Analyzing the Response of Driver Gene Targeted Drugs on Ex Vivo Treated Murine and Human Lung Cancer Tissue Slices
Kokkonen, Roosa; Mäntylä, Hanna (2019)
2019
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2019112923445
https://urn.fi/URN:NBN:fi:amk-2019112923445
Tiivistelmä
Worldwide the lung cancer continues being the leading cause of cancer-related mortality. By detecting the different genes involved in driving the development of lung cancer, different approaches to the treatment of lung cancer have been found. This thesis was done for the Verschuren group from the Institute for Molecular Medicine Finland (FIMM), Helsinki. The purpose of this thesis was to analyze the results of driver gene targeted drugs on murine and human lung cancer tissue slices.
We worked with murine and human lung cancer tissues, analyzed the results and collected data from the samples for the Verschuren group’s later use. We had on total seven samples, four murine lung cancer samples and three human lung cancer samples. Tissues were sliced using Leica VT 1200S. Tissue slices were treated using single or combined drugs, Afatinib and Trametinib, some were treated with different concentrations and treatment times. After treatment, samples were fixed, processed, embed, sectioned and stained (H&E and IHC) according to the workflow in use in the laboratory. The stained tissue sections were scanned, and pictures processed. CL2M software was used for machine learning which was done image by image to recognize viable, dead, stromal and other cell, and background from H&E stained tissue sections.
Three of the murine samples were adenosquamous carcinoma and one adenocarcinoma. Two of the human samples were adenocarcinoma and one squamous cell carcinoma. Results differed depending on type of the tumor, treatment time and drug concentration. In some cases, combinatorial treatment seemed to be more effective and some cases single Afatinib or Trametinib treatment appeared to be more effective depending on the tumor type and whether it was human or murine sample. In some cases, further quantification needs to be done.
These results led us to the conclusion that possibly the research should be continued with human samples as the drug responses appeared to differ between human and murine lung cancer tissue slices. Of course, the number of samples tested was low therefore no further conclusions can be drawn from this thesis alone.
We worked with murine and human lung cancer tissues, analyzed the results and collected data from the samples for the Verschuren group’s later use. We had on total seven samples, four murine lung cancer samples and three human lung cancer samples. Tissues were sliced using Leica VT 1200S. Tissue slices were treated using single or combined drugs, Afatinib and Trametinib, some were treated with different concentrations and treatment times. After treatment, samples were fixed, processed, embed, sectioned and stained (H&E and IHC) according to the workflow in use in the laboratory. The stained tissue sections were scanned, and pictures processed. CL2M software was used for machine learning which was done image by image to recognize viable, dead, stromal and other cell, and background from H&E stained tissue sections.
Three of the murine samples were adenosquamous carcinoma and one adenocarcinoma. Two of the human samples were adenocarcinoma and one squamous cell carcinoma. Results differed depending on type of the tumor, treatment time and drug concentration. In some cases, combinatorial treatment seemed to be more effective and some cases single Afatinib or Trametinib treatment appeared to be more effective depending on the tumor type and whether it was human or murine sample. In some cases, further quantification needs to be done.
These results led us to the conclusion that possibly the research should be continued with human samples as the drug responses appeared to differ between human and murine lung cancer tissue slices. Of course, the number of samples tested was low therefore no further conclusions can be drawn from this thesis alone.