New Technologies in Frontiers of Tumor Immunology
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New Technologies in Frontiers of Tumor Immunology
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New Technologies in Frontiers of Tumor Immunology.
Tremendous advances in immunotherapy have changed the current paradigm of cancer treatment, however, given that only a minority of patients respond to immune checkpoint blockade and other immunotherapy strategies, more new technologies are needed to decipher the relationship between tumor cells and the tumor immune microenvironment (TME) . ) complex interactions between components.
Tumor immuno-omics refers to the comprehensive study of TME using multi-omics data reflecting tumor immune status, such as immunogenomics, immune proteomics, and immune bioinformatics, which relies on the rapid development of next-generation sequencing technology.
High-throughput genomic and transcriptomic data can be used to calculate the abundance of immune cells and predict tumor antigens, however, because bulk sequencing represents the average characteristics of heterogeneous cell populations, different cell subtypes cannot be distinguished.
Single-cell-based techniques enable better resolution of the TME through precise immune cell subpopulation and spatial structure studies.
In addition, deep learning models based on radiomics and digital pathology have largely contributed to the study of tumor immunity, and these AI techniques performed well in predicting immunotherapy responses. Advances and breakthroughs in these new technologies have far-reaching implications for cancer treatment.
Tumor immune microenvironment
Advances in the study of tumor immunity in the past few years have fundamentally changed our understanding of tumors.
The definition of a tumor has also evolved from a simple aggregation of tumor cells to a complex organ-like structure composed of tumor cells, immune cells, fibroblasts, vascular endothelial cells, and other surrounding stromal cells.
The structure composed of various cells and components near the tumor, such as immune infiltrating cells, blood vessels, and extracellular matrix, also known as the tumor immune microenvironment, has become one of the most popular research topics in oncology.
TME has been shown to play a decisive role in carcinogenesis, tumor progression, metastasis and recurrence.
The TME contains an extremely diverse subset of immune cells, including T lymphocytes, B lymphocytes, natural killer ( NK ) cells, macrophages, dendritic cells ( DC ), granulocytes, and myeloid-derived suppressor cells ( MDSCs ) wait. Generally, T cells, B cells, NK cells, and macrophages help suppress tumor growth, while MDSCs and regulatory T cells ( Treg ) tend to suppress antitumor immunity.
However, existing studies have demonstrated that, given the complex interactions with tumor cells, the specific roles of immune cells may change dynamically or even become completely opposite.
In conclusion, a wide variety of immune cell types, and even different functional states of specific immune cell types, may have diametrically opposed effects on antitumor immunity.
Therefore, it is necessary to use the most advanced bioinformatics technology to systematically describe the immunological characteristics of tumors to the greatest extent and provide more information to enhance our understanding of tumor immunity.
Immunogenomics in the NGS Era
Over the past two decades, NGS including whole genome sequencing ( WGS ), whole exome sequencing ( WES ) and RNA sequencing ( RNA seq ) have been successfully developed and applied to obtain whole human genome information.
NGS generates high-throughput genomic and transcriptional data, laying the foundation for studying multistep immune responses.
Quantification of immune cells in the TME
The TME is composed of a variety of immune cells, and for the quantification of tumor immune cell components, traditional methods, such as flow cytometry and immunohistochemistry ( IHC ), are not suitable for large-scale analysis due to their high cost and low tissue availability.
With the rapid development of NGS, we have been able to estimate the abundance of dozens of immune cell types from NGS data, and these data have also proven to be reliable.
The source of these analyzes is primarily DNA and RNA sequencing, especially the latter.
Regarding RNA-seq data, the principles of computational methods are mainly divided into gene set enrichment analysis ( GSEA ) and deconvolution.
In general, representative algorithms based on GSEA include ESTIMATE, xCell, and MCP counting.
A common feature of GSEA-based approaches is the need to establish specific gene sets for each immune cell subset of interest.
The deconvolution of cellular components is the inverse of the convolution of cellular subtypes in somatic tissues based on gene expression signatures.
Deconvolution-based tools include decornaseq, PERT, CIBERSORT, TIMER, EPIC, quantIseq, and deconf.
