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Detection of somatic genetic drivers is important for risk stratification and treatment selection in pediatric leukemias; however, newly recognized genetic markers may not be detected by routine karyotyping and fluorescence in situ hybridization (FISH). To identify the combination of assays that provides the highest detection rate for clinically significant molecular abnormalities, we tested 160 B- lymphoblastic leukemia (B-ALL) by karyotyping, FISH, chromosomal microarray analysis (CMA) and the custom next-generation sequencing (NGS) panel, OncoKidsⓇ. In addition, we tested 40 myeloid malignancies with karyotyping, chromosomal microarray analysis (CMA), and OncoKidsⓇ; 36/40 myeloid malignancies were also tested with FISH. In B-ALL, individual testing methods had the following diagnostic yields for the key genetic drivers: karyotype 34%; basic FISH panel 45%; FISH panel with IGH and CRLF2 probes 65%; CMA 48%; OncoKidsⓇ 39%. CMA and OncoKidsⓇ testing allowed detection of key genetic drivers in 42% of the samples that remained unknown upon testing by conventional methods. In myeloid malignancies, OncoKidsⓇ had the highest yield for detection of both primary and secondary DNA mutations and RNA fusions. Our data highlights the complementarity between CMA and NGS and conventional cytogenetics/FISH in pediatric leukemia diagnostics. Due to rapid turn-around-time, FISH may be useful as an initial screening method in B-ALL. Our data also suggests NGS testing with a comprehensive panel, despite a longer turnaround time, is a good alternative to karyotyping and FISH in pediatric AML due to its superior detection rate.

The 2016 World Health Organization (2016 WHO) classification of hematopoietic malignancies classifies neoplasms with a fusion between the FIP1L1 and PDGFRA genes in 4q12 into a group called “myeloid and lymphoid neoplasms with eosinophilia and abnormalities of PDGFRA, PDGFRB or FGFR1 or with PCM1-JAK2”. Neoplasms characterized by this fusion are pluripotent stem cell disorders that can show both myeloid and lymphoid differentiation. They typically occur in adult patients and most are characterized by eosinophilia. We describe identification of a FIP1L1-PDGFRA fusion in a 13-year-old boy who presented with T-lymphoblastic leukemia/lymphoma without eosinophilia. Detection of FIP1L1-PDGFRA driven neoplasms at diagnosis is usually critical for proper treatment, since almost all reported cases responded to tyrosine kinase inhibitors. However, our patient’s leukemia was refractory to standard chemotherapy, and did not show a meaningful response to tyrosine kinase inhibitor therapy. Testing for a FIP1L1-PDGFRA rearrangement is at present limited to patients with idiopathic hypereosinophilia, and we hypothesize that this abnormality may be under-diagnosed in children with acute leukemias.

Next-generation sequencing (NGS) is a powerful platform for identifying cancer mutations. Routine clinical adoption of NGS requires optimized quality control metrics to ensure accurate results. To assess the robustness of our clinical NGS pipeline, we analyzed the results of 304 solid tumor and hematologic malignancy specimens tested simultaneously by NGS and one or more targeted single-gene tests (EGFR, KRAS, BRAF, NPM1, FLT3, and JAK2). For samples that passed our validated tumor percentage and DNA quality and quantity thresholds, there was perfect concordance between NGS and targeted single-gene tests with the exception of two FLT3 internal tandem duplications that fell below the stringent pre-established reporting threshold but were readily detected by manual inspection. In addition, NGS identified clinically significant mutations not covered by single-gene tests. These findings confirm NGS as a reliable platform for routine clinical use when appropriate quality control metrics, such as tumor percentage and DNA quality cutoffs, are in place. Based on our findings, we suggest a simple workflow that should facilitate adoption of clinical oncologic NGS services at other institutions.