Assessing clonal diversity in acute myeloid leukemia

Clonal diversity in cancer has been proposed as a mechanism underlying patientto-patient variability in therapeutic response, as well as the variability in the likelihood of relapse and the time to relapse of acute myeloid leukemia (AML) as well as other cancers. As a neoplasm develops it often continues to mutate, diversifying into differing clonal populations. Darwinian evolutionary pressures such as inherent fitness imbalances, immune system interactions, and chemotherapy treatments target sensitive clones and drive competition between the clonal populations; selecting for dynamic and resistant cell lines. In this way clonal diversity is conceivable as an impediment to a complete remission with more populations offering more opportunities for therapy resistance. Bulk next generation sequencing (NGS) is currently used to assess clonal composition in leukemia but requires several broad assumptions be made, which can result in incorrect assessments of diversity. Factors such as differences in zygosity of mutations, convergent evolution, or contamination with wild-type/non-cancerous cells can artificially raise or lower reported variable allele frequencies (VAF), leading to errors in clonal assessments. To examine discrepancies between the actual clonal structure and the clonal structures determined through bulk sequencing we developed a novel method of sampling the cell population to identify concurrent mutations. We first created an in silico model which randomly draws cell samples from a simulated tumor multiple times and calculates the VAF for each mutant allele in each sample. By tracking the correlation of mutations