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Nuclear topology modulates the mutational landscapes of cancer genomes

Abstract

Nuclear organization of genomic DNA affects processes of DNA damage and repair, yet its effects on mutational landscapes in cancer genomes remain unclear. Here we analyzed genome-wide somatic mutations from 366 samples of six cancer types. We found that lamina-associated regions, which are typically localized at the nuclear periphery, displayed higher somatic mutation frequencies than did the interlamina regions at the nuclear core. This effect was observed even after adjustment for features such as GC percentage, chromatin, and replication timing. Furthermore, mutational signatures differed between the nuclear core and periphery, thus indicating differences in the patterns of DNA-damage or DNA-repair processes. For instance, smoking and UV-related signatures, as well as substitutions at certain motifs, were more enriched in the nuclear periphery. Thus, the nuclear architecture may influence mutational landscapes in cancer genomes beyond the previously described effects of chromatin structure and replication timing.

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Figure 1: Somatic mutation frequencies differ between chromosomes located at the nuclear core versus the periphery.
Figure 2: Somatic mutation patterns differ between genomic regions located at the nuclear core versus the periphery.
Figure 3: Differences in somatic mutation patterns between the nuclear core and periphery are not solely due to chromatin.
Figure 4: Nuclear-pore-proximal genomic regions have characteristic somatic mutation patterns.

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Acknowledgements

The authors acknowledge financial support from T15LM009451 (K.S.S.), U54CA193461 (F.M.), P30CA072720, the American Cancer Society, and the Boettcher Foundation (S.D.). The authors thank other members of the laboratories of F.M. and S.D. for helpful discussions. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Author information

Authors and Affiliations

Authors

Contributions

S.D. conceived the project with F.M.; K.S.S., L.L.L., F.M., and S.D. designed the experiments. K.S.S., L.L.L., and S.D. performed the experiments. K.S.S., L.L.L., S.G., F.M., and S.D. interpreted the results. F.M. and S.D. wrote the manuscript with input from other authors.

Corresponding authors

Correspondence to Franziska Michor or Subhajyoti De.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Summary of the samples analyzed for the six cancer cohorts.

A) Mutation frequencies for the cohorts were comparable to those published elsewhere (Lawrence et al. Nature, 499, 214-218, 2013. Whiskers in the boxplot indicate upper and lower quartiles. B) Mutation callers used for the different cohorts are listed.

Supplementary Figure 2 Chromosome-level differences in patterns of genomic alterations.

(A) There was no significant difference in the chromosome average copy number log2 ratios between chr18 and chr19 in the LUSC cohort (p-value >0.1; Mann Whitney test), indicating that lower AMR in chr19 was not due to recurrent copy number loss of segments of this chromosome. (B) Chromosome-wise AMR and copy number log2 ratios for cLAD and iLADs in the LUSC cohort, shown after grouping according to chromosomes show that difference in AMR between cLADs and iLADs was not due to systematic difference in copy number gain or loss. Copy number log2 ratios were obtained from the cBio portal, and were reported as segment means. Whiskers in the boxplot indicate upper and lower quartiles.

Supplementary Figure 3 Effects of nuclear localization on somatic mutation patterns after refinement of the list of cLAD and iLAD regions.

A) After excluding regions that overlap with nucleolus-associated domains (NAD), filtered constant nuclear periphery (cLAD-NAD) regions tend to have a significantly higher adjusted mutation rate compared to filtered constant nuclear core (iLAD-NAD) regions (Mann Whitney U test p-value <0.05 in all cohorts). Whiskers in the boxplot indicate upper and lower quartiles. B) Constant periphery regions that are also conserved between human and mouse in their nuclear localization (cLADc) tend to have a significantly higher adjusted mutation rate (AMR) compared to constant core regions with equivalent evolutionary conservation in nuclear localization (iLADc; Mann Whitney U test p-value <0.05 in all cohorts). Whiskers in the boxplot indicate upper and lower quartiles.

Supplementary Figure 4 Mutation-signature analysis for genomic regions in the nuclear core and periphery.

