Abstract
Purpose:
Colon adenocarcinoma (COAD) is a commonly diagnosed malignancy in the world. While immunotherapy, specifically PD-1/PD-L1, shows potential as a treatment for COAD, its efficacy is limited to a minority of patients. This study sought to explore new biomarker that could predict the response to anti-PD-1/PD-L1 therapies in COAD.
Methods:
In this study, potassium channel tetramerization domain containing 9 (KCTD9) was validated as a novel biomarker for predicting immunotherapy efficacy in colon cancer through transcriptomic expression analysis, cellular pathway activity analysis, immune infiltration analysis, and in vivo mouse xenograft tumor formation assays.
Results:
Our findings revealed that KCTD9 was correlated with prognosis across multiple cancer types, and as a protective factor, the expression level of KCTD9 exhibited the most significant impact on colon cancer. Furthermore, positive correlations between KCTD9 expression and both microsatellite instability and tumor mutational burden were observed across multiple tumor types, with the strongest correlations being identified in colon cancer. Finally, high expression of KCTD9 was positively correlated with CD8+ T-cell infiltration and CD8-positive α-β T-cell activation pathway activity and significantly enhanced the anti-tumor efficacy of PD-1 inhibitor.
Conclusions:
Our data validated the strong association between KCTD9 expression and colon cancer prognosis, as well as its capacity to enhance immunotherapy efficacy, indicating that KCTD9 may represent a promising biomarker for colon cancer.
Keywords: colon adenocarcinoma, immunotherapy, KCTD9
Introduction
Colon adenocarcinoma (COAD) is one of the most common cancers worldwide[1]. Although immune checkpoint inhibitors like anti-PD-1/PD-L1 have shown benefits in treating COAD, their effectiveness varies widely among patients. The main challenges include drug resistance and limited treatment response rates. Finding reliable biomarkers to predict anti-PD-1/PD-L1 therapy success is important for improving treatment outcomes in COAD patients.
The tumor microenvironment (TME) contains various tumor infiltrating immune cells (TIICs). Previous studies have shown that TIICs affect how patients respond to immunotherapy[2,3]. COAD is infiltrated by various types of TIIC types, which include T cells, dendritic cells, or macrophages. Research indicates that the type and density of TIICs in COAD play a significant role in determining the prognosis of the disease[4-6]. Additionally, microsatellite instability (MSI)-H tumors, which represent an immunotherapy-sensitive subtype of colon cancer, show increased CD8 + T-cell infiltration in the TME. These CD8 + T cells play a key role in controlling immune “shutdown” or “activation” within the TME[7,8].
Potassium channel tetramerization domain containing 9 (KCTD9) is a protein-coding gene from the KCTD family[9]. Previous studies showed that KCTD9 is highly expressed in liver T cells of mice with MHV-3-induced fulminant hepatitis and serves as an important regulator of immune cell functions[10]. Recent research has confirmed a positive relationship between KCTD9 expression and CD8+ T-cell infiltration levels in non-small cell lung cancer[11]. However, it remains unclear whether KCTD9 can influence CD8+ T-cell infiltration in colon cancer and thereby affect the sensitivity of colon cancer to immunotherapy treatments.
Currently, little is known about how KCTD9 relates to colon cancer immunotherapy or its potential role in regulating immune infiltration in COAD. This study provides evidence that KCTD9 may serve as a predictor for COAD patient outcomes and could influence immunotherapy effectiveness. This work has been reported in line with the TITAN criteria[12].
Materials and methods
Ethics approval statement
All animal experimental protocols and procedures were approved by the Institute of Animal Care and Use Committee at our university (No. W202303060205). All animal experiments were conducted in accordance with the experimental protocol. The operating procedures were strictly regulated, and no ethical requirements were violated. The work has also been reported in accordance with the Animals in Research: Reporting In Vivo Experiments guidelines[13].
Mice and tumor cell lines
MC38 murine colon cancer cells were obtained from Cas9x Biological Technology Co. Ltd. The cells were cultured in RPMI-1640 medium supplemented with 10% FBS. Culture conditions involved maintaining the cells in a humidified atmosphere at 37°C with 5% CO2 concentration. C57BL/6 male mice (6–7 weeks old) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd, and housed in specific pathogen-free conditions. After 5 days of acclimatization, mice were randomly allocated to experimental groups using a random number table. Sample size calculation was based on preliminary experimental results. Tumor volume measurements were performed by researchers blinded to group assignments to ensure objectivity. Baseline characteristics of mice at experiment initiation are as follows: 1. All mice healthy with no obvious abnormal signs; 2.Tumor inoculation success rate: 100%.
