Digital <i>Fusion-Gene</i> expression profiling in acute leukemia (AL): Clinical validation of throughput molecular technology in laboratory medicine.
Bibliographic record
Abstract
7066 Background: AL is a heterogenous and aggressive disease with dismal prognosis. Chromosomal translocations constitute the basis of current WHO classification and are central to AL pathogenesis. FISH technique is utilized to detect variable translocations for patient prognosis and therapy selection. It is a labor intensive and expensive technique, which may not support rapidly expanding scope of additional translocations of clinical importance in AL patients. Hence, throughput automated technologies may play a critical in the management of AL patients. Methods: Nanostring platform utilizes a novel digital color-coded automated technology that is based on direct multiplexed measurement of gene expression. The “nCounter Leukemia Fusion Gene Expression Assay Kit” allows profiling a comprehensive set of 25 fusion genes that result from balanced translocations in AL. It also includes probes for 12 clinically proven AL-related biomarkers. RNA extracted from FFPE tissue from 50 AL patients with known balanced chromosomal translocations and validated the fusion gene expression on this platform. Results: We observed highly significant concordance between Nanostring fusion gene results with FISH data in various translocation such as t(9;22) (BCR-ABL); t(15;17) PML-RARA; t(8;21) (AML-ETO); t(4;11) (MLL-AF4) and Inv(16) (CBFB-MYH11) (P< 0.05). Nanostring technology failed to validate fusion gene transcript in patients with t(12;21) (TEL-AML). High expression of BAALC, a prognostic biomarker associated with poor outcome in AL patients was noted in t(9;22) (58%), t(15;17) (12%), t(4;11) (50%), t(12, 21) (20%), t(8;21) (20% ) and Inv(16) (63%). Conclusions: We have validated the application of automated throughput technology for AL patients in a clinical laboratory. Our study provides an efficient, viable and economical solution for the rapidly expanding molecular repertoire of laboratory testing for AL patients, which is critical to determine prognosis and select effective therapy. This approach also provides a promise to seamlessly incorporate newly discovered (up to 800) targets of diagnostic and prognostic importance on this digital platform.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".