Technical Note: Informatics

Immuno-oncology Informatics Applications Using
Next-Generation Sequencing
Improved informatics tools enable neoantigen discovery and tumor microenvironment analysis
with standard Illumina sequencing applications.
Introduction
Immuno-oncology is an emerging field of research that represents
tremendous promise for both patients and those fighting the war
on cancer. It is also an opportunity for the scientific community togain a better understanding of the underlying biological mechanisms
involved in the interactions between tumors and the immune system.
New discoveries are leading to improved genomic solutions capableof driving development of novel therapeutics and predict patient
response to immunotherapies. Next-generation sequencing (NGS)
is a key technology being applied to these discoveries. Illumina hasdeveloped sequencing tools and applications that make it easy for any
research group to begin their own immuno-oncology experiments.
Some forms of immunotherapy were developed to block the ability ofcancers to avoid immune destruction, allowing researchers to target
tumors in a personalized way.1 Ideally, mutated proteins resulting fromtumors with nonsynonymous somatic mutations are presented as
antigens on the surface of cancer cells for recognition by the immunesystem. However, cancer cells have implemented various strategies toavoid recognition and eradication by the immune system. The first is toexpress components of the immune checkpoint pathway that inhibit T
cell activation, such as PD-L1. A second strategy is to alter the tumor
microenvironment by recruiting tumor associated macrophages andregulatory T cells that block infiltration of cytotoxic T cells.2
Checkpoint inhibitors, such as anti-CTLA4 and anti-PD1 antibodies,
have recently received FDA approval and shown efficacy in a subsetof patients with certain cancers such as melanoma and nonsmall
cell lung cancer. Evidence also suggests that cancer patients witha higher number of nonsynonymous mutations have improved
response to checkpoint inhibitor therapy.3-5 A more personalized
type of immunotherapy is the development of a cancer vaccine
based on neoantigens created by tumor-specific nonsynonymous
DNA alterations.6 Neoantigens can persistently stimulate cytotoxic
T lymphocytes, which may eradicate cancer cells in the presence ofcheckpoint inhibitors or a favorable immune microenvironment.
This application note describes 2 immuno-oncology workflows. The
first workflow uses whole-exome sequencing (WES) and wholetranscriptome sequencing (WTS) to predict neoantigens that cancer
cells present. Studies show that overall mutational load and specificneoantigen signatures may predict response to immuno-oncology
therapies.3 The second workflow uses WTS to characterize thetumor microenvironment in a broad survey of important expression
markers (eg, immune modulatory genes, tumor-specific variants,and fusions). These tools enable detection of low-expressing
targets with high confidence. In contrast, it is challenging forhybridization-based technologies to reach such breadth andFor Research Use Only. Not for use in diagnostic procedures.

sensitivity. WES and WTS sequencing can be performed using theNextSeq® or HiSeq® Series of Illumina sequencing systems. Illumina
sequencing kits are optimized for library preparation from FFPE or
fresh/frozen samples. Illumina TruSeq® Exome and TruSeq RNAAccess Library Preparation Kits use the same sequence-specificcapture technologies to target exonic regions. The matchedcontent enables researchers to target and compare equivalentregions of RNA and DNA. Both workflows use the BaseSpace®
Informatics Suite, providing any researcher with rapid analysis ofNGS data and flexible characterization ofthe immuno-oncology profile of a tumor sample.

BaseSpace Sequence Hub
Isaac Enrichment

RNA-Seq Alignment

Tumor/Normal WES FASTQ

Tumor WTS FASTQ

Variant Calling
Assessment Tool
Tumor
WES VCFTumor
WTS VCFIntersected VCF

VariantStudio
NormalWES VCF
Nonsynonymous Variants
Gene Table

Neoantigen
prediction

GeneFPKM

Figure 1: Neoantigen DIscovery Workflow—Using BaseSpace Sequence
Hub apps, NGS analysis of immune pathways is efficient and accessible toresearchers without informatics experience. VCF = variant call file.
FPKM = fragments per kilobase of transcript per million mapped reads.

