Thea Nikolaou/Dr. Solomon - Week 6

 This week I spent my time in the molecular pathology lab with the lab technicians. I was able to see how patient samples are handled and processed in the lab from when they are received from surgical pathology to the time that they are placed in the sequencer. It was interesting to see with how much care the samples are being handled by the technicians. Often, tissue samples are very limited so no amount of the sample can be wasted when it comes to getting a successful test that can be confidently interpreted and can provide meaningful diagnostic or prognostic information to the patient's oncologist. Because tissue is received on glass slides and is formalin fixed, technicians can scrape the cells off of the glass slides. Typically, multiple slides are necessary to get to the required extracted DNA concentration. For example, from biopsy samples a rough average of 9 glass slides will be scraped to produce enough DNA for the NGS gene panels available at the lab. After scraping, and nucleic acid extraction and quantification, the procedure varies based on the assay that was requested by the prescribing oncologist/pathologist. The lab is currently focused on performing two assays TSO 500 and Oncomine v3. Each technician specializes in one of the two assays to increase efficiency. Once the genetic material is quantified, it goes through library preparation where it is labeled with a unique patient identifier and hybridization beads and further amplified. The libraries also undergo normalization so that each is represented uniformly in the pooled libraries. The pooled libraries are then loaded onto the NGS machine for sequencing. Data output goes through several analysis pipelines prior to getting reviewed by the pathologist.

Throughout the week I also worked each of the projects and have been able to gain some clarity on the goals and extent to which I can contribute to each. For the mutational signatures for TSO 500 data, I will be focused on validating the existing R package and some of the data filtering methods I added using some samples with known or suspected signatures. This offers some initial insights to the bioinformatics team on mutational signature analysis using this data. For the project focused on getting a cell count and cell type percentage, I have gotten help from the bioinformatics team in setting up the trained deep learning model which was available on github and they have been able to get it ready to run inference on some of the images Dr. Solomon retrieved from the surgical pathology department. I was able to get started on a python script that can provide the cell count and cell type percentage on a slide image from the one of the model's output files.

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