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Machine learning and bioinformatics analysis revealed classification and potential treatment strategy in stage 3–4 NSCLC patients

Abstract

Background

Precision medicine has increased the accuracy of cancer diagnosis and treatment, especially in the era of cancer immunotherapy. Despite recent advances in cancer immunotherapy, the overall survival rate of advanced NSCLC patients remains low. A better classification in advanced NSCLC is important for developing more effective treatments.

Method

The calculation of abundances of tumor-infiltrating immune cells (TIICs) was conducted using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), xCell (xCELL), Tumor IMmune Estimation Resource (TIMER), Estimate the Proportion of Immune and Cancer cells (EPIC), and Microenvironment Cell Populations-counter (MCP-counter). K-means clustering was used to classify patients, and four machine learning methods (SVM, Randomforest, Adaboost, Xgboost) were used to build the classifiers. Multi-omics datasets (including transcriptomics, DNA methylation, copy number alterations, miRNA profile) and ICI immunotherapy treatment cohorts were obtained from various databases. The drug sensitivity data were derived from PRISM and CTRP databases.

Results

In this study, patients with stage 3–4 NSCLC were divided into three clusters according to the abundance of TIICs, and we established classifiers to distinguish these clusters based on different machine learning algorithms (including SVM, RF, Xgboost, and Adaboost). Patients in cluster-2 were found to have a survival advantage and might have a favorable response to immunotherapy. We then constructed an immune-related Poor Prognosis Signature which could successfully predict the advanced NSCLC patient survival, and through epigenetic analysis, we found 3 key molecules (HSPA8, CREB1, RAP1A) which might serve as potential therapeutic targets in cluster-1. In the end, after screening of drug sensitivity data derived from CTRP and PRISM databases, we identified several compounds which might serve as medication for different clusters.

Conclusions

Our study has not only depicted the landscape of different clusters of stage 3–4 NSCLC but presented a treatment strategy for patients with advanced NSCLC.

Peer Review reports

Background

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, which is the leading cause of cancer-related death worldwide [1]. The majority of NSCLC cases are often first diagnosed at an advanced stage when curative treatment is less effective [2]. The overall survival of non-small cell lung cancer patients is dissatisfied, and the high rate of invasion and metastasis are major problems [3, 4]. During the past two decades, ICIs (immune checkpoint inhibitors), including monoclonal antibodies targeting programmed death 1 (PD-1) and cytotoxic T-lymphocyte antigen-4 (CTLA-4) and combination immunotherapy, have begun to alter clinical treatment strategy in multiple cancers, especially in NSCLC [5]. Response to immune checkpoint inhibitors treatment is associated with multiple factors, such as tumor mutation burden (TMB), microsatellite instability (MSI), and PDL1 expression [6]. The efficacy of cancer immunotherapy also depends on the tumor stage [7]. Despite recent advances in cancer immunotherapy, the 5-year overall survival rate of advanced NSCLC patients remains low [8, 9]. Understanding the tumor microenvironment and heterogeneity in advanced NSCLC is important for developing more effective treatments [10].

The tumor microenvironment is a highly complex ecosystem. We assumed that the heterogeneity of advanced NSCLC could be distinguished based on the major cellular components of TME. The development of next-generation sequencing and public database have made it possible to explore novel treatment in multiple cancers [11, 12]. To obtain insight into the tumor microenvironment, many computational methodologies have been developed (including CIBERSORT, TIMER algorithms). For example, the CIBERSORT algorithm, which was termed as cell deconvolution approach, has been developed to infer lymphocytes and other immune cells proportions from bulk transcriptome data. These computational approaches help researchers identify specific cell types, and have been widely used in cancer studies [13]. Multi-omics analysis has deepened our understanding of the biological basis of cancer and precise survival prediction of patients, which is in line with the concept of precision medicine [14]. In this study, we attempted to classify advanced NSCLC patients, depict their characteristics, and identify novel therapeutic molecular targets or potential drugs for different clusters of patients.

Method

Data pre-processing

The bulk RNA-seq TCGA-LUAD and TCGA-LUSC data for NSCLC were downloaded as HTSeq-FPKM files from UCSC Xena (https://xenabrowser.net/datapages/). The corresponding clinical information including follow-up data was also collected from UCSC Xena database. TCGA-LUAD and TCGA-LUSC microRNAs data were derived from TCGA data portal (https://portal.gdc.cancer.gov/). The expression profiles of TCGA-LUAD and TCGA-LUSC were pre-processed by the following steps: 1) Removing samples without follow-up information; 2) Preserving stage 3 or stage 4 samples; 3) The expression profile (FPKM values) was transformed into TPMs; 4) Preserving the genes of log2 (TPM + 1) > 0. From this, 195 advanced NSCLC samples from TCGA cohort were sorted out for further analysis.

Additional cohorts of NSCLC patients were derived from Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/, platform Illumina GPL6884 (n = 116): GSE41271 and GSE42127; platform Affymetrix GPL570 (n = 45): GSE29013 and GSE37745). The potential batch effects (Specifically, between GSE41271 and GSE42127, and between GSE29013 and GSE37745) were eliminated using ComBat function (“SVA” package in R) [15]. The detailed information of the studying cohorts was summarized in Additional file 2: Table S1-S2 (Combined affy cohort, GSE29013 + GSE37745, N = 45, Additional file 2: Table S1; Combined illumina cohort, GSE41271 + GSE42127, N = 116, Additional file 2: Table S2).

Transcriptomic and the corresponding clinical information of patients with urothelial cancer treated with atezolizumab (anti-PD-L1) was downloaded from Imvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/), clinical endpoints including complete or partial response (CR or PR), stable disease (SD), and the progressive disease (PD). Another two transcriptomic datasets from patients with NSCLC treated with PD-1 blockade were downloaded from GSE126044 and GSE135222 (Two anti-PD1 treatment cohorts, N = 27, N = 16, Additional file 2: Table S3; Imvigor cohort, N = 348, Additional file 2: Table S4).

Estimation of the immunological characteristics of advanced NSCLC

The abundance of LM22 (22 immune cell types) was calculated using CIBERSORT algorithm [16] (model = relative, permutation = 1000, disable quantile normalization = True, https://cibersort.stanford.edu/). To avoid calculation errors, we comprehensively calculated the abundance of immune cells using another four algorithms: TIMER, xCell, EPIC, MCP-counter [17,18,19,20]. The immune score, stromal score, and ESTIMATE score for each sample were calculated by applying “ESTIMATE” function in R [21]. In addition, Eighteen immune-related therapeutic signatures were collected from the Jiao Hu et al. study [22], and 23 immune-related gene sets were collected from MSigDB database and previous publications [23]. Effector genes of immune cells were identified from previous publications [24]. To predict clinical outcome and response to ICI therapy among different sub-clusters, four response signatures reported previously [25,26,27] were calculated using ssGSEA. We used these TIICs abundances or genesets to depict the immune-related parameters of the studying cohorts.

Identification of clusters based on consensus clustering

Unsupervised clustering methods were performed (K-means, “ConsensusClusterPlus” package in R) to determine sub-clusters (applied in TCGA cohort and two independent external validation cohorts, including affy-combined cohort and illumina-combined cohort) based on LM22 [28, 29]. This procedure was repeated 1000 times to ensure classification stability.

