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Glioblastoma immune landscape remodeling following treatment with CD70-targeted CAR-T cells.

Code ↔ Paper

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  1. [1] § Materials and methods › Animal housing and welfare ↔ resources/analysisTools/rnaseqworkflow/rnaseq_processing.sh, lines 1 to 41 · score 0.56 · German Cancer Research, DKFZ · Report a mismatch

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The authors' code

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  1. #!/usr/bin/env bash
  2. #
  3. # Copyright (c) 2018 German Cancer Research Center (DKFZ).
  4. #
  5. # Distributed under the MIT License (license terms are at https://github.com/DKFZ-ODCF/RNAseqWorkflow).
  6. #
  8. set -vx
  10. ##################################################################
  11. ## ##
  12. ## HIPO2 RNAseq workflow ##
  13. ## Authors: Naveed Ishaque, Michael Heinold, Jeongbin Park ##
  14. ## ##
  15. ##################################################################
  17. # TODO : 21/03/2017 : a : (a.1) parse read length, (a.2) specificy sbjdOverhang based on read length and (a.2) pick appropiate genome index
  18. # TODO : 21/03/2017 : b : (b.1) parse strand specificity from RNAseQC, (b.2) only compute read counts for the correct direction using feature counts
  19. # TODO : 31/07/2017 : c : (c.1) add single end mode support "useSingleEndProcessing=true" https://eilslabs-phabricator.dkfz.de/T2787
  22. ##################################################################
  23. ## ##
  24. ## SETUP ENV ##
  25. ## ##
  26. ##################################################################
  28. source $TOOL_NAV_LIB
  30. echo_run $DO_FIRST
  32. # Check software stack before running workflow
  33. check_executable "$STAR_BINARY"
  34. check_executable "$FEATURECOUNTS_BINARY"
  35. check_executable "$SAMBAMBA_BINARY"
  36. check_executable "$SAMTOOLS_BINARY"
  37. check_executable "$RNASEQC_BINARY"
  38. check_executable "$KALLISTO_BINARY"
  39. check_executable "$QUALIMAP_BINARY"
  40. check_executable "$ARRIBA_BINARY"
  41. check_executable "$ARRIBA_DRAW_FUSIONS"
  43. ########################################################################
  44. ## ##
  45. ## WORK FLOW ##
  46. ## ##
  47. ########################################################################
  49. set -u
  51. #make_directory $RESULTS_PID_DIR
  53. ##
  54. ## PRINT env
  55. ##
  57. env | sort > $DIR_EXECUTION/${RODDY_JOBNAME}.env_dump.txt
  59. ##
  60. ## STAR 2-PASS ALIGNMENT: 12 core, 50Gb, 6 hours
  61. ##
  63. make_directory $SCRATCH
  65. if [ "$RUN_STAR" == true ]
  66. then
  67. make_directory $ALIGNMENT_DIR
  68. cd $ALIGNMENT_DIR
