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The Tumor Therapy Landscape of Synthetic Lethality

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The Tumor Therapy Landscape of Synthetic Lethality ARTICLE https://doi.org/10.1038/s41467-021-21544-2 OPEN The tumor therapy landscape of synthetic lethality Biyu Zhang1,4, Chen Tang1,4, Yanli Yao2,4, Xiaohan Chen1, Chi Zhou 1, Zhiting Wei1, Feiyang Xing1, Lan Chen2, ✉ ✉ Xiang Cai3, Zhiyuan Zhang2, Shuyang Sun 2 & Qi Liu 1 Synthetic lethality is emerging as an important cancer therapeutic paradigm, while the comprehensive selective treatment opportunities for various tumors have not yet been explored. We develop the Synthetic Lethality Knowledge Graph (SLKG), presenting the tumor therapy landscape of synthetic lethality (SL) and synthetic dosage lethality (SDL). SLKG 1234567890():,; integrates the large-scale entity of different tumors, drugs and drug targets by exploring a comprehensive set of SL and SDL pairs. The overall therapy landscape is prioritized to identify the best repurposable drug candidates and drug combinations with literature supports, in vitro pharmacologic evidence or clinical trial records. Finally, cladribine, an FDA-approved multiple sclerosis treatment drug, is selected and identified as a repurposable drug for treating melanoma with CDKN2A mutation by in vitro validation, serving as a demonstrating SLKG utility example for novel tumor therapy discovery. Collectively, SLKG forms the com- putational basis to uncover cancer-specific susceptibilities and therapy strategies based on the principle of synthetic lethality. 1 Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China. 2 Department of Oral and Maxillofacial-Head Neck Oncology, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 3 Alibaba Cloud, Hangzhou, China. 4These authors ✉ contributed equally: Biyu Zhang, Chen Tang, Yanli Yao. email: [email protected]; [email protected] NATURE COMMUNICATIONS | (2021) 12:1275 | https://doi.org/10.1038/s41467-021-21544-2 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-21544-2 ynthetic lethality occurs when the inhibition of two genes is yet been explored. To this end, we developed the Synthetic Slethal while the inhibition of each single gene is not. It can Lethality Knowledge Graph (SLKG, https://www.slkg.net/), which be harnessed to selectively treat cancer by identifying presents the comprehensive tumor therapy landscape of SL and inactive genes in a given cancer and targeting their synthetic SDL from a drug repositioning perspective. SLKG integrates the lethal partners. For most cancer mutations caused by a loss-of- large-scale entity of different tumors, drugs, and drug targets by function, there are no targeted therapies available, and SL pro- exploring a comprehensive set of 19,987 SL and 3039 SDL pairs. vides additional opportunities. The best-studied example of tar- By curation with a well-defined drug repositioning scoring geted therapies exploiting the synthetic lethality (SL) principle is schema, 155 and 88 best drug repositioning results were obtained the use of poly-ADP ribose polymerase (PARP) inhibitors in according to the integrative analysis of SL or SDL with tumor and breast and ovarian cancer harboring mutations in BReast CAncer drug annotations, respectively, and most are supported by the gene (BRCA). In this case, BRCA and PARP play vital roles in literature, in vitro pharmacologic evidence or clinical trial DNA homologous recombination repair in response to DNA records. In addition, 38 drug combinations were identified by damage, leading to the occurrence of tumorigenesis. Treatment of mining the knowledge graph for tumor treatments with max- BRCA-deficient tumors with PARP inhibitors generally selec- imized therapeutic effects and reduced side effects. Finally, cla- tively kills the cancer cells in breast and ovarian cancer1. dribine, which is a FDA-approved multiple sclerosis treatment Basically, the general concept of “synthetic lethality” can be drug, was selected and identified as a repurposable drug for divided into two categories: (1) SL, which occurs between the treating melanoma with CDKN2A mutation by a comprehensive loss-of-function mutations for tumor suppressor genes (TSGs) in vitro validation, serving as a demonstrating experimental and their partner gene. This is a genetic interaction where com- protocol to utilize SLKG for novel tumor therapy discovery. bination of two mutations or more leads to cell death, whereas a Taking together, SLKG forms the computational basis for single mutation in any of the genes does not. and (2) SDL, which exploiting the tumor therapy landscape and repurposing known occurs between the oncogene and its partner gene2. This is a drugs