Chromatin Phenotype Karyometry Can Predict Recurrence in Papillary Urothelial Neoplasms of Low Malignant Potential

• Main Authors: Rodolfo Montironi, Marina Scarpelli, Antonio Lopez-Beltran, Roberta Mazzucchelli, David Alberts, James Ranger-Moore, Hubert G. Bartels, Peter W. Hamilton, Janine Einspahr, Peter H. Bartels
• Format: Article
• Language: English
• Published: Hindawi Limited 2007-01-01
• Series: Cellular Oncology
• Online Access: http://dx.doi.org/10.1155/2007/356464
• ISSN: 1570-5870; 1875-8606

Background: A preceding exploratory study (J. Clin. Pathol. 57(2004), 1201–1207) had shown that a karyometric assessment of nuclei from papillary urothelial neoplasms of low malignant potential (PUNLMP) revealed subtle differences in phenotype which correlated with recurrence of disease. Aim of the Study: To validate the results from the exploratory study on a larger sample size. Materials: 93 karyometric features were analyzed on haematoxylin and eosin-stained sections from 85 cases of PUNLMP. 45 cases were from patients who had a solitary PUNLMP lesion and were disease-free during a follow-up period of at least 8 years. The other 40 were from patients with a unifocal PUNLMP, with one or more recurrences in the follow-up. A combination of the previously defined classification functions together with a new P-index derived classification method was used in an attempt to classify cases and identify a biomarker of recurrence in PUNLMP lesions. Results: Validation was pursued by a number of separate approaches. First, the exact procedure from the exploratory study was applied to the large validation set. Second, since the discriminant function 2 of the exploratory study had been based on a small sample size, a new discriminant function was derived. The case classification showed a correct classification of 61% for non-recurrent and 74% for recurrent cases, respectively. Greater success was obtained by applying unsupervised learning technologies to take advantage of phenotypical composition (correct classification of 92%). This approach was validated by dividing the data into training and test sets with 2/3 of the cases assigned to the training sets, and 1/3 to the test sets, on a rotating basis, and validation of the classification rate was thus tested on three separate data sets by a leave-k-out process. The average correct classification was 92.8% (training set) and 84.6% (test set). Conclusions: Our validation study detected subvisual differences in chromatin organization state between non-recurrent and recurrent PUNLMP, thus allowing a very stable method of predicting recurrence of papillary urothelial neoplasms of low malignant potential by karyometry.