Construction of poor prognosis signature

The NSCLC samples in TCGA cohort were randomly assigned into the training/validation cohort (6:4). A total of 2720 immune-related genes were collected from InnateDB (https://www.innatedb.com) and Immport (https://www.immport.org/). The immunological characteristics mentioned above (Estimation of the immunological characteristics of advanced NSCLC) were calculated separately using respective methods (The abundance of immune cells, calculated by TIMER, xCell, EPIC, MCP-counter algorithm; The immune score, stromal score, and ESTIMATE score, calculated using the ESTIMATE algorithm; The enrichment of immune-related signatures, calculated using ssGSEA). All variables were merged into a feature matrix, and feature engineering was performed to filter survival-unrelated and cluster 2-irrelevant variables. Then all features were standardized across all samples (features were standardized using Z-score normalization), and LASSO-penalized regression was conducted [30] to further reduce the number of features (“glmnet” package in R). Among features identified in LASSO analysis, multivariate cox regression analysis was conducted, and Poor Prognosis Signature (PPS) was constructed by applying the regression coefficients.

Construction of classifiers to distinguish different subclusters based on machine learning

To simplify and find the best approach to distinguish different advanced NSCLC sub-clusters (determined by K-M clustering), four different algorithms, including SVM (Support Vector Machine) [31], RF (Randomforest) [32], Xgboost (eXtreme Gradient Boosting) [33, 34] and Adaboost (Adaptive boosting) [35], were recruited to build up the classifier. We attempted to find the best parameters of different algorithms. Specifically, for SVM, cross-validation and grid search were applied to find out the best model parameters (cost = 8 and gamma = 0.00391); for RF, we selected mtry = 18 and ntree = 800 as the best parameters, and random forest method has an internal validation method; for Xgboost and Adaboost, we extracted 80% samples randomly to assess the classifier and this procedure was repeated 1000 times. We built up the classifier in the training cohort and compared their performance in the validation cohort. For every algorithm, the performance measures included accuracy, precision, recall, F1 score, and AUC.

Drug sensitivity

CTRP (Cancer Therapeutics Response Portal) and PRISM (Profiling Relative Inhibition Simultaneously in Mixture), which contains the sensitivity data for more than 1000 compounds, were used to generate drug sensitivity data [36, 37]. Both databases provide AUC values as a measure of drug sensitivity, and higher AUC values indicate decreased sensitivity to specific compounds. Any compound or drug with more than 20% missing values was excluded before inferential analysis [14].

Calculation of TMB

Non-synonymous mutations were defined as "Frame_Shift_Del", "Frame_Shift_Ins", "Missense_Mutation", "Nonsense_Mutation", "Splice_Site", "In_Frame_Del", "In_Frame_Ins", "Translation_Start_Site", "Nonstop_Mutation", and the exome size was defined as 38 Mb [38]. TMB was calculated by this formula:

TMB = (Non-synonymous mutations)/ (exome size).

Copy number variation, DNA Methylation, and miRNA analysis

The TCGA CNV data (Masked copy number Segment hg38) was derived from TCGA database. Values of segment mean bigger than 0.1 were defined as gain and less than -0.1 as a loss. All CNV data was analyzed using GISTIC 2.0 [39].

Methylation data using Illumina Human Methylation 450 k was obtained from UCSC Xena browser. R package “Champ” was utilized for normalization and “limma” for the identification of differentially methylated probes [40].

R package “edgeR” was utilized to determine differentially expressed miRNA. MiRNA-DEG links were predicted by different miRNA databases (miRDB, mirTarbase, Targetscan, predictions in at least two databases were defined as positive predictions) [41,42,43].

Gene set enrichment analysis and differentially expressed gene analysis

To determine which pathways or biological functions differ between different sub-clusters, GSEA (version: 4.0) was performed. C5.go.bp.v7.2.symbols.gmt, c2.cp.kegg.v7.2.symbols.gmt and h.all.v7.2.symbols.gmt set as reference gene sets. Differentially expressed genes were identified using “limma” package in R, and the thresholds were set as |log2-fold change |> 1.0 and Fdf < 0.05.

protein–protein interaction network

The PPI network of the key proteins identified in the multi-omic analysis was constructed using the STRING database (https://string-db.org/), and parameters were set to default values [44].

Bioinformatic analysis

The bioinformatic analysis involved in our study included: (a). Preprocessing and analysis of the transcriptome data, mutation data, and copy number alteration data. (b). Calculation of immune cell abundance using CIBERSORT, TIMER, xCell, EPIC, MCP-counter, and ESTIMATE algorithms. (c). GSEA and ssGSEA (single sample GSEA) were used to calculate an enrichment level of certain signatures in different groups or samples. (d). miRDB, mirTarbase, and TargetScan databases were used for the miRNA target prediction. (e). Classified patients into different groups using unsupervised KM clustering. (f). Construction of PPS model using LASSO-COX analysis. (g). Construction of the classifier using different MLs (RF, XGBoost, Adaboost, and SVM) and DL (NNet). (h). Drug sensitivity data (derived from CTRP and PRISM) analysis using ridge regression. (i). Protein–protein interaction analysis using STRING.

Statistical analysis

Normality was calculated via the Shapiro–Wilk normality test. Wilcoxon test and Kruskal–Wallis test were utilized to analyze the ordered categorical variables. Student’s t- or chi-square test was used to compare continuous or discrete variables. Statistical analysis was two-sided, and P < 0.05 was considered to be statistically significant. To avoid false positives in multiple tests as much as possible, we performed the false discovery rate correction. All these analyses were conducted through R software.

Result

The landscape of advanced NSCLC TME

CIBERSORT algorithm was performed to quantify the abundance of LM22 in TCGA advanced NSCLC samples (stage 3 and stage 4 TCGA-LUSC, TCGA-LUAD, N = 195, Table 1). To avoid the calculation errors due to marker gene sets of tumor-infiltrating immune cells (TIICs), we estimated the abundance of immune cells using four other algorithms (TIMER, xCell, EPIC, MCP-counter), and compared the correlations among them. Five TIICs overlapping in different algorithms, including CD8 + T cell, M2.macrophage, M1.macrophage, Neutrophil, Dendritic cell, have shown a high degree of similarity with the results calculated by CIBERSORT (Additional file 1: Figure S1). E.g., the enrichment level of CD8 + T cell quantified by the four independent algorithms was in line with the previous CIBERSORT results (Spearman correlation, TIMER: 0.67, xCell: 0.80, EPIC: 0.71, MCP-counter: 0.65, Additional file 1: Figure S1, Additional file 2: Table S5), which demonstrated the stability of calculation. Unsupervised clustering (K-means) was performed to classify the advanced NSCLC into different sub-clusters based on TIICs level of the 195 tumor samples. We assessed the clustering parameters (Additional file 1: Figure S2 A-B) and the optimal cluster number was set as three. Samples from the TCGA cohort were then assigned to three separate clusters (cluster 1, n = 79; cluster 2, n = 61; cluster 3, n = 55). The clinical information was shown in Supplementary Material (Additional file 2: Table S6).

Table 1 Clinical information of patients in stage 3–4 TCGA-NSCLC cohort

Cluster analysis revealed distinct immune infiltration patterns among these three clusters (Fig. 1A, Additional file 1: Figure S2C): cluster-1 was characterized by increases in the infiltration of resting DCs, M2.marcrophages, activated mast cells, monocytes, activated NKs, and resting CD4 + T memory cells; cluster-2 showed an evident increase in the infiltration of plasma, M1.macrophages, activated CD4 + T memory cells, CD8 + T cells, T follicular helper cells, and Tregs; cluster-3 exhibited a high infiltration of M0.macrophages and resting mast cells and exhibited decreases in other TIICs. The significant difference of TIICs infiltration in these three clusters was confirmed by Kruskal–Wallis tests (Fig. 2C). To investigate the association between TME phenotypes and clinical characteristics, clinical factors, including age, gender, tumor stage, lymph node metastasis, and distant metastasis, were analyzed. However, there was no significant difference in these clinical characteristics among the three clusters (Additional file 2: Table S6).

