J Clin Oncol: Bone marrow growth abnormality syndrome | Individualized prognostic prediction based on clinical and genetic characteristics

Bone marrow proliplation abnormal syndrome (MDS) is a heterogeneic clonic hematosis disease characterized by an increased risk of exostocyte reduction and development of acute myeloid leukemia (AML).
have identified certain relapsed mutant genes and chromosomal abnormalities in myeloid dysplative syndrome.
a study conducted by Beersanelli et al., which retrospectively recruited 2,043 patients, aimed at integrating genomic features found in MDS into disease classification and prognostic groupings.
researchers combined 47 genetic mutations with cytogenetic abnormalities to identify genetic associations and subgroups.
and independently verified in 318 cases.
genetic characteristics based on unique genetic characteristics, the researchers identified a total of 8 MDS subgroups.
in five subgroups, the main genetic characteristics include shear gene mutations (SF3B1, SRSF2, and U2AF1), which occur early in the disease, determine unique esotypes and drive disease progress.
these subgroups showed different prognosms (the prevention of SF3B1 mutation groups tended to be better).
specific co-mutation patterns that affect the correlation between the number and prognosticity of genetic variants explain the clinical heterogeneity in SF3B1 and SRSF2-related MDS.
MDS with complex nucleotypes and/or TP53 gene abnormalities, and MDS patients with acute leukemia-like mutations had the worst prognostic prognostics.
the number of mutant genes and/or the absence of TP53 mutations, MDS with 5q missing can be divided into two different groups.
the probability of survival after transplantation of allogeneic hematocytes in different groups of patients, by integrating 63 clinical and genomic variables, the researchers developed a new prognostic model that can generate personalized survival predictions.
and observed results are well relevant in internal cross-validation and independent external queues.
model greatly improves the predictive accuracy of the forecasting tools currently available.
researchers and created a Web portal that allows predictive results to be generated for user-defined genomes and clinical features.
, the genome map of MDS reveals different subgroups associated with specific clinical characteristics and discrete evolutionary patterns, providing proof of concept for the classification and prognosto prediction of second-generation diseases.