Understanding correlations between mutations and therapeutic approaches

NGS methods enable efficient assessment of tumor mutational burden and identification of neoantigens

Tumor Mutational Burden & Neoantigen Analysis

Tumor mutational burden (TMB), the number of somatic mutations within the coding region of a tumor genome, is an emerging biomarker that correlates with response to immunotherapeutic agents such as checkpoint inhibitors.1-4. Recent studies indicate that a high tumor mutational burden, or load, increases the likelihood that immunogenic neoantigens expressed by tumor cells may induce a response to immunotherapy.1-4 

Next-generation sequencing (NGS) can help researchers estimate TMB, identify neoantigens, study innovative therapies to boost the immune response, and understand how genetic variation can influence their efficacy. Characterization of expressed neoantigens has also contributed to development of vaccines and cell-based therapeutic methods. 

Using NGS to Aid the Immune System in Targeting Cancers

An overview of exciting new fields of immunotherapy research, and how NGS helps to move them forward.

Access PDF

While the clinical utility of tumor mutational burden is being defined, continuing efforts to standardize TMB analysis are ongoing.

Recent studies have demonstrated that tumor mutational burden can be effectively estimated using targeted sequencing panels.5-6 Specific features are recommended to enhance accuracy in TMB scoring, such as a minimum size of 1.5 Mb genomic content, and algorithms to filter variants that are unlikely to contribute to immunogenicity. 5-6

TMB and MSI Analysis with Targeted NGS

Comprehensive coverage of cancer-related variants and improved filtering algorithms enable robust analysis of immunotherapy biomarkers.

Access PDF

Mutations in protein coding genes of cancer cells are a source of potential neoantigens that the immune system can target. NGS has enabled the predictive selection of novel tumor antigens that can be applied to elicit a tumor-specific response.

DNA and/or RNA is efficiently characterized by exome sequencing and/or transcriptome sequencing. Improved bioinformatics tools aid neoantigen selection by predicting the presentation of mutant peptides for recognition by the immune system.

Informatics Solutions for Neoantigen Discovery

Improved informatics tools enable NGS-based neoantigen prediction and tumor microenvironment research.

Read Tech Note
Measuring Tumor Mutational Burden
The Importance of Panel Size in Measuring TMB

A study by Dr. Albrecht Stenzinger and his colleagues investigated the influence of gene panel size on the precision of tumor mutational burden measurement.

Read Article
Deciphering the Role of lncRNA in Cancer
RNA Editing

Learn why Professor Jo Vandesompele, PhD says RNA editing could be valuable in assessing tumor mutational burden and identifying neoantigens through gene expression.

Read Interview

Illumina offers several library preparation and sequencing options with access to data analysis options for tumor mutational load and neoantigen identification. Streamlined workflows and flexible kit configurations accommodate multiple study designs.

Approximately 90% of the world’s sequencing data are generated using Illumina sequencing by synthesis (SBS) chemistry.*

Click on the below to view products for each workflow step.

TruSight Oncology 500

Assay targeting multiple variant types, including tumor mutational burden and microsatellite instability (MSI), even from low-quality samples.

TruSeq DNA Exome

A cost-effective library preparation and exome enrichment solution.

TruSeq RNA Exome

A low-cost solution for analyzing human RNA isolated from limited or low-quality samples, including FFPE.

TruSeq Stranded Total RNA Library Prep Kit

A robust, highly scalable whole-transcriptome analysis solution for a variety of species and sample types, including FFPE.

NextSeq 550 System

Flexible configurations that support up to 12 exomes per run.

NovaSeq 6000 System

Flexibility and scalable throughput for virtually any genome, sequencing method, and scale of project.

BaseSpace Sequence Hub Apps

Filter by RNA-Seq.

Enrichment BaseSpace App

The Enrichment app rapidly aligns samples using Isaac and performs indel, SNV, CNV, and SV analysis and annotation.

Size Matters: Dissecting Key Parameters for Panel-Based Tumor Mutational Burden (TMB) Analysis.

Int J Cancer 2019;144(4):848-858

Read Abstract

Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy.

Science 2018;362(6411)

Read Abstract

Tumor mutational load predicts survival after immunotherapy across multiple cancer types.

Nat Genet. 2019 doi: 10.1038/s41588-018-0312-8.

Read Abstract

Exome Sequencing

Representing less than 2% of the genetic code, exome sequencing is a cost-effective alternative to whole-genome sequencing. It is a commonly used method for identifying neoantigens.1-3

Learn More
RNA Sequencing

RNA sequencing provides not only gene expression measurement, but also information on single nucleotide variants and splicing variants.

Learn More
Targeted Cancer Sequencing

For some applications, targeted sequencing panels may provide efficient assessment of tumor mutational burden.4,5

Learn More
Participant Stratification in Immuno-Oncology
Participant Stratification in Immunotherapy Research

Genomics scientists discuss how advances in immuno-oncology research resulting from NGS help stratify subjects for clinical trials.

View Webinar
Alt Name
Using RNA-Seq to Aid the Immune System in Targeting Cancers

Annika Sonntag discusses the Immatics platforms for identification and validation of targets for T cell-based immunotherapies.

Access PDF
Interested in receiving newsletters, case studies, and information on cancer genomics?
Sign Up
References
  1. Rizvi NA, Hellmann MD, Snyder A, et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348 (6230):124-128.
  2. Snyder A, Makarov V, Merghoub T, et al. Genetic Basis for Clinical Response to CTLA-4 Blockade in Melanoma. N Engl J Med. 2014;371(23):2189-2199.
  3. Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015;350(6257):207-211.
  4. Garofalo A, Sholl L, Reardon B, et al. The impact of tumor profiling approaches and genomic data strategies for cancer precision medicine. Genome Med. 2016;8(1):79. doi:10.1186/s13073-016-0333-9.
  5. Buchhalter I, Rempel E, Endris V, et al. Size Matters: Dissecting Key Parameters for Panel-Based Tumor Mutational Burden (TMB) Analysis. Int J Cancer. 2018. doi: 10.1002/ijc.31878.
  6. Chalmers ZR, Connelly CF, Fabrizio D, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 2017;9(1):34. doi: 10.1186/s13073-017-0424-2.

*Data calculations on file, Illumina, Inc, 2017.