CURRENT LANDSCAPE OF COMPLEX BIOFABRICATED MSK TUMOUR MICROENVIRONMENT MODELS FOR THERAPEUTIC SCREENING: WHERE DO WE GO FROM HERE?
Bibliographic record
Abstract
Up to 80% of patients with primary tumors of the breast, prostate or lung will develop bone metastases. Metastases to the spine cause severe pain, functional deficit and highly diminished health related quality of life through fracture, vertebral instability and spinal cord and nerve-root injury. This is also directly associated with high socioeconomic and healthcare costs. Resection of spine metastases leaves large bone defects which cannot self-repair and bone grafts are often needed yet insufficient to facilitate healing, tissue repair and local control of tumor burden. Despite extensive resection during surgery, some tumor cells can remain hidden and therefore drive cancer recurrence. Following resection and reconstruction, chemotherapy is often systemically administered in these patients resulting in many side-effects. These therapies often lack site specific efficacy and promote stress induced senescence resulting in chronic low-level inflammation in the tumor and surrounding stroma. This can result in therapy-resistance and further tumor progression, and new drugs are necessary to combat these events. Drug discovery is a long-term and costly endeavor, and more than 70% of new drugs fail to reach market due to limited advanced screening technology. Tissue engineering has emerged as an advanced strategy to apply biomaterials scaffolds, cells and biologics to mimic functional tissues such as tumor microenvironment. Developing new 3D physiological tissue as therapeutic screening tools provides a platform for screening new biomaterial local drug delivery devices. Such devices could allow more accurate drug screening scenario whereby the new therapies can reduce negative side-effects, promote bone repair and block cancer recurrence directly at the site of resection. Moreover, the proposed implantable drug delivery system could be adapted and applied to any tissue and cancer type. The main goal of this talk is to provide a historical overview of the progression of complex biofabricated 3D MSK tumor microenvironment models and how they've been used for screening and developing novel therapies, as well as a brief overview of state of the art. The talk will also focus on how our group is currently positioned to developing new models of bone metastasis for screening local therapy devices and novel senlolytic adjuvant therapies.
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) | 0.001 | 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".