Tumour immune cell infiltration and response to FOLFOX or FOLFIRI chemotherapy in colorectal cancer

doi:10.21203/rs.3.rs-4859735/v1

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Tumour immune cell infiltration and response to FOLFOX or FOLFIRI chemotherapy in colorectal cancer

https://doi.org/10.21203/rs.3.rs-4859735/v1

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Background: The immune cell infiltration of tumors has been demonstrated to have a significant part in the response to different cancer treatments. We sought to gain greater understanding of the connections between the immune landscape of colorectal cancer (CRC) tumors and the clinical outcomes of infusional 5-fluorouracil/leucovorin with either oxaliplatin (FOLFOX) or irinotecan (FOLFIRI)-based chemotherapy.

Methods: The immune cell infiltration of CRC tumors was evaluated using data from the TCGA and GEO databases with the CIBERSORTx algorithm. The relative proportion of 22 immune cell subtypes was measured, and their associations with response, progression-free survival (PFS), and overall survival (OS) were investigated. Consensus clustering analysis was utilized for the identification of the immune cell patterns.

Results: We analyzed data from 387 CRC patients and found that drug response rates were negatively correlated with M1 macrophages in the context of FOLFOX chemotherapy. Poor PFS was linked to resting dendritic cells and eosinophils in this regimen. An inverse relationship between drug response rate and M0 macrophages was observed within the FOLFIRI chemotherapy regimen. Activated natural killer cells were connected to poor PFS and OS. Using consensus clustering analysis, we identified three immunological clusters, with a cluster associated with better medication response rates and improved patient outcomes following FOLFOX treatment. This cluster was characterized by higher proportions of M0 macrophages, quiescent memory CD4⁺ T cells, and quiescent B cells.

Conclusion: Our findings emphasize the significant impact of the CRC tumor-immune milieu on the response to FOLFOX or FOLFIRI chemotherapy. Further investigation of its regulatory mechanisms may contribute to advancements in therapeutic interventions.

Biological sciences/Cancer

Biological sciences/Immunology

Health sciences/Diseases

Health sciences/Gastroenterology

colorectal cancer

overall survival

tumor-immune infiltration

drug response

progression-free survival.

As one of the most prevalent cancers globally, colorectal cancer (CRC) has experienced a rise in incidence and death rates in recent years [1, 2]. By 2030, it is anticipated that there will be about 2.2 million newly diagnosed patients and 1.1 million fatalities, highlighting the urgent need for effective prevention, diagnosis, and treatment strategies [3]. Numerous studies have identified various risk variables, such as age, gender, genetics, and lifestyle, that contribute to the prevalence of CRC [1]. Chemotherapy is a crucial modality in the treatment regimens for patients with CRC, with the potential to be administered before, after, or for palliative purposes in advanced or metastatic cases. Infusional 5-fluorouracil/leucovorin in combination with either oxaliplatin (FOLFOX) or irinotecan (FOLFIRI) remains a first-line treatment for CRC patients [4]. Unfortunately, only roughly 50% of patients could benefit from the FOLFOX or FOLFIRI treatment; the majority of these patients experience chemotherapeutic resistance as a result of drug resistance, and their median OS is just about 20 months [5]. Studies have shown that survival benefits can be explained by higher tumor response rates in colorectal cancer patients[6]. Factors affecting the response to FOLFOX or FOLFIRI-based chemotherapy in CRC include the activation of the Wnt/β-catenin signaling pathway [7], sustained activation of the multiple drug resistance pathway [8], and so on. However, the mechanism of the resistance to FOLFIRI or FOLFOX-based chemotherapy is not fully understood. Enhancing the understanding of the processes underlying acquired chemoresistance may aid in the prevention and ultimate targeting of these processes to improve the prognosis of CRC patients.

