Introduction

BLCA poses a significant global health challenge, ranking as the fourth most common cancer and the eighth leading cause of cancer-related deaths in men [1]. The introduction of immune checkpoint inhibitors (CPIs) in BLCA treatment, sanctioned by the Food and Drug Administration (FDA) in 2017, marked a pivotal shift in therapeutic strategies [2]. However, the efficacy of CPIs remains limited, with the majority of patients showing minimal or no response. Patients with progressive disease (PD) at their best overall response accounted for 48.5% with no survival benefit compared to the second-line chemotherapy [3]. Therefore, understanding the molecular intricacies governing CPI response and exploring innovative methods to enhance CPI effectiveness are imperative.

Genetic alterations in fibroblast growth factor receptor 3 (FGFR3) are frequently identified in BLCA [4, 5]. Previous studies have linked FGFR3 alterations (aFGFR3) to luminal papillary tumors characterized by diminished T-cell infiltrations [6]. While initial assumptions suggested reduced sensitivity of aFGFR3 tumors to CPIs, recent research contradicts this notion, demonstrating comparable CPI effects between aFGFR3 and intact FGFR3 (iFGFR3) BLCA cases [7, 8]. Consequently, exploring the tumor microenvironment (TME) in the context of FGFR3 status emerges as a promising avenue for novel therapeutic interventions. To address this, we conducted a comprehensive multi-omics analysis encompassing 389 BLCA cases and 35 adjacent normal tissues, aiming to unravel the intricate relationship between aFGFR3 and the TME.

Results

FGFR Alterations in NMIBC and MIBC

The cohort in the present study included 124 non-muscle invasive bladder cancer (NMIBC: 5-year overall survival (OS) rate: 83%) and 265 muscle-invasive bladder cancer (MIBC: 5-year OS rate: 35%) with a median follow-up of 36 and 22 months, respectively (Supplementary Fig. 1A). We first assessed mRNA expression levels of FGFR family genes in the cohort. FGFR3 is actively transcribed among the FGFR families in the bladder, especially in BLCA (Supplementary Fig. 1B). FGFR3 was the most frequently mutated gene (81/389; 21%) among the FGFR families including FGFR1 (19/389; 5%), FGFR2 (8/389; 2%), and FGFR4 (12/389; 3%) (Supplementary Fig. 1C). A previous TCGA publication defined 58 significantly mutated genes (SMGs) in BLCA [9], which was largely recapitulated in our cohort including the clinicopathological characteristics (Fig. 1A, B).

Fig. 1
figure 1

FGFR Alterations in NMIBC and MIBC. A Mutation landscape of 58 significantly mutated genes defined by the TCGA publication [9] in 389 bladder cancer (BLCA) samples from the OMPU-NCC cohort. The patients were classified into pTa (n = 59), pT1 (n = 65), and ≥ pT2 (n = 265, MIBC: muscle-invasive bladder cancer). B Recurrent mutation rate of 58 significantly mutated genes according to pathological T stages. C Schematic of the FGFR3 fusions identified in our cohort. FGFR3-TACC3 fusions were found in 14 of 289 patients, and the most frequent pattern (7 of 11) is shown. NSD2 and SPON2 are newly identified fusion partners. D FGFR3 mRNA expression levels according to the FGFR3 alterations. The difference was assessed by the Mann–Whitney U test; p < 0.05*, p < 0.001**, p < 0.0001***. E, F Kaplan–Meier curves demonstrating progression-free survival (PFS) in non-muscle-invasive bladder cancer (NMIBC) (E) and overall survival (OS) in MIBC (F). A log-rank test was used to assess the survival difference between the two groups; p < 0.05*

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To delineate the allelic difference in FGFR3 among ethnicity, we sought to assess whether there is a specific allelic variant in the germline for the Asian population. GnomADv3.0, an integrative germline dataset of 71,702 individuals (mostly Western population), was utilized for the control [10]. We referred the Asian germline dataset (jMorp-14KJPN) [11] and identified five significantly enriched non-synonymous single nucleotide polymorphisms (SNPs) on the FGFR3 gene locus that are specific to the Asian population (Q29H, G65R, L164V, T450M, and A720S) (Supplementary Fig. 1D). However, these SNPs were not enriched in BLCA samples (Supplementary Table 1). Compared to BLCA with iFGFR3, FGFR3 mRNA expression level was significantly upregulated in patients with recurrent FGFR3 mutations but not in patients with the SNPs (Supplementary Fig. 1E). There seemed to be no survival difference based on the FGFR3 status (Supplementary Fig. 1F), indicating no clinical implication of these Asian-specific SNPs in FGFR3.

