Apolipoprotein C1 as a Novel Biomarker for Evaluating Immunotherapy Drug Responses and Clinical Prognosis for Microsatellite Unstable Colorectal Cancer

doi:10.21203/rs.3.rs-1827558/v1

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Apolipoprotein C1 as a Novel Biomarker for Evaluating Immunotherapy Drug Responses and Clinical Prognosis for Microsatellite Unstable Colorectal Cancer

https://doi.org/10.21203/rs.3.rs-1827558/v1

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Background: Colorectal cancer (CRC) is the third most common cancer in men and women worldwide. Although improved screening has significantly reduced its incidence, the occurrence and death rates of CRC in younger people have increased in the last few decades. Following initial successes in melanoma treatment, immunotherapy has rapidly become a major treatment modality for deficient mismatch repair/microsatellite instability-high (dMMR/MSI-H) CRCs. Previous reports have demonstrated the association of apolipoprotein C1 (APOC1) with several cancers; however, the function of APOC1 in MSI-H CRC remains unknown. Therefore, this study aimed to investigate the role of APOC1 in clinical prognosis and immunotherapy drug responses in CRC.

Results: The Cancer Genome Atlas (TCGA) database was searched, and through relevant machine learning algorithms and bioinformatics correlation analysis, APOC1 was identified as a gene possibly associated with CRC. The correlation between APOC1 expression, clinical prognosis, and response to immunotherapeutic drugs was also analysed based on public data. The findings were then validated using independent datasets from different technology platforms, including single-cell datasets. MSI-H CRC transcriptome data were downloaded from TCGA, and the tumour microenvironment (TME) model classified the TME of MSI-H CRC patients into four clusters; the IE (immune-enriched, non-fibrotic) subtype and other subtypes had less overlap and were relatively discrete. The screened differentially expressed genes (DEGs) were overlapped, and one common DEG (APOC1) was screened out. The results showed that APOC1 expression in CRC was positively correlated with tumour mutation burden, MSI, neoantigen, and signalling pathways such as myeloid cells and cytokines and immune scores. However, APOC1 expression in CRC was significantly negatively correlated with MMR-related genes. APOC1 was highly correlated with immune-related cell enrichment and immune checkpoints in colon adenocarcinoma patients and was more highly expressed in malignant cells than in normal cells, according to the single-cell data analysis. APOC1 expression in tumour tissues was significantly higher than that in normal tissues. The same conclusions can be obtained from different detection techniques and platforms.

Conclusions: It was revealed that the APOC1 tumour level in MSI-H CRC was correlated with clinical prognosis and response to immunotherapeutic drugs.

apolipoprotein C1

microsatellite unstable colorectal cancer

clinical prognosis

immunotherapy drug responses

According to the 2020 World Cancer Report (cancer research for cancer prevention by the International Agency for Research on Cancer [IARC]), colorectal cancer (CRC) is the third most common cancer and the most common malignant tumour of the digestive tract in humans. In 2020, there were 1.9 million new cases, accounting for 10.7% new cases and 9.4% of malignant tumour deaths [1].

With the increasing number of clinical studies in recent years, cancer immunotherapy has become an important treatment for CRC following surgery, chemotherapy, radiotherapy, and targeted therapy. Immune checkpoints play an important role in cancer occurrence and development by regulating the adaptive immune response and promoting the immune escape of cancer cells [2]. Microsatellites are tandem repeats of 1–6 nucleotides in the DNA genome and are mainly distributed in the coding regions of the genome. Microsatellite instability (MSI) is any change in microsatellite length due to an insertion or deletion of repeats in the microsatellite region in tumour cells compared to normal tissues, and the essence of its occurrence is the loss of DNA mismatch repair (MMR) protein expression [3].

