Personalized Survival Prediction of Patients With Acute Myeloblastic Leukemia Using Gene Expression Profiling.
Ontology highlight
ABSTRACT:
SUBMITTER: Mosquera Orgueira A
PROVIDER: S-EPMC8040929 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundAcute myeloid leukemia (AML) is a genetically heterogeneous blood disorder. AML patients are associated with a relatively poor overall survival. The objective of this study was to establish a machine learning model to accurately perform the prognosis prediction in AML patients.MethodsWe first screened for prognosis-related genes using Kaplan-Meier survival analysis in The Cancer Genome Atlas dataset and validated the results in the Oregon Health & Science University dataset. With a random forest model, we built a prognostic risk score using patient's age, TP53 mutation, ELN classification and normalized 197 gene expression as predictor variable. Gene set enrichment analysis was implemented to determine the dysregulated gene sets between the high-risk and low-risk groups. Similarity Network Fusion (SNF)-based integrative clustering was performed to identify subgroups of AML patients with different clinical features.ResultsThe random forest model was deemed the best model (area under curve value, 0.75). The random forest-derived risk score exhibited significant association with shorter overall survival in AML patients. The gene sets of pantothenate and coa biosynthesis, glycerolipid metabolism, biosynthesis of unsaturated fatty acids were significantly enriched in phenotype high risk score. SNF-based integrative clustering indicated three distinct subsets of AML patients in the TCGA cohort. The cluster3 AML patients were characterized by older age, higher risk score, more frequent TP53 mutations, higher cytogenetics risk, shorter overall survival.ConclusionsThe random forest-based risk score offers an effective method to perform prognosis prediction for AML patients.
| S-EPMC8892720 | biostudies-literature
Project description:BackgroundThirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data.MethodsGene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set.ResultsTwo hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942).ConclusionOur results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness.
| S-EPMC7579992 | biostudies-literature
Project description:We examined the gene expression profiles of two independent cohorts of patients with acute myeloid leukemia [n=247 and n=214 (younger than or equal to 60 years)] to study the applicability of gene expression profiling as a single assay in prediction of acute myeloid leukemia-specific molecular subtypes. The favorable cytogenetic acute myeloid leukemia subtypes, i.e., acute myeloid leukemia with t(8;21), t(15;17) or inv(16), were predicted with maximum accuracy (positive and negative predictive value: 100%). Mutations in NPM1 and CEBPA were predicted less accurately (positive predictive value: 66% and 100%, and negative predictive value: 99% and 97% respectively). Various other characteristic molecular acute myeloid leukemia subtypes, i.e., mutant FLT3 and RAS, abnormalities involving 11q23, -5/5q-, -7/7q-, abnormalities involving 3q (abn3q) and t(9;22), could not be correctly predicted using gene expression profiling. In conclusion, gene expression profiling allows accurate prediction of certain acute myeloid leukemia subtypes, e.g. those characterized by expression of chimeric transcription factors. However, detection of mutations affecting signaling molecules and numerical abnormalities still requires alternative molecular methods.
| S-EPMC2625407 | biostudies-literature
Project description:BackgroundAcute myeloid leukemia (AML) is a heterogeneous hematologic malignancy characterized by different genetic alterations that cause changes in the normal mechanisms of differentiation, which are associated with chemoresistance. The ABCB1 gene is part of a family of ATP-binding cassette (ABC) transporter genes involved in the progression of various types of cancer. The following work aimed to evaluate the expression levels of the ABCB1 gene and the C3435T SNP with the response to first-line treatment and survival in patients with AML.MethodsIn total 135 samples were taken to isolate total RNA and DNA at the beginning of the treatment. Expression analysis by RT-qPCR and SNP C3435T assessment method were performed for real-time Polymerase chain reaction (qPCR).ResultsThe expression levels impact on the survival of patients with AML compared to low or absent levels; the CC genotype was found in 22.9%, the CT genotype was found in 47.4%, and the TT genotype was found in 29.6%, the presence of the C3435T SNP, the TT genotype also impacts with a lower survival compared to CT and CC genotypes. In addition, it was shown that the dominant model significantly impacts survival.ConclusionIn conclusion, we have found that the overexpression of the ABCB1 gene, as well as the presence of the TT genotype of the C3435T SNP, contributes to a worse prognosis in AML.
