KRAS Alleles: The Devil Is in the Detail

Abstract

KRAS is the most frequently mutated oncogene in cancer and KRAS mutation is commonly associated with poor prognosis and resistance to therapy. Since the KRAS oncoprotein is, as yet, not directly druggable, efforts to target KRAS mutant cancers focus on identifying vulnerabilities in downstream signaling pathways or in stress response pathways that are permissive for strong oncogenic signaling. One aspect of KRAS biology that is not well appreciated is the potential biological differences between the many distinct KRAS activating mutations. This review draws upon insights from both clinical and experimental studies to explore similarities and differences among KRAS alleles. Historical and emerging evidence supports the notion that the specific biology related to each allele might be exploitable for allele-specific therapy.

Keywords: KRAS; RAS; alleles; cancer genetics; oncogene.

Figure 1. KRAS Mutations in Human Cancers

(A) The spectrum of cancer-associated KRAS mutations. Across all cancers, mutations in 51 different amino acids have been identified in at least one case. The functional significance of many of these mutations is unclear. Only 5 amino acids are mutated recurrently: codons 12, 13, 61, 117, and 146. Important functional regions of the protein are highlighted below the linear representation of the KRAS coding region. (B) Clustering of KRAS mutations. All of the common activating mutations alter the balance of KRAS•GDP to KRAS•GTP by affecting the active site of the enzyme. Commonly mutated residues are color-coded to correspond with panel A. This GDP-bound structure of full length KRAS (PDB code 5TAR) comes from [81]. (C) The diversity of KRAS alleles. Codon 12 mutations predominate in the “big 3” cancers: NSCLC, PDAC, and CRC. In NSCLC, codon 13 mutations also comprise a significant percentage of cancers, while in PDAC codon 61 mutations are more frequent. CRC stands out in the diversity of KRAS alleles. Here, codon 12 mutations account for only 65% of KRAS alleles. (D) Codon 12 allele choice. In NSCLC, the two most common alleles, G12C and G12V, result from the same type of mutation, a G to T transversion. In PDAC and CRC, codon 12 allele choice does not appear to be driven by mutational pattern. All data were collected from cBioportal [6].

Figure 2. The Prognostic Significance of KRAS Alleles

(A) Correlation between different codon 12 alleles and survival in pancreatic cancer. In PDAC, G12D mutations are associated with worse overall survival than are G12V mutations. G12R mutations, by contrast, correlate with better overall survival. Data are derived from [23]. (B) Correlation between different codon 12 alleles with survival in colorectal cancer. Unlike in PDAC, where G12V survival is significantly different from G12D, both mutations are associated with reduced overall survival in CRC. Data are derived from [26]. (C) Correlation between progression and mutations at different amino acids in colorectal cancer. Codon 12 mutations are associated with reduced progression-free survival in CRC, whereas progression is not statistically different in patients with WT or codon 13 mutant cancer. Data are derived from [29].

Figure 3. In vitro Prediction of HRAS and KRAS Allele Frequencies

(A) HRAS codon 61 mutations resulting from single nucleotide changes. Q61 can be changed to six different amino acids. (B) Frequency of HRAS codon 61 alleles across human cancers. Of the six codon 61 alleles that can result from a single nucleotide change, only 4 are naturally occurring in human cancers. Data were collected from cBioportal [6]. (C) Correlation between in vitro transforming activity and allele frequency for HRAS codon 61 mutants. Alleles that do not occur in human cancers are not transforming in mouse fibroblasts. Data for transforming activity (defined as the number of NIH3t3 foci that develop per ng of transfected HRAS cDNA expression vector) were taken from [51]. (D) Relative frequencies of different KRAS alleles across all cancers. Note that this graph includes data only for the alleles listed. Data were collected from cBioportal [6]. (E) Correlation between in vitro biochemical activity and allele frequency for KRAS mutants. The biochemical activation score was calculated by multiplying the rate of intrinsic hydrolysis [K_hydrolysis 10⁻⁵ (sec⁻¹)] by the affinity for the RAS binding domain (RBD) of RAF-1 [nmol/L]. Data for biochemical activity were taken from [16].

Figure 4. A Non-switch Model for KRAS Activation

(A) The classical switch analogy. KRAS proteins are typically described as being “OFF” or “ON” as a function of nucleotide binding state. In a normal cell, the homeostatic balance of hydrolysis and exchange keeps KRAS in a GDP-bound state and the switch is OFF. Cancer cells can flip the switch by (1) increasing exchange, (2) decreasing hydrolysis, or (3) doing both. This model views the entire cellular population of KRAS molecules as a binary switch. (B) A dimmer analogy. This model does not consider the nucleotide binding state of individual proteins, but rather the “OFF” or “ON” state of the entire population of KRAS molecules in a cell. This model allows for different quantitative levels of KRAS activation, which is more consistent with the distinct mechanisms of activation of different activating mutations.