OSCR

Patient-derived organoids predict personalized drug response and reveal alternative therapeutic options in glioblastoma.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

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  1. [1] § Results › Transcriptomic analysis reveals potential mechanisms underlying both innate and acquired TMZ resistance in GBOs with MGMT methylation ↔ Code/RNA_analysis.R, lines 276–352 · score 0.69 · Gene Ontology, pathway enrichment, fold changes, DEGs, KEGG, GO
  2. [2] § Results › Transcriptomic analysis reveals potential mechanisms underlying both innate and acquired TMZ resistance in GBOs with MGMT methylation ↔ Code/RNA_analysis.R, lines 276–352 · score 0.56 · fold changes, expressed genes, enriched, DEGs, enrichment, pathways
  3. [3] § Results › Transcriptomic analysis reveals potential mechanisms underlying both innate and acquired TMZ resistance in GBOs with MGMT methylation ↔ Code/RNA_analysis.R, lines 51–104 · score 0.54 · mismatch repair, KEGG pathway, MMR, gene

Paper

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The authors' code

R · 352 lines · 13 KB · no license · 3 matches

  1. # rna analysis
  3. library(org.Hs.eg.db)
  4. library(clusterProfiler)
  5. library(GSEABase)
  6. library(GSVA)
  7. library(edgeR)
  8. library(tidyverse)
  9. library(ggthemes)
  10. library(ggpubr)
  11. library(dplyr)
  12. library(ComplexHeatmap)
  13. library(limma)
  14. library(pheatmap)
  15. library(RColorBrewer)
  16. library(enrichplot)
  19. # RNA expression correlation
  21. ## TMM normalize
  22. anno = read.delim("anno.txt", row.names = 1)
  24. ### load data
  25. counts <- read.delim("RNA_readcount.txt", sep="\t")
  26. colnames(counts)[3:14] <- colnames(counts)[3:14] %>% gsub("\\.","-",.)
  28. counts_dedup <- counts[,c(2:14)]
  29. counts_dedup <- counts_dedup[!duplicated(counts_dedup$Gene_id), ]
  31. ### normalize
  32. group=anno$Type
  33. count_input <- counts_dedup[,c(anno$RNA_ID)]
  34. row.names(count_input) <- counts_dedup$Gene_id
  36. y <- DGEList(counts=count_input, group=group)
  38. keep <- filterByExpr(y, group=group)
  39. y <- y[keep, , keep.lib.sizes=FALSE]
  40. y <- calcNormFactors(y)
  41. bcv <- 0.4 # well-controlled experiments are 0.4 for human data
  43. et <- exactTest(y, dispersion=bcv^2)
  44. top <- topTags(et)
  46. tmm_log <- cpm(y, log=TRUE)
  48. exp.df <- t(tmm_log)
  49. exp.df <- merge(anno, exp.df, by="row.names")
  51. ## draw
  52. pdf("RNA_cor_per_patient.pdf", 16, 4)
  53. exp.df %>% filter(Sample %in% c("GBO-001", "GBO-002", "GBO-003", "GBO-005", "GBO-008")) %>% column_to_rownames("RNA_ID") %>%
  54. select(Type, Patient, colnames(exp.df)[23:21125]) %>% pivot_longer(colnames(exp.df)[23:21125], names_to = "Gene", values_to = "Expression") %>%
  55. pivot_wider(names_from = Type , values_from = Expression ) %>%
  56. ggplot(aes(x=Tissue, y=Organoid)) +
  57. stat_density_2d(aes(fill = stat(level)), alpha=0.9, geom = "polygon", show.legend = T , color="black", size = 0.2, bins = 9) +
