key: cord-0572789-yrhftsmw
authors: He, Bing; Garmire, Lana X.
title: ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs
date: 2021-09-14
journal: nan
DOI: nan
sha: b5538e71f40c5d920d250e7017a3ae2ff60cdb82
doc_id: 572789
cord_uid: yrhftsmw

Intercellular heterogeneity is a major obstacle to successful precision medicine. Single-cell RNA sequencing (scRNA-seq) technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potentials for precision medicine are yet to be reached. Towards this, we propose A Single-cell Guided pipeline to Aid Repurposing of Drugs (ASGARD). To precisely address the intercellular heterogeneity within each patient, ASGARD defines a novel drug score predicting drugs for multiple cell clusters. We tested ASGARD on three independent datasets, including advanced metastatic breast cancer, acute lymphoblastic leukemia, and coronavirus disease 2019 (COVID-19). On single-drug therapy, ASGARD shows significantly better average accuracy (AUC=0.95) compared to two other single-cell pipelines (AUC 0.69 and 0.57) and two other bulk-cell-based drug repurposing methods (AUC 0.80 and 0.75). The top-ranked drugs, such as fulvestrant and neratinib for breast cancer, tretinoin and vorinostat for leukemia, and enalapril for severe COVID19, are either approved by FDA or in clinical trials treating corresponding diseases. In conclusion, ASGARD is a promising pipeline guided by single-cell RNA-seq data, for repurposing drugs towards precision medicine. ASGARD is free for academic use at https://github.com/lanagarmire/ASGARD.

Heterogeneity, or more specifically the diverse cell populations within the diseased tissue, is the main cause of treatment failure for many complex diseases, such as cancers 1 , Alzheimer's disease 2 , stroke 3 , and coronavirus disease 2019 (COVID- 19) 4 , etc., as well as a major obstacle to successful precision medicine [5] [6] [7] . Recent significant advances of single-cell technologies, especially the single-cell RNA sequencing (scRNA-seq) technology, have enabled the analysis of intercellular heterogeneity at a very fine resolution 8, 9 and helped us to have many breakthroughs in understanding the disease mechanisms 10 , such as breast cancer 11 , liver cancer 12 and COVID-19 13 . However, its full potentials for precision medicine have not been fulfilled 14, 15 .

Drug repurposing (also known as drug reposition, reprofiling, or re-tasking) is a strategy to identify new uses of a drug outside the scope of its original medical approval or investigation 16 .

So far, very few drug repurposing methods have been developed to utilize the highly valuable information residing in scRNA-seq data. The pipeline by Alakwaa identifies significantly differentiated genes (DEGs) for a specific group of cells, then predicts candidate drugs for DEGs using the Connectivity Map Linked User Environment (CLUE) platform, followed by prioritizing these drugs using a comprehensive ranking score system 17 . This pipeline identified didanosine as a potential treatment for COVID-19 using scRNA-seq data 17 . Another pipeline by Guo et al. uses a simple combination of Seurat 18 , a tool for scRNA-seq analysis, and CLUE to identify 281 FDA-approved drugs that have the potential to be effective for treating COVID-19 19 . In general, the above pipelines predict drugs for each individual cell cluster within the patient. However, in heterogeneous diseases caused by multiple types of cells, efficient drugs should be able to address multiple diseased cell clusers 20 . Neither of these above-mentioned pipelines is capable of predicting drugs for multiple cell clusters, limiting their utility in the era of precision medicine.

Here we propose A Single-cell Guided pipeline to Aid Repurposing of Drugs (ASGARD) to overcome the issue above. ASGARD defines a novel drug score to predict drugs for multiple diseased cell clusters within each patient. We applied ASGARD on several scRNA-seq datasets, including those from Patient-Derived Xenografts (PDXs) models of advanced metastatic breast cancers and from Pre-T acute lymphoblastic leukemia patients. The performance of ASGARD on single drugs is both more accurate and more robust compared to the two other pipelines mentioned earlier. Additionally, with the on-going worldwide COVID-19 pandemic, we applied ASGARD to scRNA-seq data from severe COVID-19 patients and predict potential therapies to reduce deaths of severe COVID-19 patients.

