复制时间改变与癌症进化过程中的突变获取有关-动脉网

Replication timing alterations are associated with mutation acquisition during breast and lung cancer evolution

Nature 等信源发布 2024-07-18 10:17

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可切换为仅中文

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AbstractDuring each cell cycle, the process of DNA replication timing is tightly regulated to ensure the accurate duplication of the genome. The extent and significance of alterations in this process during malignant transformation have not been extensively explored. Here, we assess the impact of altered replication timing (ART) on cancer evolution by analysing replication-timing sequencing of cancer and normal cell lines and 952 whole-genome sequenced lung and breast tumours.

摘要在每个细胞周期中，DNA复制时间的过程受到严格调控，以确保基因组的准确复制。恶性转化过程中这一过程改变的程度和意义尚未得到广泛探索。在这里，我们通过分析癌症和正常细胞系以及952个全基因组测序的肺和乳腺肿瘤的复制时间测序，评估复制时间改变（ART）对癌症进化的影响。

We find that 6%–18% of the cancer genome exhibits ART, with regions with a change from early to late replication displaying an increased mutation rate and distinct mutational signatures. Whereas regions changing from late to early replication contain genes with increased expression and present a preponderance of APOBEC3-mediated mutation clusters and associated driver mutations.

我们发现6%–18%的癌症基因组表现出ART，从早期复制到晚期复制的区域显示出增加的突变率和明显的突变特征。而从晚期复制到早期复制的区域包含表达增加的基因，并且呈现出APOBEC3介导的突变簇和相关驱动突变的优势。

We demonstrate that ART occurs relatively early during cancer evolution and that ART may have a stronger correlation with mutation acquisition than alterations in chromatin structure..

我们证明ART在癌症进化过程中相对较早发生，并且ART与突变获得的相关性可能比染色质结构的改变更强。。

IntroductionCancer development is an evolutionary process where mutations serve as a substrate upon which selection can act. Thus, understanding the mechanisms underlying mutational accumulation is crucial to illuminate the processes that shape tumour evolution1. DNA replication during each cell cycle is an essential biological process that involves the duplication of the entire genome faithfully2.

引言癌症的发展是一个进化过程，突变作为选择的底物。因此，了解突变积累的潜在机制对于阐明塑造肿瘤进化的过程至关重要1。每个细胞周期中的DNA复制是一个重要的生物学过程，涉及整个基因组的复制2。

To ensure efficient and accurate replication, and to limit the potential for acquisition of somatic alterations, each chromosome is divided into segments that are replicated in a defined and highly organised temporal order, termed the replication timing (RT) programme2,3. In non-malignant cells, the RT programme is highly conserved across 50%–70% of the genome, while the remaining 30%–50% can dynamically vary during normal development, contributing to tissue-specificity3.

为了确保有效和准确的复制，并限制获得体细胞改变的可能性，将每个染色体分成以定义和高度组织的时间顺序复制的片段，称为复制时间（RT）程序2,3。在非恶性细胞中，RT程序在50%–70%的基因组中高度保守，而其余30%–50%可以在正常发育过程中动态变化，从而有助于组织特异性3。

Changes to the RT programme during normal lineage differentiation are associated with differences in the transcription level of genes4.The RT programme has been linked to the non-uniform acquisition of somatic mutations across the genome during cancer development5. Multiple studies have shown that late replicated regions, which often coincide with condensed chromatin regions, accumulate more mutations including both single nucleotide variants (SNVs)6,7,8 and somatic copy number alterations (SCNAs)9,10,11, compared to regions replicated early during S phase which often are actively transcribed and exhibit open chromatin6,12.

。多项研究表明，与S期早期复制的区域相比，晚期复制的区域通常与浓缩的染色质区域一致，积累了更多的突变，包括单核苷酸变异（SNVs）6,7,8和体细胞拷贝数改变（SCNAs）9,10,11。S期早期复制的区域通常被积极转录并表现出开放的染色质6,12。

Linked to this, prior work has revealed that the patterns of mutational signatures observed across the genome are strongly associated with the RT programme13.However, despite the association between the RT programme and the overall genetic alterations in cancer, few studies have focused o.

与此相关的是，先前的研究表明，在整个基因组中观察到的突变特征模式与RT程序密切相关13。然而，尽管RT程序与癌症的整体遗传改变之间存在关联，但很少有研究关注o。

Correction for reference bias

参考偏差校正

The Illumina Isaac pipeline51 has been used in the 100,000 Genomes Project to align and process the whole-genome sequencing data to the hg38 assembly. However, recent studies have demonstrated that the soft clipping of semi-aligned reads performed by the Isaac aligner leads to a reference bias which affects the calling of somatic copy number alterations (SCNAs) as well as purity and cancer cell fraction (CCF) estimations in cancer52.

Illumina Isaac管道51已用于100000个基因组计划，以将全基因组测序数据与hg38装配体进行比对和处理。然而，最近的研究表明，Isaac aligner进行的半比对读数的软剪切会导致参考偏差，从而影响体细胞拷贝数改变（SCNA）的调用以及癌症中纯度和癌细胞分数（CCF）的估计52。

To address this caveat, a tool called fixVAF52 was developed by Cornish et al. (https://github.com/danchubb/FixVAF) to remove sources of reference bias ensuring a robust CCF estimation. We applied fixVAF to the BAM files and VCF files of the GEL lung cohort which were produced by the Genomics England core pipeline..

为了解决这个警告，Cornish等人开发了一种名为fixVAF52的工具(https://github.com/danchubb/FixVAF)消除参考偏差的来源，确保可靠的共因失效估计。我们将fixVAF应用于Genomics England core pipeline产生的GEL-lung队列的BAM文件和VCF文件。。

Variant calling

变体调用

As part of the Genomics England core pipeline, Strelka53 has been applied for somatic variant calling. The resulting VCF files were corrected for biases in the variant allele frequency (VAF) by applying fixVAF52 which includes multiple filtering and QC steps. Additional filters for single nucleotide variants (SNVs) and INDELs, informed by the TRACERx pipeline54, were applied.

作为英国基因组学核心管道的一部分，Strelka53已被应用于体细胞变异调用。通过应用fixVAF52（包括多个过滤和QC步骤），纠正了所得VCF文件中变异等位基因频率（VAF）的偏差。应用了由TRACERx pipeline54通知的单核苷酸变体（SNV）和插入缺失的其他过滤器。

This includes that any variant located within a blacklist region of the genome, as used in the TRACERx pipeline and informed by the “blacklisted” regions reported on ENCODE55, were removed..

这包括删除位于基因组黑名单区域内的任何变体，如TRACERx管道中使用的，并由ENCODE55上报告的“黑名单”区域通知。。

Additional filters that an SNV had to pass to be included in the final mutation table:

SNV必须通过的其他过滤器才能包含在最终突变表中：

VAF ≥5%

VAF≥5%

alternative reads ≥5

替代读数≥5

Germline VAF <1%

生殖系VAF<1%

Germline number of alternative reads <5

Total depth ≥30

总深度≥30

Additional filters that an INDEL had to pass to be included in the final mutation table:

插入缺失必须通过的其他过滤器才能包含在最终突变表中：

VAF ≥5%

VAF≥5%

alternative reads ≥10

替代读数≥10

Germline VAF <1%

生殖系VAF<1%

Germline number of alternative reads <5

替代读物的种系数<5

Total depth ≥50

总深度≥50

Copy number calling

副本号码呼叫

We have applied Battenberg56 (https://github.com/Wedge-lab/battenberg) to the DNA sequencing data for the estimation of the copy number profile, ploidy and purity of the lung tumours. For this a nextflow pipeline57 was developed using the fixVAF corrected tumour and normal BAM files as well as the corrected VCF file per tumour as input.

我们已经应用了巴滕贝格56(https://github.com/Wedge-lab/battenberg)DNA测序数据用于估计肺肿瘤的拷贝数分布，倍性和纯度。为此，使用fixVAF校正的肿瘤和正常BAM文件以及每个肿瘤校正的VCF文件作为输入，开发了nextflow管道57。

As a first step of the pipeline, the initial profiling of copy number alterations was conducted using Battenberg. Afterwards, multiple assessment steps were applied to evaluate the estimated profile. If any of these criteria were not met, the sample was re-processed up to 4 times using an updated purity estimate.

作为流程的第一步，使用Battenberg对拷贝数变化进行了初步分析。然后，应用了多个评估步骤来评估估计的概况。如果不符合这些标准中的任何一个，则使用更新的纯度估计值将样品重新处理多达4次。

The quality assessment included the evaluation of the concordance of the copy number profile with the VAF distribution of the mutations. The sample failed if the absolute difference between the sample purity computed by Battenberg and the VAF estimated purity was >5%. Furthermore, the correct calling of whole-genome doubling (WGD) was evaluated.

