Next-generation tumor immune technology

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Next-generation tumor immune technology: from immunoomics to single cell analysis and artificial intelligence.

Preface

The tremendous progress of immunotherapy has changed the current mode of cancer treatment. However, given that only a small number of patients respond to immune checkpoint blockade and other immunotherapy strategies, more new technologies are needed to decipher the tumor cells and tumor immune microenvironment ( TME). Complex interactions between ingredients.

Tumor immunoomics refers to the comprehensive study of TME using immunogenomics, immunoproteomics, immunobioinformatics and other multi-omics data reflecting the immune status of tumors.

It relies on the rapid development of next-generation sequencing technology. High-throughput genome and transcriptome data can be used to calculate the abundance of immune cells and predict tumor antigens.

However, because batch sequencing represents the average characteristics of a heterogeneous cell population, it is impossible to distinguish between different cell subtypes.

Single-cell-based technology can better analyze TME through precise immune cell subgroups and spatial structure studies.

In addition, deep learning models based on radiomics and digital pathology are helpful to the research of tumor immunity to a large extent, and these artificial intelligence technologies perform well in predicting the response of immunotherapy.

The progress and breakthroughs of these new technologies have far-reaching significance for cancer treatment.

Tumor immune microenvironment

In the past few years, the research progress on tumor immunity has radically changed our understanding of tumors.

The definition of tumor has also evolved from a simple aggregation of tumor cells to a complex organ-like structure consisting of tumor cells, immune cells, fibroblasts, vascular endothelial cells and other surrounding stromal cells.

Various cells and components near the tumor, such as immune infiltrating cells, blood vessels, and extracellular matrix, are also called tumor immune microenvironment , which has become one of the most popular research topics in oncology.

TME has been proven to play a decisive role in cancer occurrence, tumor progression, metastasis and recurrence.

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 to inhibit tumor growth, while MDSC and regulatory T cells ( Treg ) tend to inhibit anti-tumor immunity.

However, existing studies have confirmed that, given the complex interaction with tumor cells, the specific role of immune cells may change dynamically or even become completely opposite.

Next-generation tumor immune technology

In short, a variety of immune cell types, and even different functional states of specific immune cell types, may have diametrically opposite effects on anti-tumor 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

In 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 human whole genome information.

NGS generates high-throughput genome and transcription data, laying the foundation for studying multi-step immune responses.

Quantify immune cells in TME

TME is composed of a variety of immune cells. 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 can estimate the abundance of dozens of immune cell types through NGS data, and these data have also been proven to be reliable.

The sources of these analyses are mainly DNA and RNA sequencing, especially the latter.

Regarding RNA sequence data, the principles of calculation methods are mainly divided into gene set enrichment analysis ( GSEA ) and deconvolution.

Generally, representative algorithms based on GSEA include ESTIMATE, xCell, and MCP counting.

A common feature of GSEA-based methods is the need to establish a specific gene set for each immune cell subpopulation of interest.

The deconvolution of cellular components is the reverse process of the convolution of cell subtypes in body tissues based on gene expression characteristics.

Tools based on deconvolution include decornaseq, PERT, CIBERSORT, TIMER, EPIC, quanTIseq, and deconf.

Identification of tumor antigens

Somatic DNA mutations, including single nucleotide variations ( SNV ) and insertions and deletions ( INDEL ), are the main sources of abnormal antigens.

Currently, the Genome Analysis Toolkit ( GATK ) is the industry standard for identifying SNV and INDEL by analyzing WES, WGS and RNA sequence data.

Its scope is also expanding to cover copy number variation ( CNVs ) and structural variation ( SVs ).

In addition, abnormal peptides need to be combined with HLA to assist T cell receptor ( TCR ) recognition, thereby triggering an immune response. Predicting HLA typing is essential 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 have four, six and eight resolutions in different cancers The performance is quite outstanding.

Among these tools, Polysolver is currently one of the recognized standard tools for using 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 matrix ( PSSM ), such as the widely used tools NetMHC and NetMHCpan.

Due to the diversity of MHCII binding peptide length 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 use of NGS technology to study tumor immunity has greatly promoted the development of oncology, batch sequencing may cause the signal to be diluted below the detection limit and mask the response of individual cells. This may obscure many important biological phenomena.

Until recently, technological breakthroughs in single-cell-related methods have completely changed 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 multi-parameter analysis to distinguish different immune cell subpopulations functionally and physically has prompted the development of flow cytometers into 8-parameter flow cytometers that are routinely used.

In addition, with the advancement of technology, instrument designs that can measure more parameters have been realized, such as 30-parameter and 50-parameter flow cytometers.

However, due to the lower accuracy of more measurable parameters, or the limited measurable parameters with higher accuracy, especially due to the overlap between the emission spectra of fluorescent dyes, these shortcomings have limited the application of multicolor flow cytometry to a certain extent. And further development.

