Identification of cytotoxic agents disrupting synovial sarcoma oncoprotein interactions by proximity ligation assay
Bibliographic record
Abstract
// Aimée N. Laporte 1, 2 , Jennifer X. Ji 2 , Limin Ma 3 , Torsten O. Nielsen 1, 2 , Bertha A. Brodin 3 1 Department of Pathology and Laboratory Medicine, Vancouver Coastal Health Research Institute and Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada 2 Centre for Translational and Applied Genomics, British Columbia Cancer Agency, Vancouver, BC, Canada 3 Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden Correspondence to: Torsten O. Nielsen, email: torsten@mail.ubc.ca Keywords: synovial sarcoma, proximity ligation assay, drug screening, HDAC inhibitors, protein-protein association Received: September 09, 2015 Accepted: April 02, 2016 Published: April 21, 2016 ABSTRACT Conventional cytotoxic therapies for synovial sarcoma provide limited benefit. Drugs specifically targeting the product of its driver translocation are currently unavailable, in part because the SS18-SSX oncoprotein functions via aberrant interactions within multiprotein complexes. Proximity ligation assay is a recently-developed method that assesses protein-protein interactions in situ . Here we report use of the proximity ligation assay to confirm the oncogenic association of SS18-SSX with its co-factor TLE1 in multiple human synovial sarcoma cell lines and in surgically-excised human tumor tissue. SS18-SSX/TLE1 interactions are disrupted by class I HDAC inhibitors and novel small molecule inhibitors. This assay can be applied in a high-throughput format for drug discovery in fusion-oncoprotein associated cancers where key effector partners are known.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".