Identification of Tumor Antigens
Somatic DNA mutations, including single nucleotide variants ( SNVs ) and insertions and deletions ( INDELs ), are a major source of abnormal antigens.
Currently, the Genome Analysis Toolkit ( GATK ) is the industry standard for identifying SNVs and INDELs by analyzing WES, WGS, and RNA-seq data.
Its scope is also expanding to cover copy number variations ( CNVs ) and structural variations ( SVs ).
In addition, abnormal peptides need to bind to HLA to assist T cell receptor ( TCR ) recognition to trigger an immune response. Predictive HLA typing is critical for identifying tumor antigens.
HLA miner and Seq2HLA are two early tools for HLA typing from NGS data, PHLAT, HLAreporter, SNP2HLA, HLA-HD, optype and HLA-VBSeq in four-digit, six-digit and eight-digit resolution in different cancers The performances are quite good.
Among these tools, Polysolver is currently one of the recognized standard tools for working with low-coverage WES data.
In addition to identifying abnormal peptides and HLA typing, antigen MHC binding affinity is the next focus of tumor antigen prediction.
Many peptide-MHC-I ( pMHC-I ) affinity prediction tools are based on artificial neural network ( ANN ) training methods and position-specific scoring matrices ( PSSM ), such as the currently widely used tools NetMHC and NetMHCpan.
However, due to the diversity of MHCII-binding peptide lengths and the “openness” of the binding region, predicting pMHC II affinity is more challenging, and the number of available pMHC II affinity prediction methods is far less than that of pMHC-I.
Immunomics in the single-cell era
Although the study of tumor immunity using NGS technology has greatly advanced the development of oncology, bulk sequencing can cause dilution of signal below the limit of detection and mask the response of individual cells. This could obscure many important biological phenomena.
Until recently, technological breakthroughs in single-cell correlation methods have revolutionized our understanding of tumor immunity and transitioned the level of research from the regional level to the single-cell level.
Multicolor flow cytometry
The ability of multiparameter analysis to functionally and physically distinguish different immune cell subsets has led to the development of flow cytometry into the routinely used 8-parameter flow cytometer.
In addition, as technology advances, instrument designs that can measure more parameters have been realized, such as 30-parameter and 50-parameter flow cytometers.
However, due to the lower precision of the more measurable parameters, or the limited number of parameters measurable with higher precision, especially due to the overlap between the emission spectra of fluorochromes, these shortcomings limit the application of multicolor flow cytometry to some extent. and further development.
Mass cytometry
A recent innovation in this field is mass spectrometry, also known as time-of-flight ( CyTOF ) flow cytometry, which combines flow cytometry with mass spectrometry.
In contrast to traditional flow cytometry, mass spectrometers label antibodies with metal isotopes instead of fluorophores, and then quantify the signal using a time-of-flight detector that detects at least 40 parameters and avoids spectral overlap issues.
CyTOF has been proven to be an accurate method for high-dimensional analysis of tumor tissue for exploratory immunoassays and biomarker discovery.
While theoretically mass cytometry allows us to measure up to 100 parameters per cell, processing speed and throughput are limited by ion flight.
After nebulization and ionization, cells are completely destroyed during pretreatment, making subsequent cell sorting applications infeasible.
Furthermore, with respect to measuring some low-expression molecular features, CyTOF may not be suitable due to its low sensitivity.
Spectral Flow Cytometry
Spectral flow cytometry is another recent technological advancement that has boosted the efficacy of traditional flow cytometry.
Unlike mass spectrometers, spectral flow cytometers still label antibodies with fluorescent dyes, but replace traditional optics and detectors with dispersive optics and new detectors that measure the full emission spectrum.
Based on the same principle, conventional flow cytometry and spectral flow cytometry maintain fairly good compatibility, especially with regard to the availability of commercial antibodies, but can better remove confounding factors such as spectral overlap to improve efficiency .
With the development of compensation techniques, spectral flow cytometry has the potential to replace multicolor flow cytometry.
