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Longitudinal clinico-genomic data generation, drugable target development and multi-sites validation for drug development enhancement

ABSTRACT: Longitudinal clinico-genomic data generation, drugable target development and multi-sites validation for drug development enhancement

PROVIDER: PRJNA762571 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR15859900_1.fastq.gz	Fastqsanger.gz
	SRR15859900_2.fastq.gz	Fastqsanger.gz
	SRR15859901_1.fastq.gz	Fastqsanger.gz
	SRR15859901_2.fastq.gz	Fastqsanger.gz
	SRR15859902_1.fastq.gz	Fastqsanger.gz

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Project description:The Rationale: Molecular markers of disease activity that are predictive of forced vital capacity (FVC) progression in idiopathic pulmonary fibrosis are needed. Objectives: Develop a predictor using longitudinal within-patient gene expression differences (ΔGE) in peripheral blood mononuclear cells (PBMC) to predict of FVC progression. Methods: Patients in the training cohort (n=74) experiencing ≥10% relative reduction in FVC% of predicted over 12 months were categorized as progressors in contrast to the remaining stable patients. Baseline to 4-month within-patient ΔGE were correlated with FVC status. FVC-predictor genes were prioritized by two-group comparison with FDR<5%, logistic LASSO regression with p<0.05, and 10-Fold Cross-Validation with ≥50% support. Receiver operating characteristic with area under the curve (AUC) analyses were conducted in training subsets and independent validation cohorts from UChicago (n=27), UPMC (n=35), and Imperial (n=24) where different transcriptome assay platforms and varying transcriptome sampling times were used to derive ΔGE. Results: Intra-subject Compared to cross-sectional analysis of baseline GE, our longitudinal ΔGE variation approach demonstratedlargely reduced within-group sample variation and increased statistic power fin progressor and stable groups for prediction model development. A 25-gene FVC-predictor separated “progressors” from “stable” by Principal Component Analysis in the training and subsets of the training cohort. The resulting FVC-predictor consistently demonstrated high discriminatory performance independent of transcriptome assay platforms and varying sampling times in validation cohorts (AUC= 0.77-0.80). TGF beta was the highest-ranking canonical pathway by Gene Set Enrichment Analysis. Conclusions: Our novel short-term longitudinal within-patient ΔGE approach identified a FVC-predictor which may reflect disease activity and prove to be a reliable biomarker predictive of future FVC decline.

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Longitudinal clinico-genomic data generation, drugable target development and multi-sites validation for drug development enhancement

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