Up-regulation of <i>BAALC/MN1/MLLT11/EVI1</i> gene cluster in relation to <i>MYC</i> / BCL2 protein co-expression and poor overall survival in acute myeloid leukemia (AML).
Bibliographic record
Abstract
7045 Background: Molecular heterogeneity is the basis of dismal prognosis in AML. Current clinical risk evaluation by karyotyping and a few genetic mutations appear insufficient to provide defined directions for novel targeted therapies. Complex molecular technologies require transition into widely available laboratory platforms, like immunohistochemistry, for better integration into routine clinical / laboratory practices. MYC and BCL2 protein co-expression is associated with aggressive clinical behaviour in diffuse large B-cell lymphoma; however, this proteomic signature is not studied in AML patients, as risk stratification tool. Methods: In a defined subset (MYC+/BCL+ or MYC-/BCL-) of AML patients (n = 20), we utilized diagnostic bone marrow biopsy material to examine expression signature of several genes (n = 12) of established prognostic value in AML (TP53; RB1; NRAS; EVL1; MN1; ERG; MLLT11; PRAME; BAALC; FLT3; WT1; SOCS2; GUSB; TBP). Digital RNA quantification was determined, utilizing nCounter , Nanostring plate form. Transcript levels were correlated with MYC/BCL2 protein expression pattern; karyotype (favourable, intermediate and unfavourable risk group) and overall survival (OS), irrespective (+/-) of therapy or cause of death. Results: Unsupervised K-means++ clustering defined two distinct groups with high and low transcript levels of BAALC/MN1/MLLT11/EVI1/SOCS2 genes (> 2.0 fold difference; P < 0.001). Higher mRNA signature correlated with higher prevalence of MYC/BCL2 co-expression (P < 0.05) and poor OS (log rank test, P < 0.036) but remains unrelated to conventional cytogenetic risk groups (fisher exact test, P < 0.084). Conclusions: This pilot study present limited but useful data, to stipulate direction to refine the prognostication scheme of AML patients outside conventional cytogenetic risk groups. It also presents some biologic rationale to the use of novel agents targeting MYC and or BCL2 genes in combinational chemotherapy for AML patients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".