Mutation or not, what directly establishes a neoplastic state, namely cellular immortality and autonomy, still remains unknown and should be prioritized in our research

Zhu, Shengming; Wang, Jiangang; Zellmer, Lucas; Xu, Nuo; Liu, Mei; Hu, Yun Hang; Ma, Hong; Deng, Fei; Yang, Wenxiu; Liao, D. Joshua

doi:10.7150/jca.72628

Cited by 8 publications

(7 citation statements)

References 632 publications

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“…This would allow to experimentally test the transition of linearity from a dose-effect relationship to a dose-risk relationship. The development of multidisciplinary approaches involving radiobiology, epidemiology, and modelling , such as the adverse outcome pathway approach (Chauhan et al 2022) or mechanistic models (NCRP 2020), should make it possible to better understand the discrepancies between experimental results in animals and observational results in humans (Zhu et al 2022).…”

Section: Synthesis and Outlookmentioning

confidence: 99%

The scientific basis for the use of the linear no-threshold (LNT) model at low doses and dose rates in radiological protection

Laurier¹,

Billarand²,

Klokov³

et al. 2023

J. Radiol. Prot.

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The Linear No-Threshold (LNT) model was introduced into the radiological protection system about 60 years ago, but this model and its use in radiation protection are still debated today. This article presents an overview of results on effects of exposure to low linear-energy-transfer (LET) radiation in radiobiology and epidemiology accumulated over the last decade and discusses their impact on the use of the LNT model in the assessment of radiation-related cancer risks at low doses. The knowledge acquired over the past 10 years, both in radiobiology and epidemiology, has reinforced scientific knowledge about cancer risks at low doses. In radiobiology, although certain mechanisms do not support linearity, the early stages of carcinogenesis comprised of mutational events, which are assumed to play a key role in carcinogenesis, show linear responses to doses from as low as 10 mGy. The impact of non-mutational mechanisms on the risk of radiation-related cancer at low doses is currently difficult to assess. In epidemiology, the results show excess cancer risks at dose levels of 100 mGy or less. While some recent results indicate non-linear dose relationships for some cancers, overall, the LNT model does not substantially overestimate the risks at low doses. Recent results, in radiobiology or in epidemiology, suggest that a dose threshold, if any, could not be greater than a few tens of mGy. The scientific knowledge currently available does not contradict the use of the LNT model for the assessment of radiation- related cancer risks within the radiological protection system, and no other dose-risk relationship seems more appropriate for radiological protection purposes.

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Section: Synthesis and Outlookmentioning

confidence: 99%

The scientific basis for the use of the linear no-threshold (LNT) model at low doses and dose rates in radiological protection

Laurier¹,

Billarand²,

Klokov³

et al. 2023

J. Radiol. Prot.

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“…Il est probable que ces mécanismes peuvent potentialiser ou atténuer le risque de cancer, mais leur impact sur le risque de cancer associé à de faibles doses est difficile à évaluer (UNSCEAR, 2021 ;Wojcik, 2022). La poursuite des recherches sur ces mécanismes non-mutationnels devrait permettre de consolider les connaissances sur leur implication dans le développement des cancers dans les années à venir (Zhu et al, 2022).…”

Section: Relation Dose-effet à Faibles Dosesunclassified

Fondements scientifiques de l’utilisation du modèle linéaire sans seuil (LNT) aux faibles doses et débits de dose en radioprotection