Immune checkpoint inhibitors, such as programmed cell death receptor 1 (PD-1), programmed cell death-ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4), play an important role in CRC treatment, especially in microsatellite instability-high (MSI-H)/mismatch repair deficient (dMMR) CRC[2]. On 29 June 2020, the Food and Drug Administration (FDA) approved the use of pembrolizumab (KEYTRUDA, Merck & Co.) as a first-line treatment for patients with unresectable or metastatic MSI-H or dMMR CRC[4]. On 31 July 2017, the FDA granted accelerated approval for the use of nivolumab (OPDIVO, Bristol-Myers Squibb Company) as a treatment for patients aged ≥ 12 years with dMMR and MSI-H metastatic (mCRC) that has progressed following treatment with fluoropyrimidine, oxaliplatin, and irinotecan[5]. On 10 July 2018, the FDA granted accelerated approval for the use of ipilimumab (YERVOY, Bristol-Myers Squibb Company Inc.) in combination with nivolumab as a treatment for patients aged ≥ 12 years with MSI-H or dMMR mCRC that has progressed following treatment with fluoropyrimidine, oxaliplatin, and irinotecan [6].

The CRC Subtyping Consortium (CRCSC) identified four gene expression consensus molecular subtypes: CMS1 (immune), CMS2 (canonical), CMS3 (metabolic), and CMS4 (mesenchymal). Among these, CMS1 and CMS4 have a relatively "hot" immune microenvironment [7]. CRCs with dMMR/MSI-H features have a relatively "hot" immune microenvironment and have been proven effective in multiple clinical trials. However, less than 50% of patients benefit from immune checkpoint inhibiting drugs, and 10–30% of patients experience disease progression [6-9].The efficacy of immunotherapy is affected by several factors simultaneously. Therefore, more sensitive and accurate biomarkers to distinguish immunotherapy responders from non-responders are required to improve the immunotherapeutic effect in CRC.

In this study, The Cancer Genome Atlas (TCGA) database was searched and (apolipoprotein C1) APOC1 was identified as a possible gene associated with CRC using relevant machine learning algorithms and bioinformatics correlation analysis. The correlation between APOC1 expression, clinical prognosis, and response to immunotherapeutic drugs was also analysed based on public data. The findings were then validated using independent datasets from different technology platforms, including single-cell datasets.

2.1 TME Classification and Mutation Mapping of MSI-H Colorectal Cancer Patients

Bagaev et al. identified four tumour microenvironment (TME) subtypes. The four TME subtypes predict response to immunotherapy in multiple cancers, including CRC. A visual tool revealing the TME subtypes integrated with targetable genomic alterations provides a planetary view of each tumour that can aid in oncology clinical decision-making [10]. The MSI-H CRC transcriptome data were downloaded from TCGA based on a visual algorithm model of machine learning integrating transcriptomics and genomics, and the TME model classified the TME of MSI-H CRC patients into four clusters (Figure 1. A), Cluster1, immune-depleted (D), Cluster2, fibrotic (F), Cluster3, immune-enriched, fibrotic (IE/F), and Cluster4, immune-enriched, non-fibrotic (IE).

Among these, a significant enrichment of angiogenic fibroblasts was predominant in type F. Enrichment of tumour immune infiltrate cells were largely absent in type D, whereas type IE was dominated by significant enrichment of anti-tumour immune infiltrate (ATI) cells. Type IE/F was dominated by significant enrichment of both angiogenic fibroblasts and ATI cells.

Based on the signal distribution associated with the four staging types of CRC patients, principal component analysis (PCA), the most commonly used method for reducing data dimensionality, was performed on the four immunophenotypes (Figure 1. B). PCA analysis showed that type IE was an independent group compared to other types, only the IE and IE/F subtypes had partial overlap, whereas IE and other subtypes had less overlap and were relatively discrete.

2.2 Screening Confirmation of Specific Genes in Patients with IE Type

Based on the results of TME classification (IE, IE/F, F, and D types) of MSI-H CRC patients, a differential expression profiling was performed for IE and IE/F types, IE and F types, and IE and D types for screening relevant differentially expressed candidate genes. Among these, 62, 44, and 51 associated differentially expressed genes (DEGs) were found in IE vs IE/F, IE vs F, and IE vs D, respectively. Concurrently, the screened DEGs were overlapped, and one common DEG (APOC1) was screened out (Figure 1. C).

