Energy oversupply to tissues: a single mechanism possibly underlying multiple cancer risk factors

A modelling framework for cancer ecology and evolution

Cancer incidence increases rapidly with age, typically as a polynomial. The somatic mutation theory explains this increase through the waiting time for enough mutations to build up to generate cells with the full set of traits needed to grow without control. However, lines of evidence ranging from tumour reversion and dormancy to the prevalence of presumed cancer mutations in non-cancerous tissues argue that this is not the whole story, and that cancer is also an ecological process, and that mutations only lead to cancer when the systems of control within and across cells have broken down. Aging thus has two effects: the build-up of mutations and the breakdown of control. This paper presents a mathematical modelling framework to unify these theories with novel approaches to model the mutation and diversification of cell lineages and of the breakdown of the layers of control both within and between cells. These models correctly predict the polynomial increase of cancer with age, show how germline defects in control accelerate cancer initiation, and compute how the positive feedback between cell replication, ecology and layers of control leads to a doubly exponential growth of cell populations.

How Should Cancer Models Be Constructed?

Choosing and optimizing treatment strategies for cancer requires capturing its complex dynamics sufficiently well for understanding but without being overwhelmed. Mathematical models are essential to achieve this understanding, and we discuss the challenge of choosing the right level of complexity to address the full range of tumor complexity from growth, the generation of tumor heterogeneity, and interactions within tumors and with treatments and the tumor microenvironment. We discuss the differences between conceptual and descriptive models, and compare the use of predator-prey models, evolutionary game theory, and dynamic precision medicine approaches in the face of uncertainty about mechanisms and parameter values. Although there is of course no one-size-fits-all approach, we conclude that broad and flexible thinking about cancer, based on combined modeling approaches, will play a key role in finding creative and improved treatments.

Oversupply of Limiting Cell Resources and the Evolution of Cancer Cells: A Review

Cancer prevention is superior to cancer treatment—indeed, understanding and controlling cancer risk is a key question in the fields of applied ecology and evolutionary oncology. Ecological cancer risk models offer the dual benefit of being generalizable across cancer types, and unveiling common mechanisms underlying cancer development and spread. Understanding the biological mechanisms of cancer risk may also guide the design of interventions to prevent cancer. Ecological considerations are central to many of these mechanisms; as one example, the ecologically-based hypothesis of metabolic cancer suppression posits that restricted vascular supply of limiting resources to somatic tissues normally suppresses the evolution of somatic cells toward cancer. Here we present a critical review of published evidence relevant to this hypothesis, and we conclude that there is substantial evidence that cancer risk does increase with an abnormal excess of limiting cell resources, including both dietary macronutrients as well as certain micronutrients.

Interactions between the MicroRNAs and Microbiota in Cancer Development: Roles and Therapeutic Opportunities

The human microbiota is made up of the fungi, bacteria, protozoa and viruses cohabiting within the human body. An altered microbiota can provoke diseases such as cancer. The mechanisms by which a modified microbiota can intervene in the onset and progression of neoplastic diseases are manifold. For instance, these include the effects on the immune system and the onset of obesity. A different mechanism seems to be constituted by the continuous and bidirectional relationships existing between microbiota and miRNAs. MiRNAs emerged as a novel group of small endogenous non-coding RNAs from that control gene expression. Several works seem to confirm the presence of a close connection between microbiota and miRNAs. Although the main literature data concern the correlations between microbiota, miRNAs and colon cancer, several researches have revealed the presence of connections with other types of tumour, including the ovarian tumour, cervical carcinoma, hepatic carcinoma, neoplastic pathologies of the central nervous system and the possible implication of the microbiota-miRNAs system on the response to the treatment of neoplastic pathologies. In this review, we summarise the physiological and pathological functions of the microbiota on cancer onset by governing miRNA production. A better knowledge of the bidirectional relationships existing between microbiota and miRNAs could provide new markers for the diagnosis, staging and monitoring of cancer and seems to be a promising approach for antagomir-guided approaches as therapeutic agents.

Aging and cancer: Is glucose a mediator between them?

Aging can increase cancer incidence because of accumulated mutations that initiate cancer and via compromised body control of premalignant lesions development into cancer. Relative contributions of these two factors are debated. Recent evidence suggests that the latter is rate limiting. In particular, hyperglycemia caused by compromised body control of blood glucose may be a factor of selection of somatic mutation-bearing cells for the ability to use glucose for proliferation. High glucose utilization in aerobic glycolysis is a long known characteristic of cancer. The new evidence adds to the concepts that have been being developed starting from mid-1970ies to suggest that age-related shifts in glucose and lipid metabolism increase the risk of cancer and compromise prognoses for cancer patients and to propose antidiabetic biguanides, including metformin, for cancer prevention and as an adjuvant means of cancer treatment aimed at the metabolic rehabilitation of patients. The new evidence is consistent with several effects of glucose contributing to aging and acting synergistically to enhance carcinogenesis. Glucose can affect (i) separate cells (via promoting somatic mutagenesis and epigenetic instability), (ii) cell populations (via being a factor of selection of phenotypic variants in cell populations for higher glucose consumption and, ultimately, for high aerobic glycolysis); (iii) cell microenvironment (via modification of extracellular matrix proteins), and (iv) the systemic levels (via shifting the endocrine regulation of metabolism toward increasing blood lipids and body fat, which compromise immunological surveillance and promote inflammation). Thus, maintenance of youthful metabolic characteristics must be important for cancer prevention and treatment.

Metabolic syndrome and risk of ovarian and fallopian tube cancer in the United States: An analysis of linked SEER-Medicare data

OBJECTIVE

To clarify associations between metabolic syndrome, its components, and ovarian cancer risk.

METHODS

Using a case-control study within the U.S.-based Surveillance, Epidemiology and End Results (SEER)-Medicare linked database, we examined metabolic syndrome, its components (obesity, impaired fasting glucose, hypertension, HDL cholesterol, triglycerides), and ovarian/fallopian tube cancer risk. Cases (n = 16,850) were diagnosed with cancer between age 68-89 from 1994 through 2013. Controls (n = 281,878) were Medicare enrollees without these cancers living in registry areas. We estimated adjusted odds ratios (OR) and 95% confidence intervals (CI) with logistic regression.

RESULTS

Women with metabolic syndrome had reduced ovarian cancer risk compared to women not meeting the diagnostic criteria (OR 0.86, CI 0.82-0.89). Having one or two syndrome components was associated with increased risk, but having ≥3 was not, when compared to women without any components. Impaired fasting glucose, which was highly prevalent among those with metabolic syndrome, was associated with reduced risk (OR 0.90, CI 0.87-0.93). Hypertension and high triglycerides, the most prevalent components among women without metabolic syndrome, were associated with increased risks (OR 1.08, CI 1.04-1.12; OR 1.05, CI 1.01-1.08, respectively).

CONCLUSIONS

Specific metabolic syndrome components may have modest associations with ovarian cancer. These associations varied in direction and the prevalence of the components influenced the overall association between metabolic syndrome and ovarian cancer. Evaluating metabolic syndrome as a composite exposure could be misleading in ovarian cancer research, but further study of the syndrome components is warranted.