A 17-Gene Stemness Score for Rapid Identification of High-Risk AML Patients
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE76009 | GEO | 2016/12/02
SECONDARY ACCESSION(S): PRJNA306002
REPOSITORIES: GEO
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Project description:In AML, most patients are initiated on standard chemotherapy and afterwards assigned to a post-remission strategy based on genetically-defined risk categories. However, outcomes remain heterogeneous, indicating the need for novel biomarker tests that can rapidly and accurately identify high-risk patients, allowing better stratification of both induction and post-remission therapy. As patient outcomes are linked to leukemia stem cell (LSC) properties that confer therapy resistance and drive relapse, LSC-based biomarkers may be highly informative. We tested 227 CD34/CD38 cell fractions from 78 AML patients for LSC activity in xenotransplantation assays. Comparison of microarray-based gene expression (GE) profiles between 138 LSC+ and 89 LSC? fractions identified 104 differentially-expressed LSC-specific genes. To obtain prognostic signatures, we performed statistical regression analysis of LSC GE against patient outcome using a training cohort of 495 AML patients treated with curative intent. A score calculated as the weighted sum of expression of 17 LSC signature genes (LSC17) was strongly associated with survival in 4 independent datasets (716 AML cases) spanning all risk categories in multi-variate analysis; an optimized 3-gene sub-score (LSC3) was prognostic in favorable risk subsets. These scores were robust across GE technology platforms, including the clinically serviceable NanoString system (LSC17: HR=2.73, P<0.0001; LSC3: HR=6.3, P<0.02). The LSC17 and LSC3 scores provide rapid and accurate identification of high-risk patients for whom conventional chemotherapy is non-curative. These scores will enable evaluation in clinical trials of whether such patients may benefit from novel and/or more intensified therapies during induction or in the post-remission setting.
2016-12-02 | GSE76008 | GEO
Project description:In AML, most patients are initiated on standard chemotherapy and afterwards assigned to a post-remission strategy based on genetically-defined risk categories. However, outcomes remain heterogeneous, indicating the need for novel biomarker tests that can rapidly and accurately identify high-risk patients, allowing better stratification of both induction and post-remission therapy. As patient outcomes are linked to leukemia stem cell (LSC) properties that confer therapy resistance and drive relapse, LSC-based biomarkers may be highly informative. We tested 227 CD34/CD38 cell fractions from 78 AML patients for LSC activity in xenotransplantation assays. Comparison of microarray-based gene expression (GE) profiles between 138 LSC+ and 89 LSC? fractions identified 104 differentially-expressed LSC-specific genes. To obtain prognostic signatures, we performed statistical regression analysis of LSC GE against patient outcome using a training cohort of 495 AML patients treated with curative intent. A score calculated as the weighted sum of expression of 17 LSC signature genes (LSC17) was strongly associated with survival in 4 independent datasets (716 AML cases) spanning all risk categories in multi-variate analysis; an optimized 3-gene sub-score (LSC3) was prognostic in favorable risk subsets. These scores were robust across GE technology platforms, including the clinically serviceable NanoString system (LSC17: HR=2.73, P<0.0001; LSC3: HR=6.3, P<0.02). The LSC17 and LSC3 scores provide rapid and accurate identification of high-risk patients for whom conventional chemotherapy is non-curative. These scores will enable evaluation in clinical trials of whether such patients may benefit from novel and/or more intensified therapies during induction or in the post-remission setting.