Fig. 1
figure 1

Unsupervised clustering of TIICS in stage 3–4 NSCLC. A. Top: Consensus clustering of the pairwise correlation of TIICs. Three modules associations were indicated in the heatmap. Middle: Five representative immune cells (T cells CD8, M1.Macrophage, Monocyte, M2.Macrophage, M0.Macrophage) from each module, with heatmap indicating the abundance (Dark colour represents high expression level, while the light colour represents the low expression level). Bottom: Distribution of the five selected TIICs within the three clusters (row), with dashed line indicating the median. B Radar graph indicates the ESTIMATE scores and four immunotherapy-related signature scores in three clusters. Line color represents the three clusters: Red for cluster-1, green for cluster-2, and blue for cluster-3

Fig. 2
figure 2

TME characteristics in each clusters. A Kaplan–Meier curve displaysdifferences of overall survival among three clusters. Log-rank statistic was conducted to test statistical significance. B Comparison of GSVA score of CD.Sig, IFNG.sig, EIGS, 12-chemokine signature among different clusters. Kruskal–Wallis statistic was conducted to test statistical significance. C 22 TIICs abundance among three clusters were shown in the box plot. ***, P < 0.0001; **, P < 0.001; *, 0.001 < P < 0.01

To unravel the biological differences among these clusters, selected chemokine and cytokine mRNA expression in the 195 samples were analyzed. Grossly, immune-activated-related molecules (CD8A, CXCL10, CXCL9, GZMA, GZMB, IFNG, PRF1, TBX2, and TNF) were relatively higher in cluster-2 compared to the other clusters; cluster-3 was associated with relatively low expression of immune-checkpoint-related molecules (CD274, CTLA4, HAVCR2, IDO1, LAG3, PDCD1, and PDCD1LG2), whereas expression of TGFβ/EMT-pathway-related molecules (ACTA2, CLDN3, COL4A1, SMAD9, TGFBR2, TWIST1, VIM, and ZEB1) were high (Additional file 1: Figure S3A-B). Then, we referred to a database of co-inhibitory, co-stimulatory, and MHC-related molecules to better compare these immunomodulators among these three clusters. Overall, the result showed that cluster-2 had a higher expression of co-inhibitors and co-stimulating molecules than the other clusters, while MHC-related molecules showed no significant difference among these clusters (Additional file 1: Figure S3C-D). In addition, cluster-2 was associated with higher expression of effector genes of CD8 + T cells as compared to cluster-1 and cluster-3 (Additional file 1: Figure S3E-F). These results indicated that cluster-2 tended to be an inflammatory phenotype, which indicated that patients classified into cluster-2 might have a better clinical outcome. Kaplan–Meier curve indicated that cluster-2 had an overall survival advantage (log-rank test, p = 0.017, Fig. 2A). ESTIMATE score and immune score were higher in cluster-2 (Fig. 1B). In addition, the GSVA score of four immunotherapy-related signatures was significantly higher in cluster-2 as compared to the other clusters (Kruskal–Wallis tests, CD8.sig, IFNG.sig, EIGS, 12-chemokines.sig, all p value < 0.001), which indicated that patients in cluster-2 might have a better response to ICI (immune checkpoint inhibitors) therapy (Fig. 2B).

Construction of the poor prognosis-associated signature

We sought to establish a poor prognostic signature by using the samples' immune status. The samples in TCGA cohorts were randomly separated into training cohort (n = 117) and validation cohort (n = 78). We collected immune-related genes (from InnateDB and Immport databases), immune-related signatures (from MsigDB and previous studies), immune-related therapeutic signature (from Jiao Hu et al. study), immune-related scores (calculated by ESTIMATE algorithm), and abundance of TIICs (calculated by CIBERSORT algorithm). Feature engineering was conducted to filter OS-unrelated and cluster2-irrelevant variables (Additional file 3: Table S7-9. Firstly, the univariate cox test was conducted to seek out features that were associated with overall survival outcome. Then, the Wilcoxon test was used to find out features related to cluster-2. The features obtained finally were used in the PPS model construction). Then 25 gene-based LASSO-COX model was constructed, which we defined as PPS (Additional file 3: Table S10). PPS for each patient was calculated and patients were classified into high/low-risk groups according to the optimal cut-off determined by X-tile software (Fig. 3A). It could be observed that patients in the PPS-low group had a distinct survival advantage (log-rank test, p < 0.001, Fig. 3B) as compared to the PPS-high group. AUC of the PPS prediction for overall survival was 0.830 at 12 months, 0.894 at 36 months, and 0.869 at 60 months in the training cohort (Fig. 3C), which showed quite a good prediction efficiency. The same results were shared in the validation cohort (Additional file 1: Figure S4). Kaplan–Meier curves showed patients in PPS-low had a better overall survival (log-rank test, p = 0.004, Additional file 1: Figure S4A), and AUC of PPS prediction for OS was 0.725 at 12 months, 0.681 at 36 months, and 0.621 at 60 months (Additional file 1: Figure S4B). In addition, PPS was confirmed to be an independent prognostic factor both in the training and validation cohorts (Table 2). Then, we validated the PPS with two external data sets (Additional file 1: Figure S4), and the results were consistent with expectations (Additional file 1: Figure S4C-H).

Fig. 3
figure 3

Construction of immune-related poor prognosis signature. A The distribution of PPS score, OS, and expression patterns of genes involved in the signature. B Kaplan–Meier curve of OS among PPS-high and PPS-low group patients. Log-rank statistic was conducted to test statistical significance. C Performance assessment of the PPS by AUC. ROC analysis revealed the AUC was 0.83 at 12 months, 0.894 at 36 months, and 0.869 at 60 months

Table 2 Univariate and multivariate analyses of clinicopathological characteristics and PPS with overall survival in training and validation cohort

According to previous studies, several prognostic models have been proposed based on NSCLC, lung adenocarcinoma, or lung squamous cell carcinoma [45,46,47,48,49,50]. However, there was almost no signature proposed based on advanced NSCLC, and the actual use of the former models might lead to fallacies due to this. In our study, the AUC of PPS was 0.784 at 12 months and 0.808 at 36 months, and 0.764 at 60 months in our entire cohort (N = 195) (Additional file 1: Figure S4I-J), and the AUC of PPS in the luad/lusc subgroup were shown in the figure (Additional file 1: Figure S4K-L, LUAD: 0.802 at 12 M, 0.806 at 36 M, 0.727 at 60 M; LUSC: 0.757 at 12 M, 0.812 at 36 M, 0.788 at 60 M). Here, we compared the efficiency of our PPS model with other models and evaluated the AUC as a measure of accuracy. As shown in the table (Table 3, Additional file 1: Figure S4M-R), the PPS model always reached the highest AUC whether in advanced NSCLC, adenocarcinoma, or squamous, suggesting that our PPS had favorable efficacy for predicting overall survival in advanced NSCLC.

Table 3 Comparison of the performance of PPS with other previous signatures

The PPS score predicts immunotherapeutic benefits

To explore the biological significance of the PPS, the correlations between PPS and immune-related parameters were analyzed. Among 8 main TIICs, PPS was found to be positively correlated with M0 and M2 macrophages, and negatively correlated with CD8 + T cells, Tfh, activated CD + T memory cells, Tgd, M1 macrophage, and plasma (Additional file 1: Figure S5). In addition, PPS was negatively correlated with the majority of immunomodulatory factors. Notably, PPS was positively correlated with the expression of TGFβ/EMT-pathway-related molecules (COL4A1, ZEB1, ACTA2, TWIST1, VIM, TGFBR2), and several immunotherapy-associated signatures (Additional file 1: Figure S6). GSEA results (Additional file 1: Figure S7) revealed that many immune-related functions or pathways were enriched in the PPS-low group (such as “Adaptive_immune_response”, “Inflammatory_response”, “T_cell_receptor_signaling_pathway” and “B_cell_receptor_signaling_pathway”).

