[go: up one dir, main page]

WO2024040006A3 - Ai and ml-based system to predict cancer from epigenetic data - Google Patents

Ai and ml-based system to predict cancer from epigenetic data Download PDF

Info

Publication number
WO2024040006A3
WO2024040006A3 PCT/US2023/072104 US2023072104W WO2024040006A3 WO 2024040006 A3 WO2024040006 A3 WO 2024040006A3 US 2023072104 W US2023072104 W US 2023072104W WO 2024040006 A3 WO2024040006 A3 WO 2024040006A3
Authority
WO
WIPO (PCT)
Prior art keywords
cancer
biomarkers
identified
methylation
amount
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2023/072104
Other languages
French (fr)
Other versions
WO2024040006A2 (en
Inventor
Ray BAHADO-SINGH
Sangeetha VISHWESWARAIAH
Uppala RADHAKRISHNA
George David WILSON
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Bioscreening and Diagnostics LLC
Original Assignee
Bioscreening and Diagnostics LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Bioscreening and Diagnostics LLC filed Critical Bioscreening and Diagnostics LLC
Publication of WO2024040006A2 publication Critical patent/WO2024040006A2/en
Publication of WO2024040006A3 publication Critical patent/WO2024040006A3/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/154—Methylation markers

Landscapes

  • Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Pathology (AREA)
  • Medical Informatics (AREA)
  • Genetics & Genomics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • Organic Chemistry (AREA)
  • Molecular Biology (AREA)
  • Biotechnology (AREA)
  • Zoology (AREA)
  • Biophysics (AREA)
  • Public Health (AREA)
  • Wood Science & Technology (AREA)
  • Immunology (AREA)
  • Theoretical Computer Science (AREA)
  • Oncology (AREA)
  • Evolutionary Biology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Primary Health Care (AREA)
  • Hospice & Palliative Care (AREA)
  • Data Mining & Analysis (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Microbiology (AREA)
  • Biomedical Technology (AREA)
  • Databases & Information Systems (AREA)
  • Epidemiology (AREA)
  • Biochemistry (AREA)
  • General Engineering & Computer Science (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

An artificial intelligence-based method for diagnosing cancer or determining susceptibility to cancer includes a step of obtaining a biological sample from a target subject (e.g., a human). The degree of methylation in one or a plurality of cancer biomarkers is identified in the biological sample. The cancer biomarkers can be intragenic and/or extragenic biomarkers. Each cancer biomarker is identified as being an indicator of the presence of or risk of developing cancer. Characteristically, the at least one or the plurality of cancer biomarkers have been identified by a machine learning technique or by logistic regression. Finally, the target subject is identified as being at risk for cancer if the amount of methylation of one or more cancer biomarkers differs from the amount of methylation established in control subjects (for the same genes) not having cancer by a predetermined amount.
PCT/US2023/072104 2022-08-15 2023-08-11 Ai and ml-based system to predict cancer from epigenetic data Ceased WO2024040006A2 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202263397910P 2022-08-15 2022-08-15
US63/397,910 2022-08-15

Publications (2)

Publication Number Publication Date
WO2024040006A2 WO2024040006A2 (en) 2024-02-22
WO2024040006A3 true WO2024040006A3 (en) 2024-04-18

Family

ID=89942331

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2023/072104 Ceased WO2024040006A2 (en) 2022-08-15 2023-08-11 Ai and ml-based system to predict cancer from epigenetic data

Country Status (1)

Country Link
WO (1) WO2024040006A2 (en)

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190390253A1 (en) * 2016-12-22 2019-12-26 Guardant Health, Inc. Methods and systems for analyzing nucleic acid molecules

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190390253A1 (en) * 2016-12-22 2019-12-26 Guardant Health, Inc. Methods and systems for analyzing nucleic acid molecules

