Identification of immune subtypes and response prediction to immune-checkpoint inhibitors for MDM4 gain/amplification luminal A type breast cancer

doi:10.21203/rs.3.rs-2954307/v1

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Identification of immune subtypes and response prediction to immune-checkpoint inhibitors for MDM4 gain/amplification luminal A type breast cancer

https://doi.org/10.21203/rs.3.rs-2954307/v1

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Objective:

The aim of this work was to identify the consensus immune subtypes and predict the response to immune checkpoint inhibitor (ICIs) therapy for MDM4 gain/amplification luminal A type breast cancer (BC).

Materials and Methods:

Luminal A type BC expression data, copy number and corresponding clinical information were downloaded and pre-processed for subsequently analysis from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC).

Furthermore, gene set enrichment analysis (GSEA) was performed to identify transcripts functions between MDM4 gain/amplification and control samples. Subsequently, weighted gene co-expression network analysis (WGCNA) was applied to screen out gene modules related biomarkers for ICIs therapy response in luminal A type BC.

We perform an unsupervised consensus clustering in MDM4 gain/amplification luminal A type BC from TCGA BC dataset based immune-related gene signatures (IRGs) and then used luminal A type BC from METABRIC BC as validation datasets. We performed the Tumor Immune Dysfunction and Exclusion (TIDE) analysis to predict ICIs response and explore significant relationship with immune subtype.

Results:

The results from GSEA indicated that luminal A type BC with MDM4 gain/amplification were significantly enriched in immunological signature gene sets. Significantly, we also identified three gene modules significantly association with immune checkpoint, DNA damage, and immune cell infiltering in luminal A type BC.

Luminal A type BC could be categorized into two distinct immune subtypes based on the expression of IRGs. Luminal A type BC in one subtype showed high response to ICIs therapy, characterized by higher immune checkpoint genes score and CD8⁺T-cell score compared to tumors in a second subtype.

Conclusion:

Our findings demonstrated that immune subtype for MDM4 gain/amplification luminal A type BC was beyond the current luminal A/B classification and a subset of luminal A type BC patients may benefit from ICIs therapy.

Response prediction to ICIs

Immune subtype

Luminal A type BC

MDM4 gain/amplification

Bioinformatics

Data from global cancer statistics 2020[1, 2] demonstrated that breast cancer (BC) has surpassed lung cancer as the most frequently diagnosed cancer and ranks the fifth leading cause of cancer mortality worldwide. Inevitably, in despite of the development of new diagnosis and treatment strategies, BC incidence and recurrence rates are increasing year by year, and the onset age of it exhibits a younger trend globally[3, 4].

Several studies have indicated that the molecular classification represented by the Prediction Analysis of Microarray (PAM50) promotes the accurate classification of BC [5], and is more consistent with the results of immunohistochemistry subtype[6]. Of them, luminal type BC, which included luminal A and luminal B subtype, is the predominant subtype of BC and showed the greatest heterogeneity [7–9].

Frequently mutation of TP53, PIK3CA and gain/amplification of MDM2/MDM4 are one of the main hallmarks of BC[10]. Of them, MDM2/4 protein binds directly with TP53 and acts as a negative regulator of TP53. Notably, MDM2/4 gain/amplification and TP53 mutation showed closely associated with hyper-progression of ICI treatment in metastatic acral and mucosal melanoma[11].

Overall, ICIs therapy is showing great promise adjusted certain molecular subtype[12] despite BC patients with a low degree of immune cell infiltration. With currently limited data available on the effect of ICIs treatment for BC, it is very meaningful to predict ICIs therapy response through biomarkers related to ICIs[13], such as increased expression of PD-L1/PD-1[14], high tumor mutational burden (TMB) [15] and human leukocyte antigen (HLA) complex[15], and stronger immune cell infiltering[16] (especially CD8⁺)

Facilitated by high-throughput sequencing technology and corresponding bioinformatics algorithms, we have selected the MDM4 gain/amplification luminal A type BC as the research object through various screenings and focused on immune-related biological function in luminal A type BC.

On this basis, WGCNA methods was applied to identify gene modules related biomarkers for ICIs in luminal A type BC. Subsequently, we identify consensus immune subtypes based on IRGs. Furthermore, it seems that there are different immune infiltering patterns and ICIs response in consensus immune subtypes.

