Description of the technology
Tumor rejection antigens allow tumors sufficiently distinct from normal tissue to
activate the immune system and induce an efficient anti-tumor response. Tumor mutated
specific antigens (TMSA, neoantigens) without central tolerance are major tumor rejection
antigens. The recent developments of innovative deep sequencing technologies (at an
affordable cost) along with advances in bioinformatics have enabled systemic analysis
of the mutation load of the tumor as well as identification of the potentially immunogenic
neoantigens. T cell reactivity against these predicted neoantigens can then be analyzed
[1, 2]. This novel approach allows the discovery of the mutated genes in individual
tumors and assessment of the immunogenicity of these neoepitopes. It consists of several
key steps as illustrated in Fig. 1, including a) sample collection and storage, b)
whole exome sequencing to identify the mutations by using different computational
and mutation calling tools, c) RNA-seq analysis to focus specifically on the expressed
mutations, d) identification of neoepitopes in silico with computational algorithms
for MHC class I and class II binding as well as e) use of tandem minigene libraries
for class II epitope screening and f) neoantigen specific T cell assays to differentiate
trueimmunogenic neoepitopes from putative ones. Tumor and non-transformed cells (usually
PBMCs) from the same patients can be sequenced to determine the mutation load and
the full range of genomic alterations within a tumor, such as nucleotide substitutions,
structural rearrangements and copy number alterations. The data to date indicate that
the vast majority of mutated antigens are not shared between patients, and are considered
patient-specific [1]. The genetic landscape and the full spectrum of genomic alterations
in each individual tumor provide potential guidance for personalized cancer immunotherapy
and precision oncology.
Fig. 1
Current potential pipelines of whole exome sequencing for neoantigen discovery and
precision oncology. After sample collection, whole exome sequencing can be performed
on both tumor and non-transformed cells from the same patient. Once tumor specific
mutations are identified, RNA-seq can be utilized to determine the level of expression
of the mutations. Computational tools and/or a tandem minigene library are used to
identify the neoepitopes, T cell assays to narrow down the true immunogenic neoepitope
for efficient assessment and precise prediction and neoantigen vaccination targets.
Neoantigen discovery also provides guidance for adaptive neoantigen T cell transfer
therapy and combination immunotherapy
Type of data obtained/readout
Deep sequencing to assess the mutations present within the protein-encoding regions
of the genome (the exome) of an individual tumor will generate a unique set of data
for each tumor. Whole exome sequencing data from the tumor sample and non-transformed
cells will be used to detect nonsynonymous somatic mutations with the use of mutation
calling tools. RNA seq analysis will be used to identify expressed mutations in order
to predict potential neoantigens. Epitope prediction algorithms based on published
or submitted MHC Class I and II binding data will provide estimates of binding affinity
to identify putative T cell neoepitopes. Data resulting from functional assays, including
combinatorial encoding of MHC multimer screening flow cytometry assays, or functional
read outs such as cytokine production, will provide an indication of T cell reactivity
to validate the tumor-specific immunogenic neoepitopes. The analyses of mutations
in MHC class I and II genes as well as key molecules affecting antigen processing
and presentation are vital to provide a better assessment of their potential impact
on cytolytic T cell responses. The genetic landscape, the pool of neoepitopes and
functional tumor rejection measures of neoantigen-specific T cells (tumor recognition)
could be used to further assess their relevance to clinical outcome, design therapeutic
tumor-specific neoantigen (TSNA) vaccination, apply adoptive neoantigen T cell transfer
therapy and to guide more effective immuno-oncology combination immunotherapy.
