Cancer is an ever-relevant disease with many genetic, social, environmental, and behavioral risk factors. One factor which has been garnering interest is the impact of nutrition on cancer. As a disease process, cancer is primarily driven by an accumulation of genetic aberrations. Recent epidemiological, pre-clinical, and clinical studies have demonstrated various impacts of bioactive food molecules on the promotion or prevention of these oncogenic mutations. This work explores several of these molecules and their relation to cancer prevention and provides a sample meal plan, which highlights many additional molecules that are currently being studied.

Cancer is an ever-relevant disease with many genetic, social, environmental, and behavioral risk factors. One factor which has been garnering interest is the impact of nutrition on cancer. As a disease process, cancer is primarily driven by an accumulation of genetic aberrations. Recent epidemiological, pre-clinical, and clinical studies have demonstrated various impacts of bioactive food molecules on the promotion or prevention of these oncogenic mutations. This work explores several of these molecules and their relation to cancer prevention and provides a sample meal plan, which highlights many additional molecules that are currently being studied.

Cancer treatments such as chemotherapy and radiation are expensive, painful, and often ineffective, as they compromise the patient’s immune system. Genetically-modified Salmonella Typhimurium (GMS) strains, however, have been proven to target tumors and suppress tumor growth. The GMS then undergo programmed lysis, optimally leaving no trace of Salmonella in the body. Additionally, constant culturing of S. Typhimurium changes the pH of the culture medium. The objective of this research is to investigate using Salmonella to induce changes in the typically acidic tumor microenvironment (TME) pH, ideally hindering tumor growth. Future studies involve utilizing Salmonella to treat a multitude of cancers.

Molecular pathology makes use of estimates of tumor content (tumor percentage) for pre-analytic and analytic purposes, such as molecular oncology testing, massive parallel sequencing, or next-generation sequencing (NGS), assessment of sample acceptability, accurate quantitation of variants, assessment of copy number changes (among other applications), determination of specimen viability for testing (since many assays require a minimum tumor content to report variants at the limit of detection) may all be improved with more accurate and reproducible estimates of tumor content. Currently, tumor percentages of samples submitted for molecular testing are estimated by visual examination of Hematoxylin and Eosin (H&E) stained tissue slides under the microscope by pathologists. These estimations can be automated, expedited, and rendered more accurate by applying machine learning methods on digital whole slide images (WSI).

Age is the most significant risk factor for cancer development in humans. The somatic mutation theory postulates that the accumulation of genomic mutations over time results in cellular function degradation which plays an important role in understanding aging and cancer development. Specifically, degradation of the mechanisms that underlie somatic maintenance can occur due to decreased immune cell function and genomic responses to DNA damage. Research has shown that this degradation can lead to the accumulation of mutations that can cause cancer in humans. Despite recent advances in our understanding of cancer in non-human species, how this risk factor translates across species is poorly characterized. Here, we analyze a veterinarian cancer dataset of 4,178 animals to investigate if age related cancer prevalence is similar in non-human animals. We intend for this work to be used as a primary step towards understanding the potential overlap and/or uniqueness between human and non-human cancer risk factors. This study can be used to better understand cancer development and how evolutionary processes have shaped somatic maintenance across species.

Cancers of the reproductive tissues make up a significant portion of the cancer burden and mortality experienced by humans. Humans experience several proximal causative carcinogens that explain a portion of cancer risk, but an evolutionary viewpoint can provide a unique lens into the ultimate causes of reproductive cancer vulnerabilities. A life history framework allows us to make predictions on cancer prevalence based on a species’ tempo of reproduction. Moreover, certain variations in the susceptibility and prevalence of cancer may emerge due to evolutionary trade-offs between reproduction and somatic maintenance. For example, such trade-offs could involve the demand for rapid proliferation of cells in reproductive tissues that arises with reproductive events. In this study, I compiled reproductive cancer prevalence for 158 mammalian species and modeled the predictive power of 13 life history traits on the patterns of cancer prevalence we observed, such as Peto’s Paradox or slow-fast life history strategies. We predicted that fast-life history strategists will exhibit higher neoplasia prevalence risk due to reproductive trade-offs. Furthering this analytical framework can aid in predicting cancer rates and stratifying cancer risk across the tree of life.