A new blood-based RNA signature (R9), for monitoring effectiveness of tuberculosis treatment in a South Indian longitudinal cohort

Abstract

Tuberculosis (TB) treatment involves a multidrug regimen for six months, and until two months, it is unclear if treatment is effective. This delay can lead to the evolution of drug resistance, lung damage, disease spread, and transmission. We identify a blood-based 9-gene signature using a computational pipeline that constructs and interrogates a genome-wide transcriptome-integrated protein-interaction network. The identified signature is able to determine treatment response at week 1-2 in three independent public datasets. Signature-based R₉-score correctly detected treatment response at individual timepoints (204 samples) from a newly developed South Indian longitudinal cohort involving 32 patients with pulmonary TB. These results are consistent with conventional clinical metrics and can discriminate good from poor treatment responders at week 2 (AUC 0.93(0.81-1.00)). In this work, we provide proof of concept that the R₉-score can determine treatment effectiveness, making a case for designing a larger clinical study.

Keywords: Biological sciences; Clinical finding; Medical microbiology; Medicine.

Graphical abstract

Figure 1

CONSORT flow diagram for BLTB and Healthy cohort (A)Diagnosis and inclusion of subjects with active TB in BLTB cohort. Total 204 blood samples from the included 32 subjects were collected at week 2, 3, month 1, 2, 3, 4, 5, 6, of TB treatment and month 8, 10, and 12 after treatment. (B) Diagnosis and inclusion of healthy individuals. Included 22 subjects were IGRA-ve (Interferon Gamma Release Assay), HIV-ve (HIV), with normal chest X-ray and hemogram profile. ∗Demographic profile for subjects enrolled in study as BLTB and Healthy cohort is mentioned in Tables 1,S1C, and S1D.

Figure 2

The biomarker discovery pipeline for blood-based TB treatment prognosis markers The various filtration steps of the pipeline (blue) along with the number of genes selected at each step (black) are represented. Datasets used for the study are colored in red. Two public datasets, GSE89403-Thompson and GSE122485-Sambarey, were used for biomarker discovery. For validation and performance evaluation of the identified biomarkers, two independent public datasets GSE40553-Bloom and GSE31348-Cliff along with newly developed BLTB cohort were used. TB: active-TB/treatment-naive TB, TR(6M): 6-month TB treatment, HC: Healthy control ∗Demographic profile for subjects enrolled in study as BLTB and Healthy cohort is mentioned in Tables 1,S1C, and S1D.

Figure 3

The response network of TB₀ vs.TR The response network of TB₀ vs. TR showing the top-ranked perturbations which form a connected subnetwork and are common between discovery datasets (GSE89403-Thompson and GSE122485-Sambarey). Genes belonging to same functional category are clustered into uniquely color-coded modules. Significantly enriched (q-value < 0.05) pathway information from the Reactome database for these modules is also indicated. The network is provided in Table S2B.∗Steps required to carry out network analysis along with the scripts are detailed in Data S1, S2, and S3.

Figure 4

Performance of the 9-gene panel in public cohorts (A and B) Receiver operating characteristics (ROC) curves for distinguishing TR and TB₀ at different treatment time points for (A) Discovery dataset (GSE89403-Thompson) and,(B) Validation datasets (GSE40553-Bloom (SA) and GSE31348-Cliff). The area under the curve (AUC) with 95% CI (CI) values are indicated at each time point for a given dataset in a different color. AUC was observed to increase from the start (week 1 or week 2) to end (week 24 or month 6) of TB treatment depicting treatment progression in subjects with TB.

Figure 5

R₉-scores for treatment response groups of subjects with active TB in the BLTB cohort (A) A boxplot representing a significantly higher R₉-scores (t-test p < 10⁻⁵) in good responders compared to poor responders across all the time points of patients in BLTB cohort. The horizontal line in the middle of the box shows the medians; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5 times the IQR from the 25th and 75th percentiles. The x axis represents the response category for patients in BLTB cohort, and the y axis indicates the R₉-score. The black dots represent R₉-score for individual subjects at an individual time point. The median R₉-score value (red dot) is connected by a red line to indicate the score trend between patient groups. A zoomed-in portion for the R₉-score comparison between the patient category is shown (left panel). (B) A heatmap representing the R₉-score for individual subjects (columns) at each time point (rows).R₉-score >1.5 is green, 1 ±0.5 is yellow and < 0.5 is red. Gray cells indicate missing data points. An agreement (qualitative match) between R₉-score and clinical evaluation is represented by a tick and disagreement is represented by a cross. (C) The R₉-score matrix for additional seven good responders with data for less than four treatment time points (see Figure 1A). (D) A stacked bar plot representing the agreement between the R₉-score and the clinical data for each treatment response category in the BLTB cohort. The x axis represents the patient response group, and the y axis represents all available R₉-score data points for that response group's subjects. A qualitative match (agreement) between clinical evaluation and R₉-score trend is colored in cyan and the total percentage of match is represented. A mismatch (disagreement) in the clinical evaluation and R₉-score trend is colored in red and the total percentage is represented above the bar. The 95% CI (CI) value is also indicated for each patient category. (E) Receiver operating characteristics (ROC) curves for distinguishing good and poor responders at Week 2 of TB treatment therapy using R₉-score. The area under the curve (AUC) with 95% CI value is indicated for the ROC curve. The optimal threshold for the curve is 0.64 where the TPR is high and FPR is low. ∗qRT-PCR data for each patient with calculated RQ and RCN values are provided in Data S4.

Figure 6

R₉-score, TB-score, and RESPONSE5 score comparison in three public cohorts and the BLTB cohort (A–N) Boxplot showing R₉-score for patients from (A) GSE31348-Cliff, (B) GSE40553-Bloom, (C and D) GSE89403-Thompson, and (J and M) BLTB dataset; TB-score for patients from (E) GSE31348-Cliff, (F) GSE40553-Bloom, (G and H) GSE89403-Thompson, and (I and L) BLTB dataset; RESPONSE5 score for patients from (K and N) BLTB dataset. The horizontal line in the middle of the box shows the medians; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5 times the IQR from the 25th and 75th percentiles, and dots represent outliers. The y axis represents the score, and the x axis represents the treatment time. For the BLTB dataset, the individual patient score is represented in a black dot. The median (red dot) at different time points are connected through a red trace. The R₉-score was observed to increase upon TB treatment indicative of response to therapy (A–C and J). After month 6 of TB treatment, the R₉-score showed no change/decrease in poor responders of BLTB (M) and “not cured” or failure cases of the GSE89403-Thompson (D) dataset, depicting poor or no response to TB treatment therapy. TB-score (I and L) and RESPONSE5 score (K and N) were observed to decrease upon TB treatment in patients in BLTB cohort (both good and poor responders), thus failing to capture poor responders. RESPONSE5 score could not be computed for the three public cohorts as the score formulation is based upon the qRT-PCR based Ct values of the genes, which were not available. Multigroup comparison was performed with ANOVA for each cohort, and two-group comparisons (for selected time points) were performed using Wilcoxon-test, and the p value is represented. ∗p< 0.05, ∗∗p< 0.01, ∗∗∗p<0.001. ∗Individual patient scores at different treatment time points are provided in Table S6.