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Project description:The incidence of acute myeloid leukaemia (AML) increases with age and mortality exceeds 90% when diagnosed after age 65. Most cases arise without any detectable early symptoms and patients usually present with the acute complications of bone marrow failure1. The onset of such de novo AML cases is typically preceded by the accumulation of somatic mutations in preleukaemic haematopoietic stem and progenitor cells (HSPCs) that undergo clonal expansion2,3. However, recurrent AML mutations also accumulate in HSPCs during ageing of healthy individuals who do not develop AML, a phenomenon referred to as age-related clonal haematopoiesis (ARCH)4-8. Here we use deep sequencing to analyse genes that are recurrently mutated in AML to distinguish between individuals who have a high risk of developing AML and those with benign ARCH. We analysed peripheral blood cells from 95 individuals that were obtained on average 6.3 years before AML diagnosis (pre-AML group), together with 414 unselected age- and gender-matched individuals (control group). Pre-AML cases were distinct from controls and had more mutations per sample, higher variant allele frequencies, indicating greater clonal expansion, and showed enrichment of mutations in specific genes. Genetic parameters were used to derive a model that accurately predicted AML-free survival; this model was validated in an independent cohort of 29 pre-AML cases and 262 controls. Because AML is rare, we also developed an AML predictive model using a large electronic health record database that identified individuals at greater risk. Collectively our findings provide proof-of-concept that it is possible to discriminate ARCH from pre-AML many years before malignant transformation. This could in future enable earlier detection and monitoring, and may help to inform intervention.
| S-EPMC6485381 | biostudies-literature
Project description:To identify biomarkers and transcriptomic pathways associated with the development of rheumatoid arhtritis, we performed a transcriptional analysis using a hole genome microarray to demostrate that a specific transcriptional signature is associated with the development of RA, and it exists since the beginning and presence of auto-antibodies against citrulinated proteins.
2017-06-20 | GSE100191 | GEO
Project description:Some patients with acute myeloid leukemia (AML) who are in complete remission after induction chemotherapy harbor persisting pre-leukemic clones, carrying a subset of leukemia-associated somatic mutations. There is conflicting evidence on the prognostic relevance of these clones for AML relapse. Here, we characterized paired pre-treatment and remission samples from 126 AML patients for mutations in 68 leukemia-associated genes. Fifty patients (40%) retained ?1 mutation during remission at a VAF of ?2%. Mutation persistence was most frequent in DNMT3A (65% of patients with mutations at diagnosis), SRSF2 (64%), TET2 (55%), and ASXL1 (46%), and significantly associated with older age (p?<?0.0001) and, in multivariate analyses adjusting for age, genetic risk, and allogeneic transplantation, with inferior relapse-free survival (hazard ratio (HR), 2.34; p?=?0.0039) and overall survival (HR, 2.14; p?=?0.036). Patients with persisting mutations had a higher cumulative incidence of relapse before, but not after allogeneic stem cell transplantation. Our work underlines the relevance of mutation persistence during first remission as a novel risk factor in AML. Persistence of pre-leukemic clones may contribute to the inferior outcome of elderly AML patients. Allogeneic transplantation abrogated the increased relapse risk associated with persisting pre-leukemic clones, suggesting that mutation persistence may guide post-remission treatment.
| S-EPMC6035153 | biostudies-literature
Project description:BackgroundLittle is known regarding the mechanisms underlying the loss of tolerance in the early and preclinical stages of autoimmune diseases. The aim of this work was to identify the transcriptional profile and signaling pathways associated to non-treated early rheumatoid arthritis (RA) and subjects at high risk. Several biomarker candidates for early RA are proposed.MethodsWhole blood total RNA was obtained from non-treated early RA patients with <1 year of evolution as well as from healthy first-degree relatives of patients with RA (FDR) classified as ACCP+ and ACCP- according to their antibodies serum levels against cyclic citrullinated peptides. Complementary RNA (cRNA) was synthetized and hybridized to high-density microarrays. Data was analyzed in Genespring Software and functional categories were assigned to a specific transcriptome identified in subjects with RA and FDR ACCP positive. Specific signaling pathways for genes associated to RA were identified. Gene expression was evaluated by qPCR. Receiver operating characteristic (ROC) analysis was used to evaluate these genes as biomarkers.ResultsA characteristic transcriptome of 551 induced genes and 4,402 repressed genes were identified in early RA patients. Bioinformatics analysis of the data identified a specific transcriptome in RA patients. Moreover, some overlapped transcriptional profiles between patients with RA and ACCP+ were identified, suggesting an up-regulated distinctive transcriptome from the preclinical stages up to progression to an early RA state. A total of 203 pathways have up-regulated genes that are shared between RA and ACCP+. Some of these genes show potential to be used as progression biomarkers for early RA with area under the curve of ROC > 0.92. These genes come from several functional categories associated to inflammation, Wnt signaling and type I interferon pathways.ConclusionThe presence of a specific transcriptome in whole blood of RA patients suggests the activation of a specific inflammatory transcriptional signature in early RA development. The set of overexpressed genes in early RA patients that are shared with ACCP+ subjects but not with ACCP- subjects, can represent a transcriptional signature involved with the transition of a preclinical to a clinical RA stage. Some of these particular up-regulated and down-regulated genes are related to inflammatory processes and could be considered as biomarker candidates for disease progression in subjects at risk to develop RA.