Lentiviral expression system
The KCTD9-overexpressing lentivirus was constructed and synthesized by WZ biosciences Co., Ltd. (Shandong, China). The vector chosen for this study was pLent-EF1a-FH-CMV-RFP-P2A-Blasticidin. The target gene was modified by adding a Kozak sequence (GCCACC) upstream of the ATG start codon. Additionally, a C-terminal fusion of the HA protein tag was employed. Notably, the red fluorescent protein (RFP) and the target gene were expressed separately without fusion. This vector design allows for efficient expression and detection of the target gene while simultaneously visualizing RFP fluorescence.
Animal model
5 × 105 cells (MC38-pLent-EF1a-FH-CMV-RFP-P2A-KCTD9) were subcutaneously injected into the right flank of C57BL/6 mice (each experimental group included at least 6 mice). Starting from the 7th day post-inoculation, the mice received intraperitoneal injections of either 10 mg/kg anti-PD-1 (KP-2025, provided by Kyinno Biotechnology) or an isotype control antibody on days 7, 10, 13, and 16.
Tumor volume (mm3) was measured every 3 days using the formula: tumor volume = (length × width2)/2. At experimental endpoints, mice were deeply anesthetized with isoflurane followed by cervical dislocation. Tissue collection was performed immediately after euthanasia. One-way analysis of variance was used to compare tumor volumes among the three groups. No mice died or were prematurely terminated due to adverse events. The tumor masses were collected from the mice, with a portion of the specimens undergoing RNA high-throughput sequencing analysis and another portion being utilized for pathological examination. The RNA sequencing analysis was conducted by Lianchuan BioTech Co., Ltd. (Hangzhou, China).
HIGHLIGHTS
Potassium channel tetramerization domain containing 9 (KCTD9) serves as a protective prognostic biomarker in colon adenocarcinoma.
KCTD9 expression strongly correlates with CD8+ T-cell infiltration and immune activation pathways, providing mechanistic insights for immunotherapy enhancement
Experimental validation demonstrates that KCTD9 overexpression significantly improves anti-PD-1 therapeutic efficacy in vivo, supporting clinical translation.
Data acquisition
STAR-counts data for COAD from the TCGA database (https://portal.gdc.cancer.gov). We then extracted data in TPM format and performed normalization using the log2(TPM + 1) transformation.
The uniformly normalized TCGA Pan-Cancer dataset (PANCAN, N = 10 535, G = 60 499) was obtained from the UCSC Xena Browser database (https://xenabrowser.net/), from which the expression data of KCTD9 gene was extracted across all samples. The significance of two sample groups is determined by the Wilcoxon test.
Correlation analysis of immune cell infiltration and KCTD9 in COAD
The correlation between KCTD9 and immune cells in COAD was analyzed using immunedeconv, an R package which integrates TIMER algorithms. The algorithm calculated the infiltration scores of six immune infiltration cells: CD4+ T cells, CD8+ T cells, neutrophils, dendritic cells, B cells, and macrophages.
Gene set enrichment analysis (GSEA)
CAMOIP is a tool for analyzing the expression data and mutation data from the TCGA and the ICI-treated projects, using a standard processing pipeline[14]. The “Pathway Enrichment” module was used to explore the GSEA of KCTD9 in colon cancer. Our own RNA-sequencing data was analyzed and visualized using the GseaVis R package.
Statistical analyses
Statistical analysis was performed using the R software (Version 4.2.3). It was considered statistically significant when the P-value was less than 0.05. The asterisks represent the level of significance, where *P < 0.05, **P < 0.01, ***P < 0.001.
Results
Prognostic significance and immunotherapy efficacy prediction of KCTD9
To investigate the correlation between KCTD9 gene expression and patient prognosis, we performed survival analysis across different cancer types using TCGA database. We first conducted Cox regression analysis to examine the relationship between KCTD9 expression levels and overall survival (OS) in various cancers. The results showed that KCTD9 expression was significantly associated with OS in multiple cancer types. Patients with high KCTD9 expression displayed markedly longer OS in KIRC, COAD, and COADREAD. Among these cancer types, KCTD9 showed the most significant protective effect in colon adenocarcinoma (COAD), with a hazard ratio of 0.62 (Fig. 1A). This finding suggests that KCTD9 may serve as a favorable prognostic marker in colon cancer.
