Abstract 1112: Feasibility of real-time personalized patient-derived Tumorgraft® models for guiding systemic treatment in recurrent and/or metastatic head and neck cancer patients
Bibliographic record
Abstract
Abstract Background: Recurrent/metastatic head and neck squamous cell carcinoma (RMHNSCC) is associated with poor quality of life and a poor prognosis with a median overall survival of 7 months. With limited systemic therapy options, there is an urgent need to identify predictive biomarkers for drug response. Patient derived xenografts (PDXs) have been demonstrated to preserve the histological features, heterogeneity, epigenetic and genetic profiles of the original tumor and appear to correlate well with objective tumor response in patients. This study aimed to test the feasibility of personalized PDXs for guiding systemic treatment in RMHNSCC. Methods: Eligible patients were consented and a fresh biopsy/surgical sample was obtained and implanted into mice (5-15 mice/patient) to establish TumorGraft® models. Engrafted tumors were excised and propagated into second generation models for drug testing with up to 4 drugs selected by the treating medical oncologist. Tumor dimensions were measured twice weekly and were reported as one of: progressive disease, stable disease (SD), partial response (PR), or complete response (CR) based on the percentage of tumor regression. Patients alive and suitable for chemotherapy were prescribed the regimen(s) observed to have the greatest response rate in their TumorGraft® models. Patients' responses to therapy were then observed. Results: Nine of 10 eligible patients had samples successfully engrafted with an average time to engraftment of 89.2 days (± SD 41.7 days). Drug testing was not performed on 5 patients as the patient either died or was not suitable for treatment. The remaining 4 patient TumorGraft® models underwent drug testing with the average time from engraftment completion to drug testing completion being 83.8 ± 59.9 days. Two of these patients then received xenograft-guided therapy. In one patient, paclitaxel demonstrated a partial response in the Tumorgraft®, however the patient's tumor did not respond and their clinical status rapidly deteriorated leading to death. In the second patient, cetuximab and paclitaxel demonstrated the best response in the TumorGraft® model. This patient had a sequential partial response to each drug including a 17 month response to cetuximab before progressing and transitioning to nivolumab. The patient remains alive with stable disease 3.5 years after diagnosis of recurrent disease. Conclusions: The main limitation of Tumorgraft® testing for this population is the time delay to obtain Tumorgraft® results. Despite this, Tumorgraft® testing is feasible for a subset of patients and appears to correlate with clinical benefit. Citation Format: Morgan D. Black, Allison Berger, Nicole Pinto, John Yoo, Kevin Fung, Danielle MacNeil, David A. Palma, Joseph S. Mymryk, Sara Kuruvilla, John W. Barrett, Suzanne Richter, Angela Davies, Eric W. Winquist, Anthony C. Nichols. Feasibility of real-time personalized patient-derived Tumorgraft® models for guiding systemic treatment in recurrent and/or metastatic head and neck cancer patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1112.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".