First Results of a 31-Gene Panel Targeted to Investigate Myeloid Malignancies by Next-Generation Amplicon Deep-Sequencing
Authors: Kohlmann Alexander , Weissmann Sandra , Schoeck Ulrike , Grossmann Vera , Kern Wolfgang , Haferlach Claudia , Schnittger Susanne , Haferlach Torsten 
Published: October 2019

Abstract
Abstract 883

Introduction:
Massively parallel next-generation sequencing data have changed the landscape of molecular mutations in acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS). The number of molecular markers used to characterize myeloid malignancies continues to constantly increase. As such, physicians and laboratories face a great unmet need to test panels of genes at a high level of sensitivity and throughput.

Methods:
We developed a sensitive next-generation deep-sequencing assay for routine diagnostics. In total, 31 genes with relevance in myeloid malignancies providing both favorable and adverse molecular prognostic information were chosen: ASXL1, BCOR, BCORL1, BRAF, CBL, DNMT3A, ETV6, EZH2, FLT3, IDH1, IDH2, JAK2, KDM6A, KIT, KRAS, NOTCH1, NPM1, NRAS, PHF6, PRPF40B, PTPN11, RUNX1, SF1, SF3A1, SF3B1, SRSF2, TET2, TP53, U2AF1, U2AF2, and ZRSR2. Targets of interest comprised either complete coding gene regions or hotspots. In summary, 1,375 amplicons were designed with a median length of 175 bp (range 109–194 bp), representing a total target sequence of 140.35 kb. The sequencing library was constructed starting off 2.2 μg genomic DNA per patient using a singleplex microdroplet-based assay (RainDance, Lexington, MA). Sequencing data was generated using the MiSeq instrument (Illumina, San Diego, CA) loading 4 patients per run. Using the 300 cycles sequencing-by-synthesis chemistry in median 6.099 millions of paired-end reads were generated per run. This resulted in a median coverage per gene of 1,766 reads (range 992-2,432). The total turn-around time of the analysis with this assay was less than 4 days. 49 clinically well-annotated patients harboring myeloid malignancies were analyzed during the evaluation phase. These included 9 acute myeloid leukemia (AML), 9 myelodysplastic syndrome (MDS), 13 chronic myelomonocytic leukemia (CMML), and 18 mixed phenotype acute leukemia, T/myeloid (MPAL-TM) cases. The median age was 69 years (range: 23 – 90 years).

Results:
In median, the coverage per amplicon harboring a mutation was 2,095-fold, thus enabling a sensitive detection of variants. In total, 146 mutations in 28 of the 31 genes were detected in 47/49 patients with a range of 1–7 mutations per case (median: 3). According to chromosome banding analysis 31/49 cases presented with a normal karyotype. In 30/31 cases with a normal karyotype at least one molecular mutation was observed using this screening panel. 42/146 mutations were detected with a clone size <20%, thus being detected only due to the higher sensitivity of this technique in comparison to direct capillary Sanger sequencing. In this cohort, the most frequently mutated genes were RUNX1 (14/49), DNMT3A (14/49), SRSF2 (11/49), ASXL1 (9/49), and TET2 (9/49). The mutation types comprised 97 missense, 17 duplications, 24 deletions, 5 insertions and 3 insertion/deletions alterations. Novel variants were verified using direct capillary Sanger sequencing (n=19) or sensitive amplicon deep-sequencing (n=65) (454 Life Sciences, Branford, CT). With respect to the technical limit of detecting larger insertions or deletions both a 27-bp insertion (RUNX1, p.Thr121delins9) and a 23-bp deletion variant (ASXL1, p.Glu635ArgfsX15) were successfully sequenced. The highest number of mutations was observed for CMML patients (mean of 3.6 per case; CMML vs remainder: P=0.201). Also, in CMML patients we observed the highest frequency of mutations in major splicing machinery genes such as SF1, SF3A1, SF3B1, SRSF2, U2AF1, U2AF2, and ZRSR2 (11/13 CMML, 84.6% vs 14/36 remainder cases, 38.9%; P<0.001). Importantly, a number of patients (39/49) was detected to harbor mutations in genes reported to be associated with decreased overall survival, both in AML (e.g. TP53, RUNX1, ASXL1, DNMT3A, IDH1, or TET2) and low- or intermediate-1 IPSS risk categories in MDS (e.g. ASXL1, EZH2, ETV6, RUNX1, TP53). As such, detecting these adverse somatic alterations may influence the course of therapy for these patients.

Conclusion:
We here demonstrated that microdroplet-based sample preparation enabled to target 31 candidate genes for next-generation sequencing in myeloid malignancies in a routine diagnostic environment. This approach provides the potential to screen for prognostically relevant mutations in a fast and comprehensive way providing actionable information suitable to guide therapy.

Disclosures:
Kohlmann: MLL Munich Leukemia Laboratory: Employment. Weissmann:MLL Munich Leukemia Laboratory: Employment. Schoeck:MLL Munich Leukemia Laboratory: Employment. Grossmann:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Equity Ownership. Schnittger:MLL Munich Leukemia Laboratory: Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Equity Ownership.