{"id":3799,"date":"2023-08-04T17:59:22","date_gmt":"2023-08-04T17:59:22","guid":{"rendered":"https:\/\/dailyai.com\/?p=3799"},"modified":"2023-08-04T18:05:23","modified_gmt":"2023-08-04T18:05:23","slug":"ai-powered-blood-test-detects-cancers-in-single-dna-molecules","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nl\/2023\/08\/ai-powered-blood-test-detects-cancers-in-single-dna-molecules\/","title":{"rendered":"AI-gestuurde bloedtest detecteert kankers in afzonderlijke DNA-moleculen"},"content":{"rendered":"

Onderzoekers van het Johns Hopkins Kimmel Cancer Center werken aan een nieuwe bloedtesttechnologie die bekend staat als GEMINI (Genome-wide Mutational Incidence for Non-Invasive detection of cancer).\u00a0<\/b><\/p>\n

Door genoombrede sequentiebepaling van afzonderlijke DNA-moleculen die door tumoren worden weggegooid, samen te voegen met machinaal leren (ML), kan de test mogelijk longkanker en andere vormen van kanker eerder opsporen.<\/span><\/p>\n

De GEMINI-test analyseert celvrij DNA (cfDNA)-fragmenten die door tumoren worden afgescheiden, waarbij ML wordt gebruikt om verschillen in mutatiefrequenties tussen kanker en niet-kanker in het genoom te detecteren. Hogere scores die door het model worden gegenereerd duiden op een hogere waarschijnlijkheid van kanker.<\/span><\/p>\n

In laboratoriumtests werd GEMINI toegepast op computertomografiebeelden en werden meer dan 90% longkankers gedetecteerd, zelfs in vroege stadia. De <\/span>onderzoek<\/span><\/a> bevindingen werden gepubliceerd in Nature Genetics.\u00a0<\/span><\/p>\n

De onderzoekers richtten zich vooral op het opsporen van longkanker bij hoogrisicopopulaties. <\/span><\/p>\n

Er werden echter ook veranderde mutatieprofielen waargenomen in cfDNA van pati\u00ebnten met leverkanker, melanoom en lymfeklierkanker, dus deze aanpak zou voor verschillende soorten kanker gebruikt kunnen worden.\u00a0<\/span><\/p>\n

Hoe de onderzoekers GEMINI cre\u00eberden<\/span><\/h2>\n

Om GEMINI te ontwikkelen bestudeerde het team genoomsequenties van 2.511 individuen bij 25 verschillende vormen van kanker.\u00a0<\/span><\/p>\n

Verschillende tumortypes vertoonden verschillende mutatieprofielen. De onderzoekers ontdekten dat genomische gebieden met een hoge mutatiefrequentie vergelijkbaar waren tussen tumorweefsel en cfDNA uit bloed bij pati\u00ebnten met longkanker, melanoom of B-cel non-Hodgkin lymfoom.\u00a0<\/span><\/p>\n

De GEMINI-test werd vervolgens toegepast op cfDNA van 365 personen met een hoog risico op longkanker en leverde hogere scores op bij mensen met kanker dan bij mensen zonder kanker.\u00a0<\/span><\/p>\n

Onderzoekers onderzochten ook de integratie van GEMINI met een eerdere test die bekend staat als DELFI (DNA-evaluatie van fragmenten voor vroegtijdige onderschepping),<\/span><\/p>\n

In totaal identificeerden GEMINI en DELFI kankers 91% van de tijd correct in 89 monsters.<\/span><\/p>\n

\"ML
Machine learning hielp onderzoekers bij het analyseren van kankermarkers in cellen. Bron: Natuur<\/a>.<\/figcaption><\/figure>\n

Opmerkelijk genoeg detecteerde de GEMINI-test afwijkingen in cfDNA-mutatieprofielen jaren v\u00f3\u00f3r de standaarddiagnose bij zeven pati\u00ebnten zonder aantoonbare tumoren ten tijde van de bloedafname.\u00a0<\/span><\/p>\n

Zes van hen testten positief met behulp van GEMINI en kregen later de diagnose longkanker, 231 tot 1.868 dagen nadat de monsters waren genomen.<\/span><\/p>\n

