Abstract B10: Antibacterial defense and metastatic progression—lessons from head and neck cancer
Bibliographic record
Abstract
Abstract HNSCC is the 6th most common malignancy in the world. The prognosis is favorable during early stages; however, the disease is rarely diagnosed early due to the lack of symptoms. Most HNSCC patients present with metastatic disease for which the survival rates remain low. Hypoxia serves as a bad prognostic factor in HNSCC correlating with worse survival and resistance to radiotherapy. We aimed to discover novel targets in metastatic HNSCC utilizing a unique collection of matched sets of cell lines derived from primary tumors and their respective metastatic sites. We carried out expression profiling across the lines to reveal potential common changes in gene expression between the cells derived from primary and metastatic sites in each patient as well as between normoxia and hypoxia. This analysis revealed significant changes in gene expression between the described conditions. Interestingly, beta defensin 2 was one of the genes that came up as overexpressed in metastasis as well as in cells cultured in hypoxia. Beta defensins are small cationic peptides that belong to the innate immune system and exhibit antimicrobial and antiviral activities. Few studies reported their abnormal expression patterns in various cancers including HNSCC. While various novel anticancer therapies are currently being developed, early diagnosis remains one of the biggest challenges in cancer research. A discovery of soluble serum factors that can reliably detect the presence of primary or metastatic disease would serve as an extremely valuable tool in defining diagnosis and prognosis, predicting the response to treatment, and monitoring disease progression. We hypothesized the beta defensin 2 can serve as a serum biomarker of hypoxia and metastasis in HNSCC patients. Utilizing a commercial ELISA kit developed to detect and quantify levels of beta-defensin in human sera, we demonstrated that media collected from cell lines derived from metastases contained higher levels of beta-defensin compared to the cell lines derived from the primary tumors. Moreover, sera from HNSCC patients showed higher levels of beta defensin compared to the normal controls. As a next step, we tested sera samples from 40 HNSCC patients, of whom 20 had lymph node metastasis and 20 did not. While these data are still under analysis, preliminary results suggest that higher concentrations of beta-defensin correlate with the presence of lymph node metastasis in HNSCC patients. Utilizing two large chemical libraries that together contain about 4,000 FDA-approved drugs, we performed high-throughput screening of 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. Interestingly, many of the metastasis-specific drugs were antibiotics. Together with the findings described above, these data clearly suggest a connection between patient microbiome and the metastatic process in HNSCC. Citation Format: Maria Kondratyev, Aleksandra Pesic, Carl Virtanen, Marianne Koritzinsky, Brad Wouters. Antibacterial defense and metastatic progression—lessons from head and neck cancer [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr B10.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".