In summary, our study provided precision treatment reference for ICIs therapy of specific subtypes of in MDM4 gain/amplification luminal A type breast cancer.

Data collection and Data preprocessing

We collected two breast cancer datasets in the present study with complete expression profiles, copy number and PAM50 molecular subtype data from Breast Invasive Carcinoma (TCGA, PanCancer Atlas, train dataset) and Breast Cancer (METABRIC, Nature 2012 & Nat Commun 2016, validation set). The data from TCGA batch-corrected gene expression profile, curated clinical information, and unified mutation and segmented data were download from PanCanAtlas (https://gdc.cancer.gov/about-data/publications/pancanatlas). As was described by Guan et al[17], quartile normalization was applied to group sample using “normalizeBetweenArrays” of R packages limma (Version 3.48.1) and Log2(TPM+0.5) transformation was conducted for subsequent data analysis.

As for the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium), the corresponding data were processed to extract from cBioPortal for Cancer Genomics[18] (cBioPortal, http://www.cbioportal.org), and was exported in “mRNA expression z-scores relative to all samples (log microarray)” format. Specifically, genes with zero expression were excluded in all samples and all gene names were remapped to official gene symbols according to the Multi-symbol checker tools (https://www.genenames.org/tools/multi-symbol-checker/).

The stringent inclusion and exclusion criteria were developed for the current study based on the objective of the study and bias reduction as follows. We only retained the “primary breast invasive ductal carcinoma” histopathological subtype and extracted luminal A molecular subtype from “subtype” field of TCGA and “PAM50 + Claudin-low subtype” field of METABRIC.

Copy number alteration analysis of MDM4

The cBioPortal database[18] (https://www.cbioportal.org) developed by research team from Memorial Sloan-Kettering Cancer Center, was user-friendly and public available versatile online platform with exploring and visualizing multi-dimensional cancer genomics, cancer transcriptomics.

As was mentioned above, we performed the corresponding search expression patterns to filter eligible samples based on cBioPortal Onco Query Language (OQL) as follows: TP53: MUT=R175 R245 R248 R249 R273 R282; MDM2: CNA >= GAIN; MDM4: CNA >= GAIN; Moreover, the oncoprint, mutual exclusivity and co-occurrence of MDM4-related genes were analyzed accordingly. Besides, relations between the CNV of MDM4 and expression level of MDM4 were visualized by Vioplot and correlation plot with R package ggstatsplot (Version 0.8.0).

mRNA expression profile analysis of MDM4

UALCAN platform[19] (http://ualcan.path.uab.edu/index.html) designed by researchers from the University of Alabama Birmingham, aims to provide frequently updated and interactive bioinformatics platform for analyzing cancer multi-omics data. Besides, bc-GenExMiner v4.7[20] (Breast Cancer Gene-Expression Miner v4.7) online data set (http://bcgenex.centregauducheau.fr) was used to finely validate the findings above.

Gene set enrichment analysis (GSEA)

Gene set enrichment analysis (GSEA), which showed a significant improvement in the use of gene expression profile data, was performed by R package fgsea (Version 1.18.0) with parameters: minSize= 5, maxSize= 1500, nperm=15000 using MSigDB C7: immunologic signature gene sets (5219 gene sets available, V7.4), C8: cell type signature gene sets (671 gene sets available, V7.4) and CP: canonical pathways (2922 gene sets available, V7.4). The Gene-pathway data file is available for download on publicly available MSigDB website[21]: http://www.gsea-msigdb.org/gsea/msigdb/index.jsp. Gene sets with a false discovery rate (FDR) value < 0.05 was considered as significantly enriched.

Retrieving immune gene signatures of biomarkers for ICIs therapy

Increasingly the literature [22-24]suggests that response of cancer immune check-point therapy was predicted by a series of biomarkers, including PD1/PD-L1, DNA damage response related gene, major histocompatibility complex (MHC), IFN responses and so on.

