DOI: 10.5061/dryad.f1vhhmh7v
title: Genotype stratified adjunctive dexamethasone for tuberculous meningitis in HIV-negative Adults: the LAST ACT trial

description: Tuberculous meningitis (TBM) is the most severe form of tuberculosis.
 Adjunctive corticosteroids are recommended for HIV-negative adults,
 although their benefit appears modest and may depend on host leukotriene
 A4 hydrolase (LTA4H) genotype. The LAST ACT trial (NCT03100786) was a
 genotype-stratified, randomised, double-blind, placebo-controlled Phase
 III trial that evaluated dexamethasone in HIV-negative Vietnamese adults
 with TBM. A total of 613 adults with LTA4H CC or CT genotypes were
 randomised to receive dexamethasone or placebo; 89 TT-genotype
 participants received open-label dexamethasone. The trial found no benefit
 from adjunctive dexamethasone in CC/CT-genotype participants. This dataset
 supports the analysis of a secondary outcome: changes in blood and CSF
 inflammatory responses. Whole-blood RNA sequencing was performed for 202
 participants after quality control (day 0: n=202; day 14: n=188; day 60:
 n=153). CSF inflammatory proteins were measured in 646 participants using
 the Olink Explore 384 Inflammation panel (day 0: n=638; day 30: n=391),
 resulting in 1029 CSF samples with high-quality data after quality
 control. Ten pre-specified cytokines were targeted, but four (IL-2, IL-4,
 IL-5, IL-13) were excluded due to poor detection. Analyses focused on five
 inflammation-related pathways: TNF signalling, interferons, cytokines,
 neutrophils, and eicosanoids. Pathway activity (enrichment scores) was
 calculated using single-sample gene set enrichment analysis (ssGSEA) and
 z-score metrics. Longitudinal changes (rate of reduction) in pathway
 activity by treatment and LTA4H genotype were assessed using Bayesian
 joint models (JMbayes2), which combined linear mixed effects and survival
 components, accounting for early mortality bias. This dataset enabled a
 detailed investigation of corticosteroid effects on host inflammatory
 responses in TBM.
description: CSF inflammatory proteins were measured using the Olink Explore
 384 Inflammation Panel (Olink Proteomics, Uppsala, Sweden). Olink
 measurements were conducted for 675 participants on day 0 (n=675) and day
 30 (n=397) at the Human Genomics Facility of the Genetic Laboratory,
 Department of Internal Medicine, Erasmus MC (Rotterdam, Netherlands). Raw
 protein expression data were reported as Normalized Protein eXpression
 (NPX) units, which were log₂-transformed and normalized using the plate
 control method to minimize technical variation. To correct for batch
 effects, the NPX values then were further adjusted using the ComBat
 function from the sva R package.<sup>31</sup> Quality control
 (QC) procedures were conducted at both the sample and protein levels. At
 the sample level, poor-quality samples were excluded if they exhibited a
 failure rate of ≥50% across protein assays, as determined by Olink’s
 internal QC criteria. Outliers were identified via principal component
 analysis (PCA) and excluded if they deviated by more than three standard
 deviations from the first principal component. At the protein level,
 proteins with a limit of detection exceeding 75% of samples were filtered
 out. Finally, 17 participants who died before randomization were excluded,
 resulting in a final dataset of 275 proteins in 1029 CSF samples from 646
 participants available for analysis (day 0: *n=<em>638; day
 30</em>: n=*391). Among the 10 planned CSF cytokines - TNF-α,
 interleukin (IL)-1β, IL-2, IL-6, IL-12b, interferon (IFN)-γ, IL-4, IL-5,
 IL-10, and IL-13 - four (IL-2, IL-4, IL-5, and IL-13) did not pass quality
 control due to their limit of detection (LOD) exceeding 75% of
 samples. Whole blood RNA sequencing was performed for
 the first 207 consecutively enrolled participants on day 0 (n=207), day 14
 (n=191), and day 60 (n=156). Whole blood samples were preserved in PAXgene
 Blood RNA collection tubes at -80°C. Total RNA was subsequently extracted
 using the PAXgene Blood RNA Kit (Qiagen, Valencia, CA, USA), following the
 manufacturer's protocol. Extracted RNA was shipped to the Ramaciotti
 Centre for Genomics (University of New South Wales, Sydney, Australia) for