  69. ## the STAR temp directory must not exist, otherwise STAR will fail
  70. remove_directory $SCRATCH/${SAMPLE}_${pid}_STAR
  71. if [ "$useSingleEndProcessing" == true ]
  72. then
  73. echo_run "${STAR_BINARY} ${STAR_PARAMS} --readFilesIn ${READS_STAR_LEFT} --readFilesCommand ${READ_COMMAND} --outSAMattrRGline ${PARM_READGROUPS}"
  74. else
  75. echo_run "${STAR_BINARY} ${STAR_PARAMS} --readFilesIn ${READS_STAR_LEFT} ${READS_STAR_RIGHT} --readFilesCommand ${READ_COMMAND} --outSAMattrRGline ${PARM_READGROUPS}"
  76. fi
  77. check_or_die $STAR_SORTED_BAM alignment
  78. check_or_die $STAR_NOTSORTED_BAM alignment
  79. check_or_die $STAR_CHIMERA_SAM alignment
  80. remove_directory $SCRATCH/${SAMPLE}_${pid}_STAR
  81. echo_run "mv ${SAMPLE}_${pid}_merged.Chimeric.out.junction ${SAMPLE}_${pid}_chimeric_merged.junction"
  83. ## BAM-erise and sort chimera file: 1 core, 1 hours, 200mb
  84. echo_run "$SAMTOOLS_BINARY view -Sbh $STAR_CHIMERA_SAM | $SAMTOOLS_BINARY sort - -o $STAR_CHIMERA_BAM_PREF.bam"
  85. check_or_die ${STAR_CHIMERA_BAM_PREF}.bam chimera-sam-2-bam
  86. echo_run "$SAMBAMBA_BINARY markdup --tmpdir=$SCRATCH -t $CORES ${STAR_CHIMERA_BAM_PREF}.bam ${STAR_CHIMERA_MKDUP_BAM}"
  87. check_or_die $STAR_CHIMERA_MKDUP_BAM chimera-post-markdups
  88. remove_file ${STAR_CHIMERA_BAM_PREF}.bam
  89. echo_run "$SAMBAMBA_BINARY index -t $CORES $STAR_CHIMERA_MKDUP_BAM"
  90. check_or_die ${STAR_CHIMERA_MKDUP_BAM}.bai chimera-alignment-index
  92. ## markdups using sambamba (requires 7Gb and 20 min walltime (or 1.5 hrs CPU time) for 200m reads)
  93. echo_run "$SAMBAMBA_BINARY markdup --tmpdir=$SCRATCH -t $CORES $STAR_SORTED_BAM $STAR_SORTED_MKDUP_BAM"
  94. check_or_die $STAR_SORTED_MKDUP_BAM post-markdups
  96. ## index using samtools (requires 40MB and 5 minutes for 200m reads)
  97. echo_run "$SAMBAMBA_BINARY index -t $CORES $STAR_SORTED_MKDUP_BAM"
  98. check_or_die ${STAR_SORTED_MKDUP_BAM}.bai alignment-index
  100. ## md5sum
  101. echo_run "md5sum $STAR_SORTED_MKDUP_BAM | cut -f 1 -d ' ' > $STAR_SORTED_MKDUP_BAM.md5"
  102. check_or_die ${STAR_SORTED_MKDUP_BAM}.md5 alignment-md5sums
  103. echo_run "md5sum $STAR_CHIMERA_MKDUP_BAM | cut -f 1 -d ' ' > $STAR_CHIMERA_MKDUP_BAM.md5"
  104. check_or_die ${STAR_CHIMERA_MKDUP_BAM}.md5 alignment-md5sums
  106. ## flagstats (requires 4MB and 5 minutes for 200m reads)
  107. echo_run "$SAMBAMBA_BINARY flagstat -t $CORES $STAR_SORTED_MKDUP_BAM > ${STAR_SORTED_MKDUP_BAM}.flagstat"
  108. check_or_die ${STAR_SORTED_MKDUP_BAM}.flagstat alignment-qc-flagstats
  109. fi
  111. # Run the fingerprinting. This requires the .bai file.
  112. if [[ "${runFingerprinting:-false}" == true ]]
  113. then
  114. cd $ALIGNMENT_DIR
