Classification of Acute Myeloid Leukemia (AML) Based On Apoptosis and Myeloid Signaling Networks.
Bibliographic record
Abstract
Abstract Abstract 325 Background: Acute Myeloid Leukemia (AML) is the most common myeloid malignancy in adults and represents an aggressive disease with significant biological and clinical heterogeneity. Currently, cytogenetics and molecular changes are used to inform treatment strategies. However a wide range of clinical responses are observed in these patient subgroups necessitating alternative methodologies to provide information that could inform clinical decisions for AML disease management. Since the net result of the cytogenetic and molecular changes is necessarily a functional alteration of proteins within signal transduction networks the current study was undertaken to understand the diversity of signaling responses in AML patient samples. Critically, in these studies treatment of samples with a variety of input stimuli allowed intracellular phospho-signaling and apoptosis network properties to be revealed that would otherwise remain unseen in resting cells. Objectives: Single cell network profiling (SCNP) using muliparameter flow cytometry was used to identify intracellular pathway responses to treatment with myeloid cytokines and growth factors in addition to apoptosis-inducing agents in individual AML patients. Identification of unique signaling profiles in patient sample sub-groups may inform the choice of specific therapeutic regimens. Methods: JAK/STAT, PI3K/S6 and apoptosis signaling pathways were measured after in vitro exposure of 34 diagnostic non-M3 AML samples to a panel of myeloid growth factors (e.g FLT3L, SCF), cytokines (e.g G-CSF, GM-CSF) interleukins (e.g IL-6, IL-27) and apoptosis-inducing agents (etoposide, staurosporine). Samples were processed for cytometry by paraformaldehyde/methanol fixation and permeabilization followed by incubation with fluorochrome-conjugated antibody cocktails that recognize both cell surface proteins to delineate cell subsets in addition to intracellular signaling molecules. Results: JAK/STAT and PI3K/S6 signaling responses to individual modulators revealed a range of activity in these pathways within individual patients. Analysis of JAK/STAT and PI3K/S6 pathways across individual patient samples identified subgroups of samples with distinct pathway profiles: A) high JAK/STAT activity, B) high PI3K/S6 activity, C) high activity in both pathways, and D) low activity in both pathways. In vitro exposure of samples to staurosporine and etoposide revealed three distinct “apoptosis” profiles: 1) Staurosporine Refractory 2) Etoposide Refractory and samples that were 3) Apoptosis Competent in response to both these agents. In this study, elevated PI3K/S6 pathway activity and elevated IL-27/IL-6 induced JAK/STAT pathway activity was associated with in vitro refractoriness to apoptosis inducing agents. Analysis of JAK/STAT, PI3K/S6 and Apoptosis pathway activities characterized biologically distinct patient-specific signatures, even within cytogenetically and phenotypically uniform patient subgroups. Notable were differences in signaling profiles between samples from different patients and in cell subpopulations within the same sample. Conclusions: SCNP revealed a range of signaling responses within AML samples consistent with the heterogeneity of the disease. The data suggest the importance of characterizing and tracking signaling profiles within the subpopulations of an AML sample over time and in response to therapeutic pressure with the aim of guiding the choice of a targeted agent to be used alone or in combination with chemotherapy to improve patient response rates. Disclosures: Rosen: Nodality, Inc.: Employment, Equity Ownership. Putta:Nodality, Inc.: Employment, Equity Ownership. Covey:Nodality, Inc.: Employment, Equity Ownership. Huang:Nodality Inc.: Employment, Equity Ownership. Cesano:Nodality Inc.: Employment, Equity Ownership. Fantl: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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".