质量评估包括评估拷贝数谱与突变的VAF分布的一致性。如果Battenberg计算的样品纯度与VAF估计纯度之间的绝对差异>5%，则样品失败。此外，评估了全基因组倍增（WGD）的正确调用。

If >30% of the genome presented an average total copy number state of about 0.5 or 1.5, it was assumed that Battenberg had incorrectly not called WGD. Also, if >20% of the genome presented a copy number state 2:2 (tetraploid) or 3:3, and <10% of the genome presented an odd copy number state, and no peak corresponding to a multiplicity of 1 was observed in the VAF distribution of SNVs in 2:2 (tetraploid) regions, then it was assumed that Battenberg had incorrectly called WGD.

如果>30%的基因组呈现出约0.5或1.5的平均总拷贝数状态，则认为巴滕伯格没有正确地称为WGD。此外，如果>20%的基因组呈现拷贝数状态2:2（四倍体）或3:3，而<10%的基因组呈现奇数拷贝数状态，并且在2:2（四倍体）区域的SNV的VAF分布中未观察到对应于多重数1的峰，则假定巴滕伯格错误地称为WGD。

The clonal architecture was characterised using DPClust to assess the presence of a clonal mutation cluster consisting of at least 5% of all variants with a CCF > 0.9 and <1.1. A sample failed if a “super-clonal” cluster could be identified by DPClust which contains at least 5% of all variants with a CCF > 1.1.

使用DPClust表征克隆结构，以评估克隆突变簇的存在，该簇由至少5%的所有变体组成，CCF>0.9和<1.1。如果DPClust可以识别出“超克隆”簇，则样本失败，该簇包含CCF>1.1的所有变体的至少5%。

Furth.

Furth。

Applying multiple exclusion criteria as mentioned above resulted in a total GEL lung cohort of 1027 tumours of which 470 were lung adenocarcinomas.

应用如上所述的多重排除标准，产生了1027个肿瘤的总凝胶肺队列，其中470个是肺腺癌。

The 560 breast cancer whole-genome cohortMutation and copy number calls for the 560 breast cancer WGS cohort were provided by the publication “Landscape of somatic mutations in 560 breast cancer whole-genome sequences” by Nik-Zainal et al.22. This data was aligned to the hg19 assembly. Only tumours for which somatic variants and copy number profiles were provided were used in this study.

Nik Zainal等人22的出版物“560个乳腺癌全基因组序列中的体细胞突变景观”提供了560个乳腺癌WGS队列的560个乳腺癌全基因组队列突变和拷贝数要求。。本研究仅使用提供了体细胞变异和拷贝数谱的肿瘤。

In the literature the human mammary epithelial cells (HMEC) have been reported to be the originating tissue for lobular and ductal breast cancer subtypes26, hence only tumours with this subtype were included in this analysis. This resulted in a total cohort of 482 breast cancer tumours (from 479 female patients and 3 male patients).Publicly available datasetsThe Cancer Genome Atlas (TCGA) dataGene expression and copy number calls for BRCA and LUAD tumours generated by The Cancer Genome Atlas pilot project established by the NCI and the National Human Genome Research Institute were downloaded.

在文献中，据报道人乳腺上皮细胞（HMEC）是小叶和导管乳腺癌亚型26的起源组织，因此该分析仅包括具有该亚型的肿瘤。这导致总共482例乳腺癌肿瘤（来自479名女性患者和3名男性患者）。公开可用的数据集下载了由NCI和国家人类基因组研究所建立的癌症基因组图谱试点项目产生的BRCA和LUAD肿瘤的癌症基因组图谱（TCGA）数据基因表达和拷贝数调用。

The data were retrieved through database of Genotypes and Phenotypes (dbGaP) authorisation (accession no. phs000854.v3.p8). Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at https://cancergenome.nih.gov/. Raw read counts were downloaded for 830 BRCA (ductal and lobular) tumours and 517 LUAD tumours to identify expressed genes in each cancer type.

通过基因型和表型数据库（dbGaP）授权（登录号phs000854.v3.p8）检索数据。有关TCGA以及构成TCGA研究网络的调查人员和机构的信息，请访问https://cancergenome.nih.gov/.下载了830个BRCA（导管和小叶）肿瘤和517个LUAD肿瘤的原始读取计数，以鉴定每种癌症类型中的表达基因。

For 149 of these tumours (91 BRCA and 58 LUAD), RNA-seq data for their adjacent normal tissues were available. These 149 paired normal and tumour samples were used for the differential expression analysis. ASCAT58 initiated copy number profiles were downloaded for 766 BRCA (ductal and lobular) and 708 LUAD tumours to evaluate whether differences in gene expression were driven by copy numb.

对于这些肿瘤中的149个（91个BRCA和58个LUAD），可以获得其相邻正常组织的RNA-seq数据。这149对配对的正常和肿瘤样品用于差异表达分析。下载了766个BRCA（导管和小叶）和708个LUAD肿瘤的ASCAT58启动的拷贝数谱，以评估基因表达的差异是否由拷贝数驱动。

Add 1 volume of phenol: chloroform: isoamyl alcohol (25:24:1) to each sample, and vortex or shake by hand thoroughly for approximately 20 s;

向每个样品中加入1体积的苯酚：氯仿：异戊醇（25:24:1），并用手彻底涡旋或摇动约20秒；

Centrifuge at room temperature for 5 min at 16,000 × g;

在室温下以16000×g离心5分钟；

Carefully remove the upper aqueous phase, and transfer the layer to a fresh tube (Be sure not to carry over any phenol during pipetting);

小心地除去上层水相，并将该层转移到新管中（在移液过程中确保不要携带任何苯酚）；

Add 4 µl glycogen and then 1 volume of propanol, mix well. Store at −80 °C around dry ice for >1 h;

。在-80°C的干冰周围储存>1小时；

Centrifuge at 16,000 × g for 30 min at 4 °C. Discard the supernatant, add 750 µl of cold 70% ethanol to the pellet;

在4°C下以16000×g离心30分钟。弃去上清液，向沉淀中加入750µl冷的70%乙醇；

Centrifuge at 16,000 × g for 5 min at 4 °C. Remove all ethanol (using 10 µl tips) as much as possible, let the pellet air dry;

在4°C下以16000×g离心5分钟。尽可能去除所有乙醇（使用10µl尖端），使颗粒风干；

Resuspend the pellet in 50 µl of 1× low TE (10 mM of 1 M Tris-HCl and 0.01 mM of 0.5 M EDTA) at 37 °C for 1 h with 350–400 rpm shaking.

将沉淀重悬于50μl1x低TE（10mM的1M Tris-HCl和0.01mM的0.5MEDTA）中，在37℃下振荡1小时，转速为350-400rpm。

Then the purified DNA samples were stored at 4 °C in the dark.Library constructionPurified DNA was then fragmented using a Covaris ultrasonicator to achieve an average length of 200 bp. The NEBNext Ultra DNA Library Prep Kit for Illumina (NEB; E7370) and the NEBNext Multiplex Oligos for the Illumina kit were applied to construct the library by ligating adaptors before BrdU immunoprecipitation.

然后将纯化的DNA样品在黑暗中保存在4°C。文库构建然后使用Covaris超声波仪将纯化的DNA片段化，以达到200 bp的平均长度。应用用于Illumina的NEBNext Ultra DNA文库制备试剂盒（NEB；E7370）和用于Illumina试剂盒的NEBNext多重寡核苷酸，通过在BrdU免疫沉淀之前连接衔接子来构建文库。

Two commercially available kits were used at this step, by following the manufacturer’s instructions: the NEBNext Ultra DNA Library Prep Kit which was used for end repair, and the NEBNext Multiplex Oligos for Illumina kit which was used for adaptor ligation and some enzyme treatment. Firstly, the end repair enzyme and the reaction buffer were added to the fragmented DNA.

在此步骤中，按照制造商的说明使用了两种市售试剂盒：用于末端修复的NEBNext Ultra DNA文库制备试剂盒和用于衔接子连接和一些酶处理的用于Illumina试剂盒的NEBNext Multiplex Oligos。首先，将末端修复酶和反应缓冲液添加到片段化的DNA中。

After a thorough mixing and quick spinning down, the sample was put in the thermocycler starting from 20 °C for 30 min, followed by 65 °C for another 30 min. After the end repair, the sample could be held at 4 °C before the next step. Second, according to the manufacturer’s instructions for the NEBNext Multiplex Oligos for Illumina kit, the NEBNext Adaptor for Illumina, the Ligation Enhancer and other buffer included in the kit were added to the repaired samples from the last step, followed by the incubation at 20 °C for 15 min.

彻底混合并快速旋转后，将样品从20℃开始放入热循环仪中30分钟，然后在65℃下再放置30分钟。末端修复后，样品可以在下一步之前保持在4℃。其次，根据制造商对用于Illumina试剂盒的NEBNext多重寡核苷酸的说明，将用于Illumina的NEBNext衔接子，试剂盒中包含的连接增强子和其他缓冲液添加到最后一步修复的样品中，然后在20℃下孵育15分钟。

Lastly, the uracil-specific excision reagent (USER) enzyme digestion from the NEBNext Multiplex Oligos for Illumina kit was performed by adding the USER enzyme to the ligated sample for further incubation at 37 °C for 15 min. The digested DNA sample was then purified using the QIAquick PCR Purification Kit and the DNA was eluted in 50 μl molecular biology grade water.BrdU immunoprecipitationEluted DNA samples after the library construction were immunoprecipitated with 40ul mouse anti-BrdU .

最后，通过将用户酶添加到连接的样品中，在37℃下进一步孵育15分钟，从NEBNext Multiplex Oligos for Illumina试剂盒中进行尿嘧啶特异性切除试剂（USER）酶消化。然后使用QIAquick PCR纯化试剂盒纯化消化的DNA样品，并将DNA在50μl分子生物学级水中洗脱。文库构建后的BrdU免疫沉淀稀释的DNA样品用40ul小鼠抗BrdU免疫沉淀。

Forward, GACCCTCTTCTCTGCACAGCTC

前进，GACCCTCTTGCACAGCTC

Reverse, GCTACCGAGGCTCCAGCTTAAC

倒车档，GCTACCGGCTCCAGCTTAAC

MMP15:

MMP15：

Forward, CAGGCCTCTGGTCTCTGTCATT

前进，CAGGCCTGTCTGTCATCAT

Reverse, AGAGCTGAGAAACCACCACCAG

反向，AGAGAGAGAACCACCAG

BMP1:

BMP1：

Forward, GATGAAGCCTCGACCCCTAGAT

前进，GATGAAGCTCGACCTAGAT

Reverse, ACCCGTCAGAGACGAACTTGAG

反向，ACCCGTCAGACGAACTTGAG

PTGS2:

PTGS2：

Forward, GTTCTAGGCTGGTGTCCCATTG

Reverse, CTTTCTGTACTGCGGGTGGAAC

反向，CTTTCTGTACTGCGGGTGGAAC

NETO1:

NETO1：

Forward, GGAGGTGGAATGCTAGGGACTT

转发，GGAGGTGGAATGCTAGGGACTT

Reverse, GCTGAGTGTGGCCTTAAGAGGA

反向，GCTGTGGGCCTTAAGAGGA

SLITRK6:

slirk6：

Forward, GGAGAACATGCCTCCACAGTCT

前锋，GGAGACATGCCTCCACAGTCT

Reverse, GTCCTGGAAGTTGAGTGGATGG

倒车档，GTCCTGGAGAGTTGAGTGGATGG

ZFP42:

ZFP42：

Forward, CTTGTGGGGACACCCAGATAAG

前进，CTTGGGACACCCCAGATAG

Reverse, AACCACCTCCAGGCAGTAGTGA

反面，AACCACCAGGGCAGTAGTGA

DPPA2:

DPPA2：

Forward, AGGTGGACAGCGAAGACAGAAC

前进，AGGTGACAGCGAGAGAGAAC

Reverse, GGCCATCAGCAGTGTCCTAAAC

The primers for mitochondrial DNA are as follows:

线粒体DNA的引物如下：

Forward, CTAAATAGCCCACACGTTCCC

Reverse, AGAGCTCCCGTGAGTGGTTA

RVerse,AGACCGCGAGTAGTAGTA

To quantify the replication timing using qPCR, we calculated the relative abundance of G1, early and late S samples per gene per cell line using this equation:$$\begin{array}{c}{RelativeAbundace}\left({s}_{i},{{gene}}_{j}\right)=\frac{{2}^{\left({{Ct}}_{{{gene}}_{j}}\left({S}_{i}\right)-{{Ct}}_{{mitochondria}}\left({s}_{i}\right)\right)}}{{\sum }_{t=1}^{3}{2}^{\left({{Ct}}_{{{gene}}_{j}}\left({S}_{t}\right)-{{Ct}}_{{mitochondria}}\left({s}_{t}\right)\right)}}\\ {with}\, {i}=1,2,3\, {and}\, {j}=1,...,n\end{array}$$.

为了使用qPCR量化复制时间，我们使用以下等式计算了每个细胞系每个基因的G1，早期和晚期S样品的相对丰度：$$\ begin{array}{c}{relativebandace}\ left({s}_{i} ，{{gene}}}uj}\ right）=\ frac{{2}^{\ left（{{Ct}}uu{{{gene}}}uj}\ left({S}_{i} \右）-{{Ct}}{{{线粒体}}\左({s}_{i} \右）}}{{\和}}{t=1}^{3}{2}^{\左（{{Ct}}}{{{基因}}{j}}\左({S}_{t} \右）-{{Ct}}{{{线粒体}}\左({s}_{t} （右）}}\{带}\，{i}=1,2,3\，{和}\，{j}=1，。。。，结束{数组}$$。

(1)

In this equation, ${S}_{i}$ represents one of the three FACS sorted samples (${S}_{i}\epsilon \{G1,\,{earlyS},{lateS}\}$) and ${{gene}}_{j}$ the j-th gene of n total genes. Therefore, ${{Ct}}_{{{gene}}_{j}}({S}_{i})$ describes the Ct value of the i-th FACS sorted sample and the j-th gene.

\({S}_{i} \）代表三个FACS分类样本之一(\({S}_{i} epsilon{G1，\，{earlyS}，{lateS}}）和\（{gene}}uj}）是n个总基因的第j个基因。因此，\（{{Ct}}}{{{{gene}}}{j}}({S}_{i} ）\）描述了第i个FACS分选样品和第j个基因的Ct值。

Similarly, ${{Ct}}_{{mitochondria}}({s}_{i})$ describes the Ct value of the mitochondrial DNA abundance of the i-th sample. The ${RelativeAbundace}({s}_{i},{{gene}}_{j})$ was calculated for each cell line separately.Multiplex WGSPurified BrdU-immunoprecipitated DNA samples were applied for indexing and PCR amplification using the NEBNext Ultra II Kit (NEB; M0544).

类似地，\（{{Ct}}{{{线粒体}}({s}_{i} ）\）描述了第i个样品的线粒体DNA丰度的Ct值。“（{相对基准}”({s}_{i} 分别计算每个细胞系的{{基因}}{j}）\）。使用NEBNext Ultra II试剂盒（NEB；M0544）将多重WGSPurified BrdU免疫沉淀的DNA样品用于索引和PCR扩增。

Primers annealing to the adaptors to determine the optimal PCR cycle number were as follows:.

引物退火到衔接子以确定最佳PCR循环数如下：。

adqPCR_Forward: ACACTCTTTCCCTACACGACGC

adqPCR_转发：ACACTTTCCCTACGACGC

adqPCR_Reverse: GACTGGAGTTCAGACGTGTGC

Next, PCR reactions were purified using AMPure XP beads (Beckman Coulter; A63880) and DNA was eluted in 10 mM Tris-HCl. After quantifying the DNA concentration of each sample using the Qubit dsDNA HS Assay Kit (Life Technologies; Q32854), libraries were pooled, followed by checking the size distribution of DNA fragments.

接下来，使用AMPure XP珠子（Beckman Coulter；A63880）纯化PCR反应，并将DNA在10mM Tris-HCl中洗脱。使用Qubit dsDNA HS分析试剂盒（Life Technologies；Q32854）定量每个样品的DNA浓度后，合并文库，然后检查DNA片段的大小分布。

Whole genome sequencing with 100 bp paired end reads was performed on an Illumina HiSeq4000 with 6 or 12 samples per lane.Repli-seq bioinformatics pipelineThe bioinformatics pipeline was based on the pipeline provided by the 4D Nucleosome Data Coordination and Integration Center74 in combination with the pipeline published in Marchal et al.72.AlignmentTo clean up the raw sequencing data and to remove any unwanted left-over adaptor sequences, TrimGalore (v0.6.5) a wrapper tool around Cutadapt75 and FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/), was applied with default settings to perform quality and adaptor trimming for each set of paired-end fastq files.

在Illumina HiSeq4000上进行具有100 bp配对末端读数的全基因组测序，每个泳道有6或12个样品。Repli-seq生物信息学管道生物信息学管道基于4D核小体数据协调与整合中心74提供的管道，并结合Marchal等人72.Alignment中发布的管道。为了清理原始测序数据并删除任何不需要的残留衔接子序列，TrimGalore（v0.6.5）是围绕Cutadapt75和FastQC的包装工具(https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)使用默认设置应用，以对每组成对末端的fastq文件执行质量和适配器修剪。

The resulting fastq files were provided to bwa-mem (v0.7.17)76 for alignment to both reference genomes hg19 and hg38 in two separate runs to account for differences in the genome build used in the downloaded WGS datasets of lung and breast tumours. The samtools software (v1.8)77 was used for further quality and filtering steps.

