Recurrrent mutations

…we developed a binomial statistical model that incorporates several aspects of underlying mutational processes including
- nucleotide context mutability,
- gene-specific mutation rates and
- major expected patterns of hotspot mutation emergence

A binomial model for mutation hotspots. by Chang et al. (2016)

Data

  • TCGA, ICGC + published studies
  • coorinates converted to GRCh37 using LiftOver.
  • VEP v77 + vcf2maf v1.5
  • remove duplicates

Definition

  • driver cancer gene: a molecular abnormality leads to a fitness advantage for the affected cancer cell.
  • initialing + later in tumor progression
  • hotspot: amino acid position in a protein coding gene mutated more frequently than would be expected in the absence of selection. All of the following mutation types result in the same hotspot:
    1. mutations in different nucleotide positions in the same codon of a gene.
    2. different nucleotide substitutions at the same site in the same codon that result in different amino acid changes.
    3. mutations where the amino acid substitution is identical but the nucleotide change are different.

model

\[ Pr(X=k)=\binom{n}{r}P^{k}(1-p)^{n-k} \]

  1. \[\label{eq:test} \frac{\alpha}{\beta} = \gamma \]

  2. \(\alpha+\beta=\gamma\)

using autoref: . using ref: using @: (2); (1)

References

Chang, Matthew T., Saurabh Asthana, Sizhi Paul Gao, Byron H. Lee, Jocelyn S. Chapman, Cyriac Kandoth, JianJiong Gao, et al. 2016. “Identifying Recurrent Mutations in Cancer Reveals Widespread Lineage Diversity and Mutational Specificity.” Nature Biotechnology 34 (2): 155–63. doi:10.1038/nbt.3391.