Intensity of Survivin Expression Correlates with Clinical and Biological Markers of Aggressive R/R DLBCL
Bibliographic record
Abstract
Introduction: The SPiReL phase II clinical trial evaluated combination immunotherapy with an immunogenic vaccine formulation to the tumor antigen survivin comprised of maveropepimut-S (MVP-S), pembrolizumab and cyclophosphamide in survivin-expressing relapsed or refractory (R/R) diffuse large B cell lymphoma (DLBCL). The efficacy of this survivin-directed T cell educating therapy may be influenced by the survivin antigen burden in the DLBCL. To further explore this potential relationship, we evaluated the heterogeneity of tumor survivin expression in the SPiReL study cohort and its associations with established clinical-pathological DLBLC variables. Methods: Survivin immunohistochemistry (IHC) was performed at NeoGenomics Laboratories (Aliso Viejo, California, USA) on formalin fixed paraffin embedded (FFPE) tumor slides from diagnostic biopsies obtained at trial screening. Inclusion criteria stipulated >10% of tumor cells expressing survivin by IHC. Semi-quantitative survivin expression metrics, as determined by pathologist assessment, included: total percentage of survivin-positive cells; intensity of survivin staining scored as 0 = None, 1+ = Weak, 2+ = Moderate, 3+ = Strong; and H-score, a linear weighted average (% positive cells x staining intensity level). Radiologic tumor burden was measured at up to 4 time points during the trial by computed tomography (CT). Other pathology (e.g. cell of origin) and laboratory biomarkers (e.g. lactate dehydrogenase, LDH) were extracted from clinical results. Results: The mean and median percentage of survivin-positive tumor cells for all enrolled participants (n= 25) was 91% and 99%, respectively. At the per participant level, the range of survivin expression was 50-100% of the cellular content. The heterogeneity of survivin expression intensity among participants is shown in Figure 1. All participants had some proportion of biopsied cells expressing strong (3+) intensity survivin (range: 5-100% of cells, mean: 30% while only 52% of tumors contained non-survivin expressing (0) cells, ranging from 0% to 50% (mean: 9%) Greater proportions of cells that expressed moderate (2+) and/or strong (3+) survivin significantly correlated with clinical and biological markers of aggressive DLBCL. Non-GCB tumours (11/25) showed greater H-scores compared to GCB tumours (212 vs 167, p = 0.0287). Non-GCB tumours contained greater proportions of strong intensity (3+) survivin cells compared to GCB tumours (44% vs 25%, p= 0.045). In addition, participants with LDH above the upper limit of normal at screening (52%, 13/25) had a greater mean proportion of cells expressing moderate and strong intensity survivin (proportion of 2+ and 3+ combined) (72% vs 54%, p = 0.0447) and a lower mean proportion of weak survivin intensity (1+) cells (p = 0.0374) compared to those with normal LDH. A relationship between total tumor area at baseline and survivin intensity levels was explored. Higher proportions of cells expressing weak intensity (1+) survivin were associated with smaller total tumour area at baseline (p=0.0201). In contrast, higher combined proportions of moderate (2+) and high (3+) intensity survivin was associated with larger total tumour area at baseline (p=0.0285). The significant association with the 2+/3+ intensity of survivin with LDH, cell of origin and tumour burden may suggest a mechanistic threshold in DLBCL pathobiology that drives aggressive clinical behavior. Discussion: Here we demonstrate substantial heterogeneity of survivin protein expression in baseline tumour biopsies of R/R DLBCL patients. Given our findings, it may be of clinical and pathobiological significance to assess both the proportion of survivin-expressing cells as well as the intensity of survivin expression. As the known functions of survivin include protection from apoptosis and cell-cycle progression, the associations demonstrated here support the relevance of survivin and in particular high intensity (2+, 3+) survivin as a biomarker linked with aggressive R/R DLBCL features. These results further support the targeting of survivin pathways in interventional trials and development of validated clinical laboratory survivin assays. Further analysis by multiplex immunofluorescence is in progress to further explore how 2+/3+ vs. 0 host immune cells may also influence survivin immunogenicity and clinical response post-MVP-S immunotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".