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BMC Cancer
Published in: BMC Cancer 1/2019

Open Access 01-12-2019 | Androgens | Research article

Biological molecular layer classification of muscle-invasive bladder cancer opens new treatment opportunities

Authors: Lucía Trilla-Fuertes, Angelo Gámez-Pozo, Guillermo Prado-Vázquez, Andrea Zapater-Moros, Mariana Díaz-Almirón, Jorge M. Arevalillo, María Ferrer-Gómez, Hilario Navarro, Paloma Maín, Enrique Espinosa, Álvaro Pinto, Juan Ángel Fresno Vara

Published in: BMC Cancer | Issue 1/2019

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Abstract

Background

Muscle-invasive bladder tumors are associated with a high risk of relapse and metastasis even after neoadjuvant chemotherapy and radical cystectomy. Therefore, further therapeutic options are needed and molecular characterization of the disease may help to identify new targets. The aim of this study was to characterize muscle-invasive bladder tumors at the molecular level using computational analyses.

Methods

The TCGA cohort of muscle-invasive bladder cancer patients was used to describe these tumors. Probabilistic graphical models, layer analyses based on sparse k-means coupled with Consensus Cluster, and Flux Balance Analysis were applied to characterize muscle-invasive bladder tumors at a functional level.

Results

Luminal and Basal groups were identified, and an immune molecular layer with independent value was also described. Luminal tumors showed decreased activity in the nodes of epidermis development and extracellular matrix, and increased activity in the node of steroid metabolism leading to a higher expression of the androgen receptor. This fact points to the androgen receptor as a therapeutic target in this group. Basal tumors were highly proliferative according to Flux Balance Analysis, which makes these tumors good candidates for neoadjuvant chemotherapy. The Immune-high group showed a higher degree of expression of immune biomarkers, suggesting that this group may benefit from immune therapy.

