Abstract
Chromosomal aberration and DNA copy number change are robust hallmarks of cancer. Imaging of spots generated using fluorescence in situ hybridisation (FISH) of locus specific probes is routinely used to detect copy number changes in tumour nuclei. However, it often does not perform well on solid tumour tissue sections, where partially represented or overlapping nuclei are common. 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 homogenous Poisson Point Process model for automated spot counting. We benchmarked the performance of FrenchFISH against previous approaches in a controlled simulation scenario and exemplify its use in 12 ovarian cancer FFPE-tissue sections, for which we assess copy number alterations in three loci (c-Myc, hTERC and SE7). We show that FrenchFISH outperforms standard spot counting approaches and that the automated spot counting is significantly faster than manual without loss of performance. FrenchFISH is a general approach that can be used to enhance clinical diagnosis on sections of any tissue.
Author summary Cancer genomes can look very chaotic, because cancer cells are unable to fully repair errors in DNA replication during cell division. While a healthy genome has two copies of every chromosome, in a cancer genome some pieces can be lost completely and others can appear in 50 copies. To diagnose cancers and to decide on the right therapeutic strategy for a patient, it can be very important to know how many copies of a particular piece of DNA exist in a cell. The standard technique used in the clinic to assess DNA copy number is called FISH, short for fluorescence in situ hybridisation. This technique uses fluorescent probes that bind to a DNA piece of interest and show up as glowing spots in a microscopic image. Counting the spots in an image is a labour-and time-intensive process that is generally done by well-trained experts. Here we present a statistical approach to automatically count FISH spots, which outperforms previously proposed methods, and has the potential to substantially speed up clinical diagnostics.
Footnotes
↵* james.brenton{at}cruk.cam.ac.uk, florian.markowetz{at}cruk.cam.ac.uk
