Genetic instability from a single S phase after whole-genome duplication

Gemble, Simon; Wardenaar, René; Keuper, Kristina; Srivastava, Nishit; Nano, Maddalena; Macé, Anne‐Sophie; Tijhuis, Andréa E.; Bernhard, Sara; Spierings, Diana C.J.; Simon, Anthony; Goundiam, Oumou; Hochegger, Helfrid; Piel, Matthieu; Foijer, Floris; Storchová, Zuzana; Basto, Renata

doi:10.1038/s41586-022-04578-4

“…Fig. 4b-f), supporting the established role of WGD 32 and TP53 31 loss in the ubiquitous generation of large genomic alterations. As mentioned earlier (see Fig.…”

Section: Genomic and Clinical Features Associated With Structural Var...supporting

confidence: 68%

“…We next investigated differences in four well-studied genomic instability markers: chromosomal aneuploidy score 31 , loss of heterozygosity (LOH) genome fraction in diploid tumors, WGD 32 , and TP53 alterations 32,33 (Fig. 1e, Supp.…”

Section: Global Genomic Characteristics Of Primary and Metastatic Tumorsmentioning

confidence: 99%

“…5b), indicating that alterations of these genes may enhance aggressiveness by disturbing pan-cancer hallmarks of tumorigenesis. In fact, TP53 alterations have been extensively linked to genomic instability 32,33 , while CDK2NA and TERT are key regulators of cell proliferation, two pathways that are often perturbed in metastatic tumors 59 (see earlier). Finally, some driver genes were strongly enriched in primary tumors, such as KMT2D mutations in nonsmall cell lung cancer (primary 13.7%, and metastatic 2.1%) and POLE mutations in primary colorectal carcinomas (primary 13%, metastatic 0.5%).…”

Section: Cancer Driver Gene Alterations In Primary and Metastatic Tumorsmentioning

confidence: 99%

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

Cuppen

¹

,

Martínez-Jiménez

²

,

Movasati

³

et al. 2022

Preprint

0

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Metastatic cancer remains almost inevitably a lethal disease. A better understanding of disease progression and response to therapies therefore remains of utmost importance. Here, we characterize the genomic differences between early-stage untreated primary tumors and late-stage treated metastatic tumors using a harmonized pan-cancer (re-)analysis of 7,152 whole-genome-sequenced tumors. In general, our analysis shows that metastatic tumors have a low intra-tumor heterogeneity, high genomic instability and increased frequency of structural variants with comparatively a modest increase in the number of small genetic variants. However, these differences are cancer type specific and are heavily impacted by the exposure to cancer therapies. Five cancer types, namely breast, prostate, thyroid, kidney clear carcinoma and pancreatic neuroendocrine, are a clear exception to the rule, displaying an extensive transformation of their genomic landscape in advanced stages. These changes were supported by increased genomic instability and involved substantial differences in tumor mutation burden, clock-based molecular signatures and the landscape of driver alterations as well as a pervasive increase in structural variant burden. The majority of cancer types had either moderate genomic differences (e.g., cervical and colorectal cancers) or highly consistent genomic portraits (e.g., ovarian cancer and skin melanoma) when comparing early- and late-stage disease. Exposure to treatment further scars the tumor genome and introduces an evolutionary bottleneck that selects for known therapy-resistant drivers in approximately half of treated patients. Our data showcases the potential of whole-genome analysis to understand tumor evolution and provides a valuable resource to further investigate the biological basis of cancer and resistance to cancer therapies.

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“…5b), indicating that alterations of these genes may enhance aggressiveness by disturbing pan-cancer hallmarks of tumorigenesis. In fact, TP53 alterations have been extensively linked to genomic instability 32,33 , while CDK2NA and TERT are key regulators of cell proliferation, two pathways that are often perturbed in metastatic tumors 59 (see earlier). Finally, some driver genes were strongly enriched in primary tumors, such as KMT2D mutations in non-small cell lung cancer (primary 13.7%, and metastatic 2.1%) and POLE mutations in primary colorectal carcinomas (primary 13%, metastatic 0.5%).…”