Figure 1.
Prognostic significance and immunotherapy efficacy prediction of KCTD9. (A). Cox regression analysis of KCTD9 on OS for pan-cancer was depicted via forest diagram. (B-C) The correlation between KCTD9 expression and TMB or MSI. (D) A comparison of KCTD9 mRNA expression levels between normal and colon tumor patients. (E) CD8-positive α-β T-cell activation pathway was enriched in KCTD9-related COAD in the GO-BP category; NES, normalized enrichment score.
Within the TME, MSI and tumor mutational burden (TMB), as key determinants of anti-cancer immune responses, represent reliable predictors for immunotherapy efficacy[15,16]. To evaluate the potential role of KCTD9 in immune-based treatments, we conducted a comprehensive pan-cancer analysis examining its relationship with TMB and MSI across different malignancies. Remarkably, COAD represented the sole malignancy where KCTD9 expression consistently exhibited positive correlations with both immune biomarkers, and the significance of these positive correlations was consistently higher than observed in other tumor types (Fig. 1B-1C). This unique pattern suggests that elevated KCTD9 levels may predict enhanced immunotherapy responsiveness in colon cancer patients.
Given these findings, we next focused specifically on colon adenocarcinoma. Analysis of the transcriptome data from TCGA revealed a decrease in KCTD9 expression in colon cancer patients compared to the normal group (Fig. 1D). Immune cell infiltration levels in tumors, especially CD8+ T cells, have been associated with success in immunotherapy. Subsequently, we evaluated the expression of six major infiltrating immune cell types in COAD patients and compared them with KCTD9 expression levels. The group with high expression of KCTD9 exhibited a higher percentage of CD8+ T cells, neutrophils, dendritic cells, and B cells compared to the group with low expression of KCTD9. Of these, CD8+ T cells was most significantly altered (P = 5.5e-16) (Fig. 1E).
In order to elucidate the underlying mechanism, GSEA was conducted on the TCGA-COAD dataset to compare colon cancer patients with differing levels of KCTD9 expression. The objective of this analysis was to identify signal pathways that are associated with KCTD9. The GSEA results indicated a positive correlation between KCTD9 and the CD8-positive α-β T-cell activation pathway (Fig. 1F). KCTD9 demonstrates the ability to influence immunotherapy efficacy in colon cancer, potentially through modulation of the CD8-positive α-β T-cell activation pathway.
CT imaging and tumor models in vivo
To investigate the influence of KCTD9 on the effectiveness of PD-1 immunotherapy, CT imaging was conducted on mice using the animal micro-CT modality (SkyScan1276, Bruker) to accurately determine the location of the tumor and the surrounding area. Prior to the CT scan, the animals were anesthetized with isoflurane inhalation and maintained under isoflurane anesthesia during the scanning process. The micro-CT analysis revealed a significant reduction in tumor volume in the group receiving both KCTD9 overexpression (KCTD9-OE) and PD-1 treatment, compared to the group receiving PD-1 treatment alone. Additionally, analysis of the peritumoral tissue architecture indicated the presence of identifiable adipose lacunae surrounding the tumor mass in the group treated with KCTD9-OE in conjunction with PD-1. Conversely, tumors in the PD-1 monotherapy group demonstrated extensive infiltration into the surrounding adipose tissue and intercostal muscles, forming cohesive masses that fused with the intercostal tissue (Fig. 2A). These results underscore the significant influence of KCTD9-OE in augmenting the therapeutic effectiveness of PD-1 immunotherapy, as demonstrated by notable reductions in tumor size, maintenance of adipose tissue gaps surrounding the tumor, and prevention of extensive invasion into neighboring tissues.
Figure 2.
