Abstract 6908: Autophagy disruption via PIKfyve inhibition as a novel strategy to enhance immunotherapy responses in multiple myeloma
Bibliographic record
Abstract
Abstract Despite advancements in the treatment of multiple myeloma (MM), most patients ultimately relapse due to innate and acquired drug resistance. Our search for novel therapeutic strategies led to the development of PIK001, a potent and selective PIKfyve inhibitor, as a promising therapeutic approach targeting lysosomal function and autophagy. In addition to its robust single agent anti-MM activity, PIK001 presented synergistic activity with relevant anti-MM therapeutics, including selinexor, venetoclax, and pomalidomide (IMiDs), in vitro. Notably, PIK001 retained efficacy in IMiD-resistant isogenic human myeloma cell line (HMCL) models, underscoring its potential in drug-resistant MM. To investigate the determinants of PIKfyve resistance, we generated PIK001-resistant HMCLs by culturing three PIK001-sensitive HMCLs (KMS26, KMS11, and JJN3) in escalating doses of the PIKfyve inhibitor (up to 5uM). These isogenic models of resistance were characterized by whole genome and transcriptome sequencing and mass spectrometry-based proteomics. Ex vivo downstream effects of PIK001 treatment with 500nM for 16h were further assessed in patient-derived CD138+ MM samples using single-cell multiomic sequencing. PIK001 resistance was associated with a marked upregulation of genes and proteins involved in lysosomal function, autophagy regulation, and cholesterol homeostasis, along with downregulation of MYC targets. These findings were also found in primary patient samples following PIK001 treatment. KMS26 PIK001-resistant showed a clonal PIKFYVE kinase domain mutation, previously described in a resistant diffuse large B-cell lymphoma cell line. Importantly, we observed a two-fold increase in canonical and noncanonical Major Histocompatibility Complex (MHC) class I and a four-fold increase in MHC class II gene and protein expression in the PIK001-resistant compared to PIK001-sensitive KMS11. Increased cell surface expression of MHC Class I and II in KMS11 PIK001-resistant was confirmed by flow cytometry. PIK001-resistant KMS26 and JJN3 also presented an upregulation of cell surface expression of MHC Class I and, to a lesser extent, II. Similarly, PIK001 treatment also resulted in increased in MHC Class I gene expression on primary patient samples. Since downregulation or loss of MHC Class I has been shown as a mechanism of immune evasion in cancer, these findings suggest that PIKfyve inhibition may enhance MM immunotherapy responses by upregulating MHC surface expression. This hypothesis aligns with recent studies demonstrating increased tumor-specific MHC Class I expression and improved cancer immunotherapy efficacy following PIKfyve inhibition in solid tumors. Together, these results highlight the potential of PIKfyve inhibitors to synergize with existing anti-MM therapeutics and sensitize MM cells to MHC-dependent immunotherapies via autophagy disruption. Citation Format: Cecilia Bonolo de Campos, Dor D. Abelman, Ruijuan He, Tessa Pelino, Ding Yan Wang, David S. Scott, Zhihua Li, Michael St Paul, Trevor J. Pugh, Olga Issakova, Nikolai Sepetov, Tak W. Mak, Suzanne Trudel, A Keith Stewart. Autophagy disruption via PIKfyve inhibition as a novel strategy to enhance immunotherapy responses in multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6908.
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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 | 0.001 |
| 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".