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.

Frequent copy number gains of SLC2A3 and ETV1 in testicular embryonal carcinomas.

Testicular germ cell tumours (TGCTs) appear as different histological subtypes or mixtures of these. They show similar, multiple DNA copy number changes, where gain of 12p is pathognomonic. However, few high-resolution analyses have been performed and focal DNA copy number changes with corresponding candidate target genes remain poorly described for individual subtypes. We present the first high-resolution DNA copy number aberration (CNA) analysis on the subtype embryonal carcinomas (ECs), including 13 primary ECs and 5 EC cell lines. We identified recurrent gains and losses and allele-specific CNAs. Within these regions, we nominate 30 genes that may be of interest to the EC subtype. By in silico analysis of data from 150 TGCTs from The Cancer Genome Atlas (TCGA), we further investigated CNAs, RNA expression, somatic mutations and fusion transcripts of these genes. Among primary ECs, ploidy ranged between 2.3 and 5.0, and the most common aberrations were DNA copy number gains at chromosome (arm) 7, 8, 12p, and 17, losses at 4, 10, 11, and 18, replicating known TGCT genome characteristics. Gain of whole or parts of 12p was found in all samples, including a highly amplified 100 kbp segment at 12p13.31, containing SLC2A3. Gain at 7p21, encompassing ETV1, was the second most frequent aberration. In conclusion, we present novel CNAs and the genes located within these regions, where the copy number gain of SLC2A3 and ETV1 are of interest, and which copy number levels also correlate with expression in TGCTs.

DNA copy number motifs are strong and independent predictors of survival in breast cancer.

Somatic copy number alterations are a frequent sign of genome instability in cancer. A precise characterization of the genome architecture would reveal underlying instability mechanisms and provide an instrument for outcome prediction and treatment guidance. Here we show that the local spatial behavior of copy number profiles conveys important information about this architecture. Six filters were defined to characterize regional traits in copy number profiles, and the resulting Copy Aberration Regional Mapping Analysis (CARMA) algorithm was applied to tumors in four breast cancer cohorts (n = 2919). The derived motifs represent a layer of information that complements established molecular classifications of breast cancer. A score reflecting presence or absence of motifs provided a highly significant independent prognostic predictor. Results were consistent between cohorts. The nonsite-specific occurrence of the detected patterns suggests that CARMA captures underlying replication and repair defects and could have a future potential in treatment stratification.

Author Correction: Pan-cancer analysis of homozygous deletions in primary tumours uncovers rare tumour suppressors.

The original version of this Article omitted a declaration from the competing interests statement, which should have included the following: 'K.P.W. is President of Tempus Lab, Inc., Chicago, IL, USA'. This has now been corrected in both the PDF and HTML versions of the Article.

Author Correction: Pan-cancer analysis of homozygous deletions in primary tumours uncovers rare tumour suppressors.

The original version of this Article contained an error in the author affiliations. The affiliation of Kevin P. White with Tempus Labs, Inc., Chicago, IL, USA was inadvertently omitted.This has now been corrected in both the PDF and HTML versions of the Article.

Pan-cancer analysis of homozygous deletions in primary tumours uncovers rare tumour suppressors.

Homozygous deletions are rare in cancers and often target tumour suppressor genes. Here, we build a compendium of 2218 primary tumours across 12 human cancer types and systematically screen for homozygous deletions, aiming to identify rare tumour suppressors. Our analysis defines 96 genomic regions recurrently targeted by homozygous deletions. These recurrent homozygous deletions occur either over tumour suppressors or over fragile sites, regions of increased genomic instability. We construct a statistical model that separates fragile sites from regions showing signatures of positive selection for homozygous deletions and identify candidate tumour suppressors within those regions. We find 16 established tumour suppressors and propose 27 candidate tumour suppressors. Several of these genes (including MGMT, RAD17, and USP44) show prior evidence of a tumour suppressive function. Other candidate tumour suppressors, such as MAFTRR, KIAA1551, and IGF2BP2, are novel. Our study demonstrates how rare tumour suppressors can be identified through copy number meta-analysis.

Intra-patient Inter-metastatic Genetic Heterogeneity in Colorectal Cancer as a Key Determinant of Survival after Curative Liver Resection.

Chromosomal instability is a well-defined hallmark of tumor aggressiveness and metastatic progression in colorectal cancer. The magnitude of genetic heterogeneity among distinct liver metastases from the same patient at the copy number level, as well as its relationship with chemotherapy exposure and patient outcome, remains unknown. We performed high-resolution DNA copy number analyses of 134 liver metastatic deposits from 45 colorectal cancer patients to assess: (i) intra-patient inter-metastatic genetic heterogeneity using a heterogeneity score based on pair-wise genetic distances among tumor deposits; and (ii) genomic complexity, defined as the proportion of the genome harboring aberrant DNA copy numbers. Results were analyzed in relation to the patients' clinical course; previous chemotherapy exposure and outcome after surgical resection of liver metastases. We observed substantial variation in the level of intra-patient inter-metastatic heterogeneity. Heterogeneity was not associated with the number of metastatic lesions or their genomic complexity. In metachronous disease, heterogeneity was higher in patients previously exposed to chemotherapy. Importantly, intra-patient inter-metastatic heterogeneity was a strong prognostic determinant, stronger than known clinicopathological prognostic parameters. Patients with a low level of heterogeneity (below the median level) had a three-year progression-free and overall survival rate of 23% and 66% respectively, versus 5% and 18% for patients with a high level (hazard ratio0.4, 95% confidence interval 0.2-0.8, P = 0.01; and hazard ratio0.3,95% confidence interval 0.1-0.7, P = 0.007). A low patient-wise level of genomic complexity (below 25%) was also a favorable prognostic factor; however, the prognostic association of intra-patient heterogeneity was independent of genomic complexity in multivariable analyses. In conclusion, intra-patient inter-metastatic genetic heterogeneity is a pronounced feature of metastatic colorectal cancer, and the strong prognostic association reinforces its clinical relevance and places it as a key feature to be explored in future patient cohorts.

