Next generation tumor immunization technology: Single-cell analysis and artificial intelligence from immunohistogenesis


The huge progress of immunotherapy has changed the model of cancer treatment, however, in view of only a few patients have responding to the blocked the immunogenic examination and other immunotherapy strategies, more new technologies need to be used to decipher the micro-micro-microenvironment of tumor cells and tumors (TME The complex interaction between the ingredients.

Tumor immunougueology refers to a comprehensive study of TME using multi-synthetic data using immunologically, immunological proteomics, immunogenic information, etc. reflects tumor immunization state, depending on the rapid development of next-generation sequencing technology. The high-throughput genome and transcriptum data can be used to calculate the abundance of immunocytes and predict tumor antigens, however, due to batch sequencing represents the average characteristics of heterogeneous cell populations, different cell subtypes cannot be distinguished. Single-cell-based technology can better analyze TME through accurate immune cell subpopulations and spatial structures. In addition, the depth learning model based on radiographics and digital pathology has a large extent to the study of tumor immunity, which is well shown in predicting immunotherapy response. The progress and breakthroughs of these new technologies have far-reaching significance for cancer treatment.

Tumor immunization micro environment

In the past few years, the research progress of tumor immunity has made us fundamentally changed the understanding of tumors. The definition of tumors also evolved from a simple tumor cell to a complex organ pattern structure, consisting of tumor cells, immunocytes, fibroblasts, vascular endothelial cells, and other matrix cells. Various cells and components near tumors, such as structural, such as immunofuncing cells, blood vessels, extracellular matrices, etc., also known as tumor immunochemical, and have become one of the topics of tumors. TME has been proven to play a decisive role in cancer, tumor progression, metastasis and recurrence.

TME includes extremely diverse immune cells, including T lymphocytes, B lymphocytes, natural killing (NK) cells, macrophages, dendritic cells (DC), granulocytes and mycinoid inhibitory cells (MDSCs) Wait. Typically, T cells, B cells, NK cells and macrophages help inhibit tumor growth, while MDSC and regulatory T cells (Treg) tend to inhibit anti-tumor immunity. However, existing studies have confirmed that in view of the complex interaction with tumor cells, the specific role of immune cells may have changed, and even completely opposite.

In summary, various immune cell types, even different functional conditions of specific immune cell types may have an opposite effect against tumor immunity. Therefore, this requires the most advanced bioinformatics techniques to minimize the immunological characteristics of tumors to the greatest extent, and provide more information to enhance our understanding of tumor immunity.

Immune genomics in NGS era

Over the past twenty years, NGS, including all genome sequencing (WGS), full exon group sequencing (WES), and RNA sequencing (RNA SEQ) has been successfully developed and applied to acquire human genome information. NGS produces high-throughput genome and transcriptional data to lay the foundation for the study of multi-step immune responses.

Immune cells in TME

TME consists of a variety of immunocytes, for quantification of tumor immunocytocyte components, conventional methods, such as flow cytometry and immunohistochemistry (IHC), due to its high cost and low tissue availability, is not applicable to large scale analysis. With the rapid development of NGS, we can estimate the abundance of dozens of immune cell types through NGS data, which are also proven to be reliable. These analyzes are mainly DNA and RNA sequencing, especially the latter. Regarding RNA sequence data, the principle of calculation methods is mainly divided into gene set enrichment analysis (GSEA) and inverse volume.

Typically, the representative algorithm based on GSEA includes Estimate, XCell, and MCP counts. A common feature of a GSEA-based approach is to establish specific genomic sets for each of the immune cell subsets of interest. The inverse volume of the cellular component is the reverse process of cell subtype convolution in a body tissue based on gene expression characteristics. Tools based on reverse volume include Decornaseq, Pert, Cibersort, Timer, EPIC, QuantiseQ and Deconf.

Identification of tumor antigens

Body cell DNA mutation, including mononucleotide variation (SNV) and insertion and deletion (INDEL), is the main source of abnormal antigen. Currently, the Genome Analysis Toolkit (GATK) is the industry standard of SNV and Indel by analyzing WES, WGS and RNA sequence data. It is also expanded to encompass copy number variation (CNVS) and structural variation (SVS).

Furthermore, the abnormal peptide needs to be combined with HLA to assist the T cell receptor (TCR) to initiate an immune response. The prediction of HLA is essential to identify tumor antigens. HLA Miner and SEQ2HLA are two early tools for HLA typing from NGS data, Four, six and eight-bit resolutions in PHLAT, HLAREPORTER, SNP2HLA, HLA-HD, Optype, and HLA-VBSEQ in different cancers. The performance is quite good. In these tools, Polysolver is one of the recognized standard tools currently using low coverage WES data.

In addition to identifying an abnormal peptide and HLA type, antigen MHC binding pro and 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 score matrices (PSSM), as currently widely used tools NetMHC and NetMHCPAN. Due to the diversity of MHCII binding peptide and "openness" of the binding zone, predicting PMHC II affinity is more challenging, the number of PMHC II affinity prediction methods is far less than PMHC-I.