(A) Substitution patterns and (B) contribution of those substitution classes to the major somatic mutation signatures in the six cancer types – melanoma (SKCA), lung cancer (LUSC), gastric cancer (STAD), lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), and prostate cancer (PRAD). Mutation signatures were generated based on non-negative matrix factorization using the somaticSignature R package. (C) AMR for cLAD and iLAD considering only C:G>A:T mutations was calculated, and AMR(cLAD)/AMR(iLAD) was plotted against the number of pack years smoked (Spearman correlation coefficient: 0.29; p-value >0.05), suggesting that the relative strength of signature of oxidative damage cause by smoking in the nuclear periphery was proportionally higher for heavy smokers compared to light smokers.

Supplementary Figure 5 Multivariate analysis to determine the effects of nuclear localization on mutation frequencies.

A) Effect of LAD on average mutation frequencies across tumors for different cancer types after adjusting for conservation, GC%, gene density, replication timing, and heterochromatin mark H3K9me3 signals using multiple linear regression. B) Effect sizes of different features on mutation rates, including phastCons score, GC%, gene density, H3K9me3, LAD density, and replication timing based on multivariate linear regression. C) cLAD regions tend to have a significantly higher adjusted mutation rate compared to iLAD regions after analyzing the effects separately for euchromatic and heterochromatic genomic regions (Mann Whitney U test p-value <0.05 for pairwise iLAD/cLAD comparisons for all cohorts). Euchromatin and heterochromatin regions were identified using Giemsa staining data (Cheung et al., Nature, 409,953-8, 2001) such that heterochromatic cLADp and iLADp regions had ≥75% Giemsa-positive staining and were deemed predominantly heterochromatic, while cLADn and iLADn had ≤25% Giemsa-positive staining and were considered predominantly euchromatic. Whiskers in the boxplot indicate upper and lower quartiles. D) cLAD regions tend to have a significantly higher adjusted mutation rate compared to iLAD regions after analyzing the effects separately for constant early replicating and constant late replicating genomic regions (Hansen et al., PNAS, 107, 139-44, 2010). cLADe and iLADe regions had early replication timing in a cell type-invariant manner. cLADl and iLADl regions had late replication timing in a cell type-invariant manner. Whiskers in the boxplot indicate upper and lower quartiles.

Supplementary Figure 6 Comparison of mutation patterns between the nuclear core and periphery in benign human cell types.

A) Data for somatic mutations detected in benign fibroblasts were obtained from Abyzov et al. Genome Res. 27, 512-523, 2017. Mutations were called based on human induced pluripotent stem cell (hiPSC) lines generated from reprogrammed skin fibroblast cells from families of donors. The SNV data was processes and analyzed in a manner similar to that described in Figure 2 and 3. B) Adjusted mutation rate (AMR) for cLAD was higher than that for iLADs, showing a trend consistent with that observed in the cancer genomes (Figure 2). But the the number of somatic mutations in each category per donor was small, and the difference was not statistically significant (Mann Whitney U test, p-value >0.05) or in the multivariate analysis similar to that presented in Figure 3. Whiskers in the boxplot indicate upper and lower quartiles.

Supplementary Figure 7 Residual plot for multivariate analysis to determine effects of nuclear localization on mutation frequencies.

A) Residual plot of multiple linear regression ‘Mutation rates ~ LAD + replication timing + GC% + gene density + H3K9me3 + phastCons’. Red lines: smoothers between residuals and fitted values from linear model. B) Residual plot of random forest regression ‘Mutation rates ~ LAD + replication timing + GC% + gene density + H3K9me3 + phastCons’. Red lines: smoothers between residuals and fitted values from the nonparametric random forest regression.

Supplementary Figure 8 Conditional variable importance analysis for nuclear localization.

A) Conditional variable importance computed by sub-dividing 1MB windows into 10 groups. Whiskers in the boxplot indicate upper and lower quartiles. B) Subsampling lymphoma cohort to test the robustness of the variable importance rankings to different sample sizes.

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Smith, K., Liu, L., Ganesan, S. et al. Nuclear topology modulates the mutational landscapes of cancer genomes. Nat Struct Mol Biol 24, 1000–1006 (2017). https://doi.org/10.1038/nsmb.3474

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