Identification and Validation of a Previously Missed Mutational Signature in Colorectal Cancer

Mariya Kazachkova; Burçak Otlu; Marcos Díaz-Gay; Ammal Abbasi; Sarah Moody; Sandra Perdomo; David C Wedge; Paul Brennan; Michael R Stratton; Ludmil B Alexandrov

doi:10.1101/2025.06.06.658321

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[Preprint]. 2025 Jun 7:2025.06.06.658321. [Version 1] doi: 10.1101/2025.06.06.658321

Identification and Validation of a Previously Missed Mutational Signature in Colorectal Cancer

Mariya Kazachkova ^1,^2,³, Burçak Otlu ⁴, Marcos Díaz-Gay ^2,^3,⁵, Ammal Abbasi ^2,^3,⁶, Sarah Moody ⁷, Sandra Perdomo ⁸, David C Wedge ⁹, Paul Brennan ⁸, Michael R Stratton ⁷, Ludmil B Alexandrov ^1,^2,^3,^6,^10,^*

PMCID: PMC12258970 PMID: 40661437

Abstract

Mutational signature analysis has greatly enhanced our understanding of the mutagenic processes found in cancer and normal tissues. As part of a recent study, we analyzed 802 treatment-naïve, microsatellite-stable colorectal cancers (CRC) and identified a de novo signature, SBS_D, which was conservatively decomposed into SBS18, a signature associated with reactive oxygen species. Here, we re-evaluate this decomposition and provide evidence that SBS_D represents a distinct mutational process from that of SBS18.

Through an independent analysis of 2,616 whole-genome sequenced microsatellite-stable CRCs across three distinct cohorts, we demonstrate that SBS_D is consistently present at a similar prevalence, suggesting that this signature may have been previously overlooked. Using a naïve decomposition approach, we demonstrate that the pattern of SBS_D better aligns with signatures previously associated with deficiencies in DNA polymerase delta (POLD1) proofreading and mismatch repair. However, multiple lines of evidence, including the absence of pathogenic mutations in the exonuclease domain of POLD1 or in mismatch repair-associated genes, indicate that SBS_D is not driven by canonical defects in these DNA repair pathways.

Overall, this study identifies a previously unrecognized mutational signature in microsatellite-stable CRC and proposes that its etiology may be linked to DNA repair infidelity emerging late in tumor development in samples without canonical defects in DNA repair pathways.

INTRODUCTION

Over the past decade, mutational signature analysis has become a standard component of genomics research^1,2, greatly enhancing our understanding of the extrinsic and intrinsic mutagenic factors that influence the genomes of both cancer³ and normal somatic cells⁴. This has resulted in the development of global reference sets of mutational signatures, including those maintained by the Catalogue of Somatic Mutations in Cancer (COSMIC) database^5,6.

In principle, mutational signature analysis can be performed in at least two ways: by assigning known reference signatures from databases like COSMIC to individual samples⁷ or by conducting de novo extraction of mutational signatures⁸. This latter approach is commonly employed in the analysis of large datasets containing at least several hundred samples, enabling the identification of novel mutational signatures by detecting de novo signatures that do not correspond to known references⁸. Specifically, all de novo extracted signatures are either directly matched to known reference signatures or, more frequently, decomposed into a combination of multiple reference signatures. Given the extensive catalog of known reference signatures, the decomposition process can be optimized to prioritize biologically relevant signatures, minimizing the risk of overfitting^9-11. For example, a mutational signature associated with ultraviolet (UV) light exposure would typically be excluded from the decomposition of de novo signatures detected in colorectal or brain tumors as UV-induced mutations are not expected in these contexts⁵. Following decomposition, any signature that cannot be accurately reconstructed using a combination of known signatures with a cosine similarity of at least 0.90 is generally considered novel⁹.

In most cases, it is straightforward to determine whether a mutational signature is known or novel. Many de novo extracted signatures are easily decomposed into known reference signatures with high similarity. Conversely, some signatures exhibit patterns that cannot be fully reconstructed with substantial similarity, indicating the presence of a novel signature absent from the current reference set. Nevertheless, borderline cases exist where it remains uncertain whether a signature represents a truly distinct mutational process or merely a complex combination of previously known signatures. Scientifically, there is always a temptation to report a new signature, as it suggests the discovery of a potentially novel mutagenic process. However, validating such a discovery requires robust evidence in independent cohorts, particularly for signatures with unknown etiology, to ensure their validity. As a result, studies may adopt a conservative approach, potentially leading to the underreporting of genuinely novel mutational signatures.

Here, we revisit a recent de novo mutational signature analysis of 802 whole-genome sequenced microsatellite-stable colorectal cancers (CRC) from the Mutographs project⁹. In the original analysis, we identified a de novo single base substitution (SBS) signature, designated SBS_D, which had a cosine similarity of 0.90 when decomposed into known COSMICv3.4 reference signatures. At the time, we adopted a conservative approach and did not classify SBS_D as a novel signature⁹. Instead, we reported its decomposition into known COSMIC signatures, including SBS18, which has been associated with reactive oxygen species¹². Here, we demonstrate that SBS_D represents a distinct mutational process from SBS18. Furthermore, we successfully extract SBS_D from three independent, previously published CRC cohorts^13-15, none of which had identified or reported it. Finally, based on associations with known molecular phenotypes in CRC, we propose that SBS_D may result from POLD1 proofreading and mismatch repair infidelity occurring late in the evolution of tumors without detectable canonical defects in either pathway. Collectively, our findings reveal the existence of a previously unrecognized mutational signature in microsatellite-stable CRC samples and underscore the challenges associated with identifying novel mutational signatures.

