Guideline-adherent, Evidence-based NGS Workflow for Myeloid Leukemia


Myeloid leukemias are a group of bone marrow disorders driven by various somatic mutations in a set of oncogenic and tumor suppressor genes. While Next Generation Sequencing (NGS) has proven to be a useful tool to understand leukemia molecular profile and its potential correlation to disease characteristics, it remains a subject of debate to what extent each mutation is clinically actionable and thus practically important for pathology labs to test. Increasing panel size also needs to be balanced with higher costs and data analysis burden. Furthermore, as professional organizations including ASCO, AMP and CAP continue to update their guidelines, it is increasingly challenging for laboratories to remain abreast of all current data and information. To address these issues, we established and compared several NGS workflows in parallel to identify evidence-based dataset for sequencing.
We also cross-referenced multiple interpretation databases to ensure realtime access to updated guideline information.


For the first workflow, we used QIAseq myloid panel with MiSeq sequencer. For the second workflow, we implemented the GeneReader NGS system and QIAGEN Clinical Insight (QCI) software suite for variant analysis and interpretation. The same set of samples were run in parallel on each workflow. Results were compared using both sequencing data and interpreted reports, then cross-referenced to ASCO, AMP and CAP


Our refined myeloid panel contains 25 genes. All variants identified using this panel were supported by guideline and other evidence. A high level of concordance in sequencing results was achieved between the two workflows. Each interpretation software offered unique advantages in assigning variant clinical significance and incorporating
guideline information.


Our NGS workflows provide a reliable solution for routine implementation of myeloid sample testing. The content as defined in our experience offers a ‘necessary and sufficient’ set of data supported by clinical evidence. By incorporating a robust interpretation software we are able to access and adhere to latest guidelines. Our lab’s
experience provides an example for others that may wish to implement myeloid NGS testing.

R. Kohle
Augusta University, Augusta, GA.

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