Treatment with eFT-508 increases chemosensitivity in breast cancer cells by modulating the tumor microenvironment
Bibliographic record
Abstract
Abstract Background Patients with triple-negative breast cancer (TNBC) are better responders to neoadjuvant chemotherapy; however, they are poor in the durability of response with decreased overall and progression-free survival. Methods Given that significant improvements have been reported with PD-L1-PD-1 blockade in different cancers, we evaluated the in vitro and in vivo effectiveness of Tomivosertib (eFT-508), an anthracycline, adriamycin, and MNK1/2 inhibitor, which has been previously shown to inhibit translation of PD-L1 in mice model of liver cancer, alone or in combination using BC cell lines and an orthotopic xenograft mice model using the TNBC cell line MDA-MB-231. Results Within the context of The Cancer Genome Atlas (TCGA) dataset, expression of CD274 mRNA, which encodes programmed death-ligand 1 (PD-L1), was found to be significantly overexpressed in TNBC patients compared to patients with HER2 + or luminal breast cancer (BC). Even within TNBC sub-types, CD274 expression was significantly higher in the immune modulatory subtype (TNBC-IM). BC cells exhibited high IC50 = 0.85 ± 0.07 nM with Adriamycin and significantly lower IC50 = 0.23 ± 0.04 nM with eFT-508 (P < 0.01). Combination treatment showed in vitro synergism on chemosensitivity. Combination therapy also exhibited a synergistic effect on inhibition of tumor growth and lung colonization in vivo. Mass cytometry-based evaluation of the tumor microenvironment revealed significant attenuation of both PD-L1 and PD-L2 following mono- or combination therapy with eFT-508. Conclusions Treatment with eFT-508 restored effector and cytotoxic function of tumor-infiltrating CD8 + T cells in mice. The remarkable efficacy observed both in vitro and in vivo, and clinical synergism with adriamycin, highlights the potential of eFT-508 as an alternative, yet more efficacious, therapeutic option for patients with TNBC.
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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) | 0.002 | 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".