Facts

  • Gene–environment interaction indicates that combination of a genetic and an environmental factor modulates the risk of cancer more than either one alone.

  • Lifetime risk of cancers of many different types was shown to strongly correlate with the total number of divisions of the normal self-renewing stem cells.

  • Specific genetic polymorphisms and mutations increase susceptibility to certain carcinogens.

  • p53 is a prototype genetic factor for cancer, it can be somatically or inheritably mutated, as well as subjected to polymorphism.

  • All carriers of inherited BAP1 mutations have developed one or more cancers during their lifetime. Exposure to asbestos and UV-light further increases their risk of develo** mesothelioma, melanoma and skin cancers: GXE interaction.

Questions

  • Why environmental carcinogens cause cancer only in a fraction of exposed individuals?

  • Alterations in TP53 and BAP1 genes both predispose to cancer, but with a different spectrum. What are the underlining molecular determinants of this specificity?

  • Will single cell analysis resolve the difficulties in diagnosis and study associated to tumour heterogeneity?

  • Can we design prophylactic or therapeutic anti-cancer approaches based on genetic of polymorphisms and mutations?

The origin of cancer: studies clash over ‘intrinsic’ versus ‘extrinsic’ risk

Hypotheses on the origin of cancer are integral part of the history of medicine. The first three evidences supporting the presence of extrinsic chemical risk factors date back to the XVIII century: John Hill’s record on tobacco snuff and nasal cancer; Percivall Pott’s association between exposure to soot and scrotal cancer; Samuel von Sömmerring’s observation of a relationship between lip carcinoma and clay-pipe smoking [1]. Recognition of the existence of intrinsic genetic risk factors is more recent, and originated from analyses of familial clustering of cancer cases and is experimentally supported by numerous efforts to generate murine models of hereditary cancers [2]. Epidemiological studies have demonstrated that environmental carcinogens cause cancer only in a fraction of exposed individuals; the biological reasons are often unknown.

Taking advantage of the extremely large amount of data generated in the last 20 years, bioinformatics represents a powerful tool to investigate both the interactions between genes and environment in determining cancer risk and the origin of cancer itself. In the last few years, a great interest has been raised on this topic by a series of articles. Tomasetti & Vogelstein’s in 2015 reported that lifetime risk of cancers of many different types is strongly correlated with the total number of divisions of the normal self-renewing stem cells maintaining tissues’ homoeostasis [3], thus supporting a prevalent ‘bad-luck’ intrinsic origin of cancer. Wu et al. instead proposed that the overwhelming majority of cancers develop following exogenous damage, with only ~ 10–30% of the risk attributable to intrinsic factors, thus supporting the ‘toxic insults’ theory [4]. These studies provide correlations, without entering into causal molecular mechanisms, but the message of these papers could not be in more strident contradiction, with the effect of igniting a public debate on this very hot topic.

Unique occurrences of cancer epidemics in remote regions of the world have provided definitive proof for the gene X environment hypothesis. In particular, studies of an epidemic of mesothelioma in Cappadocia, Turkey, where over 50% of the population exposed to carcinogenic erionite fibers dies of mesothelioma, the most devastating cancer epidemic ever recorded in medicine, revealed that susceptibility to mesothelioma was transmitted in a Mendelian fashion and that the cause of the epidemic was gene–environment interaction [5,6,7].

Expanding the concept of gene–environment interaction (G × E)

In the complex picture of cancer pathogenesis and progression, it is now clear that both external environmental and genetic factors affect the so-called microenvironment, i.e. the local cellular milieu where cancer develops, which has a critical role in determining cell fate (Fig. 1).

