Abstract Duplications and deletions of large-scale genomic regions, including whole chromosomes, are a hallmark of malignant tumors and represent an important predictor of outcome for many tumor types. In multiple myeloma, a core set of structural variants are regularly used for disease prognosis (e.g., del17p, t(4;14), t(14,16)). The presence of one or more of these lesions is predictive of a more aggressive disease and poorer outcomes for patients. The application of next-generation sequencing (NGS) to cancer samples has enhanced our ability to detect prognostic and predictive copy number aberrations (CNAs). However, most NGS-based methods for the identification of CNAs in tumors require whole genome sequencing (WGS). Relatively fewer methods exist for the detection of CNAs from exome sequencing and, to our knowledge, these methods all rely on having matched normal data. However, WES is still far more common than WGS and matched normal samples are frequently not available. Therefore, we have developed a novel method for the identification of CNAs from single-sample WES data. Our method combines LOWESS smoothing and discrete-wavelet-transformation to normalize the exome coverage data with an HMM for coverage segmentation. We have applied our new method to WES data from 40 myeloma cell-lines for which aCGH copy number calls were also available for training and validation. Our method shows a high correlation with the aCGH calls (mean = 95%). In addition we have currently analyzed 42 (of 102) tumor samples that were collected during screening of relapsed/refractory multiple myeloma patients on three Onyx-sponsored Phase 2 clinical trials of the proteasome inhibitor carfilzomib (PX-171-003,PX-171-004, PX-171-005). Based on analysis of the completed subset we find that the frequency of known and novel lesions within these samples is similar to what has previously been observed in other publically available myeloma CNA datasets. The most frequent event that we find is loss of chr13 followed by gains on chr1q and chr15 all at near 40% frequency. We also find gains of chr3, chr5, chr9 and chr11 in ∼30% of patients. These results indicate that we can apply this new method to our larger set of clinical samples in order to discover new prognostic markers for patient outcomes and response to therapy in MM. Citation Format: Jeremiah D. Degenhardt, Kenneth B. Hoehn, Kevin A. Kwei, Kristi Stephenson, Jonathan J. Keats, Chris J. Kirk, Brian B. Tuch. A method to identify copy number aberrations (CNAs) from whole exome sequence (WES) data and its application to multiple myeloma cell lines and patient samples. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2375. doi:10.1158/1538-7445.AM2014-2375