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Original Article Molecular Signature and Pathway Analysis of Human Primary Squamous and Adenocarcinoma Lung Cancers

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Original Article Molecular Signature and Pathway Analysis of Human Primary Squamous and Adenocarcinoma Lung Cancers Am J Cancer Res 2012;2(1):93-103 www.ajcr.us /ISSN:2156-6976/ajcr0000091 Original Article Molecular signature and pathway analysis of human primary squamous and adenocarcinoma lung cancers Nikolai Daraselia1, Yipeng Wang2, Adam Budoff2, Alexander Lituev3, Olga Potapova3, Gordon Vansant2, Joseph Monforte2, Ilya Mazo1, and Valeria S Ossovskaya4 1Ariadne Inc., Rockville, MD, USA; 2AltheaDx, San Diego, CA, USA; 3Cureline Inc., South San Francisco, CA, USA; 4BiPar Sciences, Inc. (subsidiary of Sanofi), South San Francisco, CA, USA Received September 29, 2011; accepted October 22, 2011; Epub November 19, 2011; Published January 1, 2012 Abstract: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with a poor response to che- motherapy and low survival rate. This unfavorable treatment response is likely to derive from both late diagnosis and from complex, incompletely understood biology, and heterogeneity among NSCLC subtypes. To define the relative contributions of major cellular pathways to the biogenesis of NSCLC and highlight major differences between NSCLC subtypes, we studied the molecular signatures of lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), based on analysis of gene expression and comparison of tumor samples with normal lung tissue. Our results suggest the existence of specific molecular networks and subtype-specific differences between lung ADC and SCC subtypes, mostly found in cell cycle, DNA repair, and metabolic pathways. However, we also observed similarities across major gene interaction networks and pathways in ADC and SCC. These data provide a new insight into the biology of ADC and SCC and can be used to explore novel therapeutic interventions in lung cancer chemoprevention and treatment. Keywords: NSCLC, adenocarcinoma, squamous cell carcinoma, molecular signature, gene expression, pathway Introduction tion and histologic heterogeneity. This heteroge- neity is reflected by the fact that the majority of Worldwide, over 1.3 million people are diag- prostate, breast, and colorectal carcinomas are nosed each year with lung cancer, with over 1.1 ADC, while only 30% of NSCLC cases are of this million deaths [1, 2]. Lung cancer is the most subtype [6, 8]. For the most part, and until re- common global cause of cancer death in men cently, NSCLC subtypes (SCC, LCC, and ADC) and second only to breast cancer in women were treated similarly, regardless of the biologic (17.6% of cancer-related deaths in both sexes) heterogeneity associated with histology. It is [1-3]. There is a high fatality rate with this dis- likely that poor historic lung cancer response ease, with only 15% of patients still alive 5 rates may be attributable in part to a relatively years after diagnosis [4]. Non-small cell lung homogenous approach to treat a heterogene- cancer (NSCLC) is the most common type of ous disease. Therefore, a better understanding lung cancer, accounting for 85% of cases, and and molecular characterization of the NSCLC can be divided into three main subgroups: ade- subtypes could contribute to improved design of nocarcinoma (ADC; 30-50% of cases), treatment schedules and management of lung squamous cell carcinoma (SCC; ~30%), and cancer. large cell carcinoma (LCC; ~10%), according to the predominant morphology of the tumor cells Gene expression profiling using microarrays is a as determined by light microscopy [4-7]. NSCLC robust and straightforward way to study the mo- is associated with high rates of proliferation and lecular features of different types and subtypes metastases, as well as poor prognosis for ad- of cancer at a systems level. Although it is possi- vanced-stage disease compared with other can- ble to distinguish the different NSCLC sub- cers. There are several potential explanations groups histologically, genomic profiling demon- for the disparity between lung cancer survival strates that there are significant and distinct and other common tumors, including late detec- differences in the molecular signature associ- Molecular signature of lung cancers ated with each subtype. The objective of this ST Arrays (Affymetrix, Santa Clara, CA) along study was to enhance the understanding of with GeneChip Eukaryotic Hybridization controls NSCLC pathogenesis through characterization (Affymetrix, Santa Clara, CA). Samples were in- of molecular and pathway signatures in primary cubated at 45°C in an Affymetrix hybridization lung ADC and SCC samples, and to compare oven 640 at 60 rpm for 16 hours, and washed these signatures with those of normal lung tis- with an Affymetrix GeneChip Fluidics Station sue. 450 according to the manufacturer’s