  58. scale_fill_distiller(palette = "Spectral") +
  59. theme_base() + stat_cor(method = "spearman") +
  60. facet_wrap(~ Patient, scales = "free", ncol = 5) + labs(x = "mRNA expression (Tissue)", y = "mRNA expression (Organoid)")
  61. dev.off()
  64. # KEGG pathway GSVA
  66. ## load data
  67. tpm_final <- read.delim("RNA_TPM.txt")
  68. colnames(tpm_final)[3:15] <- colnames(tpm_final)[3:15] %>% gsub("\\.","-",.)
  69. tpm_final.df <- tpm_final[,c(2:15)]
  70. tpm_final.df <- tpm_final.df[!duplicated(tpm_final.df$Gene_id), ]
  72. tpm_final_mat = as.matrix(tpm_final.df %>% remove_rownames() %>% column_to_rownames("Gene_id"))
  74. kegg_geneSets = getGmt("c2.cp.kegg_medicus.v2023.2.Hs.symbols.gmt")
  76. ## run GSVA
  77. tpm_final_mat=as.matrix(tpm_final.df %>% remove_rownames() %>% column_to_rownames("Gene_id"))
  79. kegg_gsva=gsva(tpm_final_mat, kegg_geneSets, method="gsva", mx.diff=TRUE, verbose=FALSE, parallel.sz=1)
  81. kegg_gsva=data.frame(t(kegg_gsva), check.names = F, stringsAsFactors = F)
  83. gbo_rna_names = c("GBO-001-OR3-R", "GBO-002-OR5-R", "GBO-003-OR4-R", "GBO-005-OR4-R", "GBO-006-OR7-R", "GBO-008-OR-p-3-R", "GBO-019-OR2-R", "GBO-015-OR-R")
  85. ## draw
  87. pdf("KEGG_MMR_barplot_8GBO.pdf", 5, 5)
  89. kegg_gsva[gbo_rna_names,c("KEGG_MEDICUS_REFERENCE_MISMATCH_REPAIR", "KEGG_MEDICUS_REFERENCE_HOMOLOGOUS_RECOMBINATION")] %>%
  90. rownames_to_column("Sample") %>%
  91. pivot_longer(c("KEGG_MEDICUS_REFERENCE_MISMATCH_REPAIR", "KEGG_MEDICUS_REFERENCE_HOMOLOGOUS_RECOMBINATION"),
  92. names_to = "KEGG", values_to = "GSVA") %>%
  93. mutate(KEGG = gsub("KEGG_MEDICUS_REFERENCE_MISMATCH_REPAIR","Mismatch Repair", KEGG)) %>%
  94. filter(KEGG == "Mismatch Repair") %>%
  95. mutate(Sample = substr(Sample, 1, 7)) %>%
  96. ggplot(aes(x = Sample, y = GSVA)) +
  97. geom_bar(position=position_dodge(), stat="identity", color="black") +
  98. theme_base(base_size = 15) +
  99. theme(axis.text.x=element_text(color="black",angle = 45, hjust=1))+
  100. labs(x = NULL, y = "GSVA score", subtitle = "KEGG Mismatch Repair") +
  101. geom_hline(yintercept=0, size=0.1) +
  102. theme(panel.grid.major.y = element_line(color = 'gray80', linewidth = 0.5))
  104. dev.off()
  107. # enrichment analysis
  109. ## filter gene
  110. tpm_final.df %>% select("Gene_id", "GBO-005-OR4-R", "GBO-019-OR2-R") %>% remove_rownames() %>%
  111. column_to_rownames("Gene_id") %>%
  112. apply(2, function(x) {x > 5}) %>% as.data.frame %>% rowSums() %>%
  113. as.data.frame %>% rename(exp_thres = ".") %>%
  114. filter(exp_thres > 0) %>% row.names() -> gbo005_over5_gene_id
  116. ## select gene
  117. tpm_final.df %>% select("Gene_id", "GBO-005-OR4-R", "GBO-019-OR2-R") %>% filter(Gene_id %in% gbo005_over5_gene_id) %>%
  118. mutate(GBO005 = log2(`GBO-005-OR4-R` + 1)) %>% mutate(GBO005R = log2(`GBO-019-OR2-R` + 1)) %>%
  119. mutate(FC = GBO005R - GBO005) %>% filter(FC >= 2) %>% arrange(desc(FC)) %>% select(Gene_id) %>% deframe -> GBO5R_fc_over2
  121. ## enrichment analysis
  122. GBO5R_fc_over2_ego <-enrichGO(GBO5R_fc_over2, OrgDb='org.Hs.eg.db', keyType = "SYMBOL", ont = "ALL",
