Abstract 4340: Integrating whole genome and transcriptome analysis to inform treatment decisions in the metastatic cancer clinical setting
Bibliographic record
Abstract
Abstract Genomic analysis is an established clinical tool for positioning patients to specific treatment rationales. However, given the vast range of genomic complexity that is observed between tumors the extent to which genomic characterization must be applied in order to generate a true picture of the actionable and informative genomic events in an individual's tumour is still a matter for intense debate. Historically, cost and the complexity of the bioinformatics analysis needed has limited the adoption of comprehensive genomic characterization. However with the emerging clinical significance of genomic signatures, mutation burden and immunogenomic profiling, information that can best be recovered from comprehensive genomic analyses, a better understanding of the utility of extensive genomic characterization of tumours in a clinical setting is needed. For the past 6 years, the Personalized Oncogenomics Project (POG) at the BC Cancer Agency has used paired tumour/normal Whole Genome and Transcriptome Analysis (WGTA) to comprehensively characterize tumour samples from patients with incurable metastatic cancer to inform treatment planning for each patient within a clinically relevant timeframe. To date tumours from over 700 patients have been genomically profiled using this approach. These data represent a cohort of tumours from the post-treatment metastatic setting that have been comprehensively characterized at a whole genome level augmented by comprehensive clinical annotation, including information about the primary diagnosis, progression of the disease and treatment history, as well as follow-up information including the response of the tumour to genome analysis-informed therapy. We will discuss findings from this powerful genomic cohort focusing on aspects that are particularly well represented by using a WGTA approach including; mutation signatures of biological and clinical significance, integration of expression data to complement genomic findings and highlighting the discovery of novel NRG1 gene fusions as a demonstration of how using a comprehensive multi-omic approach for genomic analysis to infer the oncogenic impact of novel genomic events can impact patient care. Citation Format: Martin R. Jones, Yaoqing Shen, Erin Pleasance, Elisa Majounie, Laura Williamson, Eric Zhao, Eric Chuah, Karen L. Mungall, Andrew J. Mungall, Richard A. Moore, Yussanne Ma, Stephen Yip, Howard Lim, Daniel Renouf, Steven J. Jones, Janesssa Jaskin, Marco A. Marra. Integrating whole genome and transcriptome analysis to inform treatment decisions in the metastatic cancer clinical setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4340.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".