RESUMEN
Background: Tumor genetic anomalies and immune dysregulation are pivotal in the progression of multiple myeloma (MM). Accurate patient stratification is essential for effective MM management, yet current models fail to comprehensively incorporate both molecular and immune profiles. Methods: We examined 776 samples from the MMRF CoMMpass database, employing univariate regression with LASSO and CIBERSORT algorithms to identify 15 p53-related genes and six immune cells with prognostic significance in MM. A p53-TIC (tumor-infiltrating immune cells) classifier was constructed by calculating scores using the bootstrap-multicox method, which was further validated externally (GSE136337) and through ten-fold internal cross-validation for its predictive reliability and robustness. Results: The p53-TIC classifier demonstrated excellent performance in predicting the prognosis in MM. Specifically, patients in the p53low/TIChigh subgroup had the most favorable prognosis and the lowest tumor mutational burden (TMB). Conversely, those in the p53high/TIClow subgroup, with the least favorable prognosis and the highest TMB, were predicted to have the best anti-PD1 and anti-CTLA4 response rate (40 %), which can be explained by their higher expression of PD1 and CTLA4. The three-year area under the curve (AUC) was 0.80 in the total sample. Conclusions: Our study highlights the potential of an integrated analysis of p53-associated genes and TIC in predicting prognosis and aiding clinical decision-making in MM patients. This finding underscores the significance of comprehending the intricate interplay between genetic abnormalities and immune dysfunction in MM. Further research into this area may lead to the development of more effective treatment strategies.
RESUMEN
OBJECTIVE: To investigate the presence of leukemia stem cells (LSC) in acute myeloid leukemia (AML) and find out the relative position of leukemia cells in the figures of flow cytometry, and to analyze the relationship between minimal residual diseases (MRD) and the level of LSC, so as to explore the correlation of LSC changes with the curative effect and the prognosis during chemical therapy. METHODS: A total of 85 samples were collected from 50 AML (except M3) patients, including 50 samples from the newly diagnosed patients, 7 samples of AML patients with non-remission and 28 samples of AML patients with complete remission. All samples were used for detection of LSC from immune phenotype of CD34+/CD38-/CD123+ by flow cytometry. The detection of immune phenotypic of leukemia cells was performed in the newly diagnosed patients. The detection of leukemia- associated immune phenotypes (LAIP) was implemented in the non-newly diagnosed patients. RESULTS: The LSC was identified in the CD34+/ CD38-/ CD123+ in AML and consistent with the relative position of the leukemia cell in flow cytometry figures. Statistical analysis showed significant difference in LSC content between the newly diagnosed AML group and the post-chemotherapy complete remission group(P<0.01),but did not between the newly diagnosed AML group and the post-chemotherapy non-remission group(P>0.05).There was significant positive correlation between the LSC content and MRD level in 28 AML patients with complete remission (r=0.680,P<0.01). CONCLUSION: LSC exist in AML and the relative position are consistent with the leukemia cells in flow cytometry figures, the size characteristics and weak expression of CD45 are also similar to leukemia cells. The proportion of LSC decreases after chemotherapy. Detecting and tracking the LSC changes in bone marrow and combination with detecting minimal resident disease(MRD) may contribute to evaluate the theraputic efficacy and prognosis of leukemia patients.