We obtained scRNA-seq datasets from the Gene Expression Omnibus (GEO) database.

Epithelial cells from Patient-Derived Xenografts (PDXs) models of 2 patients with advanced metastatic breast cancer and adult human breast epithelial cells from 3 healthy women are from GEO with accession numbers GSE123926 and GSE113197, respectively. ScRNA-seq data of pediatric bone marrow mononuclear cells (PBMMC) from 2 Pre-T acute lymphoblastic leukemia patients and 3 healthy controls are from GEO with accession number GSE132509. ScRNA-seq data of cells from bronchoalveolar lavage fluid (BALF) of 15 severe COVID-19 patients (4 deceased and 11 cured) are from GEO with accession numbers GSE145926 and GSE158055.

ASGARD accepts processed scRNA-seq data from the Seurat package 18 . In this study, genes identified in fewer than 3 cells are removed from the dataset. We used the same criteria as their original studies to filter cells 11, 13, 21 . Epithelial cells from breast cancer PDXs and healthy breast tissues with fewer than 200 unique genes are removed from the dataset. PBMC cells from leukemia patients and healthy controls with fewer than 200 unique genes are removed from the dataset. BALF cells from COVID-19 patients with fewer than 200 unique genes or more than 6000 unique genes or have a proportion of mitochondrial genes larger than 10% are removed from the dataset 13 . We used cell cycle marker genes and linear transformation to scale the expression of each gene and remove the effects of the cell cycle on gene expressions.

ASGARD suggests using functions from Seurat for cell pairwise correspondences. In this study, gene counts for each cell were divided by the total counts for that cell and multiplied by a scaling factor (default is set to 10000). The count matrix was then transformed by log 2(count+1) in R. To identify gene variance across cells, we firstly fitted a line to the relationship of log(variance) and log(mean) using local polynomial regression (loess). Then we standardized the feature values using the observed mean and expected variance (given by the fitted line).

Gene variance was then calculated on the standardized values. In this study, we used the 2,000 genes with the highest standardized variance for downstream analysis. Then we identified the K-nearest neighbors (KNNs) between disease and normal cells, based on the L2-normalized canonical correlation vectors (CCV). Finally, we built up the cell pairwise correspondences by identifying mutual nearest neighbors 18 .

We applied principal component analysis (PCA) from Seurat on the scaled data to perform the linear dimensional reduction. Then we used a graph-based clustering approach 18 . In this approach, we firstly constructed a KNN graph based on the euclidean distance in PCA space and refined the edge weights between any two cell pairs using Jaccard similarity. Then we applied the Louvain algorithm of modularity optimization to iteratively group cell pairs together.

We further ran non-linear dimensional reduction (UMAP) to place similar cells within the graph-based clusters determined above together in low-dimensional space. To annotate clusters of cells, we ran an automatic annotation of single cells based on similarity to the referenced single-cell panel using the SingleR package 22 . We used the dominant cell type (>50% cells) as the cell type of the cluster.

ASGARD supports multiple methods for differential gene analysis, including Limma 23 , Seurat (Wilcoxon Rank Sum test) 18 , DESeq2 24 , and edgeR 25 . The differentially expressed gene list in disease is transformed into a gene rank list. ASGARD uses 21,304 drugs/compounds with response gene expression profiles in 98 cell lines from the LINCS L1000 project 26 . A differential gene expression list in response to drug treatment is also transformed into a gene rank list.