Section: Resultsmentioning

confidence: 99%

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

Martínez-Jiménez

¹

,

Movasati

²

,

Brunner

³

et al. 2022

Preprint

View full text Add to dashboard Cite

Metastatic cancer remains almost inevitably a lethal disease. A better understanding of disease progression and response to therapies therefore remains of utmost importance. Here, we characterize the genomic differences between early-stage untreated primary tumors and late-stage treated metastatic tumors using a harmonized pan-cancer (re-)analysis of 7,152 whole-genome-sequenced tumors. In general, our analysis shows that metastatic tumors have a low intra-tumor heterogeneity, high genomic instability and increased frequency of structural variants with comparatively a modest increase in the number of small genetic variants. However, these differences are cancer type specific and are heavily impacted by the exposure to cancer therapies. Five cancer types, namely breast, prostate, thyroid, kidney clear carcinoma and pancreatic neuroendocrine, are a clear exception to the rule, displaying an extensive transformation of their genomic landscape in advanced stages. These changes were supported by increased genomic instability and involved substantial differences in tumor mutation burden, clock-based molecular signatures and the landscape of driver alterations as well as a pervasive increase in structural variant burden. The majority of cancer types had either moderate genomic differences (e.g., cervical and colorectal cancers) or highly consistent genomic portraits (e.g., ovarian cancer and skin melanoma) when comparing early- and late-stage disease. Exposure to treatment further scars the tumor genome and introduces an evolutionary bottleneck that selects for known therapy-resistant drivers in approximately half of treated patients. Our data showcases the potential of whole-genome analysis to understand tumor evolution and provides a valuable resource to further investigate the biological basis of cancer and resistance to cancer therapies.

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“…A cellular automaton (CA) is a discrete dynamical system that consists of a huge number of identical finite-state automata ( Abou-Jaoudé et al, 2016 ; Xiao et al, 2020 ). Self-replication is a fundamental feature of life in biological resources, and it is a process of biosynthesis in which the original structure is replicated in the exact same structure ( Cea et al, 2015 ; Baris et al, 2022 ; Gemble et al, 2022 ). Research of self-replication on CAs was founded by von Neumann (1966) and was viewed as one of the origins of artificial life research ( Marchal, 1998 ; Gindin et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the diversity of self-replicating structures using active self-adapting mechanisms

Xu

¹

,

Wu

²

,

Peng

³

et al. 2022

Front. Genet.

1

0

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Numerous varieties of life forms have filled the earth throughout evolution. Evolution consists of two processes: self-replication and interaction with the physical environment and other living things around it. Initiated by von Neumann et al. studies on self-replication in cellular automata have attracted much attention, which aim to explore the logical mechanism underlying the replication of living things. In nature, competition is a common and spontaneous resource to drive self-replications, whereas most cellular-automaton-based models merely focus on some self-protection mechanisms that may deprive the rights of other artificial life (loops) to live. Especially, Huang et al. designed a self-adaptive, self-replicating model using a greedy selection mechanism, which can increase the ability of loops to survive through an occasionally abandoning part of their own structural information, for the sake of adapting to the restricted environment. Though this passive adaptation can improve diversity, it is always limited by the loop’s original structure and is unable to evolve or mutate new genes in a way that is consistent with the adaptive evolution of natural life. Furthermore, it is essential to implement more complex self-adaptive evolutionary mechanisms not at the cost of increasing the complexity of cellular automata. To this end, this article proposes new self-adaptive mechanisms, which can change the information of structural genes and actively adapt to the environment when the arm of a self-replicating loop encounters obstacles, thereby increasing the chance of replication. Meanwhile, our mechanisms can also actively add a proper orientation to the current construction arm for the sake of breaking through the deadlock situation. Our new mechanisms enable active self-adaptations in comparison with the passive mechanism in the work of Huang et al. which is achieved by including a few rules without increasing the number of cell states as compared to the latter. Experiments demonstrate that this active self-adaptability can bring more diversity than the previous mechanism, whereby it may facilitate the emergence of various levels in self-replicating structures.

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Genetic instability from a single S phase after whole-genome duplication

Cited by 83 publications

References 52 publications

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

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

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

Enhancing the diversity of self-replicating structures using active self-adapting mechanisms

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