The use of blocking antibodies targeting E-cadherin as sensitizing agents for anti-cancer therapy
Bibliographic record
Abstract
Together, the results suggest that antibodies targeting cell-cell adhesion molecules such as E-cadherin may be useful as sensitizing agents for anti-cancer therapies, including those designed to involve a host anti-cancer immune response. Multicellular resistance, a subtype of therapeutic resistance manifested in cancer cells grown as three-dimensional multicellular masses, such as spheroids in vitro and solid tumors in vivo, occurs with respect to a wide variety of anti-cancer treatment strategies. Building upon previous work from our laboratory, this thesis describes proof-of-principle studies examining the ability of a monoclonal antibody targeting the potent cell adhesion molecule E-cadherin to disrupt three-dimensional aggregates of human cancer cells as a means of overcoming multicellular resistance. Chapter 3 outlines a separate but related set of experiments in which disruption of E-cadherin-mediated adhesion via SHE78-7 was shown to sensitize multicellular spheroids of HT29 in vitro to treatment with 5-fluorouracil, paclitaxel, vinblastine and etoposide, but not cisplatin. Anti-adhesion via SHE78-7 caused the release of cells from contact-mediated growth arrest, increased intracellular accumulation of administered chemotherapeutics, and decreased expression of particular PKC isoforms shown previously to influences the therapeutic resistance of HT29 cells; however, the expression and activity levels of one of these isoforms, PKC beta1, were shown to be unrelated to the manifestation of multicellular resistance. Chapter 2 describes the novel ability of one such anti-E-cadherin antibody, called SHE78-7, to play a dual role as participant in, and sensitizing agent for, host immune-mediated anti-tumor activity. SHE78-7 disrupted preformed multicellular aggregates of HT29 human colorectal carcinoma cells both in vitro and in vivo, but had no direct cytotoxic effect in vitro. In vivo, however, i.p. injection of SHE78-7 significantly prolonged the survival of nude mice carrying established i.p. HT29 ascites tumor xenografts. This anti-tumor effect was subsequently shown to be dependent upon both the anti-adhesive effect of SHE78-7 and its ability to recruit an Fc-mediated host immune response, namely antibody-dependent cellular cytotoxicity.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".