The tumor immune microenvironment (TIM) is defined as the surrounding tissue environment surrounding the tumor, including immune cells, cytokines, blood vessels, and stroma, which is crucial to the development and spread of tumors [9, 10]. According to recent research, the tumor immunological milieu of CRC is associated with the prognosis of patients. For instance, elevated levels of M0 macrophages are significantly associated with poor OS [11]. High infiltration of effector and memory T lymphocytes [12] or tumor-infiltrating plasmacytoid dendritic cells [13] is favorably connected to the patients' prognosis with CRC. Research has suggested that tumor-infiltrating immune cells are associated with responses to various therapies for cancer, such as endocrine therapy in breast cancer [14], platinum-based chemotherapy for ovarian cancer with high grade serous [15], and tumor immune checkpoint inhibitors in melanoma, clear cell renal cell carcinoma, and urothelial cancer [16]. The prognostic significance of tumor-infiltrating immune cells in colorectal cancer (CRC) after FOLFOX or FOLFIRI treatment, however, has not been thoroughly studied.

In this study, the TIM landscape of CRC and the relationship between immune cellular subpopulations, OS, RFS, and response to FOLFOX or FOLFIRI-based chemotherapy were comprehensively investigated. A bioinformatics algorithm, CIBERSORTx [17], was utilized to estimate the immune cell composition from tumor tissue mRNA profiles based on mixed cellular gene expression data in 387 CRC patients. Utilizing the consensus cluster, different immune-infiltration patterns of CRC were identified, and their associations between drug response and patient survival were evaluated. Additional research on the quantitative cellular immune infiltrations in tumors that have an effect on patient prognosis after chemotherapy may aid in the prediction and management of CRC chemotherapy resistance.

Chemotherapy patient gene expression data and data processing

Our study utilized publicly available databases. To determine the CRC whole-genome expression patterns of genes, gene expression datasets were systematically searched and downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Raw microarray data under the accession numbers GSE28702 [18], GSE62080 [19], GSE19860, and GSE72970 [20] were obtained from datasets in the GEO database. The data were standardized using the R package Affy, resulting in probe-set-level expression matrices. Probe IDs were then converted into gene symbol IDs. Probe sets without particular gene annotations were removed for every sample. The mean value for all probe sets aimed at a particular gene was determined in situations where more than one probe set belonged to that gene. Expression data for all genes in CRC, along with clinical data for tumor patients from TCGA (TCGA_COAD and TCGA_READ projects) were downloaded with the R package TCGAbiolinks. Clinical information was retrieved from the supplementary data from the original publications. The samples that were utilized at each stage of the analysis are shown in Fig. 1.

Calculate immune score using ESTIMATE to evaluate immune response gene characteristics

In assessing gene characteristics related to immune response, this study relied on the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) algorithm [21]. By employing a pre-selected set of immune genes, immune cell infiltration levels inside tumorous tissues were evaluated across different datasets, generating corresponding immune scores. The relationship among these scores and responses, PFS, and OS was then examined.

Assessing immune cell infiltration by computing the makeup of tumor-infiltrating immune cells using CIBERSORTx

To estimate the composition of immune cell subsets in tumor tissue, we used CIBERSORTx [17] (Cell type Identification By Estimating Relative Subsets Of RNA Transcripts, https://cibersortx.stanford.edu/). It is a deconvolution algorithm specifically designed for analyzing mixed gene expression data. Its aim is to distinguish and quantify the relative abundance of various cell types from a complicated combination of gene expression. The algorithm employs Support Vector Regression (SVR) to improve prediction accuracy and robustness.

CIBERSORTx utilizes a dataset containing gene expression features specific to different immune cell types. In order to study immune cells that have infiltrated tumors, it typically employs the LM22 signature matrix, which comprises 547 genes that identify 22 distinct human hematopoietic cell subtypes. Through batch correction using the B mode, quantification of the relative ratios of the 22 tumor-infiltrating subtypes of immune cells in each dataset is conducted, along with a correlation coefficient indicating the accuracy of estimating each cell type in the mixed sample. Confidence in the results of the deconvolution for each sample is computed, represented by an empirically defined global p-value.