RESULTS

Mutational Signatures and Their Predefined Schemas

All analyses of mutational signatures intrinsically rely on a predefined schema for classifying somatic mutations¹². For somatic single base substitutions, the SBS-96 context is the most widely used classification schema and serves as the foundation for the current set of COSMIC reference SBS signatures⁵. In the SBS-96 framework, somatic mutations are classified into six base substitution types (C>A, C>G, C>T, T>A, T>C, and T>G) based on the pyrimidine base change within the mutated Watson-Crick base pair (e.g., C>T representing a C:G>T:A mutation). Each of the six types is further subdivided into 16 subtypes by incorporating the trinucleotide context, which accounts for the adjacent 5′ and 3′ bases of the mutated pyrimidine. While SBS-96 is the most used schema for SBSs, higher-resolution schemas like SBS-288 and SBS-1536 have been applied in recent large-scale analyses, including in previous Mutographs studies^9-11,16. These schemas enhance resolution by incorporating additional features, such as transcriptional strand information in SBS-288 or an extended pentanucleotide context in SBS-1536¹⁷ (Methods).

Original Mutographs De Novo Signature Analysis

In the original analysis of 802 treatment-naïve, microsatellite-stable, DNA repair-proficient CRC samples from Mutographs, de novo extraction was performed using the SBS-288 classification schema⁹. This resulted in the identification of 16 de novo mutational signatures, labeled SBS_A through SBS_P (note that the use of letters distinguishes these from COSMIC reference signatures, which typically use numbers, e.g., SBS18). Of these, 12 signatures were decomposed into COSMIC reference signatures, with the decomposition optimized to exclude signatures associated with defective DNA repair, as the cohort included only DNA repair-proficient samples. The remaining four signatures represented mutational patterns not previously cataloged in COSMICv3.4 and were, as such, classified as novel. One of the 12 de novo signatures that was decomposed into COSMIC signatures, SBS_D, achieved a borderline reconstruction cosine similarity of 0.90. However, there was insufficient evidence to classify it as a novel signature, as no other large CRC mutational signature study had previously reported it. Adopting a conservative approach and given insufficient evidence at the time, we reported SBS_D as a mixture of previously known signatures while acknowledging the possibility that it may represent a novel signature⁹. Importantly, SBS_D was found in 428/802 microsatellite-stable CRC samples, contributing 8.5% of all mutations (Figure 1a).

Reevaluating the Pattern of SBS_D

The pattern of SBS_D was decomposed into COSMICv3.4 signatures SBS1 (14.5%), SBS5 (40.9%), SBS18 (32.6%), and SBS34 (12.0%) (Figure 1a). SBS1 and SBS5 are ubiquitous clock-like mutational signatures present in all types of cancer¹² and normal tissues¹⁸. Due to their pervasive nature, some degree of SBS1 and SBS5 contamination is expected in the decomposition of any de novo signature⁸, and, as anticipated, these signatures were found in many de novo signatures extracted from the Mutographs CRC cohort⁹. As such, if the decomposition was accurate, SBS_D would represent a combination of the reactive oxygen species-associated signature SBS18, the unknown etiology signature SBS34, and contributions from ubiquitous clock-like signatures (SBS1 and SBS5), with SBS18 being the predominant non-clock-like contributor. However, another de novo signature extracted from this cohort, SBS_B, exhibited a near perfect match to SBS18 (Figure 1b). Moreover, the C>A component, which constitutes the majority of SBS18 mutations, showed substantial differences between SBS18 and SBS_D (cosine similarity [CS]=0.80; Spearman correlation [CORR]=0.72) but was nearly identical between SBS18 and SBS_B (CS=0.98; CORR=0.97; Figure 1c). Considering the extremely high similarity between SBS18 and SBS_B, we will henceforth refer to SBS_B as SBS18^. Additionally, as part of the Mutographs study, additional extractions were performed using the SBS-96 and SBS-1536 classification schemas. Both approaches yielded two signatures closely resembling SBS18^ and SBS_D (Supplementary Figure 1). This consistency across different mutational schemas highlights the robustness of the patterns for these signatures, irrespective of the underlying classification framework. Overall, these findings raised doubts about the assignment of SBS18 as a contributor to SBS_D and suggested that SBS_D could represent a distinct mutational process.

SBS_D Represents a Mutational Process Distinct from SBS18

To enhance our understanding of SBS_D and its relationship with SBS18^, we performed molecular timing on the microsatellite-stable, DNA repair proficient Mutographs CRC samples where copy number information was available (774 of 802 samples; 96.5%) by categorizing SBS mutations based on their timing relative to copy number gains during cancer development into two groups: early clonal and late clonal (Methods). This stratification allowed us to analyze the contributions of SBS_D and SBS18^ across distinct stages of tumor evolution. Our analysis revealed that SBS_D was preferentially active in late clonal mutations compared to early clonal mutations (p-value<0.0001; Figure 2a). In contrast, SBS18^ was typically active exclusively early or both early and late but was rarely observed exclusively in late clonal mutations (p-value<0.0001, Figure 2b). Notably, prior studies have similarly demonstrated that SBS18 is either preferentially early or constant in CRC development^19,20. These findings indicate that SBS_D is an evolutionarily late signature, likely representing a mutational process distinct from that of SBS18^.

Figure 2: — ***(a)*** *(Left)* Relative SBS_D activity in early and late clonal mutations. Each line represents one sample: gray lines indicate SBS_D activity in both early and late clonal mutations, blue lines indicate activity only in early clonal mutations, and red lines indicate activity only in late clonal mutations. *(Right)* Number of samples with SBS_D activity in each category. Samples lacking SBS_D activity in both early and late clonal mutations are excluded from the plots. P-value is based on a McNemar test. ***(b)*** Same format as panel *(a)*, but for SBS18^, showing a different pattern of activity across early and late clonal mutations. ***(c)*** Nucleosome occupancy pattern for SBS_D (*left*) and SBS18^ (*right*), as identified by SigProfilerTopography. Each plot is centered on the mutation site, with 1,000 base pairs upstream (5’) and downstream (3’). Solid lines represent the average nucleosome signal for observed mutations; dashed lines show the signal for simulated mutations. ***(d)*** Nucleosome occupancy patterns for COSMIC signature SBS18 across all cancer types (*left*) and restricted to colorectal cancer (CRC) samples (*right*) from the COSMIC website. Plot features and layout match those in panel *(c)*.