产生的fastq文件被提供给bwa mem（v0.7.17）76，以便在两次单独的运行中与参考基因组hg19和hg38进行比对，以解释下载的肺和乳腺肿瘤WGS数据集中使用的基因组构建的差异。samtools软件（v1.8）77用于进一步的质量和过滤步骤。

In cases where one Repli-seq run resulted in multiple sets of fastq-files, samtools merge (flags: -n -f -b) was used to combine them into one. Afterwards samtools view (flags: -bhq 20) and samtools sort (flags: -m 16G –threads 4) were applied to exclude reads with a mapping quality lower than 20 and to sort reads in the bam files regarding their genomic position, respectively.

如果一次Repli-seq运行产生多组fastq文件，则使用samtools merge（标志：-n-f-b）将它们合并为一个。然后应用samtools视图（标志：-bhq 20）和samtools排序（标志：-m 16G–threads 4）来排除映射质量低于20的读数，并分别对bam文件中有关其基因组位置的读数进行排序。

Samtools stats were applied to quality check the different alignment and filterin.

Samtools统计数据用于质量检查不同的对齐和过滤器。

(2)

Instead of counting each mutation as 1 in the 50 kb bins, each mutation was counted as their mutCN divided by the total copy number (nMinor + nMajor) of the segment that this mutation was located on.$${{mutLoad}}_{j}=\sum\limits_{i=1}^{{n}_{j}}\frac{{{mutCN}}_{i}}{({{nMinor}}_{i}+{{nMajor}}_{i})}$$.

每个突变都被计算为mutCN除以该突变所在片段的总拷贝数（nMinor+ nMajor），而不是将50 kb区域中的每个突变计算为1^{{n}_{j} }\ frac{{{{mutCN}}}{{i}}{（{{nMinor}}}{i}+{{nMajor}}}}}$$。

(3)

with ${n}_{j}$ representing the total number of mutations and ${{mutLoad}}_{j}$ the final copy number corrected mutation load of the j-th 50 kb bin (Fig. 1C). The mutation load in 50 kb bins was calculated on a cohort level and a per-tumour level. For the cohort analysis, all mutations within a certain 50 kb bin were pooled together across all tumours of a certain cohort for the mutation load calculation.ART timing relative to the mutation accumulation in the most recent common ancestor (MRCA)For each mutation, its CCF and a 95% confidence interval (CI) (as described in ref.

与\({n}_{j} \）代表突变总数，而\（{{mutLoad}}uuj}）代表第50 kb bin的最终拷贝数校正突变负载（图1C）。在队列水平和每个肿瘤水平上计算50kb箱中的突变负荷。对于队列分析，将某个50kb bin内的所有突变汇集在某个队列的所有肿瘤中，以进行突变负荷计算。ART时间相对于每个突变的最新共同祖先（MRCA）中的突变积累，其CCF和95%置信区间（CI）（如参考文献所述）。

80) were calculated. Mutations with an upper 95%-CI greater equals 1 were classified as clonal and everything else as subclonal. The copy number adjusted mutation load was calculated for the aggregated data of the BRCA and LUAD cohort by only considering clonal mutations. The resulting mutation load estimates were z-transformed to be able to compare the final results across the two cancer types.

80）进行了计算。95%-CI大于等于1的突变被归类为克隆突变，其他突变被归类为亚克隆突变。通过仅考虑克隆突变，计算了BRCA和LUAD队列的汇总数据的拷贝数调整突变负荷。对产生的突变负荷估计值进行了z变换，以便能够比较两种癌症类型的最终结果。

Next, the mean mutation load for the different altered and unaltered replication timing regions was estimated by applying a bootstrapping method to account for the high variability in the number of 50 kb bins classified as presenting different types of altered or unaltered RT. The lowest number of bins regarding the different timing classifications was determined per cancer type (BRCA: 4128, LUAD: 1498) and used as the number of bins that were randomly sampled with replacement from each timing classification.

接下来，通过应用自举方法来估计不同改变和未改变的复制时间区域的平均突变负荷，以解释被分类为呈现不同类型的改变或未改变的RT的50kb箱的数量的高度可变性。关于不同时间分类的最低箱数是根据癌症类型确定的（BRCA:4128，LUAD:1498），并用作从每个时间分类中随机抽样替换的箱数。

The sampling step was conducted 10,000 times and during each iteration, the mean mutation load per RT and ART classification was calculated. This resulted in a distribution of mean mutation load estimates for each timing per cancer type. To estimate the proportion of mutations that w.

采样步骤进行了10000次，在每次迭代中，计算每个RT和ART分类的平均突变负荷。这导致了每种癌症类型的每个时间的平均突变负荷估计值的分布。估计w突变的比例。

(4)

$${d\left({Early}-{to}-{Late}\right)}_{i}= \frac{{{|}{mutLoad}({Early}-{to}-{Late})}_{i}-{mutLoad}({Late})_{i}{|}}{{|}{mutLoad}({Late})_{i}-{mutLoad}({Early})_{i}{|}} \\ {with} \ i= 1,...,10000$$

$${d \左({Early}-{to}-{Late}\ right）}{ui}=\frac{{{{|}{mutLoad}({Early}-{to}-{迟了}}_{i}-{mutLoad}（{Late}）{i}{|}}{{|}{mutLoad}（{Late}）_{i}-{mutLoad}（{Early}）{i}{|}}\\{其中}\i=1，。。。，10000个$$

(5)

The equivalent was calculated to estimate the proportions of mutations accumulated before the EarlyN-to-LateT alterations occurred during tumour evolution. This resulted in a distribution of proportions accumulated prior to ART (Supplementary Fig. 8F). The mean values of these proportions were used separately as the final timing estimates for the two different RT shifts relative to mutation accumulation in the most recent common ancestor.

计算等效值以估计在肿瘤进化过程中发生早期到晚期改变之前累积的突变比例。这导致ART之前累积的比例分布（补充图8F）。这些比例的平均值分别用作相对于最近共同祖先中突变积累的两个不同RT偏移的最终时间估计。

A low proportion suggests ART to be an early evolutionary event whereas a high proportion means that the ART event occurred closer to the emergence of the MRCA.Simulations of different ART time pointsTo validate the estimation of the ART timing relative to the mutation accumulation in the MRCA, different fractions of mutations that were accumulated before the shift in RT were simulated as different time points.

低比例表明ART是早期进化事件，而高比例意味着ART事件更接近MRCA的出现。不同ART时间点的模拟为了验证ART时间相对于MRCA中突变积累的估计，将RT移位之前累积的突变的不同部分模拟为不同的时间点。

For this, a fixed mutation rate in early and late replicated regions was assumed, which was estimated based on the fractions of mutations per Mb in unaltered RT regions for each cancer type (Supplementary Fig. 8A). A higher disparity in the fraction of mutations accumulated in LateN+T versus EarlyN+T replicated regions was observed in lung cancer in comparison to breast cancer.

为此，假设早期和晚期复制区域的突变率是固定的，这是根据每种癌症类型的未改变RT区域中每Mb突变的分数估算的（补充图8A）。与乳腺癌相比，在肺癌中观察到LateN+T与EarlyN+T复制区域累积的突变比例差异更大。

For this reason, in breast cancer, the mutation rate in late replicated regions was set to be 1.3 times the mutation rate in early replicated regions whereas in lung cancer the mutation rate was set to be 1.5 times higher in late versus early replicated regions for the simulations. Next, the accumulation of mutations during 10,000 iterations (representing cell divisions) in 1000 genomic bins with different replication timings and corresponding mutation rates was simulated to explore the resulting mean mutation load patterns when certain pr.

因此，在乳腺癌中，晚期复制区域的突变率被设置为早期复制区域突变率的1.3倍，而在肺癌中，晚期与早期复制区域的突变率被设置为1.5倍。接下来，模拟了1000个具有不同复制时间和相应突变率的基因组箱中10000次迭代（代表细胞分裂）期间突变的积累，以探索当某些pr时产生的平均突变负荷模式。

Data availability

数据可用性

Processed data to reproduce the analyses of this study including the replication timing signal data in 50 kb bins for the 31 cell lines analysed in this study can be accessed via Zenodo82. This repository does not include data from the Genomics England lung cohort due to restricted access. The Genomics England lung cohort is part of the 100,000 Genomes Project whose data are held in a secure research environment and are only available to registered users.

可以通过Zenodo82访问用于重现本研究分析的处理数据，包括本研究中分析的31个细胞系的50kb箱中的复制定时信号数据。由于访问受限，该存储库不包括来自Genomics England lung队列的数据。Genomics England lung队列是100000个基因组计划的一部分，其数据保存在安全的研究环境中，仅可供注册用户使用。

For further information on how to obtain access visit https://www.genomicsengland.co.uk/research/academic. Somatic variants for the 560 WGS breast cancer dataset are available on the International Cancer Genome Consortium Data Portal (https://dcc.icgc.org/) and were retrieved via ftp://ftp.sanger.ac.uk/pub/cancer/Nik-ZainalEtAl-560BreastGenomes/.

有关如何获得访问权限的更多信息，请访问https://www.genomicsengland.co.uk/research/academic.560 WGS乳腺癌数据集的体细胞变异可在国际癌症基因组联盟数据门户网站上获得(https://dcc.icgc.org/)并通过ftp://ftp.sanger.ac.uk/pub/cancer/Nik-ZainalEtAl-560BreastGenomes/.

