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. 2022 Apr 15;12(4):1766–1783.

Association of KDR mutation with better clinical outcomes in pan-cancer for immune checkpoint inhibitors

Yanan Cui 1,*, Pengpeng Zhang 2,*, Xiao Liang 1,*, Jiali Xu 1, Xinyin Liu 1, Yuemin Wu 1, Junling Zhang 3, Wei Wang 2, Fang Zhang 4, Renhua Guo 1
PMCID: PMC9077071  PMID: 35530271

Abstract

Kinase insert domain receptor (KDR) activation is associated with the immunosuppressive microenvironment. However, the efficacy of immunotherapy in patients with KDR mutations is still unclear. To investigate the relationship between KDR gene mutations and the prognosis of pan-cancer, and whether immune checkpoint inhibitors (ICIs) may improve the prognosis of patients with KDR mutations, we analyzed public cohorts of pan-cancer immunotherapeutic patients including genomic and clinical data.Further analysis was performed on an internal validation data set including 67 non-small cell lung cancer. Through bioinformatics analysis, potential mechanism was studied in TCGA data. We found better responses to ICIs in patients with KDR mutation from pan-cancer public datasets (objective response rate [ORR], 45.0% vs 25.1%, P=0.0058; progression-free survival [PFS], P=0.039, HR=0.586, 95% CI 0.353-0.973) and validation cohort (overall survival (OS), P=0.05, HR=0.62; 95% CI, 0.38-1.00). Our NSCLC cohort verified the value of KDR mutation in predicting better clinical outcomes, including ORR (70.0% vs 22.81%, P=0.0057) and PFS (HR=0.158; 95% CI, 0.045-0.773, P=0.007). KDR mutation was associated with tumor mutation burden high, neoantigen burden and immune cellular activities. Meanwhile, KDR mutation was indicative of an immune-hot status, characterized by higher expression of PD-L1 and abundance of cytotoxic lymphocytes. KDR mutations may be potential positive predictors for pan-cancer received ICIs.

Keywords: KDR, pan-cancer, immune checkpoint inhibitors, biomarker, NSCLC

Introduction

Immune Checkpoint Inhibitors (ICIs) are transforming the landscape of treatment in cancers [1]. With ICIs initially approved for advanced melanoma (MM) [2], immunotherapy has become an important treatment for cancers, including non-small cell lung cancer (NSCLC), breast cancer (BRC), clear cell renal cell carcinoma (ccRCC), head and neck squamous cell carcinoma (HNSCC), etc [3-5].

However, ICIs are not effective for all patients. Patients with different molecular, histological, or genetic characteristics may have different response rates to ICIs. The response rate in monotherapy of ICIs is just 15%-20% [6-9]. Although some biomarkers were proven to be significant positive predictors of ICIs treatment, such as microsatellite instability (MSI), PD-L1, and tumor mutation burden (TMB), nearly half of patients could not benefit from ICIs even if the biomarker was positive [10-12]. The development of biomarkers lacks a breakthrough. The expression of PD-L1 is the most widely accepted predictor of immunotherapy. It has been included in the majority of clinical trials of immunotherapy as an important observation indicator. It is generally believed that patients with PD-L1 positive have a better response rate and prognosis during immunotherapy [13,14]. There are still many technical limitations in PD-L1 expression detection [7,15]. Cytoplasmic PD-L1 protein seems to have an uncertain function in immunotherapy. The antibody used to detect of PD-L1 protein expression remains specific. Tumor specimens collected from bronchoscopy or percutaneous lung biopsy are not enough to get stable results. TMB is evolving as a potential biomarker of ICIs efficacy. It also has some limitations. Each tumor mutation was given equal weight, but not all mutations had the same efficacy to the ICIs. For example, RCC patients have promising efficacy with moderate TMB level [16,17]. In addition, some genes mutations, such as NORTH 1/2/3 mutation, were associated with good ICIs efficacy, while JAK1/2 mutations, MDM2/4 amplification, and EGFR mutations were associated with poor efficacy [18-20]. As a biomarker, these results were not imperfect. Therefore, new biomarkers are urgently needed to screen more patients suitable for ICIs treatment.

