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US 2022/0034894 A1Published application
Methods of predicting progression of barrett's esophagus
AssigneeCernostics, Inc.
InventorRebecca J. THORNE
Life of this patent
Estimating the term…
Abstract
Embodiments described herein provide methods of determining a risk of progression of Barrett's esophagus in a subject, classifying Barrett's esophagus in a subject, and detecting a field effect associated with malignant transformation of an esophagus of a subject suffering from Barrett's esophagus. The disclosure also provides kits for determining a risk of progression of Barrett's esophagus in a subject and classifying Barrett's esophagus in a subject.
Claims17
1-58. (canceled)
A method of identifying Barrett's esophagus, high-grade dysplasia, or esophageal adenocarcinoma in a subject, comprising:
- a) labeling cell nuclei in an upper gastrointestinal sample from the subject with a panel of reagents;
- b) labeling a plurality of biomarkers in the sample, wherein the plurality of biomarkers are p53, HIF-1α, COX2, p16, alpha-methylacyl-CoA racemase (AMACR), CD68, CD45RO, K20, and HER2/neu;
- c) detecting the labeled plurality of biomarkers and cell nuclei with an optical scanner;
- d) generating digital image data from the detected labeled plurality of biomarkers and cell nuclei;
- e) analyzing the labeled sample using a digital image platform for multi-channel fluorescence whole slide imaging to produce high dimensional quantitative image analysis features;
- f) analyzing the image analysis features associated with the plurality of biomarkers and cell nuclei, wherein the image analysis features are p53 nuclear sum intensity, p53 nuclear mean intensity, ratio of mean HER2/neu intensity:mean K20 intensity in nuclei clusters, ratio of 95th quantile HER2/neu intensity:95th quantile K20 intensity in nuclei clusters, coexpression cellular COX2 and CD68, p53 mean intensity in nuclei clusters, nuclear solidity in p53-overexpressing p16-negative cells, CD45RO plasma membrane sum intensity, AMACR microenvironment standard deviation, COX2 texture in cytoplasm, HIF-1α microenvironment cell mean intensity, HIF-1α microenvironment cell moment (product of mean and standard deviation), p16 cytoplasm mean intensity, nuclear area in p53-overexpressing p16-negative cells, and Hoechst nuclear 95th quantile intensity.
3 more elements
A kit for identifying Barrett's esophagus, high-grade dysplasia, or esophageal adenocarcinoma in a subject, comprising:
- a) one or more reagents to label cell nuclei in an upper gastrointestinal sample from the subject;
- b) one or more probes capable of detecting a plurality of biomarkers in the sample with an optical scanner, wherein the plurality of biomarkers are p53, HIF-1α, COX2, p16, alpha-methylacyl-CoA racemase (AMACR), CD68, CD45RO, K20, and HER2/neu;
- and
- c) instructions for:
- i) generating digital image data from the detected labeled plurality of biomarkers and cell nuclei,
- ii) analyzing the labeled sample using a digital image platform for multi-channel fluorescence whole slide imaging to produce high dimensional quantitative image analysis features, and
- iii) analyzing image analysis features associated with the plurality of biomarkers and cell nuclei, wherein the image analysis features associated with the plurality of biomarkers and cell nuclei, wherein the image analysis features are p53 nuclear sum intensity, p53 nuclear mean intensity, ratio of mean HER2/neu intensity:mean K20 intensity in nuclei clusters, ratio of 95th quantile HER2/neu intensity:95th quantile K20 intensity in nuclei clusters, coexpression cellular COX2 and CD68, p53 mean intensity in nuclei clusters, nuclear solidity in p53-overexpressing p16-negative cells, CD45RO plasma membrane sum intensity, AMACR microenvironment standard deviation, COX2 texture in cytoplasm, HIF-1α microenvironment cell mean intensity, HIF-1α microenvironment cell moment (product of mean and standard deviation), p16 cytoplasm mean intensity, nuclear area in p53-overexpressing p16-negative cells, and Hoechst nuclear 95th quantile intensity.
4 more elements
A method of transforming digital image data into a high dimensional quantitative image analysis feature data set, the method comprising:
- a) labeling cell nuclei in an upper gastrointestinal sample from a subject with a panel of reagents;
- b) labeling a plurality of biomarkers in the sample, wherein the plurality of biomarkers are p53, HIF-1α, COX2, p16, alpha-methylacyl-CoA racemase (AMACR), CD68, CD45RO, K20, and HER2/neu;
- c) detecting the labeled plurality of biomarkers and cell nuclei with an optical scanner;
- d) generating digital image data from the detected labeled plurality of biomarkers and cell nuclei;
- and
- e) transforming the digital image data into a high dimensional quantitative image analysis feature data set.
3 more elements
Dependencies are inferred from the claim text.