Browsing by Subject "Acute leukemia"

Acute leukemia is a life-threatening disease of blood and bone marrow, which is caused by malignant transformation of immature white blood cells. These malignant white blood cells invade space in bone marrow decreasing its ability to produce normal blood cells, eventually leading to death within weeks after the diagnosis without treatment. The acute leukemia can be broadly divided into its lymphoblastic and myeloid form, based on the affected cell lineage. Furthermore, acute leukemias can be classified based on different genomic features, such as gene fusions. Fusion genes are strong drivers in various cancers such as acute leukemias, and they are formed when two or more original genes join together forming a novel hybrid gene. If the novel hybrid gene is transcribed, it can lead to a translation of an abnormal fusion protein with altered function. The detection of the gene fusions is very important, since it affects to diagnosis and treatment of the patient. Various techniques can be used for fusion gene detection, of which the RNA sequencing is the method of choice, due to its ability to provide an unbiased identification of all known and novel gene fusions from the sample in a single experiment. In this thesis, the overarching aim was to develop an optimal sampling protocol for fusion gene detection using RNA sequencing for acute leukemia diagnostics. First, the whole blood samples in EDTA-tubes were collected from acute leukemia patients based on the findings from routine diagnostics. Next, the RNA was extracted at three different timepoints (0h, 8h, and 32h). The samples were stored at 4°C between the extractions. Finally, the RNA sequencing libraries were constructed, and the RNA sequencing was performed. After the sequencing, the data was analyzed using the FusionCatcher algorithm for fusion gene detection and the EdgeR-package for differential expression analysis. The FusionCatcher detected the same gene fusion in all the four fusion gene positive patients compared to routine diagnostics. However, the FusionCatcher failed to recognize the gene fusion in some of the samples with very low number of fusion breakpoint-spanning reads. These reads were visualized with IGV, suggesting that the detection failure resulted from the very low number of break-point-spanning reads. Furthermore, the sample storage did not affect on gene fusion detection. In addition, FusionCatcher detected PIK3AP::BLNK gene fusion from one of the fusion gene negative patients, suggesting a possibility that the patient truly was fusion gene positive. The differential expression analysis revealed changes in gene expression between the different timepoints. The results showed changes in various pathways related for example to cell death and protein biosynthesis, but also to pathways related to cancer. The results showed that prolonged sample storage alters the gene expression profile thus affecting the results of a gene expression study.