Technical Note: Informatics

Neoantigen Discovery byIntegrating WES and WTS Analysis

60%

This protocol summarizes the steps for generating neoantigen
candidates, using BaseSpace Sequence Hub apps to analyze datafrom a combined WES and WTS study (Figure 1). To begin with, WES
tumor-normal and WTS tumor data are streamed from the sequencer
directly to BaseSpace Sequence Hub. After sequencing is complete,
the respective FASTQ files are made available in Sequence Hub. Data
analysis can be performed in 4 main steps:

FPKM
40%

RNA VAF

1. Use the Isaac Enrichment v2.1 App in BaseSpace Sequence Hubfor WES alignment and variant calling of matched tumor-normal
samples. Inputs are tumor WES FASTQ and normal WES FASTQ
files. The output of the Isaac Enrichment App is a fully annotatedvariant call file (VCF).
2. Perform WTS alignment, gene expression estimation, and variant
calling in parallel using the RNA-Seq Alignment v1.1 App in
BaseSpace Sequence Hub. The inputs to the app are tumor WTS
FASTQ files. The outputs from the app are a WTS VCF file and agene expression count file (raw and FPKM). The gene expression
quantification is used in the final step of neoantigen prediction.
3. Filter out false positive variants from the tumor data to focus on
expressed variants from a pairwise intersection of the WES andWTS tumor variants using the Variant Calling Assessment Toolv2.3 App in BaseSpace Sequence Hub. Inputs are the tumor WES
VCF file from step 1, and the tumor WTS VCF file from step 2. The
output is an intersected VCF file, which contains only the variants
present in both the WES and WTS tumor data.
4. Determine the variants relevant for neoantigen prediction, theWES/WTS-intersected tumor VCF file, and the WES normal
VCF file are loaded into the VariantStudio App in BaseSpaceSequence Hub. With the click of a button, all intersected variants
are annotated with transcript info, consequences, population
frequencies, and materials extracted from other external databases(such as Cosmic, ClinVar, and dbSNP). The intersected variants
are then filtered in VariantStudio by first subtracting out the variantsfrom the WES normal samples. The remaining variants are filtered
to retain nonsynonymous variants and variants that are present inless than 1% of the population. These variants are then exported
and used for neoantigen prediction.

50%

100

50

30%

2052120%

Variant type
Missense

10%

0%
0%

NeoORF10%20%HLA Genotyping Using WES Data

HLA Class I Binding Affinity Prediction

50%60%70%80%

Figure 2: Neoantigen Characterization with Bubble Plots—Identification ofpotential neoantigens can be based on mutational type (blue: missense, yellow:neoORF), DNA and RNA allele frequencies (x and y values), and gene expression
levels (circle sizes). VAF = variant allele frequencies. FPKM = fragments perkilobase of transcript per million mapped reads.

Ideal neoantigen candidates have high variant allele frequencies (VAF)
in both tumor DNA and RNA samples. Additional prioritizations occurby variant type, such as frameshift, splice site mutations resulting in
neo-open-reading-frames (neoORFs). Some of these variants are entirely
specific to the tumor compared to missense mutations. Medium to highRNA expression can be selected to further filter candidate neoantigens.
Based on the above criteria, final neoantigen candidates can be prioritizedbased on DNA and RNA VAFs, mutational type (missense vs neoORF),
and gene expression levels (Figure 2).
Potential neoantigens can be further filtered according to their affinity
to individual HLA class 1 types. Commonly used tools include mass
spectrometry analysis and/or in silico algorithms for MHC-associatedneoantigen prediction. Several in silico prediction algorithms areavailable online (Table 1).

Algorithm

URL

Polysolver

www.broadinstitute.org/cancer/cga/polysolver7

OptiType

github.com/FRED-2/OptiType8

netMHCwww.cbs.dtu.dk/services/NetMHC/9

SYFPEITHI

www.syfpeithi.de/10

For Research Use Only. Not for use in diagnostic procedures.

