Correlation of TP53 Genetic Alterations with p53 Immunohistochemical Expression and Their Prognostic Significance in DLBCL
Bibliographic record
Abstract
TP53 genetic alterations represent a critical molecular feature in diffuse large B-cell lymphoma (DLBCL), with well-established associations with aggressive disease behavior and therapeutic resistance. However, significant controversy persists regarding the clinical utility of p53 immunohistochemical (IHC) expression as a surrogate marker. This study presents a thorough investigation of TP53 genetic alterations and their correlation with p53 protein expression in 664 cases of DLBCL. Using targeted next-generation sequencing (tNGS), we identified TP53 alterations (mutations and/or copy number losses (CNLs)) in 170 cases (25.6%). Among them, 161 cases had mutations. Concurrent analysis of copy number variations (CNVs) in 109 cases revealed TP53 CNLs in 17.4% (19/109), with 68.4% (13/19) of these showing coexisting mutations. Immunohistochemical evaluation of p53 expression in 371 cases demonstrated strong positivity (≥65% cells) in 21% (78/371), complete negativity (<1%) in 5.7% (21/371), and wild-type pattern (1–65%) in 73.3% (272/371) of cases. The p53 IHC laboratory-developed test (LDT) showed 79.2% sensitivity and 91.6% specificity for detecting TP53 alterations overall, though sensitivity varied significantly by mutation type: 86.2% for missense mutations but only 14.3% for nonsense mutations. Clinically, cases with TP53 alterations exhibited more aggressive disease characteristics, including higher ECOG performance scores, increased frequency of B symptoms, and poorer initial treatment responses (complete response rate 68.3% vs. 82.5% in wild-type cases). Most importantly, TP53 genetic alterations, but not p53 protein expression patterns, emerged as an independent prognostic factor for progression-free survival. Our findings demonstrate that tNGS effectively identifies most TP53 alterations and complementary CNV analysis enhances detection of copy number losses. The p53 IHC LDT serves as a useful but imperfect screening tool, with high specificity but variable sensitivity depending on mutation types. These results have important implications for molecular diagnostics in DLBCL, supporting the necessity for comprehensive genetic testing rather than reliance on protein expression analysis alone for accurate risk stratification and treatment planning.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".