Analysis of spatial heterogeneity in normal epithelium and preneoplastic alterations in mouse prostate tumor models.
Cancer involves histological changes in tissue, which is of primary importance in pathological diagnosis and research. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue with all its variables. On the other hand, understanding connections between genetic alterations and histological attributes requires development of enhanced analysis methods suitable also for small sample sizes. Here, we set out to develop computational methods for early detection and distinction of prostate cancer-related pathological alterations. We use analysis of features from HE stained histological images of normal mouse prostate epithelium, distinguishing the descriptors for variability between ventral, lateral, and dorsal lobes. In addition, we use two common prostate cancer models, Hi-Myc and Pten+/- mice, to build a feature-based machine learning model separating the early pathological lesions provoked by these genetic alterations. This work offers a set of computational methods for separation of early neoplastic lesions in the prostates of model mice, and provides proof-of-principle for linking specific tumor genotypes to quantitative histological characteristics. The results obtained show that separation between different spatial locations within the organ, as well as classification between histologies linked to different genetic backgrounds, can be performed with very high specificity and sensitivity.
Prostate Cancer Research Center, Faculty of Medicine and Life Sciences and BioMediTech, University of Tampere, Tampere, Finland.
Mira
M
Valkonen
Prostate Cancer Research Center, Faculty of Medicine and Life Sciences and BioMediTech, University of Tampere, Tampere, Finland.|Tampere University of Technology, Pori, Finland.
Pekka
P
Ruusuvuori
Prostate Cancer Research Center, Faculty of Medicine and Life Sciences and BioMediTech, University of Tampere, Tampere, Finland.|BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland.
Kimmo
K
Kartasalo
Prostate Cancer Research Center, Faculty of Medicine and Life Sciences and BioMediTech, University of Tampere, Tampere, Finland.
Matti
M
Nykter
Prostate Cancer Research Center, Faculty of Medicine and Life Sciences and BioMediTech, University of Tampere, Tampere, Finland.|Fimlab Laboratories, Tampere University Hospital, Tampere, Finland.
Tapio
T
Visakorpi
Prostate Cancer Research Center, Faculty of Medicine and Life Sciences and BioMediTech, University of Tampere, Tampere, Finland.|Fimlab Laboratories, Tampere University Hospital, Tampere, Finland.
Leena
L
Latonen
D000818:Animals; D015415:Biomarkers; D001706:Biopsy; D019295:Computational Biology / Q000379:methods; D004195:Disease Models, Animal; D004848:Epithelium / Q000378:metabolism / Q000473:pathology*; D020869:Gene Expression Profiling; D018740:Genetic Heterogeneity; D005838:Genotype; D006801:Humans; D007150:Immunohistochemistry; D008297:Male; D051379:Mice; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D051059:PTEN Phosphohydrolase / Q000235:genetics / Q000378:metabolism; D011230:Precancerous Conditions / Q000235:genetics / Q000378:metabolism / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000235:genetics / Q000378:metabolism / Q000473:pathology*; D012372:ROC Curve; D059467:Transcriptome
D015415:Biomarkers; D051059:PTEN Phosphohydrolase