we used Varscan to identify tumor-specific SNVs by simultaneously comparing read counts, base quality, and allele frequency between the blood/normal tissue and the tumor tissue genomes. After identifying the SNVs, we also used ANNOVAR for annotation and classification. A statistical analysis of the SNV distribution was generated to evaluate the number of SNVs located in different gene regions. By analyzing the somatic mutation spectrum of each sample, we found that for the normal versus DFSP genomes, 
G:C.T:A accounted for the majority of all detected SNVs. The x-axis denotes the number of SNV mutations, and the y- 3 Imatinib Resistance in DFSP axis lists each mutation. We also analyzed the SNVs in coding sequences and splice regions, and found that, in normal versus tumor genomes, the G:C.T:A change was still the most common type of mutation. The x-axis denotes the number of SNV mutations, and the y-axis lists the mutation types. 3) InDel. We used paired-end reads for gap alignment using SAMtoolsmpileup software to detect InDels and ANNOVAR to annotate and classify them. A statistical analysis of InDel distribution was generated in order to evaluate the number of InDels in different gene regions. To identify the somatic InDels, those also present in normal samples 10646850 were filtered out. Hence, we used a program developed in-house to filter the .vcf files which included the InDel information for normal and tumor samples. A statistical analysis of somatic InDel distribution was generated in order to evaluate the number of InDels in different gene regions. 4) CNV. Differences in CNVs between the normal and tumor genomes were detected by software developed in-house using an algorithm similar to Segseq developed by the Broad institute. After identifying the CNVs, we used ANNOVAR to annotate and classify them. A statistical analysis of CNV distribution was generated in order to evaluate the number of CNV located in different gene regions. and ZFYVE9. No significant copy number alterations, insertion, and deletions were idenfied during imatinib treatment as shown in supplementary material. Discussion Using whole-genome sequencing of both pre-treatment and post-treatment tumor tissues, we identified eight non-synonymous and one stop-gain mutations in eight genes that emerged at the same time that the tumor became resistant to imatinib. The accuracy of our 506 sequencing depth and mutation calling method has been validated in another dataset with 82.2% of sensitivity and more than 95% of specificity. This allowed us to identify potential drug resistance mechanisms in this DFSP patient who initially responded well to imatinib but suffered rapidly progressive disease 6 months after treatment. DFSP is a very rare soft tissue sarcoma, and misdiagnosis, multiple equivocal biopsies, and/or a delay in accurate diagnosis are common in the clinical history of the patients with this disease. For a more accurate diagnosis of DFSP, the National Comprehensive Cancer Network guidelines recommend hematoxylin and eosin staining along with immunostaining for markers such as CD34, factor XIIIa, tenascin, and/or stromelysin-3. The guidelines do not however recommend re-biopsy when the diagnosis of 20065018 DFSP is clinically suspicious but not SU6668 biological activity supported by initial pathology. Surgical resection is the standard management approach for localized disease, but systemic treatment with imatinib is the standard first-line treatment for inoperable and metastatic DFSP. Imatinib mesylate