 high-throughput sequencing. Library preparation was performed using the
 TruSeq Stranded Total RNA with Ribo-Zero Globin kit (Illumina, San Diego,
 CA, USA) to deplete globin transcripts and ribosomal RNA. Sequencing was
 conducted on the Illumina NovaSeq 6000 platform, generating approximately
 30 million 100 bp paired-end reads per sample. Raw sequencing data was
 subjected to quality control and aligned to the human reference genome
 (GRCh38 build 99) with the STAR aligner
 (v2.5.2a).<sup>32</sup> Gene-level quantification from aligned
 reads was performed using FeatureCounts (v2.0.0), generating raw counts
 for 60,067 genes.<sup>33</sup> Prior to analysis, five
 participants were excluded: two who died before randomization and three
 whose RNA sequencing data were poor quality (RNA integrity number &lt;
 4 and uniquely mapped reads &lt; 10 million). This resulted in a final
 sequencing dataset of 202 participants (day 0: n=202, day 14: n=188, day
 60: n=153). To further clean the dataset, hemoglobin genes, ribosomal RNA
 genes, and genes with low expression (median count &lt; 10) were
 filtered out, reducing the gene set to 20,533. Gene expression values were
 then normalized and log₂-transformed using the variance stabilizing
 transformation algorithm implemented in the DESeq2 package in R (v1.34.0)
 to enable downstream statistical analyses.<sup>34</sup> For
 each targeted pathway, a single sample enrichment score was calculated
 using the z-score method to evaluate the activity of the pathway at each
 time point for both whole blood transcriptomics and CSF proteomics, for
 each patient. <strong>Statistical
 analysis</strong> We followed the published
 statistical analysis plan of the LAST ACT clinical trial, a leukotriene A4
 hydrolase–stratified non-inferiority trial of adjunctive corticosteroids
 in HIV-negative adults with tuberculous meningitis (DOI:
 10.12688/wellcomeopenres.22498.2). The present dataset was used to analyse
 secondary endpoints described in Section 6, Measurements of blood and
 cerebrospinal fluid inflammation. We employed a joint
 modelling framework that combined a survival model with a linear
 mixed-effects model for longitudinal blood and CSF markers. The survival
 sub-model consisted of time to all-cause mortality within three months as
 the outcome, with covariates including treatment allocation and the
 subject-specific fitted values of longitudinal marker measurements. Models
 were estimated in a Bayesian framework using the R package JMbayes2. This
 approach accounts for informative dropout due to early death, occurring
 within the first 60 days for transcriptomic analyses and the first 30 days
 for proteomic analyses after randomisation.
description: # Data From: Genotype stratified adjunctive dexamethasone for tuberculous
 meningitis in HIV-negative adults: the LAST ACT trial Dataset DOI:
 [10.5061/dryad.f1vhhmh7v](10.5061/dryad.f1vhhmh7v) ## Dataset Overview
 This dataset contains the source data and codes required to replicate
 analyses for secondary endpoints - Measurements of blood and cerebrospinal
 fluid inflammation in the submitted manuscript. ### Principal Investigator
 Contact Information ``` Name: Thuong Nguyen Thuy Thuong
 Institution: Oxford University Clinical
 Research Unit - VN Email: thuongntt@oucru.org ``` ### Alternate Contact
 Information ``` Name: Nhat Le Thanh Hoang Institution: Oxford University
 Clinical Research Unit - VN Email: nhatlth@oucru.org ``` ``` Name: Hai
 Hoang Thanh Institution: Oxford University Clinical Research Unit - VN
 Email: haiht@oucru.org ``` ### Funding This work was supported by Wellcome
 Investigator award [110179/Z/15/Z] to GET and Wellcome Trust Fellowship in
 Public Health and Tropical Medicine to NTTT [206724/Z/17/Z]. ###
 Sharing/Access information The datasets generated and analysed during this
 study are published under the CC0 license waiver. We encourage you to cite
 this dataset and the associated publication when using the data. ##
 Description of the data and file structure Multiple files in this
 repository serve as source data for generating tables and figures related
 to the analysis of secondary endpoint number 6: Measurements of blood and
 cerebrospinal fluid inflammation (DOI: 10.12688/wellcomeopenres.22498.1).