  123. pvalueCutoff = 0.05, pAdjustMethod = "BH",
  124. qvalueCutoff = 0.25, minGSSize = 10, maxGSSize = 500)
  126. ## draw
  127. pdf("GBO-019_FC_ego.dotplot.pdf", 7.5, 7)
  128. dotplot(GBO5R_fc_over2_ego %>% filter(ONTOLOGY == "BP"), showCategory = 15, label_format = 50,
  129. title="GBO-019, Biologial process")
  130. dev.off()
  133. # MGMT mRNA expression
  135. tpm.anno.df <- merge(anno, tpm_final.df %>% remove_rownames() %>% column_to_rownames("Gene_id") %>% t() , by="row.names")
  137. pdf("MGMT_mRNA_barplot.pdf", 4.5, 4)
  138. tpm.anno.df %>% filter(Type == "Organoid") %>% remove_rownames() %>% column_to_rownames("Row.names") %>%
  139. select("Sample", "MGMT") %>%
  140. ggplot(aes(x = Sample, y = MGMT)) +
  141. geom_bar(position=position_dodge(), stat="identity", color="black") +
  142. theme_base(base_size = 15) +
  143. theme(axis.text.x=element_text(color="black",angle = 45, hjust=1))+
  144. geom_hline(yintercept=0, size=0.1) +
  145. labs(x = NULL, y = "TPM", subtitle = "MGMT RNA epxression") +
  146. theme(panel.grid.major.y = element_line(color = 'gray80', linewidth = 0.5))
  147. dev.off()
  150. # VEGFA, VEGFR expression
  152. pdf("VEGFA_VEGFR_tpm.pdf", 7, 4)
  153. tpm_final.df %>% filter(Gene_id %in% c("VEGFA", "FLT1", "KDR", "FLT4")) %>%
  154. pivot_longer(colnames(tpm_final.df)[-1], names_to = "Sample", values_to = "TPM") %>%
  155. mutate(Type = ifelse(grepl("-T-R" , Sample), "Tissue", "Organoid")) %>%
  156. mutate(log2TPM = log2(TPM+1)) %>%
  157. mutate(Gene_id = factor(Gene_id, levels = c("VEGFA", "FLT1", "KDR", "FLT4"))) %>%
  158. ggplot(aes(x=Type, y=log2TPM, fill=Type) ) + theme_base() +
  159. geom_dotplot(binaxis = "y", stackdir = "center", binpositions="all", binwidth = 0.3,color="black") +
  160. facet_grid(~ Gene_id)+ labs(y="log2 TPM", x =NULL) +
  161. theme(axis.text.x=element_text(color="black", angle = 45, hjust=1))
  162. dev.off()
  166. # Read TPM expression data and set gene IDs as row names
  167. GBO_TPM <- read.delim("GBM_GBO_TPM.txt")
  168. rownames(GBO_TPM) <- GBO_TPM$Gene_id
  170. # Keep only columns with "OR" in their names (GBO samples)
  171. GBO_TPM <- GBO_TPM[,grepl("OR", colnames(GBO_TPM))]
  173. # Filter out genes with low average expression (<5 TPM)
  174. GBO_TPM <- GBO_TPM[rowMeans(GBO_TPM) >= 5, ]
  176. # Filter out genes where ≤3 samples have expression >1
  177. GBO_TPM <- GBO_TPM %>%
  178. filter(rowSums(. > 1) > 3)
  180. # Log2 transform the data (adding 1 to avoid log(0))
  181. log_GBO_TPM.raw <- log2(GBO_TPM +1)
  182. # Save current row names as a new column for filtering
  183. log_GBO_TPM.raw$rownames <- rownames(log_GBO_TPM.raw)
  185. # Keep genes with expression >0 in more than 3 samples
  186. log_GBO_TPM <- log_GBO_TPM.raw %>%
  187. filter(rowSums(across(-rownames, ~ . > 0)) > 3)
  189. # Restore original row names after filtering
  190. rownames(log_GBO_TPM) <- rownames(GBO_TPM)
  192. ### Separate samples into TMZ-resistant and TMZ-sensitive groups
