Whole genome copy number analyses reveal a highly aberrant genome in TP53 mutant lung adenocarcinoma tumors

Background Genetic alterations are common in non-small cell lung cancer (NSCLC), and DNA mutations and translocations are targets for therapy. Copy number aberrations occur frequently in NSCLC tumors and may influence gene expression and further alter signaling pathways. In this study we aimed to characterize the genomic architecture of NSCLC tumors and to identify genomic differences between tumors stratified by histology and mutation status. Furthermore, we sought to integrate DNA copy number data with mRNA expression to find genes with expression putatively regulated by copy number aberrations and the oncogenic pathways associated with these affected genes. Methods Copy number data were obtained from 190 resected early-stage NSCLC tumors and gene expression data were available from 113 of the adenocarcinomas. Clinical and histopathological data were known, and EGFR-, KRAS- and TP53 mutation status was determined. Allele-specific copy number profiles were calculated using ASCAT, and regional copy number aberration were subsequently obtained and analyzed jointly with the gene expression data. Results The NSCLC tumors tissue displayed overall complex DNA copy number profiles with numerous recurrent aberrations. Despite histological differences, tissue samples from squamous cell carcinomas and adenocarcinomas had remarkably similar copy number patterns. The TP53-mutated lung adenocarcinomas displayed a highly aberrant genome, with significantly altered copy number profiles including gains, losses and focal complex events. The EGFR-mutant lung adenocarcinomas had specific arm-wise aberrations particularly at chromosome7p and 9q. A large number of genes displayed correlation between copy number and expression level, and the PI(3)K-mTOR pathway was highly enriched for such genes. Conclusions The genomic architecture in NSCLC tumors is complex, and particularly TP53-mutated lung adenocarcinomas displayed highly aberrant copy number profiles. We suggest to always include TP53-mutation status when studying copy number aberrations in NSCLC tumors. Copy number may further impact gene expression and alter cellular signaling pathways.

Whole Genome Copy Number Analyses Reveal a Highly Aberrant Genome in TP53 Mutant Lung Adenocarcinoma Tumors

Background: Genetic alterations are common in non-small cell lung cancer (NSCLC), and DNA mutations and translocations are targets for therapy. Copy number aberrations occur frequently in NSCLC tumors and may influence gene expression and further alter signaling pathways. In this study we aimed to characterize the genomic architecture of NSCLC tumors and to identify genomic differences between tumors stratified by histology and mutation status. Furthermore, we sought to integrate DNA copy number data with mRNA expression to find genes with expression putatively regulated by copy number aberrations and the oncogenic pathways associated with these affected genes. Methods: Copy number data were obtained from 190 resected early-stage NSCLC tumors and gene expression data were available from 113 of the adenocarcinomas. Clinical and histopathological data were known, and EGFR-, KRAS- and TP53 mutation status was determined. Allele-specific copy number profiles were calculated using ASCAT, and regional copy number aberration were subsequently obtained and analyzed jointly with the gene expression data.Results:The NSCLC tumors tissue displayed overall complex DNA copy number profiles with numerous recurrent aberrations. Despite histological differences, tissue samples from squamous cell carcinomas and adenocarcinomas had remarkably similar copy number patterns. The TP53-mutated lung adenocarcinomas displayed a highly aberrant genome, with significantly altered copy number profiles including gains, losses and focal complex events. The EGFR-mutant lung adenocarcinomas had specific arm-wise aberrations particularly at chromosome7p and 9q. A large number of genes displayed correlation between copy number and expression level, and the PI(3)K-mTOR pathway was highly enriched for such genes.Conclusions: The genomic architecture in NSCLC tumors is complex, and particularly TP53-mutated lung adenocarcinomas displayed highly aberrant copy number profiles. We suggest to always include TP53-mutation status when studying copy number aberrations in NSCLC tumors. Copy number may further impact gene expression and alter cellular signaling pathways.

