NUS Graduate School for Integrative Sciences and Engineering

XU Jianxin

Research Areas
Brief Description of Research
1. Cancer Research
2. Immunology
3. Population/Evolutionary Genetics
4. Systems Biology


Area 1.
Cancer immunotherapy has been explored more than one century. It is recently cancer immunotherapy has shown breakthrough, and nowadays become the hottest topic not only in academic research but also clinical oncology, as can be seen in recent ASCO and EMSO. For instance, PD1/PDL1 immune checkpoint inhibitor has shown at least 20% response rate to many kinds of cancers. CAR-T applying the latest trans genetic technology, achieve remarkable response in lymphoma. With immunotherapy, cancer cure is no longer a dream even for terminal stage cancer patients.

However, most research focuses on microbiology, which is fundamental but short of systemic view. Note that knowing a leaf is not equal to knowing the functionality of a tree, and likewise, knowing a tree is not equal to knowing the forest.

Our immune system is a super complex system, involves dozens of subgroups of immune cells such as that of T, NK, macrophage, DC, etc. as well as numerous cytokines. They work as a system to coordinate and fight against cancer cells.

System biology offers a useful methodology. A systemic and mathematical model can be established that describe the interactions between cancer cells and immune cells. Clinical data can be used to determine model parameters, thus the systemic model can be further customized for individual patient when personal clinical data are available. The systemic model can be used to predict the efficacy of cancer immunotherapy under different treatment protocols.


Area 2.

In cancer therapy, the biggest challenging is the drug resistance, no matter for chemo drug  or targeted drug. Recently, it has been made clear that the drug resistance to targeted drug is due to new genetic mutations. Further study shows that cancer cells have the ability to evolve genetically to adapt to a hostile environment. A tumor always generates new mutation genes, though in a very low rate everyday. Those new mutation genes may make the cancer cells less sensitive or resistant to certain drugs. Therefore, we have at least two types of cancer cells, drug-sensitive and drug-resistant. At the beginning of cancer treatment, drug-sensitive cells are dominant, hence tumor responses to the drug. However, the drug-resistant cancer cells, though very few and most likely mutated from drug-sensitive cancer cells, may take the opportunity to grow. For example, the most well known T790M mutation from the primary EGFR+ cancer cells, will make the 1st and 2nd generation TKI drugs ineffective. Clinical evidence shows that 50% of EGFR+ patients will eventually evolve T790M. To make things worse, current drug dose scheme is based on maximum tolerance dose(MTD),and one dosage applies to all patients regardless of actual drug concentration in blood. With MTD, the tumor micro environment become more hostile to the drug-sensitive cells, and more friendly to the drug-resistant cells. This is something we try to avoid. As the individual variability is concerned, we know the drug concentration could easily reach 20 time difference between patients. Some fine tuning in drug administration is necessary. These two issues can be addressed together by 1) feedback: applying the latest technology including radiological images such as CT or PETCT to assess tumor growth condition, liquid biopsy to assess the mutation loads of and the ratio between drug-sensitive and drug-resistant cancer cells, and some tumor markers, and 2) a system biological model: that can predict the evolutionary process of cancer cells of both sensitive and resistant. The ultimate objective of feedback and prognostic model is to guide patient in dose administration, conduct finer drug tuning in a fixed interval, reduce the selection pressure for drug-sensitive cells, in the sequel keep the dominance, and left little room for drug-resistant cells to evolve.