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S. Tam
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MA17 - Genetic Drivers (ID 409)
- Event: WCLC 2016
- Type: Mini Oral Session
- Track: Biology/Pathology
- Presentations: 1
- Moderators:M. Satouchi, G.R. Simon
- Coordinates: 12/07/2016, 14:20 - 15:50, Lehar 1-2
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MA17.06 - Landscape of Somatic Mutations Involving Lung Cancer Associated Genes in Non-Small Cell Lung Cancer (NSCLC) Patient-Derived Xenografts (ID 6084)
14:56 - 15:02 | Author(s): S. Tam
- Abstract
- Presentation
Background:
Patient-derived tumor xenografts (PDXs) have high fidelity to their histological origins, and maintain the molecular heterogeneity and genetic aberrations of the donor patient tumors more faithfully than established in non-small cell lung cancer (NSCLC) cell lines. This study evaluated whether our panel of PDX models recapitulate known cancer-related gene mutations.
Methods:
Whole-exome sequencing was completed on 103 NSCLC PDX models, 47 adenocarcinoma (AdC) and 56 squamous (SqCC), with a mean coverage of 84x. After filtering for contaminating mouse reads, the exome data were aligned using the Burrows-Wheeler Aligner, processed using the standard GATK pipeline, and mutations were identified using MuTect. Additional filtering using dbSNP, ExAC and ESP was performed for cases without corresponding normal adjacent lung exome data (n = 80). The identified mutations were compared to 1260 frequently mutated cancer-related genes, which were compiled from a panel of cancer-related mutated genes (555) and a panel of lung cancer-specific mutated genes (1082).
Results:
High rates of somatic mutations were observed in both AdC (mean of 12.4 mutations/megabase) and SqCC (mean of 11.7 mutations/megabase) PDX models. Compared to the rates observed in primary lung cancers in The Cancer Genome Atlas studies (mean of 8.9 mutations/megabase in AdC; 8.1 mutations/megabase in SqCC), these values appear higher, but may be inflated due to the lack of data from corresponding normal tissues. AdC models had a total of 953 mutated genes (median: 57 genes/model; range: 5-307), while SqCC models were characterized by 1007 mutated genes (median: 55 genes/model; range: 21-354). Specific mutation frequencies were compared to those determined in a recent study involving genomic alterations in human primary lung AdC and SqCC (Nature Genetics 2016; 48; 607–616). This comparison, based on mutated genes common in both studies, demonstrated significant correlation of the frequencies in 791 genes in AdC (ρ=0.78; p<2.2×10[-16]), as well as in 799 genes in SqCC (ρ=0.73; p<2.2×10[-16]). Three genes that were reported as significantly mutated in both AdC and SqCC primaries, and had higher mutation frequencies in SqCC, were also observed to be higher in our SqCC PDX models (TP53: 48.9% in AdC vs. 55.4% in SqCC; CDKN2A: 4.3% vs. 7.1% and PIK3CA: 2.1% vs. 23.2%); however, the statistical significance of these differences needs to be tested.
Conclusion:
Mutation landscapes in cancer genes are recapitulated in AdC and SqCC PDX models. The fidelity of these landscapes in matched patient primary tumour samples is being investigated.
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P1.05 - Poster Session with Presenters Present (ID 457)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Early Stage NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 12/05/2016, 14:30 - 15:45, Hall B (Poster Area)
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P1.05-006 - Identification of miRNAs and mRNAs Associated with Metastasis in Early-Stage Non-Small Cell Lung Cancer (NSCLC) (ID 5829)
14:30 - 14:30 | Author(s): S. Tam
- Abstract
Background:
Early-stage NSCLC patients whose tumours can form primary xenografts (XG) in immune deficient mice have significantly shorter disease-free survival and are at a greater risk of early metastasis compared with patients whose tumours do not form xenografts (non-XG). Genomic and proteomic characterization of XG and non-XG-forming primary patient tumours may reveal clinically relevant genetic aberrations that are associated with early metastasis.
Methods:
miRNA-seq and RNA-seq data of 100 early-stage NSCLC patients with known engraftment status were acquired. The cohort includes 62% adenocarcinoma (ADC) and 38% squamous cell carcinoma (SQCC). Least absolute shrinkage and selection operator (LASSO) was applied to identify features associated with XG status using integrated miRNA and mRNA abundance profiles. Gene Ontology (GO) annotation was subsequently performed to elucidate biological processes that may be altered between the two patient groups.
Results:
Using miRNA and mRNA data alone, ADC patients were classified as XG and non-XG with 88.7% and 95.2% accuracy. The integration of these two data types classified the patients with 100% accuracy using 20 features (7 miRNAs and 13 mRNAs). While less is known regarding the roles of the identified miRNAs in lung ADC, several of the genes have been suggested to affect the metastatic ability of lung cancer cells; these include PITX1, GPNMB and KRT14. In SQCC, both the miRNA and mRNA data alone and the integrated profiles were able to classify patients into XG and non-XG-forming groups with 100% accuracy. However, the roles of the selected features (1 miRNA and 11 mRNAs) in the metastasis of SQCC are not well defined. GO annotation of the identified mRNAs in ADC revealed enrichment of biological processes related to B cell differentiation, wound healing and regulation of the immune response and signalling pathway, while catabolic and metabolic processes were enriched in SQ.
Conclusion:
The use of single-dimensional data to classify patients into different prognostic groups may not be sufficient in the presence of heterogeneous patient populations. Integrative analysis of multi-omic data can provide greater insights into clinically relevant genetic aberrations, which can be used to improve the molecular classification of NSCLC.