Supplementary files from Nik-Zainal et al.2 were downloaded for additional information, including clinical data. The TCGA data were retrieved through the database of Genotypes and Phenotypes (dbGaP) authorisation (accession no. phs000854.v3.p8). Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at https://cancergenome.nih.gov/.

下载了Nik Zainal等人2的补充文件，以获取更多信息，包括临床数据。通过基因型和表型数据库（dbGaP）授权（登录号phs000854.v3.p8）检索TCGA数据。有关TCGA以及构成TCGA研究网络的调查人员和机构的信息，请访问https://cancergenome.nih.gov/.

The accession numbers for the raw Repli-seq data of the 16 cell lines downloaded from ENCODE are listed in Supplementary Table 2. The accession numbers for the Hi-C data downloaded from ENCODE are provided in Supplementary Table 3. The raw data of the 13 in-house repli-sequenced cell lines has been made publicly available on SRA under the BioProject accession number PRJNA1096133.

补充表2中列出了从ENCODE下载的16个细胞系的原始Repli-seq数据的登录号。补充表3提供了从ENCODE下载的Hi-C数据的登录号。13个内部repli测序细胞系的原始数据已在SRA上以生物项目登录号PRJNA1096133公开提供。

The raw data of the TRACERx PDCs (from the TRACERx study) used during this study has been deposited at the European Genome–phenome Archive (EGA), which is hosted by The European Bioinformatics Institute (EBI) and.

本研究中使用的TRACERx pDC的原始数据（来自TRACERx研究）已保存在欧洲基因组-表型档案（EGA）中，该档案由欧洲生物信息学研究所（EBI）和。

Code availability

代码可用性

The majority of statistical analyses were performed in the R statistical environment version ≥3.5.1. The code for the data analysis can be accessed via Zenodo82. The Repli-seq pipeline developed for this study is available for download from https://github.com/McGranahanLab/RepliSeqPipeline.

大多数统计分析是在R统计环境版本≥3.5.1中进行的。数据分析代码可以通过Zenodo82访问。为这项研究开发的Repli-seq管道可从以下网站下载https://github.com/McGranahanLab/RepliSeqPipeline.

ReferencesMartincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 173, 1823 (2018).Article

参考文献Martincorena，I。等人。癌症和体细胞组织中的普遍选择模式。细胞1731823（2018）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Rhind, N. & Gilbert, D. M. DNA replication timing. Cold Spring Harb. Perspect. Biol. 5, a010132 (2013).Article

Rhind，N。＆Gilbert，D.M。DNA复制时间。冷泉兔。透视图。。文章

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Ryba, T. et al. Evolutionarily conserved replication timing profiles predict long-range chromatin interactions and distinguish closely related cell types. Genome Res. 20, 761–770 (2010).Article

Ryba，T。等人。进化上保守的复制时间曲线预测长程染色质相互作用并区分密切相关的细胞类型。基因组研究20761-770（2010）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Rivera-Mulia, J. C. et al. Replication timing alterations in leukemia affect clinically relevant chromosome domains. Blood Adv. 3, 3201–3213 (2019).Article

Rivera-Mulia，J.C。等人。白血病的复制时间改变影响临床相关的染色体结构域。血液杂志33201-3213（2019）。文章

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Kenigsberg, E. et al. The mutation spectrum in genomic late replication domains shapes mammalian GC content. Nucleic Acids Res. 44, 4222–4232 (2016).Article

Kenigsberg，E。等人。基因组晚期复制域中的突变谱塑造了哺乳动物的GC含量。核酸研究444222-4232（2016）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Woo, Y. H. & Li, W.-H. DNA replication timing and selection shape the landscape of nucleotide variation in cancer genomes. Nat. Commun. 3, 1004 (2012).Article

Woo，Y.H.＆Li，W.-H。DNA复制时间和选择塑造了癌症基因组中核苷酸变异的格局。国家公社。。文章

ADS

PubMed

Google Scholar

谷歌学者

Liu, L., De, S. & Michor, F. DNA replication timing and higher-order nuclear organization determine single-nucleotide substitution patterns in cancer genomes. Nat. Commun. 4, 1502 (2013).Article

Liu，L.，De，S。＆Michor，F。DNA复制时间和高阶核组织决定了癌症基因组中的单核苷酸取代模式。国家公社。41502（2013）。文章

ADS

PubMed

Google Scholar

谷歌学者

Supek, F. & Lehner, B. Differential DNA mismatch repair underlies mutation rate variation across the human genome. Nature 521, 81–84 (2015).Article

Supek，F。＆Lehner，B。差异DNA错配修复是整个人类基因组突变率变化的基础。《自然》521,81-84（2015）。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Peycheva, M. et al. DNA replication timing directly regulates the frequency of oncogenic chromosomal translocations. Science 377, eabj5502 (2022).Article

Peycheva，M。等人。DNA复制时间直接调节致癌染色体易位的频率。科学377，eabj5502（2022）。文章

CAS

中科院

PubMed

Google Scholar

谷歌学者

Koren, A. et al. Genetic variation in human DNA replication timing. Cell 159, 1015–1026 (2014).Article

Koren，A。等人。人类DNA复制时间的遗传变异。细胞1591015-1026（2014）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

De, S. & Michor, F. DNA replication timing and long-range DNA interactions predict mutational landscapes of cancer genomes. Nat. Biotechnol. 29, 1103–1108 (2011).Article

De，S。＆Michor，F。DNA复制时间和远程DNA相互作用预测癌症基因组的突变景观。美国国家生物技术公司。291103-1108（2011）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Akdemir, K. C. et al. Somatic mutation distributions in cancer genomes vary with three-dimensional chromatin structure. Nat. Genet. 52, 1178–1188 (2020).Article

Akdemir，K.C。等人。癌症基因组中的体细胞突变分布随三维染色质结构而变化。纳特·吉内特。521178-1188（2020）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Yaacov, A., Rosenberg, S. & Simon, I. Mutational signatures association with replication timing in normal cells reveals similarities and differences with matched cancer tissues. Sci. Rep. 13, 7833 (2023).Article

Yaacov，A.，Rosenberg，S。＆Simon，I。突变特征与正常细胞中复制时间的关联揭示了与匹配的癌组织的相似性和差异。科学。。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Ryba, T. et al. Abnormal developmental control of replication-timing domains in pediatric acute lymphoblastic leukemia. Genome Res. 22, 1833–1844 (2012).Article

Ryba，T。等人。小儿急性淋巴细胞白血病中复制时间域的异常发育控制。基因组研究221833-1844（2012）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Sasaki, T. et al. Stability of patient-specific features of altered DNA replication timing in xenografts of primary human acute lymphoblastic leukemia. Exp. Hematol. 51, 71–82.e3 (2017).Article

Sasaki，T.等人。原发性人类急性淋巴细胞白血病异种移植物中DNA复制时间改变的患者特异性特征的稳定性。实验血液学。51,71-82.e3（2017）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Du, Q. et al. Replication timing and epigenome remodelling are associated with the nature of chromosomal rearrangements in cancer. Nat. Commun. 10, 416 (2019).Article

Du，Q。等人。复制时间和表观基因组重塑与癌症中染色体重排的性质有关。国家公社。10416（2019）。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Blumenfeld, B. et al. Chromosomal coordination and differential structure of asynchronous replicating regions. Nat. Commun. 12, 1035 (2021).Article

Blumenfeld，B。等人。异步复制区的染色体配位和差异结构。国家公社。121035（2021）。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Rivera-Mulia, J. C. et al. Allele-specific control of replication timing and genome organization during development. Genome Res. 28, 800–811 (2018).Article

Rivera Mulia，J.C.等人。发育过程中复制时间和基因组组织的等位基因特异性控制。基因组研究28800-811（2018）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Pope, B. D. et al. Topologically associating domains are stable units of replication-timing regulation. Nature 515, 402–405 (2014).Article

Pope，B.D.等人。拓扑关联域是复制定时调节的稳定单元。自然515402-405（2014）。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Makova, K. D. & Hardison, R. C. The effects of chromatin organization on variation in mutation rates in the genome. Nat. Rev. Genet. 16, 213–223 (2015).Article

Makova，K.D。和Hardison，R.C。染色质组织对基因组突变率变化的影响。Genet自然Rev。16213-223（2015）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Caulfield, M. et al. The National Genomics Research and Healthcare Knowledgebase. figshare https://doi.org/10.6084/M9.FIGSHARE.4530893.V5 (2019).Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).Article

国家基因组学研究和医疗保健知识库。figshare公司https://doi.org/10.6084/M9.FIGSHARE.4530893.V5（2019年）。Nik Zainal，S.等人。560个乳腺癌全基因组序列中体细胞突变的概况。自然534,47-54（2016）。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).Article