  115. echo_run "$PYTHON_BINARY $TOOL_FINGERPRINT $fingerprintingSitesFile $STAR_SORTED_MKDUP_BAM > $STAR_SORTED_MKDUP_BAM.fp.tmp"
  116. mv "$STAR_SORTED_MKDUP_BAM.fp.tmp" "$STAR_SORTED_MKDUP_BAM.fp"
  117. fi
  119. ##
  120. ## RNAseQC (requires 10Gb and 6 hours for 200m reads)
  121. ##
  123. if [ "$RUN_RNASEQC" == true ]
  124. then
  125. make_directory $RNASEQC_DIR/${SAMPLE}_${pid}
  126. cd $RNASEQC_DIR/${SAMPLE}_${pid}
  127. DOC_FLAG=" "
  128. if [ "$disableDoC_GATK" == true ]
  129. then
  130. DOC_FLAG="-noDoC"
  131. fi
  132. if [ "$useSingleEndProcessing" == true ]
  133. then
  134. echo_run "$RNASEQC_BINARY -r $GENOME_GATK_INDEX $DOC_FLAG -singleEnd -t $GENE_MODELS_NOGENE -n 1000 -o . -s \"${SAMPLE}_${pid}|${ALIGNMENT_DIR}/${STAR_SORTED_MKDUP_BAM}|${SAMPLE}\" &> $DIR_EXECUTION/${RODDY_JOBNAME}.${SAMPLE}_${pid}_RNAseQC.log &"
  135. else
  136. echo_run "$RNASEQC_BINARY -r $GENOME_GATK_INDEX $DOC_FLAG -t $GENE_MODELS_NOGENE -n 1000 -o . -s \"${SAMPLE}_${pid}|${ALIGNMENT_DIR}/${STAR_SORTED_MKDUP_BAM}|${SAMPLE}\" &> $DIR_EXECUTION/${RODDY_JOBNAME}.${SAMPLE}_${pid}_RNAseQC.log &"
  137. fi
  138. echo_run "$RNASEQC_BINARY -r $GENOME_GATK_INDEX $DOC_FLAG -t $GENE_MODELS_NOGENE -n 1000 -o . -s \"${SAMPLE}_${pid}|${ALIGNMENT_DIR}/${STAR_SORTED_MKDUP_BAM}|${SAMPLE}\" &> $DIR_EXECUTION/${RODDY_JOBNAME}.${SAMPLE}_${pid}_RNAseQC.log &"
  139. fi
  141. ##
  142. ## QualiMap2 (requires 6Gb and 3 hours for 200m reads)
  143. ##
  145. if [ "$RUN_QUALIMAP" == true ]
  146. then
  147. make_directory $QUALIMAP_DIR/${SAMPLE}_${pid}
  148. cd $QUALIMAP_DIR/${SAMPLE}_${pid}
  149. if [ "$useSingleEndProcessing" == true ]
  150. then
  151. echo_run "$QUALIMAP_BINARY rnaseq -gtf $GENE_MODELS -s --java-mem-size=60G -outfile ${SAMPLE}_${pid}.report -outdir $QUALIMAP_DIR/${SAMPLE}_${pid} -bam $ALIGNMENT_DIR/$STAR_NOTSORTED_BAM"
  152. else
  153. echo_run "$QUALIMAP_BINARY rnaseq -gtf $GENE_MODELS -s -pe --java-mem-size=60G -outfile ${SAMPLE}_${pid}.report -outdir $QUALIMAP_DIR/${SAMPLE}_${pid} -bam $ALIGNMENT_DIR/$STAR_NOTSORTED_BAM"
  154. fi
  155. check_or_die rnaseq_qc_results.txt qc-qualimap2
  156. fi
  158. ##
  159. ## feature Counts htseq like requires 1gb, and 30 min of 4 cores
  160. ##
  162. if [ "$RUN_FEATURE_COUNTS" == true ]
  163. then
  164. make_directory $COUNT_DIR
  165. cd $COUNT_DIR
  166. COUNT="-t exon -g gene_id -Q 255 -T $FEATURE_COUNT_CORES -a $GENE_MODELS -F GTF --tmpDir $SCRATCH/${SAMPLE}_${pid}_featureCounts "
  167. make_directory $SCRATCH/${SAMPLE}_${pid}_featureCounts
  168. for S in {0..2}
  169. do
  170. if [ "$useSingleEndProcessing" == true ]
  171. then
  172. echo_run "$FEATURECOUNTS_BINARY $COUNT --donotsort -s $S -o ${SAMPLE}_${pid}.featureCounts.s$S $ALIGNMENT_DIR/$STAR_SORTED_MKDUP_BAM"