ASGARD further identifies potential candidate drugs that yield reversed gene expression patterns from that in the diseased vs. normal cells, using the DrInsight package 27 (version: 0.1.1). Specifically, it identifies consistently differentially expressed genes, which are up-regulated in cells from diseased tissue but down-regulated in cells with drug treatment, or down-regulated in cells from diseased tissue but up-regulated in cells with drug treatment, to calculate the outlier-sum (OS) statistic 28 . The OS statistic represents the reversed differential gene pattern by the drug treatment. The Kolmogorov-Smirnov test (K-S test) is then applied to the OS statistic, to show the significance level of one drug treatment relative to the background of all other drugs in the dataset. The reference drug dataset contains gene rank lists of 591,697 drug/compound treatments from the LINCS L1000 data, as mentioned above. The Benjamini-Hochberg (BH) false discovery rate (FDR) is used to adjust P-values from the K-S test to avoid false significance due to multiple hypothesis testing 29 .

ASGARD defines a novel drug score (Formula 1) to evaluate the drug efficacy across multiple single-cell clusters. The drug score estimates drug efficacy using the cell type proportion, the significance of reversed differential gene expression pattern (FDR), and the ratio of reversed significantly deregulated genes over disease-related (or selected) single-cell clusters. The drug score is estimated by the following formula:

In this formula, is a particular single-cell cluster, represents all disease-related (or selected) single-cell clusters, represents cellular proportion of cluster in all diseased cells, ) represents the significance of reversed differential gene pattern in cluster by drug − 10 treatment, represents the ratio of reversed disease-related genes by drug is the number of cells in cluster . is the drug FDR (significance of reversed ( ) differential gene pattern) for cluster . is the number of significantly ( ) deregulated genes in cluster , while is the number of significantly ( ) deregulated genes in cluster that can be reversed by the drug.

Besides the drug score, ASGARD further provides a Fisher's combined P-value 30 ASGARD uses the drug score for drug selection. Drugs with higher value of drug score are supposed to have better therapeutic effects than those with lower value.

We use the receiver operating characteristic curves (ROCs) and the areas under the ROC curves (AUCs) to compare the performance of ASGARD with those of the other two pipelines, as well as bulk methods. Since these pipelines/ methods report both drugs and compounds, we let the ASGARD report both drugs and compounds in the comparisons with other pipelines/methods. For the performance estimation of ASGARD with different methods of differential gene analysis, we let the ASGARD only report drugs. ROCs and AUCs are calculated for each pipeline using the pROC package 31 . True positive datasets in ROC and AUC estimation of single-drug are taken from FDA-approved drugs for the corresponding disease, as well as compounds and drugs in advanced clinical trials or have been proved effective in animal models. To assess the robustness of the three pipelines on different sizes of single-cell populations, simulation data are generated by randomly drawing the same number of disease and normal cells from GSE123926 and GSE113197 using the Bootstrapping method in R 32 . To assess the robustness of the three pipelines on different similarities of single-cell populations, simulation data are generated by adjusting differential gene expression levels from 20% to 90% of original differential levels of the single-cell cluster, based on GSE123926 and GSE113197. To assess the robustness of the three pipelines on unbalanced single-cell populations, simulation data are generated by randomly drawing 5000 cells with diseased cells proportion ranging from 20% to 90%. The true positive datasets, results of all the five methods, 

Using scRNA-seq data, ASGARD repurposes drugs for disease by fully accounting for the cellular heterogeneity of patients ( Figure 1 , Formula 1 in method). In ASGARD, every cell cluster in the diseased sample is paired to that in the normal (or control) sample, according to "anchor" genes that are consistently expressed between diseased and normal cells. It then identifies consistently differentially expressed genes (P-value < 0.05) between the paired diseased and normal clusters in the scRNA-seq data. These individual clusters can be optionally annotated to specific cell types. To identify drugs for each single cluster (cell type), then ASGARD uses these consistently differentially expressed genes as inputs to identify drugs that can significantly (single-cluster FDR < 0.05) reverse their expression levels in the L1000 drug response dataset 26 . To identify drugs for multiple clusters, ASGARD defines a novel drug score (Formula 1 in method) to evaluate the drug efficacy across multiple cell clusters selected by the user. The drug score estimates drug efficacy using the cell type proportion, the significance of the reversal of the differential gene expression pattern (single-cluster FDR) by the drug treatment in each selected cell cluster , and the ratio of significantly deregulated genes (adjust P-value<0.05) that can be reversed by the drug treatment in each selected cell cluster.