KCTD9 overexpression enhances the efficacy of immunotherapy. (A). Micro-CT images obtained using an orthotopic mouse model. Micro-CT images of tumors in mice in the PD-1 alone, and combination of PD-1 and KCTD9-overexpression groups at the indicated weeks after injection MC38 cells. (B) At the end of the experiment, tumor tissues were completely dissected from mice. All tumor tissues from all three groups (NC control group, PD-1 group, and KCTD9-OE plus PD-1) are shown. (C and D) Tumors were measured every 3 days. Tumor volume was estimated using the formula: volume = (length × width2)/2. *P <0.05, ***P <0.001. At the end of the experiment, following a 3-day interval after the last dosage, tumor tissues were excised from mice.
Subsequently, we conducted in vivo graft disaggregation and precise in vitro measurements on the transplanted tumors. In line with the in vivo findings, tumor growth was markedly decreased in the group receiving combined KCTD9-OE and PD-1 treatment compared to the group receiving PD-1 monotherapy (Fig. 2B).
Upon careful analysis of the tumor growth curves across different treatment cohorts, we observed a notable inhibition of tumor growth in the PD-1 monotherapy and KCTD9 + PD-1 groups (P < 0.05). The average tumor volumes were significantly decreased in the PD-1 monotherapy group, with a more pronounced trend seen in the KCTD9 + PD-1 group (P < 0.05, Fig. 2C-2D). These findings indicate that the upregulation of KCTD9 could augment the responsiveness to PD-1 immunotherapy.
Pathway enrichment analysis of different treatment samples by bulk RNA-seq
In order to enhance our comprehension of the mechanisms by which KCTD9 influences the immune response of PD-1, ex vivo tumor tissue from mice was obtained and subjected to RNA sequencing for a thorough analysis. The KCTD9-OE in conjunction with PD-1 group exhibited a significant increase in the “CD8 positive alpha-beta T cell activation” pathway compared to the PD-1 monotherapy group (Fig. 3A). This pathway is essential in immune therapy and serves as a crucial foundation. Subsequent examination through GSEA demonstrated a significant enrichment of the pathways “CD8 positive alpha-beta T cell activation” and “CD8 positive alpha-beta T cell differentiation” in the PD-1 plus KCTD9-OE combination group as opposed to the PD-1 monotherapy group (Fig. 3B-3C). These findings suggest that the presence of KCTD9 affects the activation and differentiation of CD8+ T cells in the immune microenvironment of colon cancer.
Figure 3.
KCTD9 overexpression enhances the efficacy of immunotherapy. (A). Gene Ontology pathway enrichment analysis revealed significant enrichment of pathways (PD-1 plus KCTD9-OE vs PD-1). (B and C) GSEA demonstrated the enrichment of gene sets associated with CD8+ T-cell activation and differentiation (PD-1 plus KCTD9-OE vs PD-1). (D) GSEA revealed an enrichment of gene sets related to the intestinal immune network (PD-1 plus KCTD9-OE group). (E) GSEA indicated an enrichment of gene sets unrelated to the intestinal immune network (PD-1 monotherapy group).
It is well known that the activation of immune-related signaling pathways in the intestinal tract plays a crucial role in colon cancer. Notably, our observations indicate a significant enrichment of the “Intestinal immune network for IgA production” pathway in the PD-1 plus KCTD9-OE treatment group compared to the control group (Fig. 3D, P = 0.01), whereas the PD-1 monotherapy group exhibited unfavorable outcomes in this context (Fig. 3E, P = 0.06). These results reveal how KCTD9-OE increases colon cancer sensitivity to PD-1 immunotherapy, shedding light on the transcriptional landscape of the disease.
Discussion
COAD represents a leading contributor to cancer-related deaths globally. The development of PD-1/PD-L1 checkpoint inhibitors has revolutionized conventional cancer therapy, offering new therapeutic opportunities for COAD patients. While these immunotherapeutic approaches demonstrate clinical efficacy in MSI-high COAD cases, the majority of patients exhibit limited responsiveness to PD-1/PD-L1 blockade therapy[17]. Consequently, discovering reliable and innovative biomarkers remains essential for improving immunotherapeutic outcomes in COAD treatment.
