Nothing Special   »   [go: up one dir, main page]

Jackson et al., 2020 - Google Patents

The single-cell pathology landscape of breast cancer

Jackson et al., 2020

View PDF
Document ID
13204528792975877934
Author
Jackson H
Fischer J
Zanotelli V
Ali H
Mechera R
Soysal S
Moch H
Muenst S
Varga Z
Weber W
Bodenmiller B
Publication year
Publication venue
Nature

External Links

Snippet

Single-cell analyses have revealed extensive heterogeneity between and within human tumours,,–, but complex single-cell phenotypes and their spatial context are not at present reflected in the histological stratification that is the foundation of many clinical decisions …
Continue reading at www.repository.cam.ac.uk (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay
    • G01N33/574—Immunoassay; Biospecific binding assay for cancer
    • G01N33/57407—Specifically defined cancers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/01—Social networking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce, e.g. shopping or e-commerce

Similar Documents

Publication Publication Date Title
Jackson et al. The single-cell pathology landscape of breast cancer
Danenberg et al. Breast tumor microenvironment structures are associated with genomic features and clinical outcome
Ali et al. Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer
Kuett et al. Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment
Wei et al. Spatial charting of single-cell transcriptomes in tissues
Schapiro et al. MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging
Wu et al. Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens
Berry et al. Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade
Kather et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
Jaber et al. A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival
Corredor et al. Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non–small cell lung cancer
Manak et al. Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning
Cheng et al. Identification of topological features in renal tumor microenvironment associated with patient survival
Yuan et al. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling
Dyrskjøt et al. Identifying distinct classes of bladder carcinoma using microarrays
Wang et al. Sprod for de-noising spatially resolved transcriptomics data based on position and image information
Green et al. Identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers
Mori et al. Characterizing the tumor immune microenvironment with tyramide-based multiplex immunofluorescence
Natal et al. Exploring collagen parameters in pure special types of invasive breast cancer
Uttam et al. Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks
Allam et al. Spatially variant immune infiltration scoring in human cancer tissues
Acosta et al. Intratumoral resolution of driver gene mutation heterogeneity in renal cancer using deep learning
Zhong et al. A curated collection of tissue microarray images and clinical outcome data of prostate cancer patients
Fischer et al. Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
Wu et al. 7-UP: Generating in silico CODEX from a small set of immunofluorescence markers