PIKfyve inhibition in MM disrupts autophagy and lysosome function, increasing MHC expression and cholesterol metabolism
Bibliographic record
Abstract
ABSTRACT: We previously reported a chemogenomics screen that unexpectedly identified phosphatidylinositol-3-phosphate 5-kinase (PIKfyve) as a vulnerable target in multiple myeloma (MM). PIKfyve is an essential regulator of lysosomal function and autophagy. Given the high basal requirement for autophagy in MM for sustainable immunoglobulin synthesis, targeting autophagy holds clinical potential as a novel therapeutic avenue. Here, we report the development and characterization of PIK001 and analogs, potent and selective novel small-molecule inhibitors of PIKfyve. PIK001 demonstrated potent anti-MM activity in vitro, as well as synergistic activity with established anti-MM agents (including venetoclax and selinexor), while retaining efficacy in lenalidomide-resistant models. Multiomic characterization of isogenic cell lines sensitive and resistant to PIK001 identified a catalytic domain mutation (PIKFYVE N1939K) and heterogenous alterations in autophagy capabilities. Importantly, we noted that PIK001 exposure also resulted in significantly increased cholesterol metabolism and upregulation of major histocompatibility complex (MHC) class I expression, with potential implications in tumor immunity. Beyond MM, PIKfyve inhibition also shows selective cytotoxicity in acute myeloid leukemia, melanoma, and renal cancer, highlighting broader therapeutic potential. These findings establish PIKfyve inhibition as a valid target for MM and other hematologic malignancies, provide insights into mechanisms of sensitivity and resistance, and lay the foundation for further preclinical (particularly the role of cholesterol metabolism and tumor immunity) and clinical development.
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 | 0.000 | 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".