Cancer Genome Landscapes

Vogelstein et al have published a very informative review on the genomic landscapes of cancer.

As the cost of genome sequencing has fallen 100 fold in the last ten years it is becoming commonplace to sequence the exomes of sets of 100+ tumours, which is allowing us to study the genomic make up of tumours.

The number of mutations a tumour has is dependent on its type:  solid adult tumours have between 33-66, where as childhood cancers far fewer.

FP1Exceptions to this are lung cancers in smokers and melanomas that have far more mutations due to the mutagenic impact of the carcinogen (smoke/sunlight) that initiates them. Tumours with defects in DNA Damage Response also accrue disproportionally large number of mutations.

Interesting, the number of mutations observed in solid adult tumours in self-renewing tissues (e.g. colon) are proportional to the age of the patient implying that these mutations may be present at the pre-neoplastic stage.

It is estimated that between two to eight sequential alterations that develop over the course of 20 to 30 years are actually causative of the cancer.  These are termed  “driver mutations” and occur in driver genes.  Each alteration causes a selective growth advantage to the cell in which it resides.  The other mutations occur because the cancer is genetically unstable, are termed passenger mutations, and confer no selective advantage to the cell in which they reside,

Driver genes are genes that contain driver mutations and there are two types:

Tumour suppressors that confer a selective advantage to the cell when the are “broken”

Oncogenes who confer a selective advantage to the cell, if they are “activated”.

Vogelstein et al. estimate how many driver genes exist using the 20:20 rule. In tumour suppressors at least 20% of the mutations cause truncation of the gene product, where in oncogenes at least 20% of the missense mutations occur in a single position along the polypeptide chain. (see figure 2)


PIK3CA and IDH1 are oncogenes, where as RB1 and VHL are tumour suppressors.

Using the 20:20 rule Vogelstein et al.identify ~140 genes whose intragenic mutations contribute to cancer (so-called Mut-driver genes).

Interesting this is far fewer than the ~500 genes identified in the Cancer Gene Census as being causative of cancer.  They suggest that other genes (Epi-driver genes) that are altered by epigenetic mechanisms and cause a selective growth advantage, but the definitive identification of these genes has been challenging.

Although every individual tumor, even of the same histopathologic subtype as another tumor, is distinct with respect to its genetic alterations, but the pathways affected in different tumors are similar.  These driver genes function through a dozen signaling pathways that regulate three core cellular processes: cell fate determination, cell survival, and genome maintenance.


They also briefly discuss that currently most anti-cancer drugs available inhibit the activity of an enzyme. However, of ~140 driver genes identified, only 31 could be targeted in this manner.

Indeed the majority of Mut-driver genes encode tumor suppressors, not oncogenes. Drugs generally interfere in the function of a protein – conceptually very difficult to produce a drug that will restore the function of a protein.

They conclude that in the future, the most appropriate management plan for a patient with cancer will be informed by an assessment of the components of the patient’s germline genome and the genome of his or her tumor.  That the inherent heterogeneity of tumours and their metastases makes resistance to targeted therapies ‘inevitable’ and that it is important to research the efficacy of combination therapies. They also suggest that the information from cancer genome studies should be exploited to improve methods for prevention and early detection of cancer, which will be essential to reduce cancer morbidity and mortality.

Assessment of Cancer Genes for Drug Discovery

The Cancer Gene Census documents a list of genes which when genetically altered are known to contribute directly to cancer.

A recent paper by Patel et al describes a systematic, computational protocol, that they have used to identify which of these genes code for proteins that would be possible candidate targets, suitable for therapeutic modulation in the treatment of cancer.  A suite of analyses were undertaken to explore the biological and chemical space of these proteins (shown below).


Following the computational analysis, the authors prioritize these proteins for drug development.  First they identified twenty-five proteins already known to be drug targets, with compounds with full FDA approval.  They suggest that some of the compounds may be useful for repurposing in different types of cancer.  For instance Smoothened SMO is the target of Vismodegib was recently approved for the treatment of basal cell carcinoma.  By mining multi-omic data from The Cancer Genome Atlas the authors suggest that Vismodegib might also be of use in treating Multiforme Glioblastoma (GB), as SMO was over-expressed in 95% of the GB samples analysed.

A  further eight-six proteins had active chemical compounds with submicromolar activity in biochemical or binding assays reported in the Chembl database.

They also explored which proteins had a known structure and predicted potential druggable pockets. Figure 2 illustrates the three-dimensional structure of GNAS with the druggable cavity displayed as a surface.  GNAS has an activating dominant mutation in pituitary adenoma, and further activating mutations have also been identified in kidney, thyroid, adenocortical, colorectal and Leydig tumours.  The authors suggest that small-molecule inhibitors of this enzyme regulator may have potential therapeutic applications.


Of the 488 cancer gene census proteins, the authors identify 103 with good evidence of chemical tractability and group them by  “drug development” risk. They identify 46 proteins, whose genes are known to be genetically altered in cancer, whose structures are predicted to be druggable, with few or no know active small molecule modulators, that may be potential therapeutic targets. They suggest that these targets indicate new biological areas for chemical exploration in the treatment of cancer, but they also represent a high potential drug development risk.