Abstract 3910: The Atlas of Blood Cancer Genomes: A resource for therapeutic and biomarker development
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.034 |
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".