Abstract 4713: High-throughput multiplex Sequenom MassARRAY clinical diagnostic assay for the identification of actionable genetic variants in hematologic malignancies
Bibliographic record
Abstract
Abstract Recent cancer genome sequencing efforts have led to an enhanced understanding of the somatic mutation profile of hematologic malignancies in the context of known cytogenetic abnormalities. For cytogenetically normal acute myeloid leukemia (AML), mutations in NPM1, DNMT3A, CEBPA, TET2 and IDH1/2 are thought to have potential predictive utility and may impact patient management with regard to initial and consolidation therapy, including stem cell transplantation. As most potentially clinically relevant variants currently are not tested for within the diagnostic setting, we established a high-throughput multiplex assay capable of simultaneous detection of a range of somatic mutations in hematologic malignancies. Using iPlex chemistry and the Sequenom MassARRAY platform, we established the Princess Margaret Cancer Centre (PM) Hematologic Malignancies Panel v1.0, comprised of 110 individual assays profiling 186 mutations in 22 relevant genes, in a 16-well assay format. We conducted an in silico analysis to identify genes with hotspot mutations capable of being analyzed by Sequenom; genes with mutations distributed throughout the coding sequence (e.g., CEBPA, TET2) were excluded from the panel. To best integrate this technology in the workflow of our routine clinical molecular diagnostic testing we developed the PM Panel as an RNA-based test, requiring 1 μg of patient RNA for the initial reverse transcription reaction. To validate the assay we tested 222 patient-derived samples, including 5 normal samples, 82 AML (57 with normal cytogenetics, 24 with cytogenetic changes and 1 therapy-related), 44 myelodysplastic syndromes, 32 myeloproliferative neoplasms, 21 additional cases tested for KIT mutation, and 38 B-lymphoid malignancies (including 3 multiple myeloma cases),. Concordance with established lab assays for NPM1, FLT3-TKD and JAK2 mutations was 100%; other identified mutations were verified by Sanger sequencing at 98% concordance (2% of cases were below the limit of sensitivity of Sanger technology). Approximately 50% of samples were positive for at least one mutation with 174 mutations detected overall. Cytogenetically normal AMLs and myelodysplastic syndromes were most informative, with 2.3 and 1.8 mutations per positive case respectively. Samples already known to carry a leukemogenic driver fusion protein (e.g., PML-RARα, AML1-ETO, or BCR-ABL) had a significantly decreased identified somatic mutation load than their cytogenetically normal counterparts (average of 1.4 mutations per case, compared to 0.3, p < 10^-6). The PM Hematologic Malignancies Panel v1.0 is now being integrated into the current clinical diagnostic workflow. We therefore report the development of a versatile, RNA-based high-throughput multiplex platform for the identification of somatic mutations present in hematologic malignancies for use in a clinical diagnostic setting. Citation Format: Mahadeo A. Sukhai, Mariam Thomas, Tong Zhang, Cuihong Wei, Suzanne Trudel, Karen Yee, Mark D. Minden, Andre Schuh, Tracy Stockley, Suzanne Kamel-Reid. High-throughput multiplex Sequenom MassARRAY clinical diagnostic assay for the identification of actionable genetic variants in hematologic malignancies. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4713. doi:10.1158/1538-7445.AM2014-4713
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".