  173. else
  174. echo_run "$FEATURECOUNTS_BINARY $COUNT -p -B -s $S -o ${SAMPLE}_${pid}.featureCounts.s$S $ALIGNMENT_DIR/$STAR_SORTED_MKDUP_BAM"
  175. fi
  176. check_or_die ${SAMPLE}_${pid}.featureCounts.s${S} gene-counting
  177. done
  178. ## RPKM TPM calculations
  179. echo_run "$TOOL_COUNTS_TO_FPKM_TPM ${SAMPLE}_${pid}.featureCounts.s0 ${SAMPLE}_${pid}.featureCounts.s1 ${SAMPLE}_${pid}.featureCounts.s2 $GENE_MODELS $GENE_MODELS_EXCLUDE > ${SAMPLE}_${pid}.fpkm_tpm.featureCounts.tsv"
  180. check_or_die ${SAMPLE}_${pid}.fpkm_tpm.featureCounts.tsv counting-featureCounts
  181. # cleanup
  182. make_directory ${SAMPLE}_${pid}_featureCounts_raw
  183. echo_run "mv ${SAMPLE}_${pid}.featureCounts* ${SAMPLE}_${pid}_featureCounts_raw"
  184. echo_run "tar --remove-files -czvf ${SAMPLE}_${pid}_featureCounts_raw.tgz ${SAMPLE}_${pid}_featureCounts_raw"
  185. #remove_directory $SCRATCH/${SAMPLE}_${pid}_featureCounts
  186. fi
  188. ##
  189. ## feature Counts DEXSEQ like
  190. ##
  192. if [ "$RUN_FEATURE_COUNTS_DEXSEQ" == true ]
  193. then
  194. make_directory $COUNT_DIR_EXON
  195. cd $COUNT_DIR_EXON
  196. COUNT_EXONS="-f -O -F GTF -a $GENE_MODELS_DEXSEQ -t exonic_part -Q 255 -T $FEATURE_COUNT_CORES --tmpDir $SCRATCH/${SAMPLE}_${pid}_featureCountsExons "
  197. make_directory $SCRATCH/${SAMPLE}_${pid}_featureCountsExons
  198. for S in {0..2}
  199. do
  200. if [ "$useSingleEndProcessing" == true ]
  201. then
  202. echo_run "$FEATURECOUNTS_BINARY $COUNT_EXONS--donotsort -s $S -o ${SAMPLE}_${pid}.featureCounts.dexseq.s$S $ALIGNMENT_DIR/$STAR_SORTED_MKDUP_BAM"
  203. else
  204. echo_run "$FEATURECOUNTS_BINARY $COUNT_EXONS -p -s $S -o ${SAMPLE}_${pid}.featureCounts.dexseq.s$S $ALIGNMENT_DIR/$STAR_SORTED_MKDUP_BAM"
  205. fi
  206. check_or_die ${SAMPLE}_${pid}.featureCounts.dexseq.s${S} exon-counting
  207. done
  208. ## RPKM TPM calculations
  209. echo_run "$TOOL_COUNTSDEXSEQ_TO_FPKM_TPM ${SAMPLE}_${pid}.featureCounts.dexseq.s0 ${SAMPLE}_${pid}.featureCounts.dexseq.s1 ${SAMPLE}_${pid}.featureCounts.dexseq.s2 $GENE_MODELS $GENE_MODELS_EXCLUDE > ${SAMPLE}_${pid}.fpkm_tpm.featureCounts.dexseq.tsv"
  210. check_or_die ${SAMPLE}_${pid}.fpkm_tpm.featureCounts.dexseq.tsv counting-featureCounts_dexseq
  211. cleanup
  212. make_directory ${SAMPLE}_${pid}_featureCounts_dexseq_raw
  213. echo_run "mv ${SAMPLE}_${pid}.featureCounts* ${SAMPLE}_${pid}_featureCounts_dexseq_raw"
  214. echo_run "tar --remove-files -czvf ${SAMPLE}_${pid}_featureCounts_dexseq_raw.tgz ${SAMPLE}_${pid}_featureCounts_dexseq_raw"
  215. #remove_directory $SCRATCH/${SAMPLE}_${pid}_featureCountsExons
  216. fi
  218. ##
  219. ## Kallisto (2 hours walltime (12 hrs CPU) and 70 gb for 200m reads) PER RUN!
  220. ##