The tumor immune microenvironment is significantly influenced by TIICs. Various immune cell populations, encompassing T cells, B cells, and macrophages, within the tumor architecture substantially affect immunotherapeutic responses[18,19]. CD8+ T cells, characterized by their cytolytic function and direct tumor cell targeting capacity, are well-established as pivotal mediators of anti-cancer immune responses. Upon antigen encounter, CD8+ T cells proliferate and mature into cytotoxic T lymphocytes, subsequently trafficking via circulation to penetrate tumor sites[20]. A previous study found that patients with MSI-H colon cancer who received anti-PD-1 immunotherapy had significantly higher levels of tumor-infiltrating CD8+ T cells compared to MSS patients[21]. Moreover, tumor mutation burden (TMB) represents an important indicator for predicting immunotherapy success. Numerous studies have confirmed that patients possessing elevated TMB levels typically exhibit enhanced therapeutic outcomes following checkpoint inhibitor treatment in various cancer types, such as melanoma, non-small cell lung carcinoma, and colon malignancies[22,23].
Our transcriptomic sequencing analysis demonstrated that KCTD9 expression showed strong positive associations with CD8 + T-cell infiltration levels and TMB in COAD. These findings indicate that heightened KCTD9 expression may serve as a predictive marker for enhanced immunotherapeutic responsiveness in COAD patients. Subsequently, in vivo mouse studies demonstrated substantial tumor growth suppression in both PD-1 monotherapy and KCTD9-OE + PD-1 combination treatment groups relative to controls. Notably, the combination therapy group exhibited the greatest tumor volume reduction. These results indicate that KCTD9 upregulation can augment PD-1 immunotherapy efficacy.
To enhance comprehension of the impact of KCTD9 on the immune response mediated by PD-1, ex vivo tumor tissue samples were collected from mice and analyzed using RNA sequencing. Interestingly, compared to the PD-1 monotherapy group, the KCTD9-OE plus PD-1 group exhibited a significant activation of the “CD8 positive alpha-beta T cell activation” pathway, which serves as a critical foundation for effective immune therapy. The activation of immune-related signaling pathways in the intestinal tract is well established for its crucial involvement in colon cancer. Interestingly, our observations indicate a notable enrichment of the “Intestinal immune network for IgA production” pathway in the PD-1 + KCTD9-OE treatment group, while the PD-1 monotherapy group showed no significant findings in this regard. These results provide valuable insights into the transcriptional profile of colon cancer under the combined treatment of anti-PD-1 and KCTD9-OE. Significantly, these results further validate the role of KCTD9 in enhancing the sensitivity of colon cancer to PD-1 immunotherapeutic drugs, shedding light on the underlying mechanisms involved.
Several considerations should be acknowledged regarding this study. While the mouse xenograft tumor model provides valuable insights into tumor biology, it has inherent differences from the human tumor environment that should be considered when translating findings to clinical applications. Our current sample size, though adequate for initial validation, would benefit from larger cohorts in future studies to strengthen statistical power. Additionally, we used the well-established MC38 cell line model, and future investigations could expand to include additional colon cancer cell lines to broaden the applicability of findings.
Our research was conducted following ethical guidelines and incorporated the 3 R principles throughout the study design. We used optimized experimental design to achieve meaningful results with minimal animal numbers (reduction) and maintained high standards of animal care including appropriate housing, humane procedures, and welfare monitoring (refinement). These approaches ensure that our research contributes valuable scientific knowledge while maintaining ethical standards in animal research.
Footnotes
Lei Wu and Shengnan Sun have contributed equally to this work and share first authorship
Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
Published online 25 June 2025
Ethical approval
This study was approved by the Central Hospital Affiliated to Shandong First Medical University (No.W202303060205). All animal experiments were conducted in accordance with the experimental protocol. The operating procedures were strictly regulated, and no ethical requirements were violate.
Consent
Not applicable.
Sources of funding
This work was supported by the Scientific Research Foundation for the Introduced Talents of Jinan Central Hospital (YJRC2022003), Shandong Provincial Natural Science Foundation (ZR2022QH245), and the Special Funding for High-Level Talents in Jinan Healthcare Sector(202412).
Author contributions
L.W.: conceptualization, data curation, formal analysis, investigation, writing – original draft; S.S.: conceptualization, investigation, data curation; N.L., P.S., C.C.: formal analysis; M.S.,W.Z.: supervision, project administration. All authors read and approved the final manuscript.
Conflicts of interest disclosure
All the authors declare to have no conflicts of interest relevant to this study.
Guarantor
Lei Wu and Weichong Zhao.
Research registration unique identifying number (UIN)
Not applicable.
Provenance and peer review
Not commissioned, externally peer-reviewed.