  194. # TMZ-resistant samples
  195. log_GBO_TPM.TMZ.resi <- log_GBO_TPM[,c("GBO.002.OR5.R", "GBO.003.OR4.R", "GBO.006.OR7.R")]
  196. rownames(log_GBO_TPM.TMZ.resi) <- rownames(log_GBO_TPM)
  198. # TMZ-sensitive samples
  199. log_GBO_TPM.TMZ.sens <- log_GBO_TPM[,c("GBO.001.OR3.R", "GBO.005.OR4.R", "GBO.008.OR.p.3.R")]
  200. rownames(log_GBO_TPM.TMZ.sens) <- rownames(log_GBO_TPM)
  203. ### Calculate fold-change between resistant and sensitive samples
  205. log_GBO_TPM <- log_GBO_TPM %>% select(-rownames)
  207. # Compute average expression per gene for each group
  208. resi.mean <- rowMeans(log_GBO_TPM.TMZ.resi)
  209. sens.mean <- rowMeans(log_GBO_TPM.TMZ.sens)
  210. fold.diff <- resi.mean - sens.mean
  212. # Select top 50 upregulated and top 50 downregulated genes (total 100)
  213. top_DEGs <- c(names(sort(fold.diff, decreasing=TRUE)[1:50]), names(sort(fold.diff)[1:50]))
  216. # Prepare data for the heatmap (excluding the last column if needed)
  217. data <- log_GBO_TPM[top_DEGs, -ncol(log_GBO_TPM)]
  219. # Rename columns for clarity
  220. colnames(data) <- c("GBO-001", "GBO-002", "GBO-003", "GBO-005", "GBO-006","GBO-008")
  222. # Define sample types for each column
  223. sample_types <- c("TMZ-sensitive", "TMZ-resistant", "TMZ-resistant",
  224. "TMZ-sensitive", "TMZ-resistant", "TMZ-sensitive")
  226. # Set colors for sample types
  227. type_colors <- c("TMZ-sensitive" = "yellow", "TMZ-resistant" = "purple")
  229. # Create column annotation data frame
  230. annotation <- data.frame(Type = sample_types)
  231. rownames(annotation) <- colnames(data)
  234. # Define annotation colors for both column and row annotations
  235. ann_colors <- list(
  236. Type = c("TMZ-sensitive" = "yellow", "TMZ-resistant" = "purple"),
  237. `TMZ-resist Expr` = c("Upregulated" = "brown", "Downregulated" = "darkgreen")
  238. )
  240. # Create row annotation indicating whether genes are up- or downregulated
  241. annotation_row <- data.frame(`TMZ-resist Expr` = factor(c(rep("Upregulated", 50), rep("Downregulated", 50))))
  242. rownames(annotation_row) <- rownames(data)
  243. colnames(annotation_row) <- "TMZ-resist Expr"
  245. # Plot the heatmap with row scaling and clustering
  246. pheatmap(
  247. as.matrix(data),
  248. annotation_col = annotation,
  249. annotation_colors = ann_colors,
  250. annotation_row = annotation_row,
  251. annotation_names_row = FALSE,
  252. scale = "row", # Standardize by rows
  253. cluster_rows = TRUE,
  254. cluster_cols = TRUE,
  255. color=colorRampPalette(c("blue", "white", "red"))(100),
  256. show_rownames=TRUE
  257. )
  261. #Figure 4C, Figure S6
  263. # Load TPM expression data and set row names to gene IDs
  264. GBO_TPM <- read.delim("GBM_GBO_TPM.txt")
  265. rownames(GBO_TPM) <- GBO_TPM$Gene_id
  266. # Keep only columns with "OR" in their names (GBO samples)
  267. GBO_TPM <- GBO_TPM[,grepl("OR", colnames(GBO_TPM))]
  269. # Log2 transform the TPM data (adding 1 to avoid log(0))
  270. log_GBO_TPM <- log2(GBO_TPM +1)