  222. KALLISTO_PARAMS="quant -i $GENOME_KALLISTO_INDEX -o . -t $CORES -b 100"
  223. if [ "$useSingleEndProcessing" == true ]
  224. then
  225. RUN_KALLISTO=false
  226. RUN_KALLISTO_RF=false
  227. RUN_KALLISTO_FR=false
  228. RUN_ARRIBA=false
  229. fi
  231. if [ "$RUN_KALLISTO" == true ]
  232. then
  233. make_directory $KALLISTO_UN_DIR/${SAMPLE}_${pid}
  234. cd $KALLISTO_UN_DIR/${SAMPLE}_${pid}
  235. echo_run "$KALLISTO_BINARY $KALLISTO_PARAMS $READS_KALLISTO"
  236. check_or_die abundance.tsv kallisto
  237. echo_run "$TOOL_KALLISTO_RESCALE abundance.tsv $GENE_MODELS_EXCLUDE > abundance.rescaled.tsv"
  238. fi
  240. if [ "$RUN_KALLISTO_RF" == true ]
  241. then
  242. make_directory $KALLISTO_RF_DIR
  243. cd $KALLISTO_RF_DIR
  244. $KALLISTO_BINARY $KALLISTO_PARAMS --rf-stranded $READS_KALLISTO
  245. check_or_die abundance.tsv kallisto
  246. $TOOL_KALLISTO_RESCALE abundance.tsv $GENE_MODELS_EXCLUDE > abundance.rescaled.tsv
  247. fi
  249. if [ "$RUN_KALLISTO_FR" == true ]
  250. then
  251. make_directory $KALLISTO_FR_DIR
  252. cd $KALLISTO_FR_DIR
  253. $KALLISTO_BINARY $KALLISTO_PARAMS --fr-stranded $READS_KALLISTO
  254. check_or_die abundance.tsv kallisto
  255. $TOOL_KALLISTO_RESCALE abundance.tsv $GENE_MODELS_EXCLUDE > abundance.rescaled.tsv
  256. fi
  258. ##
  259. ## Fusion detection
  260. ##
  262. if [ "$RUN_ARRIBA" == true ]
  263. then
  264. make_directory "$ARRIBA_DIR"
  265. cd "$ARRIBA_DIR"
  267. arribaCommand="$ARRIBA_BINARY -c '$ALIGNMENT_DIR/$STAR_CHIMERA_MKDUP_BAM' -x '$ALIGNMENT_DIR/$STAR_SORTED_MKDUP_BAM' -a '$GENOME_FA' -k '$ARRIBA_KNOWN_FUSIONS' -t '$ARRIBA_TAGS' -g '$GENE_MODELS' -b '$ARRIBA_BLACKLIST' -o '$ARRIBA_DIR/${SAMPLE}_$pid.fusions.txt' -O '$ARRIBA_DIR/${SAMPLE}_$pid.discarded_fusions.txt'"
  269. echo_run "$arribaCommand"
  271. if [[ -f "$ARRIBA_DIR/${SAMPLE}_$pid.fusions.txt" ]]
  272. then
  273. echo_run "$ARRIBA_DRAW_FUSIONS --annotation='$GENE_MODELS' --fusions='$ARRIBA_DIR/${SAMPLE}_$pid.fusions.txt' --proteinDomains='$ARRIBA_PROTEIN_DOMAINS' --cytobands='$ARRIBA_CYTOBANDS' --alignments='$ALIGNMENT_DIR/$STAR_SORTED_MKDUP_BAM' --output='$ARRIBA_DIR/${SAMPLE}_$pid.fusions.pdf'"
  274. fi
  276. echo_run "gzip -9 '$ARRIBA_DIR/${SAMPLE}_$pid.discarded_fusions.txt'"
  278. check_or_die "${SAMPLE}_$pid.fusions.txt" arriba_fusions_txt
  279. fi
  281. ##
  282. ## Produce json QC file
  283. ##
  285. if [ "$RUN_QCJSON" == true ]
  286. then
  287. make_directory $QC_DIR
  288. if [ "$RUN_RNASEQC" == true ]
  289. then
  290. echo "# Waiting for RNAseQC"
  291. wait && cd $QC_DIR
  292. else
  293. cd $QC_DIR
  294. fi
  295. if [[ -f "$RNASEQC_DIR/${SAMPLE}_${pid}/metrics.tsv" ]]
  296. then
