Molecular Classification of Bladder Cancer

Decisions in the treatment of bladder cancer today are based on clinical and pathological risk variables such as tumor stage and tumor grade. The importance of these conventional risk variables is well documented since more than 10 years, and they are used routinely in the clinics. Over the last ten years, cancer research has seen a gradual transition towards personalized medicine, i.e. the exploitation of specific molecular properties in the treatment of tumors. The starting point for personalized medicine is a taxonomy of the tumor type, where genome, transcriptome, and/or proteome data is used to define molecular subtypes that make sense from biological and clinical viewpoints. The overall aim of the work presented in this thesis is to define the major gene expression subtypes of bladder tumors. The gene expression based subtypes should be viewed as a framework which can be refined either by the integration of genomic, epigenetic, or proteomic data or by the analysis of larger patient cohorts so that the subtypes can be described in greater detail. An exhaustive tumor classification should be based on biological similarity between tumors, and not only group together tumors with similar clinical risk profile. This will increase the probability that the taxonomy is relevant in the evaluation of novel therapies that function by altering pathways or transcriptional programs. In paper 1 we define the two major subtypes of bladder cancer, termed molecular subtype 1 and 2 (MS1 and MS2). In paper 2, MS1 and MS2 are subdivided into five major subtypes named Urobasal A, Urobasal B, Genomically Unstable, SCC-like, and Infiltrated, named after their dominating molecular characteristics. The subtypes were identified in an unsupervised manner and were identified also in external data sets, showing their general applicability. Secondary to the aim of tumor classification is the evaluation of the potential prognostic value of the described subtypes. To allow for clinical comparisons, tumor classification should be possible using immunohistochemistry (IHC) on archived material. In paper 3 we make use of the same set of tumors as in paper 2 and device a simplified classifier based on IHC and histology. This classifier identifies the five subtypes with the exception of Urobasal B which could not be reliably distinguished from the related Urobasal A subtype. The molecular pathological classifier defined in paper 3 thus has room for improvement and will need to evolve as the true molecular subtypes are refined. Up to this point we have shown that the subtypes differ in prognosis, but we could not determine whether this was independent of differences observed in stage and grade. In paper 4 we use an independent population based cohort of T1 tumors to retrospectively estimate the prognostic value of the molecular subtypes. The IHC/histology classifier defined in paper 3 is applied, and the molecular subtypes are compared to a current clinical risk stratification model in multivariate analyses. The results show that the subtypes contain as much prognostic information as the current clinical model, and that the best risk stratification is achieved by combining the subtypes with clinical data and an estimate of CD3+ lymphocyte infiltration

On Molecular Classification of Bladder Cancer: Out of One, Many.

Comparative analysis showed that bladder cancer classification systems identify overlapping subtypes but at different levels. Muscle-invasive bladder cancer shows remarkable heterogeneity, and six subtypes were identified that differ in transcriptional networks, marker profiles, and expression of actionable targets

A Molecular Taxonomy for Urothelial Carcinoma.

PURPOSE: Even though urothelial cancer is the fourth most common tumor type among males, progress in treatment has been scarce. A problem in day-to-day clinical practice is that precise assessment of individual tumors is still fairly uncertain; consequently efforts have been undertaken to complement tumor evaluation with molecular biomarkers. An extension of this approach would be to base tumor classification primarily on molecular features. Here, we present a molecular taxonomy for urothelial carcinoma based on integrated genomics. EXPERIMENTAL DESIGN: We use gene expression profiles from 308 tumor cases to define five major urothelial carcinoma subtypes: urobasal A, genomically unstable, urobasal B, squamous cell carcinoma like, and an infiltrated class of tumors. Tumor subtypes were validated in three independent publically available data sets. The expression of 11 key genes was validated at the protein level by immunohistochemistry. RESULTS: The subtypes show distinct clinical outcomes and differ with respect to expression of cell-cycle genes, receptor tyrosine kinases particularly FGFR3, ERBB2, and EGFR, cytokeratins, and cell adhesion genes, as well as with respect to FGFR3, PIK3CA, and TP53 mutation frequency. The molecular subtypes cut across pathologic classification, and class-defining gene signatures show coordinated expression irrespective of pathologic stage and grade, suggesting the molecular phenotypes as intrinsic properties of the tumors. Available data indicate that susceptibility to specific drugs is more likely to be associated with the molecular stratification than with pathologic classification. CONCLUSIONS: We anticipate that the molecular taxonomy will be useful in future clinical investigations. Clin Cancer Res; 1-10. ©2012 AACR

A quantitative polymerase chain reaction based method for molecular subtype classification of urinary bladder cancer-Stromal gene expressions show higher prognostic values than intrinsic tumor genes

Transcriptome-based molecular subtypes of muscle-invasive bladder cancer (MIBC) have been shown to be both prognostic and predictive, but are not used in routine clinical practice. We aimed to develop a feasible, reverse transcription quantitative polymerase chain reaction (RT-qPCR)-based method for molecular subtyping. First, we defined a 68-gene set covering tumor intrinsic (luminal, basal, squamous, neuronal, epithelial-to-mesenchymal, in situ carcinoma) and stromal (immune, extracellular matrix, p53-like) signatures. Then, classifier methods with this 68-gene panel were developed in silico and validated on public data sets with available subtype class information (MD Anderson [MDA], The Cancer Genome Atlas [TCGA], Lund, Consensus). Finally, expression of the selected 68 genes was determined in 104 frozen tissue samples of our MIBC cohort by RT-qPCR using the TaqMan Array Card platform and samples were classified by our newly developed classifiers. The prognostic value of each subtype classification system and molecular signature scores were assessed. We found that the reduced marker set combined with the developed classifiers were able to reproduce the TCGA II, MDA, Lund and Consensus subtype classification systems with an overlap of 79%, 76%, 69% and 64%, respectively. Importantly, we could successfully classify 96% (100/104) of our MIBC samples by using RT-qPCR. Neuronal and luminal subtypes and low stromal gene expressions were associated with poor survival. In conclusion, we developed a robust and feasible method for the molecular subtyping according to the TCGA II, MDA, Lund and Consensus classifications. Our results suggest that stromal signatures have a superior prognostic value compared to tumor intrinsic signatures and therefore underline the importance of tumor-stroma interaction during the progression of MIBC