specifica- tions. Scanning was performed using the Affy- Materials and methods metrix GeneChip 7G scanner using manufac- turer-recommended default settings. Isolation of RNA Data analysis Sets of syngeneic normal and tumor samples (lung ADC and SCC) were obtained from Cure- An Affymetrix Expression Console was used to line Biobank (Cureline Inc., San Francisco, CA). generate quality control parameters, process Eighty formalin-fixed, paraffin-embedded (FFPE) probe intensity files and CEL-format data files, tissue samples from 20 patients with NSCLC and to normalize and summarize a gene expres- were analyzed (20 FFPE tissue samples for sion measurement for each probe set on the each group). For RNA isolation, five 10-µm sec- array through a Robust Multiarray Averaging tions were sliced via microtome, placed in 1.7 (RMA) algorithm [11]. All the clinical and control ml tubes, and deparaffinized with 1 ml of xylene samples in the study and the expression values (EMD Chemicals, Gibbstown, NJ). The samples were log-transformed with a base of 2 for down- were digested for 16 hours with Proteinase K stream data analysis. For each individual sam- and total RNA isolation was performed using the ple, differential expression profiles of cancer Epicentre Biotechnologies MasterPure™ Com- versus normal syngeneic tissue were calculated. plete DNA and RNA Purification Kit (Epicentre In addition, differential profiles of all cancer Biotechnologies, Madison, WI) following the samples versus all normal breast samples were manufacturer’s instructions. All samples were calculated using an unpaired t-test. subsequently treated with RNase-free DNase I (Ambion, Austin, TX). All gene expression analyses were performed in Pathway Studio 7 (Ariadne Genomics, Inc., Synthesis of cDNA, amplification, and labeling Rockville, MD) [9, 11-15], using the ResNet 7 database (Ariadne Genomics, Inc.) [9, 10]. En- RNA samples were amplified and converted to richment analysis in Pathway Studio 7 was per- cDNA using the NuGEN WT-Ovation FFPE v2 formed by Gene Set Enrichment Analysis (GSEA) RNA Amplification System (NuGEN Technolo- [16] and Sub-Network Enrichment Analysis gies, San Carlos, CA) [9, 10]. Briefly, 50 ng of (SNEA) algorithms (Supplementary Figure 1S) RNA was reverse transcribed to antisense- [11]. Functional enrichment was performed us- cDNA, amplified using kit reagents, and purified ing Fisher’s Exact Test. using a QIAGEN PCR purification kit (QIAGEN, Valencia, CA). DNA concentration was assessed SNEA enrichment in Pathway Studio was calcu- using a Nanodrop ND-100 spectrophotometer. lated using the Mann-Whitney test, a non- Sense transcript cDNA (ST-cDNA) was generated parametric method that compares the medians from 2-4 µg of purified antisense-cDNA using of non-normal distributions X and Y. Both sam- the kit reagents according to manufacturer’s ples (having sizes N and M) are combined into instructions. ST-cDNA was purified by QIAGEN one array in ascending order with each element kit and final DNA concentration determined by then replaced by its rank in the array from 1 to absorption. Up to 5 µg of purified ST-cDNA was N+M. The ranks of the first sample elements fragmented and biotin-labeled using the NuGEN are summarized and a Mann-Whitney U-value Encore Biotin Module Kit (NuGEN Technologies, calculated from Equation 1: San Carlos, CA). Hybridization, washing, and analysis Eq. 1 Biotin-labeled cDNA from each sample was di- rectly hybridized to GeneChip Human Gene 1.0 94 Am J Cancer Res 2012;2(1):93-103 Molecular signature of lung cancers If U is close to the mean of U (i.e. 0.5·N·M) then along with the sub-networks themselves in the the medians of X and Y are similar. The signifi- user interface, ranked from the lowest (best) to cance level of the U statistic can be derived the highest (worst) p-value. Note: the percent- from the distribution quantiles. When applied to age overlap is also presented in order to provide gene expression data, two distributions are typi- an adequate measurement of significance and cally derived from the gene set or sub-network, confidence in various statistical tests of overlap. and from the entire gene expression profile measured on the chip. The following steps de- Analysis of key regulators of differential gene scribe the computational steps performed by expression the SNEA algorithm. The key regulators of differential response are Preparation of sub-networks those components of signal transduction path- ways and expression regulators that are most SNEA was used to build sub-networks from the likely to be involved in the regulation of genes relationships in a database based on criteria differentially expressed between tumor and nor- specified by the user. A central ‘seed’ is initially mal samples. Such key regulatory signaling created from all relevant entities in the data- pathways are assumed to be deregulated (e.g. base, and associated
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