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

From Chaos to Opportunity: Decoding Cancer Heterogeneity for Enhanced Treatment Strategies

Cancer manifests as a multifaceted disease, characterized by aberrant cellular proliferation, survival, migration, and invasion. Tumors exhibit variances across diverse dimensions, encompassing genetic, epigenetic, and transcriptional realms. This heterogeneity poses significant challenges in prognosis and treatment, affording tumors advantages through an increased propensity to accumulate mutations linked to immune system evasion and drug resistance. In this review, we offer insights into tumor heterogeneity as a crucial characteristic of cancer, exploring the difficulties associated with measuring and quantifying such heterogeneity from clinical and biological perspectives. By emphasizing the critical nature of understanding tumor heterogeneity, this work contributes to raising awareness about the importance of developing effective cancer therapies that target this distinct and elusive trait of cancer.

To Be or to Have Been Lucky, That Is the Question

Is it possible to measure the dispersion of ex ante chances (i.e., chances “before the event”) among people, be it gambling, health, or social opportunities? We explore this question and provide some tools, including a statistical test, to evidence the actual dispersion of ex ante chances in various areas, with a focus on chronic diseases. Using the principle of maximum entropy, we derive the distribution of the risk of becoming ill in the global population as well as in the population of affected people. We find that affected people are either at very low risk, like the overwhelming majority of the population, but still were unlucky to become ill, or are at extremely high risk and were bound to become ill.

Ecological and Evolutionary Consequences of Anticancer Adaptations

Cellular cheating leading to cancers exists in all branches of multicellular life, favoring the evolution of adaptations to avoid or suppress malignant progression, and/or to alleviate its fitness consequences. Ecologists have until recently largely neglected the importance of cancer cells for animal ecology, presumably because they did not consider either the potential ecological or evolutionary consequences of anticancer adaptations. Here, we review the diverse ways in which the evolution of anticancer adaptations has significantly constrained several aspects of the evolutionary ecology of multicellular organisms at the cell, individual, population, species, and ecosystem levels and suggest some avenues for future research.

Building a richer understanding of diversity through causally consistent evenness measures

Causally consistent evenness measures can only be changed when the populations they refer to change. This novel property is deeply important for making causal inferences, and yet every prominent evenness measure is not causally consistent. This paper proposes a family of causally consistent evenness measures, and while any evenness measure can be made to be causally consistent, the family I introduce has the added benefit of a straightforward interpretation as a percentage evenness. I go on to illustrate the performance of these measures, and demonstrate the importance of causal consistency not only for causal inference but also for correctly reflecting the evenness of ecological communities. I also present several alternative transformations of my preferred measures, which work to address potential critiques in advance, communicate evenness to nontechnical audiences, and connect my work to more familiar ecological indicators.

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.

Fatty Acids and Membrane Lipidomics in Oncology: A Cross-Road of Nutritional, Signaling and Metabolic Pathways

Fatty acids are closely involved in lipid synthesis and metabolism in cancer. Their amount and composition are dependent on dietary supply and tumor microenviroment. Research in this subject highlighted the crucial event of membrane formation, which is regulated by the fatty acids' molecular properties. The growing understanding of the pathways that create the fatty acid pool needed for cell replication is the result of lipidomics studies, also envisaging novel fatty acid biosynthesis and fatty acid-mediated signaling. Fatty acid-driven mechanisms and biological effects in cancer onset, growth and metastasis have been elucidated, recognizing the importance of polyunsaturated molecules and the balance between omega-6 and omega-3 families. Saturated and monounsaturated fatty acids are biomarkers in several types of cancer, and their characterization in cell membranes and exosomes is under development for diagnostic purposes. Desaturase enzymatic activity with unprecedented de novo polyunsaturated fatty acid (PUFA) synthesis is considered the recent breakthrough in this scenario. Together with the link between obesity and cancer, fatty acids open interesting perspectives for biomarker discovery and nutritional strategies to control cancer, also in combination with therapies. All these subjects are described using an integrated approach taking into account biochemical, biological and analytical aspects, delineating innovations in cancer prevention, diagnostics and treatments.