 Missing values are coded as "NA". The files share a common
 description and structure, as outlined below. ### 1.
 01_Clinical_CSF_Cytokines_data.txt **Description:** Tab delimited text
 file containing metadata for CSF cytokines concentration and
 imunopathogenesis CSF pathway activity data **Variables:** Column headings
 defined below * ID: de-identifier for participants in Olink cohort
 (character) * day: timepoint which samples were collected (numeric - days)
 * group: treatment (binary - 0=control group; 1=treated group) * genotype:
 rs17525495 genotype (numeric - 0=0 minnor allele; 1=1 minor allele; 2=2
 minor allele) * event: 3-month mortality event (binary - 0=no; 1=yes) *
 timetoevent: time to mortality event (numeric - days), number of days from
 randomisation to either death or censoring at three months ### 2.
 02_CSF_Cytokines_data.txt **Description:** Tab delimited text file
 containing measurements for CSF cytokines concentration and
 imunopathogenesis CSF pathway activity **Variables:** Column headings
 defined names of cytokines or GO pathways * ID: de-identifier for
 participants in Olink cohort (character) * Other: Names of cytokines or GO
 pathways (numeric) **Measurement unit:**  CSF cytokine levels were
 reported as Normalized Protein eXpression (NPX) values, which were
 log₂-transformed, normalized using the plate control method, and
 batch-corrected using the ComBat function. Pathway activity was calculated
 separately using enrichment scores calculated by the z-score method (PMID:
 18989396), based on single-sample cytokine expression data from CSF
 proteomics. ### 3. 03_Clinical_Blood_pathway_data.txt **Description:** Tab
 delimited text file containing metadata for blood pathway activity data by
 RNA sequencing **Variables:** Column headings defined below * ID:
 de-identifier for participants in blood RNA sequecing cohort (character) *
 day: timepoint which samples were collected (numeric - days) * group:
 treatment (binary - 0=control group; 1=treated group) * genotype:
 rs17525495 genotype (numeric - 0=0 minnor allele; 1= 1 minor allele; 2=2
 minor allele) * event: 3-month mortality event (binary - 0=no; 1=yes) *
 timetoevent: time to mortality event (numeric - days), number of days from
 randomisation to either death or censoring at three months ### 4.
 04_Blood_pathway_data.txt **Description:** Tab delimited text file
 containing measurements for imunopathogenesis blood pathway activity
 **Variables:** Column headings defined names of cytokines or GO pathways *
 ID: de-identifier for participants in Olink cohort (character) * Other:
 Names of cytokines or GO pathways (numeric) **Measurement unit:**  Pathway
 activity was calculated separately using enrichment scores calculated by
 the z-score method (PMID: 18989396), based on single-sample gene
 expression data from both transcriptomics. ## Code/Software The majority
 of analyses used in the manuscript were done using R Environment for
 Statistical Computing version R 4.3.3. ### 1.
 05_Script_for_Tables_and_Figures.Rmd This R Markdown file contains the
 source code for generating Figures and tables related to secondary
 endpoints - Measurements of blood and cerebrospinal fluid inflammation.
 ### Dependencies or R packages * ftExtra * magrittr * ggplot2 * ggbeeswarm
 * lme4 * officer * flextable * bayesplot * gtsummary * JMbayes2 *
 data.table * C306 * rstatix * ggpubr * cowplot ## Human subjects data All
 participants provided written informed consent to take part in the study,
 or, in cases where they were incapacitated, consent was obtained from
 their legal representatives. The trial protocol was approved by the
 relevant ethics committees, including the Oxford Tropical Research Ethics
 Committee (OxTREC 52–16), the Ethics Committees of the Hospital for
 Tropical Diseases, Pham Ngoc Thach Hospital, and the Vietnam Ministry of
 Health. The dataset was de-identified by removing all direct identifiers
 (e.g., date of birth, participant ID) and replacing them with
 study-specific codes. Indirect identifiers were reviewed and aggregated,
 where necessary, to minimize the risk of re-identification. The final
 dataset contains no information that could reasonably be used to identify
 individual participants.