Uitsplitsing van het onderzoek<\/span><\/h2>\n
    \n
  1. Bloedafname<\/b>: Er wordt een bloedmonster genomen van een persoon die mogelijk risico loopt op het ontwikkelen van kanker. Dit monster bevat cfDNA, kleine stukjes DNA die door cellen in het lichaam zijn uitgescheiden, waaronder mogelijke tumorcellen.<\/span><\/li>\n
  2. DNA-extractie en sequentiebepaling<\/b>: Het cfDNA wordt uit het bloedmonster gehaald en vervolgens gesequenced. Dit betekent dat wetenschappers de volgorde van de 'bouwstenen' waaruit het DNA bestaat in kaart brengen. Dit helpt hen om eventuele veranderingen of mutaties in het DNA te identificeren.<\/span><\/li>\n
  3. Analyse van DNA-veranderingen<\/b>: Elke individuele DNA-molecule wordt geanalyseerd op sequentieveranderingen, waardoor de onderzoekers mutatieprofielen in het hele genoom in kaart kunnen brengen. In wezen zijn ze op zoek naar patronen in de mutaties die zouden kunnen wijzen op de aanwezigheid van kanker.<\/span><\/li>\n
  4. Machinaal leren<\/b>: Een machine-learning model, dat getraind is om het verschil te herkennen tussen kanker en niet-kanker mutatie frequenties in verschillende gebieden van het genoom, wordt vervolgens gebruikt om de mutatieprofielen te analyseren. Het model voor machinaal leren geeft vervolgens een score vari\u00ebrend van 0 tot 1, waarbij een hogere score duidt op een grotere waarschijnlijkheid van kanker.<\/span><\/li>\n
  5. Verdere validatie en testen<\/b>: Als de GEMINI-score hoog is, wat duidt op de aanwezigheid van kanker, worden aanvullende tests zoals beeldvorming met behulp van computertomografie en de DELFI-test (die veranderingen in de grootte en verdeling van cfDNA-fragmenten in het genoom detecteert) gebruikt om de diagnose te bevestigen en het stadium van de kanker vast te stellen. Deze combinatie van tests is zeer effectief gebleken en heeft meer dan 90% van de longkankers in het onderzoek opgespoord.<\/span><\/li>\n<\/ol>\n

    Dit is nog een intrigerend onderzoek dat de nieuwe toepassing van machinaal leren op medische diagnostiek laat zien, dit keer het analyseren van kankers op cellulair niveau. <\/span><\/p>\n

    Grotere klinische onderzoeken zijn nodig om het hulpmiddel te valideren voordat het beschikbaar kan komen voor klinisch gebruik.<\/span><\/p>\n

    Deze week, een AI-ondersteund borstkankeronderzoek<\/a> workflow de snelheid en effici\u00ebntie van het beoordelen van mammogrammen op kanker enorm verbeterd.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"

    Onderzoekers van het Johns Hopkins Kimmel Cancer Center werken aan een nieuwe bloedtesttechnologie die bekend staat als GEMINI (Genome-wide Mutational Incidence for Non-Invasive detection of cancer). Door genoomwijde sequentiebepaling van afzonderlijke DNA-moleculen die door tumoren worden weggegooid samen te voegen met machinaal leren (ML), kan de test longkanker en andere vormen van kanker eerder opsporen. De GEMINI-test analyseert celvrij DNA (cfDNA)-fragmenten die door tumoren worden weggegooid, waarbij ML wordt gebruikt om verschillen in mutatiefrequenties tussen kanker en niet-kanker in het genoom te detecteren. Hogere scores die door het model worden gegenereerd duiden op een hogere waarschijnlijkheid van kanker. In laboratoriumtests werd GEMINI toegepast op beelden van gecomputeriseerde tomografie.<\/p>","protected":false},"author":2,"featured_media":3801,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[203,274,105,101],"class_list":["post-3799","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-biotech","tag-cancer","tag-machine-learning","tag-medtech"],"yoast_head":"\nAI-powered blood test detects cancers in single DNA molecules | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/nl\/2023\/08\/ai-powered-blood-test-detects-cancers-in-single-dna-molecules\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI-powered blood test detects cancers in single DNA molecules | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers at the Johns Hopkins Kimmel Cancer Center are advancing a novel blood testing technology known as GEMINI (Genome-wide Mutational Incidence for Non-Invasive detection of cancer).\u00a0 By merging genome-wide sequencing of single DNA molecules discarded by tumors with machine learning (ML), the test may enable earlier detection of lung and other cancers. The GEMINI test analyzes cell-free DNA (cfDNA) fragments shed by tumors, using ML to detect differences in cancer and non-cancer mutation frequencies across the genome. Higher scores generated by the model suggest a higher probability of having cancer. 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