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SNMF

SNMF: Integrated learning of mutational signatures and prediction of DNA repair deficiencies

Abstract • Repository Description • License

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Abstract

+-----------------------------------------------------------------------+ | Motivation: Many tumours show deficiencies in DNA damage response | | (DDR), which influences tumorigenesis and progression, but also | | exposes vulnerabilities with therapeutic potential. Assessing which | | patients might benefit from DDR-targeting therapy requires knowledge | | of tumour DDR deficiency status, with mutational signatures | | reportedly better predictors than loss of function mutations in | | select genes. However, signatures are identified independently using | | unsupervised learning, which is not optimised to distinguish between | | different pathway or gene deficiencies. Results: We propose SNMF, a | | supervised non- negative matrix factorisation that jointly optimises | | the learning of signatures: (1) shared across samples, and (2) | | predictive of DDR deficiency. We applied SNMF to mutation profiles of | | human induced pluripotent cell lines carrying gene knockouts linked | | to three DDR pathways. The SNMF model achieved high accuracy (0.971) | | and learned more complete signatures of the DDR status of a sample, | | further discerning distinct mechanisms within a pathway. Cell line | | SNMF signatures recapitulated tumour derived COSMIC signatures and | | predicted DDR pathway deficiency of TCGA tumours with high recall, | | suggesting that SNMF-like models can leverage libraries of induced | | DDR deficiencies to decipher intricate DDR signatures underlying | | patient tumours. | | | | Code: https://github.com/joanagoncalveslab/SNMF. | +-----------------------------------------------------------------------+

SNMF model

Repository Description

Folder hierarchy:

• data: Includes all the data for feature generation or experiments.
  • raw: raw repair deficient cell line data (zou2021)
  • processed: bootstrapped cell line data and TCGA mutational profiles
• results:

  • EDA: exploratory data analysis

  • final: result from paper

• SNMF: containing all the code for the SNMF model, adapted from the SigProfiler framework
  • src: containing test.py to run the SNMF method
• src:
  • processing: code for preprocessing (bootstrapping) of data

License

• Copyright © [Sander-Goossens].