Single Cell RNA Sequencing
Flow cytometry-based techniques that bind a specific label to the corresponding cell subpopulation and identify that label indicate that the target must be identified prior to sample collection, and that initial target definition limits the information gained from these techniques, which can only be obtained through these Technology finds the “known unknown”.
The emergence of single-cell sequencing technology has pushed the single-cell field to new heights.
No longer constrained by predetermined targets such as flow cytometry, single cells can be sequenced using standard NGS protocols to obtain unbiased multi-omic analyzes that can be used to identify “unknowns”.
At present, the application of scRNA-seq is more mature than other methods, and the field of tumor immunotherapy has provided us with many very valuable discoveries and enlightenments.
However, the technical noise generated by the amplification of trace substances remains the most significant challenge.
How to isolate single cells and maintain their biological activity, how to solve the huge technical noise brought about by amplification and improve sensitivity, how to obtain the highest number of measurable genes at the lowest price, and how to analyze data more efficiently have greatly improved single cell The threshold of sequencing limits its wide application.
Immunomics and Artificial Intelligence
The technical progress of artificial intelligence in tumor immunity research mainly involves the following aspects:
(1) It reduces the workload of manual identification of immune infiltration on pathological sections;
(2) It provides an alternative technology to identify immune cells that are difficult to identify with naked eyes subpopulations and spatial structure;
(3) provide a non-invasive method to predict patient-specific TME characteristics and response to immunotherapy.
Tumor antigen prediction based on deep learning method
The first step in deciphering tumor antigens is to predict abnormal peptides.
In addition to multiple algorithms for identifying SNVs, recently designed CN learning tools have also been designed to detect CNVs, showing promising performance.
Regarding HLA typing, Bulik et al. generated a large comprehensive dataset including HLA types and HLA peptides of various types of cancer tissues and published data that can be used to train a full mass spectrometry deep learning model EDGE, which has been used in non-small Validated in patients with NSCLC .
In addition, two promising computational deep learning methods, MARIA and MixMHC2pred, were recently developed, which greatly improved MHC-II prediction accuracy.
Application of radiomics in tumor immunity
With the development of artificial intelligence in medical imaging, an image is not just a picture, but a large-scale digital data. The process of using AI technology to analyze imaging data is radiomics.
Radiomics technologies applied to tumor immunity are mainly used to identify biomarkers that reflect immune infiltration and predict therapeutic response in ICB-treated patients.
Computational Pathology in Tumor Immunity
AI in pathology, or so-called digital pathology, provides new insights into the interplay between immune cells and tumor cells and the links between key behaviors in cancer biology through computational analysis.
Similar to radiology, digital pathology combined with deep learning mines unseen information from images, allowing us to understand TME at the cellular or molecular level.
Digital pathology may be a promising approach to study the structure of the TME and the relationship between cancer biology and therapy.
Application of Immunomics in Tumor Immunotherapy
Identifying biomarkers of ICB for patient stratification
As a target of ICB, the expression level of PD-L1 detected by IHC was the first predictive biomarker discovered, but some clinical trials showed that ICB had only a slight effect on some patients with high PD-L1 expression, and ICB also Patients with low expression of PD-L1 will respond. Therefore, other biomarkers are urgently needed to fill this gap.
In 2014, researchers used WES to link tumor mutational burden ( TMB ) to clinical survival in patients treated with CTLA-4 inhibitors for the first time.
Subsequently, other retrospective studies have also demonstrated that high TMB is associated with durable clinical benefits.
Regarding the methods used to assess TMB, due to the high cost and complexity of WES, the FDA approved two alternative NGS platforms, FoundationOne CDx ( F1CDx ) and MSKCC Actionable Cancer Target Integrated Mutation Profiling ( MSK-IMPACT ), and adopted Multiple cancer prospective studies were validated.
On the other hand, immune cell infiltration, especially TIL, plays a key role in the immune response.
To discover more desirable therapeutic and prognostic biomarkers, single-cell sequencing is used to identify more immune cell subsets.
TCF7+ memory-like T cells have been found to be associated with clinical improvement in melanoma patients after anti-PD1 therapy, whereas stem cell-like TCF1+PD1+ T cells have been shown to contribute to tumor control during ICB therapy.