40%

DNA VAF

Table 1: In-silico Prediction Algorithms
Application

30%

Technical Note: Informatics

Tumor Microenvironment Analysis
To assess the tumor microenvironment, WTS samples are streamed fromthe sequencer directly to BaseSpace Sequence Hub. After sequencing
is complete, the respective FASTQ files are made available in Sequence
Hub. Data analysis can be performed in 2 steps (Figure 3A):
1. Perform WTS alignment and gene expression estimation of thetumor-normal samples using the RNA-Seq Alignment v1.1 App
in BaseSpace. To enable easy visualization of relevant tumor
microenvironment genes, an immuno-oncology filter is available todisplay gene expression count information only for genes relevant
to immune modulatory and cancer pathways (Figure 3B). Output
files, including binary alignment map (BAM) files, are automaticallyrecognized by the Differential Expression App in the next step.
The same app also performs FPKM estimation, assembly of noveltranscripts, variant calling, and optional fusion calling.

2. Perform differential expression analysis between a matched tumornormal sample using the Cufflinks Assemble and DE v2.1 or theDESeq v2.1 App in BaseSpace Sequence Hub. The DifferentialExpression App also provides the option to generate only content
relevant to the tumor microenvironment, with the results from step
1 as input. Output includes a global analysis report summarizing
statistics including sample correlations (Figure 3C), differential
expression heat map incorporating differentially expressed
immuno-oncology genes, and a gene browser with filters on
various parameters (Figure 3D).
Optionally, researchers can use the list of differentially expressed
genes (their signature) for downstream pathway analysis or gene sets
enrichment analysis. Commonly used algorithms are available online(Table 2).

Table 2: Algorithms for Pathway Analysis and Gene Set Enrichment
Application

Description

URL

BaseSpace Correlation Engine

Compares signatures to over 20,000 other studies and pathways

www.nextbio.com/b/nextbio.nb11

DAVID Bioinformatics Resources

Tests signatures for enrichment against pathways, gene ontology and moredavid.ncifcrf.gov/12

Gene Set Enrichment Analysis (GSEA)

Tests signatures for enrichment against a database of signatures

software.broadinstitute.org/gsea/index.jsp13

A
BaseSpace Sequence Hub

B

RNA-Seq Alignment(Immuno-oncology filter)

Downstream
Pathway/Gene Sets
Enrichment AnalysisCufflinks Assembly & DE
(Immuno-oncology filter)

CDFigure 3: Tumor Microenvironment Analysis Workflow Using BaseSpace Sequence Hub Apps—A. BaseSpace workflow includes an alignment step
followed by differential expression analysis. B. An immuno-oncology filter is available for both steps, enabling output that only displays genes relevant to the tumor
microenvironment. C. Output from differential expression analysis, showing sample correlations for global gene expression analysis. D. Output from differentialexpression analysis, showing heat maps and scatterplots for immuno-oncology genes.

For Research Use Only. Not for use in diagnostic procedures.

Technical Note: Informatics

Conclusion

References

NGS technology from Illumina has contributed to a wealthof information about cancer genomics, including specialized
applications for DNA and RNA profiling of tumors and the hostenvironment. Using new informatics tools, proven NGS methods
can be further customized for applications in immuno-oncology. The
BaseSpace Sequence Hub can be integrated with the NextSeq andHiSeq Series of Sequencing Systems, using TruSeq Exome and RNA
Access Library Prep Kits. The BaseSpace Informatics Suite provides
flexibility and efficiency of analysis, making immuno-oncology
research accessible to any research group.

1.

Learn More
For more information on NGS applications for immuno-oncology, visit
www.illumina.com/immuno-oncology

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For Research Use Only. Not for use in diagnostic procedures.
© 2016 Illumina, Inc. All rights reserved. Illumina, BaseSpace, HiSeq, NextSeq, TruSeq, and the pumpkin orange color are trademarks ofIllumina, Inc. and/or its affiliate(s) in the U.S. and/or other countries. Pub. No. 1170-2016-013 Current as of 07 April 2016