Identification of an immunotherapy-responsive molecular subtype of bladder cancer

Abstract

Background: Although various molecular subtypes of bladder cancer (BC) have been investigated, most of these studies have focused on muscle-invasive BC (MIBC). A few studies have investigated non-muscle-invasive BC (NMIBC) or NMIBC and MIBC together, but none has classified progressive NMIBC or immune checkpoint inhibitor (ICI)-based therapeutic responses in early-stage BC patients.

Methods: A total of 1,934 samples from seven patient cohorts were used. We performed unsupervised hierarchical clustering to stratify patients into distinct subgroups and constructed a classifier by applying SAM/PAM algorithms. We then investigated the association between molecular subtypes and immunotherapy responsiveness using various statistical methods.

Findings: We explored large-scale genomic datasets encompassing NMIBC and MIBC, redefining four distinct molecular subtypes, including a subgroup containing progressive NMIBC and MIBC with poor prognosis that would benefit from ICI treatment. This subgroup showed poor progression-free survival with the distinct features of high mutation load, activated cell cycle, and inhibited TGFβ signalling. Importantly, we verified that BC patients with this subtype were significantly responsive to an anti-PD-L1 agent in the IMvigor210 cohort.

Interpretation: Our results reveal an immunotherapeutic option for ICI treatment of highly progressive NMIBC and MIBC with poor prognosis.

Funding: This research was supported by the National Research Foundation of Korea grant funded by the Korean government, a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea, and a grant from the KRIBB Research Initiative Program.

Keywords: Bladder cancer; Disease progression; Genomic signature; Immunotherapy; Subtype.

Fig. 1

Prediction of four molecular subtypes of BC using the GSP786 classifier in the CNUH and RNA-seq cohorts. (a) Heat map of the subtype-specific signature consisting of 786 genes and associations with clinicopathological features in the CNUH cohort. Class 1 contained many low-grade NMIBCs. Class 2 included both low-grade NMIBCs and a small number of MIBCs. Class 3 exhibited similar involvement in high-grade NMIBC and MIBC. Class 4 contained the most MIBC cases. (b) Heat map of the GSP786 classifier profiled in the RNA-seq cohort combined with TCGA and UROMOL. Samples are ordered according to the four classes with pathological features together with their previous subtype assignment information from the UROMOL, SSH, and TCGA cohorts. Class 1 included many low-stage and low-grade tumours with more MIBCs (71 out of 275 in class 1, 25%) compared with the CNUH cohort (1 out of 49 in class 1, 2%). The sample compositions of classes 2 and 3 were similar to those in the CNUH cohort. Class 4 tumours consist of high-grade invasive tumours with more NMIBCs (76 out of 215, 35%) compared with the CNUH cohort (6 out of 29, 20%). The heat map ranks genes based on fold-change, and genes with the largest fold-change appear at the top. Data are presented in a matrix format in which each row represents an individual gene and each column represents a tissue sample. Each cell in the matrix represents the expression level of a gene feature in an individual sample. The colouring in the cells reflects relativity high (red) and low (green) expression levels as indicated in the scale bar (log₂ transformed scale).

Fig. 2

Prognostic significance of BC subtypes. (a–d) Kaplan-Meier curves showing time to death in the CNUH, YUSH, UHL, and SSH cohorts. Class 1 exhibited a better prognosis compared with other subclasses. Class 2 exhibited an intermediate level in patient survival, whereas classes 3 and 4 patients exhibited reduced survival rates compared with other subtypes. (e–g) Kaplan-Meier curves showing time to progression in the UHL, SSH, and RNA-seq cohorts. The frequency of NMIBC progression in class 3 was significantly increased compared with the other subclasses. P-values were obtained by log-rank tests. The + symbols in the panels indicate censored data.

Fig. 3

Mutational landscape of BC subtypes in the TCGA cohort. Frequently mutated genes (≥5%, top panel), genes involved in the oncogenic signature (middle panel) and DNA damage response and repair (bottom panel). Samples are sorted according to mutation frequency within each class. Chi-squared tests for differences in frequency pattern between the classes are presented on the left together with total mutation frequency. The mutation frequency for each subtype is presented on the right. The mutation load for each sample is presented above the mutation plot.

Fig. 4

Class 3 subtype is associated with response to ICI. (a) Heat map of the GSP786 classifier profiled in the IMvigor210 cohort. Rows of the heat map show gene expression grouped by specific functions or pathways. IC, immune cells; TC, tumour cells; CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease. (b) Response versus predicted subtype based on the GSP786 classifier, demonstrating that class 3 had a significantly increased response rate (P < 0·001, two-sided Fisher's exact test). The numbers in parentheses specify sample numbers in each class. (c) Class 3 exhibited a better survival rate compared with other subtypes (P = 0·028, log-rank test).