The Clinical Application of Oncomine Myeloid Next Generation Sequencing (NGS): Comparison of Cytogenetics and NGS in Patients with Suspected Myelodysplastic Syndrome
Bibliographic record
Abstract
Background: A common referral to hematology is for patients with suspected myelodysplastic syndrome (MDS) or cytopenias of undetermined origin. We recently implemented a Next Generation Sequencing (NGS) panel which covers a wide spectrum of genes related to the major myeloid disorders, including DNA based gene sequencing and RNA based gene fusion analysis, as a tier one clinical diagnostic test (Levy et al. Eur. J. Haematol 2019). We compared the diagnostic and prognostic information derived from conventional cytogenetics and NGS testing as well as the clinical impact on management in this patient group. Methods: We identified all new cases with suspected MDS or cytopenias of undetermined origin referred between January 2018 to February 2019 that had both NGS and cytogenetic testing at London Health Sciences Centre, a tertiary care centre servicing a population of approximately 2.5 million in Southwestern Ontario, Canada. From the retrospective review of electronic medical record, patient demographics, diagnosis and management were ascertained. Diagnosis was based on the 2016 WHO classification and the ICD-10.Previously reported definitions for ICUS, IDUS, CHIP, and CCUSwere also adopted (Bejar et al. Leukemia 2017). Patients not meeting these criteria were defined as either cytopenia not yet determined (NYD) or secondary to other systemic disease. The impact of NGS and cytogenetics results on diagnosis, prognosis, and management of each disease were assessed by referring to the latest National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines in Oncology. Either a bone marrow (BM) or peripheral blood (PB) sample from each patient was assessed by the Oncomine Myeloid NGS panel (Thermo-Fisher, MA, USA), which examines DNA sequence variants in 40 genes (17 full genes and 23 hotspot genes) along with an RNA-based panel of 29 fusion driver genes and their over 600 fusion partners. Patients' BM samples were also tested by the conventional cytogenetic G-banding method. Results: Of the 1100 samples assessed by NGS, 178 met the study inclusion criteria. Overall, 120 (67.4%) patients had both cytogenetics and NGS performed on their BM samples. Of those 120 patients, 41 (34.2%) had DNA mutations, 17 (14.2%) had cytogenetic abnormalities, 22 (18.3%) had both molecular and cytogenetic findings, and 40 (33.3%) had neither abnormality identified. NGS Information contributed in diagnosing 13 (10.8%) patients, while cytogenetics in 6 (5%) patients. In addition, of 38 patients who were diagnosed as MDS with good/intermediate risk cytogenetics, 24 (63.2%) harbored poor prognostic risk mutations as detected by NGS (Table), influencing the management of disease. Additionally, 22 (12.4%) of all patients had NGS testing using PB and cytogenetics using BM samples, and of these 13 (52.2%) patients had NGS abnormalities. Finally, 36 (20.2%) of all patients had only NGS testing by PB samples. Of those, 12 (33.3%) were found to harbor at least one gene mutation including ASXL1, TP53, ZRSR2, and STAG1 suggesting poor prognostic significance for diagnoses of MDS, or JAK2, SF3B1 which could support a disease specific diagnosis. Conclusion: NGS had enhanced diagnostic capabilities including classification of newly described entities such as ICUS or CCUS and more importantly yielded additional prognostic information compared to cytogenetics alone for this patient population. Cytogenetic findings were mainly aneuploidy or deletions, either clinically evident constitutional abnormalities such as Trisomy 21 and +X in Klinefelter syndrome, or loss of Y chromosome in a small proportion of cells and thus of questionable clinical significance. Based on this information consideration should be given to using the NGS panel as the primary molecular diagnostic and prognostic tool with karyotyping being reserved for subsets of patients being assessed for suspected MDS or cytopenias of undetermined origin. Disclosures Hsia: Amgen: Honoraria; Jansen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".