Abstract 507: Repurposing of FDA approved drugs for treatment of metastatic 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 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 HNSCC tumors and their respective metastatic sites or recurrences. While extensive efforts are being made by both industry and academia to develop novel anti-cancer drugs, this process is still very slow and expensive. Drug repurposing is becoming increasingly popular due to a reduced risk to patients and lower costs of drug development compared to “de novo” discovery. We utilized 2 large chemical libraries (Selleck and Prestwick) that together contain about 4000 drugs approved by FDA and similar agencies worldwide. The libraries were screened against the HNSCC lines described above in order to discover new drugs targeting head and neck cancer including drugs that target selectively metastatic cells compared to their primary tumor counterparts. We accomplished screening of 30 HNSCC lines for all of which we have also obtained the functional genomic, mutational and gene expression data. We 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. For this purpose, we are looking at correlations between the drugs and the shRNAs that have similar pattern of effects across the 30 cell lines. To complement the functional screens data, we performed targeted sequencing of the most commonly altered genes in HNSCC as reported by the TCGA. This data will help understand which pathways are affected by the drugs we select to investigate. Out of the 30 lines, we have 13 sets of “matched” lines that belong to the same patients, coming from primary tumors, metastatic sites or recurrences. As a first step of the analysis, we looked at drugs that affected cells derived from metastases but not those from the respective primary tumors. Interestingly, many of the metastasis-specific drugs were antibiotics. We selected 10 of the drugs for further validation; dose response-based viability assays and growth curves experiments confirmed that 5 of the drugs were selectively affecting survival and proliferation of the metastatic cell lines; the mechanism of this effect are currently being investigated. Citation Format: Maria Kondratyev, Aleksandra Pesic, Anna Dvorkin-Sheva, Troy Ketela, Jason Moffat, Marianne Koritzinsky, Brad Wouters. Repurposing of FDA approved drugs for treatment of metastatic HNSCC [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 507.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".