The correlation between APOC1 expression and tumour mutational burden (TMB) scores and each cancer type was analysed, and APOC1 expression and TMB were found to be strongly correlated with CRC (p = 0.00) (Figure 2. A). MSI is one of the biomarkers for immunotherapy in CRC [11], therefore, we first performed a correlation analysis between APOC1 expression and MSI and found that APOC1 expression in CRC was positively correlated with MSI (p = 7.3e-08) (Figure 2. B).

The correlation between APOC1 expression and MMR-related genes mutL homolog 1 (MLH1), mutS homolog 2 (MSH2), mutS homolog 6 (MSH6), PMS1 homolog 2, mismatch repair system component (PMS2), and epithelial cell adhesion molecule (EPCAM) in various cancers was also analysed （Figure 2. C). APOC1 expression in CRC was found to be significantly negatively correlated with MMR-related genes. MSI results from defective MMR protein function that leads to a highly mutated genome phenotype [12], therefore, APOC1 expression in different MMR-related genes further regulates and influences MSI status.

2.3 APOC1 is Positively Correlated with Signalling Pathways such as Myeloid Cells and Cytokines and Immune Scores

Gene Set Enrichment Analysis (GSEA) of the Kyoto Encyclopedia of Genes and Genomes (KEGG) for APOC1 expression revealed cell adhesion molecule (CAMs), haematopoietic cell lineage, and cytokine-cytokine receptor interaction pathways. (Figure 3. A)

Functional enrichment analysis of the hallmarks of cancer was performed at different APOC1 expression levels (Figure 3. C). The results showed that among the many cancer characteristic factors, complement, allograft rejection and interferon-gamma response factors were significantly positively correlated with APOC1 expression. From the GSEA of KEGG and hallmarks conducted, it was evident that APOC1 expression was associated with numerous immune-related signalling pathways, further corroborating the positive correlation between APOC1 expression and immunity.

It was revealed that APOC1 expression and immune signalling pathways were closely related through the analysis of signalling pathways. The ESTIMATE algorithm calculates the correlation between APOC1 expression and immune and stromal scores [13-16]. In CRC, the immune score is a reliable and valid clinical method for predicting the risk of recurrence in patients with colon cancer, and its prognosis assessment is superior to other prognostic factors, including tumour, nodes, and metastases (TNM) staging and MSI [17, 18]. We performed the ESTIMATE algorithm correlation coefficient analysis of the relationship between APOC1 expression and immunity in CRC (Figure 3. B) and found that APOC1 expression was positively correlated with immune scores (Spearman's rank correlation coefficient, R = 0.599, P ≈ 0).

We analysed the correlation between APOC1 expression and stromal scores using the ESTIMATE algorithm correlation analysis method (Figure 3. D). APOC1 expression was positively correlated with stromal scores in CRC patients (Spearman's rank correlation coefficient, R = 0.663, P≈ 0), indicating that high APOC1 expression had a strong positive correlation with stromal cells. The immune and stromal scores of APOC1 in CRC further demonstrated a strong correlation between APOC1 and CRC immunotherapy.

2.4 APOC1 is Highly Correlated with Immune-Related Cell Enrichment and Immune Checkpoints in COAD Patients

To further clarify the subtype of tumour immune infiltrating cells, the TIMER algorithm was applied to estimate the association between various immune cell types and the APOC1 expression signature. As shown in the scatter plot, the upregulation of APOC1 expression correlated with the infiltration of dendritic cells (DCs) and anti-tumour lymphocyte subpopulations (Spearman's rank correlation coefficient, P≈ 0) (Figure 4. A). Lymphocyte subpopulation analysis is an important indicator for cellular and humoral immune function with an overall response to the current immune function, status, and balance level of the body, and the significance of observing the efficacy of treatment and detecting prognosis [16, 19]. The analysis showed that APOC1 expression was positively correlated with immune-related cells and most closely correlated with the signal (R = 0.566) of the degree of enrichment of DCs, which possess antigen-presenting properties, called antigen-presenting cells (APCs, an important part of the immune system). DCs may be associated with APOC1 expression. To prove this conjecture, we explored the correlation of antigen-related genes with the APOC1 expression signature using Pearson's correlation coefficient (Figure 4. B). The neoantigen load was positively correlated with the APOC1 expression signature (Spearman's rank correlation coefficient, P = 1.45e-06), representing tumours with high antigen load. The high APOC1 expression can benefit immunotherapy.