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
ANONYMOUS: "UCSC Genome Browser on Human (GRCh38/hg38)", 1 January 2022 (2022-01-01), XP093164838, Retrieved from the Internet <URL:https://genome.ucsc.edu/cgi-bin/hgTracks?db=hg38&lastVirtModeType=default&lastVirtModeExtraState=&virtModeType=default&virtMode=0&nonVirtPosition=&position=chr10:119806996-119806997&hgsid=2252681644_mmNAuXSN2AHgtZzDMo1DmT2eITCQ> *
KOUJI BANNO: "Candidate Biomarkers for Genetic and Clinicopathological Diagnosis of Endometrial Cancer", INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, MOLECULAR DIVERSITY PRESERVATION INTERNATIONAL (MDPI), BASEL, CH, vol. 14, no. 6, Basel, CH , pages 12123 - 12137, XP093164840, ISSN: 1422-0067, DOI: 10.3390/ijms140612123 *
TETSUYA MINAGAWA: "Identification and Characterization of a Sac Domain-containing Phosphoinositide 5-Phosphatase", JOURNAL OF BIOLOGICAL CHEMISTRY, AMERICAN SOCIETY FOR BIOCHEMISTRY AND MOLECULAR BIOLOGY, US, vol. 276, no. 25, 1 June 2001 (2001-06-01), US , pages 22011 - 22015, XP093164839, ISSN: 0021-9258, DOI: 10.1074/jbc.M101579200 *

Also Published As

Publication number Publication date
WO2024040006A2 (en) 2024-02-22

Similar Documents

Publication Publication Date Title
US20210366577A1 (en) Predicting disease outcomes using machine learned models
Kidd et al. Blood and tissue neuroendocrine tumor gene cluster analysis correlate, define hallmarks and predict disease status
Gage et al. A validated model for identifying patients unlikely to benefit from the 21-gene recurrence score assay
Dlamini et al. AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care
Shen et al. MicroRNA signatures in diagnosis and prognosis of cutaneous T-cell lymphoma
Sherafatian Tree-based machine learning algorithms identified minimal set of miRNA biomarkers for breast cancer diagnosis and molecular subtyping
WO2021237117A1 (en) Predicting disease outcomes using machine learned models
MX2007003502A (en) Methods and computer program products for analysis and optimization of marker candidates for cancer prognosis.
Adorada et al. Support vector machine-recursive feature elimination (svm-rfe) for selection of microrna expression features of breast cancer
Delahaye et al. Performance characteristics of the MammaPrint® breast cancer diagnostic gene signature
Yang et al. Revealing EXPH5 as a potential diagnostic gene biomarker of the late stage of COPD based on machine learning analysis
Lopes et al. Digital image analysis of multiplex fluorescence IHC in colorectal cancer recognizes the prognostic value of CDX2 and its negative correlation with SOX2
WO2021154869A8 (en) Systems and methods for delivery of digital biomarkers and genomic panels
Bastani et al. A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status
WO2019246160A3 (en) Methods for assessing endometrial transformation
Stawiski et al. OmicSelector: automatic feature selection and deep learning modeling for omic experiments
Arrambide et al. Big data and artificial intelligence applied to blood and CSF fluid biomarkers in multiple sclerosis
WO2024040006A3 (en) Ai and ml-based system to predict cancer from epigenetic data
Holden et al. Local in Time Statistics for detecting weak gene expression signals in blood–illustrated for prediction of metastases in breast cancer in the NOWAC Post-genome Cohort
CN112639132A (en) L1TD1 as predictive biomarker for colon cancer
Huang et al. Analytical performance of a 15-gene prognostic assay for early-stage non–small-cell lung carcinoma using RNA-stabilized tissue
Kesharwani et al. CBS-miRSeq: a comprehensive tool for accurate and extensive analyses of microRNA-sequencing data
US20210225460A1 (en) Evaluating the robustness and transferability of predictive signatures across molecular biomarker datasets
Ciniselli et al. Workflow for circulating miRNA identification and development in cancer research: Methodological considerations
Vavourakis et al. Devising reliable and accurate epigenetic predictors: choosing the optimal computational solution

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23855580

Country of ref document: EP

Kind code of ref document: A2

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 23855580

Country of ref document: EP

Kind code of ref document: A2