Efficient Bayesian inference of phylogenetic trees from large scale, low-depth genome-wide single-cell data

A new generation of scalable single cell whole genome sequencing (scWGS) methods, allows unprecedented high resolution measurement of the evolutionary dynamics of cancer cells populations. Phylogenetic reconstruction is central to identifying sub-populations and distinguishing mutational processes. The ability to sequence tens of thousands of single genomes at high resolution per experiment is challenging the assumptions and scalability of existing phylogenetic tree building methods and calls for tailored phylogenetic models and scalable inference algorithms. We propose a phylogenetic model and associated Bayesian inference procedure which exploits the specifics of scWGS data. A first highlight of our approach is a novel phylogenetic encoding of copy-number data providing an attractive statistical-computational trade-off by simplifying the site dependencies induced by rearrangements while still forming a sound foundation to phylogenetic inference. A second highlight is an innovative phylogenetic tree exploration move which makes the cost of MCMC iterations bounded by O(|C| + |L|), where |C| is the number of cells and |L| is the number of loci. In contrast, existing off-the-shelf likelihood-based methods incur iteration cost of O(|C| |L|). Moreover, the novel move considers an exponential number of neighbouring trees whereas off-the-shelf moves consider a polynomial size set of neighbours. The third highlight is a novel mutation calling method that incorporates the copy-number data and the underlying phylogenetic tree to overcome the missing data issue. This framework allows us to realistically consider routine Bayesian phylogenetic inference at the scale of scWGS data.

Exploration of the immune cell landscape in brain cancer utilizing gene expression and copy number data

Brain cancer is a common cancer that affects more than 700,000 people in the US every year. We explore the dynamic changes in the abundance of immune cells based on RNA and DNA samples extracted from a large cohort of brain cancer patients. We used gene expression data and copy number data from a large brain cancer collections - the REMBRANDT project (REpository for Molecular BRAin Neoplasia DaTa) that includes 671 patients. We applied virtual flow cytometry tools CIBERSORT and xCell to estimate the abundance of the immune cells in the RNA of these samples. The immune cell landscape in this dataset is compared with that of the TCGA brain cancer collection, that includes 511 patients with Lower Grade Glioma (TCGA-LGG) and 156 patients with Glioblastoma (TCGA-GBM). We also discuss how well the results align with published literature, and how this computational analysis can help better understand how immune cells affect clinical outcome and survival in brain cancer patients.

Analysis of Copy Number Loss of the ErbB4 Receptor Tyrosine Kinase in Glioblastoma

ABSTRACTCurrent treatments for glioblastoma multiforme (GBM)—an aggressive form of brain cancer—are minimally effective and yield a median survival of 14.6 months and a two-year survival rate of 30%. Given the severity of GBM and the limitations of its treatment, there is a need for the discovery of novel drug targets for GBM and more personalized treatment approaches based on the characteristics of an individual’s tumor. Most receptor tyrosine kinases—such as EGFR—act as oncogenes, but publicly available data from the Cancer Cell Line Encyclopedia (CCLE) indicates copy number loss in the ERBB4 RTK gene across dozens of GBM cell lines, suggesting a potential tumor suppressor role. This loss is mutually exclusive with loss of its cognate ligand NRG1 in CCLE as well, more strongly suggesting a functional role. The availability of higher resolution copy number data from clinical GBM patients in The Cancer Genome Atlas (TCGA) revealed that a region in Intron 1 of the ERBB4 gene was deleted in 69.1% of tumor samples harboring ERBB4 copy number loss; however, it was also found to be deleted in the matched normal tissue samples from these GBM patients (n = 81). Using the DECIPHER Genome Browser, we also discovered that this mutation occurs at approximately the same frequency in the general population as it does in the disease population. We conclude from these results that this loss in Intron 1 of the ERBB4 gene is neither a de novo driver mutation nor a predisposing factor to GBM, despite the indications from CCLE. A biological role of this significantly occurring genetic alteration is still unknown. While this is a negative result, the broader conclusion is that while copy number data from large cell line-based data repositories may yield compelling hypotheses, careful follow up with higher resolution copy number assays, patient data, and general population analyses are essential to codify initial hypotheses.

CINdex: A Bioconductor Package for Analysis of Chromosome Instability in DNA Copy Number Data

The CINdex Bioconductor package addresses an important area of high-throughput genomic analysis. It calculates the chromosome instability (CIN) index, a novel measurement that quantitatively characterizes genome-wide copy number alterations (CNAs) as a measure of CIN. The advantage of this package is an ability to compare CIN index values between several groups for patients (case and control groups), which is a typical use case in translational research. The differentially changed cytobands or chromosomes can then be linked to genes located in the affected genomic regions, as well as pathways. This enables in-depth systems biology–based network analysis and assessment of the impact of CNA on various biological processes or clinical outcomes. This package was successfully applied to analysis of DNA copy number data in colorectal cancer as a part of multi-omics integrative study as well as for analysis of several other cancer types. The source code, along with an end-to-end tutorial, and example data are freely available in Bioconductor at http://bioconductor.org/packages/CINdex/ .