Fig. 1
figure 1

Gene–environment and cancer pathogenesis. Both genetics and environment interact to affect the microenvironment from which ultimately causes cancer to develop in the first place and then progress

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Gene–environment interactions arise when the combined presence of both a genetic and an environmental factor modulates the risk of cancer more than either one alone. The traditional definition of G × E interaction most often implies ‘intrinsic’ genetic mutations (i.e. polymorphisms and rare germline mutations) and ‘non-iatrogenic’ environmental factors. Moreover, in cancer patients two additional levels of complexity arise: ‘acquired’ genetic mutations and ‘acquired iatrogenic’ environmental factors (e.g. drugs, radiotherapy) contribute to the cancer phenotype (Figs 2 and 3). Understanding the mutual relationships between all these factors is crucial both from a scientific and from a clinical perspective.

Fig. 2
figure 2

The complexity of gene–environment interactions. Including somatic cancer mutations and anti-cancer treatments further increases the layers of gene–environment interactions, providing us with the opportunity to increase our understand the of complexity of the cancer phenomenon

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Fig. 3
figure 3

Environmental pressure and cancer susceptibility. A number of extrinsic factors associated to lifestyle, early-life influence or pre-existing chronic conditions exerts pressure on cancer insurgence. The genetic background, including somatic mutations, germline mutations and single nucleotide polymorphism contributes to the outcome of the impact of these environmental factors on the organism. Some extrinsic factors can also promote alteration of the genetic information producing mutagenic events that trigger cancer

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The International Weinman Conferences, held at the University of Hawaii Cancer Center, Honolulu, HI, in 2017 (January 26, 27 and November 30–December 1) were conceived to stimulate discussions among experts from different fields of cancer research on complex inter-relations between genetics, external environment and microenvironment relevant to cancer aetiology—thus trying to find common ground in the lengthy conflict between ‘bad-luck’ and ‘toxic insults’, and to cancer progression and therapy. Here, we describe exemplificative topics on G × E interactions in both solid and haematologic malignancies presented by the authors, and the conclusions they reached following the discussions.

Genome-wide association studies, environment and molecular biology

Despite the unclear contribution of each single factor, it is commonly accepted that the risk of develo** cancer is determined by a complex interplay of both genetic and environmental factors [8,9,10]. There are many well-established environmental and lifestyle risk factors for various cancers, including smoking, alcohol, ionizing and solar radiations, exposure to carcinogenic fibres present in the environment, such as asbestos and erionite, as well as occupational exposures to these same fibres, infectious agents, obesity, and physical inactivity, which together are thought to account for up to 60% of cancer deaths in the United States [11]. However, only a small fraction of individuals exposed to these risk factors develop cancer, suggesting the co-existence of intrinsic genetic susceptibility.

Recent genome-wide association studies (GWAS) and next-generation sequencing studies have identified many common and rare germline genetic variations that contribute to increased cancer risks. Many of these cancer susceptibility loci are located in or near genes involved in DNA repair, or epigenetic regulators of gene transcription; however several examples exist of variants in genes involved in carcinogen metabolism. A paradigmatic example is the interaction between N-acetyltransferase 2 (NAT2) gene polymorphisms and smoking in elevating bladder cancer risk. NAT2 is a phase II detoxification enzyme that catalyses the metabolic inactivation of aromatic amines and heterocyclic amines, constituents of cigarette smoking and other environmental exposure. NAT2 polymorphisms allow classification of the human population into rapid, intermediate and slow ‘acetylator’ phenotypes. Numerous population studies and a meta-analysis [12] confirmed that NAT2 slow acetylators, resulting in slow detoxification of tobacco carcinogens, have a 1.4-fold increased risk of bladder cancer, and identified a significant synergistic interaction between NAT2 genotype and smoking: individuals with the NAT2 slow acetylating genotype who are also heavy smokers exhibited the highest risk of develo** bladder cancer [12]. Another eminent example is represented by the interaction between aldehyde dehydrogenase (ALDH2) gene variants, alcohol metabolism and oesophageal cancer [13].

Based on these and other data, there is a clear need for so-called ‘gene–environment-wide association studies’ which take into account environmental factors together with genetic variations to explain complex traits and phenomena [14]. Moreover, while in selected cases such as those represented by NAT2 and ALDH2, it is possible to infer the mechanistic link between the genetic variants and the consequent molecular alterations, a further step is needed to improve our understanding of G × E interactions from a molecular perspective, as shown by recent efforts in this direction [15].

Taking advantage of unique epidemiological resource

The study of G × E interaction requires the capability of discerning as much as possible the contribution of each of the two elements to the observed phenotype. Compared to small families and close relatives, which often share environment and lifestyle besides genes, such interactions can be more clearly elucidated studying distant relatives and high-risk pedigrees.

A unique resource that combines extensive genealogy with cancer and exposure data, the Utah Population Data Base (UPDB), provides the opportunity to better understand the contribution of genes, of the environment, and their combined effects to cancer. The UPDB represents the genealogy of the Utah Mormon pioneers and their descendants. The original Utah genealogy included 1.6 million individuals linked in genealogies 6–7 generations deep [16], and has been expanded from the 1970s with relationship data from Utah Vital Statistics records. State-wide SEER cancer data since 1966 for over 300,000 individuals have been linked to this database. The UPDB today represents 7 million Utah residents, of which 3 million have 3–16 generations of data. The power of the UPDB for G × E interaction studies lies in the simultaneous record of exposure data (such as tobacco use or radon exposure, both available in the UPDB), data for cancer incidence, and data on genetic relationships.

Health-related predisposition genes can be identified, and G × E interactions can be elucidated in high-risk pedigrees using these powerful genealogical resources. Analysis of UPDB high-risk pedigrees identified BRCA1, BRCA2, and CDKN2A as cancer predisposition genes [17,18,19], and studies of environmental exposures in individuals sharing genetic predisposition variants have clarified G × E interactions in cancer [20,21,22].

Similar efforts in creating large databases including both environmental and genetics data are steadily increasing in number, as they provide invaluable opportunities to study the complexity of cancer. Among the most recent examples, are the Iceland cohort [23], the Swedish Family-Cancer Database [24], and the US Veterans Genealogy Project [25].

The case of malignant mesothelioma and BAP1

Malignant mesothelioma (MM) is often used as the “standard” example of a cancer caused by G × E interaction. Epidemiologically, MM is an aggressive polyclonal cancer [26] associated with occupational and/or environmental exposure to tumorigenic mineral fibres, such as asbestos, erionite, or other non-regulated asbestos-like fibres [27,28,29,30]. However, “only” ~ 5% of asbestos workers exposed to asbestos continuously for over 10 years developed MM, suggesting that monogenic or polygenic genetic predisposition also contributes to MM risk [27]. Therefore, inherited genetic variants in genes encoding for proteins involved in the DNA damage response and/or in the inflammatory response may likely modulate the risk of asbestos-induced MM. Once they reach the mesothelium, asbestos fibres cause mesothelial cell death and chronic inflammation, associated with the release of damage-associated molecular patterns (DAMPs) molecules and cytokines [27]. Among these DAMPs, the active and passive release of different isoforms of high-mobility group box 1 (HMGB1) protein [31] sustains the chronic inflammatory response, with secretion of TNF-α and reactive oxygen species (ROS) [32, 33] (Fig. 4). Moreover, asbestos fibres can also directly induce ROS production because of the iron they contain, which behaves as a catalyst for free-radical generation [34, 35]. These molecules in turn activate NF-κB, leading to the survival of mesothelial cells that have accumulated genetic damage [32]. Accordingly, anti-inflammatory drugs and HMGB1 inhibitors impair MM growth in vitro and in experimental animal models and could represent prophylactic and therapeutic approaches [36,37,38].

