The Immune Landscape of Cancer

Immunity, Journal Year: 2018, Volume and Issue: 48(4), P. 812 - 830.e14

Published: April 1, 2018

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled TCGA. Across types, we identified six immune subtypes—wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant—characterized differences in macrophage or signatures, Th1:Th2 cell ratio, extent intratumoral heterogeneity, aneuploidy, neoantigen load, overall proliferation, expression immunomodulatory genes, prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, IDH1) higher (BRAF, TP53, CASP8) leukocyte levels across all cancers. Multiple control modalities the intracellular extracellular networks (transcription, microRNAs, copy number, epigenetic processes) were involved tumor-immune interactions, both within subtypes. Our immunogenomics pipeline to characterize these heterogeneous resulting are intended serve as a resource for future targeted studies further advance field.

Language: Английский

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Applications of machine learning in drug discovery and development

Nature Reviews Drug Discovery, Journal Year: 2019, Volume and Issue: 18(6), P. 463 - 477

Published: April 11, 2019

Language: Английский

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Tumor microenvironment as a therapeutic target in cancer

Pharmacology & Therapeutics, Journal Year: 2020, Volume and Issue: 221, P. 107753 - 107753

Published: Nov. 28, 2020

Language: Английский

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Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology

Nature Reviews Clinical Oncology, Journal Year: 2019, Volume and Issue: 16(11), P. 703 - 715

Published: Aug. 9, 2019

Language: Английский

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Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data

Genome Medicine, Journal Year: 2019, Volume and Issue: 11(1)

Published: May 24, 2019

We introduce quanTIseq, a method to quantify the fractions of ten immune cell types from bulk RNA-sequencing data. quanTIseq was extensively validated in blood and tumor samples using simulated, flow cytometry, immunohistochemistry data.quanTIseq analysis 8000 revealed that cytotoxic T infiltration is more strongly associated with activation CXCR3/CXCL9 axis than mutational load deconvolution-based scores have prognostic value several solid cancers. Finally, we used show how kinase inhibitors modulate contexture reveal immune-cell underlie differential patients' responses checkpoint blockers.Availability: available at http://icbi.at/quantiseq .

Language: Английский

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Data-efficient and weakly supervised computational pathology on whole-slide images

Nature Biomedical Engineering, Journal Year: 2021, Volume and Issue: 5(6), P. 555 - 570

Published: March 1, 2021

Language: Английский

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A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study

The Lancet Oncology, Journal Year: 2018, Volume and Issue: 19(9), P. 1180 - 1191

Published: Aug. 14, 2018

Language: Английский

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Conserved pan-cancer microenvironment subtypes predict response to immunotherapy

Cancer Cell, Journal Year: 2021, Volume and Issue: 39(6), P. 845 - 865.e7

Published: May 20, 2021

Language: Английский

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A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

Proceedings of the IEEE, Journal Year: 2021, Volume and Issue: 109(5), P. 820 - 838

Published: Feb. 26, 2021

Since its renaissance, deep learning has been widely used in various medical imaging tasks and achieved remarkable success many applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that of AI mostly attributed to availability big data with annotations for a single task advances high performance computing. However, presents unique challenges confront approaches. In this survey paper, we first present traits imaging, highlight both clinical needs technical describe how emerging trends are addressing these issues. We cover topics network architecture, sparse noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, several case studies commonly found practice, including digital pathology chest, brain, cardiovascular, abdominal imaging. Rather than presenting an exhaustive literature survey, instead some prominent research highlights related study applications. conclude discussion presentation promising future directions.

Language: Английский

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Deep learning in histopathology: the path to the clinic

Nature Medicine, Journal Year: 2021, Volume and Issue: 27(5), P. 775 - 784

Published: May 1, 2021

Language: Английский

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