In the subsequent analysis, we evaluated the prognostic value of the PPS in three independent ICI immunotherapy cohorts (GSE126044 n = 16, GSE135222 n = 27, and IMvigor210 n = 348). Patients were assigned to PPS-high or PPS-low group. The survival outcome and distribution of the PPS in GSE126044 and GSE135222 cohorts were shown in the supplementary figures (Additional file 1: Figure S7D-G). However, the results were not statistically significant, which might be due to the small sample sizes. The patients who received anti-PD-L1 treatment in IMvigor210 were assigned to PPS-low or PPS-high groups, too. It was shown that the PPS-low group had a distinct overall survival advantage (log-rank test, p < 0.001, Fig. 4A). Patients benefited from the treatment (CR/PR/SD) tended to have lower PPS score as compared to those PD patients (Fig. 4B). Notably, the PPS score gradually decreased from immune-desert phenotype to immune-excluded phenotype to immune-inflamed phenotype (Fig. 4C). Overall, PPS score might have the prediction ability in patients treated with anti-PD(L)1, and a higher PPS score always associated with worse clinical outcome. The results were not statistically significant (P > 0.05) in GSE126044 and GSE135222 cohorts, which were likely to be ascribed to the small sample sizes.

Fig. 4
figure 4

The performance and distribution of PPS in IMvigor210 cohort. A Kaplan–Meier curve of patients in PPS-high and PPS-low groups. Log-rank test statistic was conducted to test statistical significance. B The PPS distribution of patients in treatment-benefit and treatment-non-benefit groups. Kruskal–Wallis statistic was conducted to test statistical significance. C The PPS distribution of patients in different immune phenotype groups. Kruskal–Wallis statistic was conducted to test statistical significance

A robust model predicts sub-clusters based on immunological parameters

To build a classifier that could distinguish different subtypes for advanced NSCLC, we applied four algorithms (RF, SVM, Xgboost, Adaboost) to build the model, and selected the best one. Candidate variables included immune-related signatures, immune-related therapeutic signature, immune-related scores (calculated by ESTIMATE and IPS), PPS scores, and abundance of TIICs, and different clusters were set as response variables. Accuracy, precision, recall, F1 score, and AUC value in the validation cohort were used to measure the efficacy of different classifiers. Before the calculation, we adjusted the parameters used in different algorithms according to grid search or other approaches (Additional file 1: Figure S8A-B). Classifiers’ performance was shown in the table (Additional file 3: Table S11, Additional file 1: Figure S8C). The results indicated that the classifiers built by RF and Adaboost had higher efficacy than others, and Adaboost seems to be better. For example, the accuracy for cluster 1–3 was0.923, 0.936, 0.987 and AUC for cluster 1–3 was0.928, 0.896, 0.992 in RF, while in SVM, the accuracy for cluster 1–3 was 0.859, 0.872, 0.885, and AUC for cluster 1–3 was 0.864, 0.826, 0.835 (Additional file 3: Table S11). The detailed information of these classifiers was uploaded into Github (https://github.com/LClungcancer/nsclc-2021_classifier). The ranking plot of variables weight indicated that CD8 + T cell and Macrophages might be the keys to distinguish different clusters in patients with advanced NSCLC (Additional file 1: Figure S8D-E).

To verify the generalization ability of our classifiers, we test the performance of the selected two classifiers (RF and Adaboost) in the combined-affy cohort and combined-illumina cohort. The same KM clustering in the testing cohort was conducted, and we used Submap (GenePattern “Submap” module) to prove the identity of the clusters was the same as the TCGA cohort. Then, we test the performance of the selected two classifiers we constructed before. The result was shown in Additional file 3: Table S12, the classifiers showed good generalization ability (Additional file 3: Table S12). In addition, we used a neural network (NNet) to learn this classification. As shown in the Additional file 3: Table S13, “T cell CD8”, “T cell CD4 memory resting”, “Macrophage M0” and “B cell plasma” was the important variables in the classification, which was similar to the results of machine learning. The validation and Nnet procedure were uploaded to https://github.com/LClungcancer/nsclc-2021_classifier.

Differences in somatic mutations related to the different clusters

To reveal the relevant genetic alterations, we analyzed the somatic mutations among different clusters (Fig. 5A–C). Total tumor mutation burden (TMB) was higher in cluster 2 as compared to cluster 1 (Fig. 5D), while TMB showed no difference between cluster 2 and cluster 3 (Kruskal–Wallis test, p = 0.094). We further analyzed the mutation situations of the top 30 genes with the highest mutant frequency (Additional file 3: Table S14-15), and selected several high-frequency mutated genes in each cluster (including LRP1B, CSMD3, RYR2, RYR3, SYNE1, TTN). In addition, we collected some cancer drive genes and immunotherapy-related genes (including EGFR, ALK, KRAS, TP53, MUC16, MET, BRCA1, BRCA2, POLE, POLD1, MSH2, STK11, BRAF, PIK3CA, HER2, FGFR1, ROS1) [51]. Combined with high-frequency mutated genes we identified before, we examined the mutation proportion of these 23 genes among different clusters. The Chi-square test result revealed that TP53, MUC16, LRP1B, SYNE1, and TTN showed higher mutation proportion in cluster 2 as compared to cluster 1, and EGFR, RYR2 showed a higher proportion in cluster 2 as compare to cluster 3 (Additional file 3: Table S16).

Fig. 5
figure 5

The landscape of mutation status among different clusters. AC Top 30 genes with the highest mutation frequencies in cluster-1 (A), cluster-2 (B) and cluster-3 (C). D Tumor mutation burden (TMB) distribution in different clusters. Kruskal–Wallis statistic was conducted to test statistical significance

Genetic and epigenetic regulation related to the different clusters

To obtain a profound understanding of the difference among different clusters, we assessed somatic copy number alterations, DNA methylation, and miRNA for these three clusters. Precisely, we made two comparisons (C2 vs C1, C2 vs C3). First, differentially expression genes (DEGs) between cluster-2 and cluster-1 or between cluster-2 and cluster-3 were analyzed. In the comparison between C2 and C1, 2318 DEGs were identified, including 2135 genes with a higher expression in cluster-1 and 183 genes with a higher expression in cluster-2. In the comparison between C2 and C3, 1242 DEGs were identified, including 1001 genes with a higher expression in cluster-1 and 241 genes with a higher expression in cluster-2 (Additional file 3: Table S17).

SCNAs are widespread in human cancers and have a profound impact on immune evasion. GISTIC 2.0 was used to conduct genomic variation analysis (Fig. 6A). In the comparison between C2 and C1, 523 DEGs upregulated in cluster-1 were encoded by the genomic region with a higher frequency for deletions in cluster-2 or copy number gains in cluster-1; 71 DEGs upregulated in cluster-2 were encoded by the genomic region with a higher frequency for deletions in cluster-1 or copy number gains in cluster-2 (Additional file 3: Table S18). In the comparison between C2 and C3, 230 DEGs upregulated in cluster-3 were encoded by the genomic region with a higher frequency of deletions in cluster-2 or copy number gains in cluster-3; 17 DEGs upregulated in cluster-2 were encoded by the genomic region with a higher frequency of deletions in cluster-3 or copy number gains in cluster-2 (Additional file 3: Table S18).