Kinase insert domain receptor (KDR) encodes a key receptor that mediates tumor angiogenesis/metastasis switches [21]. Several studies have reported that VEGF-VEGFR2 axis activation is associated with the immunosuppressive microenvironment. Activation of VEGF-VEGFR2 could induce the aggregation of immature dendritic cells, bone marrow-derived suppressor cells and regulatory T cells (Tregs), and inhibit T lymphocyte migration [22,23]. However, the clinical significance of KDR mutations for ICIs treated pan-cancer is unclear.

Methods

Clinical cohorts and TCGA cohort

Based on six published immunotherapy studies, survival and mutation dates were collected and integrated as discovery cohort for this study (Supplementary Figure 1) [24-29]. In the discovery cohort, samples were sequenced using whole-exome sequencing (WES) and the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) panel. Patients with a sample size of <10 (n=3), receiving other treatments (n=10), or unknown treatments (n=4) were excluded. 662 patients were finally included in the discovery cohort, including ccRCC (n=35), NSCLC (n=385), MM (n=205), HNSCC (n=10), and bladder cancer (BLCA) (n=27).

To verify the predictive power of KDR gene to ICIs treatment, we used an expanded pan-cancer cohort with overall survival (OS) and mutational data as the validation cohort [30]. MSK-IMPACT panel were used for the samples from this cohort. After filtering, 1333 patients were included from eight cancer types: renal cell carcinoma (RCC) (n=146), NSCLC (n=303), MM (n=302), HNSSC (n=133), esophagogastric cancer (ESCA) (n=104), colorectal cancer (CRC) (n=106), BRC (n=39), and BLCA (n=200, Figure 1B). All the clinical and mutational dates of the discovery and the validation cohort were obtained from cBioPortal.

Figure 1.

Figure 1

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KDR-MUT was associated with the clinical benefit to ICIs in the discovery cohort. A. Progression-free survival curves comparing the KDR-WT and KDR-MUT groups in patients treated with ICIs therapy from the discovery cohort. B. Overall survival curves comparing the KDR-WT and KDR-MUT groups in patients treated with ICIs therapy from the discovery cohort. C. Bar graph showing proportions of ORR in KDR-MUT and KDR-WT patients. D. Bar graph showing proportions of DCB in KDR-MUT and KDR-WT patients (Fisher’s exact test). E. Overall survival curves comparing the KDR-WT and KDR-MUT groups in the TCGA cohort. F. Progression-free survival curves comparing the KDR-WT and KDR-MUT groups in the TCGA cohort.

Sixty-seven patients from our hospital who received at least two cycles of immunotherapy (camrelizumab, nivolumab, pembrolizumab toripalimab, or tislelizumab) were included in our NSCLC cohort (Figure 1C). All patients had genomic profiling of tumor tissue before anti-PD-1 treatment (288-gene panel).

The clinical data, somatic mutation data, and RNA-seq data of the TCGA cohort were retrieved from the UCSC Xena data portal.

Calculation of the TMB and NAL

The TMB was defined as the total number of non-synonymous mutations/exome size or normalized to the exonic coverage of MSK-IMPACT panels for the discovery or validation cohorts, respectively. The high TMB cutoff was the highest 20% of TMB in each cancer, as previously described [30]. NAL (neoantigen load) and TCR (T cell receptor) diversity scores were from pan-cancer immune landscape project [31].

Immune infiltration estimation, leukocyte, lymphocyte and TIL fraction analyses

Cell-type identification was performed by estimating relative subgroup of RNA transcripts (CIBERSORT) based on an online method. Gene profiles was used to describe cellular composition of complex tissues [32]. Hematopoietic subsets were counted using mixed mRNA mixtures from TCGA database.

