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Updated: Aug 8, 2026
Using Next Generation Sequencing to Identify Mutations Associated with Repair of a CAS9-induced Double Strand Break Near the CD4 Promoter
Published on: March 31, 2022
Etiology-guided mutational signature learning from DNA repair knockouts in cell lines using supervised NMF
Sander Goossens1, Yasin I Tepeli1, Colm Seale1,2
1Pattern Recognition & Bioinformatics, Intelligent Systems Department, EEMCS Faculty, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, Netherlands.
Abstract:
Many tumours show deficiencies in DNA damage response (DDR), not only driving tumorigenesis but also exposing vulnerabilities with therapeutic potential. Assessing which patients might benefit from DDR-targeting therapy requires knowledge of tumour DDR deficiency (DDRd) status, with mutational signatures reportedly better predictors than loss-of-function mutations. Existing DDRd models offer effective prediction for pathways with well-characterized processes and mutational signatures. Nevertheless, development of models for additional DDRd and clinically relevant mechanisms could be hampered by the fact that most mutational signatures have unknown etiology. Using supervised non-negative matrix factorization (SNMF), we integrate mutational signature learning with multiclass DDR-deficiency prediction to enable etiology-guided learning of signatures from cell lines with confirmed gene knockouts. Applied to DDR gene knockout human-induced pluripotent stem cell lines, SNMF identified etiology-aligned representations of deficiency in homologous recombination, mismatch repair, and base excision repair. Even guided by pathway-level labels, SNMF captured gene-specific base excision repair submechanisms, showing the integration offered added granularity. Learned cell line signatures showed high similarity to tumour-derived COSMIC signatures, revealed associations with mutations in DDR genes, and enabled high recall of tumours with DDR deficiencies. We envision that SNMF-like methods could leverage knockout screens to learn etiology-guided signatures for improved DDRd annotation and treatment optimization. SNMF is available at: https://github.com/joanagoncalveslab/SNMF.
Insights
This study introduces a new method for identifying cancer’s DNA damage response (DDR) deficiencies. This approach helps predict which patients may benefit from targeted therapies by analyzing mutational signatures.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Tumors often exhibit DNA damage response (DDR) deficiencies, driving cancer and creating therapeutic vulnerabilities.
- Accurate assessment of tumor DDR deficiency (DDRd) status is crucial for predicting patient response to DDR-targeting therapies.
- Current DDRd models are effective for well-characterized pathways, but unknown etiologies of mutational signatures limit expansion to other DDR mechanisms.
Purpose of the Study:
- To develop a novel method integrating mutational signature learning with multiclass DDR deficiency prediction.
- To enable etiology-guided signature learning for DDR deficiencies using cell line models with confirmed gene knockouts.
- To improve the granularity and accuracy of DDRd annotation for potential treatment optimization.
Main Methods:
- Utilized supervised non-negative matrix factorization (SNMF) to integrate mutational signature learning and DDR deficiency prediction.
- Applied SNMF to human-induced pluripotent stem cell lines with specific DDR gene knockouts.
- Leveraged pathway-level labels to guide SNMF in identifying etiology-aligned signatures and submechanisms.
Main Results:
- SNMF successfully identified etiology-aligned signatures for homologous recombination, mismatch repair, and base excision repair deficiencies.
- The method revealed gene-specific submechanisms within base excision repair, demonstrating enhanced granularity.
- Learned signatures closely resembled tumor-derived COSMIC signatures and accurately recalled tumors with DDR deficiencies.
Conclusions:
- SNMF offers a powerful approach for etiology-guided learning of mutational signatures related to DDR deficiencies.
- This method can improve DDRd annotation by leveraging knockout screens and providing greater mechanistic detail.
- The findings suggest SNMF-like methods can enhance treatment selection for cancer patients based on their tumor's DDR status.
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