Limitations of the approach
One of the major limitations of this approach is in the early stage computational
tools that are used both to identify tumor-specific mutations and to guide epitope
prediction. Multiple computational tools, such as EBcall, JointSNVMix, MuTect, SomtaticSniper,
Strelka and VarScan 2, are used to compare tumor samples to normal tissue at each
variant locus to increase the accuracy of somatic single nucleotide variant (sSNV)
calling [3–7]. Because these tools use distinct variant calling algorithms, there
may be variability in the somatic mutations identified. Thus, more validation studies
are necessary to improve the calling tools and standardize their use. Computer algorithm-guided
epitope prediction and the tandem minigene library approach are used to identify MHC
Class I or II binding neoepitopes recognized by neoantigen specific CD8+ and CD4+
T cells, respectively [8–10]. The accuracy of the prediction algorithms mostly depends
upon the binding scores to the MHC complex, with the Class II prediction tools being
much less well-developed than Class I. Tumors, especially those with mutant and viral
antigens, could be sufficiently “foreign” to be recognized by the immune system. However,
current data has illustrated that autologous T cells did not recognize the vast majority
of neoepitopes. Although the epitope prediction tools have been shown to have a high
degree of overlap [11–14], it is important to improve the ability of these tools to
differentiate putative neoepitopes from real immunogenic neoepitopes [15]. This lack
of immunogenicity could also be due to the tumor’s inability to activate the immune
system because of additional resistance mechanisms, especially tumor microenvironment
factors, rather than the absence of tumor antigens. Because the activation and cytotoxic
signals in individual tumors may reflect the overall status of a neoantigen-specific
tumor response, it will be critical to further evaluate these functional signatures
and to incorporate them into future optimized pipelines.
Another potential limitation of this technology is that representative, high-quality
tissue samples are needed in order to produce reliable results. Tumor tissue from
formalin-fixed, paraffin-embedded (FFPE) samples may be used for whole exome sequencing.
However, proper collection and storage of the tumor tissue is essential to ensure
high quality DNA for deep sequencing. Because of the heterogeneity of the tumor, it
is also essential to collect representative tissue to avoid any bias. In addition,
mutational profiles may change due to disease progression or ongoing treatment. Therefore,
assessing the tumor sample closest to the intervention is best to eliminate the potential
variation and increase accuracy. Moreover, although PBMCs are commonly used as non-transformed
cells, the signal from even low frequency circulating tumor cells from whole blood
needs to be further validated for potential contribution to data noise.
Types of samples needed and special issues pertaining to samples
Tissue from the tumor sample and non-transformed cells are needed for whole exome
sequencing. However, as mentioned above, proper collection and storage of representative
tissue is essential to ensure high-quality samples for deep sequencing. For downstream
assessment of T cell reactivity in functional assays, TILs and PBMCs are needed and
must be viably preserved as a single-cell suspension.
Level of evidence
This is a novel technology that is still currently under development. Two pilot preclinical
studies in mouse models first demonstrated that whole exome sequencing is efficient
to identify neoantigen-specific CD8+ T cells with tumor elimination [16, 17]. Several
human clinical studies highlighted the feasibility and importance of understanding
the immunogenicity of neoantigens and their potential clinical application in patients
treated with tumor-infiltrating lymphocyte cells [8–10]. The level of mutational load
(or the mutational landscape) as a potential biomarker was associated with clinical
outcome to immune checkpoint blockade cancer immunotherapy in patients with advanced
melanoma, non-small cell lung cancer (NSCLC) and colorectal cancer [18–21]. Patients
with highly mutagenized tumors and activated cytolytic markers are most likely to
respond to checkpoint blockade treatment [22]. In this study, epitope prediction did
not improve clinical outcome prediction value [23]. However, some patients with a
high mutational load do not experience clinical responses, while some patients with
a low mutation profile experience substantial clinical responses [18, 19]. Assessment
of clinically relevant immunogenic mutation loads along with active cytolytic signatures
before therapy is pivotal to improve the accuracy of outcome prediction. As the study
was performed in patients with mismatched repair deficiency tumors [20], more prospective
studies must be performed to determine whether the mutation load can guide novel therapeutic
approaches to selectively enhance T cell response to neoantigens in future mono- or
combination therapies.