Pan-cancer inference and validation of hypermorphic, hypomorphic and neomorphic mutations

Area of Science:

• Genomics
• Cancer Biology
• Computational Biology

Background:

• The Cancer Genome Atlas (TCGA) contains millions of nonrecurrent cancer mutations with unknown functional significance.
• Characterizing these mutations is crucial for understanding cancer development and for informing targeted therapy.

Purpose of the Study:

• To develop and validate a sensitive reporter assay for evaluating the functional roles of oncogenic mutations, particularly those with unknown significance.
• To functionally characterize a large number of nonrecurrent mutational events across TCGA.

Main Methods:

• Utilized transcription factor activity, inferred from target gene expression, as a reporter assay for oncogene mutation function.
• Compared transcription factor activity in samples with mutations of unknown significance to known gain-of-function (hypermorph) and loss-of-function (hypomorph) mutations.
• Analyzed 583,089 individual mutational events across TCGA.
• Validated predicted mutation effects (loss-of-function, gain-of-function, neomorphic, neutral) using exogenous mutation expression assays in PIK3CA and FGFR2.

Main Results:

• Successfully characterized the functional roles of 583,089 nonrecurrent mutational events.
• Identified neomorphic mutations (gain of new function) and mutations exhibiting mutational mimicry (phenocopying other gene mutations).
• Exogenous assays confirmed the majority of predicted functional classifications for PIK3CA and FGFR2 mutations.

Conclusions:

• The transcription factor activity reporter assay is a sensitive and accurate method for functional characterization of oncogenic mutations.
• This approach can identify diverse mutation types, including neomorphic and mimicking mutations.
• Findings can guide targeted therapy decisions for patients with mutations of unknown significance in established oncogenes.