Toward understanding and exploiting tumor heterogeneity
Ash A. Alizadeh,Victoria Aranda,Alberto Bardelli,Cédric Blanpain,Christoph Bock,Christine Borowski,Carlos Caldas,Andrea Califano,Michael Doherty,Markus Elsner,Markus Elsner,Manel Esteller,Rebecca C. Fitzgerald,Jan O. Korbel,Peter Lichter,Christopher E. Mason,Nicholas Navin,Dana Pe'er,Kornelia Polyak,Charles W. M. Roberts,Lillian L. Siu,Alexandra Snyder,Hannah Stower,Charles Swanton,Roel G.W. Verhaak,Jean C. Zenklusen,Johannes Zuber,Jessica Zucman-Rossi +27 more
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TLDR
A meeting focused on identifying the obstacles that need to be overcome to advance translational research in and tumor heterogeneity and devised potential solutions are presented here.Abstract:
The extent of tumor heterogeneity is an emerging theme that researchers are only beginning to understand. How genetic and epigenetic heterogeneity affects tumor evolution and clinical progression is unknown. The precise nature of the environmental factors that influence this heterogeneity is also yet to be characterized. Nature Medicine, Nature Biotechnology and the Volkswagen Foundation organized a meeting focused on identifying the obstacles that need to be overcome to advance translational research in and tumor heterogeneity. Once these key questions were established, the attendees devised potential solutions. Their ideas are presented here.read more
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SCENIC: single-cell regulatory network inference and clustering.
Sara Aibar,Carmen Bravo González-Blas,Thomas Moerman,Vân Anh Huynh-Thu,Hana Imrichova,Gert Hulselmans,Florian Rambow,Jean-Christophe Marine,Pierre Geurts,Jan Aerts,Joost van den Oord,Zeynep Kalender Atak,Jasper Wouters,Stein Aerts +13 more
TL;DR: On a compendium of single-cell data from tumors and brain, it is demonstrated that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states.
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EMT, CSCs, and drug resistance: the mechanistic link and clinical implications
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Evidence-Based Diagnosis, Staging, and Treatment of Patients With Hepatocellular Carcinoma.
TL;DR: Studies now aim to identify molecular markers and imaging techniques that can detect patients with HCC at earlier stages and better predict their survival time and response to treatment.
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SCENIC: Single-Cell Regulatory Network Inference And Clustering
Sara Aibar,Carmen Bravo González-Blas,Thomas Moerman,Jasper Wouters,Vân Anh Huynh-Thu,Hana Imrichova,Zeynep Kalender Atak,Gert Hulselmans,Michael Dewaele,Florian Rambow,Pierre Geurts,Jan Aerts,Jean-Christophe Marine,Joost van den Oord,Stein Aerts +14 more
TL;DR: SCENIC (Single Cell rEgulatory Network Inference and Clustering) is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs.
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Rethinking cancer nanotheranostics.
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References
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Robust enumeration of cell subsets from tissue expression profiles
Aaron M. Newman,Chih Long Liu,Michael R. Green,Andrew J. Gentles,Weiguo Feng,Yue Xu,Chuong D. Hoang,Maximilian Diehn,Arash Ash Alizadeh +8 more
TL;DR: CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types when applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors.
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Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Evan Z. Macosko,Evan Z. Macosko,Anindita Basu,Anindita Basu,Rahul Satija,Rahul Satija,James Nemesh,James Nemesh,Karthik Shekhar,Melissa Goldman,Melissa Goldman,Itay Tirosh,Allison R. Bialas,Nolan Kamitaki,Nolan Kamitaki,Emily M. Martersteck,John J. Trombetta,David A. Weitz,Joshua R. Sanes,Alex K. Shalek,Alex K. Shalek,Alex K. Shalek,Aviv Regev,Aviv Regev,Aviv Regev,Steven A. McCarroll,Steven A. McCarroll +26 more
TL;DR: Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together.
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Evan Z. Macosko,Evan Z. Macosko,Anindita Basu,Anindita Basu,Rahul Satija,Rahul Satija,James Nemesh,James Nemesh,Karthik Shekhar,Melissa Goldman,Melissa Goldman,Itay Tirosh,Allison R. Bialas,Nolan Kamitaki,Nolan Kamitaki,Emily M. Martersteck,John J. Trombetta,David A. Weitz,Joshua R. Sanes,Alex K. Shalek,Alex K. Shalek,Alex K. Shalek,Aviv Regev,Aviv Regev,Aviv Regev,Steven A. McCarroll,Steven A. McCarroll +26 more
TL;DR: Drop-seq as discussed by the authors analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin, and identifies 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes.
Journal ArticleDOI
Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells
Allon M. Klein,Linas Mazutis,Linas Mazutis,Ilke Akartuna,Naren Tallapragada,Adrian Veres,Victor C. Li,Leonid Peshkin,David A. Weitz,Marc W. Kirschner +9 more
TL;DR: This work has developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing, which shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays.
Journal ArticleDOI
The prognostic landscape of genes and infiltrating immune cells across human cancers
Andrew J. Gentles,Aaron M. Newman,Chih Long Liu,Scott V. Bratman,Scott V. Bratman,Weiguo Feng,Dongkyoon Kim,Viswam S. Nair,Yue Xu,Amanda Khuong,Chuong D. Hoang,Maximilian Diehn,Robert B. West,Sylvia K. Plevritis,Ash A. Alizadeh +14 more
TL;DR: A pan-cancer resource and meta-analysis of expression signatures from ∼18,000 human tumors with overall survival outcomes across 39 malignancies is presented and it is found that expression of favorably prognostic genes, including KLRB1 (encoding CD161), largely reflect tumor-associated leukocytes.
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