Amelioration of Tumor-promoting Microenvironment via Vascular Remodeling and CAF Suppression Using E7130: Biomarker Analysis by Multimodal Imaging Modalities
Bibliographic record
Abstract
E7130 is a novel anticancer agent created from total synthetic study of the natural compound norhalichondrin B. In addition to inhibiting microtubule dynamics, E7130 also ameliorates tumor-promoting aspects of the tumor microenvironment (TME) by suppressing cancer-associated fibroblasts (CAF) and promoting remodeling of tumor vasculature. Here, we demonstrate TME amelioration by E7130 using multi-imaging modalities, including multiplexed mass cytometry [cytometry by time-of-flight (CyTOF)] analysis, multiplex IHC analysis, and MRI. Experimental solid tumors characterized by large numbers of CAFs in TME were treated with E7130. E7130 suppressed LAP-TGFβ1 production, a precursor of TGFβ1, in CAFs but not in cancer cells; an effect that was accompanied by a reduction of circulating TGFβ1 in plasma. To our best knowledge, this is the first report to show a reduction of TGFβ1 production in TME. Furthermore, multiplex IHC analysis revealed reduced cellularity and increased TUNEL-positive apoptotic cells in E7130-treated xenografts. Increased microvessel density (MVD) and collagen IV (Col IV), an extracellular matrix (ECM) component associated with endothelial cells, were also observed in the TME, and plasma Col IV levels were also increased by E7130 treatment. MRI revealed increased accumulation of a contrast agent in xenografts. Moreover, diffusion-weighted MRI after E7130 treatment indicated reduction of tumor cellularity and interstitial fluid pressure. Overall, our findings strongly support the mechanism of action that E7130 alters the TME in therapeutically beneficial ways. Importantly, from a translational perspective, our data demonstrated MRI as a noninvasive biomarker to detect TME amelioration by E7130, supported by consistent changes in plasma biomarkers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".