Analysis of cancer genomes reveals basic features of human aging and its role in cancer development

Podolskiy, Dmitriy I.; Lobanov, Alexei V.; Kryukov, Gregory V.; Gladyshev, Vadim N.

doi:10.1038/ncomms12157

Cited by 85 publications

(60 citation statements)

References 53 publications

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“…The total count of pigmented naevi decreases from middle age onwards, which may be explained by the loss of either pigment‐producing cells or a reduction in melanogenesis, and is consistent with AHM patients having an older average age . The difficulty and therefore delay in detecting AHM, and the accumulation of somatic genetic mutations with age and chronic sun exposure, could also influence the older age of AHM diagnosis …”

Section: Discussionmentioning

confidence: 52%

“…26 The difficulty and therefore delay in detecting AHM, and the accumulation of somatic genetic mutations with age and chronic sun exposure, could also influence the older age of AHM diagnosis. 27,28 Prevalence of red hair was significantly greater in our AHM population than the pigmented melanoma patients (34% vs. 15%; P = 0.01), and both are higher than the control population at 7% (data not shown). Red hair as a predictor for AHM is well recognized.…”

Section: Discussionmentioning

confidence: 60%

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Phenotypic and genotypic analysis of amelanotic and hypomelanotic melanoma patients

Rayner

McMeniman

Duffy

et al. 2019

Acad Dermatol Venereol

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Background Amelanotic/hypomelanotic melanoma is associated with poorer outcomes due to a more advanced disease stage at diagnosis. Objective To determine phenotypic risks and genotypic associations with amelanotic/hypomelanotic melanoma to develop a clinical and genetic profile that could assist in identifying high‐risk individuals. Methods The Brisbane Naevus Morphology Study conducted from 2009 to 2016 has recruited a core of 1254 participants. Participants were drawn from a combination of volunteers from dermatology outpatient clinics, private dermatology clinics, the Brisbane Longitudinal Twin Study and QSkin study. Case participants had a personal history of melanoma and control participants no personal history of melanoma. We specifically examined seven known candidate pigmentation and melanoma genes and pigmentary phenotypic characteristics in participants with amelanotic/hypomelanotic melanoma compared to pigmented melanomas. This assayed single nucleotide polymorphisms in MC1R, TYR, HERC/OCA2, IRF4, MTAP, PLA2G6 and MITF. Results Forty‐seven participants had at least one amelanotic/hypomelanotic melanoma, and 389 had pigmented melanomas, with amelanotic/hypomelanotic melanoma patients significantly older than pigmented melanoma participants (63.3 ± 13.0 vs. 54.6 ± 15.3 years; P < 0.001). Amelanotic/hypomelanotic melanoma patients were more likely than pigmented melanoma patients to have red hair (34% vs. 15%; P = 0.01), severe hand freckling (13% vs. 5%; P = 0.01) and propensity to sunburn (63% vs. 44%; P = 0.01). MC1R R/R genotype was much more frequent in our amelanotic/hypomelanotic melanoma population (31.1% vs. 11%; P < 0.001; OR 26.4 vs. 5.9; control 1.0). Amelanotic/hypomelanotic melanoma was associated with TYR rs1126809*A/A [OR (CI 95%) 2.7 (1.1–6.8) vs. 1.2 (0.8–1.9)] and PLA2G6 rs11570734*A/A [OR (CI 95%) 3.7 (1.0–13.6) vs. 1.3 (0.9–2.0)]. The MTAP melanoma risk SNP genotype, associated with darker pigmentation, (rs4636294*A/A) was less common in amelanotic/hypomelanotic melanoma patients [OR (CI 95%) 0.8 (0.3–2.1) vs. 2.0 (1.3–3.1)]. Conclusions Knowledge of phenotypic and genotypic associations of amelanotic/hypomelanotic melanoma can help predict risks and associations of this difficult to diagnose melanoma, which may ultimately assist clinical management and patient skin self‐examination.

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Section: Discussionmentioning

confidence: 52%

Section: Discussionmentioning

confidence: 60%

Phenotypic and genotypic analysis of amelanotic and hypomelanotic melanoma patients

Rayner

McMeniman

Duffy

et al. 2019

Acad Dermatol Venereol

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“…As a mutation focused theory, SMT is insufficient to explain the similar cancer incidence rates observed in organisms of disparate sizes [24–26], or why, while most mutations occur early in life during ontogeny, cancers usually do not arise until late in life [27,28]. In addition, in humans, with large pools of dividing cells, known mutation rates should make oncogenic mutations relatively common [29,30].…”

Section: Introductionmentioning

confidence: 99%

Changing mutational and adaptive landscapes and the genesis of cancer

Liggett

DeGregori

2017

Biochimica et Biophysica Acta (BBA) - Reviews on Cancer

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By the time the process of oncogenesis has produced an advanced cancer, tumor cells have undergone extensive evolution. The cellular phenotypes resulting from this evolution have been well studied, and include accelerated growth rates, apoptosis resistance, immortality, invasiveness, and immune evasion. Yet with all of our current knowledge of tumor biology, the details of early oncogenesis have been difficult to observe and understand. Where different oncogenic mutations may work together to enhance the survival of a tumor cell, in isolation they are often pro-apoptotic, pro-differentiative or pro-senescent, and therefore often, somewhat paradoxically, disadvantageous to a cell. It is also becoming clear that somatic mutations, including those in known oncogenic drivers, are common in tissues starting at a young age. These observations raise the question: how do we largely avoid cancer for most of our lives? Here we propose that evolutionary forces can help explain this paradox. As humans and other organisms age or experience external insults such as radiation or smoking, the structure and function of tissues progressively degrade, resulting in altered stem cell niche microenvironments. As tissue integrity declines, it becomes less capable of supporting and maintaining resident stem cells. These stem cells then find themselves in a microenvironment to which they are poorly adapted, providing a competitive advantage to those cells that can restore their functionality and fitness through mutations or epigenetic changes. The resulting oncogenic clonal expansions then increase the odds of further cancer progression. Understanding how the causes of cancer, such as aging or smoking, affect tissue microenvironments to control the impact of mutations on somatic cell fitness can help reconcile the discrepancy between marked mutation accumulation starting early in life and the somatic evolution that leads to cancer at advanced ages or following carcinogenic insults.

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“…While these are the only signatures with the associated mutation load consistently growing with age in all cancers (see text), their contribution to total age-dependent mutation load is relatively small. Age-dependent mutation load and cancer incidence were estimated as described in reference 5. The contributions of mutational signatures 1 and 5 to the total mutation load were estimated using the data from reference 7.…”

Section: Figurementioning

confidence: 99%

“…Age-associated malignant growth, first explained by Armitage and Doll [4], shows a strong correlation with age-dependent mutation load and burden [5]. In fact, cancer incidence and mutation load doubling rates are similar to human mortality rates.…”

mentioning

confidence: 99%