Abstract 407: Novel targets in metastatic HNSCC
Bibliographic record
Abstract
Abstract Head and neck carcinoma (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. The idea behind this selective targeting is based on discovering molecular pathways that became essential in metastatic cells, and then exploiting this vulnerability through targeted agents. 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. To complement the functional screens data, we performed targeted sequencing of the most commonly altered genes in HNSCC as reported by the TCGA profiled gene expression in the HNSCC cell lines cultured under normoxia and hypoxia using Illumina microarrays. These analyses led to the discovery of genes that are differentially essential in metastatic cells as well as in cells exposed to hypoxic conditions. Utilizing CRISPR technology, we assembled a library of guide RNAs targeting the discovered hits and are currently validating the top identified genes using both in vitro and in vivo systems. To validate hits that are essential in metastatic cells in vivo, we engineered selected “matched” pairs of HNSCC cell lines to express CAS9 protein upon induction with doxycycline. The cells are then transduced with the library of guides and injected subcutaneously into mice; tumors from control and doxycycline treated animals are compared. The difference in genes that are essential for growth of tumors seeded by primary tumor derived and metastasis derived HNSCC cell lines is then assessed in a quantitative manner. Citation Format: Maria Kondratyev, Aleksandra Pesic, Troy Ketela, Azin Sayad, Stephano Marastoni, Jason Mofat, Carl Virtanen, Natalie Stickle, Reider Grenman, Brad Wouters. Novel targets in metastatic HNSCC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 407.
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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 | 0.000 | 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) | 0.002 | 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".