Cell: Relationship between tumor micro-environmental tissue distribution characteristics and tumor immune response?

The tumor immune microenvironment iTME is made up of tumor cells, immune cells, intersotrogen cells, and extracellular components.
the organization of these components affects the effectiveness of anti-tumor immunity.
technology has been able to study the characteristics of spatial distribution in tumor micro-environment.
but the clinical application value of these characteristics has yet to be tapped.
August 6, 2020, the Garry P. Nolan team from Stanford University published an article on Cell entitled Cc Cellular Neighborhood Orchestrate Antitumoral At The Cancer Invasive Front.
study explores the relationship between cell arrangement structure and anti-tumor immune response.
a variety of imaging techniques to capture single-cell information in place.
team of authors has previously developed an imaging system called co-detectionby indexing (CODEX).
the imaging system combines DNA-coupled antibodies and polymerases, fluorescently labeled nucleotides, and crackable chemical fluorescence to achieve immunofluorescence imaging in fresh frozen tissue.
other studies have found that the identification of cell types can be easily predicted by neighboring cells.
also indicates the relationship between cell marker molecules and cell positioning.
studies have shown that the relationship between neighboring cells can predict the progression of disease in mice with autoimmune disease models.
To study iTME in patients with long-term clinical follow-up results and lifetime data tumors, the team improved the CODEX system to make it compatible with Formin-fixed paraffin-enclined tissue (formalin-fixedparaffin embedded FFPE) and tissue microarrays TMAs.
to maximize the identification of cell dedications in tissues, the authors constructed a tissue microarray sample of iTME at the forefront of colon cancer attacks and imaged them with 56 markers.
these markers can be used to identify cell dedic patterns associated with anti-tumor immunity in solid tumors.
used imaging to determine the cell type (celltypes CTs) in each patient's sample and to identify the characteristics of adjacent cells (cellularneighbords CNs) as local CT characteristics.
the authors used both CT and CN information as a collection of patient sample information.
authors used the tissue region as a unit, and even if the internal individual cells change, the corresponding spatial regions of CT and CN change relatively, and the tissue region retains its characteristics.
Tertiary lymphoid structure TLS is an allogy-like lymphocytic structure that is formed in tissues where inflammation occurs, including tumors, autoimmune, infected, or isogenous transplant rejection reactions.
the three-stage lymphatic-like structure adjacent to the tumor is called the tumor-related three-level lymphocyte structure, which contains throbbing cells, T cells, hair-like centers, and B cells.
have revealed the biological and clinical significance of TLS in a variety of tumors.
iTME feature is the formation of large amounts of TLS at the forefront of tumor immersion, i.e. the formation of a "Crohn's-like reaction CLR".
and another typical iTME feature is the rare TLS, but the diffuse inflammatory immersion (diffuseinflammatory inion DII).
previous studies have found that CLR patients have a longer overall survival than DII patients.
the survival of the two groups of patients may be affected by differences in their anti-tumor immune response, not just the inttant factors of the tumor.
authors suggest that comparing iTME in patients with CLR and DII using tissue imaging techniques may lead to the discovery of new iTME structures other than TLS to explore the relationship between cell arrangement structures and anti-tumor immune responses.
team selected 17 CLR and 18 DII patients from a data set of 715 CRC patients, and used tumor immersion frontiers to build tMAs that explored iTME.
the authors found that, consistent with previous reports, the overall survival rate of CLR patients was significantly better than that of PATIENTs with DII.
summary imaging analysis found that iTME in DII patients' tumors was different from that in CLR patients.
in patients with DII, T cells and macrophages make up CN contouring.
analysis of tissue modules suggests that tumors interfere less with major immune processes in CLR patients than in patients with DII.
is that CN-specific T-cells have different functional states.
in patients with DII, CN rich in macrophages increased the abundation of Ki-67 plus Treg cells, while CN rich in T cells reduced the abundation of Ki-67 plus CD8 plus T cells.
function of CN-rich PD-1 plus CD4 plus T cells in patients with high-yielding granulocytes was associated with a relatively good survival rate in patients with DII.
in patients with DII, macrophage-rich CN inhibited the immune response activation of T-cell-rich CN.
in CLR patients, there is a direct cell-to-cell interaction between the CN of T-cell-rich CN and the CN on the tumor boundary.
Conversely, in patients with DII, the more inhibitory macrophage-rich CN is associated with the CN of the tumor boundary, which suggests that the inhibition of macrophage-rich CN may play an important role in the anti-tumor immune response and is an important factor affecting the survival rate of patients.
provides a model framework for spatial biology of dynamic tissues, such as iTME.
analysis of CN and its correlation can be used to reveal its role in iTME, and the functional study based on CNs further clarifies the correlation between the subsets of T cells and determines the communication network between CNs.
the tools developed by the authors are applied to large-scale case cohort studies, which is helpful to identify possible clinical markers, therapeutic strategies, and to understand how spatial tissue behavior acts on anti-tumor immune effects.
authors also point out that the experimental techniques and computing frameworks they have developed have certain limitations.
systems are highly suitable for samples, consume expensive antibodies and still have fewer types of antibodies, and the imaging process is subject to more interference, some require manual correction, and further system improvements are required.
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