Determining gene module related to biomarker of ICIs response using WGCNA algorithm

WGCNA methods [25]was performed to identify gene modules that are significantly related to biomarker of ICIs response in MDM4 gain/amplification luminal A type BC. Firstly, we pre-processed the input data, including remove the outlier sample and gene zero variance. Then, optimal soft threshold for correlation matrix, adjacency matrix and topology overlap matrix was calculated and network was constructed using the blockwiseModules function with the following parameters: networkType = "signed", TOMType = "signed", corType = "bicor", minModuleSize = 30, mergeCutHeight = 0.25, minKMEtoStay = 0.8. The heatmap of module-trait associations was generated using the "labeledHeatmap" function. The statistical computing and visualization operations are carried out with functions from the R packages WGCNA (Version 1.70-3). We extracted modules with the highest correlation of gene module related to biomarker of ICIs response.

GO and KEGG enrichment analysis

Metascape[26] (http://metascape.org/gp/index.html), was applied to perform the Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes), and transcript factor enrichment analysis for gene module from WGCNA. Enriched categories with a corrected p-value < 0.05 cutoff were considered statistically significant.

Gene Set Variation Analysis

The input expression matrix and reference gene set were integrated to calculate gene signature score by R package gene set variation analysis (GSVA, Version 1.40.1) with default parameters based on single-sample gene-set enrichment analysis (ssGSEA).

Consensus clustering for immune subtypes

Unlike other clustering algorithm represented by K-means, consensus clustering analysis does not require a priori specifying the number of clusters. Breast Invasive Carcinoma (TCGA, PanCancer Atlas, training dataset) and Breast Cancer (METABRIC, Nature 2012 & Nat Commun 2016, validation set) were grouped into several subgroups according the mRNA expression levels of immune-related gene signatures (IRGs) using consensus clustering analysis by R package ConsensusClusterPlus (Version 1.56.0), respectively. Subsequently, principal component analysis (PCA) was carried out from results of consensus clustering analysis with the R package PCAtools (Version 2.4.0).

Predicting response to ICIs therapy for immune subtypes

More recent studies[27-29]indicated that ICIs therapy have proven effective in the treatment of numerous cancers, especially melanoma[30, 31] and non-small cell lung cancer[32]. However, immunotherapy seems to be less effective in breast cancer compared with another tumor. Besides, there are fewer clinical trials of ICIs in human breast cancer. Fortunately, TIDE algorithm[33](http://tide.dfci.harvard.edu/) was computationally designed to predict ICIs response.

Statistical analysis

Most of the statistical analysis were performed using R software version 4.1.0 (R Core Team, 2021). MDM4 mRNA expression in different MDM4 copy number status was were analyzed using one-way ANOVA (Welch ANOVA in cases of unequal variance) followed by the Games-Howell post hoc test. Correlation between the MDM4 gene copy number alteration and expression level of its was performed using the Spearman's rank correlation coefficient. All the P values were for a two-side test and considered statistically significant when P <0.05.

MDM4 gene gain/amplification frequently occurred in luminal A type BC

The flow diagram of the current study was described in Fig. 1, which were mainly comprised of sample filtering and selection, function enrichment analysis, identification of immune subtype and immune landscape, and response prediction to ICIs.

Subsequently, we characterized the co-alteration of TP53 mutation (coding regions alteration) and MDM2/MDM4 gain/amplification in more detail for metastatic, primary BC tissue and cell lines, respectively. As can be seen in Fig. 2A-C, TP53 mutation is often accompanied by MDM2/MDM4 gene gain/amplification, especially MDM4. Furthermore, we noticed that mRNA expression levels increased as the copy number of MDM4 increased (Fig. 2D-2E). Significantly, MDM4 lacks intrinsic E3 ligase activity despite being structurally quite similar with MDM2. Moreover, MDM4 could prolongs MDM2 stability and promotes MDM2-dependent p53 degradation in the presence of MDM2[34]. The above findings suggested that MDM2/MDM4 plays a significant role in oncogene activity for BC in the context of TP53 mutations.

MDM4 mRNA exhibited more high expression in luminal A type BC

The UALCAN platform and bc-GenExMiner v4.7 database worked together to identify the association between MDM4 expression level and BC stage, grade, and molecular subtype in order to further clarify the cancer-promoting role of MDM4. Figure 3A showed that MDM4 mRNA expression was higher in BC compared to adjacent normal tissues. And then, MDM4 mRNA significantly increased with more advanced clinical stage, histological grades (Fig. 3A). Furthermore, our analysis found that MDM4 mRNA was significantly higher in Luminal type BC compared to other subtypes (Fig. 3B). Notably, Fig. 3C highlights that Luminal type BC, especially luminal A exhibited more higher expression of MDM4 judged from multiple subtype system. All the above results suggest that MDM4 mRNA expression is promising biomarkers for luminal A type BC. Besides, that is also an important reason that we had incorporated luminal A type BC for the next step of the analysis.

