Evaluation of Cancer-testis Antigens in Osteosarcoma and Dedifferentiated Liposarcoma as Targets for Immunotherapy
Bibliographic record
Abstract
Abstract T cell based immunotherapies targeting tumor antigens are a promising alternative to traditional cancer treatments due to their ability to eliminate malignant cells, but require highly immunogenic tumor-associated proteins as targets for CD8+ T cell recognition. Additional parameters to consider in a T cell based immunotherapy include tumor T cell infiltration and expression of HLA-peptide complexes on the surface of cancer cells. The goal of this study is to identify targetable immunogenic tumor antigens and to characterize the immune profile of human dedifferentiated liposarcoma (DDLPS) and osteosarcoma (OS) sarcoma subtypes by screening for cancer-testis antigen (CTA) expression, HLA expression, and tumor T cell infiltration by immunohistochemistry (IHC). Human tissue micro-arrays composed of 80 cores of OS and 49 cores of DDLPS were obtained and stained by IHC for selected CTAs (MAGE-A3, NY-ESO-1 and SSX2) and various immune markers. In DDLPS, 11% of samples express MAGE-A3 and 2% express SSX2 and NY-ESO-1 in low percentages of tumor cells. In OS, 100% of samples express MAGE-A3 and 89% express SSX2, both with >80% of positive cases showing moderate to high expression. NY-ESO-1 is expressed in 78% of OS samples, predominantly at low levels. Brisk infiltration of CD8+ T cells is observed in over 70% of both sarcoma types. Furthermore, all sarcoma samples tested are positive for HLA expression. These results show promising expression of CTAs MAGE-A3 and SSX2 in OS, which may be used as targets for immunotherapy for OS. The data generated throughout this study will provide insight into the immune profile of these sarcomas - information that is critical for immunotherapy design.
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 | 0.000 |
| 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".