Cellular therapy approaches harnessing the power of the immune system for personalized cancer treatment
Bibliographic record
Abstract
Cancer development often implies failure of the immune system to recognize tumor antigens and kill malignant cells. While the whole immune cell repertoire is broad, that of immune cells with the ability to react to individual tumor antigens is usually very limited. The purpose of cancer immunotherapy is to augment the power, quantitative and qualitative, of the immune system such that it readily recognizes and eliminates cancer cells. As immune therapy is shifting toward more personalized medicine, different types of tumor antigens can be used as target antigens to allow T cells to destroy tumor cells. These antigens are mostly defined as tumor associated antigens (TAA), neoantigens or minor histocompatibility antigens. Their clinical usage involve either direct injection of TAA and neoantigens, administration of peptide-loaded dendritic cells in vaccination approaches, or infusion of ex vivo expanded tumor-specific T cells. However, such cellular therapies are facing several challenges including immune suppressive tumor microenvironment, lack of persistence of ex vivo expanded antigen specific T cells and potential off-target toxicity of these therapies. In this review, we will discuss recent advances allowing for better expansion of tumor reactive T cells and novel strategies used to overcome the challenges facing cellular therapy for cancer.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".