Abstract A09: PDXovo: Ultra-fast in vivo drug sensitivity matrices for renal cell carcinoma patients prior to administration of targeted therapy
Bibliographic record
Abstract
Abstract Our next generation PDX model of renal cell carcinoma (RCC) offers the ability to pre-determine de novo drug resistance in fresh patient tumor samples prior to targeted therapy. Implantation of tumor specimens into the chorioallantoic membrane (CAM) of the chicken embryo results in high engraftment efficiencies within two days permitting large-scale “tumor avatar” studies due to its angiogenic microenvironment. Functional tumor heterogeneity studies can be performed in context of drug resistance within two weeks, an approach that could guide the selection of drugs and anticipate outcomes for RCC patients. This ultrafast PDX model is mirrored by high-frequency ultrasound imaging that permits quantitation of tumor volume and tumor vascularity in a high-throughput manner. Using this “tumor avatar” model paired with a prospective RCC patient cohort, we observe intratumoral functional heterogeneity in the context of Sunitinib treatment as determined by high-frequency ultrasound imaging, highlighting its interventional potential in the clinic. Citation Format: Matt Lowerison, Hon S. Leong, Yaroslav Fedyshyn, Ann F. Chambers, James Lacefield, Nicholas E. Power. PDXovo: Ultra-fast in vivo drug sensitivity matrices for renal cell carcinoma patients prior to administration of targeted therapy. [abstract]. In: Proceedings of the AACR Special Conference: Patient-Derived Cancer Models: Present and Future Applications from Basic Science to the Clinic; Feb 11-14, 2016; New Orleans, LA. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(16_Suppl):Abstract nr A09.
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 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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".