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Real-life challenges using personalized prognostic scoring systems in acute myeloid leukemia.

ABSTRACT: Personalized medicine is a challenge for patients with acute myeloid leukemia (AML). The identification of several genetic mutations in several AML trials led to the creation of a personalized prognostic scoring algorithm known as the Knowledge Bank (KB). In this study, we assessed the prognostic value of this algorithm on a cohort of 167 real life AML patients. We compared KB predicted outcomes to real-life outcomes. For patients younger than 60-year-old, OS was similar in favorable and intermediate ELN risk category. However, KB algorithm failed to predict OS for younger patients in the adverse ELN risk category and for patients older than 60 years old in the favorable ELN risk category. These discrepancies may be explained by the emergence of several new therapeutic options as well as the improvement of allogeneic stem cell transplantation (aHSCT) outcomes and supportive cares. Personalized medicine is a major challenge and predictions models are powerful tools to predict patient's outcome. However, the addition of new therapeutic options in the field of AML requires a prospective validation of these scoring systems to include recent therapeutic innovations.

SUBMITTER: Calleja A

PROVIDER: S-EPMC10028034 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Real-life challenges using personalized prognostic scoring systems in acute myeloid leukemia.

Calleja Anne A Loschi Michael M Bailly Laurent L Morisot Adeline A Marceau Alice A Mannone Lionel L Robert Guillaume G Auberger Patrick P Preudhomme Claude C Raynaud Sophie S Subtil Fabien F Sujobert Pierre P Cluzeau Thomas T

Cancer medicine 20221116 5

Personalized medicine is a challenge for patients with acute myeloid leukemia (AML). The identification of several genetic mutations in several AML trials led to the creation of a personalized prognostic scoring algorithm known as the Knowledge Bank (KB). In this study, we assessed the prognostic value of this algorithm on a cohort of 167 real life AML patients. We compared KB predicted outcomes to real-life outcomes. For patients younger than 60-year-old, OS was similar in favorable and interme ...[more]

PMID: 36394159

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Project description:BackgroundWith the rapid development of diagnostic techniques and treatment strategies, there are notable improvements in pediatric acute myeloid leukemia (AML) prognosis. Nevertheless, the pathogenesis of AML remains largely unknown. This study aims to investigate the RAS pathway-associated genes based on bioinformatics analysis, and investigate their underlying mechanisms in the initiation and progression of AML.Materials and methodsThe UCSC Xena database was the source of the training set data, while the GSE192638 dataset was the source of the validation set data. Children in the training set were split up into two groups according to RAS pathway-associated genes, and then differentially expressed genes (DEGs) of them were screened. To discover prognosis-related genes and develop a prognostic risk-scoring model, we employed One-way Cox and LASSO regression analysis. The performance of the model was assessed by an independent validation dataset. Survival analysis was performed using the Kaplan-Meier (K-M) curve. Furthermore, we investigated the association between the prognostic risk-scoring model and the correlation between immune infiltration and drug sensitivity. The expression levels of genes associated with reverse transcription-polymerase chain reaction were quantified.ResultsWe built a prognostic risk-scoring model comprising 26 DEGs. Depending on the risk score, AML patients were split up into two groups: high- and low-risk groups. Notably, compared with the survival time of patients in the high- risk group, that in the low-risk group was substantially prolonged. Univariate (uniCox) as well as multivariate Cox (multiCox) regression analyses were carried out, demonstrating that the risk score emerged as a separate risk factor for prognosis. A nomogram that incorporates clinical factors and prognostic risk scores was proposed to increase the accuracy of survival rates estimation. Subsequent analyses revealed significant connections of the risk score with the immune infiltration and drug sensitivity. The experimental results demonstrated significantly elevated expression levels of GCSAML, MED12L, and TCF4 in AML samples compared to normal samples.ConclusionThe developed prognostic risk-scoring model, along with the identified key risk genes, holds promise as candidate prognostic biomarkers and treatment targets for pediatric AML.

Project description:Background: Emerging evidence has proven that ferroptosis plays an important role in the development of acute myeloid leukemia (AML), whereas the exact role of ferroptosis-associated genes in AML patients' prognosis remained unclear. Materials and Methods: Gene expression profiles and corresponding clinical information of AML cases were obtained from the TCGA (TCGA-LAML), GEO (GSE71014), and TARGET databases (TARGET-AML). Patients in the TCGA cohort were well-grouped into two clusters based on ferroptosis-related genes, and differentially expressed genes were screened between the two clusters. Univariate Cox and LASSO regression analyses were applied to select prognosis-related genes for the construction of a prognostic risk-scoring model. Survival analysis was analyzed by Kaplan-Meier and receiver operator characteristic curves. Furthermore, we explored the correlation of the prognostic risk-scoring model with immune infiltration and chemotherapy response. Risk gene expression level was detected by quantitative reverse transcription polymerase chain reaction. Results: Eighteen signature genes, including ZSCAN4, ASTN1, CCL23, DLL3, EFNB3, FAM155B, FOXL1, HMX2, HRASLS, LGALS1, LHX6, MXRA5, PCDHB12, PRINS, TMEM56, TWIST1, ZFPM2, and ZNF560, were developed to construct a prognostic risk-scoring model. AML patients could be grouped into high- and low-risk groups, and low-risk patients showed better survival than high-risk patients. Area under the curve values of 1, 3, and 5 years were 0.81, 0.827, and 0.786 in the training set, respectively, indicating a good predictive efficacy. In addition, age and risk score were the independent prognostic factors after univariate and multivariate Cox regression analyses. A nomogram containing clinical factors and prognostic risk-scoring model was constructed to better estimate individual survival. Further analyses demonstrated that risk score was associated with the immune infiltration and response to chemotherapy. Our experiment data revealed that LGALS1 and TMEM56 showed notably decreased expression in AML samples than that of the normal samples. Conclusion: Our study shows that the prognostic risk-scoring model and key risk gene may provide potential prognostic biomarkers and therapeutic option for AML patients.