ENCODE项目联盟。人类基因组中DNA元素的综合百科全书。。文章

ADS

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Luo, Y. et al. New developments on the Encyclopedia of DNA Elements (ENCODE) data portal. Nucleic Acids Res. 48, D882–D889 (2020).Article

Luo，Y.等人。DNA元素百科全书（ENCODE）数据门户的新发展。核酸研究48，D882–D889（2020）。文章

CAS

中科院

PubMed

Google Scholar

谷歌学者

Maccaroni, K., Balzano, E., Mirimao, F., Giunta, S. & Pelliccia, F. Impaired replication timing promotes tissue-specific expression of common fragile sites. Genes 11, 326 (2020).Article

Maccaroni，K.，Balzano，E.，Mirimao，F.，Giunta，S。＆Pelliccia，F。复制时间受损促进了常见脆弱位点的组织特异性表达。。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Sims, A. H., Howell, A., Howell, S. J. & Clarke, R. B. Origins of breast cancer subtypes and therapeutic implications. Nat. Clin. Pract. Oncol. 4, 516–525 (2007).Article

Sims，A.H.，Howell，A.，Howell，S.J。＆Clarke，R.B。乳腺癌亚型的起源和治疗意义。。实践。Oncol公司。4516-525（2007）。文章

CAS

中科院

PubMed

Google Scholar

谷歌学者

Sutherland, K. D. & Berns, A. Cell of origin of lung cancer. Mol. Oncol. 4, 397–403 (2010).Article

Sutherland，K.D。和Berns，A。肺癌起源细胞。。4397-403（2010）。文章

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Koren, A. et al. Differential relationship of DNA replication timing to different forms of human mutation and variation. Am. J. Hum. Genet. 91, 1033–1040 (2012).Article

Koren，A.等人，《DNA复制时间与不同形式的人类突变和变异的差异关系》。上午J。嗯。Genet。911033-1040（2012）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Polak, P. et al. Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518, 360–364 (2015).Article

Polak，P。等人。起源细胞染色质组织塑造了癌症的突变景观。《自然》518360–364（2015）。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Pope, B. D. & Gilbert, D. M. The replication domain model: regulating replicon firing in the context of large-scale chromosome architecture. J. Mol. Biol. 425, 4690–4695 (2013).Article

Pope，B.D。和Gilbert，D.M。复制域模型：在大规模染色体结构的背景下调节复制子的放电。J、分子生物学。4254690–4695（2013）。文章

CAS

中科院

PubMed

Google Scholar

谷歌学者

Stamatoyannopoulos, J. A. et al. Human mutation rate associated with DNA replication timing. Nat. Genet. 41, 393–395 (2009).Article

Stamatoyannopoulos，J.A。等人。与DNA复制时间相关的人类突变率。纳特·吉内特。41393-395（2009）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).Article

Alexandrov，L.B.等人。人类癌症突变过程的特征。自然500415-421（2013）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Tomkova, M., Tomek, J., Kriaucionis, S. & Schuster-Böckler, B. Mutational signature distribution varies with DNA replication timing and strand asymmetry. Genome Biol. 19, 129 (2018).Article

Tomkova，M.，Tomek，J.，Kriaucionis，S。和Schuster-Böckler，B。突变特征分布随DNA复制时间和链不对称而变化。。19129（2018）。文章

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Morganella, S. et al. The topography of mutational processes in breast cancer genomes. Nat. Commun. 7, 11383 (2016).Article

Morganella，S。等人。乳腺癌基因组突变过程的地形图。国家公社。711383（2016）。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Teh, Y. W., Jordan, M. I., Beal, M. J. & Blei, D. M. Hierarchical Dirichlet processes. J. Am. Stat. Assoc. 101, 1566–1581 (2006).Article

Teh，Y.W.，Jordan，M.I.，Beal，M.J。＆Blei，D.M。分层Dirichlet过程。J、美国统计协会第1011566-1581号（2006年）。文章

MathSciNet

CAS

中科院

Google Scholar

谷歌学者

Li, Y. et al. Patterns of somatic structural variation in human cancer genomes. Nature 578, 112–121 (2020).Article

Li，Y。等人。人类癌症基因组中体细胞结构变异的模式。自然578112-121（2020）。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Alexandrov, L. B. et al. Mutational signatures associated with tobacco smoking in human cancer. Science 354, 618–622 (2016).Article

Alexandrov，L.B.等人。与人类癌症中吸烟相关的突变特征。科学354618-622（2016）。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Degasperi, A. et al. Substitution mutational signatures in whole-genome–sequenced cancers in the UK population. Science 376, abl9283 (2022).Article

。科学376，abl9283（2022）。文章

Google Scholar

谷歌学者

Moris, A., Murray, S. & Cardinaud, S. AID and APOBECs span the gap between innate and adaptive immunity. Front. Microbiol. 5, 534 (2014).Article

Moris，A.，Murray，S。＆Cardinaud，S。AID和APOBECs跨越了先天免疫和适应性免疫之间的差距。正面。微生物。5534（2014）。文章

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Burns, M. B., Temiz, N. A. & Harris, R. S. Evidence for APOBEC3B mutagenesis in multiple human cancers. Nat. Genet. 45, 977–983 (2013).Article

Burns，M.B.，Temiz，N.A。和Harris，R.S。APOBEC3B突变在多种人类癌症中的证据。纳特·吉内特。45977-983（2013）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Roberts, S. A. et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers. Nat. Genet. 45, 970–976 (2013).Article

Roberts，S.A.等人，APOBEC胞苷脱氨酶突变模式在人类癌症中广泛存在。纳特·吉内特。。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Mas-Ponte, D. & Supek, F. DNA mismatch repair promotes APOBEC3-mediated diffuse hypermutation in human cancers. Nat. Genet. 52, 958–968 (2020).Article

Mas Ponte，D。＆Supek，F。DNA错配修复促进APOBEC3介导的人类癌症弥漫性超突变。纳特·吉内特。52958-968（2020）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Supek, F. & Lehner, B. Scales and mechanisms of somatic mutation rate variation across the human genome. DNA Repair 81, 102647 (2019).Article

Supek，F。＆Lehner，B。人类基因组中体细胞突变率变异的规模和机制。DNA修复81102647（2019）。文章

CAS

中科院

PubMed

Google Scholar

谷歌学者

Müller, C. A. & Nieduszynski, C. A. DNA replication timing influences gene expression level. J. Cell Biol. 216, 1907–1914 (2017).Article

Müller，C.A。＆Nieduszynski，C.A。DNA复制时间影响基因表达水平。J、细胞生物学。2161907-1914（2017）。文章

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Siefert, J. C., Georgescu, C., Wren, J. D., Koren, A. & Sansam, C. L. DNA replication timing during development anticipates transcriptional programs and parallels enhancer activation. Genome Res. 27, 1406–1416 (2017).Article

Siefert，J.C.，Georgescu，C.，Wren，J.D.，Koren，A。＆Sansam，C.L。发育过程中的DNA复制时间预测转录程序并与增强子激活平行。基因组研究271406-1416（2017）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Yaacov, A. et al. Cancer mutational processes vary in their association with replication timing and chromatin accessibility. Cancer Res. 81, 6106–6116 (2021).Article

Yaacov，A。等人。癌症突变过程与复制时间和染色质可及性的关系各不相同。癌症研究816106-6116（2021）。文章

CAS

中科院

PubMed

Google Scholar

谷歌学者

Dukaj, L. & Rhind, N. The capacity of origins to load MCM establishes replication timing patterns. PLoS Genet. 17, e1009467 (2021).Article

Dukaj，L。和Rhind，N。origins加载MCM的能力建立了复制时间模式。PLoS Genet。17，e1009467（2021）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Smith, O. K. & Aladjem, M. I. Chromatin structure and replication origins: determinants of chromosome replication and nuclear organization. J. Mol. Biol. 426, 3330–3341 (2014).Article

Smith，O.K。＆Aladjem，M.I。染色质结构和复制起源：染色体复制和核组织的决定因素。J、分子生物学。4263330–3341（2014）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Zhang, J. et al. An integrative ENCODE resource for cancer genomics. Nat. Commun. 11, 3696 (2020).Article

Zhang，J。等人。癌症基因组学的综合编码资源。国家公社。113696（2020）。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Dileep, V. & Gilbert, D. M. Single-cell replication profiling to measure stochastic variation in mammalian replication timing. Nat. Commun. 9, 427 (2018).Article

Dileep，V。＆Gilbert，D.M。单细胞复制分析，以测量哺乳动物复制时间的随机变化。国家公社。9427（2018）。文章

ADS

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Raczy, C. et al. Isaac: ultra-fast whole-genome secondary analysis on Illumina sequencing platforms. Bioinformatics 29, 2041–2043 (2013).Article

Raczy，C。等人。Isaac：在Illumina测序平台上进行超快速全基因组二次分析。生物信息学292041-2043（2013）。文章

CAS

中科院

PubMed

Google Scholar

谷歌学者

Cornish, A. J. et al. Correcting reference bias from the Illumina Isaac aligner enables analysis of cancer genomes. Preprint at bioRxiv 836171 https://doi.org/10.1101/836171 (2019).Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs.

