In today’s world, scientists of different fields need to work together – for their own success as well as for patients’ well-being. PhD programs, which provide an existing network, can facilitate this. Students in Zurich tell us what they think the future holds for them.
Photos & Video: Raphael Zubler
The role of big data analysis for cancer immunology
Cheng Guang Wu originally studied pharmacology, then evolved into stem cell regeneration in cancer research before receiving a scholarship from the Chinese government to complete his studies in Zurich.
“I’m doing big data analysis – bioinformatics work in cancer immunology. Most patients with late-stage lung cancer tumors will experience stage IV disease, malignant pleural effusion. There is only a three to six month survival rate. Through data and image analysis I can say how long the patient will probably live. Some patients can live for five years, some patients three months. But why?”
Wu’s goal is to find which type of immune cells could influence a patient’s survival. While Wu focuses primarily on lung cancer, the protocols he has established can be applied to other cancers as well.
One of the issues Wu has with his research is access to samples, particularly for rare cancers. “We need coordination among hospitals for sample exchange,” Wu says about the need for doctors and clinicians to collaborate with experts in data analysis. He cites his home country China as an example, where doctors see many more patients, which makes it possible to have more access to samples, but less time to do the analysis.
“You have data scientists, you have biologists, you have doctors; these three groups could work more closely together, but you need to talk with each other, to translate the data and create a common language. This will be the trend. You cannot be independent of data interpretation and data analysis in medicine today.”
- The statements by Siemens Healthineers customers described herein are based on results that were achieved in the customer’s unique setting. Since there is no “typical” hospital and many variables exist (e.g., hospital size, case mix, level of IT adoption) there can be no guarantee that other customers will achieve the same results.