Table 5 shows the risk of cancer over time within THR patients and matched referent subjects, stratified by type of cancer CPRD cohort. The risk of cancer increased over time in both THR patients and referent subjects. Figure 1 shows that risk of cancer in patients with metal-on-metal hip devices remained constant over time compared to individuals with hip implants of other bearing surface types NJR cohort. This study found that patients with metal-on-metal THR were not at increased risk of cancer compared to individuals with hip implants of other bearing surface types.
The results of the patterns of cancer risk over time did not find increases of cancer risk over time. There were substantial differences in baseline characteristics between patients who received metal-on metal hip implants and those with other bearing surfaces. Elderly patients and patients with chronic conditions were less likely to receive a metal-on-metal THR and these factors were found to be associated with the risk of cancer. Our findings are in line with two recent observational studies investigating the risk of cancer in patients with metal on metal hip replacement  , .
Similar to our study, these British and Finnish studies could not find an increased risk of cancer and reported incidence rates that were consistent with our study. However, these previous studies did not have detailed information on risk factors, with only a limited comparison of baseline characteristics between the different types of THR. The present study found that there is strong evidence for confounding between the different types of THR. A Finnish cohort study, comprising 2, patients with a mean follow-up of 17 years, showed an increased cancer-related mortality rate in patients with metal-on-metal hip implants standardised mortality ratio [SMR] of 0.
However, as they did not look at incident cancer events, this may well represent confounding by contraindication: individuals with metal-on-polyethylene prostheses are in general older and may already have developed cancer and cancer is a relative contraindication for THR. Moreover, life expectancy may be shortened in prevalent cancer patients and surgeons may therefore decide not to perform a major elective surgery such as THR in these patients. An alternative explanation of the discrepant results is that the length of follow-up up to 7 years was shorter in the present study than that in the Finnish study.
It has been shown in animal studies that there may be a long latency in the development of tumours following exposure to metal compounds, which may translate to a latency of 10 years in humans . Most other observational studies could not differentiate between bearing surface types . A meta-analysis including nine of these studies compared risk of cancer in patients with total joint arthroplasty with age- and gender-specific expected cancer rates .
In line with our findings, the authors could not find an overall increased risk of any cancer. This study demonstrates the importance of linkages between different electronic health records for health surveillance monitoring. Whilst the NJR has excellent data on the type of prosthesis, it contains limited data on clinical patient related variables and co-morbidities. In our study, we have shown substantial differences in these clinical variables between prosthesis types, and need to be considered as confounding factors. CPRD does have very extensive information on these variables, as well as comprehensive data on drug and health service utilisation but does not contain detailed surgical information.
Our study had a reasonable sample size and cancer outcomes were obtained through three independently collected databases. A limitation is the lack of information on underlying disease severity which may have influenced cancer risk and other potential confounders. Referent subjects were not matched on osteoarthritis the main indication for THR , which is associated with a decreased risk of cancer . Although this may have underestimated our observed relationship, this is likely to be constant over time and should not have had an impact on the patterns of cancer risk over time.
Moreover, this should not have influenced our comparison between bearing surface types, as they should be more or less homogenous with respect to osteoarthritis. We may not be able to extrapolate our findings to long-term situations as some cancers are known to have a prolonged latency since start of exposure. In addition, as explained above, there may be a long latency in the development of tumours following metal exposure, although this has only been based on animal studies .
This study provides reassuring results with respect to the possible signal of increased risks of cancer with metal-on-metal hip replacements. We could not find an elevated risk of cancer with metal-on-metal hip implants and the analyses of cancer risk over time did not support a causal relationship.
There were substantial differences in baseline characteristics between the different types of THR complicating the interpretation of a direct comparison between bearing surfaces. The analyses in this study will need to be repeated in the future longer follow-up data, in particular as cancer latency may be prolonged for specific cancer types and following metal exposure.
We thank the patients and staff of all the hospitals in England and Wales who have contributed data to the National Joint Registry. Performed the experiments: AL TvS. Analyzed the data: AL TvS. Wrote the paper: AL TvS. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Background There are concerns that metal-on-metal hip implants may cause cancer. Results The risk of cancer was similar in patients with hip resurfacing RR 0.
Conclusions Metal-on-metal THRs were not associated with an increased risk of cancer. Introduction Total hip replacement THR is a highly effective procedure performed in patients with moderate to severe osteoarthritis . Study Populations Motivation study cohorts. Selection of THR patients and matched referent subjects.
Outcomes All patients were followed up for an incident record of cancer excluding in situ and non-melanoma skin cancer after the index date. Confounders We reviewed the literature to identify potential confounders that were associated with cancer. Analyses The following statistical analyses were conducted: Predictors of bearing surface type NJR cohort , cancer CPRD cohort and all-cause mortality CPRD cohort : In order to assess confounding by indication, we identified predictors of bearing surface type using the NJR cohort , in which we modelled all of the potential confounders in a logistic regression model.
The outcome of interest was metal-on-metal bearing surface type stratified by stemmed or resurfacing , compared with hip devices of other materials. In the second analysis, we identified predictors of cancer and all-cause mortality within control subjects, by modelling all potential confounders in a Poisson regression model. Bias-analysis NJR cohort : We evaluated risk of cancer within six months following THR surgery versus matched referent subjects, stratified by type of implant.
Any altered cancer risk in this period is unlikely to be causally related to THR and most likely represents confounding by indication. Association between hip replacement any type and cancer risk all three cohorts : Poisson regression was used to estimate adjusted relative rates RRs for cancer incidence in the hip replacement cohorts to the referent cohorts.
This analysis was performed for all three study cohorts and repeated for the three cancer data sources. Cumulative incidence of cancer NJR cohort : A competing risk model was used to estimate long-term risk of cancer, stratified by type of bearing surface, gender, and age. Death was considered the competing risk. This analysis was conducted within THR surgery patients, as well as in referent subjects, in order to compare timing and patterns. In the second analysis NJR cohort , we analysed cancer risk over time in patients with metal-on-metal THR versus patients with other hip implant devices  — .
Minimizing second cancer risk following radiotherapy: current perspectives
Download: PPT. Table 1. Baseline characteristics of patients with different types of hip replacements and matched controls NJR cohort. In this case, the intrinsic error is necessary but not sufficient for detectable invasive carcinoma to develop. From an intervention point of view, this is critical as preventing the modifiable component i. As defined above, intrinsic risk arises from the basal mutation rate operating in all dividing cells, in the absence of any non-intrinsic factors.
We have chosen to define unmodifiable intrinsic risk in this narrow way as it corresponds to a biologically intrinsic factor that causes DNA mutations in humans that is not modifiable. Passage or fixation of randomly acquired mutations e. A requirement for more than one driver mutation to initiate cancer increases the barrier to develop cancer with intrinsic mechanisms alone. In , Tomasetti and Vogelstein asked why different tissues exhibit dramatically disparate cancer rates.
Using estimates of the number and dynamics of tissue-specific stem cells for 31 tissue types, they observed a strong correlation between estimated stem cell divisions and lifetime cancer risk at log10 scale. This hypothesis sparked debates 9 , 13 on the nature of this correlation and its implications for causality of stem cells in cancer pathogenesis. In our work, we found that the correlation between stem cells and cancer risk does not distinguish the operation of intrinsic from non-intrinsic factors and vice versa, since many non-intrinsic factors e.
Thus, tissues with much larger cell divisions are susceptible to higher intrinsic mutations as well as to higher mutations induced by external factors. This conclusion was supported in recent analysis by Nowak et al. Furthering the complexity of cancer risk factors, in one study, Klutstein et al. This correlation persisted even after correcting for the contribution of stem cells whereas the reverse did not hold. These authors concluded epigenetic changes, which can be influenced by exogenous and endogenous factors, and not only mutations contribute to cancer risk with a similar dependence on the number of cell divisions in a tissue.
Thus, while these correlative studies support total tissue cell division in the observed variation between tissue-specific cancer risks, this association is correlative and only explains a part of that risk. The direct estimation of intrinsic error to cancer risk is challenged by the technical inability to truly separate intrinsic errors from the effects of non-intrinsic factors in humans.
The recent advent and rapid development of next-generation DNA sequencing technology has revolutionized the ability to survey genome-wide somatic mutations in cancer. Analyses of these data are providing new insight into the role of intrinsic versus non-intrinsic cancer risk factors, and in some cases, linking specific signatures to specific risk factors. Here we discuss recent work from large-scale tumour sequencing studies applied to estimating the magnitude of intrinsic risk and its contribution to human cancer. Using genome sequence data, more than 30 distinct mutational signatures were recently uncovered in different cancers Of these, 10 can be associated, at least partially, to known mutagens.
Interestingly, two signature mutations demonstrated strong positive correlations with age in most cancer types, indicating that they are acquired at a relative constant rate over the lifetime of cancer patients, regardless of tissue of origin.
This pattern is most consistent with the action of an intrinsic error process, since errors arising with DNA replication during cell division would accumulate in a monotonic fashion over time. In contrast, the other signature mutations lack a consistent correlation with age, suggesting they may be acquired at different rates in life due to different influences Since all known carcinogen-specific signatures demonstrate an age-uncorrelated and tumour-specific pattern, it is reasonable to assume those with unknown causes are also a consequence of external exposures to DNA damaging agents.
Notably, a number of cancers, such as lung and skin cancer, with substantial environmental risk as determined from epidemiologic studies, also contain large percentages of non-intrinsic risk estimated from the mutational signature data Extended Data Table 3 in ref. Several studies have attempted to estimate the number of driver mutations required for the development of an invasive carcinoma. The emerging consensus is that at least three hits are necessary for solid tumours and fewer for haematologic malignancies.