APOC1 expression in different immune checkpoint factors and each cancer species was analysed using Pearson's correlation linear analysis (Figure 4. C). APOC1 was highly expressed in different immune checkpoint factors in CRC. Concurrently, APOC1 expression in clear cell renal cell carcinoma (ccRCC), uroepithelial, breast, cervical, and endometrial cancers and many other cancer types showed a more significant correlation with immune checkpoints.

2.5 Analysis of Single Cell Data Set and Other Validation Set Results in Colorectal Cancer

The GSE146889 dataset of CRC with MSI-H was downloaded from the Gene Expression Omnibus (GEO) database, and the typing pattern of the TME in this dataset was found to be the same as that of the TCGA dataset after analysis using machine learning algorithms (Figure 5. A). All four immunophenotyping patterns, including IE, IE/F, F, and D types, were more obvious, and there were significant differences in APOC1 expression in the different immunophenotypes (Figure 5. B). APOC1 expression was highest in the IE type, followed by the IE/F type and lowest in the D type.

To further explore whether high APOC1 expression was derived from tumour cells or TME, the single-cell dataset GSE132465 with MSI-H CRC was downloaded from the GEO database (mainly SMC03-T/SMC03-N, SMC06-T/SMC06-N, SMC10-T/SMC10-N, SMC24-T). The cell clustering distribution of these seven samples was obtained by the descending clustering analysis (Figure 5. C). We performed an enrichment analysis based on known canonical cell marker genes (Figure 5. D) and detected six major cell types, which were classified into epithelial cells, immune cells (T cells, B lymphocytes, and myeloid cells), stromal cell types, and other cells (Figure 6. A).

The results of cell cluster analysis revealed that CRC distribution was mainly from the epithelial tissue. The clustering of epithelial cells in CRC was further analysed by performing a dimensionality reduction clustering of epithelial cells from the seven samples (three paired samples and one tumour sample) (Figure 6. B left panel). The tumour tissue contained normal and malignant tumour cells, and the seven samples were clustered according to malignant and normal cells (Figure 6. B right panel). The correlation between the expression amount of APOC1 in malignant and normal cells in the seven samples was also analysed (Figure 6. C), APOC1 was more highly expressed in malignant cells than in normal cells (p = 4.5e-23). We analysed APOC1 expression in normal and tumour tissues in the TCGA-COAD dataset (Figure 6.D). APOC1 expression in tumour tissues was significantly higher than that in normal tissues. The same conclusion that APOC1 is highly expressed in malignant tumour cells in CRC patients can be drawn on different detection techniques and platforms,

Since APOC1 was highly expressed in malignant tumour cells, APOC1 expression in SMC03-T, SMC06-T, SMC10-T, and SMC24-T was further analysed (Figure 6. E). Although they are all tumour tissues, APOC1 expression was significantly higher in SMC06-T than in other tumour tissues. Our analysis of the factors of the expression levels may be more than just the difference between normal and tumour tissues, therefore, we further analysed the distribution of cells in these four tumour tissues. (Figure 6. F) The degree of infiltration of immune T cells in SMC06-T was the highest, and the distribution trend of T cells in the four samples was the same as that of APOC1 expression. This further confirms the correlation between APOC1 expression and immunotherapy.

