# Database of recurrent mutations, an unbiased web resource to browse recurrent mutations in cancers

## TL;DR

DORM is a new unbiased database for cancer mutations that improves accuracy by using whole-genome data and reduces biases from targeted sequencing.

## Contribution

DORM introduces a novel database derived from whole-genome/exome data to eliminate biases in cancer mutation frequency estimation.

## Key findings

- Mutation recurrence is strongly linked to oncogenic activity and poor patient survival.
- DORM identifies mutations in EGFR outside the kinase domain that are missed by other databases.
- The database improves biomarker discovery and clinical variant assessment by reducing biases.

## Abstract

Existing cancer-associated variant databases contain biases arising from duplicate entries and the inclusion of targeted sequencing panels, which interfere with accurate estimation somatic mutation frequency in cancer cohorts. To address this, we developed the Database of Recurrent Mutations (DORM), a web resource derived exclusively from whole-genome and whole-exome sequencing data. By filtering out targeted screens and non-recurrent variants, our analysis reveals that mutation recurrence significantly correlates with oncogenic activity, loss of tumor suppressor function, and unfavorable patient prognosis. In a pan-cancer analysis of EGFR, DORM identified frequent mutations outside the kinase domain that are underrepresented in other databases. This resource offers a streamlined, unbiased platform for mutation frequency analysis, enhancing biomarker discovery and the assessment of clinical variant significance.

•DORM is a fast, open-source web resource for analyzing recurrent cancer mutations•DORM mitigates biases introduced by targeted sequencing and duplicate samples•Mutation recurrence correlates with oncogenicity and poor patient survival

DORM is a fast, open-source web resource for analyzing recurrent cancer mutations

DORM mitigates biases introduced by targeted sequencing and duplicate samples

Mutation recurrence correlates with oncogenicity and poor patient survival

Biocomputational method; Computational bioinformatics; Cancer