Case studies

RNA-Seq Analysis of PDX Samples

Overview

This case study highlights our robust RNA-seq analysis pipeline for patient-derived xenograft (PDX) models to derive biological insights in cancer biology. Using advanced QC, demousing, and bioinformatics data transformation, we enabled the identification of actionable biomarkers for therapy and survival in multiple cancer models, contributing to advanced biomarker discovery efforts.

Our client

Our client

A US-based precision medicine biotech company focused on oncology research partnered with us to extract deeper insights from their PDX (patient-derived xenograft) RNA-seq data. They sought a trusted RNA-seq analysis pipeline partner capable of handling large-scale sequencing datasets, identifying therapeutic biomarkers, and integrating survival analysis for translational research in cancer biology.

Client’s challenge

Client’s challenge

  • The client had raw RNA-seq data from PDX models containing mouse stromal RNA contamination.
  • They needed to isolate human tumor-specific reads, analyze gene expression changes, and understand disease mechanisms through Volcano plots, survival analysis, and pathway enrichment for effective biomarker discovery.
Client’s goals

Client’s goals

  • Demouse RNA-seq FASTQ files from patient-derived xenograft models
  • Ensure data integrity using robust QC tools for cleaner cancer biology insights
  • Identify differentially expressed genes (DEGs) via an automated RNA-seq analysis pipeline
  • Visualize and interpret DEGs using Volcano plots and pathway enrichment
  • Integrate survival data for prognostic biomarker discovery using advanced data transformation

Our approach

We developed an end-to-end RNA-seq pipeline combining demousing, QC, quantification, and statistical modeling:

Data Quality Control

Tools like FASTQC, MultiQC, RSeQC, Picard, Preseq, dupRadar, and Qualimap ensured clean input for reliable biomarker discovery in cancer biology. 

Demousing with Xengsort

Isolated human tumor reads from PDX models to focus on relevant RNA-seq data transformation. 

Alignment & Quantification

Used STAR aligner and RSEM to produce normalized count tables across various cancer biology types. 

Differential Expression Analysis

Applied DESeq2, edgeR, and limma to identify key DEGs relevant to biomarker discovery. 

Volcano Plots & Enrichment

Visualized DEGs and enriched pathways (ORA, GSEA) related to cancer biology mechanisms and biomarker discovery. 

Survival Integration

Merged expression data with clinical outcomes, enabling insightful survival analysis and predictive biomarker discovery. 

PDX RNA-Seq Analysis Pipeline for Cancer Models & Biomarker Discovery

Our solution

  • Delivered demoused count matrices tailored for each cancer biology model
  • Created clear Volcano plots showing statistically significant DEGs for downstream biomarker discovery
  • Mapped genes to critical pathways linked with tumor progression and therapy resistance
  • Enabled advanced survival analysis by linking clinical data to gene expression profiles
  • Ensured high-quality RNA-seq data transformation using a proven RNA-seq analysis pipeline.
PDX RNA-Seq Analysis Pipeline for Cancer Models & Biomarker Discovery
PDX RNA-Seq Analysis Pipeline for Cancer Models & Biomarker Discovery

Conclusion

Excelra’s customized RNA-seq analysis pipeline for PDX models empowered the client to unlock meaningful discoveries in cancer biology. The integration of survival analysis with expression profiling led to the identification of clinically relevant biomarkers, streamlining precision medicine through expert data transformation and accelerated biomarker discovery.