Abstract For a variety of human malignancies, incidence, treatment effi- cacy and overall prognosis show considerable variation between different populations and ethnic groups. Disentangling the ef- fects related to particular population backgrounds can help in both understanding cancer biology and in tailoring therapeu- tic interventions. Because self-reported or inferred patient data can be incomplete or misleading due to migration and genomic admixture, a data-driven ancestry estimation should be pre- ferred. While algorithms to analyze ancestry structure from healthy individuals have been developed, an easy-to-use tool to assign population groups based on genotyping data from SNP profiles is still missing and benchmarking for the validity of pop- ulation assignment strategy for aberrant cancer genomes was not tested. We benchmarked the consistency and accuracy of cross- platform population assignment. We also demonstrated its high accuracy to process unaltered as well as cancer genomes. De- spite widespread and extensive somatic mutations of cancer pro- filing data, population assignment consistency between germline and highly mutated samples from cancer patients reached of 97% and 92% for assignment into 5 and 26 populations re- spectively. Comparison of our benchmarked results with self- reported meta-data estimated a matching rate between 88 % to 92%. Despite a relatively high matching rate, the ethnicity labels indicated in meta-data are vague compared to the stan- dardized output from our tool. We have developed a bioinformatics tool to assign the popula- tions from genome profiling data and validated its performance in healthy as well as aberrant cancer genomes. It is ready-to-use for genotyping data from nine commercial SNP array platforms or sequencing data. This tool is effective to scrutinize the popu- lation structure in cancer genomes and provides better measure to integrate genotyping data from various platforms instead of self-reported information. It will facilitate research on inter- play between ethnicity related genetic background and molecu- lar patterns in cancer entities and disentangling possible hered- itary contributions. The docker image of the tool is provided in DockerHub as “baudisgroup/snp2pop”.