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Background: Clustering of gene expression data is widely used to identify novel subtypes of cancer. Plenty of clustering approaches have been proposed, but there is a lack of knowledge regarding their relative merits and how data characteristics influence the performance. We evaluate how cluster analysis choices affect the performance by studying four publicly available human cancer data sets: breast, brain, kidney and stomach cancer. In particular, we focus on how the sample size, distribution of subtypes and sample heterogeneity affect the performance.

Results: In general, increasing the sample size had limited effect on the clustering performance, e.g. for the breast cancer data similar performance was obtained for n = 40 as for n = 330. The relative distribution of the subtypes had a noticeable effect on the ability to identify the disease subtypes and data with disproportionate cluster sizes turned out to be difficult to cluster. Both the choice of clustering method and selection method affected the ability to identify the subtypes, but the relative performance varied between data sets, making it difficult to rank the approaches. For some data sets, the performance was substantially higher when the clustering was based on data from only one sex compared to data from a mixed population. This suggests that homogeneous data are easier to cluster than heterogeneous data and that clustering males and females individually may be beneficial and increase the chance to detect novel subtypes. It was also observed that the performance often differed substantially between females and males.

Conclusions: The number of samples seems to have a limited effect on the performance while the heterogeneity, at least with respect to sex, is important for the performance. Hence, by analyzing the genders separately, the possible loss caused by having fewer samples could be outweighed by the benefit of a more homogeneous data.

Bone metastasis is the lethal end-stage of prostate cancer (PC), but the biology of bone metastases is poorly understood. The overall aim of this study was therefore to explore molecular variability in PC bone metastases of potential importance for therapy. Specifically, genome-wide expression profiles of bone metastases from untreated patients (n = 12) and patients treated with androgen-deprivation therapy (ADT, n = 60) were analyzed in relation to patient outcome and to morphological characteristics in metastases and paired primary tumors. Principal component analysis and unsupervised classification were used to identify sample clusters based on mRNA profiles. Clusters were characterized by gene set enrichment analysis and related to histological and clinical parameters using univariate and multivariate statistics. Selected proteins were analyzed by immunohistochemistry in metastases and matched primary tumors (n = 52) and in transurethral resected prostate (TUR-P) tissue of a separate cohort (n = 59). Three molecular subtypes of bone metastases (MetA-C) characterized by differences in gene expression pattern, morphology, and clinical behavior were identified. MetA (71% of the cases) showed increased expression of androgen receptor-regulated genes, including prostate-specific antigen (PSA), and glandular structures indicating a luminal cell phenotype. MetB (17%) showed expression profiles related to cell cycle activity and DNA damage, and a pronounced cellular atypia. MetC (12%) exhibited enriched stroma-epithelial cell interactions. MetB patients had the lowest serum PSA levels and the poorest prognosis after ADT. Combined analysis of PSA and Ki67 immunoreactivity (proliferation) in bone metastases, paired primary tumors, and TUR-P samples was able to differentiate MetA-like (high PSA, low Ki67) from MetB-like (low PSA, high Ki67) tumors and demonstrate their different prognosis. In conclusion, bone metastases from PC patients are separated based on gene expression profiles into molecular subtypes with different morphology, biology, and clinical outcome. These findings deserve further exploration with the purpose of improving treatment of metastatic PC.