally, subpopulation marker genes are typically calculated based on single-cell expression profiles without confirming statistical significance at a sample-level, leading to limited consistency across studies16. Furthermore, there has been minimal examination of how CAF phenotypes differ from healthy tissue counterparts; how comparable fibroblast phenotypes are across disease states; and how specific subpopulations impact disease progression.A significant barrier to understanding CAF biology is the paucity of well-characterised markers and lack of uniform terminology to describe different phenotypes. Technological advances, such as single-cell RNA-sequencing (scRNA-seq), are expanding the understanding of fibroblast heterogeneity. These techniques have been used to identify multiple fibroblast subpopulations in different tissue and cancer types18. These studies have progressed from initial characterisations solely at a transcriptomic level17, to the establishment of orthogonally validated subpopulations associated with specific niches in lung tissue15. This has culminated in the consistent identification of alveolar, adventitial and myofibroblast subpopulations in human and murine lung tissue18. A recent study by Buechler et al. provided further context to fibroblast heterogeneity across multiple human and murine tissues: identifying the adventitial subpopulation as a 'universal' fibroblast phenotype present across all tissues analysed; and a variety of tissue or pathology-specific subpopulations19.Considerable research has been performed using scRNA-seq to characterise fibroblast heterogeneity in control lung tissues and interstitial lung diseases21), which are further subcategorised into molecular23 and morphological25 subtypes that link to patient survival. NSCLCs also have a high level of stromal and immune cell infiltration as well as high tumour mutational burden26. Therefore, we hypothesise that NSCLCs would exhibit a high degree of fibroblast heterogeneity and represent a suitable model to broadly characterise CAF phenotypes.The robust characterisation of fibroblast phenotypes in non-cancerous lung tissues provides an excellent baseline for investigating how lung cancer impacts fibroblast phenotypes. Non-small cell lung cancer (NSCLC), the most common form of lung cancer, consists of multiple histological subtypes  to examine fibroblast heterogeneity in human NSCLC and control lung tissue: investigating phenotypic diversity between and within the major fibroblast subpopulations; comparing these phenotypes across multiple tissue types; examining their spatial distribution; and assessing their clinical significance across multiple NSCLC cohorts.Here we use transcriptomics, multiplexed immunohistochemistry (MxIHC) and digital cytometry  and stromal cells (marked by DCN and DPT) 29, which are highly expressed in mural cells. To differentiate between these cells, we identified genes differentially expressed between fibroblasts and mural cells in human lung tissues using scRNA-seq data from a human lung cell atlas18  and fibroblasts (n = 885) were detected within the stromal cell cluster. To further test whether fibroblasts and mural cells commonly cluster together when analysing whole-tissue homogenate tumour samples by scRNA-seq, we repeated this analysis on multiple publicly available datasets32 consistently observing similar results  heterogeneity within the tumour microenvironment.28. To overcome this challenge and comprehensively examine fibroblast heterogeneity in NSCLC, we repeated the process of in silico fibroblast sorting  with six further scRNA-seq datasets from human NSCLC and control tissue samples34. This generated a dataset with 9673 fibroblasts . This showed that three major clusters were consistently identified, whereas higher resolution clustering led to the identification of clusters with no or very few marker genes meeting these criteria  characterisation of fibroblast subpopulations, due to costs, sample availability and difficulties in isolating these cells from tissues leading to variable proportions across tissuessts Fig. 35. We thria Fig. .Fig. 2In18  in sample-level analysis , including synthesis of prostaglandins and bile acids/salts , with 31% of all differentially expressed genes associated with the matrisome43. Therefore, we examined whether each fibroblast subpopulation upregulated genes associated with particular matrisome categories  panel. For this analysis, the human protein atlasein Fig. . Genes pein Fig. . AdditioConsistent with previously defined nomenclature for the lung fibroblast phenotypes, our mxIHC showed the alveolar and adventitial subpopulations were found within these regions