Can chance cause cancer? A causal consideration

Latent Archetypes of the Spatial Patterns of Cancer

The cancer atlas edited by several countries is the main resource for the analysis of the geographic variation of cancer risk. Correlating the observed spatial patterns with known or hypothesized risk factors is time-consuming work for epidemiologists who need to deal with each cancer separately, breaking down the patterns according to sex and race. The recent literature has proposed to study more than one cancer simultaneously looking for common spatial risk factors. However, this previous work has two constraints: they consider only a very small (2-4) number of cancers previously known to share risk factors. In this article, we propose an exploratory method to search for latent spatial risk factors of a large number of supposedly unrelated cancers. The method is based on the singular value decomposition and nonnegative matrix factorization, it is computationally efficient, scaling easily with the number of regions and cancers. We carried out a simulation study to evaluate the method's performance and apply it to cancer atlas from the USA, England, France, Australia, Spain, and Brazil. We conclude that with very few latent maps, which can represent a reduction of up to 90% of atlas maps, most of the spatial variability is conserved. By concentrating on the epidemiological analysis of these few latent maps a substantial amount of work is saved and, at the same time, high-level explanations affecting many cancers simultaneously can be reached.

Random cancers as supported by registry data

There has been considerable interest in recent years in quantifying the rate of unavoidable or so-called random cancers, as opposed to cancers linked to environmental, genetic or other factors. We propose a data-based approach to estimate an upper limit to this probability, based on an analysis of multiple registry data. The argument is that the cumulative hazards for random cancers cannot exceed the minimum reliable cumulative hazard observed across the registries. We propose a Monte Carlo method to identify this upper limit and apply the method to data on nine different cancers recorded by 423 registries. We compare our values with estimates obtained from a random mutations argument.

High percentage of cancer stem cells in metastatic locations; a comment on a claim "variation in cancer risk among tissues can be explained by the number of stem cell divisions"

Metastasis is the major cause of cancer-related deaths. Cancer relapse and metastasis are associated with a part of cancer cells with stem cell properties. These cancer stem cells (CSCs) are resistant to treatments. In a recent survey, we observed that the population of cancer stem-like cells among metastatic tumor cells was significantly higher than that among the primary tumor cells. This high percentage can partly explain the reasons for chemoresistance and relapse in metastatic cancers. Analysis of the role of CSCs in metastasis has been mainly conceptual and speculative, and the reasons for a higher number of CSCs in the metastatic loci are questionable. Tomasetti and Vogelstein's claim can partly answer the question. They postulated that the proliferation rate of normal stem cells in some tissue is greater than that of other tissues, and accordingly, the incidence of cancer in these tissues is high. In compliance with CSCs paradigm, resident normal stem cells of tissues are the most probable source of CSCs. After homing of metastatic cancer cells in a tissue with high rate of normal stem cell proliferation, there is a big opportunity for cancer cells to convert normal stem cells to cancer stem cells. This is the powerful effect of cancerous microenvironment on resident stem cells of tissue. Therefore, in metastatic cancers, the number of CSCs in primary tumor or in each metastatic location is relevant to the proliferation rate of resident normal stem cells of the location. This concept is a confirmation of Tomasetti and Vogelstein's claim and can answer some fundamental questions about metastasis process.

The surprising implications of familial association in disease risk

BACKGROUND

A wide range of diseases show some degree of clustering in families; family history is therefore an important aspect for clinicians when making risk predictions. Familial aggregation is often quantified in terms of a familial relative risk (FRR), and although at first glance this measure may seem simple and intuitive as an average risk prediction, its implications are not straightforward.

METHODS

We use two statistical models for the distribution of disease risk in a population: a dichotomous risk model that gives an intuitive understanding of the implication of a given FRR, and a continuous risk model that facilitates a more detailed computation of the inequalities in disease risk. Published estimates of FRRs are used to produce Lorenz curves and Gini indices that quantifies the inequalities in risk for a range of diseases.

RESULTS

We demonstrate that even a moderate familial association in disease risk implies a very large difference in risk between individuals in the population. We give examples of diseases for which this is likely to be true, and we further demonstrate the relationship between the point estimates of FRRs and the distribution of risk in the population.

CONCLUSIONS

The variation in risk for several severe diseases may be larger than the variation in income in many countries. The implications of familial risk estimates should be recognized by epidemiologists and clinicians.

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.