Integrating the multiple dimensions of genomic and epigenomic landscapes of cancer

Abstract

Advances in high-throughput, genome-wide profiling technologies have allowed for an unprecedented view of the cancer genome landscape. Specifically, high-density microarrays and sequencing-based strategies have been widely utilized to identify genetic (such as gene dosage, allelic status, and mutations in gene sequence) and epigenetic (such as DNA methylation, histone modification, and microRNA) aberrations in cancer. Although the application of these profiling technologies in unidimensional analyses has been instrumental in cancer gene discovery, genes affected by low-frequency events are often overlooked. The integrative approach of analyzing parallel dimensions has enabled the identification of (a) genes that are often disrupted by multiple mechanisms but at low frequencies by any one mechanism and (b) pathways that are often disrupted at multiple components but at low frequencies at individual components. These benefits of using an integrative approach illustrate the concept that the whole is greater than the sum of its parts. As efforts have now turned toward parallel and integrative multidimensional approaches for studying the cancer genome landscape in hopes of obtaining a more insightful understanding of the key genes and pathways driving cancer cells, this review describes key findings disseminating from such high-throughput, integrative analyses, including contributions to our understanding of causative genetic events in cancer cell biology.

Fig. 1

Advances in cancer genomic landscape post Y2K. The timeframe of events are estimated based on time of publication

Fig. 2

SNP array analysis to identify areas of altered copy number and allelic composition in a clinical lung cancer specimen. Shown here are a a region that is copy-neutral with no observed allelic imbalance and regions containing a b segmental gain and c UPD. Examining the allele-specific copy number plot, the gain (in b) is likely a single-copy change, and the UPD event (in c) is signified by the shift in allele levels while maintaining total copy number neutral status

Fig. 3

Overlay of chromosomal regions of gain, loss, and UPD (copy number neutral LOH) inherent to the T47D breast cancer cell line. The chromosomal loci for PIK3CA and TP53 (modified by activating and inactivating mutations, respectively, in this cell line) are indicated. The majority of the genome is affected by any one of the three genomic alterations. Raw SNP 6.0 array data were obtained from the Sanger database with mutation status obtained from the COSMIC database [67]. Copy number and allelic status changes were determined using Partek Genomics Suite, and reference genomes used were 72 individuals from the HapMap collection. Data were visualized using the SIGMA2 software [7]

Fig. 4

Integration of copy number, allelic status, DNA methylation, and gene expression for a single lung adenocarcinoma sample. a Copy number and b allele status analyses revealed a high level allele-specific DNA amplification (highlighted in yellow, image generated with Partek Genomics Suite); c individual CpG loci within this region were assessed for differential methylation between tumor and nonmalignant tissue. Hypomethylation at the indicated CpG locus, which corresponds to the MUC1 gene, is observed (visualized with Genesis). d Expression analysis revealed fourfold overexpression of the MUC1 transcript when a tumor sample was compared to matched, adjacent nonmalignant tissue. Copy number and allele status profiling was performed using the Affymetrix SNP 6.0 array; DNA methylation profiling using the Illumina Infinium HM27 platform and gene expression using the Affymetrix Human Exon 1.0 ST array

Fig. 5

Enhanced analysis of the cancer phenotype using an integrative and multidimensional approach. a On average, a higher proportion of differential gene expression can be associated with genomic alterations when examining multiple DNA dimensions relative to single dimensions. b Using a fixed frequency threshold of 50%, more genes are revealed to be frequently disrupted when multiple mechanisms of genomic alteration (e.g., altered copy number, DNA methylation, or copy number neutral LOH) are considered (~200 genes versus more than 1,000 genes). c Pathway analyses performed using gene lists derived from a multidimensional approach identifies an enhanced number of aberrant pathways relative to those identified from a unidimensional approach. d Functional pathways identified using the integrated gene list are of relatively high significance; the top 10 such pathways are shown. This suggests that the additional identified genes associate with specific pathways rather than with random functions. The four bars represent, from left to right: all dimensions, copy number, DNA methylation, and UPD. Ingenuity Pathway Analysis was used for analyses in c and d. e Example of two genes that are missed when a single DNA dimension is studied but captured when multiple DNA dimensions are examined. Both ribonucleotide reductase M2 (RRM2) [255, 256] and retinoic acid receptor responder (tazarotene-induced) 2 (RARRES2) [257, 258] are known to be deregulated in multiple cancer types

Fig. 6

Identification of multiple disrupted components in a biological pathway. Integrative analysis identifies more genes affected in the EGFR signaling pathway than a single dimensional analysis alone. In this example, multidimensional profiling data were generated from ten lung adenocarcinomas and their paired noncancerous lung tissue. Analysis of DNA copy number (gene dosage) alterations that affected expression identified seven genes (in green) that are disrupted at ≥30% frequency. However, when alterations in copy number, DNA methylation, sequence mutation, and/or copy-neutral LOH were considered, 17 genes disrupted at ≥30% frequency were identified to be associated with a change in expression, with an additional gene, KRAS, harboring frequent mutation. The 11 additional genes are indicated in red. Genes in gray are not significant in this dataset as they did not meet the frequency criteria

Fig. 7

Automated detection of selected clonal populations of cells within a cancer biopsy tissue section. All nuclei (~150,000 in this example) are detected, and FISH probe signal counts are enumerated for each nucleus. FISH signal pattern for each cell is compared against its neighbor in order to define spatial association (or neighborhood). A mathematical model is then applied to determine clonal cell relationships. a Mapping cancer cells on a tissue section. A gain or loss of any one of three FISH markers indicates a cancer cell. This image shows the density of cancer cells (so defined) in neighborhoods as a color overlay. Red indicates high fraction of cancer cells, yellow indicates medium fraction of cancer cells, and blue indicates low to none (see scale bar). Most of the section is highlighted except for the surrounding normal stromal infiltrates. b Mapping clonal cells. The same image data were analyzed for concurrent gains of each of the three markers. The two clusters of cells, magnified within the white boxes, are cells harboring gain of all three markers