Molecular changes during progression from nonmuscle invasive to advanced urothelial carcinoma

Molecular changes occurring during invasion and clinical progression of cancer are difficult to study longitudinally in patient-derived material. A unique feature of urothelial bladder cancer (UBC) is that patients frequently develop multiple nonmuscle invasive tumors, some of which may eventually progress to invade the muscle of the bladder wall. Here, we use a cohort of 73 patients that experienced a total of 357 UBC diagnoses to study the stability or change in detected molecular alterations during cancer progression. The tumors were subtyped by gene expression profiling and analyzed for hotspot mutations in FGFR3, PIK3CA and TERT, the most frequent early driver mutations in this tumor type. TP53 alterations, frequent in advanced UBC, were inferred from p53 staining pattern, and potential genomic alterations were inferred by gene expression pattern

Integrated Genomic and Gene Expression Profiling Identifies Two Major Genomic Circuits in Urothelial Carcinoma

Similar to other malignancies, urothelial carcinoma (UC) is characterized by specific recurrent chromosomal aberrations and gene mutations. However, the interconnection between specific genomic alterations, and how patterns of chromosomal alterations adhere to different molecular subgroups of UC, is less clear. We applied tiling resolution array CGH to 146 cases of UC and identified a number of regions harboring recurrent focal genomic amplifications and deletions. Several potential oncogenes were included in the amplified regions, including known oncogenes like E2F3, CCND1, and CCNE1, as well as new candidate genes, such as SETDB1 (1q21), and BCL2L1 (20q11). We next combined genome profiling with global gene expression, gene mutation, and protein expression data and identified two major genomic circuits operating in urothelial carcinoma. The first circuit was characterized by FGFR3 alterations, overexpression of CCND1, and 9q and CDKN2A deletions. The second circuit was defined by E3F3 amplifications and RB1 deletions, as well as gains of 5p, deletions at PTEN and 2q36, 16q, 20q, and elevated CDKN2A levels. TP53/MDM2 alterations were common for advanced tumors within the two circuits. Our data also suggest a possible RAS/RAF circuit. The tumors with worst prognosis showed a gene expression profile that indicated a keratinized phenotype. Taken together, our integrative approach revealed at least two separate networks of genomic alterations linked to the molecular diversity seen in UC, and that these circuits may reflect distinct pathways of tumor development

A Systematic Study of Gene Mutations in Urothelial Carcinoma; Inactivating Mutations in TSC2 and PIK3R1

Abstract BACKGROUND: Urothelial carcinoma (UC) is characterized by frequent gene mutations of which activating mutations in FGFR3 are the most frequent. Several downstream targets of FGFR3 are also mutated in UC, e.g., PIK3CA, AKT1, and RAS. Most mutation studies of UCs have been focused on single or a few genes at the time or been performed on small sample series. This has limited the possibility to investigate co-occurrence of mutations. METHODOLOGY/PRINCIPAL FINDINGS: We performed mutation analyses of 16 genes, FGFR3, PIK3CA, PIK3R1 PTEN, AKT1, KRAS, HRAS, NRAS, BRAF, ARAF, RAF1, TSC1, TSC2, APC, CTNNB1, and TP53, in 145 cases of UC. We show that FGFR3 and PIK3CA mutations are positively associated. In addition, we identified PIK3R1 as a target for mutations. We demonstrate a negative association at borderline significance between FGFR3 and RAS mutations, and show that these mutations are not strictly mutually exclusive. We show that mutations in BRAF, ARAF, RAF1 rarely occurs in UC. Our data emphasize the possible importance of APC signaling as 6% of the investigated tumors either showed inactivating APC or activating CTNNB1 mutations. TSC1, as well as TSC2, that constitute the mTOR regulatory tuberous sclerosis complex were found to be mutated at a combined frequency of 15%. CONCLUSIONS/SIGNIFICANCE: Our data demonstrate a significant association between FGFR3 and PIK3CA mutations in UC. Moreover, the identification of mutations in PIK3R1 further emphasizes the importance of the PI3-kinase pathway in UC. The presence of TSC2 mutations, in addition to TSC1 mutations, underlines the involvement of mTOR signaling in UC

A Consensus Molecular Classification of Muscle-invasive Bladder Cancer

Background: Muscle-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. Objective: To achieve an international consensus on MIBC molecular subtypes that reconciles the published classification schemes. Design, setting, and participants: We used 1750 MIBC transcriptomic profiles from 16 published datasets and two additional cohorts. Outcome measurements and statistical analysis: We 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 limitations: We 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. Conclusions: This consensus system offers a robust framework that will enable testing and validation of predictive biomarkers in future prospective clinical trials. Patient summary: Bladder 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. An international consortium of bladder cancer expert teams establishes a consensus reconciling the diverse molecular classifications of muscle-invasive bladder cancer. This work offers a robust framework that will enable testing and validating predictive biomarkers in future prospective clinical trials