Abstract PO-095: TGF-β carrying exosomes in plasma of HNSCC as potential biomarkers of disease progression in patients with HNSCC
Bibliographic record
Abstract
Abstract Background: The contribution of TGFβ+ exosomes, now referred to as small extracellular vesicles (sEV), in plasma of cancer patients to disease progression has been unclear. Levels of TGFβ in plasma often do not correlate with clinicopathological data, and TGFβ can exert dual (yin-yang) effects as a tumor promoter and a tumor inhibitor. We evaluated the potential of tumor-derived sEV dubbed TEX to serve as liquid tumor biopsy in patients with HNSCC. Methods: Plasma was obtained from 3 different cohorts of patients with HNSCC (n=97) and HDs (n=22). Clinicopathology/demographic data were available for all patients. sEV were isolated by ultrafiltration/size exclusion chromatography (SEC) from plasma or cell supernatants. TGF-β levels in plasma were assessed by ELISA; TGF-β expression in tissues was studied by IHC and immunoblots; immunoblots, flow cytometry and LC-MS/MS were used to measure TGF-β in isolated sEV. TGFB1 gene expression was analyzed using the TCGA Head and Neck Cancer database (n=520 cases and 44 NC). TGF-β signaling and activity of EVs were quantified in the reporter cell (MFB-F11) proliferation assays. Changes of TGF-β expression levels in plasma, tissues and sEV during carcinogenesis were studied in an orthotopic 4NQO mouse model. Results: Using the TCGA we found high expression levels of the vesiculation genes regulating EV production/release and of the TGFB1 gene expression in HNSCC. In the 4NQO orthotopic model of OSCC, cancer progression correlated with TGF-β accumulations in tumor tissues and a significant increase in circulating TGF-β+ sEV. In HNSCC patients, TGF-β levels and activity in plasma and TGF-β expression levels in tumor tissues were significantly elevated relative to controls but did not correlate with clinicopathologic data, tumor burden or disease outcome. TEX produced by HNSCC cell lines carried TGF-β and initiated Smad3 signaling in reporter cells. TGF-β inhibitors blocked this signaling. High TGFβ abundance in sEV was confirmed by proteomics and by RNAseq using the TCGA data base. Levels of TGF-β expression in sEV, sEV numbers and TGF-β activity of sEV were significantly elevated in HNSCC patients vs HDs’ sEV and correlated with tumor stage and LN involvement. Conclusions: TEX and sEV from HNSCC patients’ plasma carry TGF-β which reflects disease progression in HNSCC patients and in a mouse model of oral carcinogenesis. Neither plasma levels of TGF-β (ELISA) nor levels of TGF-β expression in the tumor (IHC) correlated with disease progression. Only sEV-associated TGF-β correlates with tumor burden and shows promise as a non-invasive biomarker of HNSCC progression and response to therapy. Citation Format: Nils Ludwig, Saigopalakrishna S. Yerneni, Theresa L. Whiteside. TGF-β carrying exosomes in plasma of HNSCC as potential biomarkers of disease progression in patients with HNSCC [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-095.
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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".