Challenges in the introduction of next-generation sequencing (NGS) for diagnostics of myeloid malignancies into clinical routine use.

Bacher U1,2, Shumilov E3, Flach J4, Porret N5, Joncourt R5, Wiedemann G5, Fiedler M6, Novak U7, Amstutz U6, Pabst T8.

Author information

Abstract

Given the vast phenotypic and genetic heterogeneity of acute and chronic myeloid malignancies, hematologists have eagerly awaited the introduction of next-generation sequencing (NGS) into the routine diagnostic armamentarium to enable a more differentiated disease classification, risk stratification, and improved therapeutic decisions. At present, an increasing number of hematologic laboratories are in the process of integrating NGS procedures into the diagnostic algorithms of patients with acute myeloid leukemia (AML), myelodysplastic syndromes (MDS), and myeloproliferative neoplasms (MPNs). Inevitably accompanying such developments, physicians and molecular biologists are facing unexpected challenges regarding the interpretation and implementation of molecular genetic results derived from NGS in myeloid malignancies. This article summarizes typical challenges (Table 1) that may arise in the context of NGS-based analyses at diagnosis and during follow-up of myeloid malignancies. Table 1 Challenges accompanying the introduction of massive parallel sequencing in clinical routine diagnostics in hemato-oncology Challenge Background Current and future approach Discrimination of leukemia-related mutations from polymorphisms or passenger mutations Driver mutations expected to occur at higher allele frequency in patient samples than passenger mutations; driver mutations more likely to have an impact on protein function than polymorphisms or passenger mutations Optimization of cancer-specific databases including reporting of rare physiological gene variants Implementation of novel bioinformatic algorithms based on prediction of functional impact Quantitative and dynamic VAF monitoring (separately and together with other mutations) at follow-up Discrimination of somatic leukemia-related mutations from CHIP CHIP is presented in ~10% of individuals aged 70 to 80 and in up to 20% in the age group > 80 years Quantitative and dynamic VAF monitoring (separately and together with other mutations) at follow-up Clarifying the significance of CHIP in the context of myeloid malignancies Discrimination of leukemia-related somatic mutations from pathogenic germline alterations Challenge to differentiate acquired somatic mutations from germline pathogenic variants at diagnosis Mutation detection in germline control samples (e.g., skin fibroblasts, saliva) in mutations such as in RUNX1, CEBPA Thorough medical family history followed by molecular genetic tests in relatives if necessary High and stable VAF (e.g., 40-50%) at follow-up despite clinical response to treatment may be indicative for germline alteration Discrimination of true genetic alterations from PCR, sequencing and post-sequencing artifacts Many artefacts are known to arise during NGS library preparation, sequencing and data analysis Error correction using molecular identifiers that individually label original input DNA molecules Refinement of error-correction computational methods in post-sequencing NGS data analysis Confirmation using Sanger sequencing Limited sensitivity of NGS for minimal residual disease (MRD) assessment Mutations detected at diagnosis may be re-identified at best to a VAF of 1-2% Error-corrected sequencing using molecular identifiers Complementation of NGS by established MRD tools like real-time PCR and flow cytometry High financial burden; demand on interdisciplinary approaches Expensive technical and staff equipment, sophisticated data interpretation Complex translation of NGS results into therapeutic decisions Development of continuously updated NGS interpretation sets and algorithms for well-established mutational profiles within distinct hematological malignancies Interdisciplinary leukemia boards VAF variant allele frequency, CHIP clonal hematopoiesis of indeterminate significance, bp base pairs, G guanine, C cytosine, ITDs internal tandem duplication.