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Cell Genom
Cell Genom
Cell Genomics
2666-979X
Elsevier

S2666-979X(24)00239-8
10.1016/j.xgen.2024.100635
100635
Preview
Genomic patterns of somatic mutations provide new prognostic, therapeutic, and biological insights in cancer
Tseitline Dana 1
Cohen Yuval 1
Adar Sheera sheera.adar@mail.huji.ac.il
1∗
1 The Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, The Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
∗ Corresponding author sheera.adar@mail.huji.ac.il
14 8 2024
14 8 2024
14 8 2024
4 8 100635© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The mutational landscape of an individual’s cancer can inform on its molecular state and be used as prognostic and therapeutic markers. The study by Barbour et al.1 analyzes mutational patterns in bladder cancer samples to uncover new biological insights into the ERCC2 gene function and develop new predictive prognostic tools.

The mutational landscape of an individual’s cancer can inform on its molecular state and be used as prognostic and therapeutic markers. The study by Barbour et al. analyzes mutational patterns in bladder cancer samples to uncover new biological insights into the ERCC2 gene function and develop new predictive prognostic tools.
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pmcMain text

The mutational patterns of cancer genomes reflect the cellular processes that led to mutation accumulation during tumor development and inform about a specific individual’s cancer’s molecular capacities and state. For decades, mutations in known cancer genes have been used to inform clinicians on therapeutic strategies and prognosis. In recent years, whole-genome sequencing of cancer samples has become more affordable and prevalent. These efforts uncover thousands of mutations per cancer genome, but only a handful of these are validated, clinically recognized mutations. The rest are either passenger mutations or mutations of unknown significance. There is still a strong and unmet need for predictive tools that can refine clinically actionable information from these mutations. One approach has been to identify mutational signatures. Mutational signatures are defined not only by the frequencies of the different mutated bases but also by the frequencies of the flanking nucleotides.2 To date, ∼70 single-base substitutions (SBSs), as well as doublet-base substitutions and indel mutational signatures, have been identified and have been associated with specific carcinogenic pathways, treatments, and exposures. For example, signature SBS3 is associated with defects in homologous recombination (HR) repair in breast cancers. SBS3 was first recognized based on its prevalence in BRCA1- and BRCA2-deficient cancers3 but has since been identified in cancers that do not harbor BRCA1/2 mutations, indicating that it may serve as a better guide for treatments that are indicated for HR-defective cancers such as platinum-based therapies or PARP inhibitors.4 Thus, signature analysis provides a comprehensive functional measurement of the inactivity of a mutagenic pathway complementary to gene-mutation analysis.

In this study, Barbour and colleagues used a comprehensive mutational analysis approach to study bladder cancer (Figure 1).1 The study was built on two previous observations. First, there is a high prevalence of mutations in the Excision repair cross-complementation group 2 (ERCC2) gene in bladder cancers, which is associated with better prognosis.5 Second, the mutational signatures associated with aberrant activity of the APOBEC cytosine deaminase enzyme are enriched in bladder cancers.6 ERCC2 encodes the xeroderma pigmentosum group D (XPD) helicase, a subunit of the transcription factor IIH complex and a key factor in both transcription initiation and nucleotide excision repair (NER).7,8 In cells, NER is responsible for the removal of bulky and helix-distorting lesions. These include carcinogenic damages induced by ultraviolet radiation in sunlight or by cigarette smoke but also damages induced as a means to kill cancer cells by the chemotherapy cisplatin. Cancer cells defective in ERCC2—and, therefore, NER—respond better to cisplatin therapy.9Figure 1 The study by Barbour et al. used mutational patterns at different genomic features to predict ERCC2 mutation status

ERCC2 mutant cancers are usually discovered in early tumor stages and generally harbor a higher mutation load. Each of these confounding factors could in itself affect prognosis. Using a large cohort of 1,244 sequenced bladder urothelial cancer samples, Barbour et al. validated that the ERCC2 mutation status is an independent predictor of prognosis. Following this, they stratified 392 whole-genome sequencing of bladder cancer samples from the Genomics England cohort based on their ERCC2 mutation status and conducted a complementary analysis to uncover mutational patterns that could provide predictive prognostic and therapeutic markers.

Barbour et al. focused on the SBS2 and SBS13 signatures that consist primarily of T(C>D)N sequence contexts (where D is A, G, or T) and are associated with APOBEC activity. Aberrant APOBEC activity generates uracils in genomic DNA. Compared to ERCC2 wild-type cancers, ERCC2 mutated cancers exhibited a higher proportion of SBS2 (mainly T[C>T]N mutations) but a lower proportion of SBS13 (mainly T[C>R]N, where R is A or G) signatures. In addition, the authors found that the distribution of these typical APOBEC mutations in the genome is different in ERCC2 mutant genomes. While in ERCC2 wild-type bladder cancers these mutations are less prevalent in accessible and early replicating regions of the genome, this effect is attenuated or even reversed, with higher APOBEC mutation rates in these regions, in ERCC2 mutants. A similar shift in the distribution of APOBEC signatures is also observed in cells with deficiency in UNG glycosylase, which is responsible for removal of uracils by base excision repair, suggesting that ERCC2 could play a role in repair of uracils in accessible regions.

Additional support for ERCC2’s role in repairing uracils came from the identification of mutation hotspots specifically in the ERCC2-mutated tumors at CTCF-binding sites. The mutations in these hotspots consisted primarily of T>G and T>C mutations. These mutations could be the product of misincorporation of uracils into the genome. Indeed, more uracil were observed at CTCF-binding sites in uracil incorporation sequencing data from UNG knockout cells. Further evidence for a possible role of ERCC2 in the repair of uracils at these sites was the enrichment of XPD chromatin immunoprecipation sequencing signal at CTCF-binding sites and the enhanced NER efficiency at accessible genomic regions.

Finally, leveraging the new insights from their analysis of mutational patterns in ERCC2 mutant cancers, Barbour et al. created a machine-learning model to identify ERCC2 dysfunction from the genomic mutational patterns at different genomic elements (including CTCF-binding sites, chromatin accessibility, and replication timing). Their model was 99% accurate in classifying ERCC2-deficient samples.

In conclusion, this important study highlights the wealth of data afforded by whole-genome sequencing of cancer samples. First, this work uncovers a possible new biological function for ERCC2 and possibly other components of the NER machinery in removal of uracils. Second, the authors use their study’s findings to inform and create a model for classifying the functional ERCC2 status of cancer samples, identifying samples either that were missed by variant calling or that do not have a structural mutation but could still have similar prognosis or therapy indications. The role for ERCC2 in removal of uracils at this stage is primarily correlative and would need to be experimentally validated. Still, this work is proof of concept that mutational patterns are informative of underlying biological processes, can provide insight on new biological roles for proteins, and hold great promise for the future as a tool for clinical decision-making.

Acknowledgments

S.A. is the recipient of the Jacob and Lena Joels Memorial Fund senior lectureship. This work was funded by the Israel Science Foundation grant 482/22 , administered by the Israeli Academy for Science and Humanities.

Declaration of interests

The authors declare no competing interests.
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