| S-EPMC5870959 | biostudies-literature
Project description:In this study, we use pre-malignant cells from different Cebpa mutant acute myeloid leukemia (AML) models. We have used conditional KO models (CreLoxP) and isolated hematopoietic cells shortly after induction of recombination, in order to look at pre-leukemic cells, which have acquired the first hit, but not yet undergone full malignant transformation. We have sorted granulocyte-macrophage progenitors (GMPs) and the more immature population pre-granulocyte-macrophages (preGMs) from pre-leukemic mice. We analyzed gene-expression profiles in order to find deregulated genes, which make the cells more prone to undergo transformation.
2013-07-08 | E-GEOD-47813 | biostudies-arrayexpress
Project description:In this study, we use pre-malignant cells from different Cebpa mutant acute myeloid leukemia (AML) models. We have used conditional KO models (CreLoxP) and isolated hematopoietic cells shortly after induction of recombination, in order to look at pre-leukemic cells, which have acquired the first hit, but not yet undergone full malignant transformation.
2013-07-08 | GSE47813 | GEO
Project description:BackgroundClonal hematopoiesis of indeterminate potential (CHIP) and clonal cytopenia of undetermined significance (CCUS) are defined by somatic mutations in genes associated with myeloid neoplasms (MN) at a variant allele fraction (VAF) ≥ 0.02, in the absence and presence of cytopenia, respectively. CHIP/CCUS is highly prevalent in adults and defining predictors of MN risk would aid clinical management and research.MethodsWe analyzed sequenced exomes of healthy UK Biobank (UKB) participants (n = 438,890) in separate derivation and validation cohorts. Genetic mutations, laboratory values, and MN outcomes were used in conditional probability-based recursive partitioning and Cox regression to determine predictors of incident MN. Combined statistical weights defined a clonal hematopoiesis risk score (CHRS). Independent CHIP/CCUS patient cohorts were used to test prognostic capability of the CHRS in the clinical setting.ResultsRecursive partitioning distinguished CHIP/CCUS cases with 10-year probabilities of MN ranging from 0.0078 - 0.85. Multivariable analysis validated partitioning variables as predictors of MN. Key features, including single DNMT3A mutations, high risk mutations, ≥ 2 mutations, VAF ≥ 0.2, age ≥ 65 years, CCUS vs CHIP and red blood cell indices, influenced MN risk in variable direction. The CHRS defined low risk (n = 10018, 88.4%), intermediate risk (n = 1196, 10.5%), and high risk (n = 123, 1.1%) groups. In clinical cohorts, most MN events occurred in high risk CHIP/CCUS patients.ConclusionsThe CHRS provides simple prognostic framework for CHIP/CCUS, distinguishing a high risk minority from the majority of CHIP/CCUS which has minimal risk for progression to MN.
| S-EPMC10361696 | biostudies-literature
Project description:BackgroundBreast cancer (BC) risk assessment models are statistical estimates based on patient characteristics. We developed a gene expression assay to assess BC risk using benign breast biopsy tissue.MethodsA NanoString-based malignancy risk (MR) gene signature was validated for formalin-fixed paraffin-embedded (FFPE) tissue. It was applied to FFPE benign and BC specimens obtained from women who underwent breast biopsy, some of whom developed BC during follow-up to evaluate diagnostic capability of the MR signature. BC risk was calculated with MR score, Gail risk score, and both tests combined. Logistic regression and receiver operating characteristic curves were used to evaluate these 3 models.ResultsNanoString MR demonstrated concordance between fresh frozen and FFPE malignant samples (r = 0.99). Within the validation set, 563 women with benign breast biopsies from 2007 to 2011 were identified and followed for at least 5 y; 50 women developed BC (affected) within 5 y from biopsy. Three groups were compared: benign tissue from unaffected and affected patients and malignant tissue from affected patients. Kruskal-Wallis test suggested difference between the groups (P = 0.09) with trend in higher predicted MR score for benign tissue from affected patients before development of BC. Neither the MR signature nor Gail risk score were statistically different between affected and unaffected patients; combining both tests demonstrated best predictive value (AUC = 0.71).ConclusionsFFPE gene expression assays can be used to develop a predictive test for BC. Further investigation of the combined MR signature and Gail Model is required. Our assay was limited by scant cellularity of archived breast tissue.
| S-EPMC6900446 | biostudies-literature
Project description:We recently developed a malignancy-risk gene signature that was shown to identify histologically-normal tissues with a cancer-like profile. Because the signature was rich with proliferative genes, we postulated it might also be prognostic for existing breast cancers. We evaluated the malignancy risk gene signature to see its clinical association with cancer relapse/progression, and cancer prognosis using six independent external datasets. Six independent external breast cancer datasets were collected and analyzed using the malignancy risk gene signature designed to assess normal breast tissues. Evaluation of the signature in external datasets suggested a strong clinical association with cancer relapse/progression, and prognosis with minimal overlap of signature gene sets. These results suggest a prognostic role for the malignancy risk gene signature in the assessment of existing cancer. Proliferative biology dominates not only the earliest stages of tumor development but also later stages of tumor progression and metastasis.