  272. ### Separate samples into TMZ-resistant and TMZ-sensitive groups
  273. log_GBO_TPM.TMZ.resi <- log_GBO_TPM[,c("GBO.002.OR5.R", "GBO.003.OR4.R", "GBO.006.OR7.R")]
  274. log_GBO_TPM.TMZ.sens <- log_GBO_TPM[,c("GBO.001.OR3.R", "GBO.005.OR4.R", "GBO.008.OR.p.3.R")]
  276. # Calculate fold changes between resistant and sensitive samples
  277. resi.mean <- rowMeans(log_GBO_TPM.TMZ.resi)
  278. sens.mean <- rowMeans(log_GBO_TPM.TMZ.sens)
  279. fold.diff <- resi.mean - sens.mean
  281. # Print average expression for each group (for reference)
  282. mean(resi.mean)
  283. mean(sens.mean)
  285. # Plot a histogram of the fold changes
  286. hist(fold.diff, breaks=200, xlim=c(-3,3), xlab="Fold Change", main = "Histogram of GBO RNA expression FC")
  288. # Calculate p-value for all genes (estimating differential expressions)
  289. # Welch Two Sample t-test
  291. # Calculate p-values using the Wilcoxon test for each gene
  292. p.val <- sapply(1:nrow(log_GBO_TPM.TMZ.resi), function(i)
  293. wilcox.test(as.matrix(log_GBO_TPM.TMZ.resi[i,]),
  294. as.matrix(log_GBO_TPM.TMZ.sens[i,]), paired = FALSE)$p.value)
  296. # Adjust p-values using Benjamini-Hochberg FDR
  297. FDR <- round(p.adjust(p.val, 'BH'),3)
  299. fData <- data.frame(Accession=rownames(log_GBO_TPM.TMZ.resi), FC=fold.diff, p.value=p.val, FDR=FDR)
  300. fData
  302. ### Gene Ontology Enrichment Analysis
  304. # Identify differentially expressed genes (DEGs)
  305. fData$DEG.UP <- fData$FC > 1
  306. fData$DEG.DOWN <- fData$FC < -1
  308. # Select only DEGs (either up or down)
  309. DEG <- fData[fData$DEG.UP | fData$DEG.DOWN,]
  310. # Order DEGs by fold change (highest first)
  311. DEG <- DEG[order(DEG$FC, decreasing=T),]
  313. # Convert gene symbols to Entrez IDs using org.Hs.eg.db
  314. entrezID <- bitr(DEG$Accession, fromType = "SYMBOL",
  315. toType = "ENTREZID",
  316. OrgDb = org.Hs.eg.db)
  318. # Create a named vector of fold changes with Entrez IDs as names
  319. FC <- DEG$FC
  320. names(FC) <- entrezID$ENTREZID
  321. FC
  323. # Perform GO enrichment analysis (for all ontologies)
  324. ego <- enrichGO(entrezID$ENTREZID,
  325. OrgDb = "org.Hs.eg.db",
  326. ont = "ALL",
  327. readable = TRUE,
  328. pvalueCutoff = 0.01,
  329. qvalueCutoff = 0.05,
  330. universe = FC)
  332. # Plot GO enrichment results with a dot plot, split by ontology
  333. pdf("GO_dotplot_TMZresponse.pdf", height=16, width=12)
  334. dotplot(ego, split = "ONTOLOGY", showCategory = 20, font.size = 12, label_format = 50) +
  335. facet_grid(ONTOLOGY ~ ., scale = "free_y") +
  336. geom_count() +
  337. scale_size_area(max_size = 8)
  338. dev.off()
  341. # Perform KEGG pathway enrichment analysis
  342. kk <- enrichKEGG(entrezID$ENTREZID,
  343. organism = 'hsa',
  344. pvalueCutoff = 0.01,
  345. qvalueCutoff = 0.05)
  347. pdf("KEGG_dotplot_TMZresponse.pdf", height=16, width=12)
  348. # Plot KEGG enrichment results using a dot plot
  349. dotplot(kk, font.size = 10, showCategory = 20) +
  350. geom_count() +
  351. scale_size_area(max_size = 5)
  352. dev.off()