By analyzing Thorsson’s data, we estimated TIL level using genomics and HE-stained in the TCGA pan-cancer cohort [31]. The leukocyte fraction data was derived from DNA methylation, and an aggregation of plasma cells, NK cells, CD8 T cells, gamma-delta T cells, Tregs, follicular helper T cells, CD4 T cells, and B cells estimated by CIBERSORT. TIL fraction analysis was based on the mappings of TILs for >5,000 HE-stained images on TCGA dataset [33].

Assessment of immune signatures

Gene set enrichment analysis (GSEA) was used to associate the gene signature with KDR-MUT and KDR-WT with the “Cluster Profiler” R package. The normalized enrichment score is the main statistical data to test results of gene set enrichment. In addition, in order to study the relationship between the antitumor immunity and KDR mutation, we obtained 29 classical immune signatures from a previous study [34]. GSEA was used to quantitatively analyze enrichment levels of 29 immune signatures in each sample.

Immunohistochemistry

The paraffin sections of NSCLC were taken from pathology department of our hospital. After dewaxing and rehydrating, the tumor sections were immersed into pH 6.0 citrate buffer for antigen extraction. Endogenous peroxidase was blocked with 3% H2O2, and 3% BSA was added to evenly cover the tissue at 37°C for 30 minutes, then blocking solution was gently removed. The primary antibody was added at 4°C overnight. Then, the HRP labeled secondary antibody from the corresponding species of the primary antibody was added and incubated at 37°C for 50 minutes. DAB staining, hematoxylin dying at 37°C for 3 minutes, dehydration and neutral resin sealing were performed. Anti-CD3 (Servicebio, GB111337), anti-CD31 (Servicebio, GB11063-1), anti-ICAM1 (Servicebio, GB11106) were used. Immunohistochemical staining was quantified by HALO image analysis software.

Statistical analysis

All statistical analyses were conducted with R software (version 3.6.3). Fisher’s exact test was used to assess the KDR status and the response. Log-rank test and Cox proportional hazards regression analysis were used to analyze the differences between KDR-MUT and KDR-WT in PFS and OS. The Cox proportional hazards model was used for subgroup survival analysis and adjustments were made for available confounding. Interactions between the KDR status and age, sex, cancer type, TMB level, and drug class were assessed in the validation cohort. Statistical analysis of comparisons between two groups was conducted using the Wilcoxon test. P values were two-tailed, and <0.05 were considered statistically significant.

Results

Association between VEGF signaling pathway and ICIs efficacy in the discovery cohort

Considering that KDR is a gene in the VEGF signaling pathway, we further explored the relationships between genes in the VEGF pathway and the efficacy of immunotherapy. In the discovery cohort, eight genes involved in the VEGF signaling pathway were investigated, including KDR, FLT1, FLT3, FLT4, VEGFC, VEGFA, VEGFD, and VEGFB. Among these genes, KDR mutation was the only one that significantly prolonged PFS and OS (Supplementary Figure 2A, 2B). In addition, only KDR mutation was gathered in patients with ORR and DCB (Supplementary Figure 2C), indicating that KDR mutation may be the best biomarker for predicting the efficacy of immunotherapy.

