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Integrative Genomic Characterization Identifies Molecular Subtypes of Lung Carcinoids

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Integrative Genomic Characterization Identifies Molecular Subtypes of Lung Carcinoids Published OnlineFirst July 12, 2019; DOI: 10.1158/0008-5472.CAN-19-0214 Cancer Genome and Epigenome Research Integrative Genomic Characterization Identifies Molecular Subtypes of Lung Carcinoids Saurabh V. Laddha1, Edaise M. da Silva2, Kenneth Robzyk2, Brian R. Untch3, Hua Ke1, Natasha Rekhtman2, John T. Poirier4, William D. Travis2, Laura H. Tang2, and Chang S. Chan1,5 Abstract Lung carcinoids (LC) are rare and slow growing primary predominately found at peripheral and endobronchial lung, lung neuroendocrine tumors. We performed targeted exome respectively. The LC3 subtype was diagnosed at a younger age sequencing, mRNA sequencing, and DNA methylation array than LC1 and LC2 subtypes. IHC staining of two biomarkers, analysis on macro-dissected LCs. Recurrent mutations were ASCL1 and S100, sufficiently stratified the three subtypes. enriched for genes involved in covalent histone modification/ This molecular classification of LCs into three subtypes may chromatin remodeling (34.5%; MEN1, ARID1A, KMT2C, and facilitate understanding of their molecular mechanisms and KMT2A) as well as DNA repair (17.2%) pathways. Unsuper- improve diagnosis and clinical management. vised clustering and principle component analysis on gene expression and DNA methylation profiles showed three robust Significance: Integrative genomic analysis of lung carcinoids molecular subtypes (LC1, LC2, LC3) with distinct clinical identifies three novel molecular subtypes with distinct clinical features. MEN1 gene mutations were found to be exclusively features and provides insight into their distinctive molecular enriched in the LC2 subtype. LC1 and LC3 subtypes were signatures of tumorigenesis, diagnosis, and prognosis. Introduction of Ki67 between ACs and TCs does not enable reliable stratification between well-differentiated LCs (6, 7). It has also been reported that Lung carcinoids (LC) are an indolent and rare type of primary TCs and ACs are overdiagnosed as small cell lung carcinomas lung neoplasms that are, in general, understudied. The 2015 (SCLC) in small crush biopsy specimens (8), a situation where World Health Organization (WHO; ref. 1) classification of LCs artifacts in specimens appear as bluish clusters in which cellular includes atypical carcinoids (AC; 0.2% prevalence) and typical details are not recognizable. As SCLCs are highly malignant, incor- carcinoids (TC; 2% prevalence). TCs are slow growing tumors rect diagnosis of TC and AC tumors as SCLC can subject patients to that rarely spread beyond the lungs whereas ACs are faster growing unnecessary stress and treatment (8). More accurate molecular tumors and have a greater chance of metastasizing to other diagnostic tools and stratification for LCs will help ensure more tissues (2). The WHO classification relies mainly on morphology, appropriate treatment and clinical management. proliferation rate (mitotic index), and necrosis assessment (3). Previous genomic analysis of LC tumors has identified recur- This current method of classification has its drawbacks as studies rent mutations in MEN1, PSIP1, and ARID1A (9), whereas no have shown that the reproducibility of cancer classification and its significant mutations or focal copy alterations were observed in prognostic efficacy have high interobserver variability (3, 4), genes that are frequently mutated in non–small cell lung cancer especially for differentiating between TC and AC (5). Recent WHO (NSCLC), large cell neuroendocrine carcinoma (LCNEC), and classifications highlight use of the Ki67 cell proliferation marker SCLC (e.g., KRAS, TP53, EGFR, and RB1; ref. 10). The different to distinguish ACs from TCs (1).However, overlapping distribution mutation spectrum and low mutation burden (9) of LCs indicate they are distinct from NSCLC and high-grade lung neuroendo- 1Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, New crine tumors (NET). It is not known if there are distinct molecular Jersey. 2Department of Pathology, Memorial Sloan-Kettering Cancer Center, subtypes of LCs or their cells of origin. 