Unmet needs in T cell-based cancer therapies: opportunities for translational innovation
Bibliographic record
Abstract
Immunotherapies have greatly improved treatment options for patient suffering from a range of malignancies. After these novel, anti-cancer therapeutics like CART cells showed overwhelming clinical success in targeting hematological malignancies, the first promising signals are also seen for utilizing engineered immune cells for targeting solid tumors. However, despite numerous attempts, clinical efficacy in targeting solid tumors remains lacking. This is due to several reasons including a lack of targetable, tumor-specific antigens, immune-escape mechanisms in the more complex tumor microenvironment, tumor recurrence and metastases. In order to overcome these roadblocks, new approaches for targeting solid malignancies and improving existing immunotherapies are needed. Within this thesis, we describe and investigate multiple new strategies for utilizing γδT cells and their TCRs to improve the efficacy of T cell based immunotherapies in the field of oncology. First, we identify important TCR features dictating the behavior of tumor-reactive, Vγ9Vδ2T cells (Chapter 3). Next, we describe novel approaches for the recognition and targeting of either glioblastoma (Chapter 4) and colorectal cancer (Chapter 5) that provide potential for the development of future immunotherapies. Additionally, we identify a potential novel way to improve T-cells fitness upon in vitro expansion by investiganting on a alternative activation pathway (Chapter 6). Finally, we describe an approach to boost T cell infiltration in the tumor micro-environment in order to improve therapeutic efficacy of T cell based treatments for both hematological and solid tumors (Chapter 7). In Chapter 8 we conclude with a general discussion.
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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 | 0.018 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".