Single cell age immunohistology

Although the study of tumor immunity is used to study NGS technology to greatly promote the development of tumors, batch sequencing may cause signals to be diluted below the detection limit and mask the reaction of individual cells. This may hide many important biological phenomena. Until recently, the technical breakthrough of single cell-related methods thoroughly changed our understanding of tumor immunity, and transitioned from regional levels from regional levels to a single cell level.

Multi-color flow cytometry

Multi-parameter analysis is functional and physically distinguished by different immune cell subsets. In addition, with the advancement of technology, the instrument design of more parameters can be measured, such as 30 parameters and 50 parameter flow cytometry. However, due to the lower parameter accuracy, or higher precision measuring parameters, especially due to overlap between fluorescent dye emission spectroscopy, these disadvantages limit the application of multi-color flow cytometry to a certain extent And further development.

Matrix flow cytometry

The mass spectrometry is a new innovation in the field, also known as flight time (CYTOF), combined with streaming cytometry with mass spectrometry. Compared to conventional flow cytometry, mass spectrometry is not a fluorom antibody with metal isotope, and then quantifies the signal using the flight time detector, the detector detects at least 40 parameters, and avoids spectral overlap. Cytof has been confirmed to be a precise tumor tissue high-dimensional analysis method for explorating immunoassays and biomarkers found.

Although in theory, mass spectrometry flow cytometry allows us to detect up to 100 parameters per cell, but the processing speed and flux are limited by ion flight. After atomization and ionization, the cells are completely destroyed during the pretreatment, resulting in the end of subsequent cell classification. In addition, Cytof may not be appropriate for the measurement of certain low expression molecular features.

Spectral flow cytometry

Spectrum flow cytometry is another latest technological advancement that promotes traditional flow cytometry. Unlike the mass spectrometer, spectral flow cytometry is still labeled antibodies with fluorescent dye, but a new detector with a dispersion optical and measuring full emission spectrum replaces conventional optical and detectors. Based on the same principle, traditional flow cytometry and spectral flow cytometry maintain considerable compatibility, particularly in terms of commercial antibodies, but can better eliminate confusion factors, such as spectral overlap to improve efficiency . With the development of compensation technology, spectral flow cytometry may replace multi-color flow cytometry.

Single cell RNA sequencing

Based on streaming cytometry combines a specific label with the corresponding cell subgroup and identifies the label, indicating that the target must be determined before the sample collection, and the initial goal limits the information obtained from these technologies, only these Technologies find "known unknown".

The emergence of single cell sequencing technology pushes a single cell sequence to a new height. Single cells can be sequenced using a standard NGS protocol for predetermined targets such as streaming cytometry.

At present, SCRNA-SEQ applications are more mature than other methods, and the re-tumor immunotherapeutic field provides us with a lot of very valuable discovery and revelation. However, the technical noise produced by trace substance is still the most significant challenge. How to separate a single cell and maintain its bioactivity, how to solve the enlarged huge technical noise and improve sensitivity, how to get the highest number of measurable genes at the lowest price, how to analyze data more effectively, these are greatly improved single cells The threshold for sequencing limits its wide application.

Immunohistology and artificial intelligence

Artificial intelligence in tumor immunization studies mainly involve the following aspects: (1) Alleviation of immunological infiltration on artificial identification of pathological sections; (2) Provide an alternative technology to identify immune cells that are difficult to identify in naked eye The subpopulation and spatial structure; (3) Provides a non-invasive method to predict the TME characteristics of a particular patient and the reaction of immunotherapy.

Tumor antigen prediction based on deep learning method

The first step in deciphering the tumor antigen is to predict the abnormal peptide. In addition to identifying a variety of algorithms of SNV, the recently designed CN learning tool is also designed to detect CNV and exhibit good performance. About HLA profiles, Bulik et al. Generated a large integrated data set, including various types of cancer tissue HLA types and HLA peptides, which announced data that can be used to train intact mass spectrum depth learning model EDGEs, which is already non-small Verification is obtained in patients with cell lung cancer (NSCLC). In addition, two very promising depth learning methods Maria and MixMHC2PRED have recently been developed, which greatly increases MHC-II prediction accuracy.

Application of radiology in tumor immunity

With the development of artificial intelligence in medical imaging, the image is not just a picture, but a large-scale digital data, the process of analyzing the imaging data using AI technology is the radiographic. Radiological techniques applied to tumor immunization are mainly used to identify biomarkers reflecting immunoff and predict the therapeutic response of ICB treated patients.

Calculation of tumor immunity

AI, or so-called digital pathology, by calculating analysis, providing new insights for exploring the interaction between immune cells and tumor cells and the key behavior of cancer biology.

Similar to radiology, digital patients combine deep learning from the image to excavate the invisible information, so that we can understand TME in cell or molecular level. Digital pathology may be a promising method for studying the relationship between TME structure and cancer biology and treatment.