Fig. 4
figure 4

Asbestos pathogenesis leads to mesothelioma. Asbestos fibers travel to pleura and cause human mesothelial cells (HM) to die of necrosis, leading to the release of HMGB1 into the extracellular space. HMGB1 induces macrophages and other inflammatory cells to accumulate and triggers the inflammatory response and leads to the secretion of cytokines such as TNF-α and IL-1β, which stimulate survival signalling pathways that increase survival of asbestos-damaged HM. This allows key genetic alterations to accumulate within HM that sustain asbestos-induced DNA damage that over time may lead to HM transformation and mesothelioma development

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BRCA1-associated protein 1, BAP1 is the most frequently mutated gene in MM, as about 2/3 of sporadic cases—i.e., MM not develo** in carriers of germline BAP1 mutations—carry somatic BAP1 mutations [39, 40]. Besides single nucleotide variations and other intragenic alterations, frequently MM have large copy number variations and micro-deletions reminiscent of chromothripsis. Most common are minute deletions (ranging between 100 bp and 3000 bp) in chromosome band 3p21 in genes encoding for epigenetic modifiers such as BAP1 and others, e.g. SETD2, SCAP, SMARCC1, PBRM1 [39]. Because BAP1 mutations are very rare in lung cancer, the presence or absence of wild-type BAP1, easily detected as presence or absence of nuclear BAP1 immunostaining helps pathologists differentiate MM from lung cancer [41]. A better characterization of the most common molecular modifications in MM should pave the way to novel and more specific therapies [42].

In this respect, germline BAP1 mutations significantly increase the risk of MM, even among individuals not exposed to asbestos [43, 44]. Intriguingly, patients with MM who also carry germline BAP1 mutations have prolonged survival [45].

How to reconcile the environmental and the genetic factor in MM pathogenesis? The incidence of germline BAP1 mutations is low among sporadic MM patients, suggesting that other yet unknown genetic mutations might contribute to MM risk [46]. Based on the experimental evidence in mice, germline BAP1 mutations significantly increase the risk of asbestos-induced MM after exposure to low levels of asbestos, levels that rarely cause MM in wild-type mice [47, 48]. Germline BAP1 mutations are not only associated with MM, but also with a wider recently identified cancer predisposition syndrome which includes cutaneous and uveal melanoma, squamous and basal cell carcinoma (all associated to sunlight exposure) as well as to clear cell renal cell carcinoma (ccRCC) a malignancy not yet associated with a specific carcinogen. Moreover, although less frequent, carriers of germline BAP1 mutations develop also several other malignancies, such as breast cancer, cholangiocarcinoma, sarcomas, brain tumors, etc., suggesting that BAP1 mutations increase the overall cancer risk although those associated to environmental carcinogens predominate [49].

The case of clear cell renal cell carcinoma and BAP1

BAP1 is commonly mutated at the somatic level in ccRCC, and is one of two genes that underlie the first molecular genetic classification of ccRCC [50]. Mutations in BAP1 (found in ~ 15% of ccRCC) tend to anti-correlate with mutations in PBRM1 (found in ~ 50% of ccRCC). Both BAP1 and PBRM1 are two-hit tumour suppressor genes and they reside on chromosome 3p. Chromosome 3p harbours the VHL tumour suppressor gene, which is mutated (or silenced) in the majority of ccRCC. VHL mutation is an initiating event and this mutation is followed by loss of 3p, which eliminates the second copy of VHL as well as one copy of BAP1 and PBRM1 [217]. A mutation analysis shows that only 18% of mutations occurred in oncogene/tumour suppressors, whilst 82% of mutations were in other genes, including genes regulating immune-surveillance and epigenetic modifiers consistent with random mutation theory [217].

A recent paper by Tomasetti et al. tried to clarify the contribution of random replication errors, environmental factors and heredity, emphasising the difference between aetiology of cancer mutations and cancer prevention [218]. These authors proposed that most cancers are preventable, as mutations dependent on environmental factors –even though numerically less prevalent than those dependent on random replication errors or hereditary—still contribute to cancer development, and their absence would result in prevention of a large number of cancer cases.

The current hypothesis is that while intrinsic random DNA errors are the most frequent type of mutations in all tissues and cells, including stem-cells, development of cancer is often facilitated and accelerated by the combinatory effect of these “inevitable” errors—as cells accumulate about 3 new mutations/cell division, together with those caused by exposure to mutagenic environmental carcinogens and hereditary mutations.