3 New York, New York. Department of Surgery, Memorial Sloan-Kettering Cancer In this study, we performed genotyping on 29 LCs to detect Center, New York, New York. 4Thoracic Oncology Service, Memorial Sloan- n ¼ 5 mutations in a 354-cancer gene panel, mRNA sequencing ( 30) Kettering Cancer Center, New York, New York. Department of Medicine, Rutgers n ¼ fi Robert Wood Johnson Medical School, New Brunswick, New Jersey. and DNA methylation 450K-array analysis ( 18) and identi ed 3 molecular subtypes with distinct clinical features. We also iden- Note: Supplementary data for this article are available at Cancer Research tified 2 key biomarkers (ASCL1 and S100) to stratify these subtypes. Online (http://cancerres.aacrjournals.org/). Integration of genetic and epigenetic signatures distinguishes each Corresponding Authors: Chang S. Chan, Rutgers Cancer Institute of New Jersey, subtype of carcinoid, providing deeper insight into their distinctive 195 Little Albany Street, New Brunswick, NJ 08903. Phone: 732-235-7363; molecular signatures of tumorigenesis as well as cells of origin. E-mail: [email protected]; and Laura H. Tang, Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York 10065. Phone: 212-639-5905. E-mail: [email protected] Materials and Methods Cancer Res 2019;79:4339–47 Patient's data doi: 10.1158/0008-5472.CAN-19-0214 Retrospective and prospective reviews of 30 LC neoplasms were Ó2019 American Association for Cancer Research. performed using the pathology files and institutional database at www.aacrjournals.org 4339 Downloaded from cancerres.aacrjournals.org on October 1, 2021. © 2019 American Association for Cancer Research. Published OnlineFirst July 12, 2019; DOI: 10.1158/0008-5472.CAN-19-0214 Laddha et al. Memorial Sloan Kettering Cancer Center (MSKCC, New York, NY). dataset. The gene expression and mutational data were down- All studies were conducted in accordance with appropriate ethical loaded from supplement data files (https://www.nature.com/ guidelines (following U.S. Common Rule) and with Institutional articles/ncomms4518#supplementary-information) reported in Review Board approval. Written informed consent was obtained ref. 9. This gene expression data were reported as transcript from the patients. All patients were evaluated clinically with con- expression instead of gene expression. We used callapseRows (22) firmed pathologic diagnoses, appropriate radiological and labora- on transcript expression to convert to respective gene expression tory studies, and surgical or oncological management. Fresh-frozen using MaxVariance option. Gene expression for LCs signature (top tumor and paired normal tissues were obtained from MSKCC's 100 variant genes from our 30 LCs dataset) was extracted from this tissue bank under Institutional Review Board protocol. Targeted RNAseq dataset. Unsupervised clustering and PCA analysis on this cancer gene panel DNA sequencing, RNA sequencing (RNA-seq), dataset were performed as previously described for our 30 LCs and DNA methylation array were performed on fresh-frozen sam- dataset. ples. Relevant clinical and pathologic information is presented in Supplementary File S1. DNA methylation and analysis DNA were extracted from LC samples and interrogated for CpG DNA sequencing and analysis DNA methylation using the Illumina 450K array platform (Illu- DNA extraction from microdissected tumor samples and normal mina Inc.). For CpG DNA methylation data analysis, we used adjacent tissues was performed using a commercially available DNA ChAMP (23) package in R with default parameters. Briefly, IDAT Extraction Kit (DNeasy Blood and Tissue Kit, Catalog No. 69504; raw data files were imported in R and filtered to exclude samples Qiagen), according to the manufacturer's protocol. Targeted seq- with detection P-value <0.01 and bead count <3 in at least 5% of uencing on 29 LCs was performed using MSK-IMPACT (11) hybrid samples and normalized using FunctionNormalization (24). capturecancergenepanel(n ¼ 354). Single-nucleotide variants and Additional filtering was performed to remove probes that have short indels (<30 bp) were identified and annotated using MSK- annotated SNPs, present on X and Y chromosome, and have CpG IMPACT pipeline (11). Briefly, raw reads were filtered based on probes mapped to multiple locations. b values for 413,176 probes quality, mapped to NCBI b37 genome using BWA–MEM version were used for all subsequent analysis. Subtype specific differen- 0.7.5a (http://arxiv.org/abs/1303.3997), post-processed using tially methylated CpG probes (DMP) and CpG island were GATK (12), and variant identification using MuTect (13). Variants identified using COHCAP (default parameter; ref. 25). For DMP, were filtered based on its entry in NCBI-dbSNPs (http://www.ncbi. we focused on probes present at TSS1500/200 and first exon for nlm.nih.gov/snp), 1000G project (http://www.1000genomes.org/), subsequent analysis. We integrated gene expression and DNA and COSMIC (http://cancer.sanger.ac.uk/cosmic). Filtered variants methylation to investigate subtype-specific gene expression using were manually reviewed on IGV. We created the mutational Onco- default parameter for COHCAP.integrate.avg.by.island with FDR Print plot for our 29 LCs dataset using the online cBioPortal website P value <0.01. (http://www.cbioportal.org/oncoprinter.jsp). IHC staining RNA-seq and data analysis IHC staining was performed using commercially available Total RNA extraction from microdissected frozen tumor samples antibodies
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