Functional and Phenotypic Characterization Of Acute Myeloid Leukemia By Analysis Of Diagnostic/Relapse Paired Samples
Bibliographic record
Abstract
Abstract Treatment of relapsed acute myeloid leukemia (AML) results in lower complete remission rates compared to treatment of AML at diagnosis. Immunophenotypic changes in leukemic blasts are common from diagnosis to relapse (Baer et al. Blood. 2001), suggesting that the underlying biology of AML changes with disease progression. A better understanding of the biologic properties of AML cells at different stages of disease will facilitate the development of biomarker tools and more effective therapies. We therefore studied paired diagnostic/relapse samples obtained from AML patients. Cells from 11 paired samples were transplanted into immune deficient mice (NOD.SCID IL2Rg null) over a range of cell doses. In 10 of 11 patients, a leukemic graft could be generated after transplantation of lower cell doses from the relapse sample compared to the paired diagnostic sample. By limiting dilution analysis, leukemia stem cell (LSC) frequency was higher in relapse samples (1 in 5.8×102 to 1 in 2.4×106, median 1 in 2.0×103) compared to diagnostic samples (1 in 5.0×103 to 1 in 6.1×106, median 1 in 5.5×104); the fold increase in LSC frequency ranged from 2.2 to 745 (median 8.6). Multiparameter flow cytometric analysis carried out on 13 paired diagnostic/relapse samples demonstrated an increase in 2 known stem cell markers, CD34 and CD117, from diagnosis to relapse: CD34 was gained or increased at relapse in 7/13 (54%) of paired samples, while CD117 was gained or increased at relapse in 9/13 (69%) of paired samples. We plan to take a comprehensive approach to examine the surface marker expression of paired diagnostic/relapse samples by a high throughput flow cytometric screen (HTS) of both bulk and LSC-containing AML populations to identify markers that are altered at relapse. As a first step, we have performed HTS of 373 surface markers on 10 AML patient samples, including a relapse sample from a diagnostic/relapse pair. A significant proportion of markers (155/373, 42%) were expressed on less that 5% of cells in all 10 AML samples analyzed. We will therefore focus on the remaining markers in our HTS analysis of paired samples. Surface markers that are differentially expressed from diagnosis to relapse will be further characterized in order to gain insight into disease progression and identify potential therapeutic targets. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".