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Background Non-small cell lung cancer is a major killer world-wide. While some lung adenocarcinomas have mutations qualifying for targeted therapy, this is not the case for the squamous cell carcinomas (SqCC). TP53 mutations exist in around 85% of squamous cell carcinomas, but the mutations are difficult to target directly. We explore the biology of TP53 mutated SqCC and search for putative targets for therapy. Methods Patients undergoing surgery for squamous cell lung carcinoma from 2006 to 2015 were included in the study (n = 198). Tumours were analysed using Illumina SNP6 for copy number alterations and Agilent 60K arrays for gene expression. TP53 mutations were analysed by Sanger sequencing. For 140 patients both gene expression and copy number data were available. Results Frequency plots for tumours harbouring TP53 mutations and TP53 wild type tumours were generated separately identifying genomic regions with differential frequency of copy number alterations. TP53 mutations are more frequent in the previously published gene expression subtypes Classical and Primitive compared with Basal and Secretory, but this does not seem to affect survival. Amplifications are particularly frequent in the Classical subtype. Target gene search was performed, identifying 148 genes with amplification and over-expression in TP53 mutated tumours compared with wild type tumours. Several of these putative target genes are previously studied as putative targets of therapy. The majority of the putative target genes are located on the chromosomal arms 2p for samples of the Secretory subtype, 2q for samples of the Primitive subtype and on 12p and 17q for samples of the Classical subtype. Conclusions There are distinct copy number alterations and gene expression patterns in TP53 mutated squamous cell lung cancers that can be used to identify novel targets of therapy. Legal entity responsible for the study Oslo University Hospital. Funding The Norwegian Cancer Society, South-Eastern Norway Regional Health Authority. Disclosure All authors have declared no conflicts of interest.

Aim: Novel molecularly targeted agents such as small molecule kinase inhibitors and antibodies targeted against immune check-point inhibitors have begun to erode the dominant position held by platinum-based chemotherapy in the treatment of NSCLC. While in some cases, activating kinase mutations appear to be mutually exclusive (EGFR and KRAS), there are many overlapping molecular subsets within NSCLC. As we continue to develop novel drugs, it is important that we develop a greater understanding of the prevalence, overlap, and prognostic significance of drug targets including activating mutations, signaling pathways, and tumor immune markers, as this will aid in trial design and predictive biomarker development for drug combinations or sequential treatment regimens. Methods: Seven biomarkers - TTF1, p63, EGFR mutation, KRAS mutation, MET immunohistochemistry [IHC], PDL1 IHC, NaPI2B IHC- across two sample sets (Set 1, n = 561; Set 2, n = 310) were tested. Set 1 contained surgically resected cases obtained at MD Anderson Cancer Center (MDA) during 2003-2005. Samples from Set 2 were part of a collaboration between the University of Colorado Cancer Center (UofC), USA and The Norwegian Radium Hospital, and contained surgically-resected NSCLC tissues collected from 2006–2011. Results: The prevalence, overlap, and prognostic significance of each biomarker were compared between the two cohorts. Most endpoints were consistent between the cohorts. However, significant differences in prevalence were observed within adenocarcinomas for MET (50% vs. 34%, MDA vs. UofC; p 67% of patients in both cohorts were positive for more than one biomarker and >33% were positive for at least three biomarkers. Correlations with patient characteristics and outcomes will be described in further detail. Conclusions: These data suggest that the biomarker landscape in NSCLC is complex with most patients having multiple targetable alterations. Thus, the treatment landscape for NSCLC will become increasingly complex as more experimental agents approach pivotal testing. Disclosure: D. Shames, M. Kowanetz, Y. Xiao, Y. Choi, H. Koeppen, R. Firestein, Y. Wang, S. Mocci, C. Bowden and L.C. Amler all declare that: I am an employee of Genentech Inc. I own stock in Roche Holdings. All other authors have declared no conflicts of interest.

Aim: NaPi2b belongs to the type II family of sodium-dependent phosphate co-transporters, physiologically expressed in type II pneumocytes of lung and on the brush border membrane of small intestine. Increased expression of NaPi2b was recently described in ovary, thyroid and breast cancer. The aim of this study was to evaluate NaPi2b expression in lung cancer. Methods: Immunohistochemistry using the 10H1 primary antibody (Genentech) was performed on two cohorts of treatment naive resected NSCLC patients collected at MD Anderson, University of Texas, USA (training cohort, N = 415) and University of Oslo, Norway (testing cohort, N = 350). Moreover, 67 lung cancer cell lines were analyzed (51 non-small cell and 16 small cell lung cancer). EGFR and KRAS mutations were evaluated with the SnapShot assay. NaPi2b protein expression was scored using the H-score method (0-300). Expression was defined as high or low according to a H-score cut-off value of 200. Results: Patient characteristics did not differ significantly in the two cohorts. In the training and testing cohorts high levels of NaPi2b were detected in 48.4% and 64% of patients, respectively. Adenocarcinomas (AC) were found to express significantly higher levels of NaPi2b than squamous cell carcinoma (SqCC) (AC vs SqCC median H-score: 248 vs 11, p Conclusions: NaPi2b is a prognostic marker in NSCLC and is strongly associated with important clinicopathological and biological variables such as histology and driver mutation status. Further studies are warranted to clarify the role of NaPi2b in tumor development and progression, as well as its association with driver mutations. Disclosure: O.T. Brustugun received research funding from Roche, Astrazeneca and GlaxoSmithKline; Y. Xiao is a Genentech employee and owns stock in Roche Holdings; Y. Choi is en amployee of Genetech Inc and owns stock in Roche Holdings; Y. Wang is an employee of Genentech Inc and owns stock in Roche Holdings; R. Firestein is an employee of Genentech Inc and owns stock in Roche Holdings; A. Helland received research funding from Roche, AstraZeneca and GlaxoSmithKline; F.R. Hirsch received research funding from Celgene, Genentech, Ventana, Lilly, Imclone and Amgen, and received compensation from Genentech/Roche, Novartis, Pfizer, Brystol-Meyers Squibb, Amgen and Lilly for partecipating in their advisory boards; D. Shames is an employee of Genetech Inc and owns stock in Roche Holdings. All other authors have declared no conflicts of interest.