Additionally, we performed topography analysis on the Mutographs CRC data to elucidate the topographical characteristics of SBS_D and SBS18^ (Methods). Although the two mutational signatures were similar in replication timing and strand asymmetry analyses (Supplementary Figure 2), they displayed a notable difference in nucleosome occupancy (Figure 2c). Specifically, SBS_D mutations were depleted near nucleosomes and did not exhibit any periodicity. In contrast, SBS18^ mutations were enriched near nucleosomes and demonstrated clear periodicity as expected and previously reported²¹ for COSMIC signature SBS18 in CRC and across all cancer types (Figure 2d). These observations further highlight the clear similarity between SBS18^ and SBS18, and they suggest that SBS_D represents a mutational process dissimilar to the one captured by SBS18.

Signatures SBS_D and SBS18^ are Validated in Independent Cohorts

Although several large mutational signature extraction studies in CRC have been recently conducted^14,15, none have reported a de novo signature resembling SBS_D. These prior studies analyzed DNA repair–proficient and repair–deficient samples together, which may have masked the presence of an SBS_D-like signature. Consistent with this, when we performed signature extraction on all Mutographs CRC samples without stratifying by DNA-repair status (n=981), no signature bearing resemblance to SBS_D was observed (CS<0.85 across all 27 de novo signatures; Supplementary Figure 3).

To validate SBS_D and confirm its distinction from SBS18^, we performed de novo signature extraction on treatment-naïve, microsatellite-stable, DNA repair–proficient samples from three independent whole-genome sequenced CRC cohorts, including: (i) 390 recently whole-genome re-sequenced samples from The Cancer Genome Atlas (TCGA) project, representing patients predominantly from the United States¹³; (ii) 694 samples from a recently published study¹⁴, representing patients from Sweden; and (iii) 1,532 samples from the Genomics England (GEL) project¹⁵, representing patients from the United Kingdom (Methods). De novo extractions for the Swedish and TCGA cohorts were conducted using the SBS-288 classification schema, consistent with the approach used in the Mutographs CRC project⁹. In contrast, the GEL cohort was analyzed using the SBS-96 schema, as it is the only publicly available mutational context for these cancers²². Despite differences in classification schemas, SBS_D and SBS18^ were identified across all three cohorts with high cosine similarity to their original Mutographs CRC signatures, reinforcing their distinction and confirming the reproducibility of SBS_D extraction in independent DNA repair proficient cohorts (Figure 3a-b). Moreover, aside from the GEL cohort—which showed lower signature prevalence—the total number of mutations and the proportion of samples attributed to these two signatures were very similar across the cohorts, further supporting their robustness and consistency (Figure 3a).

Figure 3: — ***(a)*** SBS18^ (*left*) and SBS_D (*right*) signatures extracted from the Mutographs, The Cancer Genome Atlas (TCGA), Swedish, and Genomics England (GEL) colorectal cancer (CRC) cohorts. Signature extraction for the Mutographs, TCGA, and Swedish cohorts was performed using the SBS-288 context and then collapsed to SBS-96 for visualization; the GEL cohort was directly analyzed in the SBS-96 context. The number of mutations and positive samples reflects results from mutational signature attribution (MSA) using *de novo* signatures identified by SigProfilerExtractor. ***(b)*** Heatmap showing cosine similarities among SBS18^-like and SBS_D-like signatures extracted from the four CRC cohorts.

Naïve Decomposition of SBS_D

The Mutographs CRC study utilized an optimized decomposition method when decomposing de novo signatures to COSMICv3.4 signatures⁹. Specifically, since the analyzed samples were treatment-naïve, microsatellite-stable, and DNA repair-proficient, the decomposition process was designed to exclude mutational signatures associated with chemotherapy exposure or DNA repair deficiencies. In the naïve decomposition approach, which allows the use of all known COSMIC signatures, SBS_D decomposed to a mixture of SBS10c (44%), SBS15 (12%), SBS34 (7%), and the clock-like signatures (37%) with cosine similarity of 0.98 (Figure 4a). SBS10c has been shown to strictly come from samples harboring germline or somatic POLD1 exonuclease domain mutations²³, and SBS15 has been attributed to defective DNA mismatch repair in microsatellite-unstable samples¹².

Figure 4: — ***(a)*** Naïve decomposition of SBS_D using the complete COSMIC reference signature set. ***(b)*** Comparison of microsatellite-stable samples with SBS_D activity (MSS SBS_D+), *POLD1*-mutant samples, and samples harboring microsatellite instability (MSI) in terms of total number of single base substitutions (SBSs; *left*), small insertions and deletions (indels; *middle*), and fraction genome aberrant (*right*).