Fig. 6
figure 6

The differences in epigenetic regulation in different clusters. A Comparison of the copy number alterations among different clusters. Gistic scores were assessed by GISTIC 2.0 with red for amplification and blue for deletion. B In the comparison between cluster-1 and cluster-2, venn diagram summarizes the DEmiRNA-mRNA links predicted by miRtarbase, miRDB and Targetscan databases. C In the comparison between cluster-3 and cluster-2, venn diagram summarizes the DEmiRNA-mRNA links predicted by miRtarbase, miRDB and Targetscan databases

To assess the impact of DNA methylation among different clusters, DNA methylation data (Illumina Human Methylation 450 k) were analyzed. In the comparison between C2 and C1, 7 probes with higher beta values in cluster-1 were located in the proximal promoter of DEGs upregulated in cluster-2, while 10 probes with higher beta values in cluster-2 were located in the proximal promoter of DEGs upregulated in cluster-1. In the comparison between C2 and C3, 1 probe with higher beta values in cluster-3 were located in the proximal promoter of DEGs upregulated in cluster-2, while 17 probes with higher beta values in cluster-2 were located in the proximal promoter of DEGs upregulated in cluster-3 (Additional file 3: Table S19).

Next, we identified differentially expressed miRNA between C2 and C1 or between C2 and C3. In the comparison between C2 and C1, 16 miRNA were upregulated in C1 and 52 miRNA were upregulated in C2; In the comparison between C2 and C3, 54 miRNA were upregulated in C3 and 9 miRNA were upregulated in C2. We examined the reliable links between DEmiRNAs and DEGs based on three databases (miRDB, miRtarbase, Targetscan, prediction in at least two databases was considered reliable). In the comparison between C2 and C1, DEmiRNAs upregulated in cluster-1 target 3 DEGs in cluster-2, and DEmiRNAs upregulated in cluster-2 target 314 DEGs in cluster-1 (Fig. 6B, Additional file 3: Table S20). In the comparison between C2 and C3, DEmiRNAs upregulated in cluster-3 target 7 DEGs in cluster-2, and DEmiRNAs upregulated in cluster-2 target 14 DEGs in cluster-3 (Fig. 6C, Additional file 3: Table S20).

Key DEGs affected by genetic and epigenetic regulation

We assumed that DEGs affected by different genetic and epigenetic regulation might play an important role in the transformation of the phenotype. Genes identified in at least two out of three above analyses (SCNA, DNA methylation, and miRNA) were considered key DEGs. In the comparison between C2 and C1, 84 key DEGs were identified (80 key DEGs upregulated in cluster-1 and 4 key DEGs upregulated in cluster-2, Additional file 1: Figure S9A). The PPI network was constructed based on the 84 key DEGs using the STRING database, and the result highlighted HSPA8, CREB1, RAP1A as the key nodes within the network (Additional file 1: Figure S9C). In the comparison between C2 and C3, 5 key DEGs were identified (including GRM2, TBXA2R, PLEC, LUZP1, RELA, all 5 key DEGs were upregulated in cluster-3, Additional file 1: Figure S9B). These results indicated that HSPA8, CREB1, RAP1A might be the potential therapeutic targets for patients in cluster-1. GRM2, TBXA2R, PLEC, LUZP1, RELA might be associated with poor prognosis in cluster-3.

Identification of potential drugs for patients in different clusters

After preprocessing, drug sensitivity profiles of 1291 compounds in PRISM dataset and 354 compounds in CTRP were used for subsequent analysis. The drug sensitivity of entire clinical samples was predicted based on a ridge regression model (“pRRophetic” package in R), and we obtained the AUC value of each compound in each sample (lower AUC values indicate increased sensitivity to specific compounds). We assessed the compounds with higher sensitivity in cluster-1, cluster-2, cluster-3 in turn, and these analyses were conducted using CTRP and PRISM data, respectively.

Compounds with lower AUC values in specific clusters were identified (Log2FC > 0.07, p value < 0.05, Fig. 7, Additional file 3: Table S21). For cluster-1 (Fig. 7A), 5 PRISM-derived compounds (including RITA, 12-O-tetradecanoylphorbol-13-acetate, Ro-4987655, idasanutlin, PD-0325901) and 1 CTRP-derived compounds (including austocystin D) were identified; For cluster-2 (Fig. 7B), 1 PRISM-derived compounds (including gemcitabine) and 10 CTRP-derived compounds (including paclitaxel, CR-1-31B, GSK461364, BI-2536, vincristine, oligomycin A, ouabain, KX2-391, SR-II-138A, daporinad) were identified; For cluster-3 (Fig. 7C), 1 PRISM-derived compounds (including vindesine) and 1 CTRP-derived compounds (including dasatinib) were identified. All compounds identified had lower AUC values in a specific cluster as compared to the other clusters. These compounds might hold therapeutic potential in patients with advanced NSCLC of different clusters.

Fig. 7
figure 7

Identification of potential agents in each cluster. Differential drug response analysis of compounds identified in cluster-1 (A), cluster-2 (B), and cluster-3 (C). Note that higher estimated AUC values imply lower drug sensitivity. ***, P < 0.001; **, P < 0.01; *, P < 0.05

Discussion

Despite substantial advances having been made in the treatment of lung cancer within the past few decades, the therapeutic outcome of advanced NSCLC remains far from satisfactory [52]. In this study, various machine learning algorithms and bioinformatic analysis were conducted to depict landscapes of patients with advanced NSCLC. The landscape of cancer research and treatment is gradually changing with the pervading of AI (Artificial Intelligence). The frontier of cancer research involves collaborations between medical oncologists and computer scientists. Specifically, with the application of ML (machine learning), DL (deep learning), and multiple neural networks, many issues have been addressed, especially the diagnosis and prognosis prediction of cancer [53, 54]. In recent years, AI has provided a new approach for the diagnosis and prognosis of cancer and made cancer prediction performance reach a new height [54]. According to Ahmed et al. [53], the use of AI on oral oncology is in the nascent stage, and research such as digital histopathologic images is very few, indicating that we should focus on cancer at more levels. In our study, we focused on the NSCLC patients at an advanced stage. When applying ML, we used multiple methods (e.g. four MLs and a neural network were applied in the construction of the classifiers) and data from different sources (e.g. drug sensitivity data from CTRP and PRISM databases) to maximize the reliability. When we measured the performance of results, multiple indicators (e.g. accuracy, recall, precision, F1 score, and AUC were used to measure the performance of the classifier) and horizontal comparison (e.g. the PPS model was compared with the prediction model proposed by previous studies) were used to ensure the accuracy of the analysis. In general, our study was not just a “Training-Validation” pattern. We attempted to explore the issue from multi-method, multi-angle, and multi-measure.We acknowledged that the advanced NSCLC patients could be classified into three clusters, and each cluster has its characteristics: cluster-1 was characterized by increases in the infiltration of resting DCs, M2.marcrophages, activated mast cells, monocytes, activated NKs, and resting CD4 + T memory cells; cluster-2 was characterized by evident increase in the infiltration of plasma, M1.macrophages, activated CD4 + T memory cells, CD8 + T cells, T follicular helper cells, and Tregs; cluster-3 was characterized by high infiltration of M0.macrophages and resting mast cells and exhibited decreases in other TIICs. Different classifiers were then designed to distinguish different clusters based on various machine learning algorithms (including RF, SVM, Xgboost, and Adaboost), and RF/Adaboost were considered as highly efficient classifiers with the best performance. These analyses not only simplified the basis for the classification but ensured the accuracy of the classifier. CD8 + T cells and Macrophages were identified to play a major role in the classification. In other words, CD8 + T cells and Macrophages are the key TIICs to alter immune phenotypes in advanced NSCLC, which is in agreement with previous researches [55, 56]. In addition, cluster-2 was found to be correlated with better overall survival outcome and might have a better clinical response to immunotherapy.

