Abstract 2311: Identification of therapeutic targets in HNSCC
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, they can be exploited to target metastatic cells selectively, sparing toxicity in normal tissues. We used functional genomic technologies to identify new 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 HNSCC tumors and their respective metastatic sites/recurrences. 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. To complement the data from the functional screens and to further characterize our cell line collection, we utilized several high through put approaches including mutational analysis, proteomics and gene expression profiling. Moreover, we performed chemical screens using libraries of over 4000 FDA approved drugs with the aim to combine our functional genomic data with the results from the drug screens to discover drug/gene “hit” combinations that would provide a basis for development of novel anti-cancer therapies.While HNSCC derived cell lines represent a valuable tool to study this disease, their biology might not always accurately represent the biology of the tumors they were derived from. Therefore, we looked at the expression of the components of several key pathways that we discovered using the in vitro characterization in a patient material from over HNSCC surgical samples constructed into a TMA. Five of the hits belonged to the Notch signalling pathway and 4 others came from the proteomic analysis as they showed higher levels of surface expression in metastatic cells and/or under hypoxic conditions. Interestingly, higher proportion of patients with late stages of HNSCC belonged to the high Jag2 and Hey1 populations as compared to patients with low stages of HNSCC that mostly expressed low or intermediate levels of these proteins. This data is in line with our observations that Jag2 and Hey1 expression is elevated in cells derived from metastatic sites and is low in cells derived from primary tumors. Proportion of cells with high CD66 and TRAIL proteins was also higher among the late stage tumors suggesting that these biomarkers are of interest for further investigation. We discovered several molecular pathways that are important in metastatic/hypoxic HNSCC. We are investigating our ability to target these pathways using FDA approved drugs in order to achieve durable cures. Citation Format: Maria Kondratyev, Aleksandra Pesic, Anna Dvorkin-Sheva, Troy Ketela, Natalie Stickle, Laurie Aiiles, Reidar Grenman, Marianne Koritzinsky, Brad Wouters. Identification of therapeutic targets in HNSCC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2311.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".