Ex Vivo High-Throughput Flow Cytometry Screening Identifies Subsets of Responders to Differentiation Agents in Individual AML Patient Samples
Bibliographic record
Abstract
Abstract Background: Prognoses for acute promyelocytic leukemia (APL) patients improved drastically upon the introduction of differentiation therapy with all-trans-retinoic acid (ATRA) in combination with conventional chemotherapy. Unfortunately, this therapeutic approach has not translated to other genetic subtypes of acute myeloid leukemia (AML) where patients demonstrate marked heterogeneity to differentiating agents. To provide improved detection of drug-induced differentiation in AML patients, we have developed a high-throughput, flow cytometry-based personalized medicine platform. Methods: Total white blood cells were isolated from each patient sample by red cell lysis, plated in serum-free media in 384-well format and incubated with drugs for 3 days. Viable cells remaining after each drug treatment were identified and quantified using cell surface marker expression, cell membrane integrity, and morphology (FSC/SSC) to determine the compound's efficacy and specificity against the blast population. Changes in cell surface marker expression and shifts in morphology indicative of blast differentiation were also evaluated with each compound. As a control for ex vivo differentiation, two APL patient samples were treated ex vivo with ATRA and we observed the blasts gaining CD66b expression indicating granulocytic differentiation. Results: A refractory AML patient was identified whose leukemic blasts exhibited a strong differentiating response to dexamethasone treatment ex vivo. This resulted in loss of CD34 expression (a marker of immature blast cells), gain of CD163 expression (a marker of monocytic/macrophage maturation) and a significant change in cellular size and granularity. After being enrolled in a clinical trial (REB: 13-6962-C) the patient was treated based on the assay for 1 week (40 mg/day) with dexamethasone. Post-treatment samples from the peripheral blood and bone marrow of the patient exhibited the same morphological and cell surface marker changes predicted by the ex vivo assay. The CD163+ cells in the patient also gained additional markers of myeloid differentiation (CD11b, CD14, CD16). After additional cytarabine and fludarabine treatment, the patient remains in remission 4 months post-treatment. Conclusions: Following this initial study, we have continued to identify subgroups of both AML and Myelodysplastic Syndrome patients where blasts differentiate in response to dexamethasone, calcitriol, ATRA or other known differentiating agents using unique cell surface markers of monocytic and myeloid maturation. Flow cytometry expression changes correlated with changes in morphology as observed by May-Grunwald Giemsa staining. In the patient described above this included an increase in cytoplasm and vacuoles consistent with monocytic/macrophage differentiation, which positively correlates with CD163 expression. We aim to apply our assay towards the identification of subgroups of AML patients who respond to differentiation therapies and develop clinical trials to combine differentiating agents with chemotherapy. This approach has the potential to extend the clinical success of APL differentiation therapy to AML patients. Disclosures Prashad: Notable Labs: Employment, Equity Ownership. Western:Notable Labs: Consultancy. Biondi:Notable Labs: Employment. Shah:Notable Labs: Employment. Liu:Notable Labs: Employment, Equity Ownership. Nguyen:Notable Labs: Employment, Equity Ownership. Warnock:Notable Labs: Employment, Equity Ownership. Quinzio:Notable Labs: Employment, Equity Ownership. De Silva:Notable Labs: Employment, Equity Ownership. Schimmer:Novartis: Honoraria. Heiser:Notable Labs: 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 | 0.001 | 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) | 0.002 | 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".