This corresponds approximately to three new mutations per genome per cell division. Replication error rates between different cell types in an organism are roughly constant given the fundamental nature of the replication process.
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For proto-oncogenes, gain-of-function mutations typically occur at specific sites that increase action of the target protein e. In contrast, loss-of-function mutations can occur at multiple sites whereby numerous mutational events promote gene loss or dysfunction e. Thus, the probability of mutating at least one cancer relevant gene is larger than the somatic mutation rate of one nucleotide. Based on these and related data, we developed a discrete stochastic multistage cancer stem cell model, with the model parameters number of stem cells, intrinsic mutation rate, and the generations of symmetric versus asymmetric divisions estimated from the most recent literature Once the intrinsic risk due to replication errors was computed, the difference between the model estimation and the observed epidemiological cancer incidence provided an estimate of the non-intrinsic risk residual risk.
These results suggested that cancer risk due to intrinsic factors alone is very low for cancers requiring more than two hits, consistent with other independent analyses including observational studies and a mutational signature study. More recently, Tomasetti et al.
A major cornerstone of this recent work was the calculation of the intrinsic risk as the amount of risk that remains after subtracting effects of known environmental and hereditary factors. That is, the percentage of mutations due to intrinsic factors was computed as:. However, this approach inflates the effect of intrinsic factors by assuming there are no other non-intrinsic cancer-causing factors to be identified. Inclusion of a yet to be identified non-intrinsic factor can significantly drive down the contribution of the intrinsic factors as illustrated in Fig. For lung cancer, while Tomasetti et al.
This discrepancy could be due to the exclusion by Tomasetti et al. This diagram illustrates the relationship between intrinsic and non-intrinsic risks, as well as preventable cancer and overall cancer burden. One can see that by ignoring the unknown non-intrinsic risk area marked with?
Preventable cancer is a subset of cancers with known non-intrinsic causes since to be preventable, a cancer has to have a known and modifiable factor e. By the same rationale, preventable cancer is often under-estimated due to the unknown non-intrinsic risk factors.
Mechanisms of non-intrinsic risk factors thought to drive cancers are multifaceted. Some belong to the family of chemicals that induce new mutations mutagens while others, such as viruses, induce cancers through activating or repressing key cancer modulating genes activating oncogenes or inhibiting tumour suppressor genes in addition to inducing mutations. At least in the case of mutagens, these operate on cells that can divide and persist so as to facilitate tumour development. The evidence for non-intrinsic risk factors is mainly derived from studies in cancer epidemiology and cancer biology.
Several landmark epidemiological and biological studies have identified exogeneous cancer risk factors such as tobacco smoke for lung cancer, UV radiation for skin cancer, and viruses for cervical and liver cancer. More recently, several groups have reported rising colorectal cancer incidence and mortality rates in Asia approaching those in western countries. Affluent Eastern Asian countries such as South Korea, Singapore, and Japan have experienced a two-fold to four-fold increase in incidence in recent decades In the USA, a recent study confirmed prior estimates that adults born in could experience twice the risk of colon cancer and four times the risk of rectal cancer at the same age had they been born in The reasons for the rise in incidence and death rates remain unclear 38 but cannot be attributed to change in factors intrinsic to DNA replication machinery in humans and thus, strongly indicate a role for non-intrinsic factors.
Evidence for causes of cancer in human populations has historically been guided by information on cancer incidence and prevalence rates in different populations. According to GLOBOCAN 39 , incidence rates of different cancers show distinct geographic patterns where estimates in high-incidence regions can be as much as one or two orders of magnitude higher than low-incidence areas.
Consistent with this pattern, we recently analysed the World Cancer Registry data 6 and found that the age-adjusted incidence rates of most cancers show distinct geographic patterns where estimates in high-incidence regions can be as much as ten folds or more than low-incidence areas 6. Some examples, obtained by taking the ratio of the incidence rates at the 90th percentile and the 10th percentile, include: melanoma 40 fold , colorectal cancer three fold , lung adenoma seven fold , breast cancer three fold , and prostate cancer nine fold.
The difference in world cancer incidence rates and wide disparity are shown in Fig. As shown in this figure, the fold changes will be more dramatic if the ratio is between the regional maximum and minimum. Shown are the conservative non-zero minimum, the 10th and 90th percentiles, the US average, and the maximum of the lifetime cancer risk based on World cancer registry, and the stem-cell-model based minimum 6. The huge disparity between the US average and world minimum indicates that cancer is unlikely the end result of a universal endogenous carcinogenesis mechanism unaffected by exogenous factors published with permission 6.
Favouring environmental risks, seminal work demonstrated that the offspring of immigrants to high incidence regions acquire the incidence patterns of the host country in one or two generations This adoption of the host country incidence pattern is consistent with changes in factors present in each geographic region. Indeed, higher incidences of lifestyle-related cancers e.
