Leukemias are currently subclassified based on the presence of recurrent cytogenetic abnormalities and gene mutations. These molecular findings are the basis for risk-adapted therapy; however, such data are generally obtained by disparate methods in the clinical laboratory, and often rely on low-resolution techniques such as fluorescent in situ hybridization. Using targeted next generation sequencing, we demonstrate that the full spectrum of prognostically significant gene mutations including translocations, single nucleotide variants (SNVs), and insertions/deletions (indels) can be identified simultaneously in multiplexed sequence data. As proof of concept, we performed hybrid capture using a panel of 20 genes implicated in leukemia prognosis (covering a total of 1 Mbp) from five leukemia cell lines including K562, NB4, OCI-AML3, kasumi-1, and MV4-11. Captured DNA was then sequenced in multiplex on an Illumina HiSeq. Using an analysis pipeline based on freely available software we correctly identified DNA-level translocations in three of the three cell lines where translocations were covered by our capture probes. Furthermore, we found all published gene mutations in commonly tested genes including NPM1, FLT3, and KIT. The same methodology was applied to DNA extracted from the bone marrow of a patient with acute myeloid leukemia, and identified a t(9;11) translocation with single base accuracy as well other gene mutations. These results indicate that targeted next generation sequencing can be successfully applied in the clinical laboratory to identify a full spectrum of DNA mutations ranging from SNVs and indels to translocations. Such methods have the potential to both greatly streamline and improve the accuracy of DNA-based diagnostics.