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The following post contains the conclusion to my experimental trials, my statistical analysis, and my research reflection.
To Recap...
I am investigating how the addition of the antibiotic Novobiocin Sodium Salt to a PARP inhibitor treatment of RIN-m Rat Beta Cells with Insulinoma impacts the health of the cell culture. PARP inhibitors causing fatal oxidative stress in a subset of tumor cells with deficiencies in DNA repair. PARP inhibitor resistance in tumor cells is an emerging problem, and the inhibition of the DNA repair by Novobiocin Sodium Salt is a potential pathway for reversing resistance. This research will contribute to the academic conversation around increasing PARP inhibitor efficacy in tumorigenic cells, in order to improve their cancer treatment potential.
Experimental Design Changes
Since my last blog post (PREP Blog #3), I had to made some changes to my experimental design due to timing concerns and a lack of available resources. As many of you know, the NIH/3T3 cytotoxicity control cells that I was attempting to show a null affect of my treatment on were having difficulty growing. The researchers we were sourcing our cells from kindly spent a lot of time and effort re-making the frozen stock for us, to ensure that the cells would be able to grow. Unfortunately, by the time we got those new cells established in the lab, I only had three days left for research (Pic. 1). I had to eliminate the NIH/3T3 group entirely from my research, limiting my data collection to 120 data points. This affects my experiment by preventing me from being able to draw conclusions about the selective tumorigenic toxicity (will my treatments kill only cancer cells, basically) of the Olaparib and Novobiocin combination. Other than that, the experiment design remained very similar, and I collected data for the 4 RIN-m groups smoothly and without interruption.
Picture 1: This picture shows a healthy hemocytometer assay of NIH/3T3 cells, after the conclusion of my research.
Results
I collected 120 data points (30 for each group) by March 12th, 2022, allowing me to do my data analysis over Spring Break (March 13th - March 19th). The data table is 120 rows long, so I will simply link the document containing my data tables and statistical analysis tables here. The average cell counts in each group can be observed in the graph below, and those are the values that I used for my statistical analysis (Graph 1). I ran 6 Two-Sample T-Tests Assuming Unequal Variances on Microsoft Excel, comparing each of the four groups with each other. A P-Value of below .05 indicates that one treatment was statistically significant to another.
Test 1: Group 1 (Olaparib) vs. Group 2 (Novobiocin)
P-Value: 0.084157964
Conclusion: Insignificance (the treatments had no difference in efficacy).
Test 2: Group 1 (Olaparib) vs. Group 3 (Combined)
P-Value: 0.001291017
Conclusion: Significance (Group 3 was more effective than Group 1).
Test 3: Group 2 (Novobiocin) vs. Group 3 (Combined)
P-Value: 0.007440836
Conclusion Significance (Group 3 was more effective than Group 2).
Test 4: Group 1 (Olaparib) vs. Group 4 (Control)
P-Value: 0.060325968
Conclusion: Insignificance (the treatment had no difference to the Control).
Test 5: Group 2 (Novobiocin) vs. Group 4 (Control)
P-Value: 0.001411905
Conclusion: Significance (Group 2 was more effective than the Control).
Test 6: Group 3 (Combined) vs. Group 4 (Control)
P-Value: 1.07962E-05
Conclusion: Significance (Group 3 was more effective than the Control).
Graph 1: This graph shows the average cell counts of the 4 RIN-m groups, along with the standard deviation represented by the error bars.
Picture 2: This is a picture of Group 4 growing 24 hours after seeding in a 96-well plate.