Finally, ASGARD uses the drug score to rank and select drugs for the disease.

To evaluate the power of drug score, we compared ASGARD with traditional bulk-cell based repurposing methods and single-cell based repurposing methods using four independent scRNA-seq datasets, including one advanced metastatic breast cancer dataset 11 , one acute lymphoblastic leukemia dataset 33 , and two coronavirus disease 2019 (COVID-19) datasets 13, 34 (see Methods).

Before comparing ASGARD to bulk-cell based repurposing methods, we first determined the default differential expression method in ASGARD. For this, we compared several representative differential expression methods: Limma 23 , Seurat (Wilcoxon Rank Sum test) 18 To compare ASGARD with those drug repurposing methods using bulk RNA-Seq samples, we summarized scRNA-seq data into pseudo-bulk RNA-Seq data. We then applied bulk methods CLUE 35 and DrInsight 27 on the pseudo-bulk RNA-Seq query data and compared their results with ASGARD on predicting both drugs and compounds ( Figure 2B ). We took the same scRNA-seq data from the same three datasets above. Since CLUE and DrInsight predict both Figure 2B ). In summary, by paying attention to heterogeneity at single-cell levels, ASGARD shows much better drug repurposing predictability than methods that rely on bulk samples.

We also compared single drug prediction using ASGARD with two other pipelines developed by Alakwaa et al. 17, 19 and Guo et al. 17, 19 , which were reported to handle scRNA-Seq data. Note that ASGARD offers more functionalities than those two methods. Alakwaa' and Guo' pipelines can Figure 3C ). These results collectively support the conclusion that ASGARD predicts drugs more accurately than Alakwaa' and Guo'

pipelines.

Additionally, given that sample size, cell population similarity, and proportion of disease cells impact significantly on differential gene analysis 36 Figure 2C ).

Using AUCs, we have shown ASGARD is a robust pipeline, and more powerful than bulk-cell based repurposing methods and current single-cell based repurposing methods. In the following sections, we will further illustrate the results of ASGARD for breast cancer, leukemia and COVID-19, respectively, in a biological way.

We collected scRNA-seq data from 24,741 epithelial cells of advanced metastatic breast cancer We first applied ASGARD for multi-cluster drug repurposing prediction and predicted 11 drugs (FDR<0.05 and overall drug score >0.99 quantiles) for advanced metastatic breast cancer ( Figure 4C , Supplementary Table 1 ). Fostamatinib is the top 1 drug candidate ( Figure 4C ). It is a tyrosine kinase inhibitor medication approved for the treatment of chronic immune thrombocytopenia 37 . Colchicine, the second best candidate, is an alkaloid approved for treating the inflammatory symptoms of familial Mediterranean fever 38 . Both fostamatinib and colchicine have shown antitumor and anti-metastasis effects in animal models of breast cancer 39, 40 .

Moreover, the 4th candidate fulvestrant and 7th candidate neratinib have been approved by the Food and Drug Administration (FDA) for breast cancer treatment 41, 42 .

To explore the potential molecular mechanisms of the top 2 candidates, we next investigated the target genes and pathways of fostamatinib and colchicine across the eight cell clusters ( Figure   4D ). Fostamatinib and colchicine both target all the significant pathways in each cluster.

Fostamatinib and colchicine are complementary in targeting genes of these pathways. Among the 143 target genes from these significant pathways, only 29 target genes are shared by fostamatinib and colchicine ( Figure 4D ). The fostamatinib and colchicine also show biologically synergistic targeting of multiple genes on the same significant pathways. For example, fostamatinib inhibits Cyclin D1 (CCND1) to produce G1 arrest in the p53 signaling pathway, while colchicine inhibits Cyclin-dependent kinase 1 (CDK1) to produce G2 arrest in the p53 signaling pathway and cell cycle pathway 43 ( Figure 4D ). Additionally, the drug scores of top drug candidates vary from one PDX model to another (Figure 4D ), demonstrating that ASGARD is a forward-looking precision medicine strategy in silico.

We further applied ASGARD to the collected scRNA-seq data from 2 Pre-T ALL patients and 3

normal healthy controls 33 Figure   5E ). All these genes and pathways were previously shown significance in the pathogenesis of Pre-T ALL [45] [46] [47] . The drug target genes and pathways in the T cell clusters explain why ASGARD predicts tretinoin for leukemia, and how tretinoin treats leukemia.

The We identified the differential gene expression profiles of the four cell types, including neutrophil, NK cell, T cell, and monocyte, by comparing decreased severe patients to cured severe ones.

Then we put the differential gene expression profiles to ASGARD to identify drug candidates using the multi-cluster drug score. Among the predicted drugs, rescinnamine (2nd) and enalapril (4th) caught our attention ( Figure 6D , Supplementary Table 3 

In this study, we present A Single-cell Guided pipeline to Aid Repurposing of Drugs (ASGARD).

To evaluate the accuracy of ASGARD in single drug repurposing, we compared ASGARD to other repurposing methods that utilize bulk cell RNA-Seq (CLUE and DrInsight) or single-cell RNA-Seq data (Alakwaa's and Guo's) on a variety of diseases, including breast cancer, leukemia, and COVID-19. ASGARD shows much better performance than all of these methods in predicting drug/compounds (Figure 2, 3, Supplementary Figure 1 ). The performance of ASGARD is also robust across different sizes and proportions of cell populations, as well as differential expression levels (Supplementary Figure 2) . Moreover, we highlight that ASGARD can summarize drug efficiency across multiple selected cell clusters. These important functions are missing in other simple single-cell RNA-Seq drug reposition pipelines of Alakwaa and Guo.

ASGARD achieves drug ranking for the disease/patient, by a novel drug score that evaluates the treatment efficacy across the user selected cell clusters (Formula 1 in Methods). The prediction using the multi-cluster drug score shows a significantly (P-value <0.05, student's t-test) better AUC than the prediction based on individual clusters (Figure 2, 3) . It suggests that targeting an individual cell cluster isn't sufficient for successful drug prediction. Instead, targeting multiple essential diseased cell clusters is a more appropriate strategy for drug prediction. On the other hand, it is not ideal to propose drug repurposing using bulk RNA-seq, which is a mixture of all cells, as done by traditional methods either (eg. CLUE and DrInsight). There exists significant heterogeneity of different cell populations and not all these cells play equal roles in the diseases 54,55 , reflected by different gene expression responses to drug treatment 56 .

ASGARD can distinguish more important cell types from others and repurpose drugs accordingly, explaining why ASGARD has significantly (P-value <0.05, student's t-test) better AUC performance than traditional bulk methods ( Figure 2B) . Moreover, ASGARD also demonstrates variations of drug scores across different patients ( Figure 4C, 5D, and 6D ). This stresses that personalized therapy is necessary for the best therapeutic effect and utilizing single-cell sequencing information may help to achieve that.