Project description:BackgroundThe high degree of heterogeneity brought great challenges to the diagnosis and treatment of acute myeloid leukemia (AML). Although several different AML prognostic scoring models have been proposed to assess the prognosis of patients, the accuracy still needs to be improved. As important components of the tumor microenvironment, immune cells played important roles in the physiological functions of tumors and had certain research value. Therefore, whether the tumor immune microenvironment (TIME) can be used to assess the prognosis of AML aroused our great interest.MethodsThe patients' gene expression profile from 7 GEO databases was normalized after removing the batch effect. TIME cell components were explored through Xcell tools and then hierarchically clustered to establish TIME classification. Subsequently, a prognostic model was established by Lasso-Cox. Multiple GEO databases and the Cancer Genome Atlas dataset were employed to validate the prognostic performance of the model. Receiver operating characteristic (ROC) and the concordance index (C-index) were utilized to assess the prognostic efficacy.ResultsAfter analyzing the composition of TIME cells in AML, we found infiltration of ten types of cells with prognostic significance. Then using hierarchical clustering methods, we established a TIME classification system, which clustered all patients into three groups with distinct prognostic characteristics. Using the differential genes between the first and third groups in the TIME classification, we constructed a 121-gene prognostic model. The model successfully divided 1229 patients into the low and high groups which had obvious differences in prognosis. The high group with shorter overall survival had more patients older than 60 years and more poor-risk patients (both P< 0.001). Besides, the model can perform well in multiple datasets and could further stratify the cytogenetically normal AML patients and intermediate-risk AML population. Compared with the European Leukemia Net Risk Stratification System and other AML prognostic models, our model had the highest C-index and the largest AUC of the ROC curve, which demonstrated that our model had the best prognostic efficacy.ConclusionA prognostic model for AML based on the TIME classification was constructed in our study, which may provide a new strategy for precision treatment in AML.

Project description:BackgroundAcute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy. The key problem lies in the complexity of the genome, so that drug resistance and relapse have become the main problems. Recent studies have found an association between synaptotagmin-like 4 (SYTL4) and drug resistance in triple-negative breast cancer and its high expression is correlated with poor prognosis; however, it is unclear whether this gene is associated with the prognosis of AML. This study aimed to investigate the role and action mechanism of SYTL4 in AML.MethodsWe downloaded gene expression profiles and corresponding clinical data from The Cancer Genome Atlas (TCGA) public database and conducted differential and survival analyses using the Limma and survival packages in R. The receiver operating characteristic (ROC) curve, univariate COX, and multivariate COX were used for gene prediction analysis. Co-expression analysis of SYTL4 was performed using Limma, and enrichment analysis of differentially expressed genes in the SYTL4 high- and low-expression groups was conducted. We performed immune cell infiltration using the CIBERSORTx algorithm.ResultsThe expression level of SYTL4 was highest in the poor prognosis group, and lowest in the good prognosis group. Survival was better in the SYTL4 low expression group than that in the high expression group. The areas under the ROC curve for TCGA-Acute Myeloid Leukemia (TCGA-LAML) at 1, 3, and 5 years were 0.725, 0.683, and 0.787, respectively. Sushi repeat protein X-linked 2 (SRPX2), caveolae associated protein 2 (CAVIN2), and other genes were identified as positive regulators of SYTL4 expression, whereas lactoperoxidase (LPO), diacylglycerol lipase beta (DAGLB), and other genes were identified as negative regulators. Differentially expressed genes in the SYTL4 high- and low-expression groups were enriched in pathways such as the embryonic skeletal system and platelet alpha granules. Differences were observed in follicular helper T cells, Tregs, monocytes, and M2 macrophages between SYTL4 high- and low-expression groups.ConclusionsSYTL4 expression negatively correlates with AML prognosis and may be associated with exosome secretion in AML.

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Real-life challenges using personalized prognostic scoring systems in acute myeloid leukemia.

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Real-life challenges using personalized prognostic scoring systems in acute myeloid leukemia.

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