Peng Jiang Ph.D.
Dr. Jiang's research is focused on developing integrative frameworks that leverage the big-data resource in public domains to identify regulators of cancer therapy resistance. A general challenge in cancer research is the lack of data to understand the clinical efficacy of each treatment, while new drugs with distinct mechanisms of action get approved every year. To fill in the gap, we are developing statistical and machine learning infrastructures that transfer knowledge from a vast amount of previous data cohorts to the study of new cancer biology problems.
1) big data integration, 2) cancer genomics, 3) machine learning, 4) biostatistics, 5) precision medicine, 6) cancer immunotherapy
For most anticancer drugs, we do not have precise rules for response prediction and mechanistic understanding of therapy resistance. Moreover, new drugs with distinct mechanisms of action get approved every year. But it takes many years to accumulate clinical data, which creates a significant gap between our current ability and the goal of cancer precision medicine. Our vision is that the data integration approach, leveraging the ever-growing volume of data from public domains, is a cost-effective solution to fill in the gap. Many statistical and machine learning methods can achieve knowledge transfer from previous data to the study of a new problem. Therefore, the general theme of our research is to develop infrastructures that transfer knowledge from big data to inform the cancer therapy decision.
The specific focus of our current work is how to utilize both genomics and imaging data to identify new regulators in cancer immune evasion. In the first direction, we study how to predict immune evasion regulators by leveraging the vast amount of functional genomics datasets and the spatial transcriptomics data produced by recent technological progress. In the second direction, we develop machine learning infrastructures for feature selection in imaging data to understand how spatial interaction among different cells can determine the anticancer immune response. Our deliverables are infrastructures that enable the users to leverage the vast amount of data resource in public domains to find immune evasion mechanisms in their own clinical studies.
View Dr. Jiang's Google Scholar page.
Selected Recent Publications
- Nature Medicine. 24(10): 1550-1558, 2018. [ Journal Article ]
Genome-scale signatures of gene interaction from compound screens predict clinical efficacy of targeted cancer therapies.Cell Systems. 6(3): 343-354.e5, 2018. [ Journal Article ]
- Proc Natl Acad Sci U S A. 112(25): 7731-7736, 2015. [ Journal Article ]
Computational assessment of the cooperativity between RNA binding proteins and microRNAs in transcript decay.PLoS Computational Biology. 9(5): e1003075, 2013. [ Journal Article ]
- Bioinformatics. 26(8): 1105-1111, 2010. [ Journal Article ]
Dr. Peng Jiang started his research program at the National Cancer Institute (NCI) in July 2019.
Before joining NCI, he finished his postdoctoral training at the Dana Farber Cancer Institute and Harvard University. During his postdoctoral research, Peng developed computational frameworks that repurposed public domain data to identify biomarkers and regulators of cancer immunotherapy resistance. Notably, his computational model TIDE revealed that cancer cells could utilize the self-protection strategy of cytotoxic lymphocytes to resist lymphocyte killing under immune checkpoint blockade. Dr. Peng finished his Ph.D. at the Lewis Sigler Genomics Institute at Princeton University, and his undergraduate study with the highest honors at the Department of Computer Science at Tsinghua University (GPA rank 1st in his year).
He is a recipient of the NCI K99 Pathway to Independence Award and the Scholar-In-Training Award of the American Association of Cancer Research.