Glioblastoma brain tumors are among the most lethal human cancers. Treatment efforts typically involve both surgical tumor removal, as well as ongoing therapy. In this work, we propose the use of deuterium magnetic resonance imaging (MRI) to delineate tumor boundaries based on spatial distributions of deuterated leucine, as well as resolve the metabolism of leucine within the tumor. Accurate boundary identification contributes to effectiveness of tumor removal efforts, while amino acid metabolism information may help characterize tumor malignancy and guide ongoing treatment. So, we first examine the fundamental mechanisms of deuterium MRI. We then discuss the use of spin-echo and gradient recall echo sequences for mapping spatial distributions of deuterated leucine, and the use of single-voxel spectroscopy for imaging metabolites within a tumor.

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.

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.

Evolution has driven organisms to develop a wide range of biological mechanisms to protect against cancer. Some organisms, including the sponge Tethya wilhelma and the Placozoa Trichoplax adhaerens have developed particularly effective mechanisms to suppress cancer and repair DNA damage. While these mechanisms are rooted in DNA damage repair and prevention, evidence of bacteria may suggest that endosymbionts living within the organisms may plays a role as well. Likewise, other organisms, such as the flatworm Macrostomum lignano, are proven model organisms whose extensive documentation enables more in-depth analysis of biological mechanisms associated with cancer. Sponges, flatworms, and Placozoa were exposed to X-ray radiation totaling 600 Gy, 25 Gy, and up to 240 Gy, respectively. RNA sequencing and bioinformatics analyses were undergone to determine the differential gene expression of the animals at different time points. No common response to the X-ray radiation was discovered amongst all organisms. Instead, sponges showed evidence of tumor suppression and DNA repair gene upregulation including CUBN, bacterial endosymbionts showed evidence of lateral gene transfer and different DNA repair genes including FH, and flatworms showed evidence of allelic and mutational shifts in which tumorous populations became more reliant on alternate alleles and a single variant signature. This study highlights the varying mechanisms that have evolved in different organisms and the importance of studying these novel model organisms further.

An immune regulatory network was constructed for the purpose of identifying target regulators in malignant pleural mesothelioma for therapies. An identified causal flow linked a mutation of D-dopachrome tautomerase to a heightened expression of regulator ASH1L and consequent down regulation of chemokine CCL5 and invasion of CD8+ T cells. Experimental validation of this initial use case indicates mRNA expression of CCL5 within the tumor cells and subsequent protein expression and secretion. Further analyses will explore the migration of CD8+ T cells in response to the chemotactic CCL5.

Cooperative cellular phenotypes are universal across multicellular life. Division of labor, regulated proliferation, and controlled cell death are essential in the maintenance of a multicellular body. Breakdowns in these cooperative phenotypes are foundational in understanding the initiation and progression of neoplastic diseases, such as cancer. Cooperative cellular phenotypes are straightforward to characterize in extant species but the selective pressures that drove their emergence at the transition(s) to multicellularity have yet to be fully characterized. Here we seek to understand how a dynamic environment shaped the emergence of two mechanisms of regulated cell survival: apoptosis and senescence. We developed an agent-based model to test the time to extinction or stability in each of these phenotypes across three levels of stochastic environments.

Early detection of disease is essential for alleviating disease burden, increasing success rate and decreasing mortality rate especially for cancer. To improve disease diagnostics, many candidate biomarkers have been suggested using molecular biology or image analysis techniques over the past decade. The receiver operating characteristics (ROC) curve is a standard technique to evaluate a diagnostic accuracy of biomarkers, but it has some limitations especially for heterogeneous diseases. As an alternative of the ROC curve analysis, we suggest a jittered dot plot (JDP) and JDP-based evaluation measures, above mean difference (AMD) and averaged above mean difference (AAMD). We demonstrate how JDP and AMD or AAMD together better evaluate biomarkers than the standard ROC curve. We analyze real and heterogeneous basal-like breast cancer data.