RNA_analysis.R at commit 3bd53b6, no license · at the source

Overview

Authors: Jisu Park1,2, Meiyu Qiu3,2, Eun Jeong Cho4, Sanghee Seo5, Ji-Hoon Moon3,2, Minji Kim3,2, Hyemin Mun4, Hyounji Yun3,2, Hoewon Park3,2, Arim Lim3,2, Younsoo Kang4,6, Jeong-woo Oh4, Heejeong Youk4, Seon-Hwan Kim7, Yeongbeom Seo8, Sae Min Kwon9, Kyoung Su Sung10, Hyuk-Jin Oh11, Kyung Rae Cho12, Kyungtae Yoon5, Nam-Shik Kim5, Se Jin Jang4,13, Kyung Hwan Kim7, Ki-Jun Yoon3,1,2
13 affiliations
  1. Graduate School of Stem Cell and Regeneration Biology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
  2. KAIST Stem Cell Center, KAIST, Daejeon 34141, Republic of Korea
  3. Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
  4. SG BioScience, Inc., Seoul 02637, Republic of Korea
  5. Department of Biological Sciences, Chungnam National University, Daejeon 34134, Republic of Korea
  6. Department of Medical Science, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea
  7. Department of Neurosurgery, Chungnam National University Hospital, Chungnam National University School of Medicine, Daejeon 35015, Republic of Korea
  8. Department of Neurosurgery, Yeungnam University Hospital, Yeungnam University School of Medicine, Daegu 42415, Republic of Korea
  9. Department of Neurosurgery, Dongsan Medical Center, Keimyung University School of Medicine, Daegu 41931, Republic of Korea
  10. Department of Neurosurgery, Dong-A University Hospital, Dong-A University, College of Medicine, Busan 49201, Republic of Korea
  11. Department of Neurosurgery, Soonchunhyang University Hospital Cheonan, Soonchunhyang University School of Medicine, Cheonan 31151, Republic of Korea
  12. Department of Neurosurgery, Konkuk University Medical Center, Seoul 05030, Republic of Korea
  13. Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea
Institutions: Korea Advanced Institute of Science and Technology (South Korea); Chungnam National University (South Korea); Asan Medical Center (South Korea); University of Ulsan (South Korea); Chungnam National University Hospital (South Korea); Yeungnam University Medical Center (South Korea); Yeungnam University (South Korea); Keimyung University Dongsan Medical Center (South Korea); Keimyung University Dongsan Hospital (South Korea); Keimyung University (South Korea); Dong-A University Hospital (South Korea); Dong-A University (South Korea); Soonchunhyang University Hospital Cheonan (South Korea); Konkuk University Medical Center (South Korea)
Journal: Cell reports. Medicine, volume 7, issue 7, article 102850
Dates: received 22 February 2025; accepted 14 May 2026; published online 11 June 2026; in print July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.xcrm.2026.102850 · PMID 42276053 · PMCID PMC13400186 · OpenAlex W7164364482
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), other condition (population), clinical / translational (subfield)
Methods: Statistics, Connectivity
Keywords: glioblastoma, patient-derived organoid, precision oncology, personalized medicine, organoid-based drug screening, temozolomide resistance, lazertinib
MeSH: Brain Neoplasms*, Glioblastoma*, Organoids*, Precision Medicine*, Animals, Cell Line, Tumor, DNA Modification Methylases, DNA Repair Enzymes, Drug Resistance, Neoplasm, Female, Gene Expression Regulation, Neoplastic, Humans, Mice, Temozolomide, Tumor Suppressor Proteins, Xenograft Model Antitumor Assays (* major topic)
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: Ministry of Health and Welfare (RS-2024-00439675, RS-2025-24536373); National Research Foundation of Korea (RS-2025-02214081, RS-2024-00332454, RS-2026-25488918, RS-2023-00209833, RS-2024-00440778, RS-2025-02219306); Korea Health Industry Development Institute; Ministry of Science, ICT and Future Planning; Korea Foundation for Women In Science, Engineering and Technology (WISET 2024-764)
Citations: cited by 1 paper (Europe PMC); 82 references in the paper
Research resources: Mouse anti-SOX2 RRID:AB_10843364, Rabbit anti-OLIG2 RRID:AB_10861310, Rabbit anti-PTPRZ1 RRID:AB_11127912, Alexa 594 donkey-anti chicken IgG RRID:AB_2340377, Alexa 594 donkey-anti rabbit IgG RRID:AB_2340621, Alexa 488 donkey-anti-mouse IgG RRID:AB_2340846, Alexa 647 donkey-anti mouse IgG RRID:AB_2340862, Alexa 647 donkey-anti rabbit IgG RRID:AB_2492288, Rabbit anti-Ki67 RRID:AB_2756525, Mouse Anti-STEM121 RRID:AB_2801314, RRID:AB_2885016, Chicken anti-GFAP RRID:AB_304558, Mouse anti-NCAM1 RRID:AB_306945, RRID:AB_309864, NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ RRID:IMSR_JAX:005557