Mass spectrometry flow cytometry

Mass spectrometry is the latest innovation in this field, also known as time-of-flight ( CyTOF ) flow cytometry, which combines flow cytometry and mass spectrometry.

Compared with traditional flow cytometers, mass spectrometers use metal isotopes instead of fluorophores to label antibodies, and then use a time-of-flight detector to quantify the signal.

This detector can detect at least 40 parameters and avoid spectral overlap problems. CyTOF has been proven to be an accurate high-dimensional analysis method of tumor tissue for exploratory immunoassay and biomarker discovery.

Although in theory, mass spectrometry flow cytometry allows us to detect up to 100 parameters per cell, the processing speed and throughput are limited by ion flight.

After atomization and ionization, the cells are completely destroyed in the pretreatment process, which makes subsequent cell classification applications infeasible.

In addition, with regard to measuring some low-expressed molecular characteristics, CyTOF may be inappropriate due to its low sensitivity.

Spectral flow cytometry

Spectral flow cytometry is another latest technological advancement that promotes the efficacy of traditional flow cytometry.

Unlike mass spectrometers, spectral flow cytometers still use fluorescent dyes to label antibodies, but use dispersive optics and new detectors that measure the full emission spectrum to replace traditional optics and detectors.

Based on the same principle, traditional flow cytometry and spectral flow cytometry maintain a fairly good compatibility, especially in the availability of commercial antibodies, but can better eliminate confounding factors, such as spectral overlap, to improve efficiency .

With the development of compensation technology, spectral flow cytometry may replace multicolor flow cytometry.

Single cell RNA sequencing

The technology based on flow cytometry combines a specific label with the corresponding cell subpopulation and recognizes the label, indicating that the target must be determined before the sample is collected, and the initial target limitation limits the information obtained from these technologies. Technology finds “known and unknown”.

The emergence of single-cell sequencing technology has pushed the single-cell field to a new level. No longer limited by predetermined goals such as flow cytometry, single cells can be sequenced using standard NGS protocols to obtain unbiased multi-omics analysis that can be used to identify the “unknown”.

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 is still the most significant challenge.

How to isolate a single cell and maintain its biological activity, how to solve the huge technical noise brought by amplification and improve sensitivity, how to obtain the highest number of measurable genes at the lowest price, and how to analyze data more effectively, these have greatly improved the single cell The threshold of sequencing limits its wide application.

Immunoomics and artificial intelligence

The technological progress of artificial intelligence in tumor immunity research mainly involves the following aspects:

(1) Reduce the workload of artificially identifying immune infiltration on pathological slices;

(2) Provide an alternative technology to identify immune cells that are difficult to identify with the naked eye Subgroups and spatial structure;

(3) Provide a non-invasive method to predict the TME characteristics and response to immunotherapy of specific patients.

Tumor antigen prediction based on deep learning method

The first step in deciphering tumor antigens is to predict abnormal peptides. In addition to a variety of algorithms for identifying SNV, the recently designed CN learning tool is also designed to detect CNV, showing good performance.

Regarding HLA typing, Bulik et al. generated a large comprehensive data set, including HLA types and HLA peptides of various types of cancer tissues, and published data that can be used to train a complete mass spectrometry deep learning model EDGE. Proven in patients with cell lung cancer ( NSCLC ).

In addition, recently developed two promising computational deep learning methods MARIA and MixMHC2pred, which greatly improved the accuracy of MHC-II prediction.

Application of Radiomics in Tumor Immunity

With the development of artificial intelligence in medical imaging, an image is no longer just a picture, but a large-scale digital data.

The process of using AI technology to analyze imaging data is radioomics. Radioomics techniques applied to tumor immunity are mainly used to identify biomarkers that reflect immune infiltration and predict the therapeutic response of patients treated with ICB.

Next-generation tumor immune technology

Computational Pathology in Tumor Immunity

AI in pathology, or so-called digital pathology, through computational analysis, provides new insights for exploring the interaction between immune cells and tumor cells and the links between key behaviors in cancer biology.

Similar to radiology, digital pathology combined with deep learning to unearth invisible information from images allows us to understand TME at the cellular or molecular level.

Digital pathology may be a promising method for studying the structure of TME and the relationship between cancer biology and treatment.

Application of immunoomics in tumor immunotherapy

Biomarkers identifying ICB for patient stratification

As a target of ICB, the PD-L1 expression level detected by IHC is the first predictive biomarker discovered, but some clinical trials have shown that ICB has only a slight effect on some patients with high PD-L1 expression, and ICB also Will respond to patients with low PD-L1 expression. Therefore, other biomarkers are urgently needed to fill this gap.

In 2014, researchers used WES for the first time to correlate tumor mutational burden ( TMB ) with the clinical survival of patients treated with CTLA-4 inhibitors.