Association between KDR-MUT and ICIs efficacy in the discovery cohort

Six publicly available studies involving 5 cancer types were consolidated into the discovery cohort. The basic characteristics were summarized in Supplementary Table 1. Fifty-one patients were harboring KDR mutations (KDR-MUT), accounting for 7.7% of the discovery cohort, and 611 patients were KDR wild-type (KDR-WT). 3 (5.9%) patients were confirmed harboring p.S803Y mutations and 3 (5.9%) patients with p.R1032Q mutations (Supplementary Figure 3A). We found that patients with KDR-MUT had longer PFS than patients with KDR-WT (median PFS: 48.91 vs 6.63 months, log-rank test P=0.003, multivariable-adjusted P=0.039, multivariable-adjusted HR 0.586; 95% CI, 0.353-0.973, Figure 1A). Superior OS was also observed in KDR-MUT group (median OS: not reached vs 24.08 months, log-rank test P=0.028, multivariable-adjusted P=0.069, multivariable-adjusted HR=0.595; 95% CI, 0.340-1.040, Figure 1B). After adjusted for sex, age, cancer types, drug class, and TMB level, the significant difference remained in PFS (multivariable-adjusted P=0.039, multivariable-adjusted HR=0.586; 95% CI, 0.353-0.973), but only a numerically significant OS benefit (multivariable-adjusted P=0.069, multivariable-adjusted HR=0.595; 95% CI, 0.340-1.040). According to RECIST 1.1, the overall response of 594 patients was evaluable, including 40 KDR-MUT patients and 554 KDR-WT patients. As expected, the ORR in patients with KDR-MUT was almost twice as higher as in patients with KDR-WT (45.0% vs 25.1%, P=0.0058, Figure 1C). As for DCB, 56% of patients with KDR-MUT were from ICIs treatment, while 41.8% of patients with KDR-WT were from ICIs (P=0.0522, Figure 1D).

Further, to assess the potential prognostic value of KDR mutations, we performed survival analyses based on KDR status in the TCGA database. There was no significant difference in OS between the KDR-WT and KDR-MUT patients treated with standard treatment (Figure 1E), and the same results across multiple cancer types were presented in Supplementary Figure 4. Although significantly worse PFS was observed in KDR-MUT patients (median PFS 49.249 vs 70.125 months, log-rank test P=0.04, HR=1.170; 95% CI, 1.01-1.36), it was no longer significant after adjusting for sex, age, and cancer types (multivariable-adjusted P=0.36, multivariable-adjusted HR=0.93; 95% CI, 0.80-1.08, Figure 1F). Taken together, KDR-MUT may potentially predict the efficacy and favorable clinical outcomes of ICIs treatment.

Association between KDR-MUT and ICIs efficacy in the validation cohort

The basic characteristics of an expanded ICI-treated cohort (n=1333) were summarized in Supplementary Table 2. There were 73 cases of KDR-MUT, including 39 MM, 11 BLCA, 9 NSCLC, 5 HNSCC, 3 ESCA, 3 RCC, 2 CRC, and 1 BRC, accounting for 5.5% of the population. In the validation cohort, 73 of 1333 patients were confirmed to have KDR mutations (Supplementary Figure 2B). p.R1032Q (2.7%) was high frequency mutation site in KDR mutation, followed by p.P351S (2.7%), p.E469K (2.7%). Adjusted for confounding factors (sex, age, cancer types, drug class, and TMB level), KDR mutation remained an independent predictor for superior OS (median OS 46.0 vs 22.0 months, log-rank test P=0.0001, multivariable-adjusted P=0.05, multivariable-adjusted HR=0.62; 95% CI, 0.38-1.00, Figure 2A). Even compared with other oncogenes, KDR remained the most stable predictor for multiple cancers (Supplementary Table 3) [25,26,35-38]. In the stratification analysis of OS, KDR-MUT also had a survival advantage over KDR-WT in subgroups of age, sex, cancer type, TMB status, and drug class (Figure 2B, P>0.05).

Figure 2.

Figure 2

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Validation of the predictive function of KDR-MUT. A. Overall survival curves comparing the KDR-WT and KDR-MUT groups in patients treated with ICIs therapy from the validation cohort. B. Stratification analysis of OS in the validation cohort. NSCLC, non-small cell lung cancer; SKCM, melanoma; HNSC, head and neck cancer; CRC, colorectal cancer; BLCA, bladder cancer; ESCA, esophagogastric cancer; RCC, renal cell carcinoma; BRCA, Breast invasive carcinoma. C. Overall survival curves comparing KDR-MUT&TMB high, KDR-MUT&TMB low, KDR-WT&TMB high, and KDR-WT&TMB low groups in the validation cohort.

