Despite the urgency for prevention and treatment of lung adenocarcinoma (LUAD), we still do not know drivers in pathogenesis of the disease. Earlier work revealed that mice with knockout of the G-protein coupled receptor Gprc5a develop late onset lung tumors including LUADs. Here, we sought to further probe the impact of Gprc5a expression on LUAD pathogenesis. We first surveyed GPRC5A expression in human tissues and found that GPRC5A was markedly elevated in human normal lung relative to other normal tissues and was consistently down-regulated in LUADs. In sharp contrast to wild type littermates, Gprc5a-/- mice treated chronically with the nicotine-specific carcinogen NNK developed LUADs by six months following NNK exposure. Immunofluorescence analysis revealed that the LUADs exhibited abundant expression of surfactant protein C and lacked the clara cell marker Ccsp, suggesting that these LUADs originated from alveolar type II cells. Next, we sought to survey genome-wide alterations in the pathogenesis of Gprc5a-/- LUADs. Using whole exome sequencing, we found that carcinogen-induced LUADs exhibited markedly higher somatic mutation burdens relative to spontaneous tumors. All LUADs were found to harbor somatic mutations in the Kras oncogene (p. G12D or p. Q61R). In contrast to spontaneous lesions, carcinogen-induced Gprc5a-/- LUADs exhibited mutations (variants and copy number gain) in additional drivers (Atm, Kmt2d, Nf1, Trp53, Met, Ezh2). Our study underscores genomic alterations that represent early events in the development of Kras mutant LUAD following Gprc5a loss and tobacco carcinogen exposure and that may constitute targets for prevention and early treatment of this disease.
DNA methylation plays an important role in many biological processes by regulating gene expression. It is commonly accepted that turning on the DNA methylation leads to silencing of the expression of the corresponding genes. While methylation is often described as a binary on-off signal, it is typically measured using beta values derived from either microarray or sequencing technologies, which takes continuous values between 0 and 1. If we would like to interpret methylation in a binary fashion, appropriate thresholds are needed to dichotomize the continuous measurements. In this paper, we use data from The Cancer Genome Atlas project. For a total of 992 samples across five cancer types, both methylation and gene expression data are available. A bivariate extension of the StepMiner algorithm is used to identify thresholds for dichotomizing both methylation and expression data. Hypergeometric test is applied to identify CpG sites whose methylation status is significantly associated to silencing of the expression of their corresponding genes. The test is performed on either all five cancer types together or individual cancer types separately. We notice that the appropriate thresholds vary across different CpG sites. In addition, the negative association between methylation and expression is highly tissue specific.
Uterine carcinosarcoma (UCS) is a rare and aggressive form of uterine cancer. It is bi-phasic, exhibiting histological features of both malignant epithelial (carcinomatous) and mesenchymal (sarcomatous) elements. Studies have indicated that UCS arises from sarcomatous differentiation of high-grade carcinoma while others have suggested a bi-clonal nature. Given these differences, we sought to separate the carcinoma and sarcoma elements of UCS to try to understand their molecular differences and gain further insights into how these tumors develop. We macrodissected carcinomatous, sarcomatous, and normal cells from formalin fixed paraffin embedded (FFPE) uterine samples of 10 UCS patients. DNA and RNA were isolated and extracted using the Qiagen AllPrep DNA/RNA FFPE kit. Whole-genome SNP microarrays and deep sequencing of 26 cancer genes was performed, using the Illumina Infinium OmniExpressExome array and the TruSight Tumor panel respectively. Illumina HiSeq mRNA sequencing was also performed to quantify gene expression. The genomic allelic imbalance (AI) profiling, called from the SNP data by hapLOH, showed that sarcoma samples were more aberrant than their carcinoma counterparts (abstract 131, AACR 2016). From the targeted sequencing, the Illumina Amplicon-DS Somatic Variant Caller was employed to call somatic mutations. Mutations were identified in TP53 in both the sarcoma and carcinoma samples of all 10 patients. Frequently mutated genes included APC, EGFR, MET and MSH6 which were found in 60-80% of the patients. Genes mutated in less than 50% of the patients included PTEN, KRAS, KIT, FBXW7, PIK3CA, FGFR2, and CTNNB1. Current results showed no association of a mutated gene to either the sarcoma or carcinoma component of UCS. RSEM, STAR and EBSeq were applied to the RNA-seq data for gene expression quantification. Approximately 2500 genes were identified as being differentially expressed (DE) between normal and carcinoma samples. Just over 4000 genes were identified as being DE between normal and sarcoma samples. 