Clinical outcomes

Collect clinical information, including age, gender, chemotherapy regimen, chemotherapy response, PFS, and OS. The chemotherapy regimen is defined as the treatment variable with values of FOLFOX, FOLFIRI, or other. The FOLFOX regimen comprises oxaliplatin, calcium folinate, and fluorouracil, while the FOLFIRI regimen includes irinotecan, calcium folinate, and fluorouracil. Chemotherapy response is defined as the response variable with values of responder and non-responder. Individuals who show signs of stable disease (SD) or disease progression (PD) are classified as non-responders, whereas patients are categorized as responders if they experience a complete response (CR) or a partial response (PR). Clinical endpoints include treatment response, PFS, and OS. Table S1 lists the fundamental data that each dataset provides.

Statistical analysis

In different chemotherapy regimens, the relationship between the percentage of every kind of immune cell in each dataset and patient response, PFS, and OS was calculated. In the correlation analysis, the sample rate of occurrence for each immune cell type was calculated, with a threshold set at less than 1×10^− 4. If a particular immune cell type was present in over 75% of the samples, it was removed from subsequent analysis. Meanwhile, some cases that did not fit the study’s requirements were eliminated, including those who did not receive FOLFOX or FOLFIRI-based chemotherapy, cases with unknown chemotherapy information, as well as individuals with a CIBERSORTx p ≥ 0.05.

To investigate the relationship between the tumor-infiltrating immune cell subtypes under different chemotherapy regimens and PFS and OS, an analysis of survival was carried out. Patients with missing OS/PFS time and event data, as well as those with an OS time less than 30 days, which could indicate postoperative complications, were not included. We used cox regression analysis to model survival time data, treating quartiles of the immune cell scores as continuous variables. These quartiles were calculated separately for each subtype of cell type in different datasets based on three quantiles, then divided into four equal parts. In the correlation analysis, "study" was used as a strata variable to adjust for heterogeneity among different datasets. The Kaplan-Meier survival curve and log-rank test were utilized to examine differences in survival rates among different subtypes. Furthermore, the relationship between survival and age, sex, and tumor stage was evaluated.

When constructing a multivariable Cox regression model, covariates and immune cell subtypes that showed statistical significance in univariate analysis, along with relevant clinical factors, were included in the model. It is worth noting that in selecting significant results from univariate analysis, some strongly correlated factors may be overlooked to control for confounding variables [22]. The "glmnet" package in R can be used to fit multivariable logistic and Cox regression models for binary outcome variables and event time data through penalized maximum likelihood [23].The penalty factor is selected based on 1000 rounds of cross-validation.

A logistic regression analysis was carried out to evaluate the association between every immune cell subtype and response under various treatment regimens. Patients with missing response data were excluded. Each immune cell subtype's quartiles (25%, 50%, and 75%) were represented as continuous variables in the model.

To investigate the association between the categorical variables, we employed chi-square tests, while Pearson correlation coefficients were used to examine the pairwise correlation among immune cell subtypes. The aforementioned statistical tests were employed to analyze the relationship between quartiles of immune response-associated gene features and patient prognosis after receiving chemotherapy.

To study the tumor-infiltrating immune cell patterns in CRC, consensus clustering analysis was conducted on the immune cell subtypes (samples with CIBERSORTx p < 0.05). To ensure that cell subtypes with different ratios may be compared, the relative score values of individual cell types were rescaled between 0 (the actual minimum value) and 1 (the actual maximum value). For the purpose of ensuring clustering stability, Euclidean distance and the k-means algorithm were used (unsupervised clustering, "Pam selection based on Manhattan distance"), with 1000 resampling iterations conducted with the ConsensuClusterPlus R package [24].

R 3.6.2 was used for all statistical analyses in this investigation [25]. The results were considered statistically significant at p < 0.05.

Using CIBERSORTx to assess tumor immune infiltration

From the expression data of 387 samples, individual immune cell subtypes were deconvoluted and summarized (Fig. 2a). Before conducting the correlation analysis, the sample rate of occurrence for each immune cell type was calculated, with a threshold set at less than 1×10^− 4. If a particular immune cell type was present in more than 75% of the samples, it was excluded from subsequent analysis. Immature CD4⁺ T cells and T cell γδ cells were filtered out, as the occurrence rate in over 75% of the samples was less than 1×10^− 4 (Fig. S2).

The pairwise correlation between the 22 subtypes of tumor-infiltrating immune cells in CRC was generally weak to moderately correlated (Fig. 2b). In general, the highest positive correlation was observed between eosinophils and plasma cells (R = 0.47), while the strongest negative connection (R = -0.47) was seen between resting mast cells and eosinophils.

The immune content score influences the drug response in patients

We evaluated the immune-related gene features using the ESTIMATE algorithm, obtaining corresponding immune content scores, which serve as indicators of overall immune infiltration in tumors. We then analyzed their correlation with patients' responses, PFS, and OS after chemotherapy. For the FOLFOX chemotherapy regimen (Fig. S1a) and the FOLFIRI chemotherapy regimen (Fig. S1d), no significant correlation between immune content and patients' response, PFS and OS under different chemotherapy regimens was observed.

Tumor immune cells and response under different chemotherapy regimens are associated

We evaluated the associations between the proportions of immune cells and treatment responses in each dataset under different chemotherapy regimens. Prior to the analysis, samples with p ≥ 0.05 in CIBERSORTx were filtered out. For the FOLFOX chemotherapy regimen (Fig. 3a), neutrophils (OR 0.65, 95% CI 0.49–0.84; p = 1.26×10^− 3, Fig. 3c) were substantially inversely connected with the response, while no immune cells significantly positively correlated with response were found. For the FOLFIRI chemotherapy regimen (Fig. 3b), M0 macrophages (OR 1.41, 95% CI 1.01–2.01; p = 4.75×10^− 2, Fig. 3d) were significantly negatively correlated with response, while activated dendritic cells (OR 0.71, 95% CI 0.51–0.99; p = 4.75×10^− 2, Fig. 3e) were significantly positively correlated with response. For the FOLFOX (Table S2) and FOLFIRI (Table S3) chemotherapy regimens, both univariate and multivariate models of immune cells in chemotherapy-treated patients were constructed.

In the FOLFOX regimen, the results of the univariate analysis indicated that the treatment response was unrelated to age and gender(Table S2a). Multivariate analysis (Table S2b) demonstrated that pT, which represents the size and extent of invasion of the primary tumor (OR 0.05, 95% CI 0.01–0.23; p = 4.16×10^− 4), contributed to the adjusted model. The multivariate logistic regression model that employs penalized maximum likelihood estimation, M1 macrophages (OR = 1.13) showed a high OR point estimate (Table S2c).

In the FOLFIRI regimen, age, gender, and TNM tumor stage were not related to treatment response, according to univariate analysis (Table S3a). Multivariate analysis (Table S3b) showed that M0 macrophages and activated dendritic cells, which, in univariate analysis, were substantially correlated with treatment response, did not contribute to the adjusted model. The multivariate logistic regression model that employs penalized maximum likelihood estimation, M0 macrophages (OR = 1.42), resting mast cells (OR = 1.29), and CD8⁺ T cells (OR = 1.26) exhibited high OR point estimates (Table S3c).

Tumor immune infiltrating cells are related to survival

We evaluated the relationship between tumor immune infiltrating cell proportions under different chemotherapy regimens and patients' survival prognosis. It was discovered that the tumor immune infiltrating cell scores were connected to the survival rates of patients receiving different chemotherapy regimens. Prior to analysis, samples with CIBERSORTx p ≥ 0.05 were filtered out. There were 137 patients in all who had PFS information. After excluding patients with survival periods of less than one month, there were 219 patients with available OS information. Forest plots showed the HR and 95% CIs of immune cell subgroups for PFS (Fig. 4) or OS (Fig. 5) under different chemotherapy regimens. The effect of immune cells on prognosis varied across different chemotherapy regimens.

In the FOLFOX regimen, eosinophils (HR = 1.38, 95% CI = 1.04–1.83, p = 2.58×10^− 2) and resting dendritic cells (HR = 1.62, 95% CI = 1.12–2.33, p = 1.01×10^− 2) were linked to poor PFS, while no association was found between immune cells and OS. Because there aren't many patients in the FOLFOX treatment with PFS data (n = 36), these results may require further validation. The findings of the univariate Cox regression analysis indicated that gender and age were not related to PFS (Table S2d) and OS (Table S2g). Multivariate Cox regression analysis results revealed that the adjusted PFS model was influenced by eosinophils (HR = 1.56, 95% CI = 1.15–2.12, p = 4.54×10^− 3) (Table S2e). In the multivariate Cox regression model with penalized maximum likelihood estimation, M2 macrophages (HR = 1.11) had the lowest HR point estimates in PFS, while resting dendritic cells (HR = 1.59) and eosinophils (HR = 1.33) had the greatest HR point estimates. Since the proportion of immune cells was not strongly connected to OS (normal p-value < 0.05), multivariate survival analysis adjusting for OS clinical factors was not performed.

In the FOLFIRI regimen, activated natural killer cells were linked to low PFS (HR = 1.20, 95% CI = 1.02–1.40, p = 2.58×10^− 2) and OS (HR = 1.32, 95% CI = 1.11–1.56, p = 1.53×10^− 3). Additionally, follicular helper T cells (Tfh cells) were linked to low PFS (HR = 1.21, 95% CI = 1.00-1.46, p = 4.91×10^− 2), while M1 macrophages (HR = 1.26, 95% CI = 1.04–1.54, p = 2.15×10^− 2) and M2 macrophages (HR = 1.25, 95% CI = 1.03–1.53, p = 2.62×10^− 2) were linked to poor OS. The results of the univariate Cox regression analysis indicated that PFS (Table S3d) and OS (Table S3g) were not correlated with age, gender, and distant metastasis. Results of multivariate Cox regression analysis indicated that for the PFS model, immune cell subtypes did not contribute to the model (Table S3d-3e); for the OS model, following the adjustment for clinical variables (metastasis of lymph nodes and tumor size), no immune cell subtypes were included in the model (p < 0.05) (Table S3g-3h). In the multivariate Cox regression model with penalized maximum likelihood estimation, activated natural killer cells had the highest HR point estimates for both PFS (HR = 1.07, Table S3f) and OS (HR = 1.14, Table S3i).

The immune clusters are associated with drug response and survival

To investigate distinct patterns of tumor immune cell infiltration, consensus clustering was performed. The consensus matrix, consensus clustering index, proportion of fuzzy clustering values, and cumulative distribution function were all assessed in the study, determining three clusters based on these indicators (Fig. S3). The tumor immune cell infiltration rates based on immune cluster partitioning appear in Fig. 6a, and the distribution of immune cell subtypes is displayed using box plots (Fig. S4). Among them, Cluster 3 exhibited a higher proportion of resting memory CD4⁺ T cells, M0 macrophages, and resting B cells, leading to a higher drug response rate after FOLFOX treatment (Fig. 6b) and improved patient OS (Fig. 6c). In contrast, a lower ratio of M0 macrophages and a higher ratio of activated mast cells were linked to poor OS.

As one of the most common malignancies worldwide, CRC poses a major risk to human health due to its escalating death rate [1]. Tumor-infiltrating immune cells are linked to the anti-tumor effects produced by different chemotherapy regimens. Our study evaluates gene features related to immune response using the ESTIMATE algorithm [21], calculates the immune cell's makeup in tumor tissue using CIBERSORTx [17], analyzes different patterns of tumor immune cell infiltration under different chemotherapy regimens, and examines their association with the response, PFS, and OS of CRC patients after chemotherapy. Importantly, existing literature provides evidence for the reliability of this study.

In numerous studies prior to this, it has been demonstrated that immune cells infiltrating in tumor microenvironment participate in the tumor immune response [26–29]. Similarly, in the microenvironment of CRC, numerous immune cells, for example, dendritic cells, natural killer cells, macrophages, CD4⁺ T cells, and CD8⁺ T cells, are known to infiltrate tumors [28] [30–32]. Moreover, various immune cells have distinct functions inside the tumor microenvironment. Tumor-associated macrophages exert inhibitory phagocytosis by expressing PD-1; thus PD-1 therapy may exert anti-tumor effects by directly acting on macrophages [33]. The content of tumor-infiltrating plasmacytoid dendritic cells (pDCs) is substantially positively connected with CRC patients' prognosis [13]. When CRC patients have highly infiltrative effector memory T cells, pathological signs of early metastatic invasion (VELIPI) are less likely to occur. Furthermore, a better clinical outcome is linked to a greater level of CD45RO⁺ cell infiltration [34]. Galon et al. showed that the prognosis of patients with CRC is strongly connected with the high infiltration of effector and memory T lymphocytes [12]. A lower CD4⁺/CD8⁺ ratio can significantly improve CRC patients' 5-year survival rate [35].

In the current study, we utilized publicly available CRC databases to evaluate immune response-related gene features by ESTIMATE algorithm, quantified the relative proportions of 22 tumor-infiltrating immune cell subtypes in each dataset using CIBERSORTx, and systematically analyzed the effect of immune cells that infiltrate tumors on CRC patients' prognosis after chemotherapy. The results indicate that under the FOLFOX chemotherapy regimen, M1 macrophages are negatively correlated with the drug response rate. Typically, M1 macrophages are considered to have anti-tumor effects because they can secrete pro-inflammatory factors and chemokines with anti-tumor cell activity [36, 37]. Compared to the results of our study, Edin et al. found that as M1 macrophage infiltration increases, patient survival rates also tend to increase [38]. However, several investigations have demonstrated a link between macrophages and a negative prognosis for patients. Research has identified a unique way that CXCL12 influences macrophages to encourage tumor growth through GMCSF/HB-EGF paracrine signaling [39]. A negative correlation has been observed between the prognosis for patient survival and tumor-associated macrophages [40, 41]. Furthermore, we discovered that resting dendritic cells and eosinophils, two immune cells linked to patient prognosis, were linked to a poor PFS, which was in line with the findings of the univariate analysis.

Similarly, under the FOLFIRI chemotherapy regimen, the medication response rate has an adverse relationship with M0 macrophages, while activated natural killer cells are connected to patients' low PFS and OS. In the multivariable regression model, resting mast cells and CD8⁺ T cells were also negatively correlated with drug response rates. Cao et al. discovered a substantial correlation between low OS and high levels of M0 macrophages [11]. Additionally, an essential part of the tumor immune response is played by CD8⁺ T cells; there are also many studies exploring their immune function in CRC. Wang et al. showed that Th17 cells reduce CD8⁺ T cells in late-stage CRC patients through the IL-17A/STAT3/CXCR3 axis, but contrary to these study results, survival analysis revealed that higher OS is predicted by increased expression of CD8 [42]. A study in 2021 found that a favorable patient survival prognosis is linked to both high levels of fatigue and low residency programs in NK cells, as well as high levels of exhaustion and low TGFb signaling in CD8⁺ T cells and NK cells [43]. Therefore, our study results are supported to some extent by previous research findings.