FGFR3 mutations was predominantly observed in cases with lower malignant properties such as NMIBC (pTa: 51%, pT1: 29%, more than pT2: 12%), low grade and negative lymph vascular invasions (Table 1). Regarding the mutational alleles, the TCGA publication, which only consists of MIBC samples, reports S249C and Y373C as the top two frequent FGFR3 mutations in BLCA (Supplementary Fig. 2A). We noted that recurrent K650E and T757P nonsynonymous mutations at the kinase domain (KD) were frequently observed in MIBC in our cohort compared to the TCGA cohort (Supplementary Fig. 2B). Although we examined the prognosis of five MIBC cases with mutation at KD (three in K650E and two in T757P), there was no difference in OS compared to that in cases with other FGFR3 alterations (Supplementary Fig. 2C). Interestingly, we found that FGFR3 mutations at KD were more prevalent in MIBC than in NMIBC cases (p = 0.021) (Supplementary Fig. 2D).

Table 1 Clinicopathological characteristics in 389 BLCA patients according to the FGFR3 status at the collection of biospecimens
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FGFR3 mRNA expression levels were consistently upregulated in aFGFR3 compared to iFGFR3, regardless of the mutation sites (Supplementary Fig. 2E). The present study exhibited a frequency of 4% (17/389) for FGFR3 fusions (Table 1), including novel fusion partners (NSD2 and SPON2) (Fig. 1C). No histological variant was observed in cases with FGFR3 fusions (Table 1). The KD located at the C-terminus of FGFR3 has been retained in 13 of 17 (77%) fusions, and the mRNA expression level was significantly upregulated in cases with FGFR3 fusions compared to cases with iFGFR3 (Supplementary Fig. 2F). Upon stratifying our cohort into NMIBC and MIBC categories, FGFR3 mRNA expression levels were significantly higher in aFGFR3 cases than in iFGFR3 cases in both NMIBC and MIBC (Fig. 1D), with the highest median mRNA expression levels observed in MIBC patients with FGFR3 fusions. This finding underscores the clinical importance of detecting FGFR3 fusions, alongside mutations, in advanced MIBC patients and accentuates the importance of considering recently approved FGFR3 inhibitors [12]. The FGFR3 protein expression levels were increased in aFGFR3 compared to iFGFR3 cases (Supplementary Fig. 2G, H). We investigated the progression free survival (PFS) of 124 NMIBC patients (Fig. 1E). Patients with recurrent FGFR3 mutations showed a significantly better PFS compared to those with iFGFR3 (p = 0.037). However, this distinction was not evident in patients with FGFR3 fusions (p = 0.806). In the context of OS among 265 MIBC patients, no significant differences were observed based on FGFR3 status (Fig. 1F).

The Association between FGFR3 alteration and molecular Subtypes

We have adopted the established consensus MIBC subtype [6], the UROMOL subtype for NMIBC [13], and Baylor college subtype [3B-3D). In the overall cohort (n = 389), FGFR3 alterations were enriched in class_1 (54%) and class_3 (94%) for the UROMOL subtype and LumP (42%) for consensus MIBC subtype (Fig. 2B and Supplementary Fig. 3E). An elevated FGFR3 mRNA expression level was confirmed within these molecular subtypes (Fig. 2C, D and Supplementary Fig. 3F).

Fig. 2
figure 2

Association between FGFR3 Alteration and Molecular Subtypes. A Summary of FGFR3 alterations, FGFR3 mRNA expression, histological variant, consensus MIBC subtypes [6], UROMOL NMIBC subtypes [13], and Baylor college [https://github.com/Genomon-Project/GenomonFisher). The software included in GenomonPipeline was used not only to analyze cancer samples, but also to compare tumor samples, for which paired normal sample were available, in pairs with normal samples to detect somatic mutations more accurately. In addition, false-positive somatic mutations from cancer genome sequencing data were filtered by Genomon Mutation Filter (https://github.com/Genomon-Project/GenomonMutationFilter). Finally, annotating process for the filtered mutations list was organized by Genomon Mutation Annotator (https://github.com/Genomon-Project/GenomonMutationAnnotator) and ANNOVAR:20,210,202 [26]. These annotations included information on amino acid changes and allele frequencies in several public databases. For publicly available dataset of germline whole-genome sequencing, gnomADv3.0 (https://gnomad.broadinstitute.org/news/2019-10-gnomad-v3-0/) [10] and jMorp-14KJPN (https://jmorp.megabank.tohoku.ac.jp/202112/) [11] were utilized to filter out false positive somatic mutations in the analysis of single nucleotide polymorphisms (SNPs). The mutation landscape was visualized using a script based on CoMut library(https://github.com/vanallenlab/comut). Lolipop mutation plots were generated by using Matplotlib. The script also acquired domain and motif features from UniProt database via Proteins API. <  < # The proteins API: accessing key integrated protein and genome information (https://doi.org/10.1093/nar/gkx237)> >.

