Peng Jiang, Ph.D.

Peng Jiang, Ph.D.
NIH Stadtman Investigator

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.

Areas of Expertise

1) big data integration, 2) cancer genomics, 3) machine learning, 4) biostatistics, 5) precision medicine, 6) cancer immunotherapy

Contact Info

Peng Jiang, Ph.D.
Center for Cancer Research
National Cancer Institute
Building 10, Room 6N119
Bethesda, MD, 20892
Ph: 240-858-3799

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 resources in public domains to find immune evasion mechanisms in their own clinical studies.

A description of my previous research before joining NCI is available at

NIH Scientific Focus Areas:
Cancer Biology, Computational Biology, Genetics and Genomics, Immunology, Systems Biology
  1. Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, Li Z, Traugh N, Bu X, Li B, Liu J, Freeman GJ, Brown MA, Wucherpfennig KW, Liu XS.
    Nature Medicine. 24(10): 1550-1558, 2018. [ Journal Article ]
  2. Jiang P, Lee W, Li X, Johnson C, Liu JS, Brown M, Aster JC, Liu XS.
    Cell Systems. 6(3): 343-354.e5, 2018. [ Journal Article ]
  3. Jiang P, Freedman ML, Liu JS, Liu XS
    Proc Natl Acad Sci U S A. 112(25): 7731-7736, 2015. [ Journal Article ]
  4. Jiang P, Singh M, Coller HA
    PLoS Computational Biology. 9(5): e1003075, 2013. [ Journal Article ]
  5. Jiang P, Singh M
    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 (mentor: Xiaole Liu, co-mentor: Kai Wucherpfennig). 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 (advisor: Mona Singh, co-advisor: Hilary Coller), 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.

Name Position
Alex Lee Postbaccalaureate Fellow (CRTA)
BeiBei Ru Ph.D. Postdoctoral Fellow (Visiting)
Trang Vu Ph.D. Postdoctoral Fellow (iCURE)
Yu Zhang Predoctoral Visiting Fellow (Graduate Student)

Cancer Therapy Response and Resistance

TIDE (Tumor Immune Dysfunction and Exclusion)

TIDE is an infrastructure with several modules to assist cancer immunotherapy applications and research (Jiang et al., Nature Medicine, 2018). The first component is a gene expression biomarker to predict the clinical response to immune checkpoint blockade. The input is a gene expression profile of a cancer sample measured by RNA-Seq on genome-scale or Nano-String on a gene panel. The output is a likelihood score of therapy response or resistance. The second component provides gene query functions for the gene activity associations with T-cell dysfunction and immunotherapy response. The input is a gene name. The output is the associations between gene activity and cancer immune evasion potentials computed from a vast amount of datasets from human clinical studies or pre-clinical models.

CARE (Computational Analysis of REsistance)

CARE is a software developed to identify genome-scale biomarkers of targeted therapy response using compound screen data (Jiang et al., Cell Systems 2018). For each drug, its CARE score vector can serve as a pattern of good responder. Patients will be predicted as responders or non-responders depending on the Pearson correlation between the gene expression profile of cancer samples and CARE score vector. For each gene, the CARE score indicates the association between its molecular alteration and drug efficacy. A positive score indicates a higher expression value (or presence of mutation) to be associated with drug response, while a negative score indicates drug resistance. You can search the results on CCLE, CTRP and CTRP datasets here. Please use the auto-completed name when available.


Biological Network Analysis

NEST (Network Essentiality Scoring Tool)

NEST is designed to predict the gene essentiality based on protein interaction network and gene expression or epigenetic profiles (Jiang et al., Genome Bio 2015). NEST can also be used to enhance the quality of CRISPR or shRNA screen results.

RABIT (Regression Analysis with Background InTegration)

RABIT is a very efficient feature selection algorithm (Jiang et al., PNAS 2015). We applied RABIT to find gene expression regulators in shaping tumor-specific gene expression patterns. The gene expression regulator could be a transcription factor or an RNA binding protein. Besides our application here, you can use RABIT as a general algorithm for feature selection.

SPICi (Speed and Performance In ClusterIng)

SPICi is a fast local network clustering algorithm (Jiang et al., Bioinformatics 2010). SPICi runs in time O(Vlog V +E) and space O(E), where V and E are the numbers of vertices and edges in the network. It also has a state-of-the-art performance with respect to the quality of the clusters it uncovers.


Combinatorial Regulation

CCAT (Combinatorial Code Analysis Tool)

CCAT is a software package for predicting genome-wide co-binding between biological regulators such as transcription factors (TF) (Jiang et al., Nucleic Acids Res 2014) or RNA binding proteins (RBP) (Jiang et al., PLoS Comput Biol 2013). The CCAT package also includes accompanying tools to cluster similar Position weight matrix (PWM) of different TFs or RBPs into clusters, and search PWMs on multiple genome alignments for conserved motif instances.

07/07/2019: Peng Jiang formally started his group at NCI.

10/01/2019: Welcome to our first computational postdoc, Dr. Beibei Ru

03/01/2020: Welcome to our first graduate student Yu Zhang, visiting us for one year

07/01/2020: Welcome to our first post-bac fellow Alex Lee

09/02/2020: Welcome to our first wet-lab postdoc Dr. Trang Vu