Non-GCB Diffuse Large B-Cell Lymphoma With an Atypical Disease Course: A Case Report and Clinical Exome Analysis
Bibliographic record
Abstract
Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid tumor among other non-Hodgkin lymphomas (30-40% of all cases). This type of lymphoma is characterized by significant differences in treatment response and the heterogeneity of clinical traits. Approximately 60% of patients are cured using standard chemotherapy (CT), while in 10-15% of cases, the tumor is characterized by an extremely aggressive course and resistance to even the most high-dose programs with autologous stem cell transplantation (auto-SCT). The activated B-cell (ABC) subtype of DLBCL is characterized by poor prognosis. Here, we describe a clinical case of diffuse ABC-DLBCL with an atypical disease course. Complete remission was achieved after four courses of CT, followed by autologous hematopoietic stem cell transplantation (auto-HSCT). However, early relapse occurred 2 months after the completion of treatment. According to the results of cytogenetic studies, significant chromosome breakdowns were observed. Exome sequencing allowed for the detection of several novel mutations that affect components of the NOTCH2 and NF-kB signaling pathways, a number of epigenetic regulators ( KMT2D , CREBBP , EP300 , ARID1A , MEF2B ), as well as members of the immunoglobulin superfamily ( CD58 and CD70 ). Whether these mutations were the result of therapy or were originally present in the lymphoid tumor remains unclear. Nevertheless, the introduction of genomic technologies into clinical practice is important for making a diagnosis and developing a DLBCL treatment regimen with the use of targeted drugs. World J Oncol. 2022;13(1):38-47 doi: https://doi.org/10.14740/wjon1436
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".