Overestimating the Role of Environment in Cancers

ZNF92, an unexplored transcription factor with remarkably distinct breast cancer over-expression associated with prognosis and cell-of-origin

Tumor phenotype is shaped both by transforming genomic alterations and the normal cell-of-origin. We identified a cell-of-origin associated prognostic gene expression signature, ET-9, that correlates with remarkably shorter overall and relapse free breast cancer survival, 8.7 and 6.2 years respectively. The genes associated with the ET-9 signature are regulated by histone deacetylase 7 (HDAC7) partly through ZNF92, a previously unexplored transcription factor with a single PubMed citation since its cloning in 1990s. Remarkably, ZNF92 is distinctively over-expressed in breast cancer compared to other tumor types, on a par with the breast cancer specificity of the estrogen receptor. Importantly, ET-9 signature appears to be independent of proliferation, and correlates with outcome in lymph-node positive, HER2+, post-chemotherapy and triple-negative breast cancers. These features distinguish ET-9 from existing breast cancer prognostic signatures that are generally related to proliferation and correlate with outcome in lymph-node negative, ER-positive, HER2-negative breast cancers. Our results suggest that ET-9 could be also utilized as a predictive signature to select patients for HDAC inhibitor treatment.

Determining cancer risk: the evolutionary multistage model or total stem cell divisions?

A recent hypothesis proposed that the total number of stem cell divisions in a tissue (TSCD model) determine its intrinsic cancer risk; however, a different model-the multistage model-has long been used to understand how cancer originates. Identifying the correct model has important implications for interpreting the frequency of cancers. Using worldwide cancer incidence data, we applied three tests to the TSCD model and an evolutionary multistage model of carcinogenesis (EMMC), a model in which cancer suppression is recognized as an evolving trait, with natural selection acting to suppress cancers causing a significant mean loss of Darwinian fitness. Each test supported the EMMC but contradicted the TSCD model. This outcome undermines results based on the TSCD model quantifying the relative importance of 'bad luck' (the random accumulation of somatic mutations) versus environmental and genetic factors in determining cancer incidence. Our testing supported the EMMC prediction that cancers of large rapidly dividing tissues predominate late in life. Another important prediction is that an indicator of recent oncogenic environmental change is an unusually high mean fitness loss due to cancer, rather than a high lifetime incidence. The evolutionary model also predicts that large and/or long-lived animals have evolved mechanisms of cancer suppression that may be of value in preventing or controlling human cancers.

Case Studies of Gastric, Lung, and Oral Cancer Connect Etiologic Agent Prevalence to Cancer Incidence

Obtaining detailed individual-level data on both exposure and cancer outcomes is challenging, and it is difficult to understand and characterize how temporal aspects of exposures translate into cancer risk. We show that, in lieu of individual-level information, population-level data on cancer incidence and etiologic agent prevalence can be leveraged to investigate cancer mechanisms and to better characterize and predict cancer trends. We use mechanistic carcinogenesis models [multistage clonal expansion (MSCE) models] and data on smoking, Helicobacter pylori (H. pylori), and HPV infection prevalence to investigate trends of lung, gastric, and HPV-related oropharyngeal cancers. MSCE models are based on the initiation-promotion-malignant conversion paradigm and allow for interpretation of trends in terms of general biological mechanisms. We assumed the rates of initiation depend on the prevalence of the corresponding risk factors. We performed two types of analysis, using the agent prevalence and cancer incidence data to estimate the model parameters and using cancer incidence data to infer the etiologic agent prevalence as well as the model parameters. By including risk factor prevalence, MSCE models with as few as three parameters closely reproduced 40 years of age-specific cancer incidence data. We recovered trends of H. pylori prevalence in the United States and demonstrated that cohort effects can explain the observed bimodal, age-specific pattern of oral HPV prevalence in men. Our results demonstrate the potential for joint analyses of population-level cancer and risk factor data through mechanistic modeling. This approach can be a first step in systematically testing relationships between exposures and cancer risk when individual-level data is lacking.Significance: Analysis of trends in risk-factor prevalence and cancer incidence can shed light on cancer mechanisms and the way that carcinogen exposure through time shapes the risk of cancer at different ages.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/12/3386/F1.large.jpg Cancer Res; 78(12); 3386-96. ©2018 AACR.

Size matters: height, cell number and a person's risk of cancer

The multistage model of carcinogenesis predicts cancer risk will increase with tissue size, since more cells provide more targets for oncogenic somatic mutation. However, this increase is not seen among mammal species of different sizes (Peto's paradox), a paradox argued to be due to larger species evolving added cancer suppression. If this explanation is correct, the cell number effect is still expected within species. Consistent with this, the hazard ratio for overall cancer risk per 10 cm increase in human height (HR₁₀) is about 1.1, indicating a 10% increase in cancer risk per 10 cm; however, an alternative explanation invokes an indirect effect of height, with factors that increase cancer risk independently increasing adult height. The data from four large-scale surveillance projects on 23 cancer categories were tested against quantitative predictions of the cell-number hypothesis, predictions that were accurately supported. For overall cancer risk the HR₁₀ predicted versus observed was 1.13 versus 1.12 for women and 1.11 versus 1.09 for men, suggesting that cell number variation provides a null hypothesis for assessing height effects. Melanoma showed an unexpectedly strong relationship to height, indicating an additional effect, perhaps due to an increasing cell division rate mediated through increasing IGF-I with height. Similarly, only about one-third of the higher incidence of non-reproductive cancers in men versus women can be explained by cell number. The cancer risks of obesity are not correlated with effects of height, consistent with different primary causation. The direct effect of height on cancer risk suggests caution in identifying height-related SNPs as cancer causing.

Inequality in genetic cancer risk suggests bad genes rather than bad luck

Heritability is often estimated by decomposing the variance of a trait into genetic and other factors. Interpreting such variance decompositions, however, is not straightforward. In particular, there is an ongoing debate on the importance of genetic factors in cancer development, even though heritability estimates exist. Here we show that heritability estimates contain information on the distribution of absolute risk due to genetic differences. The approach relies on the assumptions underlying the conventional heritability of liability model. We also suggest a model unrelated to heritability estimates. By applying these strategies, we describe the distribution of absolute genetic risk for 15 common cancers. We highlight the considerable inequality in genetic risk of cancer using different metrics, e.g., the Gini Index and quantile ratios which are frequently used in economics. For all these cancers, the estimated inequality in genetic risk is larger than the inequality in income in the USA.