AbstractMutational signatures connect characteristic mutational patterns in the genome with biological or chemical processes that take place in cancers. Analysis of mutational signatures can help elucidate tumor evolution, prognosis, and therapeutic strategies. Although tools for extracting mutational signatures de novo have been extensively benchmarked, a similar effort is lacking for tools that fit known mutational signatures to a given catalog of mutations.

摘要突变特征将基因组中的特征突变模式与癌症中发生的生物或化学过程联系起来。突变特征的分析可以帮助阐明肿瘤的进化，预后和治疗策略。尽管从头提取突变特征的工具已经过广泛的基准测试，但对于将已知突变特征与给定突变目录相匹配的工具，缺乏类似的努力。

We fill this gap by comprehensively evaluating twelve signature fitting tools on synthetic mutational catalogs with empirically driven signature weights corresponding to eight cancer types. On average, SigProfilerSingleSample and SigProfilerAssignment/MuSiCal perform best for small and large numbers of mutations per sample, respectively.

我们通过综合评估合成突变目录上的十二种特征拟合工具来填补这一空白，这些工具具有与八种癌症类型相对应的经验驱动的特征权重。平均而言，SigProfilerSingleSample和SigProfilerAssignment/Music分别对每个样本的少量和大量突变表现最佳。

We further show that ad hoc constraining the list of reference signatures is likely to produce inferior results. Evaluation of real mutational catalogs suggests that the activity of signatures that are absent in the reference catalog poses considerable problems to all evaluated tools..

我们进一步表明，临时限制参考签名列表可能会产生较差的结果。对真实突变目录的评估表明，参考目录中不存在的签名活动给所有评估工具带来了相当大的问题。。

IntroductionSince their introduction a decade ago1,2, mutational signatures have become a widely used tool in genomics3,4. They allow researchers to move from individual mutations in the genome to biological or chemical processes that take place in living tissues5,6. The activity of various mutational signatures can also serve as prognostic or therapeutic biomarkers7,8,9.

引言自十年前引入以来，突变特征已成为基因组学中广泛使用的工具3,4。它们使研究人员能够从基因组中的单个突变转移到活组织中发生的生物或化学过程5,6。。

For example, homologous recombination deficiency leads to the accumulation of DNA damage and manifests itself in a specific mutational signature (signature SBS3 from the COSMIC catalog)10,11. Signature activities have been used to attribute mutations to endogenous, exogenous, and preventable mutational processes12 and clock-like mutational signatures can help determine the absolute timing of mutations13.Mutational signatures can be introduced for single base substitutions (SBS), doublet base substitutions, small insertions and deletions14, copy number alterations15, structural variations16, and RNA singlebase substitutions17.

。签名活动已被用于将突变归因于内源性，外源性和可预防的突变过程12，而时钟样突变签名可以帮助确定突变的绝对时间13。突变签名可以用于单碱基取代（SBS），双碱基取代，小插入和缺失14，拷贝数改变15，结构变异16和RNA单碱基取代17。

We focus here on SBS-based mutational signatures which are most commonly used in the literature. Current SBS signatures are defined using 6 possible classes of substitutions (C > A, C > G, C > T, T > A, T > C, and T > G) together with their two immediate neighboring bases, thus giving rise to 6 × 4 × 4 = 96 different nucleotide contexts into which all SBS mutations in a given sample are classified.

我们在这里重点介绍文献中最常用的基于SBS的突变签名。当前的SBS签名是使用6种可能的替代类别（C > A，C > G，C > T，T > A，T > C和T > G）及其两个直接相邻的碱基定义的，因此产生了6×××4××4 = 96个不同的核苷酸上下文，其中所有SBS突变对给定样本进行分类。

De novo extraction of signatures from somatic mutations in sequenced samples has been used to gradually map the landscape of mutational signatures in cancers. Over time, the initial catalogue of 22 SBS-based mutational signatures in the first version of the Catalogue Of Somatic Mutations In Cancer (COSMIC) released in August 2013 has expanded to 86 signatures in the COSMICv3.4 version r.

从头提取测序样品中体细胞突变的特征已被用于逐渐绘制癌症突变特征的图谱。随着时间的推移，2013年8月发布的《癌症体细胞突变目录》（COSMIC）第一版中22个基于SBS的突变特征的初始目录已扩展到COSMICv3.4版本r中的86个特征。

(1)

(1)

We then generated a synthetic mutational catalog with these weights by distributing m mutations among the 96 contexts, while the probability that a mutation is assigned to context c in sample i is Wci. This is mathematically equivalent to first choosing signature s for sample i with probability wsi and then choosing context c with probability ωcs.

