Abstract A184: Clinical application of whole genome and transcriptome sequencing in cancer care
Bibliographic record
Abstract
Abstract Aim: To investigate feasibility and utility of whole genome and RNA sequencing in cancer care. The use of comprehensive personal genomic information to guide cancer treatment decisions has gained momentum in recent years. Here we present the Personalized Oncogenomics (POG) project launched at British Columbia Cancer Agency in 2012. This project uses paired tumor/normal whole genome and transcriptome sequencing to characterize a patient's tumor and inform treatment options within a clinically relevant time frame. In the past 5 years, around 600 patients with metastatic cancers (including 49 pediatric cases) have been sequenced and analyzed, representing over 50 cancer types. The breadth and depth of data in POG enable the detection of multiple types of alterations, from simple mutations, indels, and copy number changes, to more complex alterations such as gene fusion, mutation signatures, and homologous recombination deficiency score. Incorporation of gene expression data through transcriptome sequencing informs on the impact of observed genomic alterations, and provides information regarding specific diagnosis via expression comparison to other cancer subtypes. All genomic and transcriptomic variants are integrated to build a personalized tumor-specific molecular profile, identify actionable items supported by an in-house knowledgebase and publically available molecular oncology, and characterize each individual tumor by intensive pathway analysis and literature search. Such multidimensional data also impose challenges in interpretation and communication. We developed a pipeline to translate complex genomic data into clinically actionable and hypothetical recommendations for the treatment of individual patients. The pipeline produces two reports: a panel-like report that contains known SNVs and fusions with therapeutic relevance from a collection of more than 4000 events, and a second comprehensive and manually curated report to fully characterize the tumor using the whole genome and transcriptome datasets. Genomic data are presented and discussed at a multidisciplinary molecular tumor board consisting of medical oncologists, pathologists, bioinformaticians, geneticists, and biologists. This approach has enabled clinicians to make informed clinical decisions based on the genomic data integrated with other clinical features, as well as to form new treatment-related hypothesis. POG has shown that use of both whole genome and transcriptome sequencing allows identification of therapeutic targets in a significant proportion of patients. Almost 80% cases were found to have one or more actionable alterations (100% in pediatric cases), and almost one-third of these are defined only using RNA data. Based on molecular tumor board discussion, patients are directed to clinical trials, positioned to genomically informed standard-of-care options, or treated with off-label drugs. With demonstrated effectiveness, the integrative approach developed by POG not only provides molecular insight and treatment options into individual tumors, but also provides a rich resource of molecular data with matched clinical information that will aid our understanding of tumor biology and therapy response mechanisms to inform treatment strategies in the future. Citation Format: Yaoqing Shen, Martin R. Jones, Erin Pleasance, Melika Bonakdar, Carolyn Ch'ng, Caralyn Reisle, Laura Williamson, Elisa Majounie, Greg Taylor, Simon Chan, Brandon Pierce, Wei Zhang, Amir Muhammadzadeh, Eric Y. Zhao, Dustin Bleile, Karen Mungall, Nina Thiessen, Eric Chuah, Tina Wong, Richard Corbett, Yussanne Ma, Richard A. Moore, Andrew J. Mungall, Yongjun Zhao, Stephen Yip, Anna F. Lee, Rod Rassekh, Rebecca Deyell, Howard Lim, Daniel Renouf, Robyn Roscoe, Steven J.M Jones, Janessa Laskin, Marco A. Marra. Clinical application of whole genome and transcriptome sequencing in cancer care [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A184.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".