Discussion
I hypothesized that in exposing the RIN-m islet cells to the dual treatment of Olaparib and Novobiocin, the number of live cells would decrease more than any other treatment in comparison to the negative control. I accept this hypothesis and reject the null, as I found a statistically significant reduction in cell viability for Group 3 in comparison to Group 1, 2, and 4 (Pic. 2). From this result, I can draw the conclusion that the combined treatment of Olaparib and Novobiocin is more effective in reducing the viability of RIN-m beta islet cells than either treatment alone. There were also some interesting results elsewhere in the data. While Olaparib and Novobiocin were statistically identical in reducing RIN-m cell viability, the Olaparib treatment (Group 1) showed statistical insignificance in comparison to the control, while the Novobiocin showed the opposite (Group 2). This means that for the RIN-m cells, the Olaparib treatment is ineffective in reducing total cell viability via the PARP inhibitor pathway while Novobiocin's inhibition of DNA Polymerase Theta as an alternative pathway to reducing Homologous Recombination Repair is effective. While this is not a conclusion I can draw from this research alone, lacking the ability to directly measure dsDNA breaks, it is possible that the RIN-m cells quickly developed/innately had a resistance to PARP inhibition that was treated by combining the Novobiocin with the Olaparib.
Conclusion
Concerning the data collected, we are able to conclude that the combination treatment of Olaparib and Novobiocin in RIN-m beta islet cells is more effective at reducing cell viability than either treatment alone. This demonstrates the potential benefits of combining coumarin-derived antibiotics with FDA approved PARP inhibitors in order to treat PARP-inhibitor resistant tumors in the human body, but we are a long way from being able to prove that the implementation of that method will lead to better patient outcomes. The following is a list of conclusions (corresponding with potential research that could prove/disprove the conclusions) that would need to be made before clinical trial testing.
The combination of Olaparib and Novobiocin shows selective tumorigenic toxicity.
Corresponding Research: Repeat this experiment with the NIH/3T3 cytotoxicity control.
The combination of Olaparib and Novobiocin shows selective tumorigenic toxicity on multiple cancer types.
Corresponding Research: Repeat this experiment with multiple cell lines of human cancer, including pancreatic, colon, and breast cancer (the most common cancers susceptible to PARP inhibition).
The combination of Olaparib and Novobiocin is effective in reducing tumor size in animal models of human cancer.
Corresponding Research: Induce tumor growth in a mouse model, then monitor the size of the tumor on exposure to either oral or injected Novobiocin and Olaparib.
The combination of Olaparib and Novobiocin is safe in animal models of human cancer.
Corresponding Research: Monitor health effects in the experiment listed in conclusion #3.
A Quick Aside...
In my past three years of learning the principles of scientific research (first in the RCHS Biotechnology Program, and then AP Research), I learned the importance of teamwork as it relates to research. Research is impossible without collaboration on multiple levels. You base your hypotheses off of current available research in your field, and then your conclusion goes on to drive the hypotheses of more advanced research in your field. Your methods are developed with the help of others, your results analyzed with the help of others, and your conclusions are drawn with the help of others. The major flaw of AP Research is the individualistic nature of the research it promotes—"I" instead of "we", limits on expert advice, and an emphasize on personal accomplishment. Research is not an "I" activity, and AP Research does the academic community a disservice in that sense.
That being said, based on previous research experience, teamwork and effective communication is the most valuable skill that I have taken away from my 3-year research journey. A team is research's greatest asset—they are strong where you are weak, energized when you are tired, and motivated when you fail. I have had the opportunity this year to observe team research projects that have had varying degrees of success. Teamwork is the largest factor I have seen for determining that success. The best groups are coordinated, well organized, efficient, and productive—a testament to the strength of research as a tool for scientific progress. Broken teamwork, however, leads to a broken project. Discontent permeates the group, leading to uncoordinated, self-sabotaging, unproductive research that inefficiently solves the numerous challenges of experimental design. Hate and spite have no place in the research field—the goal is scientific progress, and teamwork should be informed with that goal in mind. My hope is that I can take what I have learned about healthy, productive teamwork and positively influence the future research teams that I am a part of. I cannot acknowledge the contributions of others to my research on the basis of AP Research standards—none of which I broke, but doing so would cause confusion among the AP Research graders due to the complex nature of my project. So I would like to thank you, the reader, for following my project through its conclusion. Thank you for supporting scientific research, as it is the future for all of us.
Sources
All factual information in this post not directly obtained through my own experiment was taken directly from my AP Research Proposal, which can be accessed here.
The research that Sophia McHenry, Allie Floyd, Kailey Sprengel, and Kyla Lauretti are conducting can be viewed here.
All visuals were taken/created by the researcher.
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