Resisting Resistance: Towards Unravelling the Secrets of Cellular Immunotherapy
Bibliographic record
Abstract
The field of immune-oncology, and the development of a wide range of immunotherapy products that stemmed from it, has dramatically transformed the survival chances for many cancer patients. Nonetheless, there is a large share of patients that does not yet benefit from this success, either due to having difficult to target (solid) tumour types, or through the development of resistance during treatment. Due to the complexity of both cancer and the nature of immunotherapy products, there is a great need to develop novel methods that will allow us to study immune-cancer interactions in a simplified, controlled setting. In this thesis work, we have described optimal protocols to derive breast cancer organoid models (Chapter 2), which have further been used to study engineered immune cell product behaviour in the context of immune-organoid co-cultures (Chapter 4), and furthermore for studying processes underlying sensitivity and resistance to these cellular immunotherapy products (Chapter 5). Additionally, we have provided a thorough overview on the contributions of the field of 3D imaging to our understanding of cancer biology and treatment response (Chapter 3). Most excitingly, we have developed several novel tools to study both immunotherapy products and the tumours they target in unprecedented detail. With our imaging method and associated computational analysis platform called BEHAV3D (Chapter 4), we have provided a revolutionary method to study the behaviour of engineered immune cells, revealing that these products contain a mixture of cells with different behaviours, complex interactions, and (serial) killing capacities. With our newly developed method and analysis pipeline to detangle tumour heterogeneity called FUN-CLON (Chapter 5), we were able to explore the extensive landscape of factors influencing response to engineered T cell therapy, revealing several recurring profiles of responding and not responding cell states and characteristics. Together, these tools offer exciting opportunities for both preclinical and clinical applications, with the aim of further guiding cellular immunotherapy product development and improving patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".