Moores Cancer Center, La Jolla, CA
Razelle Kurzrock , Rahul Parulkar , Timothy Joseph Yeatman , Wafik S. El-Deiry , Timothy J. Pluard , Chad Garner , Sandeep K. Reddy
Background: Next Generation Sequencing (NGS) has gained widespread clinical adoption for the determination of molecular targets for therapy in oncology. Standard NGS panels evaluate DNA only. RNAseq has shown that molecular targets identified by NGS panels are not universally expressed. We hypothesized that heterogeneous epigenomic factors may lead to low or absent RNA expression. We sought to determine the frequency of non-expressed variants that would be tested by a standard NGS panel. Methods: Retrospective analysis of a database from a commercial DNA tumor:normal and RNAseq platform was carried out. 992 samples were identified with paired DNA(WGS or WES)/RNAseq NGS. An analysis of expressed variant status for SNVs of the following type: synonymous, silent, missense, nonsense. Expressed variants are based on whether at least 2 alternate reads in the RNA are present at that variant site. A 50 gene panel (AmpliSeq HotSpot V2) was used as the reference comparison: ABL1, EGFR, GNAS, KRAS, PTPN11, AKT1, ERBB2, GNAQ, MET, RB1, ALK, ERBB4, HNF1A, MLH1, RET, APC, EZH2, HRAS, MPL, SMAD4, ATM, FBXW7, IDH1, NOTCH1, SMARCB1, BRAF, FGFR1, JAK2, NPM1, SMO, CDH1, FGFR2, JAK3, NRAS, SRC, CDKN2A, FGFR3, IDH2, PDGFRA, STK11, CSF1R, FLT3, KDR, PIK3CA, TP53, CTNNB1, GNA11, KIT, PTEN, VHL Results: A total of 225,727 SNVs were detected in the 992 samples. 669 samples had at least 1 SNV in the 50 gene panel set for a total of 1661 SNVs, of which 1375 SNVs were expressed in the RNAseq (82.8%). Across 37 tumor types the range of expression was 57% (melanoma)-100% (uterine). Conclusions: In this retrospective analysis using a 50 gene commonly used hotspot panel as a hypothetical reference comparison, 17% of detected variants were not expressed in the RNAseq. The lack of RNA expression may contribute to less than expected clinical benefit with molecularly targeted therapies. Since the distribution is non-uniform, identification of these genes can yield improved testing algorithms and treatment strategies.
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Abstract Disclosures
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