Abstract B08: Patient-derived xenografts of oral squamous cell carcinoma: A model system for the identification of prognostic molecular signatures and for preclinical assessment of targeted therapies
Bibliographic record
Abstract
Abstract Patients treated for oral squamous cell carcinoma (OSCC) continue to have a poor overall survival rate. The existence of tumor heterogeneity produces major challenges to successfully treating OSCC. Patient-derived xenografts (PDXs) provide an important tool for translational research and for improving our understanding of tumor heterogeneity in the treatment setting. PDXs of OSCCs recapitulate the histology, tumor behavior, and biomarker expression profile of the original patient tumor and our studies show that engraftment itself associates with poor patient outcome. This clinical relevance of engraftment demonstrates the use of PDXs as a powerful research tool in the advancement of personalized medicine. We have generated a large number of OSCC PDXs. Overall, 158 of 231 OSCC samples (68%) formed tumors when implanted into immune-compromised mice. The ability to engraft varied from patient to patient and these differences were associated to patient survival outcome and lymph node (LN) status. The clinical data on 108 patient samples with a minimum of 2 years follow-up demonstrated that OSCC patients whose tumors engrafted experienced a significantly worse overall survival (OS) and disease-free survival (DFS) compared to tumors that did not engraft (OS of 59% vs. 84%; DFS of 56% vs. 79%). When combined with nodal status, LN+ and LN- patients could be further stratified, wherein the 2-year OS probability for LN-, non-engrafted patients were 100%, vs. only 46% of patients that were both LN+ and engrafted. Next, given the observed clinical relevance of engrafters vs. non-engrafters, we sought to determine whether these two cohorts display distinct genetic alterations. We designed a custom in-solution hybrid capture targeted sequencing panel of 112 genes for both mutational and copy number alteration (CNA) analyses. DNA was extracted from 120 primary snap-frozen tissues (62 engrafters and 58 non-engrafters) and the targeted panel of genes was sequenced to identify gene and pathway alterations that correlate with engraftment and may be of prognostic value. Our repertoire of OSCC PDXs includes a viable tumor bank and fully annotated OSCC samples with known engraftment ability and genetic alterations. We will be able to use the molecular information obtained from our targeted sequencing results to tailor and test therapies in PDXs to the specific mutations and/or CNAs they bear. Overall, our OSCC PDXs provide a valuable system to identify prognostic and engraftment molecular signatures that can be studied for pre-clinical drug development and will contribute toward personalized medicine. Citation Format: Christina Karamboulas, Jeff Bruce, Xin Qiu, Jalna Meens, Keira Pereira, Ella Hyatt, Shao Hui Huang, Wei Xu, Trevor Pugh, Laurie Ailles. Patient-derived xenografts of oral squamous cell carcinoma: A model system for the identification of prognostic molecular signatures and for preclinical assessment of targeted therapies. [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 B08.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".