Identification of CNGB1 as a Predictor of Response to Neoadjuvant Chemotherapy in Muscle-Invasive Bladder Cancer

Abstract

Cisplatin-based neoadjuvant chemotherapy (NAC) is recommended prior to radical cystectomy for muscle-invasive bladder cancer (MIBC) patients. Despite a 5-10% survival benefit, some patients do not respond and experience substantial toxicity and delay in surgery. To date, there are no clinically approved biomarkers predictive of response to NAC and their identification is urgently required for more precise delivery of care. To address this issue, a multi-methods analysis approach of machine learning and differential gene expression analysis was undertaken on a cohort of 30 MIBC cases highly selected for an exquisitely strong response to NAC or marked resistance and/or progression (discovery cohort). RGIFE (ranked guided iterative feature elimination) machine learning algorithm, previously demonstrated to have the ability to select biomarkers with high predictive power, identified a 9-gene signature (CNGB1, GGH, HIST1H4F, IDO1, KIF5A, MRPL4, NCDN, PRRT3, SLC35B3) able to select responders from non-responders with 100% predictive accuracy. This novel signature correlated with overall survival in meta-analysis performed using published NAC treated-MIBC microarray data (validation cohort 1, n = 26, Log rank test, p = 0.02). Corroboration with differential gene expression analysis revealed cyclic nucleotide-gated channel, CNGB1, as the top ranked upregulated gene in non-responders to NAC. A higher CNGB1 immunostaining score was seen in non-responders in tissue microarray analysis of the discovery cohort (n = 30, p = 0.02). Kaplan-Meier analysis of a further cohort of MIBC patients (validation cohort 2, n = 99) demonstrated that a high level of CNGB1 expression associated with shorter cancer specific survival (p < 0.001). Finally, in vitro studies showed siRNA-mediated CNGB1 knockdown enhanced cisplatin sensitivity of MIBC cell lines, J82 and 253JB-V. Overall, these data reveal a novel signature gene set and CNGB1 as a simpler proxy as a promising biomarker to predict chemoresponsiveness of MIBC patients.

Keywords: CNGB1; biomarker; cisplatin; machine learning; muscle invasive bladder cancer; neoadjuvant chemotherapy.

Figure 1

Overview of the study. This study represents a multi-methods analysis approach of machine learning and differential gene expression analysis following performance of gene expression profiling on a cohort of MIBC patients highly selected for an exquisitely strong response to NAC (responder) or marked resistance and/or progression (non-responder). Identified gene signatures and top ranked gene markers were subjected to validation using meta-analysis, immunohistochemistry and in vitro functional assays. FFPE, formalin-fixed paraffin-embedded; TMA, tissue microarray analysis; MIBC, muscle-invasive bladder cancer; NAC, neoadjuvant chemotherapy.

Figure 2

Machine learning approach to identify gene signatures predicting response to NAC. (a) Gene commonality of signatures identified by RGIFE machine learning algorithm, (b) A Cox proportional hazards regression analysis was performed to assess the relationship between expression of Signature 1 and overall survival in the Kim et al. dataset (Validation cohort 1) [30]. The model generated a survival risk estimate from the panel of markers comprising Signature 1 based on survival time and overall survival status. The model assigned a survival risk to each sample. The cohort was divided into two equally sized groups by the median risk (‘low risk’ and ‘high risk’). Kaplan-Meier plot shows Signature 1 significantly associated with overall survival in MIBC patients treated with NAC (n = 26, Log rank test, p = 0.02) in Validation cohort 1, (c) SHAP summary plot showing the contribution that each gene of Signature 1 has in the prediction of good response for each patient sample in the Discovery cohort (SHAP value, the estimation of the contribution that each specific gene has in the prediction of good response for a given sample). A positive SHAP value indicates contribution to predicting good response to NAC. A negative SHAP value reflects a lack of support for predicting good response to NAC. i.e., contributes to predicting bad response. Blue and red points indicate low and high expressions of a gene, respectively.

Figure 3

Differential gene expression analysis to corroborate machine learning data. (a) Heat map of genes differentially expressed by ‘responders’ and ‘non-responders’ in the Discovery cohort. Rows, single gene; columns, single patient. Each cell in the matrix represents the expression level of a single transcript in a single sample with red indicating upregulation and blue indicating downregulation compared with the median expression for that gene across all samples. (b) Top 10 genes upregulated (>3-fold) in ‘Responders’ to NAC. For full list, see Table S1. (c) Top 10 genes upregulated in ‘Non-Responders’ to NAC. For full list, see Table S2. (d) Box plot demonstrating CNGB1 gene expression was upregulated in ‘Non-Responders’ compared to ‘Responders’ (p = 0.029).

Figure 4

CNGB1 is upregulated in ‘Non-Responders’ to NAC and associates with MIBC patient survival. (a) Examples of low (CNGB1^lo) and high (CNGB1^hi) levels of CNGB1 expression in MIBC patient tissue cores. (b) Comparison of CNGB1 immunostaining score between ‘Responder’ and ‘Non-Responder’ patients, a protein-based validation of the Discovery cohort (n = 30, t-test, * p < 0.05). (c) Correlation of CNGB1 expression with cancer specific survival by Kaplan-Meier analysis (Validation cohort 2; n = 99, Log rank test, p < 0.001).

Figure 5

CNGB1 knockdown enhanced cisplatin sensitivity. (a) Confirmation of CNGB1 knockdown in J82 and 253JB-V MIBC cells by western blot analysis. α-tubulin was used as a loading control. (b) The effect of cisplatin, as a proxy for NAC, was assessed on the growth of J82 and 253JB-V cells following knockdown of CNGB1 for 48 h. Mean growth fold change relative to siCTRL was calculated for cisplatin treated cells (n = 3 experimental repeats, t-test, * p < 0.05).