Single Cell Network Profiles in Non-M3 AML Associated with Patient Response to Standard Induction Therapy.
Bibliographic record
Abstract
Abstract Abstract 1582 Poster Board I-608 Background Traditional AML prognostic markers are based on clinical characterization (e.g. age) or static measurements of leukemia biology present at diagnosis, such as cytogenetics and isolated molecular events (e.g. presence of FLT3 ITD mutation). No validated methods currently exist to predict the disease response to standard AML induction chemotherapy for individual patients. Objectives: Single Cell Network Profiling (SCNP) was used to measure intracellular signaling in response to extracellular modulators in order to develop a new proteomic tool to characterize and monitor AML biology in the context of therapeutic applications. Methods Modulated SCNP using a multiparametric flow cytometry platform was performed evaluating the phosphorylation of intracellular signaling molecules in their basal states and after treatment with modulators in specific cell populations (e.g. leukemic cells). Since multiple signaling pathways may be dysregulated in AML and contribute to the likelihood of response to a given therapy, pathways that affect proliferation, apoptosis, and DNA damage were analyzed. Analyses were aimed to assess assay reproducibility, identify a signaling profile associated with likelihood of response to standard induction chemotherapy (first training set, n=34), and test extrapolation of the identified profile to a fully independent set of AML samples (second training set; n=88). Results High assay reproducibility (Pearson correlation coefficients ≥ 0.8) was observed. In the first training study univariate analysis revealed multiple “nodes” (modulated read outs of proteins in signaling pathways) associated with disease response to conventional induction therapy (i.e. AUC of ROC >0.66; p<0.05). Importantly combination of some of the independently predictive nodes improved disease response stratification (AUC of ROC up to 1.0; p<0.05). Extrapolation of the assay to a second independent set of samples revealed similar findings after accounting for clinical covariates. Specifically, for patients <60 years, the presence of intact apoptotic pathways was correlated with complete response (CR) while in samples from patients ≥60 years increased p-Akt and p-Erk levels in response to FLT3L stimulation correlated with non response (NR). Importantly, the predictive values of these nodes was independent from cytogenetic and FLT3 mutational status. Conclusions The two studies reported here show that AML biology characterization in individual patients using modulated SCNP can be performed with high technical accuracy and reproducibility to quantitatively characterize the biology of AML. This approach can be used to generate highly predictive tests for therapeutic response independently of classic prognostic factors. Disclosures Kornblau: Nodality, Inc.: Consultancy. Rosen:Nodality, Inc.: Employment, Equity Ownership. Putta:Nodality, Inc.: Employment, Equity Ownership. Cohen:Nodality, Inc.: Employment, Equity Ownership. Covey:Nodality, Inc.: Employment, Equity Ownership. Fantl:Nodality, Inc.: Employment, Equity Ownership. Gayko:Nodality, Inc.: Employment, Equity Ownership. Cesano:Nodality, Inc.: Employment, Equity Ownership.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".