Metabolic and Cell Cycle Proteins As Novel Targets of Selinexor Activity in Diffuse Large B Cell Lymphoma
Bibliographic record
Abstract
Background Diffuse large B cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL) and has poor outcomes in the relapsed/refractory (R/R) setting. Recent years have identified Exportin-1 (XPO1), a nuclear export protein, as a negative prognostic factor in several cancer types including DLBCL. Selinexor, an orally-available inhibitor of XPO1 has modest activity in R/R DLBCL, but knowledge gaps in understanding how XPO1 drives DLBCL are preventing effective use of selinexor in the clinic. Specifically, large-scale proteomics studies investigating which proteins are exported to the cytoplasm by XPO1 are lacking, and mechanisms of activity of selinexor-mediated XPO1 inhibition in DLBCL have not been well described. Therefore, we analyzed selinexor's effects on DLBCL metabolism and conducted a mass spectrometry analysis of proteins to determine which pathways are modulated by selinexor-mediated XPO1 inhibition. Methods DLBCL cell lines (HBL-1, OCI-Ly2, OCI-Ly8) were treated with selinexor for 24 hours and changes in mitochondrial and glycolytic ATP production was measured using an XFe 96 Agilent Seahorse bioanalyzer. Cells were treated with 0.5µM selinexor for 24 hours and replicates were subject to proteomic analysis via mass spectrometry. Proteins identified as having significant changes in abundance upon selinexor treatment were interrogated with Reactome to determine which pathways are modulated by the compound in DLBCL (Milacic et al., 2024). The sequences of proteins from the top pathways, as well as those of metabolic proteins of interest, were analyzed using the LocNES nuclear export sequence identifier to determine which of these proteins may be canonical XPO1 cargo molecules (Xu et al., 2014). Results Selinexor significantly reduced both mitochondrial and glycolytic ATP production (p<0.001) in all cell lines, as measured by oxygen consumption rate (OCR) and extracellular acidification rate (ECAR). Cell viability at this time point between cells treated and untreated with selinexor were not statistically significant, indicating that these metabolic changes were due to XPO1 inhibition and not cell death. Mass spectrometry analysis identified 6313 proteins, of which 126 showed a statistically significant change (p<0.05) in abundance with selinexor treatment (87 downregulated and 39 upregulated). The top pathways modified by selinexor included mitotic cell cycle control, mRNA splicing, rRNA modification, and RNA binding proteins, all of which were downregulated by the treatment. Canonical nuclear export sequences were identified in several selinexor-modulated proteins in these pathways, including cyclin B2, ORC1, LMNB1, HNRPC, and GAR1. Several metabolic proteins were affected by selinexor treatment, most notably ACADS, which catalyzes lipid metabolism via fatty acid oxidation. ACADS showed a decreased abundance in response to selinexor treatment, suggesting a mechanism by which the drug exerts its metabolic effects via inhibition of fatty acid oxidation and subsequent ATP production. Upon further analysis, the peptide sequence of the protein was also found to harbour a nuclear export sequence, indicating it is a potentially relevant XPO1 cargo molecule to the anti-lymphoma effects of selinexor. Conclusion Selinexor inhibits glycolytic and mitochondrial metabolism in DLBCL, which appears to be driven by blocking the nuclear export of enzymes involved in metabolism such as ACADS, which catalyzes ATP production via fatty acid oxidation. Selinexor is also able to decrease the abundance of proteins involved in cell cycle progression and RNA metabolism. We have identified several novel proteins that can be modulated by selinexor in DLBCL that may contribute to its mechanisms of anti-cancer activity in lymphoma. We aim to validate XPO1 targets of interest in our dataset. Future work will verify which of these proteins are XPO1 cargo molecules via proximity-ligation assays to reveal how the treatment exerts its cytotoxic effects in DLBCL.
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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".