Identifying clusters in genomics data by recursive partitioning.

Genomics studies frequently involve clustering of molecular data to identify groups, but common clustering methods such as K-means clustering and hierarchical clustering do not determine the number of clusters. Methods for estimating the number of clusters typically focus on identifying the global structure in the data, however the discovery of substructures within clusters may also be of great biological interest. We propose a novel method, Partitioning Algorithm based on Recursive Thresholding (PART), that recursively uncovers distinct subgroups in the groups already identified. Outliers are common in high-dimensional genomics data and may mask the presence of substructure within a cluster. A crucial feature of the algorithm is the introduction of tentative splits of clusters to isolate outliers that might otherwise halt the recursion prematurely. The method is demonstrated on simulated as well as a wide range of real data sets from gene expression microarrays, where the correct clusters were known in advance. When subclusters are present and the variance is large or varies between the clusters, the proposed method performs better than two established global methods on simulated data. On the real data sets the overall performance of PART is superior to the global methods when used in combination with hierarchical clustering. The method is implemented in the R package clusterGenomics and is freely available from CRAN (The Comprehensive R Archive Network).

Copynumber: Efficient algorithms for single- and multi-track copy number segmentation.

BACKGROUND: Cancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing variety of tools for measuring genomic copy numbers, including various types of array-CGH, SNP arrays and high-throughput sequencing, calls for a coherent framework offering unified and consistent handling of single- and multi-track segmentation problems. In addition, there is a demand for highly computationally efficient segmentation algorithms, due to the emergence of very high density scans of copy number. RESULTS: A comprehensive Bioconductor package for copy number analysis is presented. The package offers a unified framework for single sample, multi-sample and multi-track segmentation and is based on statistically sound penalized least squares principles. Conditional on the number of breakpoints, the estimates are optimal in the least squares sense. A novel and computationally highly efficient algorithm is proposed that utilizes vector-based operations in R. Three case studies are presented. CONCLUSIONS: The R package copynumber is a software suite for segmentation of single- and multi-track copy number data using algorithms based on coherent least squares principles.

Analyzing cancer samples with SNP arrays.

Single nucleotide polymorphism (SNP) arrays are powerful tools to delineate genomic aberrations in cancer genomes. However, the analysis of these SNP array data of cancer samples is complicated by three phenomena: (a) aneuploidy: due to massive aberrations, the total DNA content of a cancer cell can differ significantly from its normal two copies; (b) nonaberrant cell admixture: samples from solid tumors do not exclusively contain aberrant tumor cells, but always contain some portion of nonaberrant cells; (c) intratumor heterogeneity: different cells in the tumor sample may have different aberrations. We describe here how these phenomena impact the SNP array profile, and how these can be accounted for in the analysis. In an extended practical example, we apply our recently developed and further improved ASCAT (allele-specific copy number analysis of tumors) suite of tools to analyze SNP array data using data from a series of breast carcinomas as an example. We first describe the structure of the data, how it can be plotted and interpreted, and how it can be segmented. The core ASCAT algorithm next determines the fraction of nonaberrant cells and the tumor ploidy (the average number of DNA copies), and calculates an ASCAT profile. We describe how these ASCAT profiles visualize both copy number aberrations as well as copy-number-neutral events. Finally, we touch upon regions showing intratumor heterogeneity, and how they can be detected in ASCAT profiles. All source code and data described here can be found at our ASCAT Web site ( http://www.ifi.uio.no/forskning/grupper/bioinf/Projects/ASCAT/).

High-resolution analyses of copy number changes in disseminated tumor cells of patients with breast cancer.

The presence of disseminated tumor cells (DTCs) in bone marrow (BM) identifies breast cancer patients with less favorable outcome. Furthermore, molecular characterization is required to investigate the malignant potential of these cells. This study presents a single-cell array comparative genomic hybridization (SCaCGH) method providing molecular analysis of immunomorphologically detected DTCs. The resolution limit of the method was estimated using the cancer cell line SK-BR-3 on 44 and 244k arrays. The technique was further tested on 28 circulating tumor cells and four hematopoietic cells (HCs) from peripheral blood (n = 8 patients). The SCaCGH method was finally applied to 24 DTCs, three immunopositive cells morphologically classified as probable HCs from breast cancer patients and five HC controls from BM (n = 7 patients plus n = 1 healthy donor). The frequency of copy number changes of the DTCs revealed similarities with primary breast tumor samples. Three of the patients had available profiles for DTCs and the corresponding tumor tissue from primary surgery. More than two-third of the analyzed DTCs disclosed equivalent changes, both to each other and to the corresponding primary disease, whereas the rest of the cells showed balanced profiles. The probable HCs revealed either balanced profiles (n = 2) or changes comparable to the tumor tissue and DTCs (n = 1), indicating morphological overlap between HCs and DTCs. Similar aberration patterns were visible in DTCs collected at diagnosis and at 3 years relapse-free follow-up. SCaCGH may be a powerful tool for the molecular characterization of DTCs.