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. 2024 Mar 6;27(4):109433.
doi: 10.1016/j.isci.2024.109433. eCollection 2024 Apr 19.

Evolvability of cancer-associated genes under APOBEC3A/B selection

Joon-Hyun Song  1   2 , Liliana M Dávalos  3   4 , Thomas MacCarthy  1   2 , Mehdi Damaghi  1   2   5
Affiliations

Evolvability of cancer-associated genes under APOBEC3A/B selection

Joon-Hyun Song et al. iScience. .

Abstract

Evolvability is an emergent hallmark of cancer that depends on intra-tumor heterogeneity and genetic variation. Mutations generated by APOBEC3 contribute to genetic variation and tumor evolvability. However, the influence of APOBEC3 on the evolvability of the genome and its differential impact on cancer genes versus non-cancer genes remains unclear. Analyzing over 40,000 human protein-coding transcripts, we identified distinct distribution patterns of APOBEC3A/B TC motifs between cancer and non-cancer genes, suggesting unique associations with cancer. Studying a bat species with numerous APOBEC3 genes, we found distinct motif patterns in orthologs of cancer genes compared to non-cancer genes, as in humans, suggesting APOBEC3 evolution to reduce impacts on the genome rather than the converse. Simulations confirmed that APOBEC3-induced heterogeneity enhances cancer evolution through bimodal patterns of mutations in certain classes of genes. Our results suggest the bimodal distribution of APOBEC-induced mutations can significantly increase cancer heterogeneity.

Keywords: Bioinformatics; Cancer; Evolutionary processes.

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Conflict of interest statement

The authors declare no competing interest.

Figures

None
Graphical abstract
Figure 1
Figure 1
APOBEC3A/B TC hotspots statistics of protein-coding transcripts of the human genome (A) CDUR analysis on the human genome shows two dense regions at the top-left and right-bottom extremes and relatively sparse dispersion elsewhere. (B) Genes in the top-left partition of the CDUR plot showed significant enrichment of GO Biological Processes associated with differentiation and development. (C) Genes in the top-left partition of the CDUR plot showed significant enrichment of KEGG Pathway associated with several cancers, basal cell carcinoma, breast cancer, and gastric cancer.
Figure 2
Figure 2
Cancer genes have a significantly different distribution in CDUR plots compared to non-cancer genes (A) Cancer and non-cancer genes show distinct distribution in both motif under-representation and mutational susceptibility (Kolmogorov-Smirnov test for motif-representation axis, P = 2.828 x 10-7; for mutational susceptibility axis, P = 1.175 x 10-4). (B) Motif under-representation and mutational susceptibility of BRCA1 orthologs show higher variance than sequential mutations of the gene on the CDUR plot. (C) Standard deviation of motif under-representation comparison between orthologs and sequential mutations shows significantly high variance among orthologs in both cancer genes (196 genes) and non-cancer genes (20 genes). (D) Standard deviation of mutational susceptibility comparison between orthologs and sequential mutations shows significantly high variance among orthologs in both cancer genes (196 genes) and non-cancer genes (20 genes).
Figure 3
Figure 3
APOBEC3A/B TC hotspots statistics of protein-coding transcripts of Pteropus alecto genome (A) CDUR analysis on the bat genome shows two dense regions at the top-left and right-bottom extremes and relatively sparse dispersion elsewhere as in the human genome. (B) Bat orthologs of human cancer and non-cancer genes show distinct distribution in mutational susceptibility but not in motif under-representation and (Kolmogorov-Smirnov test for motif-representation axis, P = 0.1701; for mutational susceptibility axis, P = 3.169 x 10-5).
Figure 4
Figure 4
Protein-coding transcripts from the human genome display biased distribution in APOBEC3A/B motif representation and mutational susceptibility The bimodal distribution of APOBEC3A/B motif representation displays higher heterogeneity by spatial clonal dynamics simulation. (A) The number of genotypes between with and without APOBEC3A/B shows no significant difference. (B) Heterogeneity between the two simulations with and without APOBEC3A/B shows no significant difference. (C and D) Muller plots of simulation with and without APOBEC3A/B activity show no significant difference in heterogeneity at the final time point. (E) The number of genotypes between uniform and bimodal distribution shows an increasing difference over time. (F) Heterogeneity between uniform and bimodal distribution shows an increasing difference over time. (G and H) Muller plots of simulation uniform and bimodal distribution show an increasing difference over time.
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