| S-EPMC8549154 | biostudies-literature
Project description:We determined the genome-wide digital gene expression (DGE) profiles of primary acute lymphoblastic leukemia (ALL) cells from 21 patients taking advantage of 'second-generation' sequencing technology. Patients included in this study represent four cytogenetically distinct subtypes of B-cell precursor (BCP) ALL and T-cell lineage ALL (T-ALL). The robustness of DGE combined with supervised classification by nearest shrunken centroids (NSC) was validated experimentally and by comparison with published expression data for large sets of ALL samples. Genes that were differentially expressed between BCP ALL subtypes were enriched to distinct signaling pathways with dic(9;20) enriched to TP53 signaling, t(9;22) to interferon signaling, as well as high hyperdiploidy and t(12;21) to apoptosis signaling. We also observed antisense tags expressed from the non-coding strand of ~50% of annotated genes, many of which were expressed in a subtype-specific pattern. Antisense tags from 17 gene regions unambiguously discriminated between the BCP ALL and T-ALL subtypes, and antisense tags from 76 gene regions discriminated between the 4 BCP subtypes. We observed a significant overlap of gene regions with alternative polyadenylation and antisense transcription (P<1 × 10(-15)). Our study using DGE profiling provided new insights into the RNA expression patterns in ALL cells.
| S-EPMC3377998 | biostudies-literature
Project description:To determine whether gene expression profiling could improve risk classification and outcome prediction in older acute myeloid leukemia (AML) patients, expression profiles were obtained in pretreatment leukemic samples from 170 patients whose median age was 65 years. Unsupervised clustering methods were used to classify patients into 6 cluster groups (designated A to F) that varied significantly in rates of resistant disease (RD; P < .001), complete response (CR; P = .023), and disease-free survival (DFS; P = .023). Cluster A (n = 24), dominated by NPM1 mutations (78%), normal karyotypes (75%), and genes associated with signaling and apoptosis, had the best DFS (27%) and overall survival (OS; 25% at 5 years). Patients in clusters B (n = 22) and C (n = 31) had the worst OS (5% and 6%, respectively); cluster B was distinguished by the highest rate of RD (77%) and multidrug resistant gene expression (ABCG2, MDR1). Cluster D was characterized by a "proliferative" gene signature with the highest proportion of detectable cytogenetic abnormalities (76%; including 83% of all favorable and 34% of unfavorable karyotypes). Cluster F (n = 33) was dominated by monocytic leukemias (97% of cases), also showing increased NPM1 mutations (61%). These gene expression signatures provide insights into novel groups of AML not predicted by traditional studies that impact prognosis and potential therapy.
| S-EPMC1895492 | biostudies-literature
Project description:Lapatinib is an oral-form dual tyrosine kinase inhibitor of epidermal growth factor receptor (EGFR or ErbB/Her) superfamily members with anticancer activity. In this study, we examined the effects and mechanism of action of lapatinib on several human leukemia cells lines, including acute myeloid leukemia (AML), chronic myeloid leukemia (CML), and acute lymphoblastic leukemia (ALL) cells. We found that lapatinib inhibited the growth of human AML U937, HL-60, NB4, CML KU812, MEG-01, and ALL Jurkat T cells. Among these leukemia cell lines, lapatinib induced apoptosis in HL-60, NB4, and Jurkat cells, but induced nonapoptotic cell death in U937, K562, and MEG-01 cells. Moreover, lapatinib treatment caused autophagic cell death as shown by positive acridine orange staining, the massive formation of vacuoles as seen by electronic microscopy, and the upregulation of LC3-II, ATG5, and ATG7 in AML U937 cells. Furthermore, autophagy inhibitor 3-methyladenine and knockdown of ATG5, ATG7, and Beclin-1 using short hairpin RNA (shRNA) partially rescued lapatinib-induced cell death. In addition, the induction of phagocytosis and ROS production as well as the upregulation of surface markers CD14 and CD68 was detected in lapatinib-treated U937 cells, suggesting the induction of macrophagic differentiation in AML U937 cells by lapatinib. We also noted the synergistic effects of the use of lapatinib and cytotoxic drugs in U937 leukemia cells. These results indicate that lapatinib may have potential for development as a novel antileukemia agent.
