Sarah Henretta/Dr. Cristofanilli - Week 2

This week I continued working with Dr. Cristofanilli in the clinic and made substantial progress on my research project with Dr. Munoz-Arcos. 

Generally, breast cancer patients have three oncologists: a medical oncologist, surgical oncologist, and radiation oncologist. Dr. Cristofanilli is a medical oncologist, so he is responsible for creating and prescribing the patient's medical treatment plan. He is also the oncologist with the longest lasting contact with the patients, as surgeons generally only see patients close to their surgery date and radiation oncologists only see patients during the time that they are undergoing radiation. I believe this allows medical oncologists to form unique relationships with their patient that seem to extend past simply prescribing treatments. 

For example, this week we met with a patient who was losing insurance coverage in the upcoming weeks. Naturally, the conversation shifted away from their exact treatment plan and towards how they were going to balance this new change. Additionally, we met with a patient who was struggling through many different medical problems. Some of the problems were causing them to miss treatment appointments, leading to cancer progression. Here, Dr. Cristofanilli worked with them to come up with strategies so they could become more consistent with their treatments. Throughout the week I witnessed many cases where Dr. Cristofanilli discussed treatment plans with patients who were hesitant, nervous, or scared. It was easy to empathize with the patients, as the treatments he often prescribes sound very complex when you aren’t familiar with the drug names or technologies. However, every time this was the case, Dr. Cristofanilli took the time to break down the plan, explain any potential side effects, and clarify points of confusion until patients felt comfortable proceeding. 

Overall, I feel like I learned a lot this past week. Now that I understand the basics of breast cancer oncology, Dr. Cristofanilli began to teach me why you may use one treatment versus another in different cases. I now understand the general treatment plan for patients with either ductal or lobular, hormone receptor positive or negative, and HER2 positive or negative cancers. It was interesting how the combination of each variable, such as ductal, triple negative carcinoma or lobular, ER/PR+, HER2- carcinoma, impacts the specific treatment plan. I have learned I really enjoy the combination of personal interaction and problem solving that clinicians face every day. 

For my research project, I obtained in vitro images of well-established cell lines which I will use as the training images for my program. The images were full slide scans instead of basic microscope images. This is a format I am not used to working with, as they are much larger image files, and it is more difficult to see the cells. From an image analysis perspective, it would be much easier to analyze smaller, higher quality images. However, if this technology is to be translated to patient care diagnostics (end goal), it will be more efficient and easier to image full slides. Thus, I challenged myself to analyze this new type of image. This was one of my first experiences thinking of a problem by combining basic science best practices with consideration for translation into the clinic. 

Once I figured out the best way to analyze the images, I taught myself a new cell analysis program, CellProfiler. I decided this would be the best program to use based on multiple factors. First, there are thousands of cells to analyze, making manual analysis unrealistic. Second, many of the images have cells clumped together. This introduced a need for a program able to identify and segregate objects (in this case cells) based on their shape and image intensity. Third, many people will be using this program after me. Therefore, I needed to eliminate any aspect of the analysis that could be left up to user interpretation, such as thresholding and object identification. 

After I created a suitable analysis pipeline in CellProfiler, I imported the data into MATLAB where I am currently writing a code to determine which image intensity values correlate to "high," "intermediate," and "low" protein expression levels. Once I determine suitable intensity levels for each category, I will begin using “unknown” cells to validate if the program is identifying cell phenotypes correctly and, later, use patient samples in the program. 


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