Abstract 1033: Patient-derived xenografts for prognostication and personalized treatment for head and neck squamous cell carcinoma
Bibliographic record
Abstract
Abstract Overall outcomes for HPV-negative head and neck squamous cell carcinoma (HNSCC) remain poor with 5-year overall survival rates of 50-60%. Oral squamous cell carcinoma (OSCC), the most common subtype of HPV-negative HNSCC, is typically treated with surgery, and clinico-pathologic features are used to identify patients in need of adjuvant therapies such as radiation therapy (RT), or radiation plus concurrent chemotherapy (CRT). It is clear from the rate of loco-regional or distant failures that more accurate methods of risk stratification would greatly improve outcomes for OSCC patients. This requires biomarkers to identify patients that will benefit from adjuvant RT or CRT but currently there are no validated molecular biomarkers that have been clinically implemented for the personalized treatment of OSCC. In addition to biomarkers for better risk stratification, there is also a need for novel therapeutic strategies leading to improved outcomes. Recently patient-derived xenografts (PDXs) have been shown to faithfully recapitulate human tumor biology and predict drug responses, supporting their relevance as preclinical models for new drug development. Upon subcutaneous implantation of HNSCC specimens into NOD/SCID/IL2Rγ-/- mice, 161 of 243 samples (66%) successfully formed patient-derived xenografts (PDX). Using univariable and multivariable analyses, the ability to form a PDX correlated significantly with adverse clinical outcomes, and specifically, patients with palpable PDX-formation within 8 weeks experienced particularly poor outcomes (hazard ratio for overall survival = 3.0). A cohort of engrafting and non-engrafting samples were sequenced using a targeted sequencing panel designed for both mutational and copy number alteration detection. The overall frequency of somatic genomic alterations detected was similar to The Cancer Genome Atlas cohort and interestingly, successful engraftment correlated to amplification of the CCND1 gene. Twelve PDX models were treated with the CDK4/6 inhibitor, abemaciclib; 5 of 6 models with CCND1 amplifications and/or CDKN2A mutations responded to abemaciclib treatment, whereas only 1 of 6 models lacking these alterations responded. These results demonstrate the potential of using PDX models to identify novel targeted therapies for HNSCC patients who have the poorest outcomes. In the future, PDX avatars could also be exploited to individualize treatment for patients at high risk of relapse following definitive treatment. Citation Format: Christina Karamboulas, Jeffrey P. Bruce, Andrew J. Hope, Jalna Meens, Shao Hui Huang, Jie Su, Fei-Fei Liu, Trevor J. Pugh, Scott V. Bratman, Wei Xu, Laurie E. Ailles. Patient-derived xenografts for prognostication and personalized treatment for head and neck squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1033.
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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 | 0.001 |
| 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) | 0.002 | 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".