Intrinsic Versus Extrinsic Cancer Risk Factors and Aging

Podolskiy, Dmitriy I.; Gladyshev, Vadim N.

doi:10.1016/j.molmed.2016.08.001

Cited by 16 publications

(12 citation statements)

References 11 publications

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“…), to overall cancer risk in humans (45–47). However, the interplay between all these factors makes it difficult to tease out the various contributions using epidemiological data.…”

Section: Discussionmentioning

confidence: 99%

Locoregional Effects of Microbiota in a Preclinical Model of Colon Carcinogenesis

et al. 2017

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Inflammation and microbiota are critical components of intestinal tumorigenesis. To dissect how the microbiota contributes to tumor distribution, we generated germ-free (GF) ApcMin/+ and ApcMin/+;Il10−/− mice and exposed them to specific-pathogen-free (SPF) or colorectal cancer-associated bacteria. We found colon tumorigenesis significantly correlated with inflammation in SPF housed ApcMin/+;Il10−/−, but not ApcMin/+ mice. In contrast, small intestinal neoplasia development significantly correlated with age in both ApcMin/+;Il10−/− and ApcMin/+ mice. GF ApcMin/+;Il10−/− mice conventionalized by an SPF microbiota had significantly more colon tumors compared to GF mice. Gnotobiotic studies revealed that while Fusobacterium nucleatum clinical isolates with FadA and Fap2 adhesins failed to induce inflammation and tumorigenesis, pks+ Escherichia coli promoted tumorigenesis in the ApcMin/+;Il10−/− model in a colibactin-dependent manner, suggesting colibactin is a driver of carcinogenesis. Our results suggest a distinct etiology of cancers in different locations of the gut, where colon cancer is primarily driven by inflammation and the microbiome, while age is a driving force for small intestine cancer.

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“…), to overall cancer risk in humans (45–47). However, the interplay between all these factors makes it difficult to tease out the various contributions using epidemiological data.…”

Section: Discussionmentioning

confidence: 99%

Locoregional Effects of Microbiota in a Preclinical Model of Colon Carcinogenesis

et al. 2017

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“…A robust and precise marker, which can be used to estimate the biological age of organisms and to evaluate the effect of different interventions on lifespan has been a Holy Grail of aging research since the first days of this field. Various biomarkers of age have previously been suggested, which are based on telomere length ( Harley et al, 1990 ), mutation accumulation ( Dollé et al, 2000 ; Podolskiy et al, 2016 ; Podolskiy and Gladyshev, 2016 ), gene expression levels ( de Magalhães et al, 2009 ) or T-cell-specific DNA rearrangements ( Zubakov et al, 2010 ). However, these approaches to construct a precise biomarker of age proved to be relatively limited in their potential to assess the aging process and examine its modulation by various interventions, mostly due to large variability of detected ages.…”

Section: Introductionmentioning

confidence: 99%

A whole lifespan mouse multi-tissue DNA methylation clock

Meer

Podolskiy

Tyshkovskiy

et al. 2018

eLife

158

205

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Age predictors based on DNA methylation levels at a small set of CpG sites, DNAm clocks, have been developed for humans and extended to several other species. Three currently available versions of mouse DNAm clocks were either created for individual tissues or tuned toward young ages. Here, we constructed a robust multi-tissue age predictor based on 435 CpG sites, which covers the entire mouse lifespan and remains unbiased with respect to any particular age group. It can successfully detect the effects of certain lifespan-modulating interventions on DNAm age as well as the rejuvenation effect related to the transition from fibroblasts to iPSCs. We have carried out comparative analyses of available mouse DNAm clocks, which revealed their broad applicability, but also certain limitations to the use of tissue-specific and multi-tissue age predictors. Together, these tools should help address diverse questions in aging research.

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“… 31 . A question for future work is whether mutation load agrees with the scaled Weibull function or age-specific mutational signatures 32 – 34 . Another interesting observation is that testicular germ cell cancer incidence peaks at younger age compared to other cancer types, which might be explained by accelerated aging of testis 31 .…”

Section: Discussionmentioning

confidence: 99%

A Simple 3-Parameter Model for Cancer Incidences

Zhang

Fröhlich

Grigoriev

et al. 2018

Sci Rep

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We propose a simple 3-parameter model that provides very good fits for incidence curves of 18 common solid cancers even when variations due to different locations, races, or periods are taken into account. From a data perspective, we use model selection (Akaike information criterion) to show that this model, which is based on the Weibull distribution, outperforms other simple models like the Gamma distribution. From a modeling perspective, the Weibull distribution can be justified as modeling the accumulation of driver events, which establishes a link to stem cell division based cancer development models and a connection to a recursion formula for intrinsic cancer risk published by Wu et al. For the recursion formula a closed form solution is given, which will help to simplify future analyses. Additionally, we perform a sensitivity analysis for the parameters, showing that two of the three parameters can vary over several orders of magnitude. However, the shape parameter of the Weibull distribution, which corresponds to the number of driver mutations required for cancer onset, can be robustly estimated from epidemiological data.

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Intrinsic Versus Extrinsic Cancer Risk Factors and Aging

Abstract: Two recent stimulating publications have examined the causes of cancer, comparing “bad luck” vs. environment as main risk factors for cancer incidence. However, bringing aging into the picture might question the entire debate.

Cited by 16 publications

References 11 publications

Locoregional Effects of Microbiota in a Preclinical Model of Colon Carcinogenesis

Locoregional Effects of Microbiota in a Preclinical Model of Colon Carcinogenesis

A whole lifespan mouse multi-tissue DNA methylation clock

A Simple 3-Parameter Model for Cancer Incidences

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