When a second tumor arises in the contralateral breast in a patient with a previous or synchronous breast cancer, it is of clinical importance to determine if this tumor is a new unrelated tumor or a metastasis, i.e. clone, of the primary tumor. A new, unrelated tumor may be treated similarly as the first one since treatment was successful, while a distant metastasis demands a change of therapy and has a more adverse prognosis. In clinic, a second tumor is generally regarded as a new primary. If there is clinical suspicion that the second tumor may be a metastasis, clinico-pathological characteristics of the two tumors are used assess the clonality status. Clinico-pathological characteristics, however, are not reliable predictors to determine if a second tumor is a metastasis. Recent studies have investigated tumor clonality using techniques from molecular genetics. These models appear to perform well, but have several drawbacks.

In this thesis a more advanced classification model is being developed that can detect tumor clonality based on SNP array data. For this, two segmentation algorithms, ASCAT and OncoSNP, and two comparison methods, Log LR and adapted SI, have been incorporated. For each tumor, the segmentation algorithms construct a copy number profile based on the SNP array data. Given the copy number profiles, the comparison methods compute a p-value which reflects the probability that a pair is of clonal origin. Both comparison methods are permutation methods which test the null hypothesis of independence against the alternative hypothesis assuming clonality. The proposed model consists of a decision tree which assigns each pair to one of six categories depending on the significance of the four resulting p-values.

The model has been tested on 23 fresh frozen pairs by means of expert judgment. The results were promising: the four pairs which were unanimously labeled as clonal by the experts were also regarded as such by the model. No independent pairs were assigned as clonal by the model. Moreover, the decision tree showed to have a higher sensitivity than the clinical assessments as the latter only managed to detect two out of four clonal pairs. A discordance between the clinico-pathological judgments and decision tree results was found for three out of 18 pairs for which both assessments were available.

The model appears to be suitable in practice, but is not yet applicable as a stand-alone model. There were two ambiguous pairs which were labeled as independent by the model but for which the experts had varying opinions about the clonality status. Until the ambiguous pairs can be reliably categorized, it is advised to take into account both the model results and clinical assessments when determining tumor clonality. Finally, the performance of the model remains to be tested on FFPE pairs.