In contrast, higher incidences of infection-related cancers e. Numerous hypotheses about the role of environmental exposures and cancer have been generated using retrospective case-control studies, in which the association of exposures e. Suspecting tarmac or motor car fumes as the major causes for the increased incidence in lung cancer, Doll and Hill 41 undertook a historical case-control study in Comparing lung cancer patients with matched controls, they discovered tobacco smoking was strongly overrepresented in the cases.
Their findings were subsequently confirmed in a prospective cohort of more than 30, British physicians Here, the attributable risk refers to cancer risk that is theoretically preventable. Supporting links between risk factors and cancer identified in case-control studies, numerous prospective studies have been conducted and proven highly informative.
For example, prospective studies on lung 42 , oesophagus and gastric 51 , bladder 52 and other cancers 53 , 54 , have confirmed the association of smoking in human carcinogenesis, particularly in the aerodigestive tract. The robustness of the associations has yielded reliable estimates of cancer risk among smokers. In the case of cancers associated with infectious agents 56 , prospective studies of H. Other physical factors such as ionizing 62 or UV radiation 63 contribute causally to cancer incidence, and their linkage to cancer has led to effective preventive measures.
Other sources of ionizing e. In addition to these defined exposures, more complex lifestyle and behaviour factors such as diet, physical activity, alcohol consumption and reproductive patterns have also been intensively studied in cancer risk using the prospective design.
Age-Time Patterns of Radiation-Related Cancer Risk
For example, physical activity and dietary patterns, particularly nutrient deficient and calorie-dense diets i. However, data from prospective studies on specific essential nutrients i. The European Prospective Investigation into Cancer and Nutrition EPIC study 72 supports diet as an important or moderately important factor in risk of colorectal and breast but not prostate cancer.
In efforts to increase the sensitivity and reliability between individual dietary factors and cancer, epidemiologists have developed modern analytical methods 73 , While epidemiological studies have a number of strengths, certain inherent weakness limit the reliability of findings when present in only one or a few studies. For geographic comparisons of cancer risks, information on routine medical records and death registries tend to be less accurate or complete in less developed countries and less impacted by asymptomatic, screen detected cancers.
This impacts the accuracy and interpretability of the rates. These factors may inflate findings of difference between countries. On the other hand, other considerations may obscure effects of environmental factors. For example, if common exposures exist globally, which may happen increasingly with globalization, it will be harder to recognize their contribution to cancer risk.
For retrospective especially and prospective study design, confounding effects and selection biases affect the accuracy of the risk and the estimation of the effect size. As such, while replication of findings across studies is among the more powerful criteria for establishing an association, gaining knowledge of the biological mechanisms linking an exposure to disease is a necessary component of the evidentiary process in establishing direct causal relationships.
Despite these inherent limitations, population studies have provided convincing evidence for a major contribution of exogenous factors in cancer risk. Exogenous mutagens, such as UV irradiation, have long been recognized to induce specific mutation patterns in genomes However, it was not until recently that strong signatures were identified for tobacco 80 and UV light 81 in lung cancer and melanoma genomes, respectively. These also provided the proof of principle in discovering the effect of mutagens without knowing their origins.
Particularly, capitalizing on many large consortia studies targeting sequencing of large numbers of genomes, such as The Cancer Genome Atlas TCGA , several mutational signatures have now been identified and characterized with respect to a wide range of cancer types 31 , More importantly, given the rapid progress of sequencing technologies, new specific signatures are coming into light with new research that is assigning them to specific exposures. For example, aristolochic acid, common in east Asia and parts of Europe, has been shown to predispose to cancers of the renal pelvis, and is associated with a highly specific signature Certain cancer risk factors are endogenous to the individual and many have some genetic component.
Individual levels of the sex steroid hormones and their role in breast cancer risk are among the best studied examples of an endogenous cancer risk factors As endogenous determinants of cancer risk, the steroid sex hormones vary over the life course and between individuals and are influenced by other exogenous factors e. Importantly, endogenous sex steroid hormones and cell responses to hormones are proven targets for cancer prevention supporting the modifiability of endogenous risk factors. For example, obesity has a genetic basis but most often develops as a phenotype from interaction with exogenous factors over consumption of food and sedentary behaviour and is thus, highly modifiable.
Obesity-associated changes in metabolism, hormones and inflammation are the suspect proximate biological culprits in cancer risk and they are modifiable metformin, anti-inflammatory drugs, lipid lowering drugs, hormone therapies. Deregulated sex hormones for example are causally linked to the significant increase risk of uterine cancer in obese women And unlike other cancers, endometrial cancer incidence has continued to increase worldwide 86 and in parallel with the obesity epidemic Notable is the reduction modifiability of endometrial cancer risk in the obese with weight loss surgery 88 , hysterectomy or use of progestins that oppose oestrogen effects on the endometrial lining 87 , In contrast to endometrial cancer, the mechanophysical effects of obesity i.
