Abstract A184: Clinical application of whole genome and transcriptome sequencing in cancer care
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,009 |
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 ».