SSX Cancer Testis Antigens are Expressed in Most Multiple Myeloma Patients
Bibliographic record
Abstract
Cancer testis antigens (CTAs) are tumor-specific antigens that may be useful targets for cancer vaccines. Here, CTA expression was examined in multiple myeloma (MM), a B-cell cancer characterized by malignant plasma cells (PCs) in the bone marrow (BM), and monoclonal gammopathy of undetermined significance (MGUS), a condition that can progress to MM. We screened a panel of patient BMs at different stages of malignancy for CTA expression by reverse transcription polymerase chain reaction RT-PCR. Here, SSX (synovial sarcoma, X chromosome) emerged as a promising candidate for an MM vaccine, having a profile similar to currently studied CTA, NY-ESO-1, and MAGE. SSX1, 2, 4, and 5 expression was studied further in 114 MM (total SSX, 61% of patients; SSX1, 42%; SSX2, 23%; SSX4, 38%; SSX5, 35%), 45 MGUS (total SSX, 24% of patients; SSX1, 9%; SSX4, 20%), and 12 control (0/12, 0%) subjects. Several expression patterns were observed, the most predominant being co-expression of SSX1, 2, 4, and 5 (called group A expression, in 20% of MM), which correlated with reduced survival (P=0.0006). Of the four genes, SSX2 had the strongest association with reduced survival (P=0.0001). SSX protein expression ranged from 13.5% of PCs in an SSX1/SSX4 co-expressor to as high as 88% of PCs in group A expressor, exceeding reported frequencies of NY-ESO-1 and MAGE in MM. In single PCs from group A patients, we detected variable degrees of SSX co-expression, emphasizing the heterogeneity of CTA expression within tumor cell populations. These results demonstrate that SSX is a frequently expressed CTA in MM and highlight its potential as an MM vaccine candidate.
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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".