然后，我们通过在96个上下文中分布m个突变来生成具有这些权重的合成突变目录，而在样本i中将突变分配给上下文c的概率是Wci。。

As a result, the number of mutations in contexts are multinomially distributed with mean mWci for context c and sampe i. The mean number of mutations contributed to sample i by signature s is mwsi. When this number is smaller than 10, the signature cannot be correctly identified by the evaluated tools as we use the standard procedure of setting the weights of signatures that contribute less than ten mutations to zero.

结果，上下文中突变的数量与上下文c和样本i的平均mWci呈多项式分布。签名s对样本i贡献的突变的平均数量为mwsi。当这个数字小于10时，由于我们使用标准程序将贡献少于10个突变的签名的权重设置为零，因此评估工具无法正确识别签名。

To not bias the evaluation, we: (1) For sample i and m mutations per sample, remove all signatures that have mwsi < 10 and (2) normalize the weights of the remaining signatures to one.The approach described above allows us to reproduce empirical signature weights in previously analyzed samples without resorting to assumptions such as a log-normal distribution of the number of mutations due to a given signature14,18 or adding additional zeros to the Poisson distribution to reproduce signatures that are not active in a sample20.

为了不影响评估，我们：（1）对于每个样本的样本i和m突变，删除所有具有mwsi<10的签名，以及（2）将其余签名的权重标准化为1。上述方法使我们能够在先前分析的样本中重现经验签名权重，而无需诉诸假设，例如由于给定签名而导致的突变数量的对数正态分布14,18或向泊松分布添加额外的零来重现样本中不活跃的签名20。

The code for generating synthetic mutational catalogs and evaluating the signature fitting tools, SigFitTest, is available at https://github.com/8medom/SigFitTest.For Fig. 3b, we created synthetic mutational catalogs with signature activities modeled after real CNS-GBM samples where systematic differences in the activity of signature SBS40 have been introduced.

生成合成突变目录和评估签名拟合工具SigFitTest的代码可在https://github.com/8medom/SigFitTest.For图3b，我们创建了合成突变目录，其签名活动以真实的CNS-GBM样本为模型，其中引入了签名SBS40活性的系统差异。

After generating sample weights as described in the previous paragraph, the relative weights of SBS40 were multipli.

如前一段所述生成样本权重后，SBS40的相对权重是倍数。

(2)

(2)

with respect to \({\mathsf{w}}\), where wsi is the relative weight of signature s in sample i. To allow for a unique solution, vectors representing weights of different signatures must be linearly independent. The number of reference signatures thus cannot be higher than the number of mutational contexts (96 contexts for the common SBS signatures).

关于\（{\ mathsf{w}}\），其中wsi是样本i中签名s的相对权重。为了获得唯一的解，表示不同签名权重的向量必须是线性独立的。因此，参考签名的数量不能高于突变上下文的数量（普通SBS签名的96个上下文）。

Equation (2) is thus an over-determined system of linear equations. Several fitting tools are therefore based on minimizing the difference between m and \({\mathsf{R}}{{{\boldsymbol{w}}}}\) through non-negative least squares (as the signature weights cannot be negative) or quadratic programming. We evaluated several tools that belong to this class: MutationalPatterns v3.14.026, YAPSA v1.30.029, SigsPack v1.18.030, and sigminer v2.3.131 which has three separate methods based on quadratic programming, non-linear least squares, and simulated annealing.

因此，方程（2）是一个过度确定的线性方程组。因此，有几种拟合工具是基于通过非负最小二乘法（因为签名权重不能为负）或二次规划来最小化m和\（{\ mathsf{R}}{{{\ boldsymbol{w}}}}\）之间的差异。我们评估了属于此类的几种工具：MutationalPatterns v3.14.026，YAPSA v1.30.029，SigsPack v1.18.030和sigminer v2.3.131，它们有三种基于二次规划，非线性最小二乘和模拟退火的独立方法。

We find that all these tools produce similar results.Other tools use various iterative processes by which the provided set of reference signatures is gradually reduced by removing the signatures, for example, whose inclusion does not considerably improve the match between the observed and reconstructed mutational catalogs (or, opposite, signatures are gradually added as long as the reconstruction accuracy sufficiently improves).

我们发现所有这些工具都产生了类似的结果。其他工具使用各种迭代过程，通过这些迭代过程，通过删除签名来逐渐减少所提供的参考签名集，例如，签名的包含并不能显着改善观察到的和重建的突变目录之间的匹配（或者相反，签名是逐渐添加的，只要重建精度得到充分提高）。

We evaluated deconstructSigs v1.9.032, SigProfilerSingleSample v0.0.0.2714, SigProfilerAssignment v0.1.733, mmsig v0.0.0.900024, and signature.tools.lib v2.2.019, that all belong to this category. The newest tool, SigProfilerAssignment, combines backward and forward iterative adjustment of the reference catalog and these steps are repeated until convergence.Finally, sigLASSO v1.1 combines the data likelihood in a generative model with L1 re.