At first glance, these results suggest that SBS_D is associated with POLD1 deficiency or microsatellite instability. However, there are at least three lines of evidence that argue strongly against this result. First, all POLD1-mutant and microsatellite-unstable CRC samples—confirmed by droplet digital PCR—were excluded from the 802-sample cohort prior to signature extraction. We also thoroughly reviewed all samples for any previously missed pathogenic or potentially pathogenic mutations in POLD1 or the mismatch repair-associated genes. Given that SBS10c has only been observed in tumors with pathogenic germline or somatic mutations in POLD1’s exonuclease domain²³, the absence of such mutations in the 802 samples argues against POLD1 deficiency as the cause of SBS_D (Supplementary Table 1-2). Although 12 samples carried non-pathogenic POLD1 mutations, none showed elevated SBS_D activity (Supplementary Figure 4a). Similarly, among the 38 samples with mutations in known mismatch repair genes, SBS_D activity was not elevated (Supplementary Figure 4b; Supplementary Table 1-2). Only one CRC sample was found to carry a pathogenic MSH6 nonsense mutation, but it lacked both SBS_D activity and a mutational profile consistent with microsatellite instability (Supplementary Figure 5). Similarly, we observed no difference in SBS_D activity between samples with and without loss of heterozygosity in POLD1 or DNA mismatch repair genes (POLD1: 103/774; DNA mismatch repair: 420/774; Supplementary Figure 4c-d). Second, while samples with known POLD1 mutations or microsatellite instability showed 10- to 100-fold higher substitution and indel rates, this elevated mutational burden was not observed in microsatellite-stable samples positive for SBS_D (Figure 4b). Third, tumors with pathogenic POLD1 mutations or microsatellite instability typically have stable, diploid genomes with aberrations affecting less than 20% of the genome, which is in stark contrast to the substantial genomic instability observed in most tumors with SBS_D activity (Figure 4b). While microsatellite instability—but not POLD1 deficiency—can also arise from epigenetic events, mainly the hypermethylation of the MLH1 promoter²⁴, such data were not available for the Mutographs cohort. However, epigenetically driven cases also exhibit high levels of substitutions and indels²⁵, which were not observed in SBS_D-positive samples. Together, this supports the conclusion that SBS_D is not driven by canonical defects in POLD1 or mismatch repair.

Exploring the Potential Etiology of SBS_D

Although SBS_D does not appear to result from canonical defects in POLD1 or mismatch repair genes, the similarity in mutational patterns suggests that POLD1—and possibly mismatch repair—may still contribute to its underlying mutational processes. This hypothesis is further supported by three key observations in the Mutographs CRC cohort. First, although SBS_D-positive samples lacked the characteristic features of POLD1 or DNA repair deficiency—such as hypermutation and low genome aberration (Figure 4b)—they exhibited strikingly higher indel burden compared to SBS_D-negative samples (1.3-fold increase; q-value of 2.50 × 10⁻¹² after adjusting for known covariates), whereas negligible differences were observed for SBS burden and genome aberration (Figure 5a). A markedly higher indel burden was also observed in SBS_D positive samples across the TCGA, Swedish, and GEL cohorts (q-values<10⁻¹⁰; 1.3-fold increase in TCGA; 1.3-fold increase in the Swedish cohort; 1.4-fold increase in GEL; Supplementary Figure 6). Second, a prior study²³ showed that POLD1-deficient tumors are hypermutated specifically for indel signature ID1, which arises from replication slippage of the newly synthesized strand. Notably, SBS_D was the SBS signature most strongly correlated with ID1 activity (CORR=0.66; q-value<0.0001; Figure 5b). Third, SBS10c in POLD1-defective tumors exhibits a distinct nucleosome occupancy pattern, marked by strong depletion at nucleosome-bound regions, and a similar depletion pattern was observed for SBS_D in the DNA repair proficient samples (Figure 5c). While we can be confident that SBS_D is not driven by established genomic aberrations in POLD1 and mismatch repair genes, our results suggest that there may be a link between SBS_D and infidelity in these DNA repair processes.

Figure 5: — ***(a)*** Comparison of SBS_D-positive (SBS_D⁺) and SBS_D-negative (SBS_D⁻) samples across total number of single base substitution mutations (*left*), total number of indel mutations (*middle*), and fraction of the genome with copy number aberrations (*right*). P-values were calculated using linear models adjusted for age, sex, tumor subsite, country, and tumor purity, followed by Benjamini-Hochberg multiple testing correction to yield q-values. ***(b)*** Spearman correlation between the activity of each *de novo* SBS signature and ID1 activity. Asterisks indicate significant correlations with q-value<0.001 after Benjamini-Hochberg multiple testing correction. ***(c)*** Nucleosome occupancy pattern for SBS10c in the three *POLD1*-mutant samples from the Mutographs colorectal cancer cohort (*top*); nucleosome occupancy for SBS_D in the microsatellite-stable Mutographs colorectal cancers. Each plot is centered on the mutation site, with 1,000 base pairs upstream (5’) and downstream (3’). Solid lines represent the average nucleosome signal for observed mutations; dashed lines show the signal for simulated mutations.

DISCUSSION

In this study, we revisited the Mutographs CRC mutational signature analysis⁹, where mutational signature SBS_D was previously decomposed into the reactive oxygen species-associated signature SBS18. Although our results do not alter the main finding from the Mutographs CRC project, they do indicate that the conservative approach taken by Mutographs has missed that SBS_D represents a distinct mutational process from SBS18. Furthermore, although SBS_D is consistently present at similar prevalence across multiple independent microsatellite-stable CRC cohorts, it was not previously reported in these studies.

As multiple large-scale colorectal cancer cohorts^14,15 have previously undergone mutational signature analysis, it is surprising that SBS_D has not been reported. This seems to be due to previous studies not separating DNA repair deficient and DNA repair proficient cancers prior to performing de novo signature extraction. DNA repair deficient tumors typically harbor at least an order of magnitude more single base substitutions—around 10⁵ compared to ~10⁴ in microsatellite-stable cases—which can obscure weaker signals that overlap with signatures of DNA repair deficiency. Since SBS_D shares features with signatures commonly found in hypermutated, DNA repair–deficient tumors, it is particularly prone to being masked during extraction from mixed cohorts. This challenge is further amplified by the fact that SBS_D typically accounts for only 5 to 10% of all mutations in microsatellite-stable tumors. Indeed, without stratifying DNA repair–proficient and DNA repair–deficient samples, the orders-of-magnitude-higher mutation rates from hypermutated microsatellite-unstable or polymerase proofreading-deficient tumors can mask the more subtle contribution of SBS_D.

