XPO1 Enables Adaptive Regulation of mRNA Export Required for Genotoxic Stress Tolerance in Cancer Cells
Bibliographic record
Abstract
Exportin-1 (XPO1), the main soluble nuclear export receptor in eukaryotic cells, is frequently overexpressed in diffuse large B-cell lymphoma (DLBCL). A selective XPO1 inhibitor, selinexor, received approval as single agent for relapsed or refractory (R/R) DLBCL. Elucidating the mechanisms by which XPO1 overexpression supports cancer cells could facilitate further clinical development of XPO1 inhibitors. We uncovered here that XPO1 overexpression increases tolerance to genotoxic stress, leading to a poor response to chemoimmunotherapy. Upon DNA damage induced by MYC expression or exogenous compounds, XPO1 bound and exported EIF4E and THOC4 carrying DNA damage repair mRNAs, thereby increasing synthesis of DNA damage repair proteins under conditions of increased turnover. Consequently, XPO1 inhibition decreased the capacity of lymphoma cells to repair DNA damage and ultimately resulted in increased cytotoxicity. In a phase I clinical trial conducted in R/R DLBCL, the combination of selinexor with second-line chemoimmunotherapy was tolerated with early indication of efficacy. Overall, this study reveals that XPO1 overexpression plays a critical role in the increased tolerance of cancer cells to DNA damage while providing new insights to optimize the clinical development of XPO1 inhibitors. SIGNIFICANCE: XPO1 regulates the dynamic ribonucleoprotein nuclear export in response to genotoxic stress to support tolerance and can be targeted to enhance the sensitivity of cancer cells to endogenous and exogenous DNA damage. See related commentary by Knittel and Reinhardt, p. 3.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".