Machine learning constructs a T cell-related signature for predicting prognosis and drug sensitivity in ovarian cancer

Materials and Methods

Datasets

Work flow was shown in Figure 1. The single cell expression data of ovarian cancer was obtained from GSE147082 (n = 6) dataset. Ovarian cancer related bulk transcriptomics data (FKPM) were downloaded from TCGA (n = 374) database. GSE14764 (Platforms: GPL96, n = 80), GSE26193 (Platforms: GPL570, n = 107), GSE26712 (Platforms: GPL96, n = 185) and GSE140082 (Platforms: GPL14951, n = 380) datasets were used for verifying the TRS. Inclusion criteria for selecting ovarian cancer cases should be: (1) diagnosed with ovarian cancer using histological method; (2) complete data about overall survival. While metastatic ovarian cancer and ovarian cancer along with other types of cancer should be excluded in the study. IMvigor210 and GSE91061 were used as validation cohort for predicting immunotherapy benefit.

Figure 1. Workflow of the current study.

Single-cell RNA-seq analysis

The procession of single cell expression dataset GSE147082 relied on “Seurat” R package [14]. Those genes that detected in less than 3 cells and cells with less than 50 detected gene numbers were ruled out. The mitochondria proportion was set as 5%. In order to normalize the expression data, we then performed principal component analysis. We then conducted unsupervised clustering analysis with UMAP methods [15]. In order to generate the marker genes of each cluster, we utilized “FindAllMarkers” function of “Seurat” R package and the threshold of the minimum cell population fraction in either of the two populations was set as 0.25. In order to identify the cell type of each cluster, we performed cell annotation analysis with “SingleR” package [16].

Integrative machine learning algorithms constructed a prognostic T cells-related signature

The screening of the prognostic markers in ovarian cancer in TCGA dataset relied on univariate cox regression analysis (p < 0.05). The development of prognostic TRS relied on 10 integrative machine learning algorithms in ovarian cancer. The model with the highest average C-index in all datasets was suggested as the best model. Previous investigations have used similar machine learning algorithms [17–21]. After determining the optimal prognostic TRS, we could obtain the genes in the optimal prognostic TRS and the coefficient. On the basis of gene expressions and coefficient, we could calculate risk score of each ovarian cancer patients (risk score = the sum of the coefficient × gene expression).

Evaluation of the performance of TRS

The generation of overall survival curve relied on “survival” package. Time ROC analysis was conducted to assess the performance of TRS in evaluating the prognosis of ovarian cancer with “timeROC” package. The C-indexes of TRS and stage and grade were calculated with “rms” package. Moreover, we also collected 25 ovarian cancer-related mRNA and lncRNA-related models randomly, and calculated their C-indexes. Considering clinical characters and TRS, univariate and multivariate cox analysis was conducted to identify the risk factors for the clinical outcome of ovarian cancer. We then constructed a predicting nomogram via “nomogramEx” R package on the basis of TRS and clinical characters in ovarian cancer.

Correlation between risk score and immune microenvironment and genetic mutation

The Immune score and ESTIMATE score of each ovarian cancer patient were determined by ESTIMATE algorithm [22]. A total of 7 immune algorithms, including CIBERSORT were used to evaluate the relative proportions of infiltrating immune cells in ovarian cancer. The abundance of immune cells and the score of immune-related activities or functions were relied on single sample gene set enrichment analysis using “GSVA” package. The genetic landscape was drawn with “maftools” package. Moreover, GSEA was performed to identify the biological functions linked to KEGG pathways TRS based high and low-risk groups.

Evaluation of the performance of TRS in predicting the drug sensitivity

From The Cancer Immunome Atlas (https://tcia.at/home), we generated the immunophenoscore (IPS) of ovarian cancer cases. The TIDE score of ovarian cancer cases were evaluated by TIDE methods. Drug sensitivity data were downloaded from Genomics of Drug Sensitivity in Cancer, with which we determined the half maximal inhibitory concentration (IC50) value in each ovarian cancer case using “oncoPredict” package.

Availability of data and materials

The analyzed data sets generated during the study were sourced from the TCGA database (https://portal.gdc.cancer.gov/repository) and GEO database (https://www.ncbi.nlm.nih.gov/geo).

Results

Single-cell analysis revealed cell subtypes and T cell related markers

A strong positive correlation (Cor = 0.89) was obtained between the number of genes and the sequencing depth after sample normalization (Figure 2A). We obtained 9885 high-quality cell samples from 6 ovarian cancer tissues after stringent quality control metrics (Figure 2B). These cell samples could be clustered into 17 clusters based on UMAP analysis (Figure 2C). Cell annotating performed with SingleR technique identified 10 types of cells, including T cells, B cells, =, Endothelial cells, Neuroepithelial cells, and Neurons etc. (Figure 2D). And 286 T cell-related markers were obtained (Supplementary Table 1).

Figure 2. Identification of T cell-related markers in ovarian cancer. (A) Post quality control filtering of each sequenced cell. (B) Association analysis between nFeature and nCount. (C) A total of 17 clusters of all samples were identified after UMAP analysis. (D) A total of 10 subtypes of cells were identified based on SingleR annotation methods.