  297. echo_run "mv $RNASEQC_DIR/${SAMPLE}_${pid}/metrics.tsv $RNASEQC_DIR/${SAMPLE}_${pid}/${SAMPLE}_${pid}_metrics.tsv"
  298. fi
  300. if [[ -f "$RNASEQC_DIR/${SAMPLE}_${pid}/${SAMPLE}_${pid}_metrics.tsv" ]]
  301. then
  302. echo "#FOUND FILE: RNAseQC file \"$RNASEQC_DIR/${SAMPLE}_${pid}/${SAMPLE}_${pid}_metrics.tsv\""
  303. else
  304. echo "#ERROR: file not found: \"$RNASEQC_DIR/${SAMPLE}_${pid}/${SAMPLE}_${pid}_metrics.tsv\" ... exiting!" 1>&2
  305. exit 1
  306. fi
  308. if [[ -f "$DIR_EXECUTION/${RODDY_JOBNAME}.${SAMPLE}_${pid}_RNAseQC.log" ]]
  309. then
  310. check_text_and_die $DIR_EXECUTION/${RODDY_JOBNAME}.${SAMPLE}_${pid}_RNAseQC.log "org.broadinstitute.sting.gatk.walkers.coverage.DepthOfCoverageWalker.onTraversalDone" "rerun with \$disableDoC_GATK=TRUE"
  311. check_text_or_die $DIR_EXECUTION/${RODDY_JOBNAME}.${SAMPLE}_${pid}_RNAseQC.log "Finished Successfully"
  312. fi
  313. echo_run "$TOOL_CREATE_JSON_FROM_OUTPUT $ALIGNMENT_DIR/${STAR_SORTED_MKDUP_BAM}.flagstat $RNASEQC_DIR/${SAMPLE}_${pid}/${SAMPLE}_${pid}_metrics.tsv > ${JSON_PREFIX}qualitycontrol.json"
  314. check_or_die ${JSON_PREFIX}qualitycontrol.json qc-json
  315. fi
  317. ##
  318. ## Clean up files which are no longer needed
  319. ##
  321. if [ "$RUN_CLEANUP" == true ]
  322. then
  323. if [ "$RUN_RNASEQC" == true ]
  324. then
  325. echo "# Waiting for RNAseQC"
  326. wait
  327. fi
  328. remove_directory $SCRATCH/*
  329. remove_file $RNASEQC_DIR/${SAMPLE}_${pid}/refGene.txt*
  330. remove_file $RNASEQC_DIR/${SAMPLE}_${pid}/rRNA_intervals.list
  331. if [[ -f "$RNASEQC_DIR/${SAMPLE}_${pid}/${SAMPLE}_${pid}_RNAseQC.tgz" ]]
  332. then
  333. echo "# RNAseQC results already archived... skipping"
  334. else
  335. echo_run "tar --remove-files -czvf $RNASEQC_DIR/${SAMPLE}_${pid}/${SAMPLE}_${pid}_RNAseQC.tgz $RNASEQC_DIR/${SAMPLE}_${pid}/${SAMPLE}_${pid} "
  336. fi
  337. remove_file $ALIGNMENT_DIR/$STAR_SORTED_BAM
  338. remove_file $ALIGNMENT_DIR/$STAR_NOTSORTED_BAM
  339. remove_file $ALIGNMENT_DIR/$STAR_CHIMERA_SAM
  340. remove_file $ALIGNMENT_DIR/*fifo.read1
  341. remove_file $ALIGNMENT_DIR/*fifo.read2
  342. make_directory $ALIGNMENT_DIR/${SAMPLE}_${pid}_star_logs_and_files
  343. echo_run "mv -f $ALIGNMENT_DIR/${SAMPLE}_${pid}*out $ALIGNMENT_DIR/${SAMPLE}_${pid}_star_logs_and_files 2>/dev/null"
  344. echo_run "mv -f $ALIGNMENT_DIR/${SAMPLE}_${pid}*.tab $ALIGNMENT_DIR/${SAMPLE}_${pid}_star_logs_and_files 2>/dev/null"
  345. echo_run "mv -f $ALIGNMENT_DIR/${SAMPLE}_${pid}_merged._STARgenome $ALIGNMENT_DIR/${SAMPLE}_${pid}_star_logs_and_files 2>/dev/null"
  346. echo_run "mv -f $ALIGNMENT_DIR/${SAMPLE}_${pid}_merged._STARpass1 $ALIGNMENT_DIR/${SAMPLE}_${pid}_star_logs_and_files 2>/dev/null"
  347. remove_directory $SCRATCH
  348. fi
  350. echo "DONE!"

