Background: Acute myeloid leukaemia (AML) is a severe form of blood cancer, which in many cases can not be cured. Although chemotherapeutic treatment is effective in most cases, often the disease relapses. To monitor the course of disease, as well as to early identify a relapse, the leukaemic cell burden in the bone marrow is measured. In the genome of these cells certain mutations can be found, which lead to the occurrence of leukaemia. One of those mutations is in the neucleophosmin 1 (NPM1) gene. This mutation is found in about one third of all AML patients. The burden of leukaemic cells can be derived from the proportion of NPM1 transcripts carrying this mutation in a bone marrow sample. These values are measured routinely at specific time points during treatment and are then used to categorise the patients into defined risk groups. In the studies, the data for this work originates from, the NPM1 burden was measured beyond the treatment period. That leads to a more comprehensive picture of the molecular course of disease of the patients. Hypothesis: My hypothesis is that the risk group categorisation can be improved by taking into account the dynamic time course information of the patients. Another hypothesis of this work is that with the help of statistical methods and computer models the time course data can be used to describe the course of disease of AML patients and assess whether they will experience a relapse or not. Materials and Methods: For these investigations I was provided with a dataset consisting of quantitative NPM1 time course measurements of 340 AML patients (with a median of 6 mea- surements per patient). To analyse this data I used statistical methods, such as correlation, logistic regression and survival time analysis. For a better understanding of the course of disease I developed a mechanistic model describing the dynamics of the cell numbers in the bone marrow of an AML patient. This model can be fitted to the measurements of a patient by adjusting two parameters, which represent the individual severity of disease. To predict a possible relapse within 2 years after beginning of treatment, I used data that was generated using the mechanistic model (synthetic data). For the prediction three different methods were compared: the mechanistic model, a recurrent neural network (RNN) and a generalised linear model (GLM). Both, the RNN and the GLM were trained and tuned on part of the synthetic data. Afterwards all three methods were tested using the so far unseen part of the data set (test data). Results: Following the analysis of the data I found that the decreasing slope of NPM1 burden during primary treatment as well as the absolute burden after the treatment harbour information about the further course of disease. Specifically, I found that a faster decrease of NPM1 burden and a lower final burden lead to a better prognosis. Further, I could show that the developed simple mechanistic model is able to describe the course of disease of most patients. When I divided the patients into two different risk groups using the fitted parameters from the model I could show that the patients in those groups show distinct relapse-free survival times. The categorisation using the parameters lead to a better distinction of groups than using current categorisation by the WHO. Further, I tried to predict a 2-year relapse using synthetic data and three different prediction methods. I could show that it had nearly no impact at all which method I used. Much more important, however, was the quality of data. Especially the sparseness of data, which we find in the time courses of AML patients, has a considerable negative effect on the predictability of relapse. Using a synthetic data set with measurement times oriented on the times of chemotherapy I could show that a sophisticated measurement scheme could improve the relapse predictability. Conclusions: In conclusion, I suggest to include the dynamic molecular course of the NPM1 burden of AML patients in clinical routine, as this harbours additional information about the course of disease. The involvement of a mechanistic model to asses the risk of AML patients can help to make more accurate predictions about their general prognosis. An accurate prediction of the time of relapse is not possible. All three used methods (mechanistic model, statistical model and neural network) are in general suitable to predict relapse of AML patients. For reliable predictions, however, the quality of the data needs to be drastically improved.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:87059 |
| Date | 11 September 2023 |
| Creators | Hoffmann, Helene |
| Contributors | Röder, Ingo, Bornhäuser, Martin, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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