Through single-cell sequencing technology, more T cell subsets and functional states relevant to therapy and prognosis have been identified.
Prediction of neoantigens in ACT therapy
Adoptive cell therapy ( ACT ) is the reinfusion of genetically modified or expanded autologous or allogeneic T cells into patients to enhance anti-tumor immunity.
Immunomics is mainly used to identify ideal tumor antigens in ACT therapy.
At present, neoantigen-specific TCR-T cells have not entered clinical application, however, it is reassuring that some case reports have shown immunomics-predicted T cell recognition of tumor neoantigens in patients with colorectal cancer, breast cancer, and cholangiocarcinoma. effectiveness.
Tran et al performed WGS on samples from patients with metastatic cholangiocarcinoma and identified 26 somatic mutations.
A tandem minigene consisting of mutated genes was transcribed and transfected into autologous APCs, and then neoantigen-presenting APCs were co-cultured with patient-derived TILs to finally identify antigen-specific CD4+Vb22+ T cell clones and induce epithelial cancer regression.
Traditional neoantigen selection based on autologous APC and T cell co-culture is limited due to its low-throughput, high cost, and time-consuming nature.
To remove these barriers, more high-throughput immunogenic neoantigen detection technologies have been developed. Li et al. established a trogocytosis-based platform in which surface marker proteins are transferred from APCs to T cells upon binding of TCR and pMHC.
Therefore, ideal neoantigens can be identified by analyzing marker protein positive cells. In the future, these emerging immunogenomics technologies will enable high-throughput neoantigen selection.
Selection of neoantigens for personalized tumor vaccines
Immunomics approaches have been widely used in vaccine development in clinical research.
In general, neoantigens for generating personalized vaccines are identified by analyzing WES and RNA sequences of tumor and normal tissues, and predicting effective epitopes by algorithms ( such as NetMHCpan ).
Similar to ACT, a critical parameter for tumor vaccine development is ideal neoantigen identification.
In order to improve the accuracy of neoantigen prediction and immunogenic neoepitope selection pipeline, immunomics technology is making unremitting efforts in these aspects.
In a recent study, Wells et al. compiled all neoantigen prediction and selection methods and provided a novel pipeline of candidate assays that included 14 immunogenic signatures for MHC presentation and T cell recognition.
This study lays a solid foundation for improving the efficacy of tumor vaccines and adoptive cell therapy.
Summary
In recent years, with the tremendous leap forward in emerging technologies in the field of immuno-omics, we are now able to dissect tumor immunity in unprecedented depth.
In the era of batch sequencing, it enables us to better explore the individual infiltration patterns of tumor immune cells, as well as the prediction of abnormal peptides, HLA typing and prediction of tumor antigen MHC binding affinity.
The use of immunomics technology to predict tumor antigens has been preclinical Its reliable efficacy has been proven in clinical studies.
In addition, with the development of single-cell immune-related technologies, from multicolor flow cytometry to CyTOF, single-cell tumor immune atlas helps us to classify immune cell subsets to decipher TME components.
The emergence of artificial intelligence also provides a new direction for the development of immunomics.
With the vigorous development of immunomics technology, several issues need to be considered for sustainable development.
First, although many methods of quality control and improving the principles of algorithms have been implemented, the effectiveness of these techniques still needs to be improved.
Especially in tumor antigen prediction, single-cell sequencing, and spatially resolved transcriptomics, technical noise and confounding factors hinder subsequent analysis.
Second, expect more cost-effective, accessible, and automated technologies to emerge that revolutionize the discipline.
Third, we also expect researchers to make full use of existing technologies to explore tumor immunity and promote clinical transformation.
Although there is still much work to be done, immunomics is likely to occupy a dominant position in the field of tumor immunology in the future, and its clinical value will undoubtedly greatly promote the development of this subject in the fields of immunomics, single cells and artificial intelligence.
references:
1. Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intel ligence. SignalTransduct Target Ther. 2021; 6: 312.
New Technologies in Frontiers of Tumor Immunology
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