Consensus clustering analysis was carried out on all samples' immune cell components, resulting in the identification of three immune clusters. Among them, resting memory CD4⁺ T cells, resting B cells, and M0 macrophages were more prevalent in the third immune cluster, leading to a higher drug response rate and improved OS after FOLFOX regimen treatment. Conversely, poor OS was linked to a higher proportion of activated mast cells and a lower proportion of M0 macrophages. A study identified immune cell infiltration traits (activated mast cells, M0-M2 macrophages, and resting memory CD4⁺ T cells) linked to the prognosis of CRC, with these five immune cells serving as prognostic factors for CRC. Among them, M0-M1 macrophages and resting memory CD4⁺ T cells are safeguarding elements, while M2 macrophages and activated mast cells are harmful ones [44]. Consistent with our study, M0 macrophages and resting memory CD4⁺ T cells are linked to a favorable prognosis. According to a study, IL-17A could stimulate the expansion of tumors by preventing the infiltration of CD4⁺ and CD8⁺ T cells [45].

Undoubtedly, our study has certain limitations. Firstly, there is a considerable amount of missing PFS information for patients receiving the FOLFOX chemotherapy regimen in our study; as a result, fewer people are included, which may lead to some degree of bias in the results. Secondly, the estimation of tumor-infiltrating immune cell immune content and relative proportions using algorithms in our study may not be as precise as experimental measurements. Likewise, the absence of covariates may lead to a decrease in statistical power in multivariate models. Lastly, to ensure greater reliability of the results, additional biological trials and confirmation in separate cohorts are required.

In summary, our study identified many immune cell subtypes, such as M0, M1 macrophages, resting mast cells, and activated natural killer cells associated with CRC patients’ prognosis who received FOLFOX and FOLFIRI chemotherapy. Furthermore, we discovered an immune cluster characterized by a high proportion of M0 macrophages, resting memory CD4⁺ T cells, and resting B cells associated with the survival of CRC patients receiving FOLFOX chemotherapy, improving the OS of CRC patients. Our findings can provide important clues for understanding the differences in adjusting the tumor microenvironment and immunity response with different chemotherapy regimens, aiding in the realization of personalized treatment, and offering more accurate treatment options for clinical practice.

CRC: Colorectal cancer; OS: overall survival; PFS: Progression-free survival; CR: complete response; PR: partial response; SD: stable disease; PD: progression of disease; CIBERSORT: Cell type identification by estimating relative subset of known RNA transcripts; ESTIMATE: Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data; HR: hazard ratio; CI: confidence interval; OR: odds ratio.

Data availability statement

The public databases TCGA and GEO contain the datasets needed to support the findings of this work under the accession codes TCGA_COAD and TCGA_READ, GSE28702, GSE62080, GSE19860, and GSE72970. All these studies have been previously approved by their respective institutional review boards.

Ethics statement

Not applicable.

Author Contributions

R.L. and Y.Z. contributed to the study design. J.F. collected the datasets, performed statistical analysis, and drafted the manuscript. L.Z. contribute to exam the statistical analysis. Y.Z. provide assistance for computing. All authors contributed to critical revision of the final manuscript and approved the final version of the manuscript.

Funding:

This study was supported by the Hunan Provincial Science and Technology Innovation Plan Project 2022RC1022, and the National Scientific Foundation of China 31801121.

Conflict of Interest

None.

Acknowledgements

The authors acknowledge the authors from published studies to share their data of CRC chemotherapy trials and are grateful for resources from the High Performance Computing Center of Central South University.

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No competing interests reported.

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Tumour immune cell infiltration and response to FOLFOX or FOLFIRI chemotherapy in colorectal cancer

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Version 1

Abstract

Figures

Introduction

Methods and materials

Chemotherapy patient gene expression data and data processing

Calculate immune score using ESTIMATE to evaluate immune response gene characteristics

Assessing immune cell infiltration by computing the makeup of tumor-infiltrating immune cells using CIBERSORTx

Clinical outcomes

Statistical analysis

Results

Using CIBERSORTx to assess tumor immune infiltration

The immune content score influences the drug response in patients

Tumor immune cells and response under different chemotherapy regimens are associated

Tumor immune infiltrating cells are related to survival

The immune clusters are associated with drug response and survival

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1