Analysis of 153 115 patients with hematological malignancies refines the spectrum of familial risk

Estimating familial cancer risks is clinically important in being able to discriminate between individuals in the population at differing risk for malignancy. To gain insight into the familial risk for the different hematological malignancies and their possible inter-relationship, we analyzed data on more than 16 million individuals from the Swedish Family-Cancer Database. After identifying 153 115 patients diagnosed with a primary hematological malignancy, we quantified familial relative risks (FRRs) by calculating standardized incident ratios (SIRs) in 391 131 of their first-degree relatives. The majority of hematological malignancies showed increased FRRs for the same tumor type, with the highest FRRs being observed for mixed cellularity Hodgkin lymphoma (SIR, 16.7), lymphoplasmacytic lymphoma (SIR, 15.8), and mantle cell lymphoma (SIR, 13.3). There was evidence for pleiotropic relationships; notably, chronic lymphocytic leukemia was associated with an elevated familial risk for other B-cell tumors and myeloproliferative neoplasms. Collectively, these data provide evidence for shared etiological factors for many hematological malignancies and provide information for identifying individuals at increased risk, as well as informing future gene discovery initiatives.

Assessing Changes in Inequality for Millennium Development Goals among Countries: Lessons for the Sustainable Development Goals

In 2000, the United Nations adopted the Millennium Development Goals (MDGs), a set of eight global development goals to be achieved between 2000 and 2015. We estimated the Lorenz Curve and Gini Index for determining any changes in inequality at the global level with countries as a unit of analysis for eight development indicators (proportion of population undernourished, school enrollment rates, the percentage of women in parliament, infant mortality rates, maternal mortality rates, HIV (Human Immunodeficiency Virus) rates, access to improved water sources, and access to a cellular device), representing one MDG each. All of the selected indicators improved on average between 2000 and 2015. An average improvement in an indicator does not necessarily imply a decrease in inequality. For instance, the average infant mortality rate decreased from 39.17 deaths per 1000 births in 2000 to 23.40 in 2015, but the Gini Index remained almost stable over the same period, suggesting no reduction in inequality among countries. For other indicators, inequality among countries decreased at varying rates. A significant data gap existed across countries. For example, only 91 countries had data on primary school enrollment rates in 2000 and 2015. We emphasize developing a global data collection and analysis protocol for measuring the impacts of global development programs, especially in reducing inequality across social, economic, and environmental indicators. This study will feed into currently enacted Sustainable Development Goals (SDGs) for ensuring more inclusive and equitable growth worldwide.

Introducing risk inequality metrics in tuberculosis policy development

Global stakeholders including the World Health Organization rely on predictive models for developing strategies and setting targets for tuberculosis care and control programs. Failure to account for variation in individual risk leads to substantial biases that impair data interpretation and policy decisions. Anticipated impediments to estimating heterogeneity for each parameter are discouraging despite considerable technical progress in recent years. Here we identify acquisition of infection as the single process where heterogeneity most fundamentally impacts model outputs, due to selection imposed by dynamic forces of infection. We introduce concrete metrics of risk inequality, demonstrate their utility in mathematical models, and pack the information into a risk inequality coefficient (RIC) which can be calculated and reported by national tuberculosis programs for use in policy development and modeling.

Failure to account for heterogeneity in TB risk can mislead model-based evaluation of proposed interventions. Here, the authors introduce a metric to estimate the distribution of risk in populations from routinely collected data and find that variation in infection acquisition is the most impactful.

Effect of increased body mass index on risk of diagnosis or death from cancer

Background

Whether body mass index (BMI) is causally associated with the risk of being diagnosed with or dying from any cancer remains unclear. Weight reduction has clinical importance for cancer control only if weight gain causes cancer development or death. We aimed to answer the question 'does genetically predicted BMI influence my risk of being diagnosed with or dying from any cancer'.

Methods

We used a Mendelian randomisation (MR) approach to estimate causal effect of BMI in 46,155 white-British participants aged between 40 and 69 years at recruitment (median age at follow-up 61 years) from the UK Biobank, who developed any type of cancer, among whom 6998 died from cancer. To derive MR instruments for BMI, we selected up to 390,628 cancer-free participants.

Results

For each standard deviation (4.78 units) increase in genetically predicted BMI, we estimated a causal odds ratio (COR) of 1.07 (1.02–1.12) and 1.28 (1.16–1.41) for overall cancer risk and mortality, respectively. The corresponding estimates were similar for males and females, and smokers and non-smokers.

Conclusions

Higher genetically predicted BMI increases the risk of being diagnosed with or dying from any cancer. These data suggest that increased overall weight may causally increase overall cancer incidence and mortality among Europeans.

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.