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. 2022 Jan 13;14(2):378.
doi: 10.3390/cancers14020378.

Genomic Landscape of Vinflunine Response in Metastatic Urothelial Cancer

Alejandra Bernardini  1   2   3 , Marta Dueñas  1   2   3 , María Cruz Martín-Soberon  1   4 , Carolina Rubio  1   2   3 , Cristian Suarez-Cabrera  1   2   3 , Raquel Ruiz-Palomares  1   2 , Ester Munera-Maravilla  1   2   3 , Sara Lázaro  2 , Iris Lodewijk  1   2   3 , Daniel Rueda  1 , David Gómez-Sánchez  1 , Teresa Alonso-Gordoa  5 , Javier Puente  6 , Álvaro Pinto  7 , Pilar González-Peramato  7 , Carlos Aguado  6 , Mercedes Herrera  4 , Flora López  4 , Victor M G Martinez  1   2   3 , Lucía Morales  1   2 , Daniel Castellano  1   3   4 , Jesús M Paramio  1   2   3 , Guillermo de Velasco  1   4
Affiliations

Genomic Landscape of Vinflunine Response in Metastatic Urothelial Cancer

Alejandra Bernardini et al. Cancers (Basel). .

Abstract

Background and aims: Metastatic urothelial carcinoma (mUC) remains an incurable disease with limited treatment options after platinum-based chemotherapy and immune checkpoint blockade (ICB). Vinflunine has shown a modest increase in overall survival and remains a therapeutic option for chemo- and immunotherapy refractory tumours. However, biomarkers that could identify responding patients to vinflunine and possible alternative therapies after failure to treatment are still missing. In this study, we aimed to identify potential genomic biomarkers of vinflunine response in mUC patient samples and potential management alternatives.

Methods: Formalin-fixed paraffin-embedded samples of mUC patients (n = 23) from three university hospitals in Spain were used for genomic targeted-sequencing and transcriptome (using the Immune Profile panel by NanoString) analyses. Patients who received vinflunine after platinum-based chemotherapy failure were classified in non-responders (NR: progressive disease ≤ 3 months; n= 11) or responders (R: response ≥ 6 months; n = 12).

Results: Genomic characterization revealed that the most common alteration, TP53 mutations, had comparable frequency in R (6/12; 50%) and NR (4/11; 36%). Non-synonymous mutations in KTM2C (4/12; 33.3%), PIK3CA (3/12; 25%) and ARID2 (3/12; 25%) were predominantly associated with response. No significant difference was observed in tumour mutational burden (TMB) between R and NR patients. The NR tumours showed increased expression of diverse immune-related genes and pathways, including various interferon gamma-related genes. We also identified increased MAGEA4 expression as a potential biomarker of non-responding tumours to vinflunine treatment.

Conclusions: Our data may help to identify potential genomic biomarkers of response to vinflunine. Moreover, tumours refractory to vinflunine showed immune signatures potentially associated with response to ICB. Extensive validation studies, including longitudinal series, are needed to corroborate these findings.

Keywords: biomarkers; bladder; immune signatures; urothelial cancer; vinflunine.

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Conflict of interest statement

J.P.: Honoraria/Consulting or Advisory Role—Astellas Pharma; AstraZeneca; Bayer; Bristol-Myers Squibb; Eisai; EUSA Pharma; Ipsen; Janssen-Cilag; M.S.D. Oncology; Pfizer; Roche; Sanofi Eisai; Ipsen; Merck Sharp & Dohme; A.P.: Consulting or Advisory Role—Astellas Pharma; Bayer; Bristol-Myers Squibb; Ipsen; Janssen Oncology; M.S.D. Oncology; Novartis; Pfizer; Pierre Fabre; Roche; Sanofi/Aventis. D.C.: Consulting or Advisory Role—Astellas Pharma; AstraZeneca; Bayer; Boehringer Ingelheim; Bristol-Myers Squibb; Ipsen; Janssen Oncology; Lilly; MSD Oncology; Novartis; Pfizer; Pierre Fabre; Roche/Genentech; Sanofi; Sanofi G.d.V.: Honoraria/Consulting or Advisory Role—Astellas Medivation; Bayer; Bristol-Myers Squibb; Ipsen; Janssen; M.S.D.; Merck; Novartis; Pfizer, Roche, Eusa Pharma. All other authors no relationships to disclose.

Figures

Figure 1
Figure 1
Kaplan–Meier survival curves showing progression free survival (A) (PFS) and overall survival ((B), OS) in R and NR patients in the study series to vinflunine treatment; p-Values provided by log rank test).
Figure 2
Figure 2
Genomic alterations in mUC samples. (A) Count of non-synonymous mutations per Mb in responder (R) and non-responder (NR) patients. (B) Mutation plot showing oncogenic variants annotated in OncoKB, recurrent in COSMIC or indicated deleterious by Varsome. (C) Mutation plot showing genes with two or more alterations when comparing responders and non-responders group. Gene alterations for B and C are annotated according to the colour panel.
Figure 3
Figure 3
Immune gene expression in mUC samples. (A) Heatmap of the normalized gene expression generated via unsupervised clustering. Red indicates high expression; blue indicates low expression (log2 scale). (B) Volcano plot displaying each gene’s −log10 (p-value) and log2 fold change between non-responders (NR) and responders (R). Statistically significant expressed genes (below the given p-value threshold [<0.05]) are marked in red.
Figure 4
Figure 4
Expression of MAGEA4 in responders (R) and non-responders (NR). (A) Representative examples corresponding to the different IHC scores. (B–D) Summary of the MAGEA4 expression measured by IHC (B) mRNA expression by probe hybridization in nCounter Nanostring platform (D), mRNA expression by RT-qPCR (C). p-Values were estimated by un-paired T test.
See this image and copyright information in PMC

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