Recently, TMB has been proven to be an effective immunotherapy biomarker [39]. Hence, to evaluate the predictive performances of TMB and KDR, patients were divided into four groups based on KDR status and TMB level. We found that KDR-MUT patients achieved the longest OS. Moreover, KDR mutation was associated with higher TMB (adjusted P=0.04, HR=0.40; 95% CI, 0.19-0.94, Figure 2C). We also explored the effect of the most frequent types of KDR mutations on TMB in discovery and validation cohort. As shown in Supplementary Figure 6, patients with specific KDR mutation sites had higher TMB than KDR-WT patients, but there was no difference between patients with different KDR mutation subtypes.

Association between KDR-MUT and ICIs efficacy in NSCLC cohort

In our study, 67 NSCLC patients with genomic profiling were included (Supplementary Table 4). In NSCLC cohort, 54 of the 67 patients (80.6%) were male with a median age of 64-year-old (range, 32-80). The ORR was 29.8%. In the NSCLC cohort, the frequency of KDR mutations was 14.9%. And p.R1032Q mutation was not found in NSCLC with KDR mutation (Supplementary Figure 3C).

The genomic mutational landscape of 67 patients is displayed in Figure 3A. Consistent with other studies, the rate of TP53 and LRP1B mutations were higher in responders than non-responders [37,40]. Besides these, KDR mutation enrichment was discovered in responders as well (Figure 3B). KDR mutation was associated with higher ORR (70.0% vs 22.81%, P=0.0057, Figure 3C), longer PFS (median PFS 20 months vs 7 months, log-rank test P=0.007, HR=0.158; 95% CI, 0.045-0.773, Figure 3D) in NSCLC patients. These results further demonstrate the predictive function of KDR mutation to ICIs treatment.

Figure 3.

Figure 3

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KDR-MUT was associated with a better response to ICIs in the NSCLC cohort. A. Stacked plots show mutational burden (histogram, top); lines of treatment (histogram); mutations in TP53, DPYD, LRP1B, KDR, KRAS, ERBB2, SMARCA4, FAT1, ROS1, ERBB4, NF1, PIK3CA, PDGFRA, EGFR, ARID1A, FBXW7, STK11, PTEN, NOTCH1, and POLE (tile plot, middle); their mutational rates in patients having achieved objective response or progressive disease (histogram, right); and mutational marks (bottom). B. Scatter diagram displaying the mutational rate in patients having achieved objective response or progressive disease. KDR is emphasized in red. C. Histogram depicting proportions of ORR and DCR in KDR-MUT and KDR-WT patients. D. Progression-free survival curves in the patients with or without KDR-MUT.

Association of KDR-MUT with enhanced immunogenicity and activated immune response

The mutational landscape of KDR and clinical characteristics were shown in Figure 4A, with MM patients (16.4%) having the highest levels of KDR mutations, followed by NSCLC (10.5%) and GBM (8.8%) (Figure 4B). Across all patients, the mutational frequencies of KDR was 5.0%. The somatic mutations of KDR were evenly distributed (Figure 4C, Supplementary Figure 4A), and there was no difference in PFS and OS with the common KDR mutation subtypes (Supplementary Figure 7).

Figure 4.

Figure 4

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The pan-cancer mutational landscape of KDR in TCGA cohort. A. Association between KDR status and annotated clinical characteristics in TCGA cohort. (cancer type, sex, age, CNA, TMB, PFS, and OS were annotated. Samples were sorted by KDR status, while KDR-MUT and KDR-WT samples were separated by a gap. B. The proportion of KDR-MUT tumors identified for each cancer type with alteration frequency above 1%. C. Lollipop plot showing the loci distribution of mutations across the KDR altered patient cohorts from the TCGA database. Truncating mutations included nonsense, nonstop, splice site mutations, and frameshift insertion and deletion; Non truncating mutations included missense mutations and inframe insertion and deletion.