Here we consider a few such complex endogenous factors and their modifiability. This includes considering ageing as a decline in endogenous anti-cancer processes. Over the latter half of the 20th century, numerous cellular and molecular mechanism linking inflammation to malignant cell persistence and invasion have been characterized.
These range from inflammation-induced reactive oxygen species that act in DNA damage and tumour initiation as well as inflammation-derived cytokine and chemokine effects on tumour growth, angiogenesis and tumour cell migration and invasion 91 , Most recently is the appreciation that immune cells play a significant role in suppressing anti-tumour immunity enabling tumour cell persistence and progression to life-threatening disease Such effects, and the large body of evidence from animal and human studies, have led to the inclusion of inflammation as an enabling factor to carcinogenesis 94 , where inflammation is accepted to act across the continuum of tumorigenesis in a number of cancer types.
The significance of inflammation in cancer development has been demonstrated in the chemoprevention field where randomized clinical trials and population studies of anti-inflammatory agents such as aspirin and other non-steroidal anti-inflammatory drugs have demonstrated the cancer prevention effects of suppressing pro-inflammatory mediators like prostaglandin E2 for several cancers Indeed, in the US Prevention Services Task Force recommended in favour of low dose aspirin use for the prevention of colorectal cancer in individuals at elevated risk that include patients with Hereditary Non-Polyposis Colorectal Cancer HNPCC Syndrome who carry germline mutations in mismatch repair genes While it is clear that inflammation is critical for tumour development, incorporation of inflammation in mathematical models of tumour development is lacking.
This stems in part from the lack of valid biomarkers of cancer associated inflammation. As with mutational signatures of carcinogens, and more recent efforts to assess ageing, integration of inflammatory signals with the genomic and sequence data may offer insights on the magnitude of cancer burden that can be attributed to inflammation—work that would greatly enhance efforts aimed at modifying inflammation as a prevention strategy for reducing cancer incidence in the population.
Ageing is considered among the most significant risk factors for cancer Yet, it is important to recognize that ageing can be defined chronologically or biologically. Chronological ageing contributes toward cancer by providing time for intrinsic risk as well as for exogenous and endogenous factors including mutagens to exert their effects. In contrast, biological ageing processes are more difficult to define or quantify since their full spectrum is not fully understood.
The strong positive association of ageing with cancer is widely believed to reflect generalized declines in cellular and molecular system functions as an endogenous risk. Ageing encompasses at least nine recently proposed hallmarks 98 for which there are numerous overlaps to the cancer hallmarks 94 : genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication.
In an effort to assess the impact of ageing, Podolskiy et al. The group reports that that the acceleration in mutation burden is higher in men and initiates earlier in life in men. This parallels higher overall cancer incidence in men and an earlier age about 10 years at which cancer incidence begins to rise in men. The authors suggest that the strong representation of age-associated mutations in tumours reflect decreases in organismal fitness with ageing that differ by gender and tissue type. Not all biological ageing is pro-tumorigenic.
For example, mechanistic studies have suggested that cell senescence and stem cell exhaustion that accelerate with ageing may explain the observed decline in incidence of cancer at the extremes of human age , It is worth mentioning that the rate and peak timing of age-related cancer risk varies from cancer to cancer and even within subgroups of specific cancers.
This suggests that there is not always a positive relationship between age and cancer risk. For breast cancers, triple negative and HER2 positive breast cancers peak earlier in adulthood and exhibit a decline with advancing age where oestrogen receptor positive breast cancer incidence rises later and continues to increase with age These results may also reflect differences in susceptible cell populations in tissues that senescence at different life-stages; life cycle biology not currently considered in modern models of cancer risk.
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To tease out ageing effects on cancer from non-intrinsic risk factors is challenging. The effect of ageing on cancer risk is commonly removed by testing the cancer risk in individuals with and without the exposure matched on age. Analysis of age-adjusted incidence rates provides the most common way to address this issue. In the geographic comparisons discussed previously, all the incidence rates are age-adjusted.
In these cases, the chronologic age effect is accounted for. Hereditary cancer can also operate through intrinsic and non-intrinsic mechanisms, by modulating the frequency of mutations per se or their repair but also by non-intrinsic mechanisms. While much of the genetic basis of cancer risk remains to be identified, it is notable that a majority of the hereditary cancer mutations as well as the germ line variants identified involve DNA repair genes thought to act by increasing mutation rates often in tissue-specific fashion.
However, it is also important to note that the increased risk may also derive from genetic mechanisms resulting in increased susceptibility to non-intrinsic factors and exposures to other DNA damaging processes. Our ability to understand and to model cancer aetiology and the impacts of exogenous and endogenous factors in risk has in recent years been extended to include consideration of effects of numerous factors on the epigenome.
