Prediction of acute myeloid leukaemia risk in healthy individuals

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 failure(1). 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 expansion(2,3). 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

Niche-Dependent Gene Expression Profile of Intratumoral Heterogeneous Ovarian Cancer Stem Cell Populations

<div><p>Intratumoral heterogeneity challenges existing paradigms for anti-cancer therapy. We have previously demonstrated that the human embryonic stem cells (hESC)-derived cellular microenvironment in immunocompromised mice, enables functional distinction of heterogeneous tumor cells, including cells which do not grow into a tumor in a conventional direct tumor xenograft platform. We have identified and characterized six cancer cell subpopulations each clonally expanded from a single cell, derived from human ovarian clear cell carcinoma of a single tumor, to demonstrate striking intratumoral phenotypic heterogeneity that is dynamically dependent on the tumor growth microenvironment. These cancer cell subpopulations, characterized as cancer stem cell subpopulations, faithfully recapitulate the full spectrum of histological phenotypic heterogeneity known for human ovarian clear cell carcinoma. Each of the six subpopulations displays a different level of morphologic and tumorigenic differentiation wherein growth in the hESC-derived microenvironment favors growth of CD44+/aldehyde dehydrogenase positive pockets of self-renewing cells that sustain tumor growth through a process of tumorigenic differentiation into CD44-/aldehyde dehydrogenase negative derivatives. Strikingly, these derivative cells display microenvironment-dependent plasticity with the capacity to restore self-renewal markers and CD44 expression. In the current study, we delineate the distinct gene expression and epigenetic profiles of two such subpopulations, representing extremes of phenotypic heterogeneity in terms of niche-dependent self-renewal and tumorigenic differentiation. By combining Gene Set Enrichment, Gene Ontology and Pathway-focused array analyses with methylation status, we propose a suite of robust differences in tumor self-renewal and differentiation pathways that underlie the striking intratumoral phenotypic heterogeneity which characterize this and other solid tumor malignancies.</p> </div

Data management workflow for gene expression profiling process.

<p><i>A</i>, Workflow of the analyses performed for gene expression profiling of cancer cell subpopulations (CCSPs) C12 and C13 <i>in </i><i>vitro</i> and <i>in </i><i>vivo</i>. <i>B</i>, Identification of differentially expressed genes between C12 and C13 <i>in </i><i>vitro</i> grown cells and between tumors generated intramuscular (i.m) and intrateratoma (i.t), and Gene Ontology annotations which correlate with tