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

Tumour immune cell infiltration and response to FOLFOX or FOLFIRI chemotherapy in colorectal cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Tumour immune cell infiltration and response to FOLFOX or FOLFIRI chemotherapy in colorectal cancer Jia Feng, Lei Zhang, You Zou, Rong Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4859735/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction 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. Methods and materials 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. Results Using CIBERSORTx to assess tumor immune infiltration From the expression data of 387 samples, individual immune cell subtypes were deconvoluted and summarized (Fig. 2 a). 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. 2 b). 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. 3 a), neutrophils (OR 0.65, 95% CI 0.49–0.84; p = 1.26×10 − 3 , Fig. 3 c) were substantially inversely connected with the response, while no immune cells significantly positively correlated with response were found. For the FOLFIRI chemotherapy regimen (Fig. 3 b), M0 macrophages (OR 1.41, 95% CI 1.01–2.01; p = 4.75×10 − 2 , Fig. 3 d) 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. 3 e) 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. 6 a, 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. 6 b) and improved patient OS (Fig. 6 c). In contrast, a lower ratio of M0 macrophages and a higher ratio of activated mast cells were linked to poor OS. Discussion 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. Abbreviations 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. Declarations 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. 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Curr Opin Immunol, 2016. 39 : p. 1-6. Shaul, M.E. and Z.G. Fridlender, Neutrophils as active regulators of the immune system in the tumor microenvironment. J Leukoc Biol, 2017. 102 (2): p. 343-349. Varn, F.S., et al., Systematic Pan-Cancer Analysis Reveals Immune Cell Interactions in the Tumor Microenvironment. Cancer Res, 2017. 77 (6): p. 1271-1282. Murdoch, C., et al., The role of myeloid cells in the promotion of tumour angiogenesis. Nat Rev Cancer, 2008. 8 (8): p. 618-31. Peddareddigari, V.G., D. Wang, and R.N. Dubois, The tumor microenvironment in colorectal carcinogenesis. Cancer Microenviron, 2010. 3 (1): p. 149-66. T, G.S., Innate and adaptive immune cells in Tumor microenvironment. Gulf J Oncolog, 2021. 1 (35): p. 77-81. Gordon, S.R., et al., PD-1 expression by tumour-associated macrophages inhibits phagocytosis and tumour immunity. Nature, 2017. 545 (7655): p. 495-499. Pagès, F., et al., Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med, 2005. 353 (25): p. 2654-66. Diederichsen, A.C., et al., Prognostic value of the CD4+/CD8+ ratio of tumour infiltrating lymphocytes in colorectal cancer and HLA-DR expression on tumour cells. Cancer Immunol Immunother, 2003. 52 (7): p. 423-8. Ma, P.F., et al., Cytotherapy with M1-polarized macrophages ameliorates liver fibrosis by modulating immune microenvironment in mice. J Hepatol, 2017. 67 (4): p. 770-779. Italiani, P. and D. Boraschi, From Monocytes to M1/M2 Macrophages: Phenotypical vs. Functional Differentiation. Front Immunol, 2014. 5 : p. 514. Edin, S., et al., The distribution of macrophages with a M1 or M2 phenotype in relation to prognosis and the molecular characteristics of colorectal cancer. PLoS One, 2012. 7 (10): p. e47045. Rigo, A., et al., Macrophages may promote cancer growth via a GM-CSF/HB-EGF paracrine loop that is enhanced by CXCL12. Mol Cancer, 2010. 9 : p. 273. Farinha, P., et al., Analysis of multiple biomarkers shows that lymphoma-associated macrophage (LAM) content is an independent predictor of survival in follicular lymphoma (FL). Blood, 2005. 106 (6): p. 2169-74. Dave, S.S., et al., Prediction of survival in follicular lymphoma based on molecular features of tumor-infiltrating immune cells. N Engl J Med, 2004. 351 (21): p. 2159-69. Wang, D., et al., Th17 cells inhibit CD8(+) T cell migration by systematically downregulating CXCR3 expression via IL-17A/STAT3 in advanced-stage colorectal cancer patients. J Hematol Oncol, 2020. 13 (1): p. 68. Foroutan, M., et al., The Ratio of Exhausted to Resident Infiltrating Lymphocytes Is Prognostic for Colorectal Cancer Patient Outcome. Cancer Immunol Res, 2021. 9 (10): p. 1125-1140. Zhang, X., et al., Combination of multiple tumor-infiltrating immune cells predicts clinical outcome in colon cancer. Clin Immunol, 2020. 215 : p. 108412. Ma, S., et al., IL-17A produced by γδ T cells promotes tumor growth in hepatocellular carcinoma. Cancer Res, 2014. 74 (7): p. 1969-82. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additionalfigure.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4859735","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":347699183,"identity":"b6fcf4fb-5fc6-42e7-a452-c8063f97529d","order_by":0,"name":"Jia Feng","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Feng","suffix":""},{"id":347699184,"identity":"0be9de7f-7207-4ccb-9eae-9eb3a10407c5","order_by":1,"name":"Lei Zhang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":347699185,"identity":"9407b059-a709-4330-9308-38b1563150f0","order_by":2,"name":"You Zou","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"You","middleName":"","lastName":"Zou","suffix":""},{"id":347699192,"identity":"e0ff4832-b9b1-4910-a069-7e4edeb4d5cc","order_by":3,"name":"Rong Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACPmYIncDAwHwAwjxAQAsbQgtbApFaGOBaeAyI1MLOY/bh447aPIMbOR8//mxjkOO7kcD4uQCvw3iMZ848c7zY4EbuZmneNgZjyRsJzNIzCGhh5m07lrjhdu42ZsY2hsQNNxKAgsRpyXnGCHRYPbFaakBa2BiADkswIKyFrZhxZtuBxJn3nxlL85yTMJx55mGzND4t/PyHNzN8bKtL7Dtz+OHHH2U28nzHkw9+xqcFCg4zKBwAMySAmLGBsAYGhjoGeaLUjYJRMApGwYgEACQnSPw9C3FyAAAAAElFTkSuQmCC","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Rong","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-08-05 06:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4859735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4859735/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64386040,"identity":"a2af0c44-d5fe-47b6-b4a4-3a1addc2eeca","added_by":"auto","created_at":"2024-09-12 12:30:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":144974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe filtering process for samples was used at each stage of the analysis. \u003c/strong\u003eOS: overall survival; PFS: progression-free survival, CRC: colorectal cancer.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4859735/v1/463f0915d7d47b9dd87c4f23.png"},{"id":64386915,"identity":"de04fbe5-82df-4f49-9fde-56e7bd7f16f2","added_by":"auto","created_at":"2024-09-12 12:38:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3319388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of immune cell subtype scores and correlation of proportions across all datasets pooled in this study. \u003c/strong\u003e(a) \u0026nbsp;Stacked bar plot of scores for all 22 immune cell subtypes. (b) Heatmap representing the correlation matrix of proportions for 22 immune cell types.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4859735/v1/38d98757fff789dd423a514b.png"},{"id":64386042,"identity":"0d11b9b6-82e1-48e4-a042-427a12711591","added_by":"auto","created_at":"2024-09-12 12:30:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1012736,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between patient treatment response and tumor-infiltrating immune cells under different chemotherapy regimens. \u003c/strong\u003eForest plots depict the odds ratios (ORs) (boxes) and 95% confidence intervals (CIs) (horizontal lines) related to patient treatment response under the FOLFOX (a) and FOLFIRI (b) regimens. The size of the boxes is inversely proportional to the standard error of the OR. * indicates OR values with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. Spine plots (c-e) show the distributional changes in response rates within quartiles of immune cell subgroups. OR, odds ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4859735/v1/ce70564c7ba0f30cb14621c0.png"},{"id":64386043,"identity":"b9847c14-1aa4-4643-a06a-74fc33220af3","added_by":"auto","created_at":"2024-09-12 12:30:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1296545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations between tumor-infiltrating immune cell subtypes and PFS. \u003c/strong\u003eForest plots depict the hazard ratios (HRs) (boxes) and 95% confidence intervals (CIs) (horizontal lines) associated with patient PFS under FOLFOX (a) and FOLFIRI (b) regimens. The results are from univariate Cox regression analysis. The box sizes are negatively proportional to the standard error of the HR. * indicates HR values with \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05. Survival pots based on three quartiles of immune cell components (c-f). The displayed p-values are calculated based on the log-rank test. HR, hazard ratio; PFS, progression-free survival; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4859735/v1/c1ba2a510b18be18a71732d2.png"},{"id":64386916,"identity":"c357971a-a984-434c-96ae-6540d29615e8","added_by":"auto","created_at":"2024-09-12 12:38:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1319953,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between tumor-infiltrating immune cell subtypes and OS.\u003c/strong\u003e Forest plots depict the hazard ratios (HRs) (boxes) and 95% confidence intervals (CIs) (horizontal lines) associated with patient OS under FOLFOX (a) and FOLFIRI (b) regimens. The findings come from univariate Cox regression analysis. The box sizes are inversely proportional to the standard error of the HR. * indicates HR values with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. Survival curves based on quartiles of immune cell components (c-e). The displayed p-values are generated with the log-rank test. HR, hazard ratio; OS, overall survival; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4859735/v1/302c4fe943abc4b0c1a10c51.png"},{"id":64386047,"identity":"8853325b-c16f-4569-87c1-f505eab11fd2","added_by":"auto","created_at":"2024-09-12 12:30:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":722401,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConsensus clustering of all samples based on immune cell proportions. \u003c/strong\u003eStacked bar plot of samples sorted by immune cluster assignment (a). Spine plots (b) display the distribution of response rates within quartiles of tumor-infiltrating immune cell subtype under the FOLFOX regimen. OS survival curves under the FOLFOX chemotherapy regimen stratified by immune clusters (c). The displayed p-values are calculated from the log-rank test. OS: overall survival.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4859735/v1/a740b07b5c005b917a8e20e2.png"},{"id":78795082,"identity":"f3c59358-3027-45d3-ad33-6b5c5c042852","added_by":"auto","created_at":"2025-03-19 05:01:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8903838,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4859735/v1/adfe4377-b5fd-4e74-8573-75892541b628.pdf"},{"id":64386914,"identity":"0623dd81-9d6e-4bd8-91ae-d1e7b2444d86","added_by":"auto","created_at":"2024-09-12 12:38:42","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25209,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4859735/v1/f4bacf5883114cb94636b83b.xlsx"},{"id":64386046,"identity":"99e1a671-d15c-4475-8956-a899936c5b34","added_by":"auto","created_at":"2024-09-12 12:30:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3632043,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-4859735/v1/769ffe9f942d0d59e0d369fa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tumour immune cell infiltration and response to FOLFOX or FOLFIRI chemotherapy in colorectal cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs one of the most prevalent cancers globally, colorectal cancer (CRC) has experienced a rise in incidence and death rates in recent years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. By 2030, it is anticipated that there will be about 2.2\u0026nbsp;million newly diagnosed patients and 1.1\u0026nbsp;million fatalities, highlighting the urgent need for effective prevention, diagnosis, and treatment strategies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Numerous studies have identified various risk variables, such as age, gender, genetics, and lifestyle, that contribute to the prevalence of CRC [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Studies have shown that survival benefits can be explained by higher tumor response rates in colorectal cancer patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Factors affecting the response to FOLFOX or FOLFIRI-based chemotherapy in CRC include the activation of the Wnt/β-catenin signaling pathway [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], sustained activation of the multiple drug resistance pathway [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], 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.\u003c/p\u003e \u003cp\u003eThe 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 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. High infiltration of effector and memory T lymphocytes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] or tumor-infiltrating plasmacytoid dendritic cells [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] 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 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], platinum-based chemotherapy for ovarian cancer with high grade serous [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and tumor immune checkpoint inhibitors in melanoma, clear cell renal cell carcinoma, and urothelial cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The prognostic significance of tumor-infiltrating immune cells in colorectal cancer (CRC) after FOLFOX or FOLFIRI treatment, however, has not been thoroughly studied.\u003c/p\u003e \u003cp\u003eIn 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 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], 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.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eChemotherapy patient gene expression data and data processing\u003c/h2\u003e \u003cp\u003eOur 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 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], GSE62080 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], GSE19860, and GSE72970 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCalculate immune score using ESTIMATE to evaluate immune response gene characteristics\u003c/h2\u003e \u003cp\u003eIn 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 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssessing immune cell infiltration by computing the makeup of tumor-infiltrating immune cells using CIBERSORTx\u003c/h2\u003e \u003cp\u003eTo estimate the composition of immune cell subsets in tumor tissue, we used CIBERSORTx [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] (Cell type Identification By Estimating Relative Subsets Of RNA Transcripts, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersortx.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersortx.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). 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.\u003c/p\u003e \u003cp\u003eCIBERSORTx 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 \u003cem\u003ep\u003c/em\u003e-value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eClinical outcomes\u003c/h2\u003e \u003cp\u003eCollect 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. \u003cb\u003eTable S1\u003c/b\u003e lists the fundamental data that each dataset provides.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn 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\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e. 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\u0026rsquo;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 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eTo 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.\u003c/p\u003e \u003cp\u003eWhen 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 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].The penalty factor is selected based on 1000 rounds of cross-validation.\u003c/p\u003e \u003cp\u003eA 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.\u003c/p\u003e \u003cp\u003eTo 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.\u003c/p\u003e \u003cp\u003eTo study the tumor-infiltrating immune cell patterns in CRC, consensus clustering analysis was conducted on the immune cell subtypes (samples with CIBERSORTx \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;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 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eR 3.6.2 was used for all statistical analyses in this investigation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The results were considered statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eUsing CIBERSORTx to assess tumor immune infiltration\u003c/h2\u003e \u003cp\u003eFrom the expression data of 387 samples, individual immune cell subtypes were deconvoluted and summarized (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). 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\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e. If a particular immune cell type was present in more than 75% of the samples, it was excluded from subsequent analysis. Immature CD4\u003csup\u003e+\u003c/sup\u003e T cells and T cell γδ cells were filtered out, as the occurrence rate in over 75% of the samples was less than 1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e (\u003cb\u003eFig. S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe pairwise correlation between the 22 subtypes of tumor-infiltrating immune cells in CRC was generally weak to moderately correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In general, the highest positive correlation was observed between eosinophils and plasma cells (R\u0026thinsp;=\u0026thinsp;0.47), while the strongest negative connection (R = -0.47) was seen between resting mast cells and eosinophils.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe immune content score influences the drug response in patients\u003c/h2\u003e \u003cp\u003eWe 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 (\u003cb\u003eFig. S1a\u003c/b\u003e) and the FOLFIRI chemotherapy regimen (\u003cb\u003eFig. S1d)\u003c/b\u003e, no significant correlation between immune content and patients' response, PFS and OS under different chemotherapy regimens was observed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTumor immune cells and response under different chemotherapy regimens are associated\u003c/h2\u003e \u003cp\u003eWe 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 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.05 in CIBERSORTx were filtered out. For the FOLFOX chemotherapy regimen (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), neutrophils (OR 0.65, 95% CI 0.49\u0026ndash;0.84; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.26\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) were substantially inversely connected with the response, while no immune cells significantly positively correlated with response were found. For the FOLFIRI chemotherapy regimen (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), M0 macrophages (OR 1.41, 95% CI 1.01\u0026ndash;2.01; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.75\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed) were significantly negatively correlated with response, while activated dendritic cells (OR 0.71, 95% CI 0.51\u0026ndash;0.99; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.75\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee) were significantly positively correlated with response. For the FOLFOX (\u003cb\u003eTable S2\u003c/b\u003e) and FOLFIRI (\u003cb\u003eTable S3\u003c/b\u003e) chemotherapy regimens, both univariate and multivariate models of immune cells in chemotherapy-treated patients were constructed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the FOLFOX regimen, the results of the univariate analysis indicated that the treatment response was unrelated to age and gender(\u003cb\u003eTable S2a\u003c/b\u003e). Multivariate analysis (\u003cb\u003eTable S2b\u003c/b\u003e) demonstrated that pT, which represents the size and extent of invasion of the primary tumor (OR 0.05, 95% CI 0.01\u0026ndash;0.23; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.16\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), contributed to the adjusted model. The multivariate logistic regression model that employs penalized maximum likelihood estimation, M1 macrophages (OR\u0026thinsp;=\u0026thinsp;1.13) showed a high OR point estimate (\u003cb\u003eTable S2c\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eIn the FOLFIRI regimen, age, gender, and TNM tumor stage were not related to treatment response, according to univariate analysis (\u003cb\u003eTable S3a\u003c/b\u003e). Multivariate analysis (\u003cb\u003eTable S3b\u003c/b\u003e) 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\u0026thinsp;=\u0026thinsp;1.42), resting mast cells (OR\u0026thinsp;=\u0026thinsp;1.29), and CD8\u003csup\u003e+\u003c/sup\u003e T cells (OR\u0026thinsp;=\u0026thinsp;1.26) exhibited high OR point estimates (\u003cb\u003eTable S3c\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTumor immune infiltrating cells are related to survival\u003c/h2\u003e \u003cp\u003eWe 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 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;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.