Onco-fetal Reprogramming of Endothelial Cells Drives Immunosuppressive Macrophages in Hepatocellular Carcinoma

Sharma, Ankur; Seow, Justine Jia Wen; Dutertre, Charles–Antoine; Pai, Rhea; Blériot, Camille; Mishra, A. N.; Wong, Richard L.; Singh, Gurmit Singh Naranjan; Selvaraju, Sudhagar; Khalilnezhad, Shabnam; Erdal, Sergio; Teo, Hui Min; Khalilnezhad, Ahad; Chakarov, Svetoslav; Lim, Tony Kiat Hon; Fui, Alexander Chung Yaw; Chieh, Alfred Kow Wei; Chung, Cheow Peng; Bonney, Glenn Kunnath; Goh, Brian K.P.; Chan, Jerry Ky; Chow, Pierce Kah‐Hoe; Ginhoux, Florent; DasGupta, Ramanuj

doi:10.1016/j.cell.2020.08.040

Cited by 451 publications

(449 citation statements)

References 57 publications

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“…Liver metastases often originate in colorectal and pancreatic tumors and are the main cause of mortality in these cancer patients (Weinberg, 2013). Single cell atlases have provided important insight into the development (Camp et al , 2017; Segal et al , 2019; Popescu et al , 2019), physiology (MacParland et al , 2018; Aizarani et al , 2019), and pathology (Zhang et al , 2019, 2020; Ramachandran et al , 2019; Sharma et al , 2020) of the human liver. Here, we reconstruct a cell atlas of the malignant human liver in patients with liver metastases or cholangiocarcinomas.…”

Section: Introductionmentioning

confidence: 99%

A single cell atlas of the human liver tumor microenvironment

Massalha

Halpern

Abu‐Gazala

et al. 2020

Molecular Systems Biology

135

114

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Malignant cell growth is fueled by interactions between tumor cells and the stromal cells composing the tumor microenvironment. The human liver is a major site of tumors and metastases, but molecular identities and intercellular interactions of different cell types have not been resolved in these pathologies. Here, we apply single cell RNA‐sequencing and spatial analysis of malignant and adjacent non‐malignant liver tissues from five patients with cholangiocarcinoma or liver metastases. We find that stromal cells exhibit recurring, patient‐independent expression programs, and reconstruct a ligand–receptor map that highlights recurring tumor–stroma interactions. By combining transcriptomics of laser‐capture microdissected regions, we reconstruct a zonation atlas of hepatocytes in the non‐malignant sites and characterize the spatial distribution of each cell type across the tumor microenvironment. Our analysis provides a resource for understanding human liver malignancies and may expose potential points of interventions.

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Section: Introductionmentioning

confidence: 99%

A single cell atlas of the human liver tumor microenvironment

Massalha

Halpern

Abu‐Gazala

et al. 2020

Molecular Systems Biology

135

114

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show abstract

“…The development of single-cell transcriptomics has enabled direct identification and quantification of cell populations within a tumor (Kim et al, 2015; Patel et al, 2014; Puram et al, 2017; Sharma et al, 2020). Corresponding scRNA-seq workflows with gene expression measurement, normalization, cell clustering and summarization ( Figure 2A ), can be coupled in principle with existing methods that predict drug response from bulk transcriptomic profiles (Ammad-ud-din et al, 2016; Basu et al, 2018; Iorio et al, 2016; Suphavilai et al, 2018; Wang et al, 2017) to obtain cell-specific response information.…”

Section: Resultsmentioning

confidence: 99%

Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

Suphavilai

Chia

Sharma

et al. 2020

Preprint

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SummaryWhile understanding heterogeneity in molecular signatures across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Single-cell RNA-seq (scRNA-seq) technologies have facilitated investigations into the role of intra-tumor transcriptomic heterogeneity (ITTH) in tumor biology and evolution, but their application to in silico models of drug response has not been explored. Based on large-scale analysis of cancer omics datasets, we highlight the utility of ITTH for predicting clinical outcomes. We then show that heterogeneous gene expression signatures obtained from scRNA-seq data can be accurately analyzed (80%) in a recommender system framework (CaDRReS-Sc) for in silico drug response prediction. Patient-derived cell lines capturing transcriptomic heterogeneity from primary and metastatic tumors were used as in vitro proxies for validating monotherapy predictions (Pearson r>0.6), as well as optimal drug combinations to target different subclonal populations (>10% improvement). Applying CaDRReS-Sc to the increasing number of publicly available tumor scRNA-seq datasets can serve as an in silico screen for further in vitro and in vivo drug repurposing studies.Graphical abstractHighlightsLarge-scale analysis to establish the impact of transcriptomic heterogeneity within tumors on clinical outcomesCalibrated recommender system for drug response prediction based on single-cell RNA-seq data (CaDRReS-Sc)Prediction of drug response in patient-derived cell lines with transcriptomic heterogeneityIn silico identification of drug combinations that work based on clonal vulnerabilities

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“…Taken together, we believe that bindSC is likely the first tool that has achieved unbiased integration of data matrices generated by different technologies and can be applied in broad settings. In the single-cell domain, bindSC can clearly be applied to align cells and features simultaneously, which are important for ongoing investigations in the Human Cell Atlas 43 , the NIH HubMap 44 , the Human Tumor Cell Network 45 and on remodeling of tumor microenvironment 46 . Further, bindSC can potentially be applied to other domains, such as integrating patient sample mRNA profiles with cell-line drug-sensitivity data 47 .…”

Section: Integrating Single-cell Rna With Protein Datamentioning

confidence: 99%

Unbiased integration of single cell multi-omics data

Dou

Liang

Mohanty

et al. 2020

Preprint

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Acquiring accurate single-cell multiomics profiles often requires performing unbiased in silico integration of data matrices generated by different single-cell technologies from the same biological sample. However, both the rows and the columns can represent different entities in different data matrices, making such integration a computational challenge that has only been solved approximately by existing approaches. Here, we present bindSC, a single-cell data integration tool that realizes simultaneous alignment of the rows and the columns between data matrices without making approximations. Using datasets produced by multiomics technologies as gold standard, we show that bindSC generates accurate multimodal co-embeddings that are substantially more accurate than those generated by existing approaches. Particularly, bindSC effectively integrated single cell RNA sequencing (scRNA-seq) and single cell chromatin accessibility sequencing (scATAC-seq) data towards discovering key regulatory elements in cancer cell-lines and mouse cells. It achieved accurate integration of both common and rare cell types (<0.25% abundance) in a novel mouse retina cell atlas generated using the 10x Genomics Multiome ATAC+RNA kit. Further, it achieves unbiased integration of scRNA-seq and 10x Visium spatial transcriptomics data derived from mouse brain cortex samples. Lastly, it demonstrated efficacy in delineating immune cell types via integrating single-cell RNA and protein data. Thus, bindSC, available at https://github.com/KChen-lab/bindSC, can be applied in a broad variety of context to accelerate discovery of complex cellular and biological identities and associated molecular underpinnings in diseases and developing organisms.

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Onco-fetal Reprogramming of Endothelial Cells Drives Immunosuppressive Macrophages in Hepatocellular Carcinoma

Cited by 451 publications

References 57 publications

A single cell atlas of the human liver tumor microenvironment

A single cell atlas of the human liver tumor microenvironment

Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

Unbiased integration of single cell multi-omics data

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