2016-12-02 | GSE76004 | GEO
Project description:A 17-Gene Stemness Score for Rapid Identification of High-Risk AML Patients
| PRJNA306002 | ENA
Project description:PurposeRisk stratification in patients with multiple myeloma (MM) remains a challenge. As clinicopathological characteristics have been demonstrated insufficient for exactly defining MM risk, and molecular biomarkers have become the focuses of interests. Prognostic predictions based on gene expression profiles (GEPs) have been the most accurate to this day. The purpose of our study was to construct a risk score based on stemness genes to evaluate the prognosis in MM.Materials and methodsBioinformatics studies by ingenuity pathway analyses in side population (SP) and non-SP (MP) cells of MM patients were performed. Firstly, co-expression network was built to confirm hub genes associated with the top five Kyoto Encyclopedia of Genes and Genomes pathways. Functional analyses of hub genes were used to confirm the biologic functions. Next, these selective genes were utilized for construction of prognostic model, and this model was validated in independent testing sets. Finally, five stemness genes (ROCK1, GSK3B, BRAF, MAPK1 and MAPK14) were used to build a MM side population 5 (MMSP5) gene model, which was demonstrated to be forcefully prognostic compared to usual clinical prognostic parameters by multivariate cox analysis. MM patients in MMSP5 low-risk group were significantly related to better prognosis than those in high-risk group in independent testing sets.ConclusionOur study provided proof-of-concept that MMSP5 model can be adopted to evaluate recurrence risk and clinical outcome for MM. The MMSP5 model evaluated in different databases clearly indicated novel risk stratification for personalized anti-MM treatments.
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Project description:Leukemia stem cells (LSCs) are linked to relapse in acute myeloid leukemia (AML). The LSC17 gene expression score robustly captures LSC stemness properties in AML and can be used to predict survival outcomes and response to therapy, enabling risk-adapted, upfront treatment approaches. The LSC17 score was developed and validated in a research setting. To enable widespread use of the LSC17 score in clinical decision making, we established a laboratory-developed test (LDT) for the LSC17 score that can be deployed broadly in clinical molecular diagnostic laboratories. We extensively validated the LSC17 LDT in a College of American Pathologists/Clinical Laboratory Improvements Act (CAP/CLIA)-certified laboratory, determining specimen requirements, a synthetic control, and performance parameters for the assay. Importantly, we correlated values from the LSC17 LDT to clinical outcome in a reference cohort of patients with AML, establishing a median assay value that can be used for clinical risk stratification of individual patients with newly diagnosed AML. The assay was established in a second independent CAP/CLIA-certified laboratory, and its technical performance was validated using an independent cohort of patient samples, demonstrating that the LSC17 LDT can be readily implemented in other settings. This study enables the clinical use of the LSC17 score for upfront risk-adapted management of patients with AML.
| S-EPMC8945314 | biostudies-literature
Project description:Acute myeloid leukemia (AML) is an aggressive malignancy characterized by clonal proliferation of neoplastic immature precursor cells. AML impacts older adults and has a poor prognosis. Despite recent advances in treatment, AML is complex, with both genetic and epigenetic aberrations in the malignant clone and elaborate interactions with its microenvironment. We are now able to stratify patients on the basis of specific clinical and molecular features in order to optimize individual treatment strategies. However, our understanding of the complex nature of these molecular abnormalities continues to expand the defining characteristics of high-risk mutations. In this review, we focus on genetic and microenvironmental factors in adverse risk AML that play critical roles in leukemogenesis, including those not described in an European LeukemiaNet adverse risk group, and describe therapies that are currently in the clinical arena, either approved or under development.
| S-EPMC8111550 | biostudies-literature
Project description:Evidence points towards the differentiate state of cells being a marker of cancer risk and progression, in line with the cancer-stem-cell hypothesis. Measuring the differentiation state of single cells in a preneoplastic population could thus enable novel strategies for early detection and risk prediction. Here we present a novel computational method called CancerStemID that estimates a stemness index of cells from single-cell RNA-Seq data. We validate CancerStemID in two human esophageal squamous cell carcinoma (ESCC) cohorts, demonstrating how it can identify undifferentiated preneoplastic cells whose transcriptomic state is overrepresented in invasive cancer. We demonstrate decreased differentiation activity of tissue-specific TFs in cancer cells compared to the basal cell-of-origin layer, and that the differentiation state correlates with differential DNA methylation at the promoters of such TFs independently of underlying NOTCH1 and TP53 mutations. In summary, these data support an epigenetic stem-cell model of oncogenesis and highlight a novel computational strategy in which to identify stem-like preneoplastic cells that undergo positive selection.