Subsequently, other retrospective studies have also demonstrated that high TMB is associated with long-lasting clinical benefits.

Regarding the method used to evaluate TMB, due to the high cost and complexity of WES, the FDA approved two alternative NGS platforms, namely FoundationOne CDx ( F1CDx ) and MSKCC Actionable Cancer Target Integrated Mutation Spectrum ( MSK-IMPACT ), and passed Several prospective studies of cancer have been validated.

On the other hand, immune cell infiltration, especially TIL, plays a key role in the immune response.

In order to discover more ideal therapeutic and prognostic biomarkers, single-cell sequencing is used to identify more immune cell subpopulations.

It has been found that TCF7+ memory-like T cells are associated with the clinical improvement of melanoma patients after anti-PD1 treatment, and stem cell-like TCF1+PD1+T cells have been proven to help tumor control in ICB therapy.

Through single-cell sequencing technology, more T cell subsets and functional status related to treatment and prognosis have been determined.

Prediction of neoantigens in ACT treatment

Adoptive cell therapy ( ACT ) is to reinfuse the patient’s body with transgenic or amplified autologous or allogeneic T cells to enhance anti-tumor immunity. Immunoomics is mainly used to identify ideal tumor antigens in ACT treatment.

At present, neoantigen-specific TCR-T cells have not yet entered clinical applications.

However, it is gratifying that some case reports have shown that immunomics predicts the ability of T cells to recognize 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.

Tandem minigenes composed of mutated genes are transcribed and transfected into autologous APCs, and then the neoantigen-presenting APCs are co-cultured with patient-derived TILs, and finally antigen-specific CD4+Vb22+T cell clones are identified to induce regression of epithelial cancer.

Traditional neoantigen selection based on co-culture of autologous APC and T cells is limited due to its low throughput, high cost and time-consuming characteristics.

In order to eliminate these obstacles, more high-throughput immunogenic neoantigen detection technologies have been developed. Li et al. established a platform based on trogocytosis, in which when TCR and pMHC are combined, surface marker proteins are transferred from APC to T cells.

Therefore, the ideal neoantigen can be identified by analyzing the labeled protein-positive cells.

In the future, these emerging immunogenomics technologies will enable high-throughput neoantigen selection.

Choosing neoantigens for individualized tumor vaccines

Immunomics methods have been widely used in vaccine development in clinical research. Generally speaking, the neoantigens used to generate personalized vaccines are identified by analyzing the WES and RNA sequences of tumors and normal tissues, and predicting effective epitopes through algorithms ( such as NetMHCpan ).

Similar to ACT, the key parameter for tumor vaccine development is ideal neoantigen identification.

In order to improve the accuracy of neoantigen prediction and the pipeline of immunogenic new epitope selection, immunoomics technology is making unremitting efforts in these aspects.

In a recent study, Wells et al. compiled all new antigen prediction and selection methods and provided a new candidate assay pipeline, which included 14 immunogenic features for MHC presentation and T cell recognition.

This research has laid a solid foundation for improving the efficacy of tumor vaccines and adoptive cell therapy.

Summary

In recent years, with the tremendous leaps in emerging technologies in the field of immunoomics, we are now able to analyze tumor immunity in an unprecedented depth.

Next-generation tumor immune technology

In the era of batch sequencing, it allows us to better explore the individual infiltration patterns of tumor immune cells, as well as the prediction of abnormal peptides, HLA typing and the prediction of tumor antigen MHC binding affinity, and the use of immunoomics technology to predict tumor antigens has been preclinical And clinical studies have proven its reliable efficacy.

In addition, with the development of technologies related to single-cell immunity, from multicolor flow cytometry to CyTOF, single-cell tumor immunity atlas helps us to classify immune cell subgroups to decipher the TME components.

The emergence of artificial intelligence also provides a new direction for the development of immunoomics.

Next-generation tumor immune technology

With the vigorous development of immunoomics technology, several issues need to be considered for sustainable development.

First, although many methods of quality control and improvement of algorithm principles have been implemented, the effectiveness of these technologies still needs to be improved.

Especially in terms of tumor antigen prediction, single-cell sequencing, and spatially resolved transcriptomics, technical noise and confounding factors hinder subsequent analysis.

Second, look forward to the emergence of more cost-effective, more accessible, and more automated technologies that will completely change the development of 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 a lot of work to be done, immunoomics is likely to dominate the field of tumor immunology in the future, and its clinical value will undoubtedly greatly promote the development of this discipline in the fields of immunoomics, single cell 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.

Next-generation tumor immune technology: from immunoomics to single cell analysis and artificial intelligence.

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Next-generation tumor immune technology

Next-generation tumor immune technology: from immunoomics to single cell analysis and artificial intelligence.

Preface

Tumor immune microenvironment