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R. Ferrara
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OA06 - Prognostic & Predictive Biomarkers (ID 452)
- Event: WCLC 2016
- Type: Oral Session
- Track: Biology/Pathology
- Presentations: 1
- Moderators:F. Shepherd, Y. Yatabe
- Coordinates: 12/05/2016, 14:20 - 15:50, Strauss 1
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OA06.06 - Druggable Alterations Involving Crucial Carcinogenesis Pathways Drive the Prognosis of Squamous Cell Lung Carcinoma (SqCLC) (ID 5342)
15:15 - 15:25 | Author(s): R. Ferrara
- Abstract
- Presentation
Background:
We previously built and validated a risk classification model for resected SqCLC by combining clinicopathological predictors to discriminate patients’ (pts) prognosis (Pilotto JTO 2015). Here we (AIRCMFAG project no. 14282) investigate the molecular portrait of prognostic outliers to identify differentially expressed, potentially druggable alterations.
Methods:
Based on the published 3-class model, 176 and 46 pts with good and bad prognosis, respectively, were identified. Somatic Mutations (SM) and Copy Number Alterations (CNA) were evaluated with Next Generation Sequencing (NGS) for 59 genes (Ion Proton system, Ion Ampliseq custom panel). Moreover, RNA expression assays, immunohistochemistry (IHC) and immunofluorescence (FISH) were performed. Descriptive statistic was adopted and continuous variables were dichotomized according to AUC or medians.
Results:
Herein, the analysis of 60 pts (good/poor 27/33) is reported. In the overall population, the median rate of SM (3.3%) is lower compared to the median rate of CNA (28.3%), without significant differences between the two prognostic groups. The most frequent SM resulted to be missense (66.7%) and nonsense (20.3%) mutations, whereas the copy number gain is the most common CNA (76.7%), The distribution of relevant alterations in the main carcinogenesis pathways in term of SM, CNA and expression (by RNA, IHC and FISH), according to the prognostic subgroups, are reported in the table.Pathway Gene [method] Good [%] Poor [%] p-value Squamous differentiation SOX [CNA] 74.1 51.5 0.11 TP63 [CNA] 37.0 21.2 0.25 Epithelial to mesenchymal transition SNAI1 [RNA] 59.2 90.9 0.006 Vimentin [RNA] 44.4 69.7 0.07 mTOR PI3KCA [SM] 0 9.0 0.24 RICTOR [CNA] 3.7 27.3 0.017 p-mTOR [IHC] 11.1 18.1 0.5 Tyrosine kinase receptors DDR2 [SM] 11.1 0 0.085 FSR2 [CNA] 3.7 18.1 0.12 MET [FISH] 11.1 24.2 0.32 FGFR3 [FISH] 25.9 42.4 0.28 Cell cycle regulators CDKN2A [CNA] 22.2 3.0 0.38 SMAD4 [CNA] 33.3 57.6 0.074 Immune checkpoints PD-L1 [IHC] 18.5 6.1 0.23 PD-1 [RNA] 51.8 93.9 <0.0001
Conclusion:
Although performed on a limited number of pts, such comprehensive analysis of DNA, RNA and proteins, using different methodologies, is feasible and allow identifying potentially druggable prognostic modulators, such as RICTOR/PI3K/mTOR signaling pathway. The possibility to inhibit this pathway with selective agents is currently under investigation in in vitro preclinical models.
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