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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SGMedical/GBO

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 3bd53b6dc092698e780cb000c474f91f8e6ebba8, 14 February 2025
Languages: R (2)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ComplexHeatmap (2 files), ggpubr (2 files), tidyverse (2 files), circlize (1 file), clusterProfiler (1 file), edgeR (1 file), limma (1 file), pheatmap (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/SGMedical/GBO

The paper's code and data availability statement is in the Data section.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: SGMedical/GBO
  • it says that the data are available on request
  • it says that the code is available on request

Read it in the paper: doi.org/10.1016/j.xcrm.2026.102850.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 24 authors, 7 keywords, 16 MeSH terms, 5 funders, 80 references, 15 RRIDs.

Cite

This paper

Park, J., Qiu, M., Cho, E. J., Seo, S., Moon, J.-H., Kim, M., Mun, H., Yun, H., Park, H., Lim, A., Kang, Y., Oh, J.-w., Youk, H., Kim, S.-H., Seo, Y., Kwon, S. M., Sung, K. S., Oh, H.-J., Cho, K. R., . . . Yoon, K.-J. (2026). Patient-derived organoids predict personalized drug response and reveal alternative therapeutic options in glioblastoma. Cell reports. Medicine, 7(7), 102850. https://doi.org/10.1016/j.xcrm.2026.102850

BibTeX

@article{park2026patient,
author = {Park, Jisu and Qiu, Meiyu and Cho, Eun Jeong and Seo, Sanghee and Moon, Ji-Hoon and Kim, Minji and Mun, Hyemin and Yun, Hyounji and Park, Hoewon and Lim, Arim and Kang, Younsoo and Oh, Jeong-woo and Youk, Heejeong and Kim, Seon-Hwan and Seo, Yeongbeom and Kwon, Sae Min and Sung, Kyoung Su and Oh, Hyuk-Jin and Cho, Kyung Rae and Yoon, Kyungtae and Kim, Nam-Shik and Jang, Se Jin and Kim, Kyung Hwan and Yoon, Ki-Jun},
title = {{Patient-derived organoids predict personalized drug response and reveal alternative therapeutic options in glioblastoma}},
journal = {Cell reports. Medicine},
year = {2026},
month = jun,
volume = {7},
number = {7},
pages = {102850},
publisher = {Elsevier},
issn = {2666-3791},
doi = {10.1016/j.xcrm.2026.102850},
url = {https://doi.org/10.1016/j.xcrm.2026.102850},
pmid = {42276053},
pmcid = {PMC13400186}
}

