How did tremendous advances in immunotherapy change cancer treatment?

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How did tremendous advances in immunotherapy change cancer treatment?

The 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 immunoomics refers to the comprehensive study of TME using immunogenomics, immunoproteomics, immunobioinformatics and other multi-omics data reflecting tumor immune status. It 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, since batch 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 studies of immune cell subsets and spatial structure.

In addition, deep learning models based on radiomics and digital pathology have largely contributed to the study of tumor immunity, and these artificial intelligence techniques have performed well in predicting immunotherapy response.

Advances and breakthroughs in these new technologies have far-reaching implications for cancer treatment.

Tumor immune microenvironment

Advances in tumor immunity research in the past few years have fundamentally changed our understanding of tumors.

The definition of a tumor has also evolved from a mere aggregate of tumor cells to a complex organ-like structure composed 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, extracellular matrix and other structures, also known as the tumor immune microenvironment , have become one of the most popular research topics in oncology.

TME has been shown to play a decisive role in cancer initiation, 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 ( DCs ), 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 ( Tregs ) tend to suppress antitumor immunity.

However, existing research has demonstrated that given the complex interactions with tumor cells, the specific roles of immune cells may change dynamically or even become completely opposite.

How did tremendous advances in immunotherapy change cancer treatment?

In conclusion, a wide variety of immune cell types, and even the different functional states of specific immune cell types, may have diametrically opposed effects on antitumor immunity.

Therefore, this requires the use of state-of-the-art bioinformatics techniques to systematically characterize tumor immunology 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 human whole genome information.

NGS generates high-throughput genomic and transcriptional data, laying the foundation for studying multistep immune responses.

Quantifying 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 were able to estimate the abundance of dozens of immune cell types from NGS data, which also proved to be reliable.

The sources of these analyses are mainly 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.

Deconvolution of cellular components is an inverse process of 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 variations ( 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 are required 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 at four-, six- and eight-bit resolution in different cancers All performed quite well.

Among these tools, Polysolver is currently one of the accepted 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 for 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.

While predicting pMHC II affinity is more challenging due to the diversity of MHCII-binding peptide lengths and the “openness” of the binding region, the number of pMHC II affinity prediction methods available is far less than that of pMHC-I.

Immunomics in the single-cell era

Although studies using NGS techniques to study tumor immunity have greatly advanced oncology, bulk sequencing can result in signal dilution below the detection limit and mask the response of individual cells.

This may mask many important biological phenomena. Until recently, technological breakthroughs in single-cell-related approaches have revolutionized our understanding of tumor immunity and transitioned 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 prompted 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, these shortcomings limit the application of multicolor flow cytometry to a certain extent due to the lower accuracy of more measurable parameters, or the limited parameters that can be measured with or to higher accuracy, especially due to the overlap between emission spectra of fluorochromes and further development.

Mass cytometry

Mass spectrometry is a recent innovation in the field, also known as time-of-flight ( CyTOF ) flow cytometry, combining flow cytometry with mass spectrometry.

In contrast to traditional flow cytometry, mass spectrometers label antibodies with metal isotopes rather than fluorophores, and then quantify the signal using a time-of-flight detector that detects at least 40 parameters and avoids spectral overlap problems.

CyTOF has been demonstrated to be an accurate method for high-dimensional analysis of tumor tissue for exploratory immunoassays and biomarker discovery.

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

After nebulization and ionization, cells were completely destroyed during pretreatment, rendering subsequent cell sorting applications infeasible.

Furthermore, CyTOF may not be suitable due to its low sensitivity for measuring certain low-expressed molecular features.

Spectral flow cytometry

Spectral flow cytometry is another recent technological advance that enhances the efficacy of traditional flow cytometry. Unlike mass spectrometers, spectroscopic 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, traditional flow cytometry and spectral flow cytometry maintain a fairly good compatibility, especially in terms of the availability of commercial antibodies, but can better eliminate 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 associate a specific tag with a corresponding subset of cells and identify that tag indicate that targeting must be established prior to sample collection, and initial targeting limits the information that can be obtained from these techniques Technology finds the “known unknown”.

The advent of single-cell sequencing technology has pushed the single-cell field to new heights. No longer limited by predetermined goals such as flow cytometry, single cells can be sequenced using standard NGS protocols to obtain unbiased multi-omics analyses 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 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 address the huge technical noise of amplification and improve sensitivity, how to obtain the highest number of measurable genes at the lowest price, and how to analyze data more efficiently, these greatly improve the 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) Reducing the workload of manually identifying immune infiltration on pathological sections;

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

(3) to provide a non-invasive method to predict patient-specific TME characteristics and response to immunotherapy.

