FrenchFISH: Poisson Models for Quantifying DNA Copy Number From Fluorescence In Situ Hybridization of Tissue Sections

Macintyre, G. et al. (2021) FrenchFISH: Poisson Models for Quantifying DNA Copy Number From Fluorescence In Situ Hybridization of Tissue Sections. JCO Clinical Cancer Informatics, 5, pp. 176-186. (doi: 10.1200/cci.20.00075) (PMID:33570999)

Abstract

Purpose: Chromosomal aberration and DNA copy number change are robust hallmarks of cancer. The gold standard for detecting copy number changes in tumor cells is fluorescence in situ hybridization (FISH) using locus-specific probes that are imaged as fluorescent spots. However, spot counting often does not perform well on solid tumor tissue sections due to partially represented or overlapping nuclei. Materials and Methods: To overcome these challenges, we have developed a computational approach called FrenchFISH, which comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes or a homogeneous Poisson point process model for automated spot counting. Results: We benchmarked the performance of FrenchFISH against previous approaches using a controlled simulation scenario and tested it experimentally in 12 ovarian carcinoma FFPE-tissue sections for copy number alterations at three loci (c-Myc, hTERC, and SE7). FrenchFISH outperformed standard spot counting with 74% of the automated counts having < 1 copy number difference from the manual counts and 17% having < 2 copy number differences, while taking less than one third of the time of manual counting. Conclusion: FrenchFISH is a general approach that can be used to enhance clinical diagnosis on sections of any tissue by both speeding up and improving the accuracy of spot count estimates.

• Item Type: Articles
• Additional Information: Supported in part by CRUK core grant C14303/A17197 as well as A19274 (F.M.) and A18072 (JDB/IAMcN).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ennis, Dr Darren and Yuan, Dr Ke and Mcneish, Professor Iain
• Authors: Macintyre, G., Piskorz, A. M., Berman, A., Ross, E., Morse, D. B., Yuan, K., Ennis, D., Pike, J. A., Goranova, T., Mcneish, I. A., Brenton, J. D., and Markowetz, F.
• College/School: College of Medical Veterinary and Life Sciences > School of Cancer Sciences; College of Science and Engineering > School of Computing Science
• Journal Name: JCO Clinical Cancer Informatics
• Publisher: American Society of Clinical Oncology
• ISSN: 2473-4276
• ISSN (Online): 2473-4276
• Published Online: 11 February 2021
• Copyright Holders: Copyright © 2021 by American Society of Clinical Oncology
• First Published: First published in JCO Clinical Cancer Informatics 5:176-186
• Publisher Policy: Reproduced under a Creative Commons Licence