Immune-related pathways were significantly enriched in MDM4 gene gain/amplification luminal A type BC

To characterize immune function in the MDM4 gene gain/amplification luminal A type BC, GSEA analysis was conducted. As compared to the control group, Fig. 4A showed that CD4_T_cell, TCR_activated_CD4_T_cell, and LPS_induced DC_cell was significantly enriched in MDM4 gene gain/amplification luminal A type BC.

Furthermore, cell type signature analysis (Fig. 4B) exhibited that genes from MDM4 gene gain/amplification luminal A type BC was closely correlated with tumor immune infiltering, such as gastric related immune cells. Besides, we inferred molecular mechanisms of immune modulation depends on the activity of transcription termination involves the action of immune related gene in MDM4 gene gain/amplification luminal A type BC (Fig. 4C).

Three gene modules were subjected to biomarker of ICIs therapy

Inspired by findings above, we focused on the immune dysfunction in MDM4 gene gain/amplification luminal A type BC. It has been reported that MDM4 gene gain/amplification is closely connected with hyper-progressive disease (HPD) in mucosal and acral melanoma [11, 35]. Besides, response to ICIs therapy can be predicted by a series of biomarker[36].

Herein, we collected biomarkers[37] gene signature related to ICIs therapy and divided them into four categories (immune_checkpoint, DNA damage and repair, MHC molecalr and antigen_presentation, and immune factors, for details, see Supplementary Material S1-1 and S1-2), and then performed the ssGSEA algorithm to calculate the score as the characteristics input of WGCNA methods (Fig. 5A-5D). As was showed in Fig. 5E, three gene module (Yellow, Red, and Magenta) showed strong positive correlation with biomarkers related to ICIs therapy.

Genes from Yellow module (Fig. 6A-6C) were significantly enriched in cell cycle, cell division, and DNA conformation changes, and were widely regulated by E2F transcription factors family, which were consistent with “DNA damage and repair” role. Genes from Red module (Fig. 6A-6C) were involved in leukocyte activation and inflammatory response, and were broadly regulated by SPI1 and BFKB1. Genes from Magenta module (Fig. 6A-6C) were involved in lymphocyte activation and lymphocyte migration, and were broadly regulated by NFKB1 and STAT4.

Two immune subtypes exist different ICIs response

The heterogeneity of the immune microenvironment leads to the failure of immunotherapy, Appropriate immunophenotyping can provide precise treatment for immunotherapy.

Herein, we downloaded immune-related gene signatures (https://github.com/Gibbsdavidl/Immune-Subtype-Clustering/tree/master/Scripts) (for details, see Supplementary Material S2), and then performed the consensus clustering algorithm to calculate optimal number of clusters by “ConsensusClusterPlus” methods (Fig. 7A1-A1, B1-B2). Immune checkpoint genes and tumor-infiltrating lymphocytes, particularly CD8 + T cells, has been verified as the core determinant of ICIs treatment efficacy[38, 39].

The results from the TIDE algorithm (Fig. 7A3-B3, A4-B4) showed that cluster B showed better responds to ICIs, with increased immune checkpoint and CD8 + T cells score compared with cluster A (Fig. 8A-8D). Bedsides, TIDE algorithms have a more satisfactory prediction of ICIs therapy response, even if there is a lack of corresponding real clinical data (Fig. 8E-8H).

Breast cancer (BC) has overtaken lung cancer as the mostly frequently diagnosed malignant cancer worldwide[1]. Although it is not as malignant as triple-negative breast cancer, luminal type BC accounts for the major part of BC. Furthermore, genetic changes represented by TP53 mutation and PIK3CA significantly were occurred in BC[40]. Considering that MDM2/MDM4 is the most important negative regulator for TP53 function regulation, thus it is very important to study the role of MDM2/MDM4 in BC[34]. Besides, MDM2/4 amplification has been reported to be closely related to the hyper-progress of immune-therapy[11]. Enlightenment from the above literature, we are concerned about the genomic and transcriptome alteration of MDM2/MDM4 in certain subtype and explore its relationship with BC immune landscape and ICIs.