For the RNA-seq analysis, STAR:2.5.2a (https://github.com/alexdobin/STAR) was used for the map** of FASTQ on GRCh38. Then, featureCounts (SUBREAD): 2.0.1 (http://subread.sourceforge.net/) was adopted to count the number of reads mapped on exon regions by gene symbol. Raw read counts were used after normalization to TPM (transcripts per million). Regarding the molecular subtypes, we utilized the consensus MIBC subtype (https://github.com/cit-bioinfo/consensusMIBC) [6], the UROMOL subtype (https://github.com/sialindskrog/classifyNMIBC) [13], and the Baylor college subtype [14]. The detection and visualization process of FGFR3-fusion transcript was performed using Arriba platform (https://github.com/suhrig/arriba). Estimated immune-related cell composition was calculated by CIBERSORT [27]. For the deconvolution of tumor microenvironment from the bulk RNA-seq data, EcoTyper (https://github.com/digitalcytometry/ecotyper) was adopted [16]. For differentially expressed gene (DEG) analysis, the DEseq2 platform (https://lashlock.github.io/compbio/R_presentation.html) was conducted using a raw read count matrix from the present cohort.

For publicly available datasets, TCGA data set was analyzed using the cBio Cancer Genomics Portal (cBioPortal; www.cbioportal.org). The raw data from the IMvigor210 trial [17] was downloaded from (http://research-pub.gene.com/IMvigor210CoreBiologies/). Heatmaps were created using Morpheus (https://software.broadinstitute.org/morpheus/).

Immunohistochemistry

PD-L1 protein expression in immunohistochemistry (IHC) was evaluated in tumor samples obtained from patients using the PD-L1 IHC 22C3 pharmDx assay (Agilent Technologies, Santa Clara, CA) and the 22C3 anti–PD-L1 antibody (Merck & Co., Kenilworth, NJ) [28]. The Combined Positive Score (CPS) method was employed to determine PD-L1 protein expression. This approach quantifies the number of PD-L1 staining cells (tumor cells, lymphocytes, macrophages) and divides it by the total number of viable tumor cells, then multiplies the result by 100. Immunohistochemical staining was conducted using the Discovery ULTRA System (Roche Diagnostics, Basel, Switzerland) as per the manufacturer's guidelines. A panel of antibodies was employed to evaluate the immune profile of the tumor samples, including TIM-3 (rabbit monoclonal antibody, D5D5R, Cell Signaling Technology, Danvers, MA, USA; diluted 1:200), CD8 (monoclonal mouse clone, C8/144B, DAKO; diluted 1:200), and FOXP3 (mouse monoclonal clone, 236A/E7, Abcam; diluted 1:100). At least two researchers independently assessed the immunohistochemistry results to ensure accuracy and reproducibility. The criteria for determining positive cell count were as follows: membrane staining of any intensity for TIM-3 and CD8, or nuclear staining for FOXP3 on ≥ 1% of cells at a high-power field. In the clinical samples, FGFR3 protein expression was evaluated using an FGFR3 rabbit monoclonal antibody (MA5-32,620, ThermoFisher Scientific). The H-score, ranging from 0 to 300, was calculated as (3 × percentage of strongly staining nuclei + 2 × percentage of moderately staining nuclei + percentage of weakly staining nuclei), allowing for a semi-quantitative assessment of protein expression levels. The CPS of PD-L1 and H-score of FGFR3 were evaluated by two board-certified pathologists to provide a robust and reliable foundation for further data analysis and interpretation in the context of molecular pathology.

Conclusions

We comprehensively investigated the biological implication of aFGFR3 in BLCA. Differential pathways were affected by aFGFR3 between NMIBC and MIBC, particularly emphasizing the significant upregulation of both luminal and basal markers in MIBC/aFGFR3 cases. Crucially, our study underscores the heterogeneous nature of the TME within MIBC/aFGFR3, leading to differential treatment outcomes for CPIs. In particular, favorable ORR in LumP/aFGFR3 and poor ORR in LumP/iFGFR3 were noted. We propose TIM3 for iFGFR3 (ORR: 20% in our cohort) and several immune checkpoint genes for LumP/iFGFR3 (ORR: 5% in our cohort), including IDO1, CCL24, IL1RL1, LGALS4, and NCAM (CD56) as potential druggable targets. These findings offer promising avenues for future precision immunotherapy, indicating a plausible direction for enhancing treatment outcomes in BLCA patients.