Multi-Omics Profiling of the Tumor Microenvironment

Oekelen, Oliver Van; Laganà, Alessandro

doi:10.1007/978-3-030-91836-1_16

Cited by 7 publications

(6 citation statements)

References 305 publications

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“…TME composition affects immune cells’ ability to locate and kill malignant cells, the bioavailability and effectiveness of chemotherapy drugs, the availability of oxygen and other nutrients needed for cancer cell growth, and the possibility of metastasis [2, 3]. For these reasons, thorough characterization of the TME is an active area of cancer research [4, 5].…”

Section: Introductionmentioning

confidence: 99%

Performance of computational algorithms to deconvolve heterogeneous bulk tumor tissue depends on experimental factors

Hippen

Omran

Weber

et al. 2022

Preprint

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Background: Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay. Results: We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method. Conclusions: Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity.

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Section: Introductionmentioning

confidence: 99%

Performance of computational algorithms to deconvolve heterogeneous bulk tumor tissue depends on experimental factors

Hippen

Omran

Weber

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…( a ) UMAP plot showing the breast cancer atlas based on two previously published single-cell RNA-seq datasets. 21,23 ( b ) Verification of the cellular annotation of the newly generated breast cancer atlas using several cell-specific genes. ( c ) Deconvolution of two cell populations in the spatial single-cell RNA-seq data using the breast cancer cell atlas (left panels) and confirmation of the deconvolution by histology analysis (right panel).…”

Section: Resultsmentioning

confidence: 99%

Cancer-associated Fibroblast Spatial Heterogeneity andEMILIN1Expression in Cancer Stroma Modulate TGF-β Activity and CD8⁺T-Cell Infiltration in Breast Cancer

Honda,

Kurozumi,

Fujii

et al. 2023

Preprint

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The tumor microenvironment (TME) and its multifaceted interactions with cancer cells are major targets for cancer treatment. Single-cell technologies have brought major insights into the TME, but the resulting complexity frequently precludes conclusions on function. Therefore, we combined single-cell RNA sequencing and spatial transcriptomic data to explore the relationship between different cancer-associated fibroblast (CAF) populations and immune cell exclusion in breast tumors. Our data show for the first time the degree of spatial organization of different CAF populations in breast cancer. We found that IL-iCAFs, Detox-iCAFs, and IFN-beta-iCAFs tended to cluster together, while Wound-myCAFs, TGF-beta-myCAFs, and ECM-myCAFs formed another group that overlapped with elevated TGF-beta signaling. Differential gene expression analysis of areas with CD8+ T-cell infiltration/exclusion within the TGF-beta signaling-rich zones identified elastin microfibrillar interface protein 1 (EMILIN1) as a top modulated gene. EMILIN1, a TGF-beta inhibitor, was upregulated in IFN-beta-iCAFs directly modulating TGF-beta immunosuppressive function. Histological analysis of 74 breast cancer samples confirmed that high EMILIN-1 expression in the tumor margins was related to high CD8+ T-cell infiltration, consistent with our spatial gene expression analysis. High EMILIN-1 expression was also associated with better prognosis of patients with breast cancer, underscoring its functional significance for the recruitment of cytotoxic T cells into the tumor area. In conclusion, our data show that correlating TGF-beta signaling to a CAF subpopulation is not enough because proteins with TGF-beta-modulating activity originating from other CAF subpopulations can alter its activity. Therefore, therapeutic targeting should remain focused on biological processes rather than on specific CAF subtypes.

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“…We also analyzed the tumor genomic heterogeneity of SERPINA1 mutations in pan-cancer [39], and a growing number of studies have found that other tumor heterogeneity analyses are also emerging clinical biomarkers in immunotherapy, clinical outcomes, and chemotherapy [55,56]. In addition, tumor heterogeneity based on SERPINA1 mutations in pan-cancer was also observed.…”

Section: Discussionmentioning

confidence: 99%

Pan-cancer analysis reveals potential for synergy between SERPINA1 molecular targeting and macrophage-related immunotherapy

Fu¹,

Miao²,

Xue³

et al. 2022

Preprint

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Background The immune response can be modulated by autophagy to alter tumor growth. SERPINA1 is not only an autophagy-related protein but also a serine protease inhibitor with the potential for immunotherapy and targeted drug therapy. Methods Based on the latest multi-omic databases, we evaluated SERPINA1 mRNA and protein expression levels, prognostic value, methylation and mutation, signaling pathway, and gene ontology analysis and explored their relevance. The relationship between SERPINA1 expression and immune and drug sensitivity was also analyzed. Single-cell sequencing was used to validate the function and immunity in different cancers. Results Many tumors are associated with abnormal SERPINA1 expression and a poor prognosis. According to our study, DNA methylation, gene mutations, and post-translational modifications of SERPINA1 were significantly and positively correlated with its expression levels in breast cancer as a diagnostic marker. In addition, we observed that SERPINA1 positively correlates with macrophages and was able to stimulate M2 macrophage polarization, It was found that SERPINA1 was associated with macrophages in glioma immune microenvironments. Conclusions Considering that SERPINA1 plays a role in cancer progression, SERPINA1 may be a new promising target for immunotherapy and drug target therapy.

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Multi-Omics Profiling of the Tumor Microenvironment

Cited by 7 publications

References 305 publications

Performance of computational algorithms to deconvolve heterogeneous bulk tumor tissue depends on experimental factors

Performance of computational algorithms to deconvolve heterogeneous bulk tumor tissue depends on experimental factors

Cancer-associated Fibroblast Spatial Heterogeneity andEMILIN1Expression in Cancer Stroma Modulate TGF-β Activity and CD8⁺T-Cell Infiltration in Breast Cancer

Pan-cancer analysis reveals potential for synergy between SERPINA1 molecular targeting and macrophage-related immunotherapy

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Multi-Omics Profiling of the Tumor Microenvironment

Cited by 7 publications

References 305 publications

Performance of computational algorithms to deconvolve heterogeneous bulk tumor tissue depends on experimental factors

Performance of computational algorithms to deconvolve heterogeneous bulk tumor tissue depends on experimental factors

Cancer-associated Fibroblast Spatial Heterogeneity andEMILIN1Expression in Cancer Stroma Modulate TGF-β Activity and CD8+T-Cell Infiltration in Breast Cancer

Pan-cancer analysis reveals potential for synergy between SERPINA1 molecular targeting and macrophage-related immunotherapy

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Product

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Cancer-associated Fibroblast Spatial Heterogeneity andEMILIN1Expression in Cancer Stroma Modulate TGF-β Activity and CD8⁺T-Cell Infiltration in Breast Cancer