Cornish，A.J.等人纠正Illumina Isaac aligner的参考偏差可以分析癌症基因组。bioRxiv 836171预印本https://doi.org/10.1101/836171（2019年）。桑德斯（Saunders，C.T.）等人（Strelka）：从测序的肿瘤-正常样本对中进行准确的体细胞小变异调用。

Bioinformatics 28, 1811–1817 (2012).Article .

生物信息学281811-1817（2012）。文章。

CAS

中科院

PubMed

Google Scholar

谷歌学者

Jamal-Hanjani, M. et al. Tracking the Evolution of Non-Small-Cell Lung Cancer. N. Engl. J. Med. 376, 2109–2121 (2017).Article

Jamal Hanjani，M.等人，追踪非小细胞肺癌的进展。N、英语。J、医学杂志3762109-2121（2017）。文章

CAS

中科院

PubMed

Google Scholar

谷歌学者

Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE blacklist: Identification of problematic regions of the genome. Sci. Rep. 9, 9354 (2019).Article

Amemiya，H.M.，Kundaje，A。＆Boyle，A.P。ENCODE黑名单：基因组问题区域的鉴定。科学。代表99354（2019）。文章

ADS

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).Article

Nik Zainal，S.等人，《21种乳腺癌的生活史》。。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Di Tommaso, P. et al. Nextflow enables reproducible computational workflows. Nat. Biotechnol. 35, 316–319 (2017).Article

Di Tommaso，P。等人Nextflow实现了可重复的计算工作流。美国国家生物技术公司。35316-319（2017）。文章

PubMed

Google Scholar

谷歌学者

Van Loo, P. et al. Allele-specific copy number analysis of tumors. Proc. Natl. Acad. Sci. USA 107, 16910–16915 (2010).Article

Van Loo，P。等人。肿瘤的等位基因特异性拷贝数分析。程序。纳特尔。阿卡德。科学。美国10716910-16915（2010）。文章

ADS

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Harris, H. L. et al. Chromatin alternates between A and B compartments at kilobase scale for subgenic organization. Nat. Commun. 14, 3303 (2023).Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).Article .

Harris，H.L。等人。染色质在亚基因组织的千碱基尺度上在A和B区室之间交替。国家公社。143303（2023）。Quinlan，A.R。＆Hall，I.M。BEDTools：用于比较基因组特征的灵活实用程序套件。生物信息学26841-842（2010）。文章。

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Greenman, C. D. et al. PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data. Biostatistics 11, 164–175 (2010).Article

Greenman，C.D.等人，《野餐：用微阵列癌症数据预测绝对等位基因拷贝数变异的算法》。生物统计学11164-175（2010）。文章

PubMed

Google Scholar

谷歌学者

Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 174, 1034–1035 (2018).Article

Bailey，M.H.等人。癌症驱动基因和突变的综合表征。细胞1741034-1035（2018）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Martínez-Jiménez, F. et al. A compendium of mutational cancer driver genes. Nat. Rev. Cancer 20, 555–572 (2020).Article

Martínez Jiménez，F.等人，《突变癌症驱动基因简编》。《国家癌症评论》20555-572（2020）。文章

PubMed

Google Scholar

谷歌学者

Frankell, A. M. et al. The evolution of lung cancer and impact of subclonal selection in TRACERx. Nature 616, 525–533 (2023).Article

Frankell，A.M.等人，《肺癌的进化和TRACERx亚克隆选择的影响》。自然616525-533（2023）。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Berger, A. H. et al. High-throughput phenotyping of lung cancer somatic mutations. Cancer Cell 32, 884 (2017).Article

Berger，A.H.等人。肺癌体细胞突变的高通量表型分析。癌细胞32884（2017）。文章

CAS

中科院

PubMed

Google Scholar

谷歌学者

Sondka, Z. et al. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat. Rev. Cancer 18, 696–705 (2018).Article

Sondka，Z.等人，《宇宙癌症基因普查：描述所有人类癌症的遗传功能障碍》。《国家癌症评论》18696-705（2018）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Ramirez, R. D. et al. Immortalization of human bronchial epithelial cells in the absence of viral oncoproteins. Cancer Res. 64, 9027–9034 (2004).Article

Ramirez，R.D.等人。在没有病毒癌蛋白的情况下，人支气管上皮细胞的永生化。癌症研究649027-9034（2004）。文章

CAS

中科院

PubMed

Google Scholar

谷歌学者

Kemp, S. J. et al. Immortalization of human alveolar epithelial cells to investigate nanoparticle uptake. Am. J. Respir. Cell Mol. Biol. 39, 591–597 (2008).Article

Kemp，S.J.等人。人肺泡上皮细胞的永生化以研究纳米粒子的摄取。Am.J.Respir。细胞分子生物学。39591-597（2008）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Hynds, R. E. et al. Expansion of airway basal epithelial cells from primary human non-small cell lung cancer tumors. Int. J. Cancer 143, 160–166 (2018).Article

Hynds，R.E.等人。原发性人类非小细胞肺癌肿瘤气道基底上皮细胞的扩增。Int.J.Cancer 143160–166（2018）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Garcia, M. et al. Sarek: a portable workflow for whole-genome sequencing analysis of germline and somatic variants. F1000Res 9, 63 (2020).Article

Garcia，M。等人。Sarek：用于种系和体细胞变异的全基因组测序分析的便携式工作流程。F1000Res 9，63（2020）。文章

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Ryba, T., Battaglia, D., Pope, B. D., Hiratani, I. & Gilbert, D. M. Genome-scale analysis of replication timing: from bench to bioinformatics. Nat. Protoc. 6, 870–895 (2011).Article

Ryba，T.，Battaglia，D.，Pope，B.D.，Hiratani，I。＆Gilbert，D.M。复制时间的基因组规模分析：从实验室到生物信息学。自然协议。6870-895（2011）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Marchal, C. et al. Genome-wide analysis of replication timing by next-generation sequencing with E/L Repli-seq. Nat. Protoc. 13, 819–839 (2018).Article

Marchal，C.等人。通过E/L Repli-seq的下一代测序对复制时间进行全基因组分析。自然协议。13819-839（2018）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Sambrook, J. & Russell, D. W. Purification of nucleic acids by extraction with phenol:chloroform. CSH Protoc. 2006, db.prot4455 (2006).

Sambrook，J。＆Russell，D.W。通过用苯酚：氯仿萃取纯化核酸。CSH协议。2006年，db.prot4455（2006）。

Google Scholar

谷歌学者

4D Nucleome Network. 4D Nucleome Policies: 2017-12-19 Release. Zenodo https://doi.org/10.5281/zenodo.1119074 (2017).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).Article

4D核小体网络。4D核小体政策：2017-12-19发布。泽诺多https://doi.org/10.5281/zenodo.1119074（2017年）。Martin，M。Cutadapt从高通量测序读数中删除了衔接子序列。。文章

Google Scholar

谷歌学者

Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).Article

Li，H。＆Durbin，R。使用Burrows-Wheeler变换进行快速准确的短读比对。生物信息学251754-1760（2009）。文章

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).Article

Li，H。等人。序列比对/图谱格式和SAMtools。生物信息学252078-2079（2009）。文章

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Watkins, T. B. K. et al. Pervasive chromosomal instability and karyotype order in tumour evolution. Nature 587, 126–132 (2020).Article

Watkins，T.B.K.等人。肿瘤进化中普遍存在的染色体不稳定性和核型顺序。自然587126-132（2020）。文章

ADS

CAS

中科院

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Zynda, G. J. et al. Repliscan: a tool for classifying replication timing regions. BMC Bioinform. 18, 362 (2017).Article

Zynda，G.J.等人。Repliscan：用于对复制时间区域进行分类的工具。BMC生物信息。18362（2017）。文章

Google Scholar

谷歌学者

McGranahan, N. et al. Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Sci. Transl. Med. 7, 283ra54 (2015).Article

McGranahan，N。等人。可操作驱动事件的克隆状态和癌症进化中突变过程的时间。科学。翻译。医学杂志7283RA54（2015）。文章

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).Article

Love，M.I.，Huber，W。＆Anders，S。用DESeq2缓和了RNA-seq数据的倍数变化和分散估计。。15550（2014）。文章

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Dietzen, M., Zhai, H. & Barrington, C. Software to Repeat the ART Analysis by Dietzen, Zhai et al. https://doi.org/10.5281/zenodo.11313291 (2024).Download referencesAcknowledgementsThis research was made possible through access to the data and findings generated by the 100,000 Genomes Project, under the guidance of the Lung-Cancer GeCIP (project RR616).

Dietzen，M.，Zhai，H。＆Barrington，C。软件重复Dietzen，Zhai等人的艺术分析。https://doi.org/10.5281/zenodo.11313291（2024年）。下载参考文献致谢本研究是在肺癌GeCIP（项目RR616）的指导下，通过访问100000个基因组项目产生的数据和发现而实现的。

The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health and Social Care) and funded by the National Institute for Health Research and NHS England. The Wellcome Trust, Cancer Research UK and the Medical Research Council have also funded research infrastructure.