Conclusions

Our approach, based on layer analyses, established a Luminal group candidate for therapy with androgen receptor inhibitors, a proliferative Basal group which seems to be a good candidate for chemotherapy, and an immune-high group candidate for immunotherapy.
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Literature
1.
2.
Shah JB, McConkey DJ, Dinney CP. New strategies in muscle-invasive bladder cancer: on the road to personalized medicine. Clin Cancer Res. 2011;17(9):2608–12.PubMedCrossRef
3.
Choi W, Porten S, Kim S, Willis D, Plimack ER, Hoffman-Censits J, Roth B, Cheng T, Tran M, Lee IL, et al. Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell. 2014;25(2):152–65.PubMedPubMedCentralCrossRef
4.
Robertson AG, Kim J, Al-Ahmadie H, Bellmunt J, Guo G, Cherniack AD, Hinoue T, Laird PW, Hoadley KA, Akbani R, et al. Comprehensive molecular characterization of muscle-invasive bladder Cancer. Cell. 2017;171(3):540–556.e525.PubMedPubMedCentralCrossRef
5.
Seiler R, Ashab HAD, Erho N, van Rhijn BWG, Winters B, Douglas J, Van Kessel KE, van de Putte EE F, Sommerlad M, Wang NQ, et al. Impact of molecular subtypes in muscle-invasive bladder Cancer on predicting response and survival after neoadjuvant chemotherapy. Eur Urol. 2017;72(4):544–54.PubMedCrossRef
6.
de Velasco G, Trilla-Fuertes L, Gamez-Pozo A, Urbanowicz M, Ruiz-Ares G, Sepúlveda JM, Prado-Vazquez G, Arevalillo JM, Zapater-Moros A, Navarro H, et al. Urothelial cancer proteomics provides both prognostic and functional information. Sci Rep. 2017;7(1):15819.PubMedPubMedCentralCrossRef
7.
Gámez-Pozo A, Berges-Soria J, Arevalillo JM, Nanni P, López-Vacas R, Navarro H, Grossmann J, Castaneda C, Main P, Díaz-Almirón M, et al. Combined label-free quantitative proteomics and microRNA expression analysis of breast cancer unravel molecular differences with clinical implications. Cancer Res. 2015;75:2243–53.PubMedCrossRef
8.
Gámez-Pozo A, Trilla-Fuertes L, Berges-Soria J, Selevsek N, López-Vacas R, Díaz-Almirón M, Nanni P, Arevalillo JM, Navarro H, Grossmann J, et al. Functional proteomics outlines the complexity of breast cancer molecular subtypes. Sci Rep. 2017;7(1):10100.PubMedPubMedCentralCrossRef
9.
Trilla-Fuertes L, Gámez-Pozo A, Arevalillo JM, Díaz-Almirón M, Prado-Vázquez G, Zapater-Moros A, Navarro H, Aras-López R, Dapía I, López-Vacas R, et al. Molecular characterization of breast cancer cell response to metabolic drugs. Oncotarget. 2018;9(11):9645–60.PubMedPubMedCentralCrossRef
10.
Zapater-Moros A, Gámez-Pozo A, Prado-Vázquez G, Trilla-Fuertes L, Arevalillo JM, Díaz-Almirón M, Navarro H, Maín P, Feliú J, Zamora P, et al. Probabilistic graphical models relate immune status with response to neoadjuvant chemotherapy in breast cancer. Oncotarget. 2018;9(45):27586–94.PubMedPubMedCentralCrossRef
11.
12.
Monti S, Tamayo P, Mesirov J, Golub T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn. 2003;52(1):91–118.CrossRef
13.
14.
Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, Mann M, Cox J. The Perseus computational platform for comprehensive analysis of (prote) omics data. Nat Methods. 2016;13(9):731–40.PubMedCrossRef
15.
Abreu G, Edwards D, Labouriau R. High-dimensional graphical model search with the gRapHD R package in. J Stat Softw. 2010;37:1–18.
16.
R Core Team. R: A language and environment for statistical computing: Vienna,Austria, R Foundation for Stattistical Computing; 2013.
17.
Lauritzen S. Graphical models. Oxford,UK: Oxford University Press; 1996.
18.
Huang dW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57.CrossRef
19.
Schellenberger J, Que R, Fleming R, Thiele I, Orth J, Feist A, Zielinski D, Bordbar A, Lewis N, Rahmanian S, et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox v2.0. In. Nat Protoc. 6, 2011:1290–307.PubMedPubMedCentralCrossRef
20.
Thiele I, Swainston N, Fleming RM, Hoppe A, Sahoo S, Aurich MK, Haraldsdottir H, Mo ML, Rolfsson O, Stobbe MD, et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol. 2013;31(5):419–25.PubMedCrossRef
21.
22.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.PubMedPubMedCentralCrossRef
23.
Prado-Vázquez G, Gámez-Pozo A, Trilla-Fuertes L, Arevalillo JM, Zapater-Moros A, Ferrer-Gómez M, Díaz-Almirón M, López-Vacas R, Navarro H, Maín P, et al. A novel approach to triple-negative breast cancer molecular classification reveals a luminal immune-positive subgroup with good prognoses. Sci Rep. 2019;9(1):1538.PubMedPubMedCentralCrossRef
24.
Varma A, Palsson BO. Parametric sensitivity of stoichiometric flux balance models applied to wild-type Escherichia coli metabolism. Biotechnol Bioeng. 1995;45(1):69–79.PubMedCrossRef
25.
Asgari Y, Zabihinpour Z, Salehzadeh A, Schreiber F, Masoudi-Nejad A. Alterations in cancer cell metabolism: the Warburg effect and metabolic adaptation. Genomics. 2015;105:275–81.PubMedCrossRef
26.
Bellmunt J, Orsola A, Leow JJ, Wiegel T, De Santis M, Horwich A, Group EGW. Bladder cancer: ESMO Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2014;25(Suppl 3):iii40–8.PubMedCrossRef
27.
Imaoka S, Yoneda Y, Sugimoto T, Ikemoto S, Hiroi T, Yamamoto K, Nakatani T, Funae Y. Androgen regulation of CYP4B1 responsible for mutagenic activation of bladder carcinogens in the rat bladder: detection of CYP4B1 mRNA by competitive reverse transcription-polymerase chain reaction. Cancer Lett. 2001;166(2):119–23.PubMedCrossRef
28.
Dadhania V, Zhang M, Zhang L, Bondaruk J, Majewski T, Siefker-Radtke A, Guo C, Dinney C, Cogdell D, Zhang S, et al. Meta-analysis of the luminal and basal subtypes of bladder Cancer and the identification of signature Immunohistochemical markers for clinical use. EBioMedicine. 2016;12:105–17.PubMedPubMedCentralCrossRef
29.
Jitao W, Jinchen H, Qingzuo L, Li C, Lei S, Jianming W, Zhenli G. Androgen receptor inducing bladder cancer progression by promoting an epithelial-mesenchymal transition. Andrologia. 2014;46(10):1128–33.PubMedCrossRef
30.
Miyamoto H, Yang Z, Chen YT, Ishiguro H, Uemura H, Kubota Y, Nagashima Y, Chang YJ, Hu YC, Tsai MY, et al. Promotion of bladder cancer development and progression by androgen receptor signals. J Natl Cancer Inst. 2007;99(7):558–68.PubMedCrossRef
31.
32.
Zhang T, Guo P, Zhang Y, Xiong H, Yu X, Xu S, Wang X, He D, Jin X. The antidiabetic drug metformin inhibits the proliferation of bladder cancer cells in vitro and in vivo. Int J Mol Sci. 2013;14(12):24603–18.PubMedPubMedCentralCrossRef
33.
De Giorgi U, Rosti G, Frassineti L, Kopf B, Giovannini N, Zumaglini F, Marangolo M. High-dose chemotherapy for triple negative breast cancer. Ann Oncol. 18, edn. England, 2007:202–3.
34.
Liedtke C, Mazouni C, Hess KR, Andre F, Tordai A, Mejia JA, Symmans WF, Gonzalez-Angulo AM, Hennessy B, Green M, et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol. 2008;26(8):1275–81.PubMedCrossRef
35.
Pico de Coaña Y, Choudhury A, Kiessling R. Checkpoint blockade for cancer therapy: revitalizing a suppressed immune system. Trends Mol Med. 2015;21(8):482–91.PubMedCrossRef
Metadata
Title
Biological molecular layer classification of muscle-invasive bladder cancer opens new treatment opportunities
Authors
Lucía Trilla-Fuertes
Angelo Gámez-Pozo
Guillermo Prado-Vázquez
Andrea Zapater-Moros
Mariana Díaz-Almirón
Jorge M. Arevalillo
María Ferrer-Gómez
Hilario Navarro
Paloma Maín
Enrique Espinosa
Álvaro Pinto
Juan Ángel Fresno Vara
Publication date
01-12-2019
Publisher
BioMed Central
Keyword
Androgens
Published in
BMC Cancer / Issue 1/2019
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-019-5858-z

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