Abstract PO-034: Optimizing chemotherapeutic regimen through metabolic interrogation to maximize radiosensitizing effects in HNSCC
Bibliographic record
Abstract
Abstract Background: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer globally. Cisplatin (CDDP) has been widely utilized as a chemotherapeutic agent and is often combined with ionizing radiation (IR) to improve overall survival of patients with advanced stage HNSCC and other malignancies. Current treatment algorithms have limited efficacy due to empiric approaches and the lack of analysis to optimize CDDP treatment effectiveness and reduce normal tissue toxicity. We utilized metabolic interrogation as a tool to optimize CDDP treatment regimens in conjunction with IR to maximize radiosensitizing effects in a pre-clinical HNSCC mouse model. Materials/Methods: We performed flow cytometry to optimize the combination regiment of CDDP and IR. The surviving fraction of cells was determined through clonogenic assays. Acute lactate production was monitored as a biomarker for metabolic perturbation induced by CDDP and IR. We compared a metabolism-based CDDP radiosensitization regimen with a standard clinical regimen in a pre-clinical HNSCC mouse model. The metabolism-based CDDP regimen involved administering CDDP (2.5 mg/kg, IV) one hour prior to irradiation (2 Gy) on day 1, followed by another irradiation (2Gy) alone on day 3, while standard clinical regimen involved administering irradiation (2 Gy) alone one day 1 and day 3, followed by CDDP (2.5 mg/kg, IV) treatment on day 4. Both regimens were repeated in the second week. Tumor size was measured using a caliper method twice a week and median survival time was recorded. Results: CDDP caused cell cycle arrest in S and G2/M phases from 24-72 hours. Although there was no significant cell apoptosis in the Sub-G1 phase, CDDP induced senescence, leading to a decreased clone formation on clonogenic survival. Both CDDP and IR triggered a dose and time-dependent, transient decrease in cellular lactate levels, which may serve as sensitive biomarker for predicting treatment response. The metabolism-based CDDP regimen resulted in delayed tumor growth and prolonged median survival time when compared to control standard clinical group. The metabolism-based CDDP regimen outperformed the standard regimen in terms of tumor growth inhibition and prolonged median survival time (p<0.005). Conclusions: Both CDDP and IR triggered acute transient perturbations in cellular lactate levels, which may predict CDDP effects and improve radiosensitization. The real-time metabolic interrogation of acute lactate changes reflects treatment effects on DNA damage, cell death and tumor growth delay. To optimize CDDP regimens prior to clinical translation, we developed an adaptive and robust preclinical platform to deliver the maximum tolerable dose and minimize toxicity. A metabolism-based CDDP regimen can significantly enhance radiosensitization, improve therapeutic effectiveness, and reduce normal tissue toxicity by potentially decreasing the CDDP dose. Minimizing normal tissue toxicity by reducing the CDDP dose and maximizing radiosensitizing effects may have a significant impact on patient survival. Citation Format: Yunyun Chen, Wangjie Yu, Ying Henderson, Vlad Sandulache, Stephen Y. Lai. Optimizing chemotherapeutic regimen through metabolic interrogation to maximize radiosensitizing effects in HNSCC [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-034.
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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 | 0.001 |
| 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".