Novel cell adhesion/migration pathways are predictive markers of HDAC inhibitor resistance in cutaneous T cell lymphoma
Bibliographic record
Abstract
BACKGROUND: Treatment for Cutaneous T Cell Lymphoma (CTCL) is generally not curative. Therefore, selecting therapy that is effective and tolerable is critical to clinical decision-making. Histone deacetylase inhibitors (HDACi), epigenetic modifier drugs, are commonly used but effective in only ~30% of patients. There are no predictive markers of HDACi response and the CTCL histone acetylation landscape remains unmapped. We sought to identify pre-treatment molecular markers of resistance in CTCL that progressed on HDACi therapy. METHODS: Purified T cells from 39 pre/post-treatment peripheral blood samples and skin biopsies from 20 patients were subjected to RNA-seq and ChIP-seq for histone acetylation marks (H3K14/9 ac, H3K27ac). We correlated significant differences in histone acetylation with gene expression in HDACi-resistant/sensitive CTCL. We extended these findings in additional CTCL patient cohorts (RNA-seq, microarray) and using ELISA in matched CTCL patient plasma. FINDINGS: Resistant CTCL exhibited high levels of histone acetylation, which correlated with increased expression of 338 genes (FDR < 0·05), including some novel to CTCL: BIRC5 (anti-apoptotic); RRM2 (cell cycle); TXNDC5, GSTM1 (redox); and CXCR4, LAIR2 (cell adhesion/migration). Several of these, including LAIR2, were elevated pre-treatment in HDACi-resistant CTCL. In CTCL patient plasma (n = 6), LAIR2 protein was also elevated (p < 0·01) compared to controls. INTERPRETATION: This study is the first to connect genome-wide differences in chromatin acetylation and gene expression to HDACi-resistance in primary CTCL. Our results identify novel markers with high pre-treatment expression, such as LAIR2, as potential prognostic and/or predictors of HDACi-resistance in CTCL. FUNDING: NIH:CA156690, CA188286; NCATS: WU-ICTS UL1 TR000448; Siteman Cancer Center: CA091842.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".