As with replication errors, epigenetic changes e. There is convincing evidence that epigenetic changes not only occur during tumour development, but they also play a direct causal role. This includes reproducible evidence that specific epigenetic silencing events, such as silencing of MLH-1 in a subset of human colon cancers, are essential alterations in human tumorigenesis Key epigenetics mechanisms in human carcinogenesis are beyond the scope of this perspective but have recently been reviewed for the major cancer types Noteworthy here for future models aimed at 1 identifying cancer risk factors and 2 for estimating contribution of factors endogenous or exogenous that impact the epigenome in cancer risk is consideration of the recent elegant work from the Baylin laboratory In their studies, they provide evidence that cigarette smoke as a chronic exposure induces time dependent alterations in the human bronchial epithelial cell epigenome that enhances their sensitivity to transform with a single KRAS mutation These data strongly suggest that a chronic exposure like smoking or obesity, nutrient deprivation, ageing epigenetic effects on immune cell function, inflammation may act by lowering the threshold of a cell to intrinsic errors for cancer development; an important interaction of the effects of the intrinsic and non-intrinsic risk factors not adequately considered in previous models.
Similar effects of other exogenous and endogenous factors to the epigenome including inflammation, obesity and ageing may similarly alter the thresholds to transformation via effects on the epigenome , Importantly, whether epigenetic changes represent reversible processes is currently debated and a subject of investigation. Studies in smokers, however, demonstrate smoking-specific changes to the epigenome persist for many years after smoking cessation, which may explain the long-lasting nature of elevated risk in former smokers.
These include height and telomere length as examples along with emerging interest in the human microbiome as a modifier of cancer risk. Given progress toward understanding the significance of complex interactions in cancer, estimating their contribution to cancer burden will be important. While beyond the scope of this review, two recent lines of work on telomere genetics and cancer risk and human height and cancer risk are worth mentioning The Telomeres Mendelian Randomization Collaboration Group recently demonstrated an association between genetic polymorphisms, telomere length and cancer.
Longer telomere associated gene variants were associated with rare cancers and strikingly, with cancers in tissues with low stem cell divisions. As noted by the authors, the positive association with telomere length is consistent with evidence that telomere shortening with aging may act as an intrinsic protective mechanism against cancer by limiting cell division, explaining the lower rates of cancer in extreme age.
While telomere length is a heritable trait, recent evidence from experimental models suggests that telomere length is malleable and influenced by numerous external stimuli Such findings provide new biological rationale for positive associations between environmental and psychosocial factors and telomere length observed in human studies that may impact cancer risk Similarly, the repeated observation between adult height and cancer risk including breast , prostate and colon is intriguing given the average height of humans continues to increase worldwide.
As such, the positive association between height and cancers has been hypothesized to reflect genetic traits that influence gestational, childhood and adolescent growth processes that also act on cancer progenitor cells. Indeed, genetic variants associated with height and Mendelian randomization analysis were reported associated with genetically predicted height and risk of oestrogen receptor positive breast cancer Confounding the interpretation of these associations though is the strong influence of maternal nutrition as an equally strong non-genetic determinant of height For example, prostate cancer has been shown to be positively associated with height at 13 years of age ; a time when early life nutrition is most important in determining stature.
This association was independent of adult height, suggesting nutrition in early life may be a modifying factor in prostate cancer risk. Like emerging evidence that obesity and other growth factor affect cancer risk via expansion on tissue stem cells , it is plausible that nutrition and height genes interact with effects on stem cells affecting an intrinsic risk factor for cancer at the tissue level. Understanding such effects will be essential for modelling the contribution of each to cancer risk. Unfortunately, integration of early life exposures including nutritional status in human studies are challenging and make it difficult to tease out the effects of early life nutrition on adult cancer Studies in animals and in birth to death cohorts, where detailed early life exposures are collected, will be critical to advancing our understanding of such factors in risk of cancer in adults These have aided in mathematical modelling approaches aimed at estimating the contributions of non-intrinsic and intrinsic factors to cancer risk and cancer burden in the population.
These estimates of non-intrinsic risk are consistent across studies and support a substantial contribution of potentially modifiable or actionable risk to cancer 77 , 78 , Evolving theories in cancer molecular pathogenesis and technological innovations for example the deeper understanding of the cancer epigenetics mechanisms are resulting in finer estimates of the impact of intrinsic and non-intrinsic processes based on biological principles.
The rapid advances in the molecular biology of human cancers, including emergent role of stem cells in cancer evolution and expansion of long lived clones with multiple mutations and epigenetic changes, favour a much more complex picture of cancer aetiology with heterogeneity among the cancers and within cancers of the same tissue type. These pave the way for development of new analytic approaches that better integrate new knowledge including considering contributions of individual factors as well as their joint effects on cancer burden.
Proportion of non-intrinsic risk estimates from four different approaches. Data were obtained from ref The two dashed horizontal lines indicate non-intrinsic risk at the levels of 0. Thus, someone who never smoked may still have a lifetime risk of lung cancer of 0. However, it is important to realize that 1 Non-intrinsic factors themselves only impart an increase in risk in developing cancer, and therefore there is still an element of luck for non-intrinsic factors.
