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OA 16 - Treatment Strategies and Follow Up (ID 686)
- Event: WCLC 2017
- Type: Oral
- Track: Early Stage NSCLC
- Presentations: 1
OA 16.08 - A Modified Pathological N1 Classification Strategy Based on Systematic Dissection of N1 Nodes from Level 10 to 14 for Non-Small Cell Lung Cancer (ID 9157)
14:30 - 16:15 | Presenting Author(s): S. Yan
It is necessary to apply a precise standard to predict the oncological outcomes among heterogeneous subgroups of N1 disease ranging from level 10 to 14. Although International Association for the Study of Lung Cancer (IASLC) proposed a new N descriptor in the 8[th] edition of the TNM Classification, lack of dissection on level 13 and level 14 may affect the efficacy of new classification. In this study, we tested a hypothesized classification strategy based on systematic dissection of N1 node from level 10 to level 14.
From March 2007 to December 2014, 156 consecutive patients of non-small cell lung cancer, treating with lobectomy and systematic mediastinal lymphadenectomy, were investigated. Nodes from level 10 to 12 were dissected during operation. Intrapulmonary lymph nodes (level 13-14) were retrieved after surgery. The data were prospectively collected and retrospectively analyzed. All cases were divided into two categories according to the 8[th] edition of the TNM Classification: pN1a was defined as N1 at a single station, while pN1b was defined as N1 at multiple stations. Then, in our proposed classification, N1a (modified) was defined as single level of N1 station involved (not including single level 10 or 11 spread) or level 13 and/or 14 involved, while N1b (modified) was defined as single level 10 or 11 spread or multiple levels of N1 node involvement (not including level 13 and 14 spread). The association between the N1 subgroup status and survival was explored separately using 8[th] IASLC classification and hypothesized classification.
In the whole cohort, a mean±SD of 13.1±7.1 N2 nodes and 12.0+5.2 N1 nodes per case were collected.There were 4.7±3.1 nodes from level 13 and 14. The difference in 5-year overall survival between pN1a and pN1b was not significant (73.9% versus 65.7%, p=0.371). However, the difference in 5-year overall survival between N1a (modified) and N1b (modified) was significant (79.1% versus 60.2%, p=0.018). Multivariate analysis showed the revised N1 classification was an independent prognostic factor for NSCLC (versus N1a, the hazard ratio [HR] of N1b for OS was 2.120, 95% confidence interval [CI]: 1.083-4.151, p=0.028). However, the 8[th] edition IASLC N1 descriptors was not an independent prognostic factor (versus pN1a, HR of pN1b was 1.419, 95% CI: 0.710-2.837, p=0.322).
The hypothesized N1 classification in present study was shown to be a better descriptor to express the outcome than 8[th] edition of the TNM Classification of IASLC. More data are needed to validate this proposal.
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P1.02 - Biology/Pathology (ID 614)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Biology/Pathology
- Presentations: 1
- Coordinates: 10/16/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
P1.02-051 - Ultra-Deep Sequencing Depicts the Genomic Landscape of Ground-Glass Nodules in Early Stage Lung Adenocarcinoma (ID 9252)
09:30 - 16:00 | Author(s): S. Yan
Ground-glass nodule (GGN) is a type of nonspecific abnormality in lung parenchyma detected by computed tomography (CT) as hazy lesion with preservation of bronchial structures and vascular margins, which has a high correlation with lung adenocarcinoma. Different from typical lung cancer, malignant GGN appears a very early stage characteristic with long and indolent course. Large-scale radiological, pathological, and genetic studies on GGNs have broadened our knowledge to develop strategies of management. However, the molecular pathogenesis of GGNs remains unclear, leaving several questions of great clinical significance unsolved.
Motivated by this, we collected a cohort of 29 GGN patients diagnosed as early stage lung adenocarcinoma and performed whole exome sequencing (WES) to clarify the comprehensive genomic features and underlying molecular mechanism of GGNs. With the expectation of low purity of these samples, we adopted an ultra-deep sequencing depth to ~1000x, which is the deepest WES strategy so far in a single sample of single sequencing experiment to our knowledge, and got a high resolution landscape of genomic alterations in GGNs.
We found the extreme heterogeneity within each GGN patient, most of mutations manifesting as low frequency, indicating that GGNs grew under neutral evolutionary dynamics. Next we analyzed the mutation signature of these mutations and identified two novel signatures, Cp*C>A and Gp*CpC>T/Gp*CpG>T. These two signatures can reflect the mutation accumulating process within a growing tumor after initiation. Seven driver genes calculated in our cohort were all known lung cancer related genes, including EGFR，MGA，PIK3CA，PPP2R1A，RBM10，SETD and TP53. Of note, copy number alterations in GGNs were significantly less than other late stage lung cancers and this would result in the specific nature of GGNs.
In summary, this analysis of exome sequencing data highlights the repertoire of cancer genes and mutational processes in GGN patients, and progresses towards a comprehensive account of the somatic genetic basis of GGNs. These results, combined with further study efforts, will accelerate the pace to the achievement of accurate diagnosis and treatment for GGN patients. Also, the endeavor here provides a framework for the research on early stage tumors and low purity tumors, which will become a subject of active investigation in the near future.