New DDLS Fellow: Qiaoli Wang
The SciLifeLab & Wallenberg National Program for Data-Driven Life Science (DDLS) continues to recruit outstanding early career scientists. Our latest Fellow, Qiaoli Wang (LU), talks about increasing the survivability of pancreatic cancer patients by analyzing population data, biobanks, clinical images, and health records, in order to develop strategies for personalized prevention, individual risk stratification, early detection, tailored treatment and outcome prediction tools., in our latest Q&A-style article. Qiaoli will be joining the DDLS Precision medicine and Diagnostics research area.
Qiaoli received her MD degree and specialization training in Oncology through the Sino-French 7-year Medical Education Program at Wuhan University in China, in cooperation with Université de Lorraine in Nancy, France. She then obtained her PhD from Karolinska Institutet in Stockholm, Sweden, with an exchange study at Harvard T.H. Chan School of Public Health in Boston, US, where she developed advanced data analytical skills in population science and cancer epidemiology. She later joined Dana-Farber Cancer Institute as a Research Fellow and became a Teaching Fellow and Course Director for the Clinical Data Science course in the MMSCI master’s program at Harvard Medical School. At Dana-Farber, Qiaoli applied large population cohorts, randomized trials, clinical biobanks, and imaging data to investigate early pre-diagnostic and prognostic features of gastrointestinal cancers.
How do you think your expertise can contribute to the program?
With the rapid advancement of life science technologies and growing public health awareness, we are entering an era characterized by the generation of vast and diverse datasets. Effectively addressing scientific questions through integrative data analysis has become critically important. Our group is highly dynamic, with a broad range of expertise. We are privileged with the application of interdisciplinary methodologies which combine oncology and epidemiology with multi-omics technologies and machine learning. By leveraging diverse data sources, we also bring strong capabilities in advanced data management and sophisticated statistical modelling using large-scale databases.
Shortly describe your research in an easy to understand way.
Pancreatic cancer is predicted to be the second most common cancer-related deaths in Europe by 2030 and only 8-13% of the patients can survive more than 5 years after diagnosis. Its high death rate is mainly due to late diagnosis of this disease in around 85% of the patients. Our goal is to increase knowledge about pancreatic cancer, developing strategies for personalized prevention, individual risk stratification and early detection, as well as to design tailored treatment and outcome prediction tools for pancreatic cancer. Our lab leverages various integrative approaches for individual risk estimation and biomarker discovery using large-scale population-based cohorts, national registries, biobanks, and electronic healthcare records and clinical images. Through innovative application of various data sources armed with advanced analytical methodologies, we try to explore key contributors to the course of pancreatic cancer development and metastasis. The knowledge will be transferred to more scientific research and development of screening programs and individualized surveillance and ultimately improve the survival of pancreatic cancer.
How do you think the program and interactions with the other DDLS-Fellows will benefit you?
I’m thrilled to be enrolled in this national program and have the opportunity to interact with other DDLS-Fellows. The program gathers so many excellent researchers in Sweden and offers a great environment for collaborations and self-development, as well as various wonderful networking activities. Interactions with other fellows will spark new research ideas and we will learn new technologies and skills from each other. In addition, we will benefit from sharing experiences as young PIs and improve our leadership skills.
Name one thing that people generally do not know about you.
I finished my first, the virtual 125th Boston Marathon in 4h and 49mins.
Where do you see yourself in five years regarding the DDLS aspect?
I am inspired by the dramatic advances in machine learning and exploding of omics during the past years. I deeply believe the data-driven life science is an integrative science with different techniques. In five years, I hope to see myself continuing working on exciting research projects with integrative methodologies in large-scale population science. Meanwhile, educating the next-generation DDLS researchers is also an important perspective.
In one word, describe how you feel about becoming a DDLS-Fellow.
Excited
