Our research group has characterized the preclinical activity of PHY906 as a modulator of cytotoxicity of anticancer agents as well as a cytoprotective agent of chemotherapy. In this project, we are addressing an important hypothesis, as suggested by our preliminary pre-clinical results, that part of the mechanism for the biological activity of PHY906 involves the modulation of certain key immuoregulatory processes and metabolic functions of tumor and normal tissues, which will be reflected in the immunokine/growth factor and metabolomic profiles from plasma of patients under treatment. We hope to identify the relevant potential biomarkers that can be used to predict the subset of patients who may benefit from the use of PHY906 using multiplex approach. The proposed studies will also to begin indentify the potential multiple sites of action and bioactive compounds of this herbal medicine. This Project has two specific aims:
Specific Aim 1 will investigate the potential biomarkers in plasma that can be used to correlate the effect of PHY906 with clinical benefit of irinotecan-based chemotherapy, either toxicity or clinical activity, in patients with metastatic colorectal cancer. For these studies, a broad series of biomarker analyses will be conducted, and they include the following: profiles of immunocytokines, chemokines, growth factors;metabolomic profiles including steroid hormones;levels of tumor DNA and mutational load distribution analysis as determined by presence of mutations of K-Ras, B-Raf, or PI3K/Akt from tumor DNA in plasma.
Specific Aim 2 will investigate the bioactive compounds involved in the action of PHY906 on immunological regulatory pathways.
This aim will characterize the chemical profile of PHY906 and their metabolites in each sample taken from patients using LC/MS spectrometry and correlate each plasma's activity against several immunological regulatory signal pathways and DNA repair pathways ex vivo with the goal of identifying bioactive compounds in plasma. The first specific aim may show how PHY906 works from a systems biology point of view, and these studies may also have a general impact on the correlation of those biomarkers with clinical outcomes for other cancer treatments. The second specific aim will confirm our preclinical studies of PHY906, and more importantly, establish new methodologies to identify bioactive candidate compounds for their potential biological actions. This Project is highly and tightly integrated with the other two proposed Projects in this Program, and the information obtained from this work may serve as a novel paradigm for combining Chinese herbal medicine with conventional cancer chemotherapy to treat human cancers.

Public Health Relevance

The proposed study could open a new paradigm for cancer chemotherapy by combining Chinese herbal medicine with conventional anticancer drugs. It will also provide the scientific basis for taking a more comprehensive system biology-based approach in studying Chinese herbal medicine as well as other herbal medicines.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA154295-04
Application #
8727479
Study Section
Special Emphasis Panel (ZCA1-GRB-S)
Project Start
Project End
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
4
Fiscal Year
2014
Total Cost
$519,643
Indirect Cost
$41,434
Name
Yale University
Department
Type
DUNS #
043207562
City
New Haven
State
CT
Country
United States
Zip Code
06520
Chen, Mengjie; Ren, Zhao; Zhao, Hongyu et al. (2016) Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model. J Am Stat Assoc 111:394-406
Li, Cong; Yang, Can; Hather, Greg et al. (2016) Efficient Drug-Pathway Association Analysis via Integrative Penalized Matrix Decomposition. IEEE/ACM Trans Comput Biol Bioinform 13:531-40
Lin, Zhixiang; Li, Mingfeng; Sestan, Nenad et al. (2016) A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data. Stat Appl Genet Mol Biol 15:139-50
Zhu, Ruoqing; Zhao, Qing; Zhao, Hongyu et al. (2016) Integrating multidimensional omics data for cancer outcome. Biostatistics 17:605-18
Wang, Tao; Chen, Mengjie; Zhao, Hongyu (2016) Estimating DNA methylation levels by joint modeling of multiple methylation profiles from microarray data. Biometrics 72:354-63
Ryslik, Gregory A; Cheng, Yuwei; Modis, Yorgo et al. (2016) Leveraging protein quaternary structure to identify oncogenic driver mutations. BMC Bioinformatics 17:137
Huang, Xiu; Stern, David F; Zhao, Hongyu (2016) Transcriptional Profiles from Paired Normal Samples Offer Complementary Information on Cancer Patient Survival--Evidence from TCGA Pan-Cancer Data. Sci Rep 6:20567
Hu, Yiming; Zhao, Hongyu (2016) CCor: A whole genome network-based similarity measure between two genes. Biometrics 72:1216-1225
Wang, Ying; Lam, Wing; Chen, Shao-Ru et al. (2016) Tylophorine Analog DCB-3503 Inhibited Cyclin D1 Translation through Allosteric Regulation of Heat Shock Cognate Protein 70. Sci Rep 6:32832
Tsou, Lun K; Lara-Tejero, María; RoseFigura, Jordan et al. (2016) Antibacterial Flavonoids from Medicinal Plants Covalently Inactivate Type III Protein Secretion Substrates. J Am Chem Soc 138:2209-18

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