Abstract 3910: The Atlas of Blood Cancer Genomes: A resource for therapeutic and biomarker development
Notice bibliographique
Résumé
Abstract Developing a novel cancer therapy is an expensive, time-consuming, high-risk endeavor that involves identifying a molecular target as well as target indications. This process could be accelerated by a comprehensive interrogation of driver variants and gene expression profiles across cancer types. The Atlas of Blood Cancer Genomes (ABCG) project was initiated to elucidate the molecular basis of all leukemias and lymphomas, building on advances in genomic technologies, our capabilities for data analysis, and economies of scale. The ABCG project includes collaborators from 25 institutions worldwide who contributed samples from 10,512 patients comprising every type of blood cancer in the World Health Organization classifications. All cases were de-identified and their associated pathology and clinical information entered into a purpose-built web-based system. All cases underwent pathology and clinical data review by experienced hematopathologists and oncologists. Samples were subjected to whole exome DNA and RNA sequencing. We examined three classes of therapeutic targets with selected examples of application: 1. Surface markers: Surface markers are targets of many approved and experimental therapies in blood cancers (CAR-T cells, monoclonal and bispecific antibodies). We found that CD22, which has been evaluated as a target in diffuse large B-cell lymphoma (DLBCL), is also highly expressed in follicular lymphoma, adult ALL, mantle cell lymphoma, and high-grade B cell lymphoma, all areas of clinical need. We can evaluate the expression of virtually any marker or combination of markers across all blood cancers while noting areas of greatest clinical need within and across diseases, providing the basis to reclassify diseases by therapeutic target. 2. Genetic targets: Our work assesses the entire spectrum of genetic alterations including mutations and fusions. We found targetable alterations including BCR-ABL1 fusions, EZH2 Y641, IDH2 R140Q and BRAF V600E mutations in multiple cancers, albeit at low frequency, pointing to potential new indications for existing drugs in subsets of rare diseases. 3. Complex targets (immune or expression signatures or combinations of gene variants): Past work defined DLBCL immune and other signatures associated with response to avadomide, a novel cereblon inhibitor. We found that these signatures are also highly expressed in large subsets of peripheral T-cell lymphomas and acute myeloid leukemia, both areas of major unmet clinical need. Thus, we can interrogate immune and other signatures across the spectrum of cancers to uncover potential biomarkers of response. The ABCG project will enable the comprehensive study of genomic and clinicopathological features of all blood cancers. We anticipate that our data, approaches and results will provide a lasting resource for molecular classification and therapeutic development in all leukemias and lymphomas. Citation Format: Jennifer Shingleton, Raju Pillai, Sarah Ondrejka, Govind Bhagat, Amy Chadburn, Matthew McKinney, Jean Koff, Dina Soliman, Magdalena Czader, Abner Louissaint, Shaoying Li, Choon Kiat Ong, Amir Behdad, Andrew Evens, Yaso Natkunam, Mette Pedersen, Sirpa Leppa, Eric Tse, Jennifer Chapman, Catalina Amador-Ortiz, Yuri Fedoriw, Andrew Evans, Jiong Yan, Mina Xu, Kikkeri Naresh, Clay Parker, David Hsu, Sandeep Dave. The Atlas of Blood Cancer Genomes: A resource for therapeutic and biomarker development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3910.
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 | 0,006 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,009 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,053 | 0,034 |
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 ».