SNP panel identification assay (SPIA): a genetic-based assay for the identification of cell lines

Translational research hinges on the ability to make observations in model systems and to implement those findings into clinical applications, such as the development of diagnostic tools or targeted therapeutics. Tumor cell lines are commonly used to model carcinogenesis. The same tumor cell line can be simultaneously studied in multiple research laboratories throughout the world, theoretically generating results that are directly comparable. One important assumption in this paradigm is that researchers are working with the same cells. However, recent work using high throughput genomic analyses questions the accuracy of this assumption. Observations by our group and others suggest that experiments reported in the scientific literature may contain pre-analytic errors due to inaccurate identities of the cell lines employed. To address this problem, we developed a simple approach that enables an accurate determination of cell line identity by genotyping 34 single nucleotide polymorphisms (SNPs). Here, we describe the empirical development of a SNP panel identification assay (SPIA) compatible with routine use in the laboratory setting to ensure the identity of tumor cell lines and human tumor samples throughout the course of long term research use

Allele-Specific Amplification in Cancer Revealed by SNP Array Analysis

Amplification, deletion, and loss of heterozygosity of genomic DNA are hallmarks of cancer. In recent years a variety of studies have emerged measuring total chromosomal copy number at increasingly high resolution. Similarly, loss-of-heterozygosity events have been finely mapped using high-throughput genotyping technologies. We have developed a probe-level allele-specific quantitation procedure that extracts both copy number and allelotype information from single nucleotide polymorphism (SNP) array data to arrive at allele-specific copy number across the genome. Our approach applies an expectation-maximization algorithm to a model derived from a novel classification of SNP array probes. This method is the first to our knowledge that is able to (a) determine the generalized genotype of aberrant samples at each SNP site (e.g., CCCCT at an amplified site), and (b) infer the copy number of each parental chromosome across the genome. With this method, we are able to determine not just where amplifications and deletions occur, but also the haplotype of the region being amplified or deleted. The merit of our model and general approach is demonstrated by very precise genotyping of normal samples, and our allele-specific copy number inferences are validated using PCR experiments. Applying our method to a collection of lung cancer samples, we are able to conclude that amplification is essentially monoallelic, as would be expected under the mechanisms currently believed responsible for gene amplification. This suggests that a specific parental chromosome may be targeted for amplification, whether because of germ line or somatic variation. An R software package containing the methods described in this paper is freely available at http://genome.dfci.harvard.edu/~tlaframb/PLASQ

High-order chromatin architecture determines the landscape of chromosomal alterations in cancer

The rapid growth of cancer genome structural information provides an opportunity for a better understanding of the mutational mechanisms of genomic alterations in cancer and the forces of selection that act upon them. Here we test the evidence for two major forces, spatial chromosome structure and purifying (or negative) selection, that shape the landscape of somatic copy-number alterations (SCNAs) in cancer1. Using a maximum likelihood framework we compare SCNA maps and three-dimensional genome architecture as determined by genome-wide chromosome conformation capture (HiC) and described by the proposed fractal-globule (FG) model2. This analysis provides evidence that the distribution of chromosomal alterations in cancer is spatially related to three-dimensional genomic architecture and additionally suggests that purifying selection as well as positive selection shapes the landscape of SCNAs during somatic evolution of cancer cells

GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers

We describe methods with enhanced power and specificity to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, we improve the estimation of background rates for each category. We additionally describe a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence. Here we detail this revised computational approach, GISTIC2.0, and validate its performance in real and simulated datasets

Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer

Introduction Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach. Methods Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n = 84), gene expression profiling (n = 47), matched pre- and post-AI aCGH (n = 19 pairs) and Ki67-based AI-response analysis (n = 39). Results Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1). Conclusions These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response

Modeling genomic diversity and tumor dependency in malignant melanoma

The classification of human tumors based on molecular criteria offers tremendous clinical potential; however, discerning critical and "druggable" effectors on a large scale will also require robust experimental models reflective of tumor genomic diversity. Here, we describe a comprehensive genomic analysis of 101 melanoma short-term cultures and cell lines. Using an analytic approach designed to enrich for putative "driver" events, we show that cultured melanoma cells encompass the spectrum of significant genomic alterations present in primary tumors. When annotated according to these lesions, melanomas cluster into subgroups suggestive of distinct oncogenic mechanisms. Integrating gene expression data suggests novel candidate effector genes linked to recurrent copy gains and losses, including both phosphatase and tensin homologue (PTEN)-dependent and PTEN-independent tumor suppressor mechanisms associated with chromosome 10 deletions. Finally, sample-matched pharmacologic data show that FGFR1 mutations and extracellular signal-regulated kinase (ERK) activation may modulate sensitivity to mitogen-activated protein kinase/ERK kinase inhibitors. Genetically defined cell culture collections therefore offer a rich framework for systematic functional studies in melanoma and other tumors

DR-Integrator: a new analytic tool for integrating DNA copy number and gene expression data

Summary: DNA copy number alterations (CNA) frequently underlie gene expression changes by increasing or decreasing gene dosage. However, only a subset of genes with altered dosage exhibit concordant changes in gene expression. This subset is likely to be enriched for oncogenes and tumor suppressor genes, and can be identified by integrating these two layers of genome-scale data. We introduce DNA/RNA-Integrator (DR-Integrator), a statistical software tool to perform integrative analyses on paired DNA copy number and gene expression data. DR-Integrator identifies genes with significant correlations between DNA copy number and gene expression, and implements a supervised analysis that captures genes with significant alterations in both DNA copy number and gene expression between two sample classes

PAK1 is a Breast Cancer Oncogene that Coordinately Activates MAPK and MET Signaling

Activating mutations in the RAS family or BRAF frequently occur in many types of human cancers but are rarely detected in breast tumors. However, activation of the RAS-RAF-MEK-ERK Mitogen-Activated Protein Kinase (MAPK) pathway is commonly observed in human breast cancers, suggesting that other genetic alterations lead to activation of this signaling pathway. To identify breast cancer oncogenes that activate the MAPK pathway, we screened a library of human kinases for their ability to induce anchorage-independent growth in a derivative of immortalized human mammary epithelial cells (HMLE). We identified PAK1 as a kinase that permitted HMLE cells to form anchorage-independent colonies. PAK1 is amplified in several human cancer types, including 33% of breast tumor samples and cancer cell lines. The kinase activity of PAK1 is necessary for PAK1-induced transformation. Moreover, we show that PAK1 simultaneously activates MAPK and MET signaling; the latter via inhibition of Merlin. Disruption of these activities inhibits PAK1-driven anchorage-independent growth. These observations establish PAK1 amplification as an alternative mechanism for MAPK activation in human breast cancer and credential PAK1 as a breast cancer oncogene that coordinately regulates multiple signaling pathways, the cooperation of which leads to malignant transformation

Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes