Evidence of Targetable Immune Dysfunction in the Bone Marrow of Patients with Intermediate/High-Risk Myelodysplastic Syndrome Refractory to Hypomethylating Agents
Bibliographic record
Abstract
Abstract Background Growing evidence indicates that a dysfunctional adaptive immune response contributes to the pathogenesis of myelodysplastic syndrome (MDS). Intermediate/high (I/H)-risk MDS is associated with immunosuppression, and median overall survival in hypomethylating agent (HMA)-refractory patients (pts) remains poor at Methods Bone marrow aspirates from HMA-refractory, I/H-risk pts (n = 18) and normal donors (NDs; n = 20) were assessed by flow cytometry (FC), immunohistochemistry and RNA sequencing (RNAseq). An independent cohort of treatment-naive, I/H-risk pts (n = 9) was assessed for regulatory T-cell (Treg) counts by FC. T-cell subsets (Treg, T-naive, T-effector memory, T-central memory, T-late effector memory) were identified by FC based on established markers (CD3, CD4, CD8, CD197, CD45RO, CD127, CD25, FOXP3). Activation/proliferation status of T-cell subsets was measured by HLA-DR and Ki-67. MDS blasts were identified by CD45, CD34 and CD117. Immune checkpoint markers programmed death-1 (PD-1) and programmed death-ligands 1 and 2 (PD-L1/2) were measured on T cells and CD34+ blasts. T-cell immunoglobulin and ITIM domain (TIGIT) was also measured on CD4 and CD8 T cells. T cells (CD4, CD8) and PD-L1 were identified in decalcified bone marrow biopsies by immunohistochemistry. Whole transcriptome gene expression analysis was performed using the Illumina TruSeq RNA Access Library Prep Kit on bulk bone marrow aspirates. Results T cells were detected in bone marrow aspirates from HMA-refractory, I/H-risk pts at levels comparable to those in NDs. Treg counts (cells/µL) were increased in HMA-refractory vs treatment-naive pts (P= 0.017), with the highest Treg count associated with complex cytogenetic karyotypes (≥ 3 abnormalities). We observed increased T-cell activation and proliferation in these pts compared with NDs (MDS vs ND medians [% positive cells]; HLA-DR−/Ki-67+: 1.23 vs 0.75; HLA-DR+/Ki-67+: 1.71 vs 0.33; HLA-DR+/Ki-67−: 14.14 vs 4.80). No trends were seen in CD4-naive and memory subsets, whereas CD8-naive cells were lower and late effector memory cells were higher than in NDs (P= 0.035 each). We observed higher expression of immune checkpoint and regulatory molecules (PD-L1, PD-1, TIGIT) in MDS except for PD-L2, which was minimally detected ( Conclusions Bone marrow in pts with I/H-risk MDS demonstrate evidence of prolonged T-cell activation, an increase in suppressive immune cell populations (i.e., Tregs) and expression of immune checkpoint molecules (PD-L1, TIGIT) compared with normal bone marrow. Patients with complex karyotype had subtle differences, potentially relevant for identifying subsets responsive to checkpoint inhibitors. These data provide new insight into the immune landscape of HMA-refractory I/H-risk MDS. Disclosures Green: Genentech, Inc.: Employment. Yan: F. Hoffmann-La Roche Ltd: Employment. Nalle: Genentech, Inc.: Employment; Roche: Equity Ownership. Ma: Genentech, Inc.: Employment. Robert: Genentech, Inc.: Employment. Zhong: F. Hoffmann-La Roche Ltd.: Employment. Krishnan: Genentech, Inc.: Employment. Phuong: Genentech, Inc.: Employment. Byon: Genentech, Inc.: Employment. Woodard: Genentech, Inc.: Employment, Equity Ownership. Adamkewicz: Genentech Inc.: Employment. Venstrom: Genentech, Inc.: Employment. Dail: Genentech, Inc.: Employment.
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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 | 0.000 | 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) | 0.002 | 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".