Lysine-Specific Demethylase 1 (LSD1)-Mediated Epigenetic Modification of Immunogenicity and Immunomodulatory Effects in Breast Cancers
Bibliographic record
Abstract
Tumor evolution to evade immune surveillance is a hallmark of carcinogenesis, and the modulation of tumor immunogenicity has been a challenge to present therapeutic responses in immunotherapies alone for numerous cancers. By altering the cell phenotype and reshaping the tumor microenvironment, epigenetic modifications enable tumor cells to overcome immune surveillance as a mechanism of cancer progression and immunotherapy resistance. Demethylase enzymatic activity of lysine-specific demethylase 1 (LSD1), a histone demethylase first identified in 2004, plays a pivotal role in the vast cellular processes of cancer. While FDA-approved indications for epigenetic therapies are limited to hematological malignancies, it is imperative to understand how epigenetic machinery can be targeted to prime immunotherapy responses in breast cancers. In this review, we discuss the potential roles of epigenetics and demethylating agent LSD1 as a potent new cancer management strategy to combat the current challenges of breast cancers, which have presented modest efficacy to immune checkpoint inhibitors till date. Additionally, we describe the combined use of LSD1-specific inhibitors and immune checkpoint inhibitors in existing breast cancer preclinical and clinical trials that elicits a robust immune response and benefit. Overall, the promising results observed in LSD1-targeting therapies signify the central role of epigenetics as a potential novel strategy to overcome resistance commonly seen in immunotherapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".