We then constructed the Poor Prognosis Signature based on the immune-related parameters, and we found out that the PPS score had survival prediction efficacy in patients treated with anti-PD(L)1 immunotherapy. Similar, similar prediction models have been proposed in previous studies. But the actual use of them might lead to fallacies since almost none of them were constructed based on the advanced tumor stage. The benchmarking results showed that our poor prognosis signature had the best prediction performance. To find out the key molecules in the differences between cluster-2 and cluster-1 or between cluster-2 and cluster-3, we turned to explore the genetic or epigenetic alterations among different clusters. The results unraveled that three key nodes (including HSPA8, CREB1, RAP1A) showed noteworthy differences between cluster-1 and cluster-2. Similarly, we found five molecules (including GRM2, TBXA2R, PLEC, LUZP1, RELA) that might be associated with poor prognosis in cluster-3. In previous studies, HSPA8 and RAP1A have been demonstrated to be associated with cancer growth and proliferation in various human cancers [57, 58]. CREB1 was considered to promote invasion and migration in human cancers, including NSCLC [59, 60]. In our analysis, we came to the point that these three molecules might serve as potential therapeutic targets for patients in cluster-1.

Finally, based on drug sensitivity data derived from CTRP and PRISM, we identified several compounds which might serve as medication for different clusters of patients with advanced NSCLC. Specifically, six compounds for cluster-1 (RITA, 12-O-tetradecanoylphorbol-13-acetate, Ro-4987655, idasanutlin, PD-0325901, austocystin D), 11 compounds for cluster-2 (gemcitabine, paclitaxel, CR-1-31B, GSK461364, BI-2536, vincristine, oligomycin A, ouabain, KX2-391, SR-II-138A, daporinad), and 2 compounds for cluster-3 (vindesine, dasatinib). These results gave us some clues. For example, MAP2K1 inhibitors (including PD-0325901 and RO-4987655) showed their capacity of improving PFS and OS of patients with solid tumors as well as the major treatment-related toxicity [61, 62]. Our study further unraveled that PD-0325901 or RO-4987655 might be more applicable to cluster-1 patients with advanced NSCLC. Common antitumor drugs, including gemcitabine and paclitaxel [63,64,65], might apply to cluster-2, and dasatinib might be more applicable to patients in cluster-3.

However, there are still shortcomings and a lot of room for improvement in our study. A limitation of the study is the small sample size. In our study, the sample size of the main cohort (TCGA cohort, N = 195) was small. However, there are not much data of advanced NSCLC available in the public database. Thus, we could only verify the PPS model and the performance of the classifier using small sample data. On the other hand, the epigenetic-related analysis could only be conducted in the main cohort due to the lack of relevant data in the other cohorts, which might cause a certain degree of bias. In future studies, we will take account of these factors to enhance our study. In addition, we only selected the most prominent shift in mutation or drug sensitivity for further analysis, which could cause a certain bias.

Conclusions

In conclusion, our study established new stratification of stage 3–stage 4 NSCLC, simplified the classifications, built an immune-related poor prognosis signature, analyzed the key therapeutic targets in cluster1/3, and explored the potential drug for patients in each cluster. With the promotion of the precision medicine concept, our study could provide more convenience for diagnosis and treatment for patients with advanced NSCLC. There are also some limitations to this study. The verification of the conclusions needs to be determined in related clinical trials in the future.

Availability of data and materials

TCGA dataset was downloaded from Xena (https://xenabrowser.net/datapages/,TCGA-LUAD, TCGA-LUSC). Multi-omics data was downloaded from data portal (https://portal.gdc.cancer.gov/,TCGA-LUAD, TCGA-LUSC). Other transcriptome datasets including anti-PD(L)1 treatment cohorts were downloaded from Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/,accession number: GSE37745, GSE29013, GSE42127, GSE41271, GSE135222, GSE126044) or Imvigor210 (http://research-pub.gene.com/IMvigor210CoreBiologies/. All data in this cohort was integrated into the R package “IMvigor210CoreBiologies”). Immune-related genes were obtained from InnateDB (https://www.innatedb.com) and Immport (https://www.immport.org/). Drug sensitivity data was obtained from PRISM (https://depmap.org/portal/prism/) and CTRP (https://portals.broadinstitute.org/ctrp). Our classifiers have been uploaded to Github, and can be found at (https://github.com/LClungcancer/nsclc-2021_classifier).

Abbreviations

NSCLC:

Non-small cell lung cancer

LUAD:

Lung adenocarcinoma

LUSC:

Lung squamous cell carcinoma

ICI:

Immune checkpoint inhibitors

MSI:

Microsatellite instability

CTLA-4:

Cytotoxic T-lymphocyte antigen-4

PD-1:

Programmed death 1

PD-L1:

Programmed cell death ligand 1

LM22:

22 Immune cell types

SVM:

Support vector machine

RF:

Randomforest

Xgboost:

EXtreme gradient boosting

Adaboost:

Adaptive boosting

TMB:

Tumor mutation burden

TIICs:

Tumor-infiltrating immune cells

MHC:

Major histocompatibility complex class

LASSO:

Least absolute shrinkage and selection operator

CR:

Complete response

PR:

Partial response

SD:

Stable disease

PD:

Progressive disease

ROC:

Receiver operating characteristic curve

AUC:

The area under curve

References

  1. Duma N, Santana-Davila R, Molina JR. Non-small cell lung cancer: epidemiology, screening, diagnosis, and treatment. Mayo Clin Proc. 2019;94(8):1623–40. https://doi.org/10.1016/j.mayocp.2019.01.013.

    CAS  Article  PubMed  Google Scholar 

  2. Leonetti A, Wever B, Mazzaschi G, Assaraf YG, Rolfo C, Quaini F, et al. Molecular basis and rationale for combining immune checkpoint inhibitors with chemotherapy in non-small cell lung cancer. Drug Resist. 2019;46:100644. https://doi.org/10.1016/j.drup.2019.100644.

    Article  Google Scholar 

  3. Reck M, Rabe KF. Precision diagnosis and treatment for advanced non-small-cell lung cancer. N Engl J Med. 2017;377(9):849–61. https://doi.org/10.1056/NEJMra1703413.

    CAS  Article  PubMed  Google Scholar 

  4. Zappa C, Mousa SA. Non-small cell lung cancer: current treatment and future advances. Transl Lung Cancer Res. 2016;5(3):288–300. https://doi.org/10.21037/tlcr.2016.06.07.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Mariniello A, Novello S, Scagliotti GV, Ramalingam SS. Double immune checkpoint blockade in advanced NSCLC. Crit Rev Oncol Hematol. 2020;152:102980. https://doi.org/10.1016/j.critrevonc.2020.102980.

    Article  PubMed  Google Scholar 

  6. Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer. 2019;19(3):133–50. https://doi.org/10.1038/s41568-019-0116-x.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. Shroff GS, de Groot PM, Papadimitrakopoulou VA, Truong MT, Carter BW. Targeted therapy and immunotherapy in the treatment of non-small cell lung cancer. Radiol Clin North Am. 2018;56(3):485–95. https://doi.org/10.1016/j.rcl.2018.01.012.

    Article  PubMed  Google Scholar 

  8. Manegold C, Dingemans AC, Gray JE, Nakagawa K, Nicolson M, Peters S, et al. The potential of combined immunotherapy and antiangiogenesis for the synergistic treatment of advanced NSCLC. J Thorac Oncol. 2017;12(2):194–207. https://doi.org/10.1016/j.jtho.2016.10.003.

    Article  PubMed  Google Scholar 

  9. Qu J, Wang L, Jiang M, Zhao D, Wang Y, Zhang F, et al. A review about pembrolizumab in first-line treatment of advanced NSCLC: focus on KEYNOTE studies. Cancer Manag Res. 2020;12:6493–509. https://doi.org/10.2147/CMAR.S257188.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Li T, Kung HJ, Mack PC, Gandara DR. Genotyping and genomic profiling of non-small-cell lung cancer: implications for current and future therapies. J Clin Oncol. 2013;31(8):1039–49. https://doi.org/10.1200/JCO.2012.45.3753.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Zhang C, Zhang Z, Zhang G, Zhang Z, Luo Y, Wang F, et al. Clinical significance and inflammatory landscapes of a novel recurrence-associated immune signature in early-stage lung adenocarcinoma. Cancer Lett. 2020;479:31–41. https://doi.org/10.1016/j.canlet.2020.03.016.

