The molecular prognostic scoring system for normal karyotype myelodysplastic syndromes
Abstract Background Molecular-clinical prognostic models for Myelodysplastic syndromes (MDS) offer more accurate prognosis predictions, yet existing models often overlook the heterogeneity of mutational profiles against the cytogenetic background. Moreover, how to apply these models in regions where...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
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| Series: | Journal of Translational Medicine |
| Online Access: | https://doi.org/10.1186/s12967-024-05995-x |
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| Summary: | Abstract Background Molecular-clinical prognostic models for Myelodysplastic syndromes (MDS) offer more accurate prognosis predictions, yet existing models often overlook the heterogeneity of mutational profiles against the cytogenetic background. Moreover, how to apply these models in regions where large panel NGS is unaffordable remains a significant challenge to be addressed. Methods A total of 237 NK MDS patients from our center were used as the training set to screen for key variables and develop a prognostic model with overall survival (OS) as the endpoint. The C-index was used as the main evaluation metric to assess the model’s performance. The IWG-PM cohort (n = 691) was used as an external independent validation set to evaluate the generalizability of the model. Results We developed a seven-parameter molecular-clinical prognostic model, the Molecular Prognostic Scoring System for NK MDS (NK-PSS-M), which only incorporates three gene mutations as parameters. The NK-PSS-M can reliably predict OS and leukemia-free survival (LFS). The performance of NK-PSS-M was comparable to that of the Molecular International Prognostic Scoring System (IPSS-M), and it significantly outperformed the Revised International Prognostic Scoring System for MDS (IPSS-R). Conclusions The NK-PSS-M model improved the risk stratification of non-molecular models and provided a reliable alternative to the IPSS-M. This strategy provides insights into how resource-scarce regions can apply molecular-clinical models. |
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| ISSN: | 1479-5876 |