Background: Patients with unfavorable AML have an estimated 3-year overall survival (OS) of < 15% with standard chemotherapy treatment. The recommendation for consolidative allogeneic transplant can be challenging and is usually an individualized assessment. Accurate and sensitive genomic data may improve the decision making process. Detailed mutational analyses using myeloid-specific gene assays have shown to refine disease prognostication from standard six-gene mutational analysis (Gerstung et al. Nature Genetics2017, Dohner et al. Blood2017). Several studies using mutation analysis for assessing minimal residual disease (MRD) have been performed. Specifically, in two large studies, isolated presence of DNMT3A, TET2, or ASXL1mutations (associated with clonal hematopoiesis) was not prognostic but detection of other mutations, independent of pathogenicity, at >2.5% predicted relapse, relapse-free survival, and overall survival (Jongen-Lavrencic et al. JEJM2018, Morita et al. JCO2018). Of note, flow cytometry was also used to detect minimal residual disease (MRD), and at a sensitivity of 0.1% provided independent prognostic value; however even when used in combination, nearly 25% of relapses were not predicted. At Rutgers, we utilize deep DNA sequencing and real-time multiplex PCR assays to detect mutant alleles in the presence of 10,000 wild-type fragments. Here, we hypothesize that detailed analysis of available mutational data and detection of mutations at allele frequencies <2.5% in pre-treatment and sequentially collected samples during treatment serves as a targeted and ultrasensitive measure of clonal heterogeneity and may correspond better to prognosis and ultimately guide further treatment decisions based on MRD sequencing.
Methods: We recruited 99 patients with newly diagnosed high-risk MDS and/or AML.Each patient had standard myeloid mutation profiles obtained at diagnosis (pre-treatment) and on the 'day 30' recovery bone marrow. Patient characteristics and bone marrow biopsy results were obtained including standard cytogenetics and conventional molecular studies. Additional and contemporaneous analyses were performed for 18 patients, diagnosed and treated from February 2017 through July 2019,utilizinga 49-gene panel (RainDance Technologies) on Illumina MiSeq at high depth (2,000x-20,000x). We identified all sites different from the reference using an inclusive variant caller and used a Bayesian approach to detect true mutations against mutation-specific background error at >0.1% allele frequencies. We tested the variants against previously sequenced non-malignant samples and guided the analysis by detected mutations in longitudinal data (Hadigol et al. BMC Bioinformatics2018, Rabadan et al. J Stat Phys2018).
Results:The median age of patients with deep-sequencing data was 55 years (range, 26-73 [n=4 male; n=14 female]). There were 14 patients with AML and 4 patients with high-risk MDS. Sixty-one percent of patients received standard 7+3 induction therapy and 14 patients received consolidative allogeneic stem cell transplant (SCT), 93% of whom received it at first complete remission (CR1). With the median follow-up of 19 months (range, 12-27), the median time to relapse in 5 patients (28%) was 13 months (range, 8-15). In the relapsed patients, there was a median of 1 mutation (range, 0-2) reported by conventional variant calling methods at CR1. However, Bayesian deep-sequencing analysis identified additional non-synonymous coding mutations (range, 1-4) at allele frequencies <2.5% in 4 of these patients for whom sequencing data was available at this time point (Table). It is important to highlight that some of these mutations were not detected at initial diagnostic sample and could be present in clones with higher fitness after treatment.
Conclusion: Our approach identified new and relevant mutations that were not reported with conventional genomic analysis at a crucial time point in the measurement of treatment response in AML and high-risk MDS. Data such as these may refine prognostication and ultimately be used as to help guide further treatment decision making for patients. Continued and larger prospective analyses with novel deep-sequencing technology are needed to better inform outcomes for AML and high-risk MDS patients.
No relevant conflicts of interest to declare.