    CAS  Article  PubMed  Google Scholar 

  12. Chen Z, Yang X, Bi G, Liang J, Hu Z, Zhao M, et al. Ligand-receptor interaction atlas within and between tumor cells and T cells in lung adenocarcinoma. Int J Biol Sci. 2020;16(12):2205–19. https://doi.org/10.7150/ijbs.42080.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. Avila Cobos F, Alquicira-Hernandez J, Powell JE, Mestdagh P, De Preter K. Benchmarking of cell type deconvolution pipelines for transcriptomics data. Nat Commun. 2020;11(1):5650. https://doi.org/10.1038/s41467-020-19015-1.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Yang C, Huang X, Li Y, Chen J, Lv Y, Dai S. Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology. Brief Bioinform. 2020. https://doi.org/10.1093/bib/bbaa164.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–3. https://doi.org/10.1093/bioinformatics/bts034.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7. https://doi.org/10.1038/nmeth.3337.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108–10. https://doi.org/10.1158/0008-5472.CAN-17-0307.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017;18(1):220. https://doi.org/10.1186/s13059-017-1349-1.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Racle J, de Jonge K, Baumgaertner P, Speiser DE, Gfeller D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. Elife. 2017. https://doi.org/10.7554/eLife.26476.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Becht E, Giraldo NA, Lacroix L, Buttard B, Elarouci N, Petitprez F, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17(1):218. https://doi.org/10.1186/s13059-016-1070-5.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612. https://doi.org/10.1038/ncomms3612.

    CAS  Article  PubMed  Google Scholar 

  22. Hu J, Yu A, Othmane B, Qiu D, Li H, Li C, et al. Siglec15 shapes a non-inflamed tumor microenvironment and predicts the molecular subtype in bladder cancer. Theranostics. 2021;11(7):3089–108. https://doi.org/10.7150/thno.53649.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Garcia-Mulero S, Alonso MH, Pardo J, Santos C, Sanjuan X, Salazar R, et al. Lung metastases share common immune features regardless of primary tumor origin. J Immunother Cancer. 2020. https://doi.org/10.1136/jitc-2019-000491.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Auslander N, Zhang G, Lee JS, Frederick DT, Miao B, Moll T, et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat Med. 2018;24(10):1545–9. https://doi.org/10.1038/s41591-018-0157-9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Xiong D, Wang Y, You M. A gene expression signature of TREM2(hi) macrophages and gammadelta T cells predicts immunotherapy response. Nat Commun. 2020;11(1):5084. https://doi.org/10.1038/s41467-020-18546-x.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Messina JL, Fenstermacher DA, Eschrich S, Qu X, Berglund AE, Lloyd MC, et al. 12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy? Sci Rep. 2012;2:765. https://doi.org/10.1038/srep00765.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, et al. IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. 2017;127(8):2930–40. https://doi.org/10.1172/JCI91190.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zhang X, Shi M, Chen T, Zhang B. Characterization of the immune cell infiltration landscape in head and neck squamous cell carcinoma to aid immunotherapy. Mol Ther Nucleic Acids. 2020;22:298–309. https://doi.org/10.1016/j.omtn.2020.08.030.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Zeng D, Li M, Zhou R, Zhang J, Sun H, Shi M, et al. Tumor microenvironment characterization in gastric cancer identifies prognostic and immunotherapeutically relevant gene signatures. Cancer Immunol Res. 2019;7(5):737–50. https://doi.org/10.1158/2326-6066.CIR-18-0436.

    CAS  Article  PubMed  Google Scholar 

  30. Li G, Xu W, Zhang L, Liu T, Jin G, Song J, et al. Development and validation of a CIMP-associated prognostic model for hepatocellular carcinoma. EBioMedicine. 2019;47:128–41. https://doi.org/10.1016/j.ebiom.2019.08.064.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genom Proteom. 2018;15(1):41–51. https://doi.org/10.21873/cgp.20063.

    CAS  Article  Google Scholar 

  32. Guo L, Wang Z, Du Y, Mao J, Zhang J, Yu Z, et al. Random-forest algorithm based biomarkers in predicting prognosis in the patients with hepatocellular carcinoma. Cancer Cell Int. 2020;20:251. https://doi.org/10.1186/s12935-020-01274-z.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Ogunleye A, Wang QG. XGBoost model for chronic kidney disease diagnosis. IEEE/ACM Trans Comput Biol Bioinform. 2020;17(6):2131–40. https://doi.org/10.1109/TCBB.2019.2911071.

    Article  PubMed  Google Scholar 

  34. Zhong J, Sun Y, Peng W, Xie M, Yang J, Tang X. XGBFEMF: an XGBoost-based framework for essential protein prediction. IEEE Trans Nanobiosci. 2018;17(3):243–50. https://doi.org/10.1109/TNB.2018.2842219.

    Article  Google Scholar 

  35. Lu H, Gao H, Ye M, Wang X. A hybrid ensemble algorithm combining AdaBoost and genetic algorithm for cancer classification with gene expression data. IEEE/ACM Trans Comput Biol Bioinform. 2019. https://doi.org/10.1109/TCBB.2019.2952102.

    Article  PubMed  Google Scholar 

  36. Rees MG, Seashore-Ludlow B, Cheah JH, Adams DJ, Price EV, Gill S, et al. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat Chem Biol. 2016;12(2):109–16. https://doi.org/10.1038/nchembio.1986.

    CAS  Article  PubMed  Google Scholar 

  37. Corsello SM, Nagari RT, Spangler RD, Rossen J, Kocak M, Bryan JG, et al. Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling. Nat Cancer. 2020;1(2):235–48. https://doi.org/10.1038/s43018-019-0018-6.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Wang S, He Z, Wang X, Li H, Liu XS. Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction. Elife. 2019. https://doi.org/10.7554/eLife.49020.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Pecina-Slaus N, Kafka A, Gotovac Jercic K, Logara M, Bukovac A, Bakaric R, et al. Comparable genomic copy number aberrations differ across astrocytoma malignancy grades. Int J Mol Sci. 2019. https://doi.org/10.3390/ijms20051251.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Tian Y, Morris TJ, Webster AP, Yang Z, Beck S, Feber A, et al. ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics. 2017;33(24):3982–4. https://doi.org/10.1093/bioinformatics/btx513.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. Wong N, Wang X. miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic Acids Res. 2015;43:D146–52. https://doi.org/10.1093/nar/gku1104.

    CAS  Article  PubMed  Google Scholar 

  42. Hsu SD, Lin FM, Wu WY, Liang C, Huang WC, Chan WL, et al. miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res. 2011;39:D163–9. https://doi.org/10.1093/nar/gkq1107.

    CAS  Article  PubMed  Google Scholar 

  43. Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015. https://doi.org/10.7554/eLife.05005.

    Article  PubMed  PubMed Central  Google Scholar 

  44. von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M, et al. STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res. 2005;33:D433–7. https://doi.org/10.1093/nar/gki005.