To further explore the potential mechanisms associated with KDR-MUT in predicting the efficacy of ICIs. We first analyzed the correlation between KDR and TMB and neoantigen burden. As shown, KDR mutations were associated with higher TMB in the TCGA, discovery and validation cohorts, as well as significantly higher predicted neoantigens in the TCGA cohort (Figure 5A; Supplementary Figure 3A), suggesting that the KDR positive patients may have a higher immune reaction to tumor neoantigens.

Figure 5.

Figure 5

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KDR-MUT was associated with enhanced tumor immunogenicity and anti-tumor immunity. A. Comparison of the TMB and NAL levels between the KDR-MUT and KDR-WT tumors in the TCGA cohort. B. Significantly enriched pathways with GSEA between KDR-MUT and KDR-WT tumors in the TCGA dataset. C. Comparison of the MHC molecules and co-stimulators expression levels between KDR-WT and KDR-MUT tumors in the TCGA cohort. D. Comparison of the expression of TCR richness between KDR-WT and KDR-MUT tumors. E. Heatmap shows clustering of tumor types based on angiogenesis and APC correlated gene sets. The heatmap is colored by the normalized enrichment score of a gene set for a tumor type. F. Comparison of the adhesion molecules expression levels in KDR-MUT and KDR-WT groups. G. Quantitative immunohistochemistry (IHC) analysis of CD31 and ICAM1 protein expression in the KDR-MUT and KDR-WT groups (n=4/group). Representative micrographs of these samples are shown. Data represent the mean ± SD. (Mann-Whitney U test; ns, not significant; *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001).

GSEA revealed the significant enrichment of antigen processing and presentation pathway in the KDR-MUT group, along with other immune activation-related pathways, including T-cell receptor signaling, NK cell-mediated immunity, CD8+ T cell activation, and IFN-gamma response (Figure 5B). In addition, KDR-MUT tumors had higher expression of MHC I- and II-associated antigen-presenting molecules than KDR-WT tumors (Figure 5C). Antigen-presenting cells (APCs) have long been known to present tumor-associated antigens to T cells, which elicited tumor-specific immune responses. Immune evasion associated with in antigen presentation to T lymphocytes defects is a common phenomenon and highlights the relevance of T cells in solid tumors rejection. These results attenuate this effect in KDR-MUT tumors, which may make tumor cells susceptible to ICIs.

Following antigen processing and presentation by APC, two signals are essential to activate the immune response against cancer cells: peptide-MHC complexes occupy TCRs and subsequent activate the costimulatory molecules [41]. The host immune system needs to maintain a diverse TCR repertoire to recognize multiple tumor neoantigens [42]. We found the TCR diversity and costimulatory molecules were significantly higher in KDR-MUT tumors than KDR-WT tumors (Figure 5C and 5D). Taken together, these results suggested that KDR-MUT was associated with enhanced immunogenicity and activation of tumor neoantigens immune response.

Notably, we also found that angiogenesis and VEGFR signaling was down-regulated in KDR-MUT tumors than KDR-WT tumors across multiple cancer types (Figure 5B and 5E). VEGFR2 activation promotes endothelial cell proliferation, migration. Consistently, significantly decreased mRNA expression levels of vascular epithelial cell migration and proliferation-related genes were observed (Supplementary Figure 5B). Abnormal angiogenesis was closely associated with immunosuppressive and is characterized by T cell infiltration overcoming higher interstitial fluid pressure, and down-regulated vasculature cell adhesion molecule-1 (VCAM-1) and intracellular cell adhesion molecule-1 (ICAM-1) to impair the T cell extravasation [43]. As shown in Figure 5E and 5F, vascular permeability and expression of ICAM1 were decreased and increased in KDR-MUT patients, respectively. Decreased tumor vessel density labeled by CD31 and increased expression of ICAM1 were further confirmed by immunohistochemistry (Figure 5G). These results implied that KDR-MUT was associated with the inhibition of angiogenesis, which could promote immune cell infiltration.