We downloaded the IMvigor210 cohort dataset, and a survival curve analysis was performed on the dataset. Patients with high APOC1 expression in the IMvigor210 cohort had a highly significant survival time advantage over those with low expression (Figure 7. A). The data of patients who had undergone PD-1/PD-L1 immunotherapy were categorised into groups, the clinical response group, according to complete response (CR) and partial response (PR), and the clinical non-response group, according to stable disease (SD) and progressive disease (PD). The potential significant difference in APOC1 expression between the CR/PR and SD/PD groups was analysed using analysis of variance (ANOVA) (Figure 7. B). The results revealed a significant difference in APOC1 expression between the two groups. High APOC1 expression was associated with more responders than low expression, suggesting a strong correlation between high APOC1 expression and response to immunotherapy.

We further validated the correlation between APOC1 expression and neoantigen in the validation dataset (Figure 7. C). Consistent with the previous findings, high APOC1 expression was positively correlated with neoantigen.

APOC1 expression in the classical immune desert, excluded, and infiltrated phenotypes were also analysed (Figure 7.D). APOC1 was significantly correlated with each immunophenotype and highly expressed in the immuno-infiltrative type (p = 2.9e-25). This immunophenotyping analysis further demonstrated that APOC1 is highly expressed in CRC patients with the immuno-infiltrative type, which has a better prognosis.

Thus, we conclude that APOC1 can be used as an immunotherapeutic prognostic marker.

APOC1 encodes a member of the apolipoprotein C1 family, and its expression influences the cell proliferation ability and motility capacity of CRC via the mitogen-activated protein kinase (MAPK) pathway [19].

This study analysed the TME (which can be classified into four clusters) in MSI CRC. Related studies have demonstrated that the response to immunotherapy indicates a dynamic evolution of the TME [17, 20, 21]. Differential gene analysis of the four clusters and the study of related signalling pathways demonstrated that APOC1 had a high positive correlation with immune scores in CRC patients and a high positive correlation with immune lymphocyte enrichment. Concurrently, our study on APOC1 and immune checkpoints revealed that APOC1 positively correlated with immune checkpoint factors. Analysis of the dataset in combination with single cells revealed that APOC1 was highly expressed in malignant tumour cells, and the degree of infiltration of immune T cells was also higher in the highly expressed tissues, indicating a high correlation between APOC1 expression and immunotherapy.

Our findings suggest that APOC1 is associated with myeloid cell and cytokine signalling pathways and has different functions and roles in some cancers. In ccRCC, genes co-expressed with APOC1 were mainly expressed in DCs, B cells, and CD4 cells [22]. DCs are the most important antigen-presenting cells that present antigens via major histocompatibility complex (MHC)-I and MHC-II [23] and play a primary role in antigen presentation to CD4 cells. They can determine cancer immune responses by regulating immune activation and tolerance [13] and improve the development of autoantibodies through B cells [14]. In cervical cancer, APOC1 was found to have a strong correlation with immune markers of monocytes, M2 macrophages, DCs, and T cells, especially exhausted T cells, and was closely associated with the tumour immune microenvironment [15].

Given the low percentage of patients who currently benefit from immune checkpoint-inhibiting drugs, it is critical to determine which patients will benefit more from these treatments. Mechanistically, we used bioinformatic methods to analyse possible pathways, and emerging evidence suggests that APOC1 may serve as a novel biomarker to assess prognosis in patients with malignancy, highlighting the potential value of APOC1 as an independent prognostic biomarker in MSI-H CRC immunotherapy in humans.

This study is the first to investigate the correlation between APOC1 expression in MSI-H CRC and clinical prognosis and response to immunotherapy drugs. Although only the feasibility of APOC1 as a prognostic marker for immunotherapy in CRC patients at the theoretical level using the available published data was confirmed, we expect that our findings will help refine the existing knowledge, improve treatment design for CRC, and enhance the accuracy of prognosis for patients with CRC. Also, despite the inability to perform validation of other platforms due to the sample size, the current APOC1 expression could be relevantly detected and validated using immunohistochemical platforms (Figure 7. E). Further clinical trials are needed to confirm the correlation of APOC1 expression in CRC with clinical prognosis and response to immunotherapeutic agents.