| S-EPMC2808422 | biostudies-literature
Project description:Context: In many cancers, specific subpopulations of cells appear to be uniquely capable of initiating and maintaining tumors. The strongest support for this cancer stem cell model comes from transplantation assays in immune-deficient mice indicating that human acute myeloid leukemia (AML) is organized as a cellular hierarchy driven by self-renewing leukemia stem cells (LSC). This model has significant implications for the development of novel therapies, but its clinical significance remains unclear. Objective: To measure associations between a leukemic stem cell expression signature and clinical outcomes in AML. Design, Setting, and Patients: We defined a gene expression signature of LSC-enriched subpopulations from primary AML patient samples and xenografts, based on a functional definition in transplantation assays. Using previously published gene expression data of bulk AML from four independent cohorts totaling 1047 patients, we performed a retrospective cohort study, defining an LSC score and evaluating it for associations with known predictors of risk including cytogenetic subtype and molecular mutations, and as an independent prognostic factor. Main Outcome Measures: Reproducible associations between a leukemic stem cell signature and overall, event-free, and relapse-free survival. Results: The LSC score was similar across most AML subtypes, but was lower in promyelocytic leukemia, and prognostically favorable cases harboring NPM1 or CEBPA mutations. Strikingly, high scores associated with inferior overall (OS), event-free (EFS), and relapse-free survival (RFS) in these independent cohorts, whether considering patients with a normal karyotype [hazard ratio (HR) range for OS 1.13-1.18, p<0.012 in all cases], or those with cytogenetic anomalies (HR range for OS 1.07-1.15, p<0.01 in all cases). In multivariate analysis, the LSC score was associated with poor outcomes independently of age, FLT3 or NPM1 mutations, and cytogenetic risk group, and added to their prognostic value. Conclusions: High expression of a leukemic stem cell gene expression signature is independently associated with adverse outcomes in AML Cellular fractionation and expression profiling of normal and leukemic subsets: Human samples were obtained at the Stanford University Medical Center according to an approved protocol of the Institutional Review Board after informed consent. Normal human bone marrow mononuclear cells were purchased from AllCells Inc. (Emeryville, CA) and human cord blood was obtained from Stanford University. For AML specimens, peripheral blood and/or bone marrow was obtained, and gene expression microarray data were generated using Affymetrix U133 Plus 2.0 microarrays from the following populations purified by fluorescence-activated cell sorting: AML LSC (Lin-CD34+CD38-CD90-, n=7), AML LPC (Lin-CD34+CD38+, n=7), AML Blasts (Lin-CD34-), normal hematopoietic stem cells (HSC, Lin-CD34+CD38-CD90+CD45RA-; bone marrow and cord blood, n=7), multipotent progenitors (Lin-CD34+CD38-CD90-CD45RA-; bone marrow and cord blood, n=7), common myeloid progenitors (Lin-CD34+CD38+CD123+CD45RA-; bone marrow, n=4), granulocyte-monocyte progenitors (Lin-CD34+CD38+CD123+CD45RA+; bone marrow, n=4), and megakaryocyte-erthythrocyte progenitors (Lin-CD34+CD38+CD123-CD45RA-; bone marrow, n=4). Raw data were deposited at the National Center for Biotechnology Information Gene Expression Omnibus (GEO, accession GSE24006). Detailed methods for purification of cellular subsets and clinical features of the corresponding AML patients have been reported previously. Microarray analysis and definition of LSC signature: Fourteen paired LSC and LPC samples from 7 patients described above were combined with 16 paired samples (8 LSC and 8 LPC) from an independent study to produce one dataset for analysis. Individual genes differentially expressed between paired LSC and LPC were identified using Significance Analysis of Microarrays, employing a paired metric (false discovery rate<10%). The ‘LSC signature’ in a given dataset was defined as the first principal component of these genes (the linear weighted sum of gene expression values that summarizes the maximum possible proportion of their total variance) across samples from that dataset. The LSC signature was evaluated across all purified subpopulations described above. To identify biological themes distinguishing LSC from LPC, all genes on microarrays were ranked by their geometric mean difference in expression between paired LSC/LPC samples, and evaluated using Gene Set Enrichment Analysis. Raw microarray data were obtained as Affymetrix CEL files for four publicly available bulk AML gene expression studies from NCBI GEO (GSE12417, n=163 normal-karyotype AML only, with OS outcomes; GSE10358, n=184, OS and EFS; GSE14468, n=527, OS, EFS and RFS) and the National Cancer Institute caArray database (accession willm-00119, n=170 non-FAB M3, OS only). Matrices of re-analyzed data linked below as supplementary files. Ingenuity Pathways Analysis was used to identify interaction networks of genes.
2010-12-22 | E-GEOD-24006 | biostudies-arrayexpress