To address this, the original Mutographs CRC study⁹ implemented a key strategy by analyzing microsatellite-stable and unstable samples separately. However, the study also adopted a conservative stance and did not classify SBS_D as a novel signature. At the time, its decomposition into known reference signatures was borderline, and without the additional evidence presented in this study, SBS_D was interpreted as a composite rather than a distinct entity. The results in this study underscore the need to further investigate signatures with borderline decompositions, including analyses of additional cohorts and evaluation of naïve signature decomposition approaches that incorporate all known reference signatures.

Overall, despite extensive studies on CRC mutational landscapes, our findings reveal a previously unrecognized mutational signature in microsatellite-stable CRC that may reflect DNA repair infidelity during the late stages of CRC evolution. However, to conclusively determine the etiology of SBS_D, further in vitro and in vivo experiments will be required. Notably, recent advances in polymerase error rate sequencing (PER-seq) have provided new insights into polymerase epsilon fidelity²⁶, and similar approaches will be needed to experimentally validate the potential role of POLD1 infidelity in this newly identified mutational signature.

METHODS

Mutational Contexts

Briefly, the SBS-96 mutational context organizes somatic mutations into six base substitution types: C>A, C>G, C>T, T>A, T>C, and T>G (according to the pyrimidine base of the Watson-Crick base-pair). Each type is divided into 16 subtypes based on the trinucleotide context, which accounts for the bases immediately preceding and following the mutation (4 possible bases at the 5′ position × 4 at the 3′ position × 6 substitution types = 96 categories)¹⁷. The SBS-1536 classification extends the SBS-96 schema by incorporating the pentanucleotide context, resulting in 1,536 mutational channels (256 per each of the six base substitution types). While its complexity has limited its widespread use, SBS-1536 has previously provided insights into mutational processes such as APOBEC-mediated cytidine deamination²⁷. The SBS-288 classification builds upon SBS-96 by introducing three additional subcategories for each mutation: (i) non-transcribed, (ii) transcribed, and (iii) un-transcribed. This schema differentiates mutations occurring in non-transcribed (i.e., non-genic) regions of the genome from those in transcribed regions of the genome. Mutations within transcribed regions are further divided based on the transcriptional strand orientation relative to the pyrimidine mutated base¹⁷, resulting in the transcribed and un-transcribed classifications. This enhanced framework enables more detailed investigations of transcription-coupled DNA damage and repair processes, for example, transcription-coupled nucleotide excision repair. Importantly, both the SBS-288 and SBS-1536 schemas are fully collapsible to SBS-96, allowing extracted signatures to be converted back to the SBS-96 schema for direct comparison with COSMIC reference signatures. The mutational context enables the transformation of mutational catalogs from cancer genomes into a mutational matrix, where each row represents a specific mutation type and each column corresponds to a tumor sample.

Mutational Signature Extraction and Decomposition

Mutational signature extraction and decomposition results from the Mutographs CRC Project⁹ were utilized in this study. Briefly, we previously used SigProfilerExtractor⁸, which involves performing nonnegative matrix factorization (NMF) on a mutational matrix, to identify the underlying mutational signatures in the cohort. Extractions were performed using the SBS-96, SBS-288, and SBS-1536 contexts, and the signatures from the SBS-288 extraction were decomposed and used for the Mutographs CRC study. Decomposition of the de novo signatures, which means identifying which known reference signatures best explain each of the de novo signatures, was performed using an optimized approach where the following signature subsets were removed from the decomposition: artifact signatures, ultraviolet signatures, lymphoid signatures, mismatch repair deficiency signatures, polymerase deficiency signatures, base excision repair deficiency signatures, and treatment signatures. This was done using SigProfilerAssignment⁷ and utilizing the exclude_signature_subgroups parameter.

Identification of Mutational Signature Activity in Each Sample

As in the original Mutographs CRC project⁹, Mutational Signature Attribution²⁸ (MSA) was used to attribute each of the de novo mutational signatures to each sample. MSA was run using the same parameters used for the original Mutographs CRC study, including setting the no_CI_for_penalties parameter to false for penalty selection. Analyses were performed using the pruned attributions produced by the tool. Somatic mutations attributed to each mutational signature after mapping to COSMICv3.4—along with SBS_D, which is now classified as a novel signature—are provided in Supplementary Table 3.

Molecular Timing of SBS18^ and SBS_D

Copy number profiles, generated using Battenberg²⁹, and somatic variant calls, generated using a consensus between the CaVEMan³⁰ and Strelka2³¹ callers, were generated as part of the Mutographs CRC project. These data were used to run DPClust3p²⁹ and DPClust²⁹ to identify the cancer cell fraction (CCF) of each somatic mutation and estimated number of chromosomes carrying each somatic mutation. The following logic was applied: in areas with copy number amplifications, if a mutation appeared on more than one chromosome copy, it was labeled as early clonal, and if a mutation appeared on one or less chromosome copies when a gain had occurred on the major allele in that location, it was labeled as likely late clonal; this logic was only applied to clonal mutations (those with CCF>0.95).

Samples with at least 256 early clonal and 256 late clonal mutations were retained for analysis (684/774), as has been done in previous studies^10,11. MSA was then used to identify the activity of each de novo mutational signature in each early clonal and late clonal set of mutations. A McNemar test was used to assess if the frequencies of samples with SBS_D activity and SBS18^ activity were different between late clonal and early clonal mutations.

Topography Analysis

SigProfilerTopography³² was used to analyze the topographical features of SBS_D, SBS18^, and SBS10c. Probability files and activity files were generated using MSA as part of the Mutographs CRC project. For the SBS_D and SBS18^ analysis, MSA was run with the SBS-288 de novo signatures and the 802 DNA repair proficient samples. For the SBS10c analysis, MSA was run using the COSMICv3.4 reference signatures and the three POLD1-mutant samples from the full Mutographs CRC cohort. Both runs of SigProfilerTopography³² used 0.75 for the average_probability parameter. For replication timing and replication strand asymmetry analyses, the HCT116 colon carcinoma cell line was used as the reference. For nucleosome occupancy, the K562 cell line was used as the reference.

