A spatial simulation approach to account for protein structure when identifying non-random somatic mutations

Abstract

Background: Current research suggests that a small set of "driver" mutations are responsible for tumorigenesis while a larger body of "passenger" mutations occur in the tumor but do not progress the disease. Due to recent pharmacological successes in treating cancers caused by driver mutations, a variety of methodologies that attempt to identify such mutations have been developed. Based on the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of cluster identification algorithms has become critical.

Results: We have developed a novel methodology, SpacePAC (Spatial Protein Amino acid Clustering), that identifies mutational clustering by considering the protein tertiary structure directly in 3D space. By combining the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC) and the spatial information in the Protein Data Bank (PDB), SpacePAC is able to identify novel mutation clusters in many proteins such as FGFR3 and CHRM2. In addition, SpacePAC is better able to localize the most significant mutational hotspots as demonstrated in the cases of BRAF and ALK. The R package is available on Bioconductor at: http://www.bioconductor.org/packages/release/bioc/html/SpacePAC.html.

Conclusion: SpacePAC adds a valuable tool to the identification of mutational clusters while considering protein tertiary structure.

Figure 1

Statistic construction. In this example, we consider r∈{3,9} and up to 3 potential mutational hotspots in the protein. First, μ and σ are calculated over each column. Next, we normalize each entry in the column by calculating $Z_{i,s,r}=\frac{X_{i,s,r}-{\mu }_{s,r}}{{\sigma }_{s,r}}$. We then take the maximum over each row to get Z₀,…,Z₁₀₀₀. The percentage of times Z₀≥Z_i, where i∈{1,…,1000}, is the p-value of our observed statistic Z₀. Note that if Z₀ is greater than Z_i for all 1000 simulations, we report a p-value of <1.00 E-03.

Figure 2

Organizing the data example. In our example, the protein has 5 residues with mutations. The residues are sorted from largest to smallest (so residue 1 has the largest number of mutations, residue 2 the second largest number of mutations, etc.), and the inside of the table is calculated as the sum of the mutations on both residues using all the samples in the study. For instance, as residue 1 has 50 mutations and residue 2 has 40 mutations, there were 90 total samples that had a mutation either on residue 1 or 2. In the actual code, only the lower half of the table is considered and then only sequentially to decrease running time, but we present the whole table here for clarity.

Figure 3

Algorithm execution example. This figure refers to the data in Figure 2. The first index, i, represents the row and the second index, j, represents the column. The third index, s, represents the total number of mutations at amino acids i and j. Beginning in position (2,1,90), we then add (3,1,80) to the stack, then {(4,1,70), (3,2,70)} and so forth. After each addition to the stack, we pick the element with the highest value in the third position of the 3-tuple.

Figure 4

Results summary. A breakout of what biologically relevant regions are overlapped by the most significant cluster for each of the 18 proteins. Overall, 77% of the hotspots overlap a binding site or a protein domain.

Figure 5

CHRM 2 clustering. The CHRM2 structure (PDB ID: 3UON) where residues 52 and 144 are highlighted.

Figure 6

FGFR3 clustering. The FGFR3 structure (PDB ID: 1RY7) where residue 248 is highlighted blue.

Figure 7

BRAF clustering. The BRAF structure (PDB ID: 3Q96) where cluster 464-466 is shown in blue, 469-471 is shown in red and 595-597 is shown in purple. The central residue in each cluster (465, 470 and 596 for the blue, red and purple clusters, respectively) is labeled.

Figure 8

ALK clustering. The ALK structure (PDB ID: 2XBA) where cluster 1173-1175 is shown in blue and cluster 1274-1276 is shown in red. The central residue in each cluster (1174 and 1275 for the blue and red clusters respectively) is labeled.