rnaseq_processing.sh at commit f41259c, no license · at the source

Overview

Authors: Alexandros Kourtesakis1, Dennis Alexander Agardy2,3, Clara Tejido Dierssen2,3, Ling Hai1, Dirk Carsten Frieder Hoffmann1, Normann Mussnig1, Ralph Sinn4, Manuel Fischer4, Andy Schacht1, Michael Breckwoldt4, Lukas Bunse2,3, Tim Sauer5, Michael Platten2,3, Wolfgang Wick1,6, Tobias Kessler1,6
  1. Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
  2. Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
  3. Department of Neurology, Medical Faculty Mannheim, Mannheim Center for Translation Neuroscience (MCTN), Heidelberg University, Mannheim, Germany
  4. Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
  5. Medicine, Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Heidelberg, Germany
  6. Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg, Germany
Institutions: German Cancer Research Center (Germany); Heidelberg University (Germany); University Hospital Heidelberg (Germany); Bernstein Center for Computational Neuroscience Heidelberg-Mannheim (Germany); University Medical Centre Mannheim (Germany)
Journal: Oncoimmunology, volume 15, issue 1, article 2745295
Dates: published online 18 October 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1080/2162402x.2026.2745295 · PMID 42894313 · PMCID PMC13682394 · OpenAlex W7222952400
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: genetics / omics (modality), mouse (organism), other condition (population)
Methods: Smoothing, state filtering, decompositions, Preprocessing, Machine learning
Keywords: Glioblastoma, CD70, CAR-T cells, scRNAseq, immunotherapy
Topic: Glioma Diagnosis and Treatment (Oncology, Medicine), according to OpenAlex
Funding: Chica and Heinz Schaller Foundation (2017-A25;575 2019_EKMS.23); Else Kröner-Fresenius-Stiftung (Clinician Scientist professorship, 2024_EKCS.06); Emmy Noether program of the German Research foundation (DFG) (BR 6153/1-1); German Research Foundation/DFG (SFB UNITE Glioblastoma (SFB1389, WP B06N), WP B03)); European Center for Neurooncology, funded by the Dietmar Hopp-Foundation; Hertie Network of excellence in clinical neuroscience fellowship by the Hertie Foundation
Citations: not cited yet (OpenAlex); 66 references in the paper
Research resources: RRID:AB_11149115, RRID:AB_11150408, RRID:AB_2563061, RRID:Addgene_12371

Abstract

Chimeric antigen receptor (CAR)-T cell therapy has shown limited efficacy in glioblastoma (GB), largely because of the immunosuppressive tumor microenvironment (TME). We have previously demonstrated that the ligand-based mCD70-directed construct mCAR27 specifically eliminates CD70⁺ GB cells in vitro, and induces durable tumor remission with prolonged survival in a syngeneic model of orthotopic GB. Despite the therapeutic promise of CD70 as a GB CAR-T cell target, the impact of CD70-directed CAR-T cells on the brain tumor immune landscape after target interception remains unexplored. Here, we independently confirmed a tumorigenic role for CD70 in murine GB by performing bulk RNA sequencing and in vivo experiments. Next, we applied single-cell RNA sequencing (scRNAseq) to decipher the transcriptome of GB-infiltrating immune cells in immunocompetent mice bearing CD70+ GB after intratumoral mCAR27 therapy. CAR-T cell treatment expanded adoptively transferred cells and was associated with increased endogenous Cd8⁺ T-cell infiltration, alongside a reduction in immunosuppressive populations, including Tregs and anti-inflammatory M2-like macrophages, but also subsets of dendritic and natural-killer cells. CD70 expression was detected on multiple GB-infiltrating immune subsets, notably Tregs and M2-like macrophages. Transcriptionally, mCAR27-mediated GB remission led to a coordinated downregulation of type-I interferon-stimulated genes and immunosuppressive mediators such as Arg1, Tgfbi, Cd274, Pdcd1lg2 and Lag3, coupled with upregulation of genes involved in inflammatory resolution, findings consistent with attenuation of GB-derived inflammatory stress and immunosuppression. Collectively, we showcase that CD70-directed CAR-T cell therapy remodels the post-treatment immune landscape towards resolution of inflammation and reduced immunosuppression, supporting sustained immune surveillance.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

DKFZ-ODCF/RNAseqWorkflow

License: none: the authors keep all their rights
State: the link answers, verified on 24 October 2026
Evidence: files inventoried
Commit: f41259c62b5f087fbeee7775adc11547ad44f378, 12 June 2024
Languages: Shell (5), Python (2), Java (1)
Size: 50 files, 8 scripts
Software Heritage: not archived
Found in: the text, “Data analysis”
Holds: README, environment (environment.yaml)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: pysam (1 file), SAMtools (1 file), Subread (featureCounts) (1 file)
Availability: 1 check, the latest on 24 October 2026: the link answers
  • 24 October 2026: the link answers
9 files
At the source: github.com/DKFZ-ODCF/RNAseqWorkflow

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 scripts, each with its path and the digest of its content;
  • 1 match 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.

Data availability statement

Manuscript-related data, materials and protocols are uploaded in the submitted article or as Supplementary material and are available upon reasonable request from the corresponding author. Bulk and single-cell RNA sequencing data have been deposited to Gene Expression Omnibus (GEO) with accession numbers GSE329446 and GSE330539 respectively. For bulk RNA sequencing data analysis, the processing pipeline and parameters were based on the DKFZ ODCF workflow (https://github.com/DKFZ-ODCF/RNAseqWorkflow).