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e or OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) under different chemotherapy regimens. The effect of immune cells on prognosis varied across different chemotherapy regimens.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the FOLFOX regimen, eosinophils (HR\u0026thinsp;=\u0026thinsp;1.38, 95% CI\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;1.83, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.58\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and resting dendritic cells (HR\u0026thinsp;=\u0026thinsp;1.62, 95% CI\u0026thinsp;=\u0026thinsp;1.12\u0026ndash;2.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.01\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) 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\u0026thinsp;=\u0026thinsp;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 (\u003cb\u003eTable S2d\u003c/b\u003e) and OS (\u003cb\u003eTable S2g\u003c/b\u003e). Multivariate Cox regression analysis results revealed that the adjusted PFS model was influenced by eosinophils (HR\u0026thinsp;=\u0026thinsp;1.56, 95% CI\u0026thinsp;=\u0026thinsp;1.15\u0026ndash;2.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.54\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) (\u003cb\u003eTable S2e\u003c/b\u003e). In the multivariate Cox regression model with penalized maximum likelihood estimation, M2 macrophages (HR\u0026thinsp;=\u0026thinsp;1.11) had the lowest HR point estimates in PFS, while resting dendritic cells (HR\u0026thinsp;=\u0026thinsp;1.59) and eosinophils (HR\u0026thinsp;=\u0026thinsp;1.33) had the greatest HR point estimates. Since the proportion of immune cells was not strongly connected to OS (normal \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), multivariate survival analysis adjusting for OS clinical factors was not performed.\u003c/p\u003e \u003cp\u003eIn the FOLFIRI regimen, activated natural killer cells were linked to low PFS (HR\u0026thinsp;=\u0026thinsp;1.20, 95% CI\u0026thinsp;=\u0026thinsp;1.02\u0026ndash;1.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.58\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and OS (HR\u0026thinsp;=\u0026thinsp;1.32, 95% CI\u0026thinsp;=\u0026thinsp;1.11\u0026ndash;1.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.53\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). Additionally, follicular helper T cells (Tfh cells) were linked to low PFS (HR\u0026thinsp;=\u0026thinsp;1.21, 95% CI\u0026thinsp;=\u0026thinsp;1.00-1.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.91\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), while M1 macrophages (HR\u0026thinsp;=\u0026thinsp;1.26, 95% CI\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;1.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.15\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and M2 macrophages (HR\u0026thinsp;=\u0026thinsp;1.25, 95% CI\u0026thinsp;=\u0026thinsp;1.03\u0026ndash;1.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.62\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) were linked to poor OS. The results of the univariate Cox regression analysis indicated that PFS (\u003cb\u003eTable S3d\u003c/b\u003e) and OS (\u003cb\u003eTable S3g\u003c/b\u003e) 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 (\u003cb\u003eTable S3d-3e\u003c/b\u003e); 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 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cb\u003eTable S3g-3h\u003c/b\u003e). 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\u0026thinsp;=\u0026thinsp;1.07, \u003cb\u003eTable S3f\u003c/b\u003e) and OS (HR\u0026thinsp;=\u0026thinsp;1.14, \u003cb\u003eTable S3i\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe immune clusters are associated with drug response and survival\u003c/h2\u003e \u003cp\u003eTo 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 (\u003cb\u003eFig. S3\u003c/b\u003e). The tumor immune cell infiltration rates based on immune cluster partitioning appear in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, and the distribution of immune cell subtypes is displayed using box plots (\u003cb\u003eFig. S4\u003c/b\u003e). Among them, Cluster 3 exhibited a higher proportion of resting memory CD4\u003csup\u003e+\u003c/sup\u003e T cells, M0 macrophages, and resting B cells, leading to a higher drug response rate after FOLFOX treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) and improved patient OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). In contrast, a lower ratio of M0 macrophages and a higher ratio of activated mast cells were linked to poor OS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs one of the most common malignancies worldwide, CRC poses a major risk to human health due to its escalating death rate [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], calculates the immune cell's makeup in tumor tissue using CIBERSORTx [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], 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.