Abstract A101: Comprehensive genomic profile analyses of small cell lung cancer patient-derived xenografts for pharmacogenomics
Authors: Vuaroqueaux Vincent , Peille Anne-Lise , Zeitouni Bruno , Fiebig Vanina , Fiebig Heinz-Herbert 
Published: May 2016

Small Cell Lung Cancers (SCLC) are fast growing tumors with frequent neuroendocrine differentiation. They represent 10-15% of lung cancers and arise in heavy smokers. Most SCLC cancer patients respond well to chemotherapy initially but rapidly develop resistance and die within a few months. Thus, the identification of alternative therapeutic approaches is urgently needed to improve patient's prognosis. Since SCLC is rarely treated by surgery, the number of SCLC models developed for pharmacogenomic research is very limited, however we have established 6 patient-derived xenograft (PDX) models from primary and metastasis SCLC. Complementary to histology, growth characteristic and response to SOCs, here we present comprehensive genomic analyses of these models, revealing similarity with patient tumors and druggable genes and pathways.

First we confirmed that the SCLC PDX models retained the morphology characteristics of SCLC tumors and all were heterogeneous in stroma and vasculature content. The median volume doubling time of SCLC-PDX was 7.4 days, which was faster than adenoma (9.6 days) or squamous (9.5 days) NSCLC and slower than large cell NSCLC-PDX (5.7 days). In vivo analyses demonstrated that the SCLC-PDX responded to the SOCs irinotecan and cyclophosphamide but were relatively resistant to cisplatin.

Whole exome sequencing, RNAseq and SNP6.0 microarray data revealed mutations in 1542 genes (bi-allelic mutations occurring in 328 of these) as well as larger chromosomal alterations including high copy number gains in 33 genes (of the 33 gene copy gains found, 28 occurred in a model with amplification of chromosome 1), 3 homozygous gene deletions and &gt;20 gene fusions. Genes involved in cell cycle/apoptosis, ECM and focal adhesion via PI3K/AKT were frequently altered, as identified using KEGG database resources and as in patient tumors, recurrent mutations were seen in TP53, RB1 and SPDYE5. Furthermore, similar to SCLC data reported by the TCGA, we saw alterations of the NOTCH signaling pathway in some models, with mutations in NOTCH 1, 2 or 4 genes as well as downstream signaling molecules (CTBP2, KAT2A, DTX2). A search for drug targetable gene alterations identified mutations in BRAF, ERBB2, ALK, mTOR and JAK3 genes while EGFR, KRAS, PI3KCA and PTEN genes were not affected in these models. We also discovered gene fusions such as IP6K1-TRAIP or TM9SF4 fused with the oncogene SRC. At the gene expression level (RNAseq and Affymetrix U133 2Plus), 5 out of 6 SCLC-PDX expressed high levels of neuroendocrine markers characteristic of SCLC such as ASCL1, DLK1, GRP or CHGA, and had gene signatures characteristic of high proliferation and expression of stem cell markers such as LGR5. Analysis of the model with amplification of chromosome 1 confirmed that gene copy number gains were frequently associated with increased expression. This included the MYC family member MCL1, suggesting MYC-driven tumors and sensitivity toward compounds such as aurora kinase inhibitors.

In conclusion, we show here SCLC-PDX closely resemble to patient tumors for histology and genomic profiles resulting in fast growing models of various sensitivity toward chemotherapeutics. Numerous drug targetable alterations have been revealed by comprehensive genomic profile analysis. These models therefore represent useful tools for pharmacogenomics investigations.

Citation Format: Vincent Vuaroqueaux, Anne-Lise Peille, Bruno Zeitouni, Vanina Fiebig, Heinz-Herbert Fiebig. Comprehensive genomic profile analyses of small cell lung cancer patient-derived xenografts for pharmacogenomics. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A101.