Expression of “DNA damage response” pathway genes in diffuse large B‐cell lymphoma: The potential for exploiting synthetic lethality
Bibliographic record
Abstract
Diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) are two of the most prevalent non-Hodgkin's lymphoma subtypes. Despite advances, treatment resistance and patient relapse remain challenging issues. Our study aimed to scrutinize gene expression distinctions between DLBCL and FL, employing a cohort of 53 DLBCL and 104 FL samples that underwent rigorous screening for genetic anomalies. The NanoString nCounter assay evaluated 730 cancer-associated genes, focusing on densely tumorous areas in diagnostic samples. Employing the Lymph2Cx method, we determined the cell-of-origin (COO) for DLBCL cases. Our meticulous analysis, facilitated by Qlucore Omics Explorer software, unveiled a substantial 37% of genes with significantly differential expression patterns between DLBCL and FL, pointing to nuanced mechanistic disparities. Investigating the impact of FL disease stage and DLBCL COO on gene expression yielded minimal differences, prompting us to direct our attention to consistently divergent genes in DLBCL. Intriguingly, our Gene Set Enrichment Analysis spotlighted 21% of these divergent genes, converging on the DNA damage response (DDR) pathway, vital for cell survival and cancer evolution. Strong positive correlations among most DDR genes were noted, with key genes like BRCA1, FANCA, FEN1, PLOD1, PCNA, and RAD51 distinctly upregulated in DLBCL compared to FL and normal tissue controls. These findings were subsequently validated using RNA seq data on normal controls and DLBCL samples from public databases like The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, enhancing the robustness of our results. Considering the established significance of these DDR genes in solid cancer therapies, our study underscores their potential applicability in DLBCL treatment strategies. In conclusion, our investigation highlights marked gene expression differences between DLBCL and FL, with particular emphasis on the essential DDR pathway. The identification of these DDR genes as potential therapeutic targets encourages further exploration of synthetic lethality-based approaches for managing DLBCL.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".