Next, the result was derived from WGCNA, we identified three genes module associated with biomarkers associated with ICIs therapy. Furthermore, GO and pathway analysis indicated that gene member from gene module were related to various immune cell activation.

In this study, we identified two immune subtypes in MDM4 gain/amplification luminal A type BC. Remarkably, subtype B exhibited higher immune checkpoint gene scores and CD8⁺ T cell score compared with subtype A. Moreover, the findings from TIDE analysis suggested that subtype B were more sensitive to ICIs therapy with respect to the subtype A. Besides, we found that TIDE algorithm has superior performance in terms of ICIs response discrimination. These finding demonstrated that heterogeneity and complexity of tumor immune microenvironments and response to ICIs therapy in MDM4 gain/amplification luminal A type BC.

However, our study has its own limitations. (A) The luminal A subtype BC dataset from TCGA and METABRIC database were still not enough, and more reliable results would be obtained from a larger sample size. (B) Our finding was mainly performed bioinformatics calculation method, which was not verified by experiments. Thus, future a large sample and experimental studies should be conducted to confirm the results of the present study. Large ICIs studies of luminal BC with treatment and outcome data are warranted to follow up on our findings. Compared to melanoma and non-small cell lung cancer, few immunotherapy treatments data are available for breast cancer.

In summary, our research provides potential ICIs therapy application for certain subtype luminal A BC treatment.

ICIs: Immune checkpoint inhibitor; IRGs: Immune-related gene signatures; TCGA: The Cancer Genome Atlas; METABRIC: Molecular Taxonomy of Breast Cancer International Consortium; Luminal A type BC: luminal A type breast tumors; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; BP: biological processes; CC: cellular components; MF: molecular functions; GSEA: Gene Set Enrichment Analysis; GSVA: Gene Set Variation analysis; ssGSEA: single sample gene set enrichment analysis; WGCNA: weighted gene co-expression network analysis; TIDE: Tumor immune dysfunction and Exclusion; PAM50: Prediction Analysis of Microarray; TMB: Tumor mutational burden; PCA: Principal component analysis; HPD: Hyper-progressive disease;

DATA AVAILABILITY STATEMENT

The datasets and detail flow work analyzed in our study were described in MATERIALS AND METHODS. Detailed descriptions of database searches, collection and classification of data are available in Methods section.

ETHICS STATEMENT

We are thankful to medical staffs from TCGA (http://cancergenome.nih.gov/), cBioPortal database (https://www.cbioportal.org/) and METABRIC (https://www.mercuriolab.umassmed.edu/metabric) for providing the continually updated genomic data. And our study met the publication guidelines stated by TCGA (https://cancergenome.nih.Gov/publications/publicationguidelines), there was no requirement for ethics committee approval.

CONFLICTS OF INTEREST

The authors declare that they have no conflict of interest.

AUTHOR CONTRIBUTIONS

Project conceptualization: methodology:; software:; validation:; formal analysis:; investigation:; resources:; data curation:; writing—original draft preparation:; writing—review and editing:; supervision:. All authors have read and agreed to the published version of the manuscript.

FUNDING

Not.

Consent for publication

Not applicable.

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Supplementary Materials S1-2 is not available with this version.

No competing interests reported.

SupplementaryS11.xlsx
Supplementary Material S1-1 and S1-2 Gene signatures for predicting immune checkpoint inhibitor therapy efficacy.
SupplementaryS12.xlsx
Supplementary Material S2 71 immune gene set for identifying immune subtype by consensus clustering algorithm.

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Identification of immune subtypes and response prediction to immune-checkpoint inhibitors for MDM4 gain/amplification luminal A type breast cancer

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Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

RESULTS

MDM4 gene gain/amplification frequently occurred in luminal A type BC

MDM4 mRNA exhibited more high expression in luminal A type BC

Immune-related pathways were significantly enriched in MDM4 gene gain/amplification luminal A type BC

Three gene modules were subjected to biomarker of ICIs therapy

Two immune subtypes exist different ICIs response

DISCUSSION

Abbreviations

Declarations

References

Supplementary Materials

Additional Declarations

Supplementary Files

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