RIS

TY - JOUR
AU - Park, Jisu
AU - Qiu, Meiyu
AU - Cho, Eun Jeong
AU - Seo, Sanghee
AU - Moon, Ji-Hoon
AU - Kim, Minji
AU - Mun, Hyemin
AU - Yun, Hyounji
AU - Park, Hoewon
AU - Lim, Arim
AU - Kang, Younsoo
AU - Oh, Jeong-woo
AU - Youk, Heejeong
AU - Kim, Seon-Hwan
AU - Seo, Yeongbeom
AU - Kwon, Sae Min
AU - Sung, Kyoung Su
AU - Oh, Hyuk-Jin
AU - Cho, Kyung Rae
AU - Yoon, Kyungtae
AU - Kim, Nam-Shik
AU - Jang, Se Jin
AU - Kim, Kyung Hwan
AU - Yoon, Ki-Jun
TI - Patient-derived organoids predict personalized drug response and reveal alternative therapeutic options in glioblastoma
T2 - Cell reports. Medicine
J2 - Cell Rep Med
PY - 2026
DA - 2026/06/11
VL - 7
IS - 7
SP - 102850
SN - 2666-3791
PB - Elsevier
DO - 10.1016/j.xcrm.2026.102850
UR - https://doi.org/10.1016/j.xcrm.2026.102850
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.xcrm.2026.102850",
"type": "article-journal",
"title": "Patient-derived organoids predict personalized drug response and reveal alternative therapeutic options in glioblastoma",
"container-title": "Cell reports. Medicine",
"author": [
{
"family": "Park",
"given": "Jisu"
},
{
"family": "Qiu",
"given": "Meiyu"
},
{
"family": "Cho",
"given": "Eun Jeong"
},
{
"family": "Seo",
"given": "Sanghee"
},
{
"family": "Moon",
"given": "Ji-Hoon"
},
{
"family": "Kim",
"given": "Minji"
},
{
"family": "Mun",
"given": "Hyemin"
},
{
"family": "Yun",
"given": "Hyounji"
},
{
"family": "Park",
"given": "Hoewon"
},
{
"family": "Lim",
"given": "Arim"
},
{
"family": "Kang",
"given": "Younsoo"
},
{
"family": "Oh",
"given": "Jeong-woo"
},
{
"family": "Youk",
"given": "Heejeong"
},
{
"family": "Kim",
"given": "Seon-Hwan"
},
{
"family": "Seo",
"given": "Yeongbeom"
},
{
"family": "Kwon",
"given": "Sae Min"
},
{
"family": "Sung",
"given": "Kyoung Su"
},
{
"family": "Oh",
"given": "Hyuk-Jin"
},
{
"family": "Cho",
"given": "Kyung Rae"
},
{
"family": "Yoon",
"given": "Kyungtae"
},
{
"family": "Kim",
"given": "Nam-Shik"
},
{
"family": "Jang",
"given": "Se Jin"
},
{
"family": "Kim",
"given": "Kyung Hwan"
},
{
"family": "Yoon",
"given": "Ki-Jun"
}
],
"container-title-short": "Cell Rep Med",
"volume": "7",
"issue": "7",
"page": "102850",
"DOI": "10.1016/j.xcrm.2026.102850",
"PMID": "42276053",
"PMCID": "PMC13400186",
"ISSN": "2666-3791",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.xcrm.2026.102850",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
11
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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