Molecular Classification of Bladder Cancer

Research output: ThesisDoctoral Thesis (compilation)


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.


Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Cancer and Oncology


  • Bladder cancer, Subtype, Classification
Original languageEnglish
Awarding Institution
Supervisors/Assistant supervisor
Award date2013 Dec 12
  • Oncology, Lund
Print ISBNs978-91-87651-11-3
Publication statusPublished - 2013
Publication categoryResearch

Bibliographic note

Defence details Date: 2013-12-12 Time: 13:00 Place: Belfrage Lecture hall BMC D15, Klinikgatan 32, Lund External reviewer(s) Name: Zwarthoff, Ellen Title: Proffessor Affiliation: Department of Pathology, Josephine Nefkens Institute, Erasmus University Medical Center, Rotterdam, Netherlands ---

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Research output: Contribution to journalArticle

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