Abstract IA08: The role of gene expression in the classification of aggressive B-cell lymphoma
Bibliographic record
Abstract
Abstract Aggressive B-cell lymphomas collectively make up half of all lymphoma diagnoses. The current classification system, the 2017 revision of the WHO 4th edition, assigns these tumors into groups based on morphology, immunophenotype, site of disease, and the presence of recurrent chromosomal rearrangements. Accurate and reproducible diagnosis is required for selection of optimal treatment, prognostication, and ongoing basic research and clinical trials aimed at improving outcomes. Ideally, the taxonomy would continue to evolve towards further defining homogeneous groups of tumors sharing targetable biology. Gene expression (GE) profiling of tumors supports the biologic validity of the entities in the current classification and, along with genomic sequencing, is driving the identification of new lymphoma subtypes. In the mid-2000s GE profiling studies identified specific signatures that distinguish aggressive B-cell entities from each other—namely, primary mediastinal large B-cell lymphoma (PMBL) from diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL) from DLBCL. The increasingly divergent treatment of these entities makes reliable diagnosis important. Prior to those studies, GE profiling identified 2 distinct subtypes of DLBCL. This binary division of DLBCL into the cell-of-origin groups of germinal center B-cell-like (GCB) and activated B-cell-like (ABC) has been foundational to our understanding of the pathology of DLBCL. However, recent failure of clinical trials to improve outcomes by adding targeted agents to R-CHOP in upfront treatment of ABC-DLBCL highlights that this binary division may not be sufficiently granular to support precision medicine. These signatures have been translated onto tractable technology platforms, including nuclease protection assay, RT-MLPA, and NanoString, allowing potential integration into diagnostic workflows. More recently, GE signatures have been described that identify distinct molecular subtypes within GCB-DLBCL. Working from the standpoint of tumors that have GE profiles sitting between BL and DLBCL (the “molecular high grade” signature [MHG]) or the GE signature of tumors with rearrangement of MYC and BCL2 (the “double hit” signature [DHITsig]), a sizeable group of GCB-DLBCL can be identified with poor prognosis. Finally, a group of DLBCL without mediastinal involvement have been shown to display the PMBL GE signature. These tumors share perturbation of the hallmark pathways of PMBL but arrive at this biology through different genetic mechanisms. These subtypes map onto, and complement, the newly minted genetics-based classifications of DLBCL. In order to arrive at a tractable unified molecular classification for aggressive B-cell lymphoma, ongoing efforts are needed to integrate GE and genetic aberrations across the disease spectrum. Such a classification framework holds the promise of improved diagnostic accuracy and reliability while providing the foundation for improving patient outcomes through precision medicine. Citation Format: David W. Scott. The role of gene expression in the classification of aggressive B-cell lymphoma [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr IA08.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".