Abstract 3501: A genome-wide screen for determinants of radioresistance in head and neck cancer
Bibliographic record
Abstract
Abstract Introduction: Head and neck cancer (HNC) is the seventh most common cancer worldwide with over 1 million new cases diagnosed each year. Radioresistance is a major cause of both treatment failure and poor prognosis, with locoregional recurrence after radiotherapy occurring in up to 50% of HNC patients. Despite the discovery of individual genes implicated in the ability of HNC cells to develop radioresistance, a systematic evaluation remains to be undertaken. This project is the first to conduct genome-wide CRISPR screens for regulators of radioresistance in HNC, with the aim to identify phenotype-driving genes and pathways. Experimental Procedures: Genome-wide negative selection screens were conducted on HNC cell lines using the Toronto KnockOut (TKO) CRISPR library. Monoclonal Cas9 cell lines were derived from UT-SCC-42A (42A), FaDu, and PE/CA-PJ41 (clone D2) with Cas9 editing efficiencies >80%. Library-transduced cells were treated with the minimum dose of radiation resulting in cessation of cell growth (10 Gy for 42A and FaDu; 8 Gy for PE/CA-PJ41). Genomic DNA from the resultant radioresistant populations was extracted and sequenced on the Illumina NextSeq 500. MAGeCK analysis of read counts identified gene targets of significantly depleted gRNAs after irradiation. Genes that correlated with overall survival (OS) in the TCGA Pan-Cancer database were selected for further investigation. The top candidate genes in the determination of radioresponse were validated using cell proliferation and clonogenic assays. Effects of loss-of-function on cell migration were assessed using scratch wound and transwell migration assays. Results: 117 putative radioresistance genes were identified in the 42A cell line screen; the top ranked hits were MMP14, CD44, CALR, and HHLA1. RNA expression levels of these genes had a significant correlation with OS of radiation-treated HNC patients in the TCGA Pan-Cancer Atlas. Loss-of-function of the candidate genes was confirmed to increase radiosensitivity through live cell imaging in 42A and PJ41 cells, which were further corroborated with clonogenic assays. Downregulation of at least one gene impaired the migration of 42A and PJ41 cells, suggesting a role in cellular invasion and migration. Conclusion: This study will contribute to a deeper understanding of mechanisms of radioresistance in HNC, which continues to be a leading cause of mortality in HNC patients. Future pathway elucidation through transcriptome analysis and functional characterization may reveal additional therapeutic targets that can improve the outcome for HNC patients treated with radiation. Citation Format: Jacqueline H. Law, Pierre-Antoine Bissey, Isabella Kojundzic, Kenneth W. Yip, Fei-Fei Liu. A genome-wide screen for determinants of radioresistance in head and neck cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3501.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".