of control  lung tissue Fig. . NotablyThis mxIHC panel was then applied to whole-tissue sections of NSCLC tissue  were then clustered into modules based on correlated expression in 'pseudotime'. This identified four modules on both the trajectories representing different phases of transdifferentiation: progenitor, early-activation, proto-differentiation and differentiation  to myofibroblasts (enriched in tumour tissue) we performed trajectory inference on our scRNA-seq dataset, using a diffusion map dimensionality reductionate Fig. . We thenate Fig. . Genes dion Fig. . Compari8 (e.g. IL6 and CCL2). Notably, similar expression profiles were found when analysing datasets consisting of only control samples or both control and NSCLC samples  were also significantly enriched in this module , which is a well-described driver of both heat shock response and fibroblast activation, were also found in this module. To investigate the role of heat shock/stress responses in myofibroblast activation further, we used mxIHC to examine HSPA1A/Hsp70 expression. This identified a significant increase in each of the fibroblast subpopulations when comparing cells that were located within the tumour to those in tumour-adjacent control regions  could act as tissue-resident progenitors for myofibroblasts. These data also suggest that the process of transdifferentiation is comparable irrespective of the progenitor subpopulation: involving a transient phase of inflammatory gene upregulation, independent of direct interaction with the tumour; followed by proto-differentiation involving heat shock response signalling, which is increased by interaction with the tumour; finally leading to a fully differentiated myofibroblast phenotype with increased capacity for ECM organisation and collagen formation, which is significantly enhanced by tumour-associated stimuli.49, HNSCC29 and colorectal cancer (CRC)50. In each case, fibroblasts were identified by unsupervised clustering followed by mural cell exclusion, as described above . This showed that all three subpopulations were identified with high probability scores  samplesn = 1669;53 four LUSC cohorts, n = 1104;55 Supplementary Data p < 0.01; Supplementary Fig. To examine the clinical relevance of fibroblast subpopulations in NSCLC, we leveraged imputed cell abundances from CIBERSORTx-mediated digital cytometry to interrogate large clinically annotated NSCLC cohorts  and low .The morphological subtype of LUAD tumours is recognised to be associated with patient survival ratesp < 0.01; Supplementary Fig. p = 0.07) was observed with micropapillary growth patterns  and 69% of PP tumours were poorly differentiated (G3). As expected, given this link, myofibroblast abundance was highest in PP tumours; whereas alveolar and adventitial fibroblasts were most prominent in TRU tumours  of LUADurs Fig. . Furtherons Fig. .57) and their correlation with fibroblast subpopulations. This demonstrated an inverse relationship between the immune cells that correlated with myofibroblasts and alveolar fibroblasts, which was consistently observed across all the LUAD transcriptomic datasets analysed  were associated with improved survival rates in LUAD, particularly alveolar fibroblasts. These findings support the rationale for developing therapeutic strategies that revert CAFs to a ‘normal’ phenotype, as described for vitamin D receptor agonists59 and NOX4 inhibition60. Our cross-tissue analysis showed that alveolar fibroblasts were relatively specific to lung tissues . However, this does not preclude the existence of similarly tumour-suppressive tissue-resident fibroblast populations in other organs. For example, a broad analysis of murine tissues has previously identified organ-specific fibroblast subsets in the intestine, as well as bone, lung, lymph node/spleen and artery/tendon19.Fibroblast heterogeneity is an emerging area of cancer research with previously described relevance to patient outcomes and treatment response across multiple tumour types19 and may represent 'universal' fibroblasts19. We identified CD34 as an effective marker of this population, which is consistent with many studies that have described CD34 + fibroblast/stromal cell populations across different organs. For example, PDGFRA + CD34 + cells represent >90% of stromal cells within the dermis;61 they have been shown to represent a key progenitor cell in tissue regeneration62 and a prominent source of tumour-reactive stromal cells63. The adventitial fibroblasts were also the closest 'relative' to iCAF phenotypes8. However, in contrast to previous studies, we found that the expression of inflammation-associated cytokines and iCAF markers were predominantly