Identification of High Risk Group for Leukemic Transformation in Higher Risk MDS Patients Using Targeted RNA-Sequencing: Hematopoietic Stem Cell Signature As a High Risk Profile for Leukemic Transformation
Bibliographic record
Abstract
Abstract Introduction Myelodysplastic syndrome (MDS) is a heterogeneous group of myeloid disorder characterized by defective bone marrow (BM) hematopoiesis with peripheral blood cytopenias and risk for progression to acute myeloid leukemia. Accurate determination of prognosis is critical to select an appropriate therapy and to detect any case progressing to leukemic transformation which brings ominous prognosis in patients with MDS. Despite clinical risk models, additional molecular data are needed to enhance the prediction of patients' clinical courses and to aid disease management. Therefore, the present study attempted to classify the higher risk MDS (HR-MDS) patients according to their molecular risk through targeted RNA-sequencing, to correlate it with clinical risk models, and analyzed molecular risk grouping for prognostic stratification power, especially for leukemic transformation in higher-risk patients with MDS treated with hypomethylating agents (HMA) including azacitidine or decitabine. Patients and Methods A total of 30 patients were included with HR-MDS by International Prognostic Scoring System (IPSS). Overall, 60 bone marrow samples (30 diagnosis and follow-up pairs) were subject for targeted RNA-seq using Illumina TruSight Pan-Cancer panel. After read mapping by Tophat2, gene count was measured using HTSeq followed by DEseq2 for differential gene expression quantification. All 60 samples as well as 30 samples from T-cell fraction (CD3+, as a control) were also subjected for DNA-seq targeting a panel of 84 commonly mutated genes in myeloid malignancies (Agilent SureSelect). All downstream computational and statistical analyses were performed using R and Python. Results The median age was 65 years (range 40-84 years) with 16 male patients (53%). Twenty-seven (90%) and 3 (10%) patients were intermediate-2 and high risk by IPSS, respectively. According to revised IPSS (IPSS-R), the distribution of risk groups was as follows: low (n=5, 17%), intermediate (n=8, 27%), high (n=11, 37%), and very high (n=6, 20%). A total of 56 mutations were detected in the diagnostic samples from 30 patients. Frequently mutated genes were DDX41 (n=5) and TP53 (n=4). Best response to HMA (16 azacitidine and 14 decitabine) was achieved in median 4 cycles (range 3-8). Complete response (CR) including marrow CR was achieved in 18 patients (60%), and 10 patients (33%) received allogeneic hematopoietic cell transplantation. Overall survival (OS) rate was not well correlated with IPSS-R risk groups. With median follow-up duration of 28.2 months (range 3.8-95), 3-years' OS rate showed 40%, 75%, 36%, and 67% in low, intermediate, high, and very high risk, respectively. Unsupervised clustering using top 100 genes with highest variance revealed 3 distinct clusters (n=8, 9, and 13 in group 1, 2, and 3), 3-years' OS rate of which showed 73%, 57%, and 35% in group 1, 2, and 3, respectively (p=0.004 between group 3 vs group 1/2). Despite inferior long-term outcomes in the group 3, the baseline clinical variables of some patients were classified as favorable implying that clinical factor does not reflect adverse long-term outcomes: 4 out of 13 patients with low risk by IPSS-R eventually experienced adverse outcome. The 3-years' leukemic transformation rate was 0%, 33% and 57% in group 1, 2, and 3 (p=0.039 between group 3 vs group 1/2). In the multivariate analyses, besides achievement of CR, the risk group 3 by RNA-seq were identified as independent adverse prognostic factors for OS (p=0.007, HR 6.75 [1.68-27.17]) as well as leukemic transformation (p=0.013, HR 6.91 [1.49-31.95]). In the gene set enrichment analysis using MSigDB, hematopoietic stem cell genes were enriched in RNA-seq group 3, suggesting that the high-risk signature on RNA-seq is linked with stemness of hematopoietic stem cells. Conclusion RNA-seq can be utilized to identify the higher risk patients with MDS. The higher risk group by RNA-seq enriched with genes with hematopoietic stem cells, which suggests that stemness in hematopoietic stem cells is linked with resistance to HMA therapy and increasing risk of leukemic transformation in HR-MDS. Figure. Figure. Disclosures No relevant conflicts of interest to declare.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".