FLT3 ITD Signaling Profiles in AML Samples Harboring Mutations.
Bibliographic record
Abstract
Abstract Abstract 1588 Poster Board I-614 Background Mutations in the receptor tyrosine kinase (RTK) Fms-like tyrosine kinase 3 (FLT3) gene are among the most common somatic mutations in AML with FLT3 internal tandem duplications (ITDs) occurring in 20-35% of adult and 5-15% of pediatric AML. While the presence of FLT3 ITD mutation does not appear to influence outcome to induction chemotherapy, this mutation has been shown to confer a poor prognosis with significantly shorter disease free and relapse free survival. For patients with intermediate risk cytogenetically normal AML, molecular testing for FLT3 ITD has recently been incorporated into the National Comprehensive Cancer Network (NCCN) guidelines for clinical practice. However, while molecular testing can identify a subset of patients at high risk for relapse, there remains clinical heterogeneity likely due to differences in activation of signal transduction networks. Objectives This study tested the ability to use single cell network profiling (SCNP), in which cells are perturbed with extracellular modulators and their response ascertained by multiparametric flow cytometry, to identify a more clinically predictive functional readout of activation state, intracellular signaling capabilities and pathway dysregulation in the context of FLT3 mutational status. Methods Modulated SCNP was performed sequentially on two independent sets of patient samples (n=32 peripheral blood and n=85 bone marrow samples respectively). 304 and 201 “node-metric” i.e. modulated read outs of dynamic elements on individual proteins in signaling pathways were measured in the two sets respectively. These were derived from pathways known to be relevant to Flt3 WT and Flt3-ITD signaling (e.g. Ras-Raf-Erk-S6, PI3K-Akt-S6, STATs), as well as in-vitro chemotherapeutic induction of apoptosis (cleaved PARP, cleaved caspases), phosphatases, drug transporters (e.g. MDR-1, ABCG2) and expression of growth factor RTKs (e.g. Flt3R, c-Kit). Results In the first study, univariate analysis revealed 76 nodes out of 304 tested that distinguished FLT3 ITD from FLT3 WT patient samples (i.e. AUC of ROC >0.7; p<0.05). Analysis of false discovery rate showed this frequency to be significantly greater than the number of nodes that can be expected by chance (p=0.0009). Although several nodes were found to be correlated, many were independent of each other and represented multiple signaling pathways. Importantly, multivariate analysis showed that combinations of independently predictive nodes improved stratification over the single nodes (AUC of ROC up to 0.99) with respect to distinguishing WT and ITD FLT3 samples. Independent analysis of a second set of samples, revealed several nodes in common between the 2 studies which distinguish FLT3 ITD from WT, including etoposide/c-PARP (apoptosis), IL-27/p-STAT3, 5 (JAK/STAT pathways) and Flt3L/p-S6 (Ras/Erk/mTOR/S6 or PI3K/mTor/S6 pathways). In both sample sets, Flt3 receptor expression did not differ significantly between FLT3 ITD and FLT3 WT samples. Conclusions Pathway analysis by SCNP revealed significant differences in signaling in FLT3 ITD relative to WT AML samples across multiple pathways. We propose that a functional signature of FLT3 signaling is distinct from the existing molecular typing and may improve the ability to predict prognostic outcomes in individual AML patients. The impact of other important prognostic, molecular markers within the FLT3 context (e.g. NPM1) are currently under investigation. Disclosures Kornblau: Nodality, Inc.: Consultancy. Rosen:Nodality, Inc.: Employment, Equity Ownership. Cleary Cohen:Nodality Inc.: Employment, Equity Ownership. Gayko:Nodality, Inc.: Employment, Equity Ownership. Putta:Nodality, Inc.: Employment, Equity Ownership. Woronicz:Nodality, Inc.: Employment, Equity Ownership. Fantl: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 | 0.000 |
| 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".