Autophagy Gene Panel-Based Prognostic Model in Myelodysplastic Syndrome

Immunologic Predictors for Clinical Responses during Immune Checkpoint Blockade in Patients with Myelodysplastic Syndromes

PURPOSE

The aim of this study is to determine immune-related biomarkers to predict effective antitumor immunity in myelodysplastic syndrome (MDS) during immunotherapy (IMT, αCTLA-4, and/or αPD-1 antibodies) and/or hypomethylating agent (HMA).

EXPERIMENTAL DESIGN

Peripheral blood samples from 55 patients with MDS were assessed for immune subsets, T-cell receptor (TCR) repertoire, mutations in 295 acute myeloid leukemia (AML)/MDS-related genes, and immune-related gene expression profiling before and after the first treatment.

RESULTS

Clinical responders treated with IMT ± HMA but not HMA alone showed a significant expansion of central memory (CM) CD8+ T cells, diverse TCRβ repertoire pretreatment with increased clonality and emergence of novel clones after the initial treatment, and a higher mutation burden pretreatment with subsequent reduction posttreatment. Autophagy, TGFβ, and Th1 differentiation pathways were the most downregulated in nonresponders after treatment, while upregulated in responders. Finally, CTLA-4 but not PD-1 blockade attributed to favorable changes in immune landscape.

CONCLUSIONS

Analysis of tumor-immune landscape in MDS during immunotherapy provides clinical response biomarkers.

Development and validation of a novel prognosis prediction model for patients with myelodysplastic syndrome

Background

Somatic mutations are widespread in patients with Myelodysplastic Syndrome (MDS) and are associated with prognosis. However, a practical prognostic model for MDS that incorporates somatic mutations urgently needs to be developed.

Methods

A cohort of 201 MDS patients from the Gene Expression Omnibus (GEO) database was used to develop the model, and a single-center cohort of 115 MDS cohorts from Northwest China was used for external validation. Kaplan-Meier analysis was performed to compare the effects of karyotype classifications and gene mutations on the prognosis of MDS patients. Univariate and multivariate Cox regression analyses and Lasso regression were used to screen for key prognostic factors. The shinyapps website was used to create dynamic nomograms with multiple variables. The time-dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) were used to evaluate the model’s discrimination, accuracy and clinical utility.

Results

Six risk factors (age, bone morrow blast percentage, ETV6, TP53, EZH2, and ASXL1) were considered as predictor variables in the nomogram. The nomogram showed excellent discrimination, with respective the area under the ROC curve (AUC) values of 0.850, 0.839, 0.933 for the training cohort at 1 year, 3 years and 5 years; 0.715, 0.802 and 0.750 for the testing cohort at 1 year, 3 years and 5 years; and 0.668, 0.646 and 0.731 for the external validation cohort at 1 year, 3 years and 5 years. The calibration curves and decision curve showed that the nomogram had good consistency and clinical practical benefit. Finally, a stratified analysis showed that MDS patients with high risk had worse survival outcomes than patients with low risk.

Conclusion

We developed a nomogram containing six risk factors, which provides reliable and objective predictions of prognosis for MDS patients.