2022-04-01 | GSE199654 | GEO
Project description:Nowadays, there are different ICU scoring systems to predict the likelihood of mortality, such as Acute Physiology And Chronic Health Condition (APACHE), Sequential Organ Failure Assessment (SOFA), and SAPS (Simplified Acute Physiology Score). Theses risk scores are based on the use of physiologic and other clinical data. However, the use of these score systems depend on the clinical trust in the reliability and predictions by physicians. In this work, we have evaluated the expression profile by microarray analysis from postsurgical patients with the aim of proposing a candidate set genes as a mortality risk score.
2020-05-05 | GSE132897 | GEO
Project description:Emerging data demonstrate important roles for the TYRO3/AXL/MERTK receptor tyrosine kinase (TAM RTK) family in diverse cancers. We investigated the prognostic relevance of GAS6 expression, encoding the common TAM RTK ligand, in 270 adults (n=71 aged<60 years; n=199 aged ?60 years) with de novo cytogenetically normal acute myeloid leukemia (CN-AML). Patients expressing GAS6 (GAS6+), especially those aged ?60 years, more often failed to achieve a complete remission (CR). In all patients, GAS6+ patients had shorter disease-free (DFS) and overall (OS) survival than patients without GAS6 expression (GAS6-). After adjusting for other prognostic markers, GAS6+ predicted CR failure (P=0.02), shorter DFS (P=0.004) and OS (P=0.04). To gain further biological insights, we derived a GAS6-associated gene-expression signature (P<0.001) that in GAS6+ patients included overexpressed BAALC and MN1, known to confer adverse prognosis in CN-AML, and overexpressed CXCL12, encoding stromal cell-derived factor, and its receptor genes, chemokine (C-X-C motif) receptor 4 (CXCR4) and CXCR7. This study reports for the first time that GAS6 expression is an adverse prognostic marker in CN-AML. Although GAS6 decoy receptors are not yet available in the clinic for GAS6+ CN-AML therapy, potential alternative therapies targeting GAS6+-associated pathways, for example, CXCR4 antagonists, may be considered for GAS6+ patients to sensitize them to chemotherapy.
| S-EPMC4047202 | biostudies-literature
Project description:ObjectiveMany healthcare systems employ population-based risk scores to prospectively identify patients at high risk of poor outcomes, but it is unclear whether single point-in-time scores adequately represent future risk. We sought to identify and characterize latent subgroups of high-risk patients based on risk score trajectories.Study designObservational study of 7289 patients discharged from Veterans Health Administration (VA) hospitals during a 1-week period in November 2012 and categorized in the top 5th percentile of risk for hospitalization.MethodsUsing VA administrative data, we calculated weekly risk scores using the validated Care Assessment Needs model, reflecting the predicted probability of hospitalization. We applied the non-parametric k-means algorithm to identify latent subgroups of patients based on the trajectory of patients' hospitalization probability over a 2-year period. We then compared baseline sociodemographic characteristics, comorbidities, health service use, and social instability markers between identified latent subgroups.ResultsThe best-fitting model identified two subgroups: moderately high and persistently high risk. The moderately high subgroup included 65% of patients and was characterized by moderate subgroup-level hospitalization probability decreasing from 0.22 to 0.10 between weeks 1 and 66, then remaining constant through the study end. The persistently high subgroup, comprising the remaining 35% of patients, had a subgroup-level probability increasing from 0.38 to 0.41 between weeks 1 and 52, and declining to 0.30 at study end. Persistently high-risk patients were older, had higher prevalence of social instability and comorbidities, and used more health services.ConclusionsOn average, one third of patients initially identified as high risk stayed at very high risk over a 2-year follow-up period, while risk for the other two thirds decreased to a moderately high level. This suggests that multiple approaches may be needed to address high-risk patient needs longitudinally or intermittently.
| S-EPMC6258600 | biostudies-literature