Abstract 1191: Translational Proof-of-Concept (TransPoC), a not-for-profit research organization enabling access to large-scale translational oncology platforms: The Patient-Derived Xenograft network
Bibliographic record
Abstract
Abstract TransPoC is a not-for-profit research organization that will deliver open-access, large-scale translational oncology platforms to enable greater clinical proof-of-concept success for new cancer therapies. TransPoC will comprise three platforms: 1. CPN - Cancer Cell “PoC” Network for screening compounds against 1000+ genomically-characterized cell lines; 2. MPN- Mouse “PoC” Network - a multi-site platform for mouse preclinical trials using genomically-characterized Patient-Derived Xenograft (PDX) models; 3. BioIT - analysis and integration of genomic information and pharmacological profiling data. Here we present an overview of the Mouse “PoC” Network, define a path to implementation of multi-center pre-clinical trials in mice and describe a pilot study to demonstrate the feasibility of implementing such a network. PDX models are increasingly used in pre-clinical studies as they capture and retain the histological, molecular, and genetic heterogeneity of the original tumor compared to cell line derived xenografts and are therefore a closer representation of a patient's tumor in situ. To enable transformative preclinical studies, models need to be characterized in a manner similar to tumor samples in The Cancer Genome Atlas and the International Cancer Genome Consortium, and must be assembled in sufficient quantity to capture clinically relevant major cancer (sub)types. To achieve this, TransPoC is building a global network of mouse PDX “hospitals” with genomic and metabolomic profiles characterized in a consistent manner. In addition, each mouse hospital will utilize common SOPs to generate comparable pharmacology data sets across sites that will include testing standard of care agents. BioIT will enable deep interrogation of data sets and provide pipelines for pharmacogenomics correlates of response to both standard and novel agents. To date, the network has collated over 2,000 PDX models and will enable sponsors to execute multi-center pre-clinical trials in a manner similar to those used in multi-institutional cooperative clinical trials. To demonstrate the viability of MPN, a pilot study has been initiated at 6 sites located in Canada, Italy, China and USA to evaluate the activity of MEK and RAF inhibitors against a panel of BRAF/KRAS mutant melanoma and colorectal cancer PDX models. An update on the initial tolerability, PK/PD/efficacy studies and molecular characterization of PDX models in the network will be presented. TransPoC continues to recruit new sites and characterize their PDX models for incorporation into MPN for use by TransPoC sponsors. Through this effort TransPoC enables rapid assessment of standard and novel investigational therapies to determine their therapeutic potential for translation to clinical trials with a mission to improve the chance of observing clinical proof-of-concept. Citation Format: Peter G. Smith, David Sutton, Andrea Bertotti, Livio Trusolino, Susan Airhart, Ming S. Tsao, Bradly G. Wouters, S. Gail Eckhardt, Lai Wang, Tim Heffernan, David Verbel, Andrea Gerken, Peter Fekkes, Lihua Yu, Lihua Yu, Markus Warmuth. Translational Proof-of-Concept (TransPoC), a not-for-profit research organization enabling access to large-scale translational oncology platforms: The Patient-Derived Xenograft network. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1191. doi:10.1158/1538-7445.AM2014-1191
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.024 |
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".