P3.01-027 3D Telomere Nuclear Organization to Distinguish Multiple Synchronous Lung Adenocarcinoma from Metastatic Lung Adenocarcinoma: Topic: Morphology

Lung cancer is the leading cause of cancer-related mortality. Adenocarcinoma (AC) representing 50% of diagnosed lung cancer. At diagnosis, 25% of pulmonary AC present as multicentric lesions and an half are considered synchronous AC (SLA) while the remaining represents intrapulmonary metastases (MAC) from a primary lung AC. Surgical resection is the treatment of choice for SLA and the outcome of the patients is generally good. On the other hand, intrapulmonary metastases (MAC) are related lesions associated with a poor prognosis and generally not amenable to surgical therapy. There is currently no way to distinguish SLA from MAC without analyzing a surgical specimen from each lesion, which is rarely possible. It is then likely that a significant proportion of patients with multiple AC do not get the appropriate treatment. There is therefore an urgent need to develop molecular tools to classify multicentric lesions. Genomic instability is one of the drivers of metastases, and the alteration of telomeric nuclear organization (TNO) is a predictor of genomic instability and tumor progression. Our hypothesis is that the profile of TNO can discriminate SLA from MAC. We assessed the parameters defining 3D-TNO using 3D quantitative fluorescence in situ hybridization, 3D imaging and 3D-TNO analyses on formalin-fixed paraffin-embedded tissue sections from 10 patients with SLA or MAC. For each patient, were analyzed two lesions: primary and metastatic lesions for MAC and two different primary tumors for SLA. The following 3D-TNO parameters were evaluated: 1) number of telomere (telomere signals), 2) telomere length (telomere signal intensities), 3) number of telomere aggregates (telomere clusters), 4) telomere distribution within a nucleus and 5) nuclear volume. Firstly, we compared 3D-TNO of cancer cells between MAC and SLA and found that four of the five parameters defining 3D-TNO showed statistical difference between the two pathological groups. Secondly, for each patient, we did pairwise comparison of parameters defining 3D-TNO between the two lesions. For the patients presenting MAC, we found that metastatic lesions had higher telomere aggregates than primary lesions. The comparison of the number of telomere aggregates did not display statistical difference between the two primary tumors from SLA. This study shows that the number of telomere aggregates is a powerful discriminative parameter that can reliably distinguish patients with SLA from patients with MAC. Our results suggest that 3D-TNO signature has the potential to provide a molecular tool that can eventually be implemented in a clinical setting.