limited to control tissues in NSCLC. Furthermore, our trajectory analysis suggests this inflammatory phenotype may represent an early and transient phase in the transdifferentiation to a myofibroblast phenotype, independent of direct interaction with the tumour. Indeed, inflammatory fibroblasts have been identified in infections and inflammatory conditions as well as cancer;64 supporting the concept that fibroblasts progress through a spectrum of phenotypes that function to initiate, support and ultimately suppress inflammation.The adventitial fibroblast subpopulation is consistent with multiple previous studies66. Our results suggest these inconsistencies are likely due to both fibroblast heterogeneity and variation across NSCLC histological subtypes. This may be because LUSC (compared to LUAD) have a more homogeneous stroma containing consistently high levels of myofibroblasts, which could limit their utility as a prognostic marker. Alternatively, the functional impact of myofibroblasts on tumour progression may vary between tumour subtypes. Multiple mechanisms for how these cells could promote tumour progression have been identified in pre-clinical cancer models: including modulating ECM organisation and tumour cell invasion67; promoting tumour cell proliferation68; regulating epithelial differentiation69 and promoting immune evasion70. However, recent studies have also demonstrated that myofibroblasts can have tumour-suppressive properties in PDAC6. The observation that myofibroblasts are not prognostic in LUSC provides further evidence that the role played by these cells in cancer may be context-dependent, and further investigation is required to determine what mechanisms underlie this difference. Given that we found no significant differences in gene expression between myofibroblasts from LUAD and LUSC tumours, the varied impact these cells have on tumour progression may be due to intrinsic properties of the malignant cells, as described in a recent study elucidating a LUAD-specific role for GREM1-KDR signalling in disease progression68.Previous studies have shown that fibroblast markers can indicate both good and bad prognosis in NSCLCIn summary, this comprehensive analysis of lung and NSCLC fibroblast heterogeneity shows how certain subpopulations are organ-specific, whilst others are consistently found in different tissues, cancers and pathologies. Myofibroblasts and alveolar fibroblasts are associated with different molecular and immunological LUAD subtypes, and their opposing effects on prognosis accurately identify high-risk patients. These findings could improve patient stratification and should refine strategies for therapeutic targeting of fibroblasts in lung cancer.The Southampton and South West Hampshire Research Ethics Committee approved the study, and written informed consent was obtained from all subjects (Target Lung study: REC number 14/SC/0186). Newly diagnosed, untreated patients with respiratory malignancies were prospectively recruited once referred. Freshly resected tumour tissue and, where available, matched control tissue was obtained from lung cancer patients following surgical resection. Control tissues were sampled from peripheral regions of the resected tissue at the furthermost point from the tumour. Tissue samples were transported (within 1 h) to the laboratory on ice in serum-free Dulbecco’s Modified Eagle Medium .28. Briefly, samples were washed, incised and incubated with Collagenase P  at 37 °C with agitation (200 rpm) for 60 min. The resulting suspension was strained; incubated with red cell lysis buffer (BioLegend); and re-suspended in PBS supplemented with 9% Optiprep (Sigma) and 0.1% bovine serum albumin (BSA). Single-cell transcriptome encapsulation was performed using a custom microfluidic platform (Drop-seq) as described previously71.Tissue disaggregation was performed as previously described36, unless otherwise stated. Initial quality control was carried out to remove low-quality events. First, we used a random forest classifier to exclude empty droplets as described previously72. We then identified outliers for the fraction of reads mapping to mitochondrial genes  to exclude apoptotic cells.ScRNA-seq data processing and analysis was performed using the Seurat package in R (v4.0.2)FindClusters function, using principal components identified as significant with JackStraw analysis (p < 1e-5) and a resolution of 0.2. Cluster markers were identified using the FindAllMarkers function (ROC classifier) and cell types were assigned based on canonical marker expression or by significant enrichment (adj. p < 0.0001) in previously described cell type markers from the Immunological