KDR-MUT was indicative of an immune-hot status

Hot tumor microenvironment (TME) characterized by the presence of tumor-infiltrating lymphocytes and PD-L1 expression is associated with increased response to anti-PD-1/L1 monotherapy [44,45]. By using CIBERSORT, we found that activated NK cells and CD8+ T cells were more abundant in KDR-MUT tumors. Besides, M0 and M1 macrophages infiltration increased, while the M2 macrophages infiltration decreased (Figure 6A). To cross-examine the above results with different immune cells assessment methods, we next analyzed the expression profiles of 29 immune signatures. As shown in Figure 6B, KDR-MUT tumors have rich immune signatures and microenvironment cell populations, such as dendritic cells (DCs) and cytotoxic lymphocytes. Then, using three different methods, we further verified the abundance of immune cells in KDR-MUT tumors. First, the larger leukocyte fraction in KDR-MUT tumors was assessed based on DNA methylation arrays (Figure 6C). Next, we obtained a similar high TIL results in KDR-MUT tumors based on TIL score estimated from HE-stained slides (Figure 6D) [33], which was consistent with the results of lymphocyte fraction estimated by the CIBERSORT method (Figure 6E). Importantly, CD3 immunohistochemical results further confirmed the presence of tumor-infiltrating T cells in KDR-MUT tumors (Figure 6F).

Figure 6.

Figure 6

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KDR-MUT was indicative of an immune-hot status. A. Comparison of the 22 immune cells infiltration levels in KDR-MUT and KDR-WT tumors. CIBERSORT was used to calculate the infiltration degree of these immune cells. B. Volcano plots of 29 immune signatures estimated by the GSEA method. Immune signatures enriched in KDR-MUT tumors and KDR-WT tumors are marked in red and blue, respectively. C. Comparison of the leukocyte fractions between KDR-WT and KDR-MUT tumors. D. Comparison of the TIL regional fractions between KDR-WT and KDR-MUT tumors. E. Comparison of the lymphocyte fractions between KDR-WT and KDR-MUT tumors. F. Quantitative immunohistochemistry (IHC) analysis of CD3 protein expression in the KDR-MUT and KDR-WT groups (n=4/group). Representative micrographs of these samples are shown. Data represent the mean ± SD. G. Boxplot comparing the immune-related genes expression levels in KDR-MUT and KDR-WT groups (Mann-Whitney U test; ns, not significant; *P<0.05, **P<0.01, ***P <0.001, ****P<0.0001).

Some chemokines (such as CXCL9, CXCL10, and CCL5) were also up-regulated in the KDR-MUT tumors, and these chemokines have been shown to attract CD8 T cells and DCs [46,47]. Additionally, the expression of genes related with cytotoxic activity (such as GZMA, PRF1), the surrogate measures of cytotoxic T lymphocyte (CTL) activity, was higher in KDR-MUT tumors, indicating enhanced tumor-killing capacity. Moreover, PD-L1 (CD274) and CTLA4 were up-regulated in KDR-MUT tumors (Figure 6G). Both of these results demonstrate KDR-MUT was associated with a hot TME and enhanced the ICIs efficacy.

