Head and neck squamous cell carcinoma (HNSCC), the sixth most common cancer

type worldwide, accounts for more than 630,000 new cases and 350,000 deaths

annually. Drug-resistance and tumor recurrence are the most challenging problems

in head and neck cancer treatment. It is hypothesized that a very small fraction

of stem-like cells within HNSCC tumor, called cancer stem cells (CSCs), is

responsible for tumor initiation, progression, resistance and recurrence. It has also

been shown that IL-6 secreted by head and neck tumor-associated endothelial cells

(ECs) enhances the survival, self-renewal and tumorigenic potential of head and

neck CSCs. In this study we will use a mathematical multi-scale model which operates

at the intracellular, molecular, and tissue level to investigate the impacts of

EC-secreted IL-6 signaling on the crosstalk between tumor cells and ECs during

tumor growth. This model will be calibrated by using the experimental in vivo

data.

Eventually the model will be modified to explore the responses of head and neck

cancer cells to combination therapy involving Tocilizumab (an anti-IL-6R antibody)

and Cisplatin (the most frequently used chemotherapy for head and neck

cancer). The model will be able to predict the final proportion of CSCs in response

to endothelial cell-secreted IL-6 and drug therapies. The model will be validated

by directly comparing the experimental treatment data and the model predictions.

This could potentially provide a condition under which we could control enlargement

of the head and neck CSC pool and tumor recurrence. It may also suggest

the best bounds for Cisplatin and/or Tocilizumab dose and frequency to be tested

in the clinical trial.

Background
In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends.
Methodology
We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data.
Conclusions
We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.