based on the principle of SL to uncover cancer-specific genetic interaction where an overexpression of oncogene (Gene susceptibilities. B) combined with the under-expression of its partner gene (Gene A) kills the tumor cell. (Fig. 1). Overall, SL and SDL provide Results important guidance for uncovering cancer-specific susceptibilities Uncover the tumor therapy landscape on the basis of SL/SDL. and identifying cancer-specific treatments. The general framework of our study is outlined in Fig. 2.We Many largescale geneknockout studies using CRISPR screening obtained SL and SDL gene pairs with comprehensive annotations and RNAi screening such as Project Score3 and Project DRIVE4 by searching the literature and databases spanning comprehen- have presented a comprehensive catalog of essential genes related sive data sources, such as SynLethDB5, DRIVE DATA PORTAL4, to certain phenotypes. Various methods have been proposed to DepMap6, and Daisy7 (Fig. 2). The relationships between tumors identify SL and SDL interactions based on these data, however, and mutant genes were organized through the DisGeNET8, the selective treatment opportunities for various tumors have not COSMIC9, ONGene10, and TSGene11 databases. By integrating a Synthetic Lethality (SL) Intervention Gene A Gene A Gene B Gene B TSG Single mutation in tumor cell Gene A Viable Lethal Gene B b Synthetic Dosage Lethality (SDL) Normal cell Intervention Gene A Gene A Gene B Gene B Oncogene Over-expression in tumor cell Fig. 1 The principle of SL and SDL. For gene A and B, if only one gene has a mutation or an overexpression in the tumor cell, the tumor cell is still alive. However, the pharmacological intervention of the partner gene will result in SL interaction in tumor cell which has a loss-of-function mutation of the tumor suppressor gene (TSG) (a). In addition, the pharmacological intervention of the partner gene will result in SDL interaction in tumor cell which has a gain-of- function mutation or an overexpression of the oncogene (b). The red star denotes a mutation. The thicker arrow denotes an overexpression. The cross line denotes a pharmacological intervention. 2 NATURE COMMUNICATIONS | (2021) 12:1275 | https://doi.org/10.1038/s41467-021-21544-2 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-21544-2 ARTICLE CESSING O ENTITY EPR PR Mutation TSG/OncoGene Gene A Gene Cancer Repositioning L Drug D S / L Known S Indications Target Gene B Drug Gene Inhibition BUILDING OF SLKG Best candidates Others Ranking 1.0 0.6 0.8 1.0 0.8 0.6 CancerScore 0.4 0.2 0.4 0.2 SLScore SCORING SCHEMA 0.0 0.0 0.2 0.4 0.6 0.8 1.0 DrugScore Wilcoxon rank Wild type sum test cell line 14 P = 0.011 13 12 11 10 AND AUC of Sunitinib Mutation type 9 cell line BRCA1_mut BRCA1_wt VALIDATION IDENTIFICATION Literature supports in Top 20 In-vitro pharmacologic validation Clinical trials validation Target Gene_B SL Drug Drug1 0.6 Drug1 Target Gene_C Mutation Remove Drug Cancers Antagonism Gene_A 0.7 Drug2 Target Gene_D Drug2 0.6 Drug3 Target Gene_E 0.3 IDENTIFICATION Integer DRUG COMBINATION Programming (IP) Fig. 2 The general framework of the SLKG and the following analysis. The study comprises five steps: (1) Entity preprocessing: the three types of entities (gene, cancer type, and drug) are collected and analyzed to exploit the tumor therapy landscape. (2) Building of SLKG: the SLKG comprises three types of entities and four types of relationships, which forms the computational basis from a drug repositioning perspective. (3) Scoring schema: a well-defined drug repositioning scoring schema by integrating three core scoring functions were developed to obtain the best repurposable drug candidates. (4) Identification and validation: the literature supports and in-vitro pharmacologic evidence were identified and validated in the top repurposable drug candidates. (5) Drug combination identification: a computational model was developed for identifying drug combinations in a weighted bipartite graph network by an IP algorithm. the TTD12, DrugBank13, and DGIdb14 databases, we further pairs, respectively. Because there is no obvious distinction organized the relationships of drugs, target genes, and known between gene A and gene B in the SL or SDL pairs, we considered indications into a knowledge graph. Based on these three types of two reciprocal scenarios during the mapping, i.e., gene A maps entities, we mapped mutants and target genes to SL or SDL gene the mutant gene and gene B maps the target gene, and vice versa NATURE COMMUNICATIONS | (2021) 12:1275 | https://doi.org/10.1038/s41467-021-21544-2 | www.nature.com/naturecommunications 3 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-21544-2
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