Lysosome targeted therapies in hematological malignancies
Bibliographic record
Abstract
Lysosomes are dynamic organelles integral to cellular homeostasis, secretory pathways, immune responses, and cell death regulation. In cancers, lysosomes become dysregulated to sustain proliferative signaling, metabolism, and invasion. In hematological malignancies such as acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), chronic myeloid leukemia (CML), and chronic lymphocytic leukemia (CLL), leukemia cells demonstrate lysosome dysregulation with increased lysosomal activity, mTORC1 signaling, catabolic reactions, and autophagy. This supports the survival, metabolism, and proliferation of the leukemia cells. Lysosomes also play a critical role in treatment resistance by promoting cell survival and sequestration of drugs. This has led to the development of lysosome-targeted therapies such as cationic amphiphilic drugs (CAD), ATPase inhibitors or autophagy inhibitors to treat hematological malignancies. Lysosome-targeted treatments have shown effectiveness at inducing cell death by inhibiting cell survival mechanisms and inducing apoptosis. In addition, the combination of lysosome-targeted therapies with standard treatments gives synergistic apoptotic responses in leukemia cells. In this review, we will describe the lysosomal functions, their dysregulation in hematological malignancies and the development of lysosomal targeted therapies for leukemia treatment. By understanding lysosome dysregulation and developing lysosome-targeted agents, innovative treatment strategies could be effective in overcoming drug resistance in hematological malignancies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".