The identification of driver or contributor GEOIs, especially the weaker or less prevalent ones, can be greatly accelerated by integrative analyses of multidimensional data and by comparisons with data from multiple model systems or species (). But identification of a GEOI is insufficient for its translation into a clinical end point. Cancer is a complex and heterogeneous collection of disease entities that are defined by clinical, histopathological and genetic parameters. Given this disease heterogeneity, even if a strong correlation between a GEOI and cancer is found in the laboratory in a test validation set (for example, a collection of genomic data, behaviour in a model system or even responses in a clinical trial), this correlation, no matter how significant, might not apply to every patient or trial subject. Without a definition of the genomic and biological context under which a GEOI exerts its cancer-associated activities, the full diagnostic and prognostic and therapeutic value of these genomic insights will not be realized.
Consider the example of EGFR
mutations in non-small-cell lung cancer and glioblastoma multiforme (GBM). Mutational activation of EGFR
in non-small-cell lung cancer is present in a subpopulation of patients who are highly responsive to targeted inhibition of EGFR. The proportion of patients with non-small-cell lung cancer who have an activating mutation in EGFR
is small (about 10% in studies carried out in the United States and somewhat higher in Asian populations)37
. Thus, the response of these patients to gefitinib, which inhibits the tyrosine-kinase activity of EGFR, would not have emerged in the absence of genetic stratification of this clinically distinct population. Conversely, amplification of a mutant form of EGFR known as EGFRvIII is prevalent in GBM (in about 45% of primary GBM cases)74
, yet EGFR-specific tyrosine-kinase inhibitors have strikingly little clinical effect. A positive, albeit transient, clinical response has been detected in subsets of patients in whom EGFR
is amplified or mutated but PTEN
is intact 75
, indicating that this key molecule downstream of EGFR in the signalling pathway can modify the biological response of the tumour (). However, these positive responses do not last, despite documented pharmacological extinction (that is, inactivation) of mutated or amplified EGFR
. In this case, the proteomic profiling of receptor-tyrosine-kinase activation patterns in solid tumours, including GBM and lung cancer, has provided a rational explanation for the patterns of clinical responses. Specifically, Jayne Stommel et al.76
showed that established GBM cell lines, GBM xenotransplants and GBM primary tumour specimens from patients contain several coactivated receptor tyrosine kinases and that inhibition of EGFR alone can lead to its replacement with other coactivated receptor tyrosine kinases in the phosphatidylinositol-3-OH-kinase (PI(3)K) signalling complex, thus maintaining downstream signalling and cell survival. Signalling downstream of PI(3)K was extinguished only when multiple receptor tyrosine kinases were targeted by RNAi or by a combination receptor-tyrosine-kinase inhibitor76
. Thus, the integration of genomic and proteomic insights with the molecular dissection of the signalling complex now provides a more accurate blueprint for the rational deployment of receptor-tyrosine-kinase inhibitors for treating GBM, tumours of the lung and other solid tumours.
Establishing the molecular basis of action of a GEOI in a specific tumour-biological context is perhaps the most difficult step in cancer genomics. Compounding the challenges of lengthy and laborious functional and clinicopathological validation (Box 2
) is the biological phenomenon of false negatives. False negatives can arise in many ways; for example, when the cancer-associated biological activities of a GEOI (such as interaction with the host stroma) are not captured by standard cell-based assays; when a GEOI has a relevant role but only in a particular cellular or genetic context that is not recreated in the validation assay; and when a GEOI contributes only part of the overall activity conferred by a genomic event (so that the activity of a single GEOI is negligible in the absence of this cooperating partner or partners). Therefore, validation must not rely on just a single type of assay that involves a single manipulation.
Gain-of-function and loss-of-function manipulations for multiple tumour phenotypes using multiple cell lines should be carried out to search for the context in which biological activity can be uncovered. This process can be aided by knowledge obtained from other analyses, such as information about the biology of the tumour, the gene family of the GEOI, the pathways that the GEOI product is involved in, and insights from integrative analyses that nominated the GEOI. For example, if a GEOI identified by integrative genomic analyses is prioritized further on the basis of its known role in neural stem-cell homeostasis, then the next step would be to assess how manipulation of the GEOI affects the renewal, maintenance and differentiation of neural stem cells, in addition to carrying out the more generic assays of anchorage independence or cell proliferation (Box 2
). Similarly, if a GEOI is identified in a subset of tumours with a particular genotype (such as with activated RAS
or a mutation in EGFR
), then its biological importance needs to be assayed in the appropriate context. This process has been demonstrated in two recent studies47,48
. Kim et al
showed that NEDD9 had gain-of-function pro-invasion activities only in cells in which BRAF
was concomitantly activated, an experimental design that was informed by the characteristics of the metastatic escapers harbouring NEDD9
amplification. Zender et al
showed that the inhibitor of apoptosis IAP1 (also known as BIRC2) and the transcription factor YAP had oncogenic activities in Tp53+/-
hepatoblasts with Myc
activation but not in those with Akt1
activation. This finding is consistent with the presence of an amplicon in the chromosomal region 9qA1 (which contains the genes encoding IAP1 and YAP) in this mouse model of hepatocellular carcinoma. In the study by Zender et al.48
, both IAP1 and YAP were shown to be targets of 9qA1 amplification, showing that a single genomic aberration can dysregulate more than one gene that contributes to the pathophysiology of the cancer. The chances of missing important GEOIs in a region of recurrent aberration can be reduced by using efficient functional genomic assays to assess the consequences of changing the expression levels of all GEOIs associated with the aberration. For example, genetic screens can be carried out with low-complexity libraries representing GEOIs resident in a particular genomic event (which is especially useful for regions that are large and gene-rich), allowing the identification of cooperating contributors (which together confer the biological advantage selected for in the cancer cells). This functional genomic approach will be important for sorting out which of the less impressive ‘hills and valleys’ are biologically important.
Similarly challenging is the issue of biological false positives. For example, an RNAi-mediated loss-of-function assay is a powerful way to determine whether the expression of a GEOI is required in a cell for a specific tumorigenic phenotype (such as cell survival, anchorage independence or invasion). However, given the innumerable genetic and epigenetic alterations that are present in established tumour cells (and, consequently, the altered signalling between pathways and networks), the observed phenotype might be an artefact. In this case, finding a complementary gain-of-function activity can help to increase the evidence in support of a particular GEOI being a true driver or contributor to cancer. In addition, the type of functional activity also conveys a different level of confidence; for example, anchorage-independent growth in soft agar is a more stringent assay than increased proliferation in fully supplemented culture medium.
Biological false positives can also emerge as a direct consequence of the artificial nature of the assays used. Consider the possibility that overexpression of a GEOI confers a strong anchorage-independent phenotype; this effect might, however, result from the supraphysiological level of expression in vitro. Conversely, knockdown of a GEOI might result in cell death because its expression is required for the survival of all cells not just cancerous ones. To this end, clinicopathological validation through analysis of the DNA, messenger RNA and protein levels in normal samples and tumour samples arranged in microarrays can provide support for cancer relevance, by demonstrating the prevalence of genomic aberrations or dysregulated GEOI expression in large independent cohorts of specific tumour types. This can be particularly informative if the tumour cohorts are annotated with the clinical outcome because such a survey will not only add to the evidence but also provide invaluable insight into possible clinical contexts for therapeutic development. Ultimately, it is the cumulative weight of evidence based on the strength of particular functional activities, the magnitude of clinicopathological data and the importance of mechanistic clues that provides the confidence to assign a GEOI as a cancer-relevant driver or contributor rather than a mere passenger.