Genome73 and LungGENS74 projects, assessed using the ToppFun gene set enrichment tool75.Initial clustering was performed using a subset of genes selected based on variance and average non-zero expression, excluding extreme outliers. Raw counts data was log-normalised and scaled (regressing out nUMI) before performing principal components analysis (PCA). Clusters were identified with the 12) and, therefore, high susceptibility to false negatives in marker detection (due to drop-out), this cluster had very few genes identified as markers. Therefore, it was unclear whether these lymphocytes were sufficiently separated from low-quality droplets. To identify lymphocytes within this cluster the AddModuleScore function was used to calculate the average expression of previously described T-cell markers  and NK cell markers . This cluster was then filtered further to remove cells negative for both gene signatures.This identified a large cluster of cells predominantly comprised of lymphocytes. However, due to the relatively low number of genes detected in lymphocytes . This identified significant (median z-score >1.96) patient-dependent batch effects in each dataset. The previously described reciprocal PCA (rPCA)35 approach for data integration was then used for intra-dataset batch correction, as implemented in the Seurat R package.Batch effects were assessed by comparing k-nearest neighbour overlap between potentially confounding sample groups and randomly sampled cells to calculate DCN, LUM and DPT) and a subset from the entire dataset. The AddModuleScore function was then used to calculate per-cell expression levels for fibroblast and mural cell gene signatures  and clustering was performed using the integrated (batch-corrected) data. The stromal cell cluster was then identified from canonical marker expression  was then identified as those expressed in >1% of fibroblasts in each dataset. The Seurat package IntegrateData function was then used to calculate rPCA corrected expression values for these consensus fibroblast genes within each dataset.A consensus list of fibroblast genes  dimensionality reduction to create a shared low-dimensional space across datasets. 1422 genes  package to calculate an enrichment score for each gene set per sample as the normalised difference in empirical cumulative distribution functions (CDFs) of ranked gene expression inside and outside the gene set. The limma (v3.44.3) R package was then used to assess whether these enrichment scores were differentially expressed between subpopulations using linear models.Marker genes were identified using the p values were then calculated to determine those genes that significantly varied across all datasets analysed, using the meta p (v1.5) R package. To identify pseudotime modules, the median was calculated (across datasets) for each gene’s loess-fitted expression profile in pseudotime and the correlation between genes was calculated as Pearson’s correlation coefficient (r). Gene modules were then identified through unsupervised clustering using “Ward’s method”, applied to a distance matrix constructed by calculating 1-r.To infer differentiation trajectories, we used the destiny (v3.2.0) R package to perform diffusion map dimensionality reduction, cluster cells by diffusion pseudotime branches and calculate pseudotime values. A loess regression model was then used to identify genes that were differentially expressed in and calculate fitted values for each gene’s expression profile in pseudotime for each dataset, using the gam (v1.20) R package. Meta-15, PDAC49, CRC50 and HNSCC29. These datasets were processed to extract fibroblasts, as described above. Integration with the NSCLC dataset was then performed by CCA, as described above. Label transfer was then performed as described previously35, generating a predicted cluster classification and associated prediction score for each class.To compare fibroblast phenotypes across pathologies we analysed publicly available data from IPF76. Four-micrometre sections of formalin-fixed paraffin-embedded (FFPE) sections were mounted on TOMO slides (Matsunami) and baked for 60 min at 60 °C. Deparaffinisation, rehydration, antigen retrieval and immunohistochemical staining were performed using the PT Link Autostainer (Dako).Immunohistochemical staining was performed using a previously-described multiplexed protocolAntigen retrieval for all antibodies was performed using the EnVision FLEX Target Retrieval Solution . Whole slide images (WSI) were captured after each staining cycle, then a conserved region that shows the target signal in the first stain is selected for all images and the stain is quantified as a relative area covered across multiple intensity bins. If the staining intensity was found to decrease after repeat 77, to create a 'pseudo-immunofluorescence' (pIF) image showing the blue (nuclear), brown (registration) and red (transient marker) signals. This produced three raster images of floating-point values ranging from 0 to ~3. Storage and processing economy was maximised by converting float values to integers in the range of 8-bit images (1–256); for each pixel, the float value was divided by 1.5, then multiplied by 256 and rounded up to a maximum of 256. Each layer was then processed to identify topological maxima above the level of background signal, measured as the moving average over a 151 × 151-pixel window . For the red signal, an additional condition was applied to account for the similarity in the colour profile of red and brown  = 0). These pIF images, saved at a resolution equivalent to x5 magnification in tiff format, were registered using the MultiStackRegistration plugin78 in Fiji79. The primary Mx image consists of a nuclear stain from iteration (i)1, registration stain from i1, target marker from i2 to in and a nuclear marker from in. For quality control (QC), an additional Mx image was generated consisting of nuclear marker staining from i1 to in.The WSI from each staining iteration was deconvoluted using the hue-saturation-density (HSD) model, implemented in Developer XD 2.7 n were marked as lost.QC analysis was performed on the nuclear stain, to identify tissue loss or registration errors. A nuclear signal was identified from i1, then any nucleus with a signal missing in i2 to iMx analysis proceeded from the import of the result from QC analysis where the remaining nuclear signal was segmented to generate individual nuclear objects. These were then grown by 5.5 µm radius to represent simulated cell regions. For each marker, a threshold of 7 was used to identify significant staining (this threshold is to exclude blush/bleed/non-specific staining) and the marker coverage for each cell was measured. This, plus XY coordinates for each cell were exported for histo-cytometry analysis.Histo-cytometry estimates of cellular expression levels were calculated as the fraction of pixels within simulated cell regions positive for staining once background levels had been subtracted. Stromal cells were identified by the absence of staining for Pan-CK, CD31 or MCAM and positivity for one of the fibroblast markers . Then subpopulations were identified by which of the four markers was most highly expressed.Tissue regions  were annotated by a consultant pathologist (GJT and ECS) and point pattern data from the histo-cytometry analysis was used to assess region enrichment.80. Clinical data for these patients was downloaded using the TCGA clinical data resource81. Mutational status and additional metadata were downloaded from cBioPortal82. Microarray and associated clinical data were downloaded from the NCBI GEO database  using the GEOquery (v2.56.0) R package83. For each LUAD dataset the molecular subtypes were calculated using the previously described nearest centroid approach23.RNA-sequencing counts data from TCGA NSCLC cohorts was downloaded using the TCGAbiolinks (v2.16.4) R package27. We used SMART-Seq2 single-cell RNA-sequencing data from the Travaglini et al. and Maynard et al. studies34 to generate a single-cell reference sample matrix, consisting of cells from control and tumour tissues. This consisted of fibroblast subpopulations; pericytes and SMCs, identified during in silico fibroblast sorting (described above); endothelial, epithelial and immune cells, identified from the general annotation provided in the dataset metadata. To prevent memory errors, we limited the size of this reference matrix by randomly downsampling each cell-type to a maximum of 500 cells. This reference sample matrix was then used to generate a signature matrix file, using the default CIBERSORTx settings  regression modelling of overall survival rates. For categorical analyses, TCGA-LUAD cohort was used as the test dataset, and optimal dichotomisation thresholds were determined using the survival (v3.2-11) survminer (v0.4.9) R packages. Statistical significance was then assessed using a log-rank test  or by CoxPH regression modelling .Survival analysis was performed for each cohort, using the p values are shown in the figures unless otherwise stated in the figure legends. All Boxplots are displayed using the Tukey method .All statistical tests carried out were two-sided and are described in the relevant figure legends, including the sample size for each experimental group. Exact Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Reporting Summary