Abstract DPOC-014: BEYOND CODING MUTATIONS: USING RETROTRANSPOSONS TO PREDICT OVARIAN CANCER DEVELOPMENT
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".