Somatic Variant Calling in Three Independent Cohorts

For the Swedish Cohort, somatic mutation calls were publicly available and had been identified using Mutect³³, Mutect2³⁴, and TNscope³⁵. For GEL, only the SBS-96 mutational matrix was available; somatic mutations for GEL were identified using Strelka³¹. Because mutation calls are not yet available for the TCGA whole-genome sequenced CRC samples, we identified mutations using Mutect2³⁴ with the pcr-indel-model parameter set to HOSTILE and all other parameters set to default values; the standard PASS filter was the applied.

Sample Filtering in Three Independent Cohorts

Samples in each independent cohort were filtered to mimic the DNA repair proficient sample set used in the Mutographs CRC project. For all three cohorts, samples with over 100,000 single base substitution (SBS) mutations or over 7,000 indel mutations were removed. In addition, any samples with less than 1,000 SBS mutations were removed. To account for mutations in MUTYH, NTLH1, and OGG1, samples with cosine similarity greater than 0.80 to any of the respective signatures (SBS36 from COSMIC³⁶, SBS30 from COSMIC³⁶, and SBS108 from SIGNAL³⁷) were removed. Like in the Mutographs CRC project, two samples from the Swedish cohort were removed for suspected microsatellite instable activity (high levels of SBS15) despite not being hypermutators.

Mutational Signature Extraction in Three Independent Cohorts

After filtering, SBS matrices were generated for the TCGA and Swedish cohort using SigProfilerMatrixGenerator¹⁷. For the GEL cohort, the publicly available SBS-96 mutational matrix was filtered and used for downstream analysis as the variant calling files are not publicly available. SigProfilerExtractor⁸ with default parameters was then used to extract mutational signatures from each cohort, and the suggested solution was used for each extraction.

Characterizing Pathogenicity of Mutations in POLD1 and DNA Mismatch Repair Genes

Ensembl Variant Effect Predictor (VEP)³⁸ was previously used to annotate mutations in the Mutographs CRC cohort. All mutations with the MODIFIER annotation (lowest impact) were filtered out. All protein-altering single base substitution and indel mutations from the 802 DNA repair proficient samples in POLD1 and DNA mismatch repair genes (MLH1, MLH3, MSH2, MSH3, MSH6, PMS1, and PMS2) were examined using CLINVAR³⁹. Results were reported in Supplementary Table 1 (single base substitutions) and Supplementary Table 2 (indels).

Linear Models for Comparing Genomic Features

A generalized linear model (Python version 3.9.7; statsmodels version 0.13.2) was used to identify the association between SBS_D status and total number of single base substitutions, total number of indel mutations, and fraction genome aberrant. Fraction genome aberrant values came from the original Mutographs CRC study⁹. A single model was used for each feature, and sex, age, tumor subsite, country, and purity were all included as covariates. This same approach was used to identify associations between SBS_D status and total number of indel mutations in the Swedish and TCGA cohorts^13,14. No linear modeling was performed for the Genomics England (GEL) cohort¹⁵ due to the lack of publicly available metadata.

Supplementary Material

Supplement 1

Supplementary Figure 1: SBS18^ and SBS_D are recapitulated across signature extractions from multiple contexts in the Mutographs colorectal cancer cohort. (a) SBS_D-like (top) and SBS18^-like (bottom) signatures extracted from the Mutographs colorectal cancer cohort using the SBS-96 context. (b) Same as (a), but signatures were extracted using the SBS-1536 context. (c) Cosine similarity between the SBS18^-like signatures and SBS_D-like signatures extracted from each of the SBS-96, SBS-288, and SBS-1536 contexts. All signatures were collapsed to the SBS-96 context for comparison.

Supplementary Figure 2: SBS_D and SBS18^ exhibit similar patterns for some topographical features. (a) Replication timing patterns for SBS_D (left) and SBS18^ (right). Blue bars represent deciles (10% bins) of the replication timing signal; the dashed line indicates the distribution of simulated somatic mutations. (b) Strand asymmetry profiles for SBS_D (top) and SBS18^ (bottom). Circles indicate statistically significant biases (Fisher’s exact test with Benjamini-Hochberg multiple testing correction), with circle color representing the genomic context. Both signatures show enrichment in intergenic regions (gray circles), while no significant differences were observed between leading versus lagging or between transcribed versus un-transcribed strands. Numbers indicate odds ratios for each comparison.

Supplementary Figure 3: SBS_D is not extracted when DNA repair proficient and deficient samples are combined. De novo mutational signatures extracted from all the Mutographs colorectal cancer samples (n=981), without stratifying by DNA repair status. Extraction was performed using the SBS-288 context; for simplicity, signatures are shown here in the SBS-96 context.

Supplementary Figure 4 SBS_D activity is not associated with mutations in POLD1 or mismatch repair genes. (a) SBS_D activity in samples with single nucleotide variants (SNVs, top) and indel mutations (bottom) in POLD1. Statistical significance was assessed using the Mann-Whitney U test for mutation categories with ≥5 samples. (b) As in (a), but for mutations in DNA mismatch repair genes (MLH1, MLH3, MSH2, MSH3, MSH6, PMS1 and PMS2). (c) Comparison of SBS_D activity in samples with and without loss of heterozygosity (LOH) in POLD1 (left) and mismatch repair genes (right). Significance was assessed using a Mann-Whitney U test. (d) SBS_D activity stratified by the number of mismatch repair genes with LOH per sample.