5.1 Data Sources

5.1.1 Data Sources- Discovery Dataset

The RNA sequencing (RNA-seq) data of CRC patients were downloaded from the publicly available TCGA database (https://portal.gdc.cancer.gov) and the National Center for Biotechnology Information (NCBI) GEO database. TCGA-colon adenocarcinoma (COAD) MSI-H transcriptome public dataset was downloaded from TCGA, and the GSE146889 dataset with CRC was downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146889). GSE146889 included tumours and paired normal tissues from 58 patients for TMB analysis using RNA-seq. Forty-six tumours were MSI-H (29 with a DNA MMR germline mutation and 17 with MLH1 [mutL homologue 1] promoter hypermethylation [HM], and 12 were microsatellite stable (MSS).

5.1.2 Data Sources-Validation Dataset

The single-cell GSE132465 dataset with MSI-H CRC was downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132465). The transcriptome profile and clinical information from immunotherapy cohorts were obtained from IMvigor210, and the IMvigor210 cohort was used as a validation dataset for relevant conclusion validation.

5.2 Data Pre-Processing

The TME model of CRC was established after preliminary data processing and unsupervised cluster analysis of TCGA of CRC using a visual tool for personalised treatment selection based on integrated transcriptomics and genomics created by Bagaev et al.

Screening and identification of specific immune genes based on the TME typing were performed to analyse differential expression profiles to obtain different immune-related genes[24]. For the obtained genes, related signalling pathways were studied to determine CRC-related signalling pathway-related genes.

To identify the final immune-related genes, Spearman's correlation coefficient analysis was performed on the TCGA dataset of the selected genes combined with CRC. The Kaplan-Meier (KM) survival curve analysis was used as the validation set. To identify the molecular pathways associated with expression in CRC and determine the correlation between genes, GSEA was performed by screening DEGs and performing functional enrichment analysis of DEGs. The correlation coefficients of APOC1 expression in different immune checkpoint factors and for each cancer species were analysed using the Pearson analysis method. Reduced-dimensional clustering analysis of single cells and taxonomic labelling of cell populations using signature genes were performed.

CRC, colorectal cancer, dMMR, deficient mismatch repair, MSI-H, microsatellite instability-high, APOC1, apolipoprotein C1, TCGA, The Cancer Genome Atlas, TME, tumour microenvironment, IARC, International Agency for Research on Cancer, MSI, microsatellite instability, PD-1, programmed cell death receptor 1, PD-L1, programmed cell death-ligand 1, CTLA-4, cytotoxic T lymphocyte antigen 4, FDA, Food and Drug Administration, mCRC, metastatic colorectal cancer, CRCSC, colorectal cancer subtyping consortium, ATI, anti-tumour immune infiltrate, PCA, principal component analysis, DEGs, differentially expressed genes, MLH1, mutL homolog 1, MSH2, mutS homolog 2, MSH6, mutS homolog 6, PMS2, PMS1 homolog 2, mismatch repair system component, EPCAM, epithelial cell adhesion molecule, CAMs, cell adhesion molecule, TNM, tumour, nodes, and metastases, DCs, dendritic cells, CR, complete response, PR, partial response, SD, stable disease, PD, progressive disease, ANOVA, analysis of variance, ccRCC, clear cell renal cell carcinoma, MHC, major histocompatibility complex, RNA-seq, RNA sequencing, NCBI, National Center for Biotechnology Information, GEO Gene Expression Omnibus, COAD, colon adenocarcinoma, TMB, tumour mutational burden, HM, hypermethylation, MSS, microsatellite stable.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the available at https://www.proteinatlas.org and the NCBI GEO repository, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132465.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 8197032867) and Natural Science Foundation of Guangdong (Grant No. 107266332019)

Authors' contributions

YTT and SYQ wrote the manuscript. OGW, WHC, YPY, WQY, WCY and CCH participated in the initial planning and discussion. All the authors have read and approved the final manuscript.

Acknowledgements

We are grateful to Dr. Zhiwen Shi for his helpful insights.

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Apolipoprotein C1 as a Novel Biomarker for Evaluating Immunotherapy Drug Responses and Clinical Prognosis for Microsatellite Unstable Colorectal Cancer

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