Pan-cancer whole-genome comparison of primary and metastatic solid tumours

Martínez-Jiménez, Francisco; Movasati, Ali; Brunner, Sascha Remy; Nguyen, Luan Thanh; Priestley, Peter; Cuppen, Edwin; Hoeck, Arne van

doi:10.1038/s41586-023-06054-z

Cited by 104 publications

(33 citation statements)

References 61 publications

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“…al. found that the metastatic tumors have a higher frequency of structural variants 8 . Incorporating additional structural mutation signature data and copy number variations (CNVs) has the potential to further improve AI algorithms from diverse multi-omics datasets.…”

Section: Discussionmentioning

confidence: 94%

“…The mutational signature contribution matrices pertaining to primary breast cancers within the Pan-Cancer Analysis of Whole Genomes (PCAWG) cohort, and metastatic breast cancers within the Hartwig Medical Foundation (HMF) cohort, were extracted from the supplementary tables of the reference 8 . The dataset encompasses two types of contribution matrices.…”

Section: Genomic Data Acquisitionmentioning

confidence: 99%

“…Somatic mutations in whole genomes have empowered the detection of multiple mutational processes active in tumorigenesis, and such processes manifested in the tumor genome during tumor progression and treatment. Several studies have investigated the genomic landscape of metastatic tumors varieties [5][6][7][8] . Deep learning methods have been increasingly applied in the field of cancer research, particularly in studying the cancer metastasis progress.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sparse Modeling of Genomic Landscape Identifies Pathogenic Processes and Therapeutic Targets in Metastatic Breast Cancer

Pu,

Tian,

Zheng

et al. 2023

Preprint

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Breast cancer is a heterogeneous disease and ranks as one of the most lethal and frequently detected disease in the world. It poses significant challenges for precision therapy. To better decipher the patterns of heterogeneous nature in human genome and converge them into common functionals, mutational signatures are introduced to define the types of DNA damage, repair and replicative mechanisms that shape the genomic landscape of each cancer patient.In this study, we developed a deep learning (DL) model, MetaWise 2.0, based on pruning technology that improved model generalization with deep sparsity. We applied it to patient samples from multiple sequencing studies, and identified statistically significant mutational signatures associated with metastatic progression using Shapley additive explanations (SHAP). We also employed gene cumulative contribution abundance analysis to link the mutational signatures with relevant genes, which could unearth the shared molecular mechanisms behind tumorigenesis and metastasis of each patient and lead to novel therapeutic target identification.Our study illustrates that MetaWise 2.0 is an effective DL tool for discovering clinically meaningful mutational signatures in metastatic breast cancer (MBC) and relating them directly to relevant biological functions and gene targets. These findings could facilitate the development of novel therapeutic strategies and improve the clinical outcomes for individual patients.

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Section: Discussionmentioning

confidence: 94%

Section: Genomic Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sparse Modeling of Genomic Landscape Identifies Pathogenic Processes and Therapeutic Targets in Metastatic Breast Cancer

Pu,

Tian,

Zheng

et al. 2023

Preprint

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“…Copy number data of primary tumors of various entities and their respective metastases unveiled highly increased ratios of chr8p loss in the metastatic tissue compared to the corresponding primary site (fig. S3A) ( 27 , 28 ). Moreover, chr8p deletion was more prominent in distant than in local metastasis sites indicating chr8p loss to be a beneficial factor for the metastatic capacity of cancer cells (fig.…”

Section: Resultsmentioning

confidence: 99%

Chromosome 8p engineering reveals increased metastatic potential targetable by patient-specific synthetic lethality in liver cancer

Huth,

Dreher,

Lemke

et al. 2023

Sci. Adv.

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Large-scale chromosomal aberrations are prevalent in human cancer, but their function remains poorly understood. We established chromosome-engineered hepatocellular carcinoma cell lines using CRISPR-Cas9 genome editing. A 33–mega–base pair region on chromosome 8p (chr8p) was heterozygously deleted, mimicking a frequently observed chromosomal deletion. Using this isogenic model system, we delineated the functional consequences of chr8p loss and its impact on metastatic behavior and patient survival. We found that metastasis-associated genes on chr8p act in concert to induce an aggressive and invasive phenotype characteristic for chr8p-deleted tumors. Genome-wide CRISPR-Cas9 viability screening in isogenic chr8p-deleted cells served as a powerful tool to find previously unidentified synthetic lethal targets and vulnerabilities accompanying patient-specific chromosomal alterations. Using this target identification strategy, we showed that chr8p deletion sensitizes tumor cells to targeting of the reactive oxygen sanitizing enzyme Nudix hydrolase 17. Thus, chromosomal engineering allowed for the identification of novel synthetic lethalities specific to chr8p loss of heterozygosity.

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“…The rate of SBS1 accumulation varies among cell types and increases with the rate of cellular turnover [3,18]. The loading of SBS1 mutations has also been found to increase in metastatic tumours relative to their primary tumour counterparts, plausibly due to accelerated cell division rates in metastasis [19]. In contrast, SBS5 has a more diffuse distribution among the 96 SBS types.…”

Section: Introductionmentioning

confidence: 99%

Disentangling sources of clock-like mutations in germline and soma

Spisak,

de Manuel,

Milligan

et al. 2023

Preprint

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The rates of mutations vary across cell types. To identify causes of this variation, mutations are often decomposed into a combination of the single base substitution (SBS) “signatures” observed in germline, soma and tumors, with the idea that each signature corresponds to one or a small number of underlying mutagenic processes. Two such signatures turn out to be ubiquitous across cell types: SBS signature 1, which consists primarily of transitions at methylated CpG sites caused by spontaneous deamination, and the more diffuse SBS signature 5, which is of unknown etiology. In cancers, the number of mutations attributed to these two signatures accumulates linearly with age of diagnosis, and thus the signatures have been termed “clock-like.” To better understand this clocklike behavior, we develop a mathematical model that includes DNA replication errors, unrepaired damage, and damage repaired incorrectly. We show that mutational signatures can exhibit clocklike behavior because cell divisions occur at a constant rate and/or because damage rates remain constant over time, and that these distinct sources can be teased apart by comparing cell lineages that divide at different rates. With this goal in mind, we analyze the rate of accumulation of mutations in multiple cell types, including soma as well as male and female germline. We find no detectable increase in SBS signature 1 mutations in neurons and only a very weak increase in mutations assigned to the female germline, but a significant increase with time in rapidly-dividing cells, suggesting that SBS signature 1 is driven by rounds of DNA replication occurring at a relatively fixed rate. In contrast, SBS signature 5 increases with time in all cell types, including post-mitotic ones, indicating that it accumulates independently of cell divisions; this observation points to errors in DNA repair as the key underlying mechanism. Thus, the two “clock-like” signatures observed across cell types likely have distinct origins, one set by rates of cell division, the other by damage rates.

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Pan-cancer whole-genome comparison of primary and metastatic solid tumours

Cited by 104 publications

References 61 publications

Sparse Modeling of Genomic Landscape Identifies Pathogenic Processes and Therapeutic Targets in Metastatic Breast Cancer

Sparse Modeling of Genomic Landscape Identifies Pathogenic Processes and Therapeutic Targets in Metastatic Breast Cancer

Chromosome 8p engineering reveals increased metastatic potential targetable by patient-specific synthetic lethality in liver cancer

Disentangling sources of clock-like mutations in germline and soma

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