Smoking increases the probability by 10 to 25 folds. Thus, exposure to risk factors does not necessitate the development of cancer; nor does the absence of exposure with a few exceptions e. In this regard, cancer risk can still be modified even when intrinsic factors contribute to some of the risk. As such, it is detrimental to prevention and cancer control measures if the risk, especially for clinically significant cancers, is over interpreted to be due solely to bad luck. This underestimates the potential impact of prevention and control measures aimed at reducing or delaying incidence and death due to cancer.
Similarly, under-estimating the fraction of preventable cancer risk impedes progress to identify modifiable exposures for cancer prevention and control measures when possible Fig. Indeed, the proportion of currently preventable cancers is mostly a subset of cancers with known non-intrinsic risk factors as shown in Fig.
For example, at present, several cancers e. Therefore, this does not negate the significant contribution of currently unidentified risk factors or that they would become modifiable. Moreover, other known non-intrinsic risk factors such as radon for lung cancer and geographic variations for breast, colorectal and prostate cancers are not currently considered in the Cancer Research UK estimates. While plausible, challenges remain in ascertaining exposure of humans to putative non-intrinsic risks with hypothesized but equivocal evidence for several suspects, including heavy metals, endocrine disruptors, cadmium, sleep deprivation, chemical mixtures especially at low doses and nutrient deficiencies folate, selenium identified from experimental systems as pro-tumorigenic.
Potential interactions among various risk factors further complicates measurement issues, though the identification of additional modifiable risk factor s will likely open new venues for prevention or at least intervention. This has been amply illustrated with the successive discovery of risk factors such as smoking, HPV, inflammation in colon cancer, and many others. The analyzed data can be adequately described even with multiple restrictions on model flexibility, such as keeping several parameter values in common for all tumor types and restricting some others to biologically plausible ranges or values.
Parameter interpretations and restrictions are listed in Table 1.
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This scheme may be plausible to some extent, for example because 1 radiogenic promotion is interpreted in the context of our model as deregulation of cell-cell signals that maintain stem cell niche sizes, 2 cell-cell signals can be modulated by radiation-induced oxidative stress and other processes [e. Consequently, radiogenic promotion may be more organ-specific than initiation. Notably, these estimates can be quite asymmetric around the best-fit values.
Consequently, the best-fit values listed for a given tumor type do not represent the optimal combination for that particular tumor type which would be based on a deviance minimum for that type alone but were obtained by optimizing the total deviance for a larger data set that includes information on other tumors. The confidence intervals, however, apply specifically to each selected tumor type, to provide a sense of how the fit for each type is affected by altering the default parameter values.
The greatest model sensitivity, represented by the tightest confidence intervals, occurred for the pre-malignant niche replication rate b because this parameter strongly affects both the background risk and the radiation-induced risk see Eqs. Sensitivity to other parameters was generally less pronounced.
For example, confidence intervals for the radiation initiation constant X were often very wide Table 2 , which is consistent with the fact that this parameter could be kept in common across tumor types without altering the fit dramatically. These results suggest that the formalism can adequately fit the selected data sets using many possible parameter value combinations, so that conclusions based on specific parameter values should be interpreted cautiously without additional information about these values, e. The model fit to the age-dependent mortality rate from all solid tumors in unirradiated mice is shown in Fig 1.
Figure 2 shows the data and best-fit model predictions for the ERR for mortality from all solid tumors at a dose of 1. Generally, the model describes the data reasonably, considering the uncertainties in the data points. The decreasing trend in the ERR with time since exposure is well accounted for. The data and best-fit model predictions for the background mortality rate from all solid tumors combined. The data in this figure and the following figures are from refs. In this and the following figures, error bars represent standard errors. The data and best-fit model predictions for the excess relative risk ERR for mortality from all solid tumors combined at a dose of 1.
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In this and the following two figures, the model predictions corresponding to the data points are represented by points filled circles , which were generated using the corresponding combinations of dose, age at exposure, and time since exposure. The lines connecting the predicted points are shown for convenience only. There is, however, some qualitative discrepancy between the data and predictions for the effects of age at exposure: The model predicts a monotonic decrease in ERR with age at exposure for a fixed attained age some effect of attained age is also seen because of progressive life shortening at increasing radiation doses, but this effect is not dominant in the data analyzed here.
The mouse data, however, suggest that ERR actually grows during the late in utero period not included in the analysis here and the first days of life and begins to decrease only after 35 days. A similar pattern has recently been observed in humans using data from Japanese atomic bomb survivors A possible interpretation is that the ERR is affected by the physiological processes during active organ growth, which occur in utero and during the neonatal period both in mice and in humans, e. The model in its current form does not account for these processes and assumes that the target cell numbers and dynamics are the same regardless of age; such assumptions were made for simplicity.