Laurier,

Billarand,

Klokov

et al. 2023

Radioprotection

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Le modèle linéaire sans seuil (LNT) a été introduit dans le système de radioprotection il y a environ 60 ans, mais ce modèle et son utilisation en radioprotection sont encore débattus aujourd'hui. Cet article résume les résultats en radiobiologie et en épidémiologie accumulés au cours de la dernière décennie sur les effets d’une exposition aux rayonnements ionisants à faible Transfert d’Energie Linéique (TEL) et discute de leur impact sur l'utilisation du modèle LNT dans l'évaluation des risques de cancer par rayonnement à faibles doses. Les connaissances acquises au cours des 10 dernières années, tant en radiobiologie qu'en épidémiologie, ont renforcé les fondements scientifiques sur les risques de cancer à faibles doses. En radiobiologie, bien que certains mécanismes ne soient pas linéaires avec la dose, les premiers stades de la cancérogenèse composés d'événements mutationnels, qui jouent un rôle clé dans la cancérogenèse, montrent des réponses linéaires à des doses aussi faibles que 10 mGy. L'impact des mécanismes non mutationnels sur le risque de cancer associé aux rayonnements à faibles doses est actuellement difficile à évaluer. En épidémiologie, les résultats montrent un excès de risques de cancer à des niveaux de dose de 100 mGy ou moins. Bien que certains résultats récents indiquent des relations non linéaires avec la dose pour certains types de cancers, le modèle LNT ne surestime pas substantiellement globalement les risques à faibles doses. Les résultats actuels, en radiobiologie ou en épidémiologie, ne démontrent pas l'existence d'un seuil de dose en dessous duquel le risque de cancer associé aux rayonnements serait nul. Des incertitudes persistent mais un tel seuil de dose, s'il existe, ne pourrait être supérieur à quelques dizaines de mGy. L'IRSN considère que les connaissances scientifiques actuellement disponibles ne remettent pas en cause l'utilisation du modèle LNT pour l'évaluation des risques de cancers radio-induits en appui au système de radioprotection. L'utilisation de ce modèle semble raisonnable d'un point de vue scientifique, et aucune autre relation dose-réponse ne semble plus adaptée ou justifiée à des fins de radioprotection.

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“…Cellular transformation and immortalization is a complex process driven primarily by the accumulation of point mutations that change protein sequences [1], but also by other genomic changes, such as structural genomic re-arrangements that produce global changes in chromosomal architecture [2,3]; numerical genomic re-arrangements that modify ploidy [4]; and changes in promoter methylation, which generally affect gene expression patterns [5]. For example, massive and rapid chromosomal re-arrangement events such as chromothripsis have been shown to promote the evolution of certain cancers [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…We computed performance metric silhouette scores to measure the quality of the clusters generated and used p-values to determine which formulation was significant for the integration of the omics data. The novelty of the work can be summarized in three major points: (1) This study introduces an interpretable integration strategy, as opposed to a black-box, neural-network-based technique, for omics integration; (2) the approach employed is simple yet efficient, utilizing a combination of regression and clustering to identify the most significant formulation for integrating omics data; and (3) the study demonstrates that LIHC can be categorized by genomic changes beyond mutations.…”

Section: Introductionmentioning

confidence: 99%

Multi-Omics Integration for Liver Cancer Using Regression Analysis

Raj,

Petreaca,

Mirzaei

2024

CIMB

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Genetic biomarkers have played a pivotal role in the classification, prognostication, and guidance of clinical cancer therapies. Large-scale and multi-dimensional analyses of entire cancer genomes, as exemplified by projects like The Cancer Genome Atlas (TCGA), have yielded an extensive repository of data that holds the potential to unveil the underlying biology of these malignancies. Mutations stand out as the principal catalysts of cellular transformation. Nonetheless, other global genomic processes, such as alterations in gene expression and chromosomal re-arrangements, also play crucial roles in conferring cellular immortality. The incorporation of multi-omics data specific to cancer has demonstrated the capacity to enhance our comprehension of the molecular mechanisms underpinning carcinogenesis. This report elucidates how the integration of comprehensive data on methylation, gene expression, and copy number variations can effectively facilitate the unsupervised clustering of cancer samples. We have identified regressors that can effectively classify tumor and normal samples with an optimal integration of RNA sequencing, DNA methylation, and copy number variation while also achieving significant p-values. Further, these regressors were trained using linear and logistic regression with k-means clustering. For comparison, we employed autoencoder- and stacking-based omics integration and computed silhouette scores to evaluate the clusters. The proof of concept is illustrated using liver cancer data. Our analysis serves to underscore the feasibility of unsupervised cancer classification by considering genetic markers beyond mutations, thereby emphasizing the clinical relevance of additional global cellular parameters that contribute to the transformative process in cells. This work is clinically relevant because changes in gene expression and genomic re-arrangements have been shown to be signatures of cellular transformation across cancers, as well as in liver cancers.

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Mutation or not, what directly establishes a neoplastic state, namely cellular immortality and autonomy, still remains unknown and should be prioritized in our research

Cited by 8 publications

References 632 publications

The scientific basis for the use of the linear no-threshold (LNT) model at low doses and dose rates in radiological protection

The scientific basis for the use of the linear no-threshold (LNT) model at low doses and dose rates in radiological protection

Fondements scientifiques de l’utilisation du modèle linéaire sans seuil (LNT) aux faibles doses et débits de dose en radioprotection

Multi-Omics Integration for Liver Cancer Using Regression Analysis

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