A Cancer Risk Study

Document Type

Capstone

Lead Author Type

MBI Masters Student

Advisors

Dr. Guenter Tusch, tuschg@gvsu.edu

Embargo Period

5-17-2016

Abstract

Cancer is a complex disease and it remains the second leading cause of death in the United States. Cancer statistics depict what happens in extensive groups of individuals and to provide a picture in time of the burden of cancer on society. Statistics can give us details such as how many people die from each year, the number of people who are currently living after cancer diagnosis and more. I analyzed cancer data provided by The American Cancer Society.

The aim of the project was twofold: to extract most impactful cancers from the data and to explore the pharmacogenetics and pharmacokinetics of those cancers. Statistical tests such as ANOVA and t-test were performed to identify the top 5 significant cancers, which contribute to the highest death rates in the US. Those were breast, colorectal, prostrate, lung cancers and myeloma. Programs in Python and Plotly were developed to analyze death trends in male and females, risk estimates for new cases, and death rates in the United States.

The above analysis was performed to identify the rate of increase in cancer incidence. To explore further the above stated cancer groups, the following analysis was performed at the level of pharmacogenetics and pharmacokinetics: cancer causing genes were collected from several biological sources and also several drugs acting upon these cancer genes. The genes were filtered to extract the most common cancer genes for the study. There are various approaches in which genes can influence reaction to certain medications depending upon whether they impact the pharmacokinetic drug reaction pathways. For instance, alterations in genes in the PK pathway may influence the absorption, distribution, metabolism or elimination of the drug. The VisANT software was used to create a gene network to see if these genes interact with other genes. As other researchers found in similar situations, it could be shown that a drug can have alternative gene targets.

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