MicroRNA Are Useful Biomarkers for Prediction of Response to Therapy and Survival of Patients with Diffuse Large B-Cell Lymphoma.
Bibliographic record
Abstract
Abstract Abstract 624 Background: Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with a variable clinical outcome even in the current era of rituximab containing anthracycline-based chemotherapy (RCHOP). We have previously reported a 6 gene model which is predictive for outcome in DLBCL (Lossos et al NEJM 2004, 350:1829 and Malumbres et al Blood 2008, 111:5509). Recently, short non-protein coding microRNAs (miRNA) that regulate gene expression by targeting the 3'UTR region of mRNA have been identified and are postulated to play an important role in oncogenesis. We have previously demonstrated that miRNAs exhibit specific expressions at different lymphocyte differentiation stages as well as distinct expression in gene expression-defined DLBCL subtypes (Malumbres et al Blood 2009; 113:3754). Herein, we evaluated whether specific miRNAs differentially expressed across DLBCL cell lines were prognostic biomarkers for prediction of outcome of DLBCL patients (pts). Patients and Methods: RNA was extracted from paraffin-embedded archived specimens (Chen et al Diagn Mol Pathol 2007, 16:61), from pts with newly diagnosed de novo DLBCL treated with R-CHOP at 4 different institutions. Expression of 11 miRNAs, including miR-146a, miR-146b-5p, miR-222, miR-500, miR-574-3p, miR-363, miR-155, and miR-21 that are differentially expressed between the ABC and GCB DLBCL subtypes, as well as expression of miR-18a, miR-140-3p, and miR-181a which has been reported to be variably expressed across DLBCL tumors were analyzed by ABI real-time PCR assays. Expression of the genes comprising the 6 gene survival prediction model were measured as reported previously. Expression was correlated to progression-free survival (PFS) and overall survival (OS). Predictive value of miRNAs was evaluated both as a continuous variable as well as a in a dichotomous model with pts grouped based on miRNA median expression. Results: The study group consisted of 176 pts with a median age 59 years (y) (range 16-92) of which 84 pts (48%) were > 60 y. 54 pts (30%) had an ECOG PS ≥2 and 86 (49%) presented with stage III or IV. Distribution according to the IPI was: 0-1 factor (n=77); 2 factors (n=50); 3 factors (n=30); and ≥4 factors (n=19). There were 41 deaths during a median follow-up of 2.6 years (range 0.04-8.1y). OS correlated with expression of miR-18a as a continuous variable, with higher expression correlating with inferior OS (p=0.038) but was not independent of the IPI in a multivariate model. PFS correlated with expression of miR-181a as a continuous variable (p=0.026; higher expression associated with longer PFS) and with expression of miR-222 as a categorical variable (miR-222cat; p=0.004, lower expression associated with longer PFS). In a multivariable model including IPI, both miR-222cat and miR-181a were IPI independent (p=0.01 and p=0.003, respectively). The mortality-prediction score calculated from the 6-gene model predicted both the OS (p=0.007) and PFS (p=0.004) and was IPI-independent. A multivariate Cox regression analysis that included IPI scores, mortality predictor scores and expression of miR-18a, miR-181a and miR-222cat revealed that all factors except miR-222cat were independent predictors of OS and all factors except miR-18a were independent predictors of PFS. Conclusions: We confirm that in RCHOP treated pts with DLBCL the 6-gene model predicts both OS and PFS independent of the IPI. Expression of miRNAs miR-18a, miR-181a and miR-222 is associated with response to therapy and outcome of pts with DLBCL, independent of the IPI and the mortality-prediction score calculated from the 6-gene model. While the expression of miR-181a and miR-222 may reflect the cell of origin of DLBCL tumors, the expression of miR-18a does not. Further studies are warranted to evaluate the precise role of these miRNAs in B-cell biology and DLBCL pathogenesis and prognosis. Disclosures: Advani: Seattle Genetics, Inc.: Research Funding.
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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 | 0.001 |
| 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) | 0.001 | 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".