Drug Response RNA Biomarker Discovery and Profiling

Drug Response RNA Biomarker Analysis

RNA sequencing (RNA-Seq) is increasingly being utilized for the discovery of and profiling for RNA-based drug response biomarkers with the aim of improving the efficiency and success rate of the drug development process. While a number of technologies have been used for this application, the capabilities of RNA sequencing promise to be of particular benefit 1,2,3. Consequently, there is a growing need to extend the accessibility of RNA sequencing-based workflow solutions for this application to a broader range of potential users, including those without prior experience with next-generation sequencing (NGS).

Towards that end, the resources below are designed for users of any level of NGS experience who are considering adopting this application. They contain information that we have found to be particularly helpful across multiple stages of the adoption process, from understanding the steps of an RNA sequencing workflow, to matching configuration options to specific program requirements, to preparing a plan for rapid navigation through the implementation process.

RNA Drug Response Biomarker Discovery and Profiling
Application
Overview

An introduction to RNA-Seq drug response biomarker discovery and profiling.

Workflow
Introduction

 

Key considerations, requirements and recommended components for multiple application use-cases.

Best
Practices

 

"How-to" guidance to facilitate workflow implementation.

Start-up
Advice

 

Tips from fellow application users and Illumina experts on how to get up and running quickly and smoothly.

Analysis Pipeline
Review

 

A screenshot-based walk-through from raw data through outputs needed to inform candidate assessment and prioritization.

Section 1: Application Overview

This section provides an overview of RNA-based drug response biomarker discovery and profiling. It includes a review of methods for this application, including quantitative PCR (qPCR) and gene expression arrays, and the respective strengths and limitations of each. It also reviews the benefits provided by NGS-based workflows and practical considerations about implementation in your program.

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Section 1: Application Overview
Section 2: Workflow Introduction

This section introduces our recommended RNA-Seq workflows for drug response biomarker discovery and profiling, and outlines the process, from starting total RNA sample through analyzing data.

At each step, the following will be included:

  • A high-level description of every step of the process
  • Key points to consider when selecting a solution
  • Outline of recommended solution(s)
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Section 2: Workflow Introduction
Workflows and Products for RNA Biomarker Discovery
Step Library Prep Sequencing Feature detection Biomarker candidate ID Filtering / prioritization
Requirements
  • FFPE sample compatibility
  • Coding transcriptome coverage; option to capture coding and noncoding RNA
  • Minimal total RNA input requirement
  • Solution compatible with 1s to mid 10s of samples/week
  • Solution compatible with 10s to low 100s of samples/week
  • Measure gene, transcript expression
  • Detect known, novel gene fusions
  • Detect known, novel single nucleotide variants (SNVs)
  • Identify expression/ response associations
  • Identify SNV, fusion/ response associations
  • Identify outliers within cohorts
  • Integrate RNA-Seq data w/array, quantitative PCR (qPCR)
  • Identify correlations w/disease outcome (false positives)
  • Identify correlations w/compounds, knockout (KO), tissue profiles
  • Identify known fusion, SNV gene locus associations
Component
Workflows and Products for RNA Biomarker Profiling
Step Library Prep Sequencing Biomarker detection
Requirements addressed
  • FFPE sample compatibility
  • Develop custom panels for known targets
  • Detect novel fusions, transcripts, SNVs in focused regions
  • Minimal total RNA requirement
  • Solution for known targets compatible with 10s to mid 1000s of samples / week
  • Solution for focused discovery compatible with 10s to mid 100s of samples / week
  • Measure gene, transcript expression
  • Call known gene fusions, or novel gene fusions in focused regions
  • Call known SNVs, or novel SNVs in focused regions
Component
Section 3: Best Practices

This section outlines sequencing-related design parameters that will need to be addressed ahead of planning your study. Included are considerations pertaining to read length, read depth, sequencer output modes, and other variables that should be considered to match the requirements of your program. Also captured are practical considerations related to how transitioning to RNA-Seq from platforms such as quantitative polymerase chain reaction (qPCR) and gene expression (GEX) arrays may affect day-to-day operations, and how you might best prepare.

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Section 3: Best Practices
Section 4: Start-up Advice

Experts across multiple functional areas, as well as users within the pharmaceutical industry currently running this application, offer advice to new users.

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Section 4: Start-up Advice
Section 5: Analysis Pipeline Review

Data analysis has historically been one of the most challenging barriers to the adoption of NGS workflows. This has been due, in part, to uncertainty about whether the desired endpoint for a particular application can be reached, what that process entails, and what level of expertise is required. This section provides a holistic view of our recommended analysis pipeline for this application, broken down into feature discovery, identification of biomarker candidates, and biomarker filtering and prioritization. For each work stream within the broader pipeline, a step-by-step, screen shot-based walk-through of the Illumina solution is provided.

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Section 5: Analysis Pipeline Review
References
  1. Zhao S, Fung-Leung W-P, Bittner A, Ngo K, Liu X. Comparison of RNA-seq and microarray in transcriptome profiling of activated T cells. PLoS ONE. 2014;9(1):e78644. doi:10.1371/journal.pone.0078644.
  2. Atak ZK, Gianfelici V, Hulselmans G, et al. Comprehensive analysis of transcriptome variation uncovers known and novel driver events in T-cell acute lymphoblastic leukemia. PLoS Genet. 2013;9(12):e1003997.
  3. Kumar-Sinha C, Kalyana-Sundaram S, Chinnaiyan AM. Landscape of gene fusions in epithelial cancers: seq and ye shall find. Genome Med. 2015;7:129.