Discussion

In this work, KDR-MUT were identified for the first time as a positive factor for better clinical benefit in pan-cancer patients treated with ICIs, particularly NSCLC. However, patients receiving standard care did not receive the clinical benefits of OS. In the exploratory analyses, higher TMB, higher neoantigen burden, and reduced expression of genes associated with VEGFR pathway activation may be potential mechanism for predicting KDR mutation in pan-cancer (Supplementary Figure 8). GSEA revealed prominent enrichment of signatures related to antigen processing and presentation in patients with KDR-MUT. Using CIBERSORT, we found cytotoxic lymphocytes were more abundant in KDR-MUT tumors. GSEA showed that KDR-MUT tumors had rich immune signatures and microenvironment cell populations, further proving that KDR-MUT was associated with a hot tumor microenvironment. These results suggest that KDR-MUT may be a potentially positive predictor of pan-cancer patients treated with ICIs.

In recent years, more and more immunotherapy treatments has been applied to clinical practice. How to select patients who can benefit from immunotherapy has become a thorny clinical problem. Some specific mutations have been proved to influence the response to immunotherapy. It was reported that MMR, POLE, and POLD1 were related to the response to immunotherapy [27,28,37-40]. These genes seem to lack powerful clinical evidence. Our study demonstrated that KDR-MUT has the potential to become a biomarker for ICIs. We showed the association between KDR gene status and the responses of immunotherapy in three independent cohorts. Importantly, in our own NSCLC cohort, we excluded some confounding factors, such as the course of treatment which were not covered in the public cohort. It can reduce the statistical bias to some extent and make the result more reliable. KDR mutation is not so rare in tumors, accounting for about 5.0% of the pan-cancer cohort, with MM ranking first (16.4%) followed by NSCLC (10.5%) and GBM (8.8%). This means that KDR-MUT could serve more patients than other rare mutants. Unlike PD-L1 expression or TMB, KDR-MUT can be easily detected by next-generation sequencing. KDR-MUT is also included in most commercially available gene panels.

KDR is a type 2 vascular endothelial growth factor receptor that plays an important role in the regulation of angiogenesis and vascular integrity. However, a possible functional association between PD-1/PD-L1 and VEGF/VEGFR has been reported. In patients with ccRCC and classical Hodgkin lymphoma addition, PD-L1 expression was associated with VEGF and microvessel density [48,49]. Our study showed similar results, with significantly higher TMB values and PD-L1 expression associated with KDR-MUT compared to KDR-WT. It produced more neoantigens, which were processed by APCs and presented to T cells. Up-regulated MHC-related molecules and TCR diversity activated this process. Meanwhile, chemokines contributed to the invasion of CD8 T cells and DCs into the tumor tissues. CTL released GZMA and PRF1 and then enhanced tumor-killing capacity. This is one of the reasons why we believe that KDR nonsynonymous mutations are associated with good clinical outcomes in ICIs.

To our knowledge, this is the first time for exploring the relationship between KDR-MUT and ICIs in pan-cancer. Some limitations still existed in our study including these inherent to a retrospective design. The analysis was based on a universal carcinomatous public cohort that received WES or panel sequencing, which may have resulted in selection bias. However, our own independent cohort of NSCLC was included in the analysis as a strong complement. Due to the limited number of patients, the relationship between KDR-MUT and the efficacy of immunotherapy cannot be further explored, but the overall efficacy of KDR-MUT has been satisfactory. These results should be confirmed in large cohorts. In addition, the potential mechanisms by which KDR mutation enhance the efficacy of immunotherapy were explored only through bioinformatics analysis. Our conclusion is only that KDR mutation is related to the immune hot environment, but the specific mechanism how KDR-MUT caused the immune alterations is worthy of further experimental study.

In conclusion, our results suggested that KDR-MUT was associated with better PFS and OS in pan-cancer patients who received ICIs. KDR-MUT might be an important indicator of the immunogenetic landscape, and should be considered as a therapeutic biomarker for immunotherapy.

Acknowledgements

We are indebted and thankful to all participants for their valuable contributions. This study was supported by the National Natural Science Foundation of China (81972188) and the Medical Important Talents of Jiangsu Province (ZDRCA2016024).

Disclosure of conflict of interest

None.

Supporting Information

ajcr0012-1766-f7.pdf (2.3MB, pdf)

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