Tumor Antigen Prediction Based on Deep Learning Methods

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 good 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 the full mass spectrometry deep learning model EDGE, which has been used in non-small Validated in patients with NSCLC .

Furthermore, two promising computational deep learning methods, MARIA and MixMHC2pred, have been recently developed, greatly improving the 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 techniques applied to tumor immunity are mainly used to identify biomarkers reflecting immune infiltration and to predict treatment response in ICB-treated patients.

How did tremendous advances in immunotherapy change cancer treatment?

How did tremendous advances in immunotherapy change cancer treatment?

Computational Pathology in Tumor Immunity

AI in pathology, or so-called digital pathology, provides new insights into the exploration of interactions between immune cells and tumor cells and connections 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 TME structure and the relationship between cancer biology and therapy.

The application of immunomics in tumor immunotherapy

Identifying biomarkers of ICB for patient stratification

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

In 2014, researchers first linked tumor mutational burden ( TMB ) to clinical survival in patients treated with CTLA-4 inhibitors using WES. Subsequently, other retrospective studies have also demonstrated that high TMB is associated with durable clinical benefit.

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 Comprehensive Mutation Profile of Actionable Cancer Targets ( MSK-IMPACT ), and approved them by Prospective studies in multiple cancers 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 subsets of immune cells.

TCF7+ memory-like T cells have been found to be associated with clinical improvement in melanoma patients following anti-PD1 therapy, while stem cell-like TCF1+PD1+ T cells have been shown to contribute to tumor control in ICB therapy.

Through single-cell sequencing technology, more T-cell subsets and functional status relevant to therapy and prognosis have been identified.

Prediction of neoantigens in ACT therapy

Adoptive cell therapy ( ACT ) is the reinfusion of transgenic or expanded autologous or allogeneic T cells into the patient to enhance antitumor immunity. Immunomics is primarily used to identify ideal tumor antigens in ACT therapy.

Currently, neoantigen-specific TCR-T cells have not yet entered clinical applications, however, reassuringly, some case reports have shown immunomic-predicted T cells in patients with colorectal, breast, and cholangiocarcinoma to recognize tumor neoantigens. effectiveness. Tran et al. performed WGS on samples from patients with metastatic cholangiocarcinoma and identified 26 somatic mutations.

Tandem minigenes consisting of mutated genes were 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 that induced regression of epithelial cancers.

Traditional neoantigen selection based on co-culture of autologous APCs and T cells 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-tagged proteins are transferred from APCs to T cells when TCR and pMHC bind. Thus, ideal neoantigens can be identified by analysis of marker protein-positive cells. In the future, these emerging immunogenomics technologies will enable high-throughput neoantigen selection.

Selection of neoantigens for individualized tumor vaccines

Immunomic approaches have been widely used for vaccine development in clinical research. Generally, 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 key parameter for tumor vaccine development is ideal neoantigen identification.

In order to improve the accuracy of neoantigen prediction and the pipeline of immunogenic neoepitope selection, immunomics technologies are making unremitting efforts in these areas.

In a recent study, Wells et al. compiled all neoantigen prediction and selection methods and provided an entirely new 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 a huge leap in emerging technologies in the field of immunoomics, we are now able to dissect tumor immunity with unprecedented depth.

How did tremendous advances in immunotherapy change cancer treatment?

How did tremendous advances in immunotherapy change cancer treatment?

In the era of batch sequencing, allowing us to better explore individual infiltration patterns of tumor immune cells, as well as prediction of abnormal peptides, HLA typing, and prediction of MHC binding affinity for tumor antigens, the use of immunomics to predict tumor antigens has been preclinical and clinical studies have demonstrated its reliable efficacy.

In addition, with the development of single-cell immunity-related technologies, from multicolor flow cytometry to CyTOF, the 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 immunoomics.

How did tremendous advances in immunotherapy change cancer treatment?

How did tremendous advances in immunotherapy change cancer treatment?

As immunomics technologies flourish, several issues need to be considered for sustainable development.

First, although many methods for quality control and improving algorithmic principles 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 revolutionize the development of the discipline. Third, we also expect researchers to make full use of existing technologies to explore tumor immunity and facilitate 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 intelligence. SignalTransduct Target Ther. 2021; 6: 312.

How did tremendous advances in immunotherapy change cancer treatment?

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How did tremendous advances in immunotherapy change cancer treatment?

How did tremendous advances in immunotherapy change cancer treatment?

Tumor immune microenvironment