We chose breast cancer or leukemia datasets to illustrate the utilities of ASGARD, given the relative abundance of prior knowledge on drugs. Many drugs predicted by ASGARD have been approved by FDA, such as fulvestrant and neratinib for treating breast cancer 41,42 ( Figure 4C ), tretinoin for treating leukemia 44 ( Figure 5D ). Fulvestrant is the 4th candidate predicted by ASGARD for advanced breast cancer. Fulvestrant has achieved licensing as the first line drug for advanced breast cancer in post-menopausal women, and the second line drug for non-operative advanced breast cancer 57 . Neratinib, a protein kinase inhibitor, is the 7th candidate predicted by ASGARD for advanced breast cancer. Neratinib was approved in July 2017 as an extended adjuvant therapy in breast cancer 42 . Recently, a randomized phase III clinical trial of 621 patients from 28 countries showed neratinib significantly improved progression-free survival of patients with advanced breast cancer 58 . Tretinoin, also known as all-trans-retinoic acid (ATRA), is the 1st candidate predicted by ASGARD for leukemia. Tretinoin targets all significant pathways, such as p53 signaling, cell cycle and apoptosis pathways, for each diseased cell cluster in the leukemia patients ( Figure 5E ). These pathways play important roles in the survival of leukemia patients 47 . Consistent with our prediction, tretinoin was approved by the FDA to induce remission in patients with acute leukemia 44 . Tretinoin significantly improves the survival of acute leukemia 59 . Tretinoin with chemotherapy has become the standard treatment for acute leukemia, resulting in cure rates exceeding 80% 60 . The successful prediction of FDA approved drugs supports the reliability of ASGARD.

Beyond these approved cases, ASGARD also predicts novel candidate drugs for breast cancer and leukemia. The fostamatinib and colchicine are the top two candidates for breast cancer ( Figure 4C ). Fostamatinib is a tyrosine kinase inhibitor. Tyrosine kinase inhibitors have been widely used either in single drug treatment or combination therapy for breast cancer 61, 62 .

Colchicine is an alkaloid used in symptomatic pain relief in attacks of gout. Colchicine inhibits proliferation of breast cancer cells and induces their apoptosis 63 . Interestingly, fostamatinib and colchicine show synergistic targeting of multiple genes in the significant pathways associated with breast cancers ( Figure 4D ). For example, fostamatinib inhibits Cyclin D1 (CCND1) to produce G1 (first growth) arrest 64 in the p53 signaling pathway, and colchicine inhibits Cyclin-dependent kinase 1 (CDK1) to produce G2 (second growth) arrest 65 COVID-19 is an ongoing and evolving pandemic, therefore drug knowledge is changing too.

Remdesivir is the only drug approved for COVID-19 right now 72 Figure 6E ). These pathways play important roles in COVID-19 patient severity and survival 75 . It may explain the observed efficiency of enalapril in reducing mortality of COVID-19 patients. On the other hand, rescinnamine was rarely studied for COVID-10. Since rescinnamine targets the same significant pathways as enalapril in COVID-19 ( Figure 6E ), rescinnamine could be an alternative candidate for further investigation.

Altogether, this study shows clear evidence that ASGARD repurposes confident drugs that were approved or in clinical trials for breast cancer, leukemia, and COVID-19, respectively. It also provides new applications for drugs that warrant further clinical studies. In all, ASGARD is a single-cell guided pipeline with significant potential to recommend repurposeful drugs using scRNA-seq data. are identified between diseased and normal cells, either within a cluster or within a cell type.

Using the consistently DE genes as the input, potential drugs that significantly reverse the pattern of DE genes are identified, using the Kolmogorov-Smirnov (K-S) test with

Benjamini-Hochberg (BH) false discovery rate (FDR) adjustment. Next ASGARD estimates and ranks the drug scores for single drugs, by targeting specific cell cluster(s) or all cell clusters. 

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The authors thank Qianhui Huang 

L.X. Garmire conceived the study of and supervised the project. B. He wrote the code and analyzed the data. B. He and L.X. Garmire wrote the manuscript.

The authors declare that they have no competing interests.

ScRNA-Seq data are available in Gene Expression Omnibus (Accession number: GSE123926, GSE113197, GSE132509, GSE158055, and GSE145926). Phase I LINCS L1000 data are available in Gene Expression Omnibus (Accession number: GSE92742). Phase II LINCS L1000 data are available in Gene Expression Omnibus (Accession number GSE70138). ASGARD is available as an R package in Github (https://github.com/lanagarmire/ASGARD). Scripts used in this study are available in Github (https://github.com/lanagarmire/Single-cell-drug-repositioning).