Application of immunohistology in tumor immunotherapy

Identifying ICB biomarkers for patient layers

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

In 2014, the researchers were linked to the clinical survival rate of tumor mutation (TMB) with patients who were treated with CTLA-4 inhibitors by WES. Subsequently, other retrospective studies also prove that high TMB is related to lasting clinical benefits. Regarding the method used to evaluate TMB, due to the high cost and complexity of WES, the FDA approves two alternative NGS platforms, which is FoundationOndongOne CDX (F1CDX) and MSKCC operable cancer target integrated mutant (MSK-IMPACT), and passed Prospective study of multiple cancers has been verified.

On the other hand, immune cell infiltration, especially TIL, a key role in immune response. In order to find more desirable treatment and prognosis biomarkers, single-cell sequencing is used to identify more immune cells. TCF7 + memory T cells have been found to be related to clinical improvement in patients with melanoma after anti-PD1 treatment, while stem cell-like TCF1 + PD1 + T cells have been confirmed, which contributes to tumor control in ICB treatment. Thorbing T cell subpopulations and functional conditions associated with treatment and prognosis are determined by single-cell sequencing techniques.

Prediction of new antigen in ACT treatment

Overpounding Cell Therapy (ACT) is re-transferred to the patient by transgene or amplified autologous or allogeneic T cells to enhance anti-tumor immunity. Immunoatology is mainly used to identify the ideal tumor antigen in ACT treatment.

At present, the new antigen-specific TCR-T cell has not yet entered the clinical application. However, it is gratifying that some case reports show the neat colorectal cancer, breast cancer and cholangiocarcinoma patients with immunohistology, T cell recognition tumor new antigen Effectiveness. TRAN et al. WGS on the sample of metastatic cholangiocarcinoma patients, determined 26 species of cell mutation. The series microchrome composed of mutant genes was transcribed and transfected into autologous APC, and then the new antigen was presented with TIL of APC and the patient’s source of TIL, and finally identified antigen-specific CD4 + VB22 + T cell clones and induced epithelial cancer.

The conventional autologous APC and T cell co-cultured new antigen selection is limited due to its low flux, high cost and time consuming characteristics. In order to eliminate these obstacles, more high-throughput immunogenic new antigen detection techniques were developed. Li et al. Established a Trogocytosis-based platform where the TCR and PMHC were combined, the surface marker protein was transferred from the APC to T cells. Therefore, the ideal new antigen can be identified by analyzing labeling positive cells. In the future, these emerging immunologies will achieve high-throughput antigen selection.

Choose a new antigen for individual tumor vaccines

The immunohistological method has been widely used in vaccine development in clinical studies. In general, a new antigen for generating a personalized vaccine is identified by analyzing a tumor and normal tissue WES and RNA sequences, and is identified by an algorithm (such as NetMHCPAN).

Similar to ACT, key parameters for tumor vaccine development are ideal for new antigen identification. In order to improve the accuracy of the new antigen prediction and the new epitope selection of the immunogenicity, immunohistology technology has made unremitting efforts in these respects. In a recent study, Wells et al. Compiled all new antigen predictions and selection methods and provided a new candidate measurement pipe, including 14 immunogenic characteristics of MHC presence and T cell recognition. This study laid a solid foundation for improving the efficacy of tumor vaccine and the adverse cell therapy.


In recent years, with the huge leap of emerging technologies in the field of immunohma, we can now analyze tumor immunity before.

In the bulk sequencing era, we can better explore the individual infiltration model of tumor immunocytes, and the prediction of abnormal peptides, HLA-based and tumor antigen MHC binding pro-and predictions, using immunogenics, predicting tumor antigens have been in front of it. It proves its reliable effect in clinical research.

In addition, with the development of single-cell immune related technologies, from multi-color flow cytometry to CYTOF, single-cell tumor immunization maps help us to classify the immune cell subtracitions to decide the TME component. The emergence of artificial intelligence also provides a new direction for the development of immunohistology.

With the booming of immunohistology, sustainable development needs several issues. First, although many methods of quality control and improvement algorithm have been implemented, the effectiveness of these technologies remains to be improved. Especially in tumor antigen prediction, single cell sequencing and space resolving transcriptional groups, technical noise and mixing factors have hindered subsequent analysis. Second, we look forward to the more cost-effective, easier to obtain and more automated techniques, thoroughly change the development of the discipline. Third, we also expect researchers to make full use of prior art to explore tumor immunity and promote clinical transformation.

Although there is still a lot of work to do, the immunohistology is likely to dominate the future in the future, and its clinical value will undoubtedly promote the development of the subject in the field of immunohistology, single cells and artificial intelligence.


1.Technological Advances in Cancer Immunity: from Immunogenomics to Single-Cell Analysis and Artificial Intelligence. SignalTransduct Target Her. 2021; 6: 312.