Abstract IA02: Utilizing patient-derived xenografts for prognostication and biomarker discovery
Bibliographic record
Abstract
Abstract Overall outcomes for human papilloma virus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) remain poor with 5-year overall survival rates of 50-60%. Treatment often includes surgery, and clinicopathologic 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 locoregional or distant failures that more accurate methods of risk stratification would greatly improve outcomes for HPV-negative HNSCC patients. This requires biomarkers to identify patients who 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 HNSCC. 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 HPV-negative HNSCC specimens into NOD/SCID/IL2Rγ-/- mice, 161 of 243 samples (66%) successfully formed PDXs. 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 nonengrafting 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. Ten HPV-negative 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 4 models lacking these alterations responded. We also carried out a PDX clinical trial on 20 models using the PI3Kα inhibitor, BYL719. Interestingly, while previous studies using in vitro cell line studies and limited numbers of xenograft models derived from various tumor types have suggested PIK3CA hotspot mutations predict for response to PI3K inhibitors, we found that BYL719 was almost globally tumoristatic, regardless of PIK3CA mutational status. Our results demonstrate the potential of using PDX models to individualize treatment for patients at high risk of relapse following definitive treatment, to identify novel therapies and predictive biomarkers, and to interrogate drug resistance mechanisms. Citation Format: Christina Karamboulas, Jeffrey P. Bruce, Kara M. Ruicci, Wei Xu, Anthony C. Nichols, Laurie Ailles. Utilizing patient-derived xenografts for prognostication and biomarker discovery [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; 2019 Apr 29-30; Austin, TX. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_2):Abstract nr IA02.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".