Machine learning algorithms based prognostic TRS

Based on 286 T cell-related markers, we performed univariate cox analysis and identified 26 potential prognostic biomarkers (Supplementary Table 2). These 26 potential prognostic biomarkers were submitted into integrative procedure including 10 machine learning methods, which could conduct a prognostic TRS. Figure 3A showed the C-index of 101 kinds of prediction models in all datasets. The plsRcox method-based model was referred as the best model and it had a highest average C-index being 0.60 (Figure 3A). And a final set of 26 T cell-related genes were used to construct the TRS (Supplementary Table 2). Classification of high and low-risk groups relied on the medium value of risk score was the cutoff. As expected, in TCGA cohort, high-risk score indicated a poor OS rate in ovarian cancer with an AUCs of 1-, 3-, and 5-year ROC curve being 0.616, 0.711 and 0.745, respectively (Figure 3B, p < 0.001). In testing cohort, the data indicated a poor clinical outcome in high-risk score group in GSE14764, GSE26193, GSE26712 and GSE140082 cohort (Figure 3C–3F, all p < 0.05). ROC analysis measured the discrimination of TRS, with 1-, 3-, and 5-year AUCs of 0.444, 0.759, and 0.681 in GSE14764 dataset; 0.745, 0.633, and 0.649 in GSE26193 dataset; 0.568, 0.624, and 0.653 in GSE26712 dataset; 0.629, and 0.73 in GSE140082 dataset, respectively (Figure 3C–3F). The risk score, survival status, and gene expression of TRS of all cohorts could be seen in Supplementary Figure 1A–1E.

Figure 3. Prognostic T cell-related signature (TRS) developed with integrative machine learning analysis. (A) The C-index of each prognostic model constructed by 10 machine learning algorithms and 101 kinds of combinations in training and testing cohort. The survival curve and corresponding ROC curve of ovarian cancer with high and low-risk score in TCGA (B), GSE14764 (C), GSE26193 (D), GSE26172 (E) and GSE140082 (F) cohort.

Evaluation of the performance of TRS

As shown in Figure 4A, compared with grade and FIGO stage, risk score had a highest C-index in all cohort. However, there was no clinical information in GSE26712 cohort. Moreover, risk score was a risk factor for the clinical outcome of ovarian cancer in all cohorts (Figure 4B). Supplementary Table 3 showed the C-index of our TRS and 25 random prognostic models () were also calculated. And the data showed that the C- index most of these prognostic signatures was lower than the current TRS (Figure 4C). These results suggested that our TRS had a better performance in evaluating the prognosis of ovarian cancer cases. Based on TRS, stage and grade, we then developed a survival prediction nomogram (Figure 4D, 4E), with which the clinicians could evaluate the clinical outcome of ovarian cancer patients.

Figure 4. The role of T cell-related signature (TRS) in predicting the prognosis of ovarian cancer. (A) C-index evaluated the overall survival rate of ovarian cancer patients in training and testing cohort. (B) Univariate and multivariate cox regression analysis considering grade, stage and TRS in training and testing cohort. (C) C-index of TRS and other established signatures evaluated discrimination of TRS in predicting the prognosis of ovarian cancer patients. (D, E) Prediction nomogram for predicting the 1-, 3-, and 5-year OS rate of ovarian cancer.

TRS showed significant correlation with tumor microenvironment in ovarian cancer

Study had suggested six types of tumor immune landscape [22]. Most of TCGA ovarian cancer cases were IFN-g dominant(C2) type and lymphocyte depleted(C4) type ranked a higher proportion in high-risk score group compared with low-risk score group (Figure 5A, p = 0.001). Further analysis suggested a higher ESTIMATE score and Immunes core in low-risk score group (Figure 5B, all p < 0.05). Moreover, risk score had a negative correlation with the abundance of most of immune cells (Figure 5C). The result of the current study showed that low-risk score indicated a lower level of s M2/M1 proportion in TCGA and GSE140082 cohort (Figure 5D). Moreover, ovarian cancer patients with high-risk score correlated a lower level of T cells, B cells, NK cells, Th1 cells, Th2 cells and neutrophils (Figure 5E). Ovarian cancer cases with high-risk score demonstrated a lower score of immune related functions in ovarian cancer, including cytolytic activity and inflammation promoting (Figure 5F, all p < 0.05). Further analysis revealed that high-risk score indicated a lower level of most of HLA-related genes (Figure 5G).

Figure 5. Dissection of T cell-related signature (TRS)-based tumor microenvironment (TME). (A) Tumor immune landscape in ovarian cancer with high and low-risk score. (B) The TME score difference in different risk score group of ovarian cancer. (C) The correlation between TRS and immune infiltration in ovarian cancer. (D) The level of macrophages M2/M1 proportion in ovarian cancer patients with high and low-risk score in TCGA and GSE140082 cohort. The difference of the score of immune cells (E), immune-related functions (F) and HLA-related genes (G) in different risk score group of ovarian cancer. ^*p < 0.05, ^**p < 0.01, ^***p < 0.001.