Figure 3 shows the data and best-fit model predictions for the ERR for incidence of specific tumor types as a function of age at exposure estimated for an attained age corresponding to the mean survival age for each experimental group for mice irradiated with 1. For these tumor types, the data support a relatively monotonic decrease in ERR with age at exposure to a better extent than the data for all solid tumors combined referred to earlier. For this reason, qualitative agreement with model predictions is better as well. The explanation for a decrease of the ERR with age at exposure within the context of our model has been described in detail in previous papers 16 , If excess risk is dominated by radiation initiation rather than radiation promotion, it occurs mainly for the following reasons: 1 cells initiated at an early age have longer to exploit their growth advantage over normal cells if the attained age of maximal cancer incidence is relatively constant ; 2 cellular proliferation rates early in life can be more rapid, making target cells more sensitive to radiogenic initiation.
When irradiation occurs at a young age, there are few existing pre-malignant cells, so promotion of these cells i. For irradiation at a much older age, the reverse is true: there are many already existing pre-malignant cells compared with the number of new cells initiated by radiation, and promotion of the existing large pre-malignant cell population becomes much more important.
The data and best-fit model predictions for the excess relative risk ERR for incidence of specific tumor types as function of age at exposure for mice irradiated with 1. The model was fitted to data for all doses used see next figure , not just to the points shown here. The radiation dose responses for the selected tumor types are shown in Fig. The structure of Eq. This generic shape describes the data reasonably well, particularly for mouse liver and pituitary tumors, among those analyzed here.
For mouse lung tumors, the model underestimates the slope of the dose response at low doses and the rate of decline at high doses.
A possible explanation is that radiogenic lung tumor risk may be substantially affected by factors such as the bystander effect, which would tend to produce plateau-like dose—response shapes at low or moderate doses [e. For bone tumors, the data may suggest a linear-quadratic rather than a linear dose—response shape.
The data and best-fit model predictions for the excess relative risk ERR for incidence of specific tumor types as function of dose for mice irradiated at age 0 days. An important goal of the present paper is to investigate the quantitative roles of different mechanisms, such as initiation and promotion, in the overall radiation-induced cancer risk. This can be done mathematically as follows:. Just after irradiation i. By setting either X or Y to zero, the RR can be decomposed into terms that contain only radiation-induced initiation RR i , only radiation-induced promotion RR p , and both initiation and promotion together RR b :.
The term RR b , which represents interactions between initiation, promotion and cell killing, can be interpreted as radiation-induced promotion of previously radiation-initiated niches. This interpretation intuitively explains the quadratic dose dependence of the term, considering the linear individual dependences of initiation and promotion. The behavior of Eq. It is shown using mouse liver tumors as an example in Fig.
The figure graphically illustrates the model property that the initiation-dependent terms RR i and RR b decline with age at exposure panels A, C , for reasons described earlier, whereas the promotion-only term RR p is independent of age at exposure, because for a single acute dose the model analyzes promotion as simply a multiplicative amplification of the number of premalignant cells. It is also notable that the contributions of initiation, promotion, initiation-promotion interactions, and cell killing are also dose-dependent, as Fig.
Contributions of initiation, promotion and initiation-promotion interactions i. Panel A shows total RR and its components for a dose of 1 Gy, and panel B shows the percentage contributions of these components as a function of age at exposure. Panels C and D show the same analysis at a dose of 5 Gy. Best-fit parameter values from Table 2 were used.
In Panels A and C the x axis starts at 50 days because for younger ages at exposure the predicted RR is too large to conveniently show on the same vertical scale with the RR at older ages at exposure, and a logarithmic vertical scale is not possible because of negative values. The negative values for initiation-promotion interactions RR b seen at older ages at exposure are, in a sense, a mathematical artifact of the definition of RR.
This can be clearly seen from the structure of Eq. Here we presented an analysis of radiation-induced mouse carcinogenesis using a data set well suited to investigate the dependences of cancer risk on age at exposure and time since exposure. The biologically based mathematical model we developed earlier 16 , 17 , which integrates the relatively short-term processes during irradiation and tissue recovery with more long-term processes that determine pre-malignant cell dynamics throughout the entire lifetime, is able to adequately describe these data, using a limited number of biologically plausible parameter values.
The best-fit model parameters generated by this analysis are of course mouse-specific and cannot be applied to humans because of life-span differences and other factors. However, our results suggest that the general patterns of radiation carcinogenesis may be relatively similar for mice and humans, at least for the cancer types analyzed.
Some conclusions drawn from analyzing the mouse data sets selected here, and which can have some importance for human risk estimation and carcinogenesis mechanisms, are presented below:. In conclusion, mechanistic analysis of animal and human data, using biologically motivated formalisms that model both initiation and promotion on both a short and a long time scale, may enhance the understanding of radiation-induced carcinogenesis. Our findings are consistent with the hypothesis that the general mechanistic patterns of radiation carcinogenesis may be relatively similar for mice and humans but that the balance between initiation and promotion may vary considerably among different cancer types.