| S-EPMC4959590 | biostudies-literature
Project description:The expression of apoptosis-related BCL2 family genes, fine-tuned in normal cells, is dysregulated in many neoplasms. In acute myeloid leukemia (AML), this problem has not been studied comprehensively. To address this issue, RNA-seq data were used to analyze the expression of 26 BCL2 family members in 27 AML FAB M1 and M2 patients, divided into subgroups differently responding to chemotherapy. A correlation analysis, analysis of variance, and Kaplan-Meier analysis were applied to associate the expression of particular genes with other gene expression, clinical features, and the presence of mutations detected by exome sequencing. The expression of BCL2 family genes was dysregulated in AML, as compared to healthy controls. An upregulation of anti-apoptotic and downregulation of pro-apoptotic genes was observed, though only a decrease in BMF, BNIP1, and HRK was statistically significant. In a group of patients resistant to chemotherapy, overexpression of BCL2L1 was manifested. In agreement with the literature data, our results reveal that BCL2L1 is one of the key players in apoptosis regulation in different types of tumors. An exome sequencing data analysis indicates that BCL2 family genes are not mutated in AML, but their expression is correlated with the mutational status of other genes, including those recurrently mutated in AML and splicing-related. High levels of some BCL2 family members, in particular BIK and BCL2L13, were associated with poor outcome.
| S-EPMC8267690 | biostudies-literature
Project description:PurposeBreast cancer is the leading cause of cancer death worldwide in women. The molecular mechanism for human breast cancer is unknown. Gene microarray has been widely used in breast cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis survival. So far, the valuable multigene signatures in clinical practice are unclear, and the biological importance of individual genes is difficult to detect, as the described signatures virtually do not overlap. Early prognosis of this disease, breast invasive ductal carcinoma (IDC) and breast ductal carcinoma in situ (DCIS), is vital in breast surgery.MethodsThus, this study reports gene expression profiling in large breast cancer cohorts from Gene Expression Omnibus, including GSE29044 (N=138) and GSE10780 (N=185) test series and four independent validation series GSE21653 (N=266), GSE20685 (N=327), GSE26971 (N=276), and GSE12776 (N=204). Significantly differentially expressed genes in human breast IDC and breast DCIS were detected by transcriptome microarray analysis.ResultsWe created a set of three genes (MAMDC2, TSHZ2, and CLDN11) that were significantly correlated with disease-free survival of breast cancer patients using a univariate Cox regression model (significance level P<0.01) in a meta-analysis. Based on the risk score of the three genes, the test series patients could be separated into low-risk and high-risk groups with significantly different survival times. This signature was validated in the other three cohorts. The prognostic value of this three-gene signature was confirmed in the internal validation series and another four independent breast cancer data sets. The prognostic impact of one of the three genes, CLDN11, was confirmed by immunohistochemistry. CLDN11 was significantly overexpressed in human breast IDC as compared with normal breast tissues and breast DCIS.ConclusionUsing novel gene expression profiling together with a meta-analysis validation approach, we have identified a three-gene signature with independent prognostic impact. Furthermore, CLDN11 may offer a biomarker to predict prognosis as well as a new target for prognostic and therapeutic intervention for human breast IDC.
| S-EPMC5067006 | biostudies-literature
Project description:Accurate prediction of long-term outcomes remains a challenge in the care of cancer patients. Due to the difficulty of serial tumor sampling, previous prediction tools have focused on pretreatment factors. However, emerging non-invasive diagnostics have increased opportunities for serial tumor assessments. We describe the Continuous Individualized Risk Index (CIRI), a method to dynamically determine outcome probabilities for individual patients utilizing risk predictors acquired over time. Similar to "win probability" models in other fields, CIRI provides a real-time probability by integrating risk assessments throughout a patient's course. Applying CIRI to patients with diffuse large B cell lymphoma, we demonstrate improved outcome prediction compared to conventional risk models. We demonstrate CIRI's broader utility in analogous models of chronic lymphocytic leukemia and breast adenocarcinoma and perform a proof-of-concept analysis demonstrating how CIRI could be used to develop predictive biomarkers for therapy selection. We envision that dynamic risk assessment will facilitate personalized medicine and enable innovative therapeutic paradigms.
| S-EPMC7380118 | biostudies-literature