Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships

Andersson, Alma; Larsson, Ludvig; Stenbeck, Linnea; Salmén, Fredrik; Ehinger, Anna; Wu, Sunny Z.; Al-Eryani, Ghamdan; Roden, Daniel; Swarbrick, Alexander; Borg, Åke; Frisén, Jonas; Engblom, Camilla; Lundeberg, Joakim

doi:10.1101/2020.07.14.200600

Cited by 28 publications

(35 citation statements)

References 59 publications

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“…While scRNA-seq has mainly been used to delineate cell subpopulations and their lineage relationships, recently developed spatial transcriptomics technologies have been designed to infer cell-cell communications and spatial architecture in the tumor microenvironment. A research group from Sweden employed an in-house spatial transcriptomics method to resolve spatial immune cell distribution from tumor tissue sections of BRCA patients diagnosed with HER2 + subtype ( 16 , 17 ). The abundance and distribution of the infiltrated immune cells in different regions of the tumor tissue including invasive cancer regions were determined.…”

Section: Spatial Mapping Of Single-cell Rna-seq Datamentioning

confidence: 99%

Single-Cell Profiling to Explore Immunological Heterogeneity of Tumor Microenvironment in Breast Cancer

et al. 2021

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Immune infiltrates in the tumor microenvironment (TME) of breast cancer (BRCA) have been shown to play a critical role in tumorigenesis, progression, invasion, and therapy resistance, and thereby will affect the clinical outcomes of BRCA patients. However, a wide range of intratumoral heterogeneity shaped by the tumor cells and immune cells in the surrounding microenvironment is a major obstacle in understanding and treating BRCA. Recent progress in single-cell technologies such as single-cell RNA sequencing (scRNA-seq), mass cytometry, and digital spatial profiling has enabled the detailed characterization of intratumoral immune cells and vastly improved our understanding of less-defined cell subsets in the tumor immune environment. By measuring transcriptomes or proteomics at the single-cell level, it provides an unprecedented view of the cellular architecture consist of phenotypical and functional diversities of tumor-infiltrating immune cells. In this review, we focus on landmark studies of single-cell profiling of immunological heterogeneity in the TME, and discuss its clinical applications, translational outlook, and limitations in breast cancer studies.

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Section: Spatial Mapping Of Single-cell Rna-seq Datamentioning

confidence: 99%

Single-Cell Profiling to Explore Immunological Heterogeneity of Tumor Microenvironment in Breast Cancer

et al. 2021

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“…These technologies have been widely applied to study the state of breast tumors [ 16 ]. Likewise, resolved molecular profiling technologies provide further opportunities to characterize the tumor microenvironment [ 22 , 23 ], but are only just emerging at a high-dimensional molecular resolution to characterize the pathways in both tumor and immune cells [ 24 , 25 ]. Characterizing the heterogeneous molecular and cellular states with greater resolution helps not only to identify novel biomarkers, but also to understand the significance of these pathways and intercellular interactions in ICI response.…”

Section: Introductionmentioning

confidence: 99%

Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response

et al. 2021

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Response to cancer immunotherapies depends on the complex and dynamic interactions between T cell recognition and killing of cancer cells that are counteracted through immunosuppressive pathways in the tumor microenvironment. Therefore, while measurements such as tumor mutational burden provide biomarkers to select patients for immunotherapy, they neither universally predict patient response nor implicate the mechanisms that underlie immunotherapy resistance. Recent advances in single-cell RNA sequencing technology measure cellular heterogeneity within cells of an individual tumor but have yet to realize the promise of predictive oncology. In addition to data, mechanistic multiscale computational models are developed to predict treatment response. Incorporating single-cell data from tumors to parameterize these computational models provides deeper insights into prediction of clinical outcome in individual patients. Here, we integrate whole-exome sequencing and scRNA-seq data from Triple-Negative Breast Cancer patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology model. The model comprises a four-compartmental Quantitative System Pharmacology sub-model to represent a whole patient and a spatial agent-based sub-model to represent tumor volumes at the cellular scale. We use the high-throughput single-cell data to model the role of antigen burden and heterogeneity relative to the tumor microenvironment composition on predicted immunotherapy response. We demonstrate how this integrated modeling and single-cell analysis framework can be used to relate neoantigen heterogeneity to immunotherapy treatment outcomes. Our results demonstrate feasibility of merging single-cell data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of clinical outcomes to immunotherapy.

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“…The spatial distribution of immune-cells among tumour cells has a long-standing role in diagnosis [28], and was proven useful in predicting prognosis and treatment response in multiple cancer types and molecular settings [67, 49, 53, 40, 13, 9, 43, 39, 54, 16, 29, 55, 37, 23, 5, 46, 11]. Recent advances in spatial transcriptomics, including technology developed for direct measurement (e.g., Visium spatial RNA-sequencing (RNA-seq) by 10x Genomics [1]), as well as approaches for inferring such information from digital pathology images [17, 41, 35, 22], have accentuated the interest in analysing the spatial distribution of molecular traits [33, 4, 61, 51, 48, 24, 6, 27, 3], with some studies already indicating its potential clinical utility [50, 41].…”

Section: Introductionmentioning

confidence: 99%

Assessing heterogeneity in spatial data using the HTA index with applications to spatial transcriptomics and imaging

Levy-Jurgenson

Tekpli

Yakhini

2021

Preprint

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Tumour heterogeneity is being increasingly recognised as an important characteristic of cancer and as a determinant of prognosis and treatment outcome. Emerging spatial transcriptomics data hold the potential to further our understanding of tumour heterogeneity and its implications. However, existing statistical tools are not sufficiently powerful to capture heterogeneity in the complex setting of spatial molecular biology. We provide a statistical solution, the HeTerogeneity Average index (HTA), specifically designed to handle the multivariate nature of spatial transcriptomics. We prove that HTA has an approximately normal distribution, therefore lending itself to efficient statistical assessment and inference. We first demonstrate that HTA accurately reflects the level of heterogeneity in simulated data. We then use HTA to analyse heterogeneity in two cancer spatial transcriptomics datasets: spatial RNA sequencing by 10x Genomics and spatial transcriptomics inferred from H&E. Finally, we demonstrate that HTA also applies to 3D spatial data using brain MRI. In spatial RNA sequencing we use a known combination of molecular traits to assert that HTA aligns with the expected outcome for this combination. We also show that HTA captures immune-cell infiltration at multiple resolutions. In digital pathology we show how HTA can be used in survival analysis and demonstrate that high levels of heterogeneity may be linked to poor survival. In brain MRI we show that HTA differentiates between normal ageing, Alzheimer's disease and two tumours. HTA also extends beyond molecular biology and medical imaging, and can be applied to many domains, including GIS. Source code and python package are available at https://alonalj.github.io/HTA.html .

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Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships

Cited by 28 publications

References 59 publications

Single-Cell Profiling to Explore Immunological Heterogeneity of Tumor Microenvironment in Breast Cancer

Single-Cell Profiling to Explore Immunological Heterogeneity of Tumor Microenvironment in Breast Cancer

Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response

Assessing heterogeneity in spatial data using the HTA index with applications to spatial transcriptomics and imaging

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