Data from Dynamic Single-Cell Network Profiles in Acute Myelogenous Leukemia Are Associated with Patient Response to Standard Induction Therapy
Bibliographic record
Abstract
Abstract Purpose: Complete response to induction chemotherapy is observed in ∼60% of patients with newly diagnosed non-M3 acute myelogenous leukemia (AML). However, no methods exist to predict with high accuracy at the individual patient level the response to standard AML induction therapy. Experimental Design: We applied single-cell network profiling (SCNP) using flow cytometry, a tool that allows a comprehensive functional assessment of intracellular signaling pathways in heterogeneous tissues, to two training cohorts of AML samples (n = 34 and 88) to predict the likelihood of response to induction chemotherapy. Results: In the first study, univariate analysis identified multiple signaling “nodes” (readouts of modulated intracellular signaling proteins) that correlated with response (i.e., AUCROC ≥ 0.66; P ≤ 0.05) at a level greater than age. After accounting for age, similar findings were observed in the second study. For patients <60 years old, complete response was associated with the presence of intact apoptotic pathways. In patients ≥60 years old, nonresponse was associated with FLT3 ligand–mediated increase in phosphorylated Akt and phosphorylated extracellular signal-regulated kinase. Results were independent of cytogenetics, FLT3 mutational status, and diagnosis of secondary AML. Conclusions: These data emphasize the value of performing quantitative SCNP under modulated conditions as a basis for the development of tests highly predictive for response to induction chemotherapy. SCNP provides information distinct from other known prognostic factors such as age, secondary AML, cytogenetics, and molecular alterations and is potentially combinable with the latter to improve clinical decision making. Independent validation studies are warranted. Clin Cancer Res; 16(14); 3721–33. ©2010 AACR.
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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 | 0.001 | 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 | 0.000 |
| 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".