Abstract 3054: Cell surface targets in head and neck cancer
Bibliographic record
Abstract
Abstract HNSCC is 6th most common malignancy in the world. Despite advances in diagnosis and treatment, the survival rates remain low due in large part to metastatic disease. The underlying biology associated with metastatic disease and poor outcome in HNSCC remains unclear. Importantly, metastatic cells acquire new properties that permit them to invade surrounding tissues and seed metastasis at distant sites. While these acquired properties contribute to aggressiveness of metastatic cancer and interfere with success of therapies, they can also potentially be exploited to target metastatic cells selectively, sparing toxicity in normal tissues. We used functional genomic technologies to identify new potential therapeutic targets for advanced disease in HNSCC. These targets were identified by conducting whole genome shRNA screens in matched sets of cell lines derived from primary tumors and their respective metastatic sites, with the goal of identifying genes that become essential for cell survival only following metastasis. Since hypoxia is an important attribute of aggressive and therapy resistant subpopulations of HNSCC tumor cells, we also aimed to identify genes that became essential when cells are exposed to hypoxia. We are particularly interested in the identification of contextual synthetic lethal oncogenes expressed on the cell surface, as those are easily targetable by therapeutic antibodies. To identify these targets, we performed high-throughput flow cytometry screening that enables evaluation of 370 validated cell surface antibodies. Cell surface targets differentially expressed in metastatic lines included CECAM and CCR6 that were previously reported to be implicated in metastasis and tumor progression as well as Thy1, a known marker of stem cells involved in regulation of cell adhesion. Cell surface targets induced under hypoxic conditions across the cell lines included CA9, an enzyme that is known to regulate pH in hypoxic cells and be associated with tumor progression, as well as CD338, CD264 and CD312, that were previously associated with stemness in a few models of cancer. Interestingly, the described proteins were also found to be differentially essential in the shRNA screens, highlighting their functional importance in tumor progression and hypoxia survival. We are currently investigating the role of these proteins in HNSCC metastasis utilizing our unique collection of matched pairs of HNSCC lines from multiple patients. Moreover, we are utilizing our pipeline of patient derived HNSCC xenografts to test the effect of knocking down the described genes in patient tumors. We are also testing the expression of selected hits in histological sections of patient tumorsthe 400-patient TMA by immunohistochemistry looking for correlation with tumor grade, aggressiveness, levels of hypoxia as well as presence/absence of metastasis in the patient. Citation Format: Maria Kondratyev, Aleksandra Pesic, Azin Sayad, Troy Ketela, Natalie Stickle, Carl Virtanen, Jason Moffat, Laurie Ailles, Marianne Koritzinsky, Brad Wouters. Cell surface targets in head and neck cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3054.
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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) | 0.003 | 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".