Translating insights from the cancer genome into clinical practice

Abstract

Cancer cells have diverse biological capabilities that are conferred by numerous genetic aberrations and epigenetic modifications. Today's powerful technologies are enabling these changes to the genome to be catalogued in detail. Tomorrow is likely to bring a complete atlas of the reversible and irreversible alterations that occur in individual cancers. The challenge now is to work out which molecular abnormalities contribute to cancer and which are simply 'noise' at the genomic and epigenomic levels. Distinguishing between these will aid in understanding how the aberrations in a cancer cell collaborate to drive pathophysiology. Past successes in converting information from genomic discoveries into clinical tools provide valuable lessons to guide the translation of emerging insights from the genome into clinical end points that can affect the practice of cancer medicine.

Figure 1. Various types of genomic and epigenomic aberration in cancers

The main types of genomic and epigenomic aberration are illustrated together with examples of how they can be detected. a, Changes in DNA sequence, such as point mutations, can be assessed by DNA-sequencing techniques. b, Changes in genomic organization can be assessed by using fluorescence in situ hybridization. In the example shown, DNA segments are exchanged between the two (blue and green) DNA molecules. c, Changes in DNA copy number, such as those that result from amplification, can be assessed by using comparative genomic hybridization. d, Changes in DNA methylation and the resultant changes in chromatin structure can be assessed by using chromatin immunoprecipitation plus microarray analysis of immunoprecipitated DNA. Each of these types of change can alter the expression levels of genes or non-coding microRNAs (referred to here as genetic elements of interest, GEOIs), alter the splicing patterns of transcripts, or change gene function through mutation or through creating chimaeric genes. Many of these events can be as assessed by microarray analysis. These changes ultimately translate into altered functions, leading to a diseased state, such as cancer.

Figure 2. Integration of complex multidimensional genomic data with insights from other model systems

The identification of cancer drivers or contributors from multidimensional genomic data (such as that shown in Fig. 1) from a particular human tumour type can be facilitated by integration with similar data from other tumour types (for example, by searching for GEOIs that are common to cancers of different lineages). Incorporating clinical information into the analysis of this genomic data helps to narrow the focus to clinically relevant GEOIs. This genomic knowledge can be further filtered by integrating it with insights obtained from studies in model systems. These systems can include non-human model organisms (such as mice, zebrafish, nematodes, fruitflies and yeast), which can be studied to identify evolutionarily conserved GEOIs, to define pathways that GEOIs influence and to elucidate the roles of GEOIs in normal development. Cell-line model systems also can be useful, particularly for functional genetic screening or monitoring responses to drugs. This type of integrative analysis, which extends beyond the cancer genome, is an informative way to identify GEOIs that are likely to be drivers or contributors. After such GEOIs have been identified, they need to undergo stringent biological and clinicopathological validation (Box 2), a labour-intensive process that can be accelerated by carrying out functional screening with a library of GEOIs rather than by assessing one GEOI at a time. For successful translation into the clinic — that is, development of a therapeutic agent that targets the GEOI or a biomarker for the GEOI — a basic understanding of the molecular mechanism of action of the GEOI is helpful, particularly in terms of the specific cellular and genetic context in which it maintains the tumour. Such a biomarker or therapeutic agent will then need to be clinically validated before it can be adopted for routine clinical practice. At each step of this process, the results can be fed back to inform and refine the analyses and to help improve the validation platforms.

Figure 3. Disruption of intracellular signalling by alterations in the cancer genome

A simplified signalling pathway is depicted to highlight known examples of bona fide oncogenes that are subjected to dysregulation by various mechanisms. It is clear that a signalling pathway can be disrupted at multiple points, and a variety of genomic and epigenomic alterations can contribute to this, ultimately leading to cancer.