TRS-based treatment strategy for ovarian cancer

Several indicators were then used to assess the functions of TRS in predicting immunotherapy benefits in ovarian cancer. As shown in Figure 6A, higher expression of many immune checkpoints was found in low-risk score group (all p < 0.05). TIDE score and T cell exclusion score could predict the immunotherapy benefits [23, 24]. Low TIDE score and T cell exclusion score indicated a better response to immunotherapy. As shown in Figure 6B, 6C, low-risk score group had a lower T cell exclusion score in ovarian cancer and TIDE score (all p < 0.05). IPS was an indicator for predicting the response to immunotherapy [25]. Low-risk score group had a increased IPS of anti-CTLA4 and anti-PD1 (Figure 6D, p < 0.05). In IMvigor210 cohort and GSE91061 cohorts, low-risk score was obtained in patients in CR/PR group with an AUC of 0.678 and 0.749 in ROC curve (Figure 6E, 6F, p < 0.001). Moreover, high-risk score indicated a poor OS rate in IMvigor210 dataset and GSE91061 dataset, with 1-, 3-, and 5-year AUCs of 0.654, 0.684, and NA; and 0.837, 0.809 and 0.852, respectively. (Figure 6G, 6H). Chemotherapy and endocrinotherapy were important therapeutic measures for ovarian cancer. We also detected the IC5O value of common drugs in high and low-risk score groups. As shown in Figure 7A–7I, the IC50 values of 5-Fluorouracil, Bortezomib, Cisplatin, Cyclophosphamide, Erlotinib, Fludarabine, Fulvestrant, Ribociclib, Topotecan were higher in high-risk score group versus low-risk score group (all p < 0.05), demonstrated that low-risk score had a better response to chemotherapy, endocrinotherapy, target therapy in ovarian cancer.

Figure 6. T cell-related signature (TRS)-based treatment strategy for ovarian cancer. The level of immune checkpoints (A), T cell exclusion score (B), TIDE score (C), immunophenoscore (D) in ovarian cancer patients with high and low-risk score. The risk score in CR/PR and SD/PD group and corresponding ROC curve in GSE91061 (E) and IMvigor210 (F) cohort. The OS curve in patients with high and low-risk score in GSE91061 (G) and IMvigor210 (H) cohort. ^*p < 0.05, ^**p < 0.01, ^***p < 0.001.

Figure 7. T cell-related signature (TRS)-based treatment strategy for ovarian cancer. The IC50 values of 5-Fluorouracil (A), Bortezomib (B), Cisplatin (C), Cyclophosphamide (D), Erlotinib (E), Fludarabine (F), Fulvestrant (G), Ribociclib (H) and Topotecan (I) in different risk score group of ovarian cancer.

TRS-based mutation landscape in ovarian cancer

The mutation landscape of ovarian cancer patients with low and high-risk scores were shown in Supplementary Figure 2A, 2B. TP53, TTN, and CSMD3 were top three most frequently mutated genes. A higher tumor mutational burden (TMB) score was seen in ovarian cancer patients with low-risk score (Supplementary Figure 2C, p = 0.0018). Moreover, negative correlation was seen between risk score and TMB score (Supplementary Figure 2D, p = 0.00055). As shown in Supplementary Figure 2E, 2F, low TMB score and high-risk score indicated a poor clinical outcome (p < 0.001).

TRS-based functional enrichment difference in ovarian cancer

The data revealed that high-risk score was mainly linked to ECM receptor interaction, focal adhesion, melanoma, pathways in cancer and ribosome (Supplementary Figure 3A). Low-risk score was mainly linked to antigen processing and presentation and type I diabetes mellitus (Supplementary Figure 3B).

TRS-based unsupervised clustering

Consensus clustering was conducted to identify unidentified subclasses in ovarian cancer. As shown in Supplementary Figure 4A, 4B, ovarian cancer patients could be well clustered into three subtypes according to the consensus CDF and delta area. Among these three clusters, cluster 1 had a best OS rate compared with cluster 2/3 in ovarian cancer (Supplementary Figure 4C, p < 0.001). Most of cluster 1 patients was correlated with low-risk while most of cluster 2 was correlated with high-risk (Supplementary Figure 4D). Further tSNE indicated significant differences of TRS gene expression among these three clusters (Supplementary Figure 4E). Moreover, these three clusters had a significant difference in TME. As shown in Supplementary Figure 4F, cluster 1 had a highest abundance of immune cells while cluster 2 had a lowest abundance. As we could see in Supplementary Figure 4G, 4H, ovarian cancer in cluster 1 had a highest level of ESTIMATE score, Immune score, and immune checkpoints (all p < 0.05).

Author Contributions

Yunzheng Zhang: Writing-Original draft preparation, Investigation, Writing-Reviewing. Lipeng Pei: Study design and Supervision, Conceptualization, Methodology. All authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflicts of interest related to this study.

Funding

This study was funded by the 2022 Liaoning Province Applied Basic Research Program (Joint Program): Mechanism and Clinical Application Research of Targeted MCM4 Therapy for Endometrial Cancer Based on Molecular Docking Technology (2022JH2/101500019).

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