Supplementary Figure 5: Mutational signature activities in a sample with a likely pathogenic MSH6 mutation. (a) Mutational profile for sample PD54054a, which carries a likely pathogenic mutation in the DNA mismatch repair gene MSH6. (b) De novo mutational signatures detected in PD54054a. Numbers on the left of each signature plot represent the number of single base mutations (subs) attributed to that signature. Percentages on the right indicate the COSMIC decomposition of the corresponding de novo signature.

Supplementary Figure 6: SBS_D positive samples have elevated numbers of indels in independent colorectal cancer cohorts. Comparison of the number of indels between SBS_D-positive (SBS_D⁺) and SBS_D-negative (SBS_D⁻) samples in The Cancer Genome Atlas (TCGA; left), Swedish (middle), and Genomics England (GEL; right) cohorts. For the Swedish and TCGA cohorts, p-values were calculated using linear models adjusted for age, sex, tumor subsite, and purity. For the GEL cohort, the p-value was calculated using a Mann-Whitney U test due to lack of available metadata.

media-1.pdf^{(1.9MB, pdf)}

Supplement 2

media-2.xls^{(191.5KB, xls)}

ACKNOWLEDGMENTS

M.K. was supported by the Cancer Biology, Informatics, and Omics Training Grant (T32CA067754) and the Merkin Fellowship through the University of California San Diego. This work was delivered as part of the Mutographs team supported by the Cancer Grand Challenges partnership funded by Cancer Research UK (C98/A24032). M.R.S. and S.M. are also supported by the Wellcome Trust core grant (206194). M.D.-G. was awarded with a fellowship within the “Generación D” initiative, Red.es, Ministerio para la Transformación Digital y de la Función Pública, for talent attraction (C005/24-ED CV1), funded by the European Union NextGenerationEU funds, through PRTR. D.C.W. is co-funded by Cancer Research UK RadNet Manchester (grant no. C1994/A28701), NIHR Manchester BRC (grant no. NIHR203308), The University of Manchester and the Christie NHS Foundation Trust. This work was supported by the US National Institute of Health grants R01ES032547, R01ES036931, R01CA269919, R01CA296974, P01CA281819, and U01CA290479 to L.B.A. as well as by L,B.A.’s Packard Fellowship for Science and Engineering and the UC San Diego Sanford Stem Cell Institute. The computational analyses reported in this manuscript have utilized the Triton Shared Computing Cluster at the San Diego Supercomputer Center of UC San Diego. The funders had no roles in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Footnotes

COMPETING INTERESTS

M.R.S. is a founder, stockholder and consultant for Quotient Therapeutics. L.B.A. is a co-founder, CSO, scientific advisory member, and consultant for io9 (now Acurion), has equity and receives income. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. L.B.A. is also a compensated member of the scientific advisory board of Inocras. L.B.A.’s spouse is an employee of Hologic, Inc. L.B.A. declares U.S. provisional applications filed with UCSD with serial numbers: 63/269,033; 63/289,601; 63/483,237; 63/412,835; and 63/492,348. L.B.A. and A.A. declare US provisional patent application filed with UCSD with serial number 63/366,392. L.B.A. is also an inventor of a US Patent 10,776,718 for source identification by non-negative matrix factorization. L.B.A., M.D.-G., M.R.S., P.B., S.P., and S.M. further declare a European patent application with application number EP25305077.7. All other authors declare that they have no competing interests.

REFERENCES

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1

media-1.pdf^{(1.9MB, pdf)}

Supplement 2

media-2.xls^{(191.5KB, xls)}

[R1] 1.Kim Y.-A. et al. Mutational signatures: From methods to mechanisms. Annu. Rev. Biomed. Data Sci. 4, 189–206 (2021). 10.1146/annurev-biodatasci-122320-120920. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R2] 2.Koh G., Degasperi A., Zou X., Momen S. & Nik-Zainal S. Mutational signatures: emerging concepts, caveats and clinical applications. Nat. Rev. Cancer 21, 619–637 (2021). 10.1038/s41568-021-00377-7. [DOI] [PubMed] [Google Scholar]

[R3] 3.Boysen G. et al. Investigating the origins of the mutational signatures in cancer. Nucleic Acids Res. 53, gkae1303 (2025). 10.1093/nar/gkae1303. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Fiala C. & Diamandis E. P. Mutations in normal tissues-some diagnostic and clinical implications. BMC Med. 18, 283 (2020). 10.1186/s12916-020-01763-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R5] 5.Alexandrov L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020). 10.1038/s41586-020-1943-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R6] 6.Sondka Z. et al. COSMIC: a curated database of somatic variants and clinical data for cancer. Nucleic Acids Res. 52, D1210–D1217 (2024). 10.1093/nar/gkad986. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R7] 7.Díaz-Gay M. et al. Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment. Bioinformatics 39, (2023). 10.1093/bioinformatics/btad756. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R8] 8.Islam S. M. A. et al. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Cell Genom 2, None (2022). 10.1016/j.xgen.2022.100179. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Díaz-Gay M. et al. Geographic and age variations in mutational processes in colorectal cancer. Nature 1–2 (2025) doi: 10.1038/s41586-025-09025-8. https://doi.org/10.1038/s41586-025-09025-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.Senkin S. et al. Geographic variation of mutagenic exposures in kidney cancer genomes. Nature 629, 910–918 (2024). 10.1038/s41586-024-07368-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.Torrens L. et al. The complexity of tobacco smoke-induced mutagenesis in head and neck cancer. Nature Genetics 57, 884–896 (2024). 10.1038/s41588-025-02134-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Alexandrov L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013). 10.1038/nature12477. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012). 10.1038/nature11252. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Nunes L. et al. Prognostic genome and transcriptome signatures in colorectal cancers. Nature 633, 137–146 (2024). 10.1038/s41586-024-07769-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Cornish A. et al. The genomic landscape of 2,023 colorectal cancers. Nature 633, 127–136 (2024). 10.1038/s41586-024-07747-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Moody S. et al. Mutational signatures in esophageal squamous cell carcinoma from eight countries with varying incidence. Nat. Genet. 53, 1553–1563 (2021). 10.1038/s41588-021-00928-6. [DOI] [PubMed] [Google Scholar]

