Abstract 4621: Validation and calibration of next-generation sequencing to identify Epstein-Barr Virus-positive gastric cancer
Bibliographic record
Abstract
Abstract Background: Gastric adenocarcinoma was recently classified by a large genomic study into four molecular subtypes, including one defined by Epstein-Barr virus (EBV)-positivity. EBV infection in tumors is conventionally assessed by in situ hybridization (ISH), but detection of viral nucleic acids by next-generation sequencing represents a potential alternative. Methods: Normalized EBV read counts were determined by whole genome, whole exome, mRNA and miRNA sequencing for 295 fresh-frozen gastric tumor samples. Formalin-fixed, paraffin-embedded tissue sections were retrieved for ISH confirmation of 13 high-EBV and 11 low-EBV cases selected at random. Results: By each sequencing method, numbers of EBV reads were bimodally distributed across tumors, with a minority having much higher counts. In pairwise comparisons, individual samples were either consistently high or consistently low by all genomic methods for which data were available. Empiric cut-offs based on molecular data of 1000 normalized reads for whole genome, 100 for exome, 4 for mRNA and 5000 for miRNA had perfect concordance identifying 26 (9%) tumors as EBV-positive. One sample tested by ISH lacked tumor cells. For the 23 tumors with EBV status determined by both approaches, EBV-positivity or -negativity by molecular testing was confirmed by EBER-ISH in all but one case (kappa = 0.91). The exception was a microsatellite instability-type cancer EBV-negative by both mRNA and miRNA sequencing, with equivocally positive ISH interpretation. Conclusions: EBV-positive gastric tumors may be accurately identified by quantifying viral sequences in genomic data. Simultaneous analyses of human and viral DNA, mRNA and miRNA could streamline tumor profiling for clinical care and research. Citation Format: M. Constanza Camargo, Reanne Bowlby, Andy Chu, Chandra S. Pedamallu, Vesteinn Thorsson, Sandra Elmore, Andrew Mungall, Adam Bass, Margaret L. Gulley, Charles S. Rabkin. Validation and calibration of next-generation sequencing to identify Epstein-Barr Virus-positive gastric cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4621. doi:10.1158/1538-7445.AM2015-4621
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".