我们评估了解构Sigs v1.9.032，SigProfilerSingleSample v0.0.0.2714，SigProfilerAssignment v0.1.733，mmsig v0.0.0.900024和signature.tools.lib v2.2.019，它们都属于此类。最新的工具SigProfilerAssignment结合了参考目录的向后和向前迭代调整，并重复这些步骤直到收敛。最后，sigLASSO v1.1将生成模型中的数据可能性与L1 re相结合。

(3)

(3)

and then averaged over all samples. The division by two is introduced for normalization purposes. The lowest fitting error, 0, is achieved when the estimated signature weights are exact for all signatures and all samples. The highest fitting error, 1, is achieved when the estimated signature weights sum to one for each sample but they are all false positives.

然后对所有样本进行平均。为了规范化的目的，引入了除以2的方法。当估计的签名权重对于所有签名和所有样本都是精确的时，可以实现最低的拟合误差0。当每个样本的估计签名权重总和为1时，拟合误差最高，为1，但它们都是误报。

For example, when a sample has 40% contribution of SBS1 and 60% of SBS4 but a tool estimates 20% contribution of SBS2 and 80% contribution of SBS13, the fitting error is 1. We further quantify the agreement between the true and estimated signature weights by computing their Pearson correlation. For each sample where at least three signatures have positive either true or estimated weights, we compute the Pearson correlation coefficient between these two vectors whilst excluding all signatures that are zero for both of them.

例如，当样本的SBS1贡献率为40%，SBS4贡献率为60%，但工具估计SBS2贡献率为20%，SBS13贡献率为80%时，拟合误差为1。。对于至少三个签名具有正的真实或估计权重的每个样本，我们计算这两个向量之间的Pearson相关系数，同时排除两者均为零的所有签名。

The obtained values are averaged over all samples in the cohort.Further common evaluation metrics are based on classifying the estimated signatures as true positives (when both \({\tilde{w}}_{si}\) and wsi are positive), false positives (when \({\tilde{w}}_{si} \, > \, 0\) and wsi = 0), true negatives (when \({\tilde{w}}_{si}=0\) and wsi = 0), and false negatives (when \({\tilde{w}}_{si}=0\) and wsi > 0)18,20,33.

获得的值在队列中的所有样本上取平均值。其他常见的评估指标是基于将估计的签名分类为真阳性（当\（{tilde{w}}}和wsi均为阳性时），假阳性（当\（{tilde{w}}}和wsi}0时），真阴性（当\（{tilde{w}}}和wsi}=0时）和假负数（当\（{\ tilde{w}}uu{si}=0 \）和wsi>0时）18,20,33。

False positive weight quantifies how much weight is assigned to the signatures that are not active in the corresponding samples. The value for sample i,$${\sum}_{s:\,{w}_{si}=0}{\tilde{w}}_{si},$$.

误报权重量化了为相应样本中不活跃的签名分配的权重。样本i的值，$${\ sum}{s:\，{w}_{si}=0}{\ tilde{w}}{si}，$$。

(4)

(4)

is averaged over all samples. The lower the value, the better. Precision quantifies the reliability of the identified active signatures. It is computed by averaging the number of true positives to all positives over all samples in the cohort. Sensitivity quantifies the ability to identify all active signatures.

是所有样本的平均值。值越低越好。精度量化已识别活动签名的可靠性。。灵敏度量化了识别所有活动签名的能力。

It is computed by averaging the number of true positives to the number of active signatures (i.e., the sum of true positives and the false negatives) over all samples. Higher sensitivity can be commonly achieved at the cost of lower precision37. This is addressed by the F1 score which is the harmonic mean of precision and recall (we compute the F1 score for each sample and then average it over all samples).

它是通过将所有样本中的真阳性数平均为活动签名数（即真阳性和假阴性的总和）来计算的。通常可以以较低的精度为代价来实现更高的灵敏度37。这可以通过F1分数来解决，F1分数是精确度和召回率的调和平均值（我们计算每个样本的F1分数，然后对所有样本进行平均）。

To achieve a good F1 score, high precision and sensitivity are necessary.Selecting reference signaturesIt has been argued that the removal of irrelevant reference signatures can improve the fitting results5. To assess this hypothesis, we test a two-step process where: (1) We fit the samples using all COSMICv3 signatures as a reference and (2) keep only the signatures whose relative weight exceeds threshold w0 for at least 5 samples in our cohorts with 100 samples.

为了获得良好的F1成绩，需要高精度和灵敏度。选择参考签名有人认为，删除不相关的参考签名可以改善拟合结果5。。

We use thresholds w0 = 0.1 (m = 100), w0 = 0.03 (m = 2000) and w0 = 0.01 (m = 50,000) which correspond to absolute signature contributions 10, 60, and 500, respectively. This pruning of reference signatures is often beneficial (Supplementary Fig. 21). However, for a high number of mutations and well-performing methods (mmsig, MuSiCal, sigLASSO, SigProfilerAssignment, SigProfilerSingleSample), this two-step process increases the produced fitting errors (m = 50,000 in Supplementary Fig. 21).

我们使用阈值w0=0.1（m=100），w0=0.03（m=2000）和w0=0.01（m=50000），分别对应于绝对签名贡献10,60和500。参考签名的这种修剪通常是有益的（补充图21）。然而，对于大量突变和表现良好的方法（mmsig，MuSiCal，sigLASSO，SigProfilerAssignment，SigProfilerSingleSample），这个两步过程增加了产生的拟合误差（补充图21中的m=50000）。

When lower thresholds are used to keep signatures, this can be avoided but there is nevertheless.

当使用较低的阈值来保留签名时，这是可以避免的，但仍然存在。

Data availability

数据可用性

The absolute contributions of signatures to sequenced tissues from various cancers were obtained from the Catalogue Of Somatic Mutations In Cancer (COSMIC), https://cancer.sanger.ac.uk/signatures/sbs/. They are also included in SigFitTest (https://github.com/8medom/SigFitTest). The reference catalogs of single base substitution (SBS) signatures are available from COSMIC, https://cancer.sanger.ac.uk/signatures/downloads/.

签名对各种癌症的测序组织的绝对贡献来自《癌症体细胞突变目录》（COSMIC），https://cancer.sanger.ac.uk/signatures/sbs/.它们也包含在SigFitTest中(https://github.com/8medom/SigFitTest)。单碱基取代（SBS）签名的参考目录可从COSMIC获得，https://cancer.sanger.ac.uk/signatures/downloads/.

The synthetic datasets that were used to evaluate the fitting tools can be generated by function generate_synthetic_catalogs() of SigFitTest. The mutational catalogs of 146 WGS PCAWG samples with at least 50,000 mutations, obtained from the ICGC, as well as the consensus ground truth based on sigLASSO, SigProfilerAssignment, and MuSiCal for four samples where the three tools agree best, are included in SigFitTest..

用于评估拟合工具的合成数据集可以通过SigFitTest的函数generate\u synthetic\u catalogs（）生成。SigFitTest中包含了从ICGC获得的146个WGS PCAWG样本的突变目录，其中至少有50000个突变，以及基于sigLASSO，SigProfilerAssignment和Music的四个样本的共识基本事实，其中三个工具最为一致。。

Code availability

代码可用性

The code of SigFitTest is available at https://github.com/8medom/SigFitTest38. Links to the code of the evaluated fitting tools are provided in Table 1.

SigFitTest的代码可在https://github.com/8medom/SigFitTest38.。

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Y. Ng & Michaela MedováIRCCS Humanitas Research Hospital, Rozzano, Milan, ItalyCharlotte K. Y. NgAuthorsMatúš MedoView author publicationsYou can also search for this author in.

Y、 Ng＆Michaela MedováIRCCS Humanitas Research Hospital，Rozzano，Milan，ItalyCharlotte K.Y.NgAuthorsMatúšMedoView作者出版物您也可以在中搜索这位作者。

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PubMed Google ScholarContributionsMa.M., C.N. and Mi.M. designed the study. Ma.M. developed the simulation code and analyzed the results. Ma.M., C.N. and Mi.M. discussed the results and contributed to the final manuscript.Corresponding authorCorrespondence to

PubMed谷歌学术贡献SMA。M、 ，C.N.和Mi.M.设计了这项研究。Ma.M.开发了仿真代码并分析了结果。Ma.M.，C.N.和Mi.M.讨论了结果并为最终手稿做出了贡献。对应作者对应

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Reprints and permissionsAbout this articleCite this articleMedo, M., Ng, C.K.Y. & Medová, M. A comprehensive comparison of tools for fitting mutational signatures.

转载和许可本文引用本文Medo，M.，Ng，C.K.Y。＆Medová，M。拟合突变签名工具的综合比较。

Nat Commun 15, 9467 (2024). https://doi.org/10.1038/s41467-024-53711-6Download citationReceived: 06 October 2023Accepted: 18 October 2024Published: 02 November 2024DOI: https://doi.org/10.1038/s41467-024-53711-6Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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