Predicting the primary origin of Cancer of Unknown Primary by using mRNA expression profiles and machine Learning

Bakker, J.A. (2017) Predicting the primary origin of Cancer of Unknown Primary by using mRNA expression profiles and machine Learning. thesis, Medicine.

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Abstract

Cancer of unknown primary (CUP) is metastasized cancer with an undetectable primary tumor. It accounts for 3-5% of all patients with cancer and it is the fourth most frequent cause of death by cancer in the western world. The perspective for patients with CUP is unfortunate since current cancer treatments are depending on the primary site of origin. This study is an attempt to build a computer model to predict the origin of CUP. To train and evaluate this model, 8068 mRNA expression profiles of relevant tissues with known origins are collected from the Gene Expression Omnibus (GEO) and ArrayExpress (AE). Two data representations, the most important 5000 probes determined with the median absolute deviation and a mixing matrix as the result of an independent component analysis, are compared. Moreover, detected batch effects are eliminated. To achieve the best predictive model with the highest accuracy, conventional machine learning algorithms are compared to stacking and neural networks. The effect of healthy tissues in the training data is determined and also the performance of the models is explored. The final model, a neural network trained on the top 5000 probes, achieved an accuracy of 0.968 on unseen evaluation data. This model is applied on a dataset of 90 CUP samples to predict their primary origin. Since evaluation of these predictions is impossible, a comparison with silver labels and known proportions obtained from literature is executed. Despite the development of an accurate model, the results of this comparison remain questionable. A clinical study should be performed to see if the predictive model improves the survival and treatment response of CUP patients.

Item Type:	Thesis (Thesis)
Supervisor name:	Supervisors: and Fehrmann, dr. R.S.N and Plank, dr. B. and Bouma, dr. G.
Faculty:	Medical Sciences
Date Deposited:	25 Jun 2020 10:58
Last Modified:	25 Jun 2020 10:58
URI:	https://umcg.studenttheses.ub.rug.nl/id/eprint/1882

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