Targeting Neural-Restrictive Silencer Factor Sensitizes Tumor Cells to Antibody-Based Cancer Immunotherapy In Vitro via Multiple Mechanisms
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Tumor cells escape clearance by complement by abundantly expressing CD59 and other membrane complement regulators. Recently, we designed a peptide derived from the neural-restrictive silencer factor (REST), REST68, which we showed to inhibit expression of CD59 in tumors lacking the full-length REST and proposed a detailed model for regulation of CD59 expression via interplay between REST and nucleolin (NCL) transcription factors. In this paper, we study in detail the mechanisms for sensitization of malignant cells to Ab-based cancer immunotherapy by the REST68 peptide and the implications of the REST/NCL model for the design of treatment resulting in higher tumor susceptibility. REST68 inhibited CD59 expression in malignant cells expressing either truncated or full-length REST, but not in nonmalignant cells. However, activation of protein kinase C (PKC) in nonmalignant cells, a process that contributes to cellular transformation, phosphorylated NCL and enabled suppression of CD59 expression by the REST68. Combined treatment of different tumor types with REST68 and PKC inhibitor synergized to further suppress CD59 expression and reduce resistance to complement lysis. The combined treatment also increased susceptibility of tumors expressing either of the REST isoforms to PBMC-mediated killing, which, at least in part, accounted for the strong promotion of apoptosis by the REST68/PKC inhibitor. These data demonstrate that REST68 sensitizes tumors to Ab-based cancer immunotherapy via multiple mechanisms. Furthermore, the REST/NCL interplay model for regulation of expression of cd59 and other genes involved in cell survival enables the design of treatments for different tumor types to achieve more efficient tumor clearance.
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) | 0.001 | 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".