Comparing MicroRNA Expression In Aggressive and Indolent Non-Hodgkin Lymphomas Identifies a Prognostic Signature for Mantle Cell Lymphoma
Bibliographic record
Abstract
Abstract Abstract 800 Mantle cell lymphoma (MCL) is a B-cell non-Hodgkin lymphoma (NHL) accounting for ~6% of all NHL. It is sensitive to combination chemotherapy, but remission durations are short without approaches such as stem cell transplantation (SCT). Most patients are incurable, but the clinical course is variable, with some patients succumbing quickly, while others survive >10 years. MicroRNAs (miRs) are small, non-coding RNAs that regulate gene expression by inhibiting mRNA translation. miRs are useful in the prognostic assessment of tumors, but work to date examining differences between MCL and normal lymphoid tissues, have only identified 2 miRs involved in MCL prognosis (Zhao JJ, Blood, 2010; Di Lisio L, Leukemia, 2010). We used a novel approach to identify a prognostic miR signature in MCL. We hypothesized that a miR signature defining aggressiveness can be obtained by comparing miR expression profiles of aggressive NHL with indolent NHL, and that this signature when applied to a set of MCL cases, may aid in MCL prognosis. Total RNA was extracted from 135 formalin-fixed paraffin-embedded samples obtained at primary diagnosis (Table 1). RNA from a training set of 19 indolent and 20 aggressive NHL cases was analyzed on a high-throughput quantitative real-time PCR (qRT-PCR) platform assessing the expression of 365 miRs and 3 endogenous controls (TaqMan Human MicroRNA Array v1.0: TLDA, ABI) using the DDCt method. A two-sample Wilcoxon Rank sum test corrected for false discovery rate was used to assess the significance of differential expression for each miR between aggressive and indolent NHL. The 14 most significantly differentially expressed miRs (p<0.001, FDR<0.02) were validated on an independent set of 25 indolent NHL and 19 aggressive NHL by qRT-PCR, and analyzed using the DDCt method. Univariate analysis using a one-sided t-test yielded 9 miRs that validated on the independent NHL set. Multivariable analysis demonstrated the ability of this 9 miR signature to distinguish between aggressive and indolent NHL (p<0.0001). Applying this signature to a set of 32 MCL patients with complete outcome data (Table 2) separated a poor prognosis group (median OS: 15 months, range: 4–40 months) from a good prognosis group (median OS: 88 months, range: 41–131 months) (Fig. 1). Among the 9 miRs were miR-29c, shown to have some prognostic value in MCL by Zhao et al., and miR-26a, shown to be important in MCL pathogenesis by Di Lisio et al. In light of the overlap with such recent studies, we believe the 9 miR prognostic signature we have identified may be of clinical utility. We are currently identifying mRNA targets for this miR signature and validating both the signature and the deregulated expression of these targets on a larger set of 200 MCL samples with known outcome data. Fig. 1. Psrincipal component analysis demonstrating separation of MCL cases into a good prognosis group in red (median OS: 88 months, range: 41–131 months) and a poor prognosis group in blue (median OS: 15 months, range: 4–40 months) based on expression of a 9 miR aggressiveness signature. Fig. 1. Psrincipal component analysis demonstrating separation of MCL cases into a good prognosis group in red (median OS: 88 months, range: 41–131 months) and a poor prognosis group in blue (median OS: 15 months, range: 4–40 months) based on expression of a 9 miR aggressiveness signature. Table 1. Sample breakdown Training set Number Aggressive cases Diffuse large B-cell lymphoma 5 Primary mediastinal B-cell lymphoma 5 Burkitt lymphoma 5 Atypical Burkitt 5 Indolent cases Small lymphocytic lymphoma/CLL 5 Extranodal marginal zone lymphoma 5 Follicular lymphoma Grade 1 3 Grade 2 3 Grade 3a 3 Validation set Aggressive cases Diffuse large B-cell lymphoma 7 Primary mediastinal B-cell lymphoma 5 Burkitt lymphoma 3 Atypical Burkitt 4 Indolent cases Small lymphocytic lymphoma/CLL 5 Extranodal marginal zone lymphoma 5 Follicular lymphoma Grade 1 5 Grade 2 5 Grade 3a 5 MCL cases Conventional 19 Blastoid/pleomorphic 11 Prolymphocytoid 1 Multiple lymphomatoid polyposis 1 Normal benign lymph nodes 20 TOTAL 135 Table 2. MCL clinical data Features Total % Gender Male 23 72 Female 9 28 ECOG 0 12 38 1 17 53 2-3 3 9 Stage 1 2 6 2 8 25 3 7 22 4 15 47 B symptoms 9 28 Extranodal sites 5 16 Lines of therapy 0 2 6 1 14 44 2 3 9 3 6 19 4 3 9 >4 4 13 Types of therapy Observation alone 2 6 Anthracycline-based 18 56 Rituximab 14 44 SCT 3 9 Bortezomib 3 9 Radiation 14 44 Median Range Age (yrs) 69 37–90 M-IPI score 6.6 5.3–8.7 Ki-67 (%) 25 7.5–90 Time to 1st treatment (months) 0.8 0.1–99.1 Overall survival (months) 34 4–131 Disclosures: Kuruvilla: Hoffman LaRoche: Honoraria, Research Funding; Celgene: Research Funding; Amgen: Honoraria; Otsuka: Honoraria; Genzyme: Honoraria.
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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".