Systems Pharmacology-Based Discovery of Natural Products for Precision Oncology Through Targeting Cancer Mutated Genes

Abstract

Massive cancer genomics data have facilitated the rapid revolution of a novel oncology drug discovery paradigm through targeting clinically relevant driver genes or mutations for the development of precision oncology. Natural products with polypharmacological profiles have been demonstrated as promising agents for the development of novel cancer therapies. In this study, we developed an integrated systems pharmacology framework that facilitated identifying potential natural products that target mutated genes across 15 cancer types or subtypes in the realm of precision medicine. High performance was achieved for our systems pharmacology framework. In case studies, we computationally identified novel anticancer indications for several US Food and Drug Administration-approved or clinically investigational natural products (e.g., resveratrol, quercetin, genistein, and fisetin) through targeting significantly mutated genes in multiple cancer types. In summary, this study provides a powerful tool for the development of molecularly targeted cancer therapies through targeting the clinically actionable alterations by exploiting the systems pharmacology of natural products.

Figure 1

Diagram of an integrated systems pharmacology framework for prioritizing new anticancer indications by mapping the polypharmacology of natural products into significantly mutated genes (SMGs) in cancers across 15 cancer types or subtypes. The abbreviations of 15 major cancer types/subtypes are: acute myeloid leukemia (LAML), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), colon and rectal adenocarcinoma (COADREAD), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), prostate adenocarcinoma (PRAD), skin cutaneous melanoma (SKCM), stomach adeno‐carcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC). The SMGs are derived from various large‐scale cancer genome projects as described in a previous study.28 The drug–target interaction network for natural products were built via integration data from several commonly used chemoinformatics and bioinformatics databases (see Methods).

Figure 2

A global bipartite drug–target interaction network for natural products. This network connects 409 FDA‐approved or clinically investigational natural products annotated in the DrugBank database and 2,210 known drug target proteins, including proteins encoded by 154 significantly mutated genes (SMGs) and 2,056 non‐SMGs in cancers.

Figure 3

The heat maps show the predicted indications for 45 FDA‐approved or clinically investigational natural products against 15 cancer types/subtypes. The predicted Z‐scores (a) and q‐values (b) for 45 natural products against 15 cancer types/subtypes. The area in gray represents the nonavailable value since no significantly mutated genes are overlapped with the known targets of a specific natural product. The area in red represents the natural product having the high Z‐score and the low q value across specific cancer indications. The abbreviations of 15 major cancer types/subtypes are: acute myeloid leukemia (LAML), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), colon and rectal adenocarcinoma (COADREAD), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), prostate adenocarcinoma (PRAD), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC).

Figure 4

The reconstructed networks for four typical natural products. The networks display the predicted indications for four typical natural products, resveratrol (a), quercetin (b), genistein (c), and fisetin (d), against 15 cancer types/subtypes and their corresponding targets of the significantly mutated genes (SMGs) in multiple cancers. The gray lines denote SMGs in a specific cancer. The dotted red lines denote the predicted indications. The thickness (value) of a dotted red line is proportional to the Z‐score (see Methods). The abbreviations of 15 major cancer types/subtypes are: acute myeloid leukemia (LAML), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), colon and rectal adenocarcinoma (COADREAD), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), ovarian serous cystadenocarcinoma (OV), prostate adenocarcinoma (PRAD), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC).