Approaches to biomarkers in human colorectal cancer: looking back, to go forward

Abstract

Like all human cancers, colorectal cancer is a complicated disease. While a mature body of research involving colorectal cancer has implicated the putative sequence of genetic alterations that trigger the disease and sustain its progression, there is a surprising paucity of well-validated, clinically useful diagnostic markers of this disease. For prognosis or guiding therapy, single gene-based markers of colorectal cancer often have limited specificity and sensitivity. Genome-wide analyses (microarrays) have been used to propose candidate patterns of gene expression that are prognostic of outcome or predict the tumor's response to a therapy regimen; however, these patterns frequently do not overlap, and this has raised questions concerning their use as biomarkers. The limitation of gene-expression approaches to marker discovery occurs because the change in mRNA expression across tumors is highly variable and, alone, accounts for a limited variability of the phenotype, such as with cancer. More robust and accurate markers of cancer will result from integrating all the information we have about the cell: genomics, proteomics and interactomics. This article will discuss traditional markers in colorectal cancer, both genomic and proteomic, including their respective approaches and limitations, then conclude with examples of systems biology-based approaches for candidate marker discovery, and discuss how this approach is reshaping our view of a biomarker.

Figure 1. Stage-wise progression of colorectal cancer

Genetic alterations commonly associated with the progession to subsequent stages are indicated above the arrow. Reproduced with permission from [104].

Figure 2. Proteomic profiling: 2D-differential gel electrophoresis

Tripartite samples of N, T and pooled control were mixed (1); samples were separated by isoelectric focusing (2); then by molecular weight (3); and each fluorophore imaged independently (4). Using the DeCyder software, spots were matched on an intragel basis with differential image analysis DeCyder (5), and on an intergel basis with biological variation analysis Decyder to assess biological variation (6). Significant spots were selected for robotic excision (7), digested by trypsin (8) and the peptides separated by reverse-phase chromatography and detected by tandem mass spectrometry (9). MS² spectra were searched against the Sequest database (10). 2D-PAGE: 2D polyacrylimide gel electrophoresis; IEF: Isoelectric focusing; MS²: Tandem mass spectrometry; N: Normal; RPLC-MS: Reverse-phase liquid chromatography-mass spectrometry; T: Tumor.

Figure 3. Relevant colorectal cancer subnetworks and pathways

(A) Protein interaction graph of the APC (center) axis. Nodes (n = 70) are indicated as glyphs (e.g., enzymes, transcription factors and receptors) and interactions with APC are indicated by lines. Evidence of the interactions is based on literature curation. The nominal cellular compartment of each protein is indicated by annotation on the right. (B) Map of the canonical WNT-signaling pathway, including APC (left, center). MetaCore©.

Figure 4. Proposed bioinformatic flow

The proteomic first approach begins with a seed of targets that are found to be significant for disease. These are used to search for hotspots in the interactome – subnetworks of well-connected proteins that reveal significant proximity and interactivity (crosstalk) to the targets in the seed. Using differential mRNA expression between normal and tumor (microarray) as a surrogate for network activity, each subnetwork is scored and pruned for its ability to discriminate normal from tumor. The pruned subnetworks are evaluated for their role in the disease state, and may be used to inform mechanistic verification experiments. HPRD: Human Protein Reference Database.

Figure 5. Discovered, significant subnetworks

(A) Discovered subnetwork that is annotated as that expressed in late-stage colorectal cancer and (B) the pruned version. Significant targets in pruned version (highlighted in gray) were extended one interaction to infer functional relevance. Proteins for which there was direct proteomic evidence (seed) have adjacent red (upregulated) or blue (downregulated) circles. Reproduced with permission from [56].