100000个基因组计划由英国基因组学有限公司（英国卫生和社会保健部的全资公司）管理，并由英国国家卫生研究院和英国国民保健服务（NHS）资助。惠康基金会、英国癌症研究中心和医学研究委员会也为研究基础设施提供了资金。

The 100,000 Genomes Project uses data provided by patients and collected by the National Health Service as part of their care and support. We thank all participants and their families for contributing to this study. The authors thank Dr Alex J. Cornish and Dr Daniel Chubb (Institute of Cancer Research, London, UK) for providing code and support for processing the variant and copy number calls of the lung cancer tumours in the 100,000 Genomes Project.

100000个基因组计划使用患者提供的数据，并由国家卫生局收集，作为他们护理和支持的一部分。我们感谢所有参与者及其家人为这项研究做出的贡献。作者感谢Alex J.Cornish博士和Daniel Chubb博士（英国伦敦癌症研究所）在100000个基因组计划中为处理肺癌肿瘤的变异和拷贝数调用提供代码和支持。

The authors also thank Professor Terry Tetley and Dr Michele Chiappi (National Heart and Lung Institute, Imperial College London, UK) for providing the TT1 cell line, the Cell Services STP at the Francis Crick Institute for providing lung cancer cell lines, and Dr Panos Zalmas (Open Targets Validation Lab, Wellcome Trust Sanger Institute, Cambridge, UK) for his advice during the initiation of this study.

作者还感谢Terry Tetley教授和Michele Chiappi博士（英国伦敦帝国理工学院国家心肺研究所）提供TT1细胞系，Francis Crick研究所的细胞服务STP提供肺癌细胞系，以及Panos Zalmas博士（英国剑桥威康信托桑格研究所开放目标验证实验室）在本研究开始期间的建议。

The authors additionally acknowledge Dr Assma Ben Aissa (UCL Cancer Institute, London, UK) who initiated the CRUK0977-CL patient-derived cell line and Dr David Pearce (UCL Cancer Institute, London, UK) who supported the cell culture and DNA .

作者还感谢启动CRUK0977-CL患者来源细胞系的Assma Ben Aissa博士（英国伦敦大学学院癌症研究所）和支持细胞培养和DNA的David Pearce博士（英国伦敦大学学院癌症研究所）。

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PubMed Google ScholarContributionsM.D. and H.Z. contributed equally and were ordered alphabetically by surname. M.D. developed the Repli-seq bioinformatics pipeline, processed the WGS data from the 100,000 Genomes Project, led and conducted bioinformatics analyses and wrote the manuscript.

PubMed谷歌学术贡献。D、 H.Z.和H.Z.的贡献相同，并按姓氏的字母顺序排列。M、 D.开发了Repli-seq生物信息学管道，处理了来自100000个基因组计划的WGS数据，领导并进行了生物信息学分析，并撰写了手稿。

H.Z. designed and initiated this study, led and conducted the cell culturing, Repli-seq and other experiments, conducted bioinformatics analyses and wrote the manuscript. O.L. conducted bioinformatics analyses and contributed to writing the manuscript. O.P. and W-T.L. contributed to method development and gave feedback on the manuscript.

H、 Z.设计并启动了这项研究，领导并进行了细胞培养，Repli-seq和其他实验，进行了生物信息学分析并撰写了手稿。O、 L.进行了生物信息学分析，并为撰写手稿做出了贡献。O、 P.和W-T.L.为方法开发做出了贡献，并对手稿提供了反馈。

C.B. processed the Hi-C data. S.W. performed the library preparation and whole-genome sequencing. Y.G. performed fluorescence-activated cell sorting. R.E.H. established the CRUK0557-CL primary cell culture and provided guidance on tissue-of-origin matching. S.Z. helped direct bioinformatics analyses and gave feedback on the manuscript.

C、 B.处理Hi-C数据。S、 W.进行了文库制备和全基因组测序。Y、 G.进行荧光激活细胞分选。R、 E.H.建立了CRUK0557-CL原代细胞培养，并为起源组织匹配提供了指导。S、 Z.帮助指导生物信息学分析，并对稿件提供反馈。

N.M., C.S. and N.K. jointly designed and supervised this study and wrote the manuscript.Corresponding authorsCorrespondence to.

N、 M.，C.S.和N.K.共同设计并监督了这项研究，并撰写了手稿。通讯作者通讯。

Nicholas McGranahan or Nnennaya Kanu.Ethics declarations

尼古拉斯·麦克格拉纳汉（NicholasMcGranahan）或尼娜娅·卡努（NnennayaKanu）。道德宣言

Competing interests

相互竞争的利益

N.M. has stock options in and has consulted for Achilles Therapeutics and holds a European patent in determining HLA LOH (PCT/GB2018/052004) and is a co-inventor to a patent to identifying responders to cancer treatment (PCT/GB2018/051912). N.K. acknowledges grant support from AstraZeneca. C.S. acknowledges grant support from AstraZeneca, Boehringer-Ingelheim, Bristol Myers Squibb, Pfizer, Roche-Ventana, Invitae (previously Archer Dx Inc.

N、 M.在Achilles Therapeutics拥有股票期权，并咨询过Achilles Therapeutics，拥有确定HLA LOH的欧洲专利（PCT/GB2018/052004），并且是识别癌症治疗反应者专利（PCT/GB2018/051912）的共同发明人。N、 K.感谢阿斯利康的资助。C、美国感谢阿斯利康、勃林格殷格翰、百时美施贵宝、辉瑞、罗氏文塔纳、因维他（原阿彻Dx公司）的资助。

- collaboration in minimal residual disease sequencing technologies), and Ono Pharmaceutical. He is an AstraZeneca Advisory Board member and Chief Investigator for the AZ MeRmaiD 1 and 2 clinical trials and is also chief investigator of the NHS Galleri trial. He has consulted for Achilles Therapeutics, Amgen, AstraZeneca, Pfizer, Novartis, GlaxoSmithKline, MSD, Bristol Myers Squibb, Illumina, Genentech, Roche-Ventana, GRAIL, Medicxi, Metabomed, Bicycle Therapeutics, Roche Innovation Centre Shanghai, and the Sarah Cannon Research Institute, C.S.

-最小残留疾病测序技术的合作）和Ono Pharmaceutical。他是阿斯利康咨询委员会成员和AZ美人鱼1号和2号临床试验的首席研究员，也是NHS Galleri试验的首席研究员。他曾为阿基里斯治疗公司、安进公司、阿斯利康公司、辉瑞公司、诺华公司、葛兰素史克公司、MSD公司、百时美施贵宝公司、Illumina公司、基因泰克公司、罗氏文塔纳公司、GRAIL公司、Medicxi公司、Metabomed公司、自行车治疗公司、上海罗氏创新中心和莎拉·坎农研究所提供咨询。

had stock options in Apogen Biotechnologies and GRAIL until June 2021, and currently has stock options in Epic Bioscience, Bicycle Therapeutics, and has stock options and is co-founder of Achilles Therapeutics. C.S. holds patents relating to assay technology to detect tumour recurrence (PCT/GB2017/053289); to targeting neoantigens (PCT/EP2016/059401), identifying patent response to immune checkpoint blockade (PCT/EP2016/071471), determining HLA LOH (PCT/GB2018/052004), predicting survival rates of patients with cancer (PCT/GB2020/050221), identifying patients who respond to cancer treatment (PCT/GB2018/051912), US patent relating to detecting tumour mutations (PCT/US2017/28013), methods for lung cancer detection (US20190106751A1) and both a European and US patent related to identifying insertion.

在2021年6月之前拥有Apogen Biotechnologies和GRAIL的股票期权，目前拥有Epic Bioscience，Bicycle Therapeutics的股票期权，拥有股票期权，并且是Achilles Therapeutics的联合创始人。C、美国拥有与检测肿瘤复发的检测技术相关的专利（PCT/GB2017/053289）；靶向新抗原（PCT/EP2016/059401），鉴定对免疫检查点阻断的专利反应（PCT/EP2016/071471），确定HLA LOH（PCT/GB2018/052004），预测癌症患者的生存率（PCT/GB2020/050221），鉴定对癌症治疗有反应的患者（PCT/GB2018/051912），与检测肿瘤突变有关的美国专利（PCT/US2017/28013），肺癌检测方法（US20190106751A1）以及与识别插入相关的欧洲和美国专利。

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Reprints and permissionsAbout this articleCite this articleDietzen, M., Zhai, H., Lucas, O. et al. Replication timing alterations are associated with mutation acquisition during breast and lung cancer evolution.

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Nat Commun 15, 6039 (2024). https://doi.org/10.1038/s41467-024-50107-4Download citationReceived: 07 January 2024Accepted: 01 July 2024Published: 18 July 2024DOI: https://doi.org/10.1038/s41467-024-50107-4Share 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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