75% of the DE genes in the carcinoma were also identified in the sarcoma. Using DAVID functional annotation tool, we characterized these gene sets with KEGG pathways. Deregulated pathways identified in both carcinoma and sarcoma include: cell cycle, transcriptional regulation, Ras and p53 signaling. Some additional pathways are putatively associated with sarcoma only, including MAPK and PI3K-Akt signaling. We report here the differences between sarcoma and carcinoma components of UCS from multiple molecular perspectives. From the genomic AI and DE analysis, the carcinoma aberrations appear to be mostly a subset of the sarcoma tumor profiles, where sarcoma samples appear to be more highly aberrant compared to the carcinoma samples. One possible inference from this is that the sarcoma originated and evolved from the carcinoma cells. Citation Format: Yihua Liu, Zachary Weber, F. Anthony San Lucas, Aditya Deshpande, Raed Sulaiman, Mary Fagerness, Natasha Flier, Joseph Sulaiman, Christel M. Davis, Jerry Fowler, Gareth E. Davies, David Starks, Luis Rojas-Espaillat, Paul Scheet, Erik A. Ehli. Tumor profiling of separated carcinomatous and sarcomatous components from uterine carcinosarcoma biopsies provides insights into their development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2463. doi:10.1158/1538-7445.AM2017-2463
Transcriptome sequencing (mRNA-seq) is becoming a very versatile technique for profiling tumors, extending beyond its original intent of transcript quantification, identification of alternative transcripts, and detection of gene fusions. For instance, through recent advancements in RNA-seq data analyses, one can now computationally assess allele-specific gene expression and generate profiles of expressed somatic mutations. Here, we demonstrate the ability to identify chromosomal allelic imbalances (AI) through detection of haplotype-specific patterns in gene transcripts. This class of RNA-based observations may potentially reveal DNA-level chromosomal allelic imbalances or uncover large regions of transcription deregulation. From the TCGA Uterine Carcinosarcoma project, we downloaded exome and RNA sequencing data for 48 patients’ tumor/normal sample pairs in addition to their clinical annotations. We also downloaded Affymetrix SNP6-based DNA copy number event calls made by the TCGA for use as a gold standard when evaluating the AI calls in the exome and RNA-seq. AI calls were made in both the exome and RNA-seq data by: (1) calling 1000 Genomes genotypes in the sequencing data, (2) phasing haplotypes and then (3) characterizing haplotype imbalances using a tool that we developed called hapLOHseq. hapLOHseq applied to the exome and RNA-seq data both resulted in a 72% specificity for identifying the gold standard AI events. In RNA-seq data we detected 43% of the chromosomal AI events identified in the exome sequencing data. When considering AI events specifically detected in the RNA-seq and not the gold standard (RNA-specific AI), the data suggest that higher RNA-specific AI loads could be negatively associated with survival (p-val = 0.076), with higher RNA-specific AI load patients having a median survival of 771 days compared to 1526 days for those patients with lower loads of RNA-specific AI. In conclusion, our results suggest that analysis of chromosomal AI in RNA-seq has equal specificity for detecting DNA-level AI when compared to exome sequencing, although at lower sensitivities. Clinically, our analyses suggest that patients with higher RNA-specific AI load may have a worse overall survival prognosis. The AI we are identifying in the RNA-seq samples may reflect large-scale transcription defects, resulting in a negative impact on the survival of patients. One possible cause of RNA-specific allelic imbalance could be the presence of cis mutations that impact a large-region of the transcription of one of the two haplotypes. Currently, we are identifying areas for improvement in our analytical methods, while interrogating and characterizing exome and RNA-seq AI in additional data sets. Citation Format: Francis A. San Lucas, Yihua Liu, Zachary Weber, Erik Ehli, Gareth Davies, Paul Scheet. Characterization of chromosomal allelic imbalances through RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3572. doi:10.1158/1538-7445.AM2017-3572
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