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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, 24 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 5 keywords, 6 funders, 66 references, 4 RRIDs.

Cite

This paper

Kourtesakis, A., Agardy, D. A., Tejido Dierssen, C., Hai, L., Hoffmann, D. C. F., Mussnig, N., Sinn, R., Fischer, M., Schacht, A., Breckwoldt, M., Bunse, L., Sauer, T., Platten, M., Wick, W., & Kessler, T. (2026). Glioblastoma immune landscape remodeling following treatment with CD70-targeted CAR-T cells. Oncoimmunology, 15(1), 2745295. https://doi.org/10.1080/2162402x.2026.2745295

BibTeX

@article{kourtesakis2026glioblastoma,
author = {Kourtesakis, Alexandros and Agardy, Dennis Alexander and Tejido Dierssen, Clara and Hai, Ling and Hoffmann, Dirk Carsten Frieder and Mussnig, Normann and Sinn, Ralph and Fischer, Manuel and Schacht, Andy and Breckwoldt, Michael and Bunse, Lukas and Sauer, Tim and Platten, Michael and Wick, Wolfgang and Kessler, Tobias},
title = {{Glioblastoma immune landscape remodeling following treatment with CD70-targeted CAR-T cells}},
journal = {Oncoimmunology},
year = {2026},
month = oct,
volume = {15},
number = {1},
pages = {2745295},
publisher = {Taylor \& Francis},
issn = {2162-4011},
doi = {10.1080/2162402x.2026.2745295},
url = {https://doi.org/10.1080/2162402x.2026.2745295},
pmid = {42894313},
pmcid = {PMC13682394}
}

RIS

TY - JOUR
AU - Kourtesakis, Alexandros
AU - Agardy, Dennis Alexander
AU - Tejido Dierssen, Clara
AU - Hai, Ling
AU - Hoffmann, Dirk Carsten Frieder
AU - Mussnig, Normann
AU - Sinn, Ralph
AU - Fischer, Manuel
AU - Schacht, Andy
AU - Breckwoldt, Michael
AU - Bunse, Lukas
AU - Sauer, Tim
AU - Platten, Michael
AU - Wick, Wolfgang
AU - Kessler, Tobias
TI - Glioblastoma immune landscape remodeling following treatment with CD70-targeted CAR-T cells
T2 - Oncoimmunology
J2 - Oncoimmunology
PY - 2026
DA - 2026/10/18
VL - 15
IS - 1
SP - 2745295
SN - 2162-4011
PB - Taylor & Francis
DO - 10.1080/2162402x.2026.2745295
UR - https://doi.org/10.1080/2162402x.2026.2745295
LA - en
ER -

CSL-JSON

{
"id": "10.1080/2162402x.2026.2745295",
"type": "article-journal",
"title": "Glioblastoma immune landscape remodeling following treatment with CD70-targeted CAR-T cells",
"container-title": "Oncoimmunology",
"author": [
{
"family": "Kourtesakis",
"given": "Alexandros"
},
{
"family": "Agardy",
"given": "Dennis Alexander"
},
{
"family": "Tejido Dierssen",
"given": "Clara"
},
{
"family": "Hai",
"given": "Ling"
},
{
"family": "Hoffmann",
"given": "Dirk Carsten Frieder"
},
{
"family": "Mussnig",
"given": "Normann"
},
{
"family": "Sinn",
"given": "Ralph"
},
{
"family": "Fischer",
"given": "Manuel"
},
{
"family": "Schacht",
"given": "Andy"
},
{
"family": "Breckwoldt",
"given": "Michael"
},
{
"family": "Bunse",
"given": "Lukas"
},
{
"family": "Sauer",
"given": "Tim"
},
{
"family": "Platten",
"given": "Michael"
},
{
"family": "Wick",
"given": "Wolfgang"
},
{
"family": "Kessler",
"given": "Tobias"
}
],
"container-title-short": "Oncoimmunology",
"volume": "15",
"issue": "1",
"page": "2745295",
"DOI": "10.1080/2162402x.2026.2745295",
"PMID": "42894313",
"PMCID": "PMC13682394",
"ISSN": "2162-4011",
"publisher": "Taylor & Francis",
"URL": "https://doi.org/10.1080/2162402x.2026.2745295",
"language": "en",
"issued": {
"date-parts": [
[
2026,
10,
18
]
]
}
}

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