Abstract DPOC-014: BEYOND CODING MUTATIONS: USING RETROTRANSPOSONS TO PREDICT OVARIAN CANCER DEVELOPMENT
Notice bibliographique
Résumé
Abstract PURPOSE: Women with endometriosis, a painful condition caused by displaced endometrial tissue, have a 3-fold increased risk of developing endometrioid ovarian cancer (ENOC) and clear cell ovarian cancer (CCOC). How two distinct cancers arise from the same precursor lesion is unknown. Sensitive biomarkers are needed to identify women with endometriosis at risk of developing cancer. We performed whole genome sequencing on 29 ENOC and 36 CCOC cases and observed a highly frequent insertion event originating from an active LINE-1 (L1) retrotransposon in the TTC28 gene. L1 retrotransposons are mobile genetic elements that can take downstream DNA pieces and insert them into random genomic locations in a process called 3' transduction. L1s are epigenetically silenced in normal tissues, but are known to become activated in a variety of cancers. A recent study showed a stepwise loss of methylation across various L1 loci between normal endometrium, contiguous endometriosis (endometriosis adjacent to tumor), and ENOC/CCOC tissues. We hypothesize that TTC28 L1 retrotransposon is an early event in the transformation of endometriosis into ENOC and CCOC and such events could be used as biomarkers for endometriosis with high cancer risk. METHODS: We compared the presence of TTC28 L1 3' transductions to six SNVs and frame shifts mutations in normal, endometriosis, and tumor tissues from different anatomical sites in four ENOC and four CCOC cases. PCR followed by Sanger sequencing was used to detect TTC28 L1 insertions, and micro-fluidic PCR assay followed by MiSeq sequencing used to detect SNV/frameshift mutations. To broaden the analysis we will use a target capture sequencing method to track novel TTC28 and other L1 transductions. In these experiments probes tiling 1kbp downstream of L1s will be used to capture DNA fragments containing L1 transductions; the captured fragments will be sequenced on the MiSeq. We will assess the difference in TTC28 L1 methylation status between normal, endometriosis, and tumor tissues via the sequencing of bi-sulfite treated DNA. RESULTS: TTC28 L1 retrotransposition insertion is present at all 5 tumor sites in 75% (6/8) of cases, and is present in 3/5 or 4/5 tumor sites in the remaining cases. Analysis shows that TTC28 L1 insertion preceded some SNV and/or frameshift mutations. Preliminary results show that TTC28 L1 promoters are unmethylated in tumors with L1 insertions. Future experiments involving additional cases with endometriosis tissues will be performed. We expect to see L1 promoter hypomethylation and L1 transductions in endometriosis tissues. CONCLUSION: TTC28 L1 promoter hypomethylation and TTC28 L1 transductions may be early events in the transformation of endometriosis to cancer that can be explored as a method to predict tumor development. The development of a target capture assay to detect novel L1 transductions will be crucial for investigating cases without whole genome sequencing data. Ultimately, we hope to detect L1 insertions in plasma samples, and use L1 insertions as a biomarker to identify high-risk endometriosis cases. Citation Format: Zhouchunyang Xia, Dawn Cochrane, Michael S Anglesio, Tayyebeh M Nazeran, Janine Senz, Amy Lum, Ali Bashashati, Yi Kan Wang, Sohrab P Shah, David Huntsman. BEYOND CODING MUTATIONS: USING RETROTRANSPOSONS TO PREDICT OVARIAN CANCER DEVELOPMENT [abstract]. In: Proceedings of the 11th Biennial Ovarian Cancer Research Symposium; Sep 12-13, 2016; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(11 Suppl):Abstract nr DPOC-014.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».