    CAS  Article  Google Scholar 

  45. Yao J, Li R, Liu X, Zhou X, Li J, Liu T, et al. Prognostic implication of glycolysis related gene signature in non-small cell lung cancer. J Cancer. 2021;12(3):885–98. https://doi.org/10.7150/jca.50274.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. Wang H, Wang MS, Wang Y, Huang YQ, Shi JP, Ding ZL, et al. Prognostic value of immune related genes in lung adenocarcinoma. Oncol Lett. 2020;20(5):259. https://doi.org/10.3892/ol.2020.12122.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. Hou J, Zhong Q. A novel immunogenomic prognostic signature in lung squamous carcinoma. Medicine (Baltimore). 2021;100(2): e24073. https://doi.org/10.1097/MD.0000000000024073.

    CAS  Article  Google Scholar 

  48. Li J, Li X, Zhang C, Zhang C, Wang H. A signature of tumor immune microenvironment genes associated with the prognosis of nonsmall cell lung cancer. Oncol Rep. 2020;43(3):795–806. https://doi.org/10.3892/or.2020.7464.

    CAS  Article  PubMed  Google Scholar 

  49. Zhu J, Wang M, Hu D. Identification of prognostic immune-related genes by integrating mRNA expression and methylation in lung adenocarcinoma. Int J Genom. 2020;2020:9548632. https://doi.org/10.1155/2020/9548632.

    CAS  Article  Google Scholar 

  50. Fu D, Zhang B, Yang L, Huang S, Xin W. Development of an immune-related risk signature for predicting prognosis in lung squamous cell carcinoma. Front Genet. 2020;11:978. https://doi.org/10.3389/fgene.2020.00978.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. Biton J, Mansuet-Lupo A, Pecuchet N, Alifano M, Ouakrim H, Arrondeau J, et al. TP53, STK11, and EGFR mutations predict tumor immune profile and the response to Anti-PD-1 in lung adenocarcinoma. Clin Cancer Res. 2018;24(22):5710–23. https://doi.org/10.1158/1078-0432.CCR-18-0163.

    CAS  Article  PubMed  Google Scholar 

  52. Rocco D, Della Gravara L, Battiloro C, Gridelli C. The role of combination chemo-immunotherapy in advanced non-small cell lung cancer. Expert Rev Anticancer Ther. 2019;19(7):561–8. https://doi.org/10.1080/14737140.2019.1631800.

    CAS  Article  PubMed  Google Scholar 

  53. Sultan AS, Elgharib MA, Tavares T, Jessri M, Basile JR. The use of artificial intelligence, machine learning and deep learning in oncologic histopathology. J Oral Pathol Med. 2020;49(9):849–56. https://doi.org/10.1111/jop.13042.

    Article  PubMed  Google Scholar 

  54. Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett. 2020;471:61–71. https://doi.org/10.1016/j.canlet.2019.12.007.

    CAS  Article  PubMed  Google Scholar 

  55. Zeng D, Ye Z, Wu J, Zhou R, Fan X, Wang G, et al. Macrophage correlates with immunophenotype and predicts anti-PD-L1 response of urothelial cancer. Theranostics. 2020;10(15):7002–14. https://doi.org/10.7150/thno.46176.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. Garrido-Martin EM, Mellows TWP, Clarke J, Ganesan AP, Wood O, Cazaly A, et al. M1(hot) tumor-associated macrophages boost tissue-resident memory T cells infiltration and survival in human lung cancer. J Immunother Cancer. 2020. https://doi.org/10.1136/jitc-2020-000778.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Shan N, Zhou W, Zhang S, Zhang Y. Identification of HSPA8 as a candidate biomarker for endometrial carcinoma by using iTRAQ-based proteomic analysis. Onco Targets Ther. 2016;9:2169–79. https://doi.org/10.2147/OTT.S97983.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. Yao R, Xu L, Wei B, Qian Z, Wang J, Hui H, et al. miR-142-5p regulates pancreatic cancer cell proliferation and apoptosis by regulation of RAP1A. Pathol Res Pract. 2019;215(6): 152416. https://doi.org/10.1016/j.prp.2019.04.008.

    CAS  Article  PubMed  Google Scholar 

  59. Rao M, Zhu Y, Cong X, Li Q. Knockdown of CREB1 inhibits tumor growth of human gastric cancer in vitro and in vivo. Oncol Rep. 2017;37(6):3361–8. https://doi.org/10.3892/or.2017.5636.

    CAS  Article  PubMed  Google Scholar 

  60. Cho JH, Hong WG, Jung YJ, Lee J, Lee E, Hwang SG, et al. Gamma-Ionizing radiation-induced activation of the EGFR-p38/ERK-STAT3/CREB-1-EMT pathway promotes the migration/invasion of non-small cell lung cancer cells and is inhibited by podophyllotoxin acetate. Tumour Biol. 2016;37(6):7315–25. https://doi.org/10.1007/s13277-015-4548-y.

    CAS  Article  PubMed  Google Scholar 

  61. van Geel R, van Brummelen EMJ, Eskens F, Huijberts S, de Vos F, Lolkema M, et al. Phase 1 study of the pan-HER inhibitor dacomitinib plus the MEK1/2 inhibitor PD-0325901 in patients with KRAS-mutation-positive colorectal, non-small-cell lung and pancreatic cancer. Br J Cancer. 2020;122(8):1166–74. https://doi.org/10.1038/s41416-020-0776-z.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. Nakamichi S, Nokihara H, Yamamoto N, Yamada Y, Fujiwara Y, Tamura Y, et al. Phase I and pharmacokinetics/pharmacodynamics study of the MEK inhibitor RO4987655 in Japanese patients with advanced solid tumors. Invest New Drugs. 2015;33(3):641–51. https://doi.org/10.1007/s10637-015-0229-3.

    CAS  Article  PubMed  Google Scholar 

  63. Lee Y, Joo J, Lee YJ, Lee EK, Park S, Kim TS, et al. Randomized phase II study of platinum-based chemotherapy plus controlled diet with or without metformin in patients with advanced non-small cell lung cancer. Lung Cancer. 2021;151:8–15. https://doi.org/10.1016/j.lungcan.2020.11.011.

    Article  PubMed  Google Scholar 

  64. Tamiya M, Tamiya A, Suzuki H, Taniguchi Y, Katayama K, Minomo S, et al. Phase 2 study of bevacizumab plus carboplatin/nab-paclitaxel followed by bevacizumab plus nab-paclitaxel for non-squamous non-small cell lung cancer with malignant pleural effusion. Invest New Drugs. 2021. https://doi.org/10.1007/s10637-021-01076-8.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Redin E, Garmendia I, Lozano T, Serrano D, Senent Y, Redrado M, et al. SRC family kinase (SFK) inhibitor dasatinib improves the antitumor activity of anti-PD-1 in NSCLC models by inhibiting Treg cell conversion and proliferation. J Immunother Cancer. 2021. https://doi.org/10.1136/jitc-2020-001496.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank Jimmy for helpful conversations.

Funding

This work was supported by the National Key R&D Program of China (2016YFC1303800) and the National Natural Science Foundation of China (81773056).

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Authors

Contributions

CL: Conceptualization, Methodology, Writing—original draft, Writing—review & editing. CT: Methodology, Software, Data curation, Writing—original draft. JL: Validation, Formal analysis. YZ: Investigation, Validation. FG: Software, Visualization. YH: Investigation, Resources. QY: Conceptualization, Supervision. LL: Project administration, Funding acquisition. All authors read and approved the final manuscript.

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Correspondence to Yue Hu or Li Liu.

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Additional file 1

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Additional file 2

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Additional file 3

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Li, C., Tian, C., Zeng, Y. et al. Machine learning and bioinformatics analysis revealed classification and potential treatment strategy in stage 3–4 NSCLC patients. BMC Med Genomics 15, 33 (2022). https://doi.org/10.1186/s12920-022-01184-1

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Keywords

  • Immunophenotypes
  • Machine learning
  • Signature
  • Multiomics
  • Cancer immunotherapy
  • Drug sensitivity
  • Treatment strategy