[R17] 17.Bergstrom E. N. et al. SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genomics 20, 685 (2019). 10.1186/s12864-019-6041-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Ren P., Zhang J. & Vijg J. Somatic mutations in aging and disease. GeroScience 46, 5171–5189 (2024). 10.1007/s11357-024-01113-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Gerstung M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020). 10.1038/s41586-019-1907-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Dentro S. C. et al. Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell 184, 2239–2254.e39 (2021). 10.1016/j.cell.2021.03.009. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Otlu B. et al. Topography of mutational signatures in human cancer. Cell Rep. 42, 112930 (2023). 10.1016/j.celrep.2023.112930. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Degasperi A. et al. Substitution mutational signatures in whole-genome-sequenced cancers in the UK population. Science 376, (2022). 10.1126/science.abl9283. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R23] 23.Robinson P. S. et al. Increased somatic mutation burdens in normal human cells due to defective DNA polymerases. Nat. Genet. 53, 1434–1442 (2021). 10.1038/s41588-021-00930-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Niv Y. Microsatellite instability and MLH1 promoter hypermethylation in colorectal cancer. World J. Gastroenterol. 13, 1767–1769 (2007). 10.3748/wjg.v13.i12.1767. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Yang R. K. et al. Microsatellite instability and high tumor mutational burden detected by next generation sequencing are concordant with loss of mismatch repair proteins by immunohistochemistry. Cancer Genet. 290–291, 44–50 (2025). 10.1016/j.cancergen.2024.12.002. [DOI] [PubMed] [Google Scholar]

[R26] 26.Tomková M. et al. Human DNA polymerase ε is a source of C>T mutations at CpG dinucleotides. Nat. Genet. 56, 2506–2516 (2024). 10.1038/s41588-024-01945-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Wong J. K. L. et al. Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions. Nat. Commun. 13, 178 (2022). 10.1038/s41467-021-27792-6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.Senkin S. MSA: reproducible mutational signature attribution with confidence based on simulations. BMC Bioinformatics 22, 540 (2021). 10.1186/s12859-021-04450-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Nik-Zainal S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012). 10.1016/j.cell.2012.04.023. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Jones D. et al. CgpCaVEManWrapper: Simple execution of CaVEMan in order to detect somatic single nucleotide variants in NGS data. Curr. Protoc. Bioinformatics 56, 15.10.1-15.10.18 (2016). 10.1002/cpbi.20. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R31] 31.Kim S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018). 10.1038/s41592-018-0051-x. [DOI] [PubMed] [Google Scholar]

[R32] 32.Otlu B. & Alexandrov L. B. Evaluating topography of mutational signatures with SigProfilerTopography. Genome Biol. 26, 134 (2025). 10.1186/s13059-025-03612-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Cibulskis K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013). 10.1038/nbt.2514. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Benjamin D. et al. Calling Somatic SNVs and Indels with Mutect2. bioRxiv (2019) doi: 10.1101/861054. https://doi.org/10.1101/861054. [DOI] [Google Scholar]

[R35] 35.Freed D., Pan R. & Aldana R. TNscope: Accurate detection of somatic mutations with haplotype-based variant candidate detection and machine learning filtering. bioRxiv (2018) doi: 10.1101/250647. https://doi.org/10.1101/250647. [DOI] [Google Scholar]

[R36] 36.Tate J. G. et al. COSMIC: The Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res. 47, D941–D947 (2019). 10.1093/nar/gky1015. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Degasperi A. et al. A practical framework and online tool for mutational signature analyses show inter-tissue variation and driver dependencies. Nat. Cancer 1, 249–263 (2020). 10.1038/s43018-020-0027-5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R38] 38.McLaren W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016). 10.1186/s13059-016-0974-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R39] 39.Landrum M. J. et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46, D1062–D1067 (2018). 10.1093/nar/gkx1153. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

This is a preprint.

Identification and Validation of a Previously Missed Mutational Signature in Colorectal Cancer

Mariya Kazachkova

Burçak Otlu

Marcos Díaz-Gay

Ammal Abbasi

Sarah Moody

Sandra Perdomo

David C Wedge

Paul Brennan

Michael R Stratton

Ludmil B Alexandrov

Abstract

INTRODUCTION

RESULTS

Mutational Signatures and Their Predefined Schemas

Original Mutographs De Novo Signature Analysis

Figure 1: SBS_D and SBS_B from the Mutographs colorectal cancer cohort.

Reevaluating the Pattern of SBS_D

SBS_D Represents a Mutational Process Distinct from SBS18

Figure 2: SBS_D and SBS18^ reflect distinct mutational processes.

Signatures SBS_D and SBS18^ are Validated in Independent Cohorts

Figure 3: Independent colorectal cancer cohorts support reproducibility of SBS_D and SBS18^.

Naïve Decomposition of SBS_D

Figure 4: Naïve decomposition of SBS_D and its distinction from POLD1-mutant and MSI tumors.

Exploring the Potential Etiology of SBS_D

Figure 5: SBS_D exhibits mutational features suggestive of DNA repair infidelity.

DISCUSSION

METHODS

Mutational Contexts

Mutational Signature Extraction and Decomposition

Identification of Mutational Signature Activity in Each Sample

Molecular Timing of SBS18^ and SBS_D

Topography Analysis

Somatic Variant Calling in Three Independent Cohorts

Sample Filtering in Three Independent Cohorts

Mutational Signature Extraction in Three Independent Cohorts

Characterizing Pathogenicity of Mutations in POLD1 and DNA Mismatch Repair Genes

Linear Models for Comparing Genomic Features

Supplementary Material

ACKNOWLEDGMENTS

Footnotes

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

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RESOURCES

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