CLASSIFICATION OF TUMOR MICROENVIRONMENTS
The disclosure provides population and non-population-based classifiers to categorize patients and cancers. The population-based classifiers disclosed integrate signatures, i.e., global scores related to the expression of genes in particular gene panels. The non-population-based classifiers are generated using machine-learning techniques (e.g., regression, random forests, or ANN). Each type of classifier stratifies patients and cancers according to tumor microenvironments (TME) as biomarker-positive or biomarker-negative, and treatment decisions are then guided by the presence/absence of a particular TME. Also provided are methods for treating a subject, e.g., a human subject, afflicted with cancer comprising administering a particular therapy depending on the classification of the cancer's TME according to the disclosed classifiers. Also provided are personalized treatments that can be administered to a subject having a cancer classified into a particular TME, and gene panels that can be used for identifying a human subject afflicted with a cancer suitable for treatment with a particular therapeutic agent..
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Patent| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Europäisches Patentamt - (2022) vom: 14. Sept. Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
BENJAMIN LAURA E [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Anmerkungen: |
Source: www.epo.org (no modifications made), First posted: 2022-09-14, Last update posted on www.tib.eu: 2024-03-05, Last updated: 2024-03-08 |
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| Patentnummer: |
EP4055609 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA014938391 |
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| 520 | |a The disclosure provides population and non-population-based classifiers to categorize patients and cancers. The population-based classifiers disclosed integrate signatures, i.e., global scores related to the expression of genes in particular gene panels. The non-population-based classifiers are generated using machine-learning techniques (e.g., regression, random forests, or ANN). Each type of classifier stratifies patients and cancers according to tumor microenvironments (TME) as biomarker-positive or biomarker-negative, and treatment decisions are then guided by the presence/absence of a particular TME. Also provided are methods for treating a subject, e.g., a human subject, afflicted with cancer comprising administering a particular therapy depending on the classification of the cancer's TME according to the disclosed classifiers. Also provided are personalized treatments that can be administered to a subject having a cancer classified into a particular TME, and gene panels that can be used for identifying a human subject afflicted with a cancer suitable for treatment with a particular therapeutic agent. | ||
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| 650 | 4 | |a G16H: Healthcare informatics, i.e. information and communication technology [ict] specially adapted for the handling or processing of medical or healthcare data | |
| 650 | 4 | |a C12Q: Measuring or testing processes involving enzymes, nucleic acids or microorganisms (immunoassay g01n0033530000); compositions or test papers therefor; processes of preparing such compositions; condition-responsive control in microbiological or enzymological processes | |
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