The Next-Generation of Combination Cancer Immunotherapy: Epigenetic Immunomodulators Transmogrify Immune Training to Enhance Immunotherapy
Bibliographic record
Abstract
Cancer immunotherapy harnesses the immune system by targeting tumor cells that express antigens recognized by immune system cells, thus leading to tumor rejection. These tumor-associated antigens include tumor-specific shared antigens, differentiation antigens, protein products of mutated genes and rearrangements unique to tumor cells, overexpressed tissue-specific antigens, and exogenous viral proteins. However, the development of effective therapeutic approaches has proven difficult, mainly because these tumor antigens are shielded, and cells primarily express self-derived antigens. Despite innovative and notable advances in immunotherapy, challenges associated with variable patient response rates and efficacy on select tumors minimize the overall effectiveness of immunotherapy. Variations observed in response rates to immunotherapy are due to multiple factors, including adaptative resistance, competency, and a diversity of individual immune systems, including cancer stem cells in the tumor microenvironment, composition of the gut microbiota, and broad limitations of current immunotherapeutic approaches. New approaches are positioned to improve the immune response and increase the efficacy of immunotherapies, highlighting the challenges that the current global COVID-19 pandemic places on the present state of immunotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".