Mutations in myeloid transcription factors and activated signaling genes predict chronic myeloid leukemia outcomes.

ABSTRACT: Advancements in genomics are transforming the clinical management of chronic myeloid leukemia (CML) toward precision medicine. The impact of somatic mutations on treatment outcomes is still under debate. We studied the association of somatic mutations in epigenetic modifier genes and activated signaling/myeloid transcription factors (AS/MTFs) with disease progression and treatment failure in patients with CML after tyrosine kinase inhibitor (TKI) therapy. A total of 394 CML samples were sequenced, including 254 samples collected at initial diagnosis and 140 samples taken during follow-up. Single-molecule molecular inversion probe (smMIP)-based next-generation sequencing (NGS) was conducted targeting recurrently mutated loci in 40 genes, with a limit of detection of 0.2%. Seventy mutations were detected in 57 diagnostic samples (22.4%), whereas 64 mutations were detected in 39 of the follow-up samples (27.9%). Carrying any mutation at initial diagnosis was associated with worse outcomes after TKI therapy, particularly in AS/MTF genes. Patients having these mutations at initial diagnosis and treated with imatinib showed higher risks of treatment failure (hazard ratio, 2.53; 95% confidence interval, 1.13-5.66; P = .0239). The adverse prognostic impact of the mutations was not clear for patients treated with second-generation TKIs. The multivariate analysis affirmed that mutations in AS/MTF genes independently serve as adverse prognostic factors for molecular response, failure-free survival, and progression risk. Additionally, there was an observable nonsignificant trend indicating a heightened risk of progression to advanced disease and worse overall survival. In conclusion, mutations in the AS/MTF genes using smMIP-based NGS can help identify patients with a potential risk of both treatment failure and progression and may help upfront TKI selection.

An improved molecular inversion probe based targeted sequencing approach for low variant allele frequency.

Deep targeted sequencing technologies are still not widely used in clinical practice due to the complexity of the methods and their cost. The Molecular Inversion Probes (MIP) technology is cost effective and scalable in the number of targets, however, suffers from low overall performance especially in GC rich regions. In order to improve the MIP performance, we sequenced a large cohort of healthy individuals (n = 4417), with a panel of 616 MIPs, at high depth in duplicates. To improve the previous state-of-the-art statistical model for low variant allele frequency, we selected 4635 potentially positive variants and validated them using amplicon sequencing. Using machine learning prediction tools, we significantly improved precision of 10-56.25% (P < 0.0004) to detect variants with VAF > 0.005. We further developed biochemically modified MIP protocol and improved its turn-around-time to ∼4 h. Our new biochemistry significantly improved uniformity, GC-Rich regions coverage, and enabled 95% on target reads in a large MIP panel of 8349 genomic targets. Overall, we demonstrate an enhancement of the MIP targeted sequencing approach in both detection of low frequency variants and in other key parameters, paving its way to become an ultrafast cost-effective research and clinical diagnostic tool.