Practical Acute Myeloid Leukemia (AML) Biomarker Testing Using Next-Generation Sequencing (NGS) Technology: A Comprehensive, Rapid, Inexpensive and Flexible Approach
Bibliographic record
Abstract
Abstract The diagnostic category of AML includes a very heterogeneous group of neoplasms but progress has been made in recent years to identify biologically and clinically relevant genomic biomarkers that aid in further sub classification, provide more accurate prognostic information and supply targets for tumor-specific therapy and monitoring of residual disease. At this time, clinically relevant biomarkers include single nucleotide variants (SNVs), small insertions and deletions (indels) and a variety of translocations. They involve many different genes, including CEBPA, which is a highly G/C nucleotide-rich gene that is extremely difficult to evaluate using standard NGS methods. Because of the number and complexity of biomarkers, it has been a challenge for clinical laboratories to provide testing that is of practical clinical use; most approaches require multiple testing modalities, including less than satisfactory commercial sequencing panels, resulting in poor clinical testing service with respect to biomarker coverage, turn-around time and cost. We have developed an integrated "laboratory-developed" NGS-based approach that evaluates all current, clinically relevant AML biomarkers simultaneously (including CEBPA and identity markers for post-transplant chimerism evaluation), in as little as 2.5 days (<6 hrs hands-on time) for a total cost of only a few hundred dollars per sample. The method is flexible with respect to future, custom addition of newly discovered biomarkers and can be used on any of the Life Technologies or Illumina NGS platforms. Briefly, the method involves extraction of both DNA and RNA, the use of 3 slightly different techniques for initial library preparation (one each for CEBPA, other small variants, and translocations), followed by addition of indexed sequencing ends for either of the major sequencing technologies, pooling of the libraries and sequencing. The data analysis pipeline is rapid and simple to use and was custom created using predominantly readily available software. The method was used successfully to evaluate 50 patient blood/liquid bone marrow aspirate samples and 25 custom-designed DNA synthetics containing previously identified variants in ASXL1, BRINP3, CEBPA, DNMT3A, EZH2, FLT3, GATA1, GATA2, HNRNPK, IDH1, IDH2, KIT, KRAS, KMT2A(a.k.a. MLL), NPM1, NRAS, PHF6, PTPN11, RAD21, RUNX1, SMC1A, SMC3, STAG2, TET2, TP53, U2AF1, WT1, and translocations CBFB/MYH11, DEK/NUP214, KMT2A(a.k.a.MLL)/X=any partner, PML/RARA, NUMA1/RARA, STAT5B/RARA, ZBTB16/RARA, RBM15/MKL1, RPN1/MECOM, RUNX1/RUNX1T1 with 100% concordance and reproducibility. It has a reliable sensitivity of at least 1% and further evaluation is underway to establish the limit of detection for each target-type using ultra-deep sequencing, to determine acceptability for residual disease detection. The assay was shown to be 100% specific when challenged with normal samples. This integrated approach is novel and makes maximal use of available sequencing technology to simplify clinical biomarker evaluation, as required for practical management of patients with AML. Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".