\u003c/p\u003e \u003cp\u003eIn numerous studies prior to this, it has been demonstrated that immune cells infiltrating in tumor microenvironment participate in the tumor immune response [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, in the microenvironment of CRC, numerous immune cells, for example, dendritic cells, natural killer cells, macrophages, CD4\u003csup\u003e+\u003c/sup\u003e T cells, and CD8\u003csup\u003e+\u003c/sup\u003e T cells, are known to infiltrate tumors [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The content of tumor-infiltrating plasmacytoid dendritic cells (pDCs) is substantially positively connected with CRC patients' prognosis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. 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\u003csup\u003e+\u003c/sup\u003e cell infiltration [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Galon et al. showed that the prognosis of patients with CRC is strongly connected with the high infiltration of effector and memory T lymphocytes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A lower CD4\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e ratio can significantly improve CRC patients' 5-year survival rate [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn 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 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Compared to the results of our study, Edin et al. found that as M1 macrophage infiltration increases, patient survival rates also tend to increase [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A negative correlation has been observed between the prognosis for patient survival and tumor-associated macrophages [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eSimilarly, 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\u003csup\u003e+\u003c/sup\u003e 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 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, an essential part of the tumor immune response is played by CD8\u003csup\u003e+\u003c/sup\u003e T cells; there are also many studies exploring their immune function in CRC. Wang et al. showed that Th17 cells reduce CD8\u003csup\u003e+\u003c/sup\u003e 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 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. 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\u003csup\u003e+\u003c/sup\u003e T cells and NK cells [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Therefore, our study results are supported to some extent by previous research findings.\u003c/p\u003e \u003cp\u003eConsensus clustering analysis was carried out on all samples' immune cell components, resulting in the identification of three immune clusters. Among them, resting memory CD4\u003csup\u003e+\u003c/sup\u003e 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\u003csup\u003e+\u003c/sup\u003e 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\u003csup\u003e+\u003c/sup\u003e T cells are safeguarding elements, while M2 macrophages and activated mast cells are harmful ones [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Consistent with our study, M0 macrophages and resting memory CD4\u003csup\u003e+\u003c/sup\u003e 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\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUndoubtedly, 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.\u003c/p\u003e \u003cp\u003eIn 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\u0026rsquo; prognosis who received FOLFOX and FOLFIRI chemotherapy. Furthermore, we discovered an immune cluster characterized by a high proportion of M0 macrophages, resting memory CD4\u003csup\u003e+\u003c/sup\u003e 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.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCRC: 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.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.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\u0026nbsp;assistance for computing. All authors contributed to critical revision of the final manuscript and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Hunan Provincial\u0026nbsp;Science and Technology Innovation Plan Project 2022RC1022, and the National Scientific\u0026nbsp;Foundation of China 31801121.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMontalban-Arques, A. and M. 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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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eWe 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\u003csup\u003e+\u003c/sup\u003e T cells, and quiescent B cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur 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.\u003c/p\u003e","manuscriptTitle":"Tumour immune cell infiltration and response to FOLFOX or FOLFIRI chemotherapy in colorectal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-12 12:30:37","doi":"10.21203/rs.3.rs-4859735/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4b14a38f-0aa6-483e-840c-f13bc92285e7","owner":[],"postedDate":"September 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36889295,"name":"Biological sciences/Cancer"},{"id":36889296,"name":"Biological sciences/Immunology"},{"id":36889297,"name":"Health sciences/Diseases"},{"id":36889298,"name":"Health sciences/Gastroenterology"}],"tags":[],"updatedAt":"2025-03-19T04:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-12 12:30:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4859735","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4859735","identity":"rs-4859735","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}