Data availability statement
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
- [1].Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 Countries. CA Cancer J Clin 2021;71:209–49. [DOI] [PubMed] [Google Scholar]
- [2].Fridman WH, Pagès F, Sautès-Fridman C, et al. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer 2012;12:298–306. [DOI] [PubMed] [Google Scholar]
- [3].Gnjatic S, Bronte V, Brunet LR, et al. Identifying baseline immune-related biomarkers to predict clinical outcome of immunotherapy. J Immunother Cancer 2017;5:44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].Saito T, Nishikawa H, Wada H, et al. Two FOXP3(+)CD4(+) T-cell subpopulations distinctly control the prognosis of colorectal cancers. Nat Med 2016;22:679–84. [DOI] [PubMed] [Google Scholar]
- [5].Galon J, Costes A, Sanchez-Cabo F, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Sci 2006;313:1960–64. [DOI] [PubMed] [Google Scholar]
- [6].Dadabayev AR, Sandel MH, Menon AG, et al. Dendritic cells in colorectal cancer correlate with other tumor-infiltrating immune cells. Cancer Immunol Immunother 2004;53:978–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Mlecnik B, Bindea G, Angell HK, et al. Integrative analyses of colorectal cancer show immunoscore is a stronger predictor of patient survival than microsatellite instability. Immunity 2016;44:698–711. [DOI] [PubMed] [Google Scholar]
- [8].Jiang P, Gu S, Pan D, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med 2018;24:1550–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Skoblov M, Marakhonov A, Marakasova E, et al. Protein partners of KCTD proteins provide insights about their functional roles in cell differentiation and vertebrate development. Bioessays 2013;35:586–96. [DOI] [PubMed] [Google Scholar]
- [10].Zhang X, Wang P, Chen T, et al. Kctd9 deficiency impairs natural killer cell development and effector function. Front Immunol 2019;10:744. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Jiang J, Li X, Zhang C, et al. Anti-cancer effects of coix seed extract through KCTD9-mediated ubiquitination of TOP2A in lung adenocarcinoma. Cell Div 2024;19:6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Agha RA, Mathew G, Rashid R, et al. Transparency in the reporting of artificial intelligence-the TITAN guideline. Premier J Sci 2025;10:100082. [Google Scholar]
- [13].Kilkenny C, Browne WJ, Cuthill IC, et al. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol 2010;8:e1000412. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Lin A, Qi C, Wei T, et al. CAMOIP: a web server for comprehensive analysis on multi-omics of immunotherapy in pan-cancer. Brief Bioinform 2022;23:bbac129. [DOI] [PubMed] [Google Scholar]
- [15].Dudley JC, Lin MT, Le DT, et al. Microsatellite instability as a biomarker for PD-1 blockade. Clin Cancer Res 2016;22:813–20. [DOI] [PubMed] [Google Scholar]
- [16].Yarchoan M, Hopkins A, Jaffee EM. Tumor mutational burden and response rate to PD-1 inhibition. N Engl J Med 2017;377:2500–01. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17].Jácome AA, Eng C. Role of immune checkpoint inhibitors in the treatment of colorectal cancer: focus on nivolumab. Expert Opin Biol Ther 2019;19:1247–63. [DOI] [PubMed] [Google Scholar]
- [18].Liu R, Hu R, Zeng Y, et al. Tumour immune cell infiltration and survival after platinum-based chemotherapy in high-grade serous ovarian cancer subtypes: a gene expression-based computational study. EBioMedicine 2020;51:102602. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Kang Y, Huang J, Liu Y, et al. Integrated analysis of immune infiltration features for cervical carcinoma and their associated immunotherapeutic responses. Front Cell Dev Biol 2021;9:573497. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [20].Zhang N, Bevan MJ. CD8(+) T cells: foot soldiers of the immune system. Immunity 2011;35:161–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [21].De Smedt L, Lemahieu J, Palmans S, et al. Microsatellite instable vs stable colon carcinomas: analysis of tumour heterogeneity, inflammation and angiogenesis. Br J Cancer 2015;113:500–09. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Hodges TR, Ott M, Xiu J, et al. Mutational burden, immune checkpoint expression, and mismatch repair in glioma: implications for immune checkpoint immunotherapy. Neuro Oncol 2017;19:1047–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [23].Samstein RM, Lee CH, Shoushtari AN, et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet 2019;51:202–06. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.