AML Drug Tolerant Persister (DTP) Cells Survive Chemotherapy By Transiently Altering Cellular Lipidomics to Increase Plasma Membrane Rigidity, but Also Increases Sensitivity to Immune Cell Killing
Bibliographic record
Abstract
Emerging evidence indicates that cancer persister cells, including AML, stochastically and transiently upregulate stress pathways to survive acute exposure to chemotherapy. Here, we explore the biology of these persister cells and identify a T cell-based immunotherapy that targets this surviving fraction. To investigate the drug-tolerant persister (DTP) state, we established a model of DTP AML cells. AML cell lines (MOLM13, OCI-AML2, OCI-AML3, MV4-11, THP1 and NB4) were treated with a combination of Daunorubicin and Ara-C, at their IC90 - a concentration at which 90% of the cells were dead by day 7. Surviving cells displayed a lag phase in their proliferation for 7 to 15 days post-chemotherapy. As determined by EdU (5-ethynyl-2'-deoxyuridine) labeling, persisting cells actively proliferated during this time, albeit slower than parental cells. Upon cell regrowth, rates of proliferation returned to normal and cells regained chemosensitivity similar to parental cells, demonstrating the DTP phenotype. To understand mechanisms of the DTP state, we used liquid chromatography-mass spectrometry (LC-MS) to analyze the metabolome and lipidome of MOLM13 and OCI-AML2 cells before Daunorubicin and Ara-C treatment, 7 days posttreatment as well as 12 days after treatment removal (i.e., recovery period). Among the most prominent changes in the DTP cells was an alteration in the global lipidome, with an increase in the levels of unsaturated and elongated fatty acids across the main lipid classes (e.g., triglycerides, phosphatidylcholines, phosphatidylethanolamines, etc.). Increased unsaturated and elongated fatty acids have been associated with increased plasma membrane rigidity and we previously demonstrated that increased plasma membrane rigidity impairs the uptake of chemotherapeutic agents such as Daunorubicin. Therefore, we measured membrane rigidity in AML persisters by staining cells with a lipophilic pyrene dye. DTP AML cells (MOLM13, OCI-AML2, OCI-AML3, MV4-11, THP1 and NB4) acutely increased membrane rigidity upon exposure to chemotherapy and membrane rigidity returned to baseline after recovery from chemotherapy when proliferation and chemosensitivity returned to baseline. We also demonstrated that primary AML cells isolated from a patient on d5 of “7+3” induction chemotherapy had increased membrane rigidity compared to their cells prior to chemotherapy. As a non-pharmacologic approach to mimic the DTP state and increase membrane rigidity, we cultured AML cells at lower temperature (31 oC). Culturing cells at 31 oC mimicked the DTP phenotype as the cells that survived culture at reduced temperature displayed slower proliferation and were resistant to Daunorubicin and Ara-C. Upon returning cells to 37 oC, proliferation and chemosensitivity returned to baseline, similar to DTP cells. Interestingly, the lipidome alterations of AML cells at lower temperatures matched the DTP state with increased unsaturation and elongated fatty acids across the main lipid classes. Increasing membrane rigidity can increase sensitivity to immune-mediated killing. Therefore, we explored the sensitivity of persisting AML cells to immune-mediated killing using Double Negative T (DNT) cells, a subset of CD3+ CD4- CD8- T cells that possess anti-cancer properties. We established a mouse model of AML persisters by engrafting primary AML cells into immune-deficient mice and treating mice with Ara-C or vehicle control. Eight days after treatment, primary AML cells were isolated from Ara-C treated or control mice. Isolated primary AML cells were treated with DNTs for 2 hours. AML DTPs from mice treated with Ara-C were more susceptible to DNT cell-mediated killing compared to AML cells from vehicle-treated mice. Likewise, AML cells that persisted at 31 oC displayed increased sensitivity to DNT-mediated killing compared to cells cultured at 37 oC. In conclusion, AML persister cells adapt and survive chemotherapy by transiently increasing fatty acid unsaturation and elongation resulting in increasing plasma membrane rigidity. Although these cells survive chemotherapy, they have increased susceptibility to T cell-mediated killing, thus highlighting a potential therapeutic strategy for this disease.
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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".