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

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.

SUBMITTER: Trilla-Fuertes L

PROVIDER: S-EPMC6599340 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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

Trilla-Fuertes Lucía L Gámez-Pozo Angelo A Prado-Vázquez Guillermo G Zapater-Moros Andrea A Díaz-Almirón Mariana M Arevalillo Jorge M JM Ferrer-Gómez María M Navarro Hilario H Maín Paloma P Espinosa Enrique E Pinto Álvaro Á Vara Juan Ángel Fresno JÁF

BMC cancer 20190628 1

<h4>Background</h4>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.<h4>Methods</h4>The TCGA cohort of muscle-invasive bladder cancer patients wa ...[more]

PMID: 31253132

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Project description:BackgroundMuscle-invasive bladder cancer (MIBC) is a molecularly diverse disease with heterogeneous clinical outcomes. Several molecular classifications have been proposed, but the diversity of their subtype sets impedes their clinical application.ObjectiveTo achieve an international consensus on MIBC molecular subtypes that reconciles the published classification schemes.Design, setting, and participantsWe used 1750 MIBC transcriptomic profiles from 16 published datasets and two additional cohorts.Outcome measurements and statistical analysisWe performed a network-based analysis of six independent MIBC classification systems to identify a consensus set of molecular classes. Association with survival was assessed using multivariable Cox models.Results and limitationsWe report the results of an international effort to reach a consensus on MIBC molecular subtypes. We identified a consensus set of six molecular classes: luminal papillary (24%), luminal nonspecified (8%), luminal unstable (15%), stroma-rich (15%), basal/squamous (35%), and neuroendocrine-like (3%). These consensus classes differ regarding underlying oncogenic mechanisms, infiltration by immune and stromal cells, and histological and clinical characteristics, including outcomes. We provide a single-sample classifier that assigns a consensus class label to a tumor sample's transcriptome. Limitations of the work are retrospective clinical data collection and a lack of complete information regarding patient treatment.ConclusionsThis consensus system offers a robust framework that will enable testing and validation of predictive biomarkers in future prospective clinical trials.Patient summaryBladder cancers are heterogeneous at the molecular level, and scientists have proposed several classifications into sets of molecular classes. While these classifications may be useful to stratify patients for prognosis or response to treatment, a consensus classification would facilitate the clinical use of molecular classes. Conducted by multidisciplinary expert teams in the field, this study proposes such a consensus and provides a tool for applying the consensus classification in the clinical setting.

Project description:BackgroundRecently, immunotherapies have been approved for advanced muscle invasive bladder cancer (MIBC) treatment, but only a small fraction of MIBC patients could achieve a durable drug response. Our study is aimed at identifying tumor microenvironment (TME) subtypes that have different immunotherapy response rates.MethodsThe mRNA expression profiles of MIBC samples from seven discovery datasets (GSE13507, GSE31684, GSE32548, GSE32894, GSE48075, GSE48276, and GSE69795) were analyzed to identify TME subtypes. The identified TME subtypes were then validated by an independent dataset (TCGA-MIBC). The subtype-related biomarkers were discovered using computational analyses and then utilized to establish a random forest predictive model. The associations of TME subtypes with immunotherapy therapeutic responses were investigated in a group of patients who had been treated with immunotherapy. A prognostic index model was constructed using the subtype-related biomarkers. Two nomograms were built by the subtype-related biomarkers or the clinical parameters.ResultsTwo TME subtypes, including ECM-enriched class (EC) and immune-enriched class (IC), were found. EC was associated with greater extracellular matrix (ECM) pathways, and IC was correlated with immune pathways, respectively. Overall survival was significantly greater for tumors classified as IC, whereas the EC subtype had a worse prognosis. A total of nine genes (AKAP12, APOL3, CXCL13, CXCL9, GBP4, LRIG1, PEG3, PODN, and PTPRD) were selected by computational analyses to construct the random forest model. The area under the curve (AUC) values for this model were 0.827 and 0.767 in the testing and external validation datasets, respectively. Therapeutic response rates were greater in IC patients than in EC patients (28 percent vs. 18 percent). Patients with a high prognostic index had a poorer prognosis than those with a low prognostic index. The nomogram constructed from nine genes and stage achieved a C-index of 0.71.ConclusionThe present investigation defined two distinct TME subtypes and developed models to assess immunotherapeutic treatment outcomes.

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

Publications

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

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