Abstract B72: Assessing specific biological mechanisms of drug resistance in adult and pediatric AML.
Bibliographic record
Abstract
Abstract Background: Acute myeloid leukemias (AML) non-responsive to induction chemotherapy generally have poor prognoses. Understanding the biological mechanisms of drug resistance, or sensitivity, specific to each individual AML could inform biologically-based treatment selection in the salvage setting. The current study was undertaken to identify resistance mechanisms to commonly used AML drugs. Methods: Single Cell Network Profiling (SCNP) is a multiparametric flow cytometry-based assay that simultaneously measures, in a quantitative fashion and at the single cell level, both extracellular surface marker levels and changes in intracellular signaling proteins in response to extracellular modulators (Kornblau et al. Clin Cancer Res 2010). Herein we use SCNP to investigate biological mechanisms of in vitro resistance to chemotherapeutics relevant to AML treatment (Ara-C/Daunorubicin, Gemtuzumab Ozogamicin, Decitabine, Azacitidine and Clofarabine) by assessing in a quantitative fashion the induction of DNA Damage Responses (as measured by phospho-Chk2 and phospho-H2A.X levels) and apoptosis (as measured by cleaved PARP levels and loss of membrane integrity) in 6 pediatric and 6 elderly AML samples. In addition, SCF and FLT3L induced survival signaling (as measured by phospho-Akt and phospho-Erk levels) were simultaneously assessed. Results: Treatment with Ara-C/Dauno and GO revealed cross-resistance among AML samples and was associated with elevated SCF induced PI3K/Erk survival signaling. In vitro treatment of AML samples with the different genotoxic agents (Ara-C/Dauno, GO or CLO), resulted in three distinct profiles of DNA Damage and apoptosis responses: A) Samples with induced DNA Damage Response and apoptosis, B) Samples with induced DNA Damage response but no detectable apoptosis and C) Samples lacking significant induced DNA Damage response and apoptosis. In the latter group the presence of drug efflux activity in some samples, as measured by sensitization with an MDR1 (MultiDrug Resistance) inhibitor, may explain the observation. Of note, for each sample the drug response varied with the specific agent tested. Differential sensitivity among individual patient samples was also observed between treatment with two hypomethylating agents (Decitabine and Azacitidine). Additionally, distinct kinetics of DNA Damage and apoptosis were observed among drugs: genotoxic agents (CLO, Ara/Dauno, GO) induced DNA Damage prior to induction of apoptosis while hypomethylating agents (Decitabine and Azaciditine) induced DNA Damage coincident with induction of apoptosis. Conclusions: Using SCNP to dissect in vitro response to clinically used AML drugs reveals: 1) heterogeneity of individual patient biology characteristic of AML, 2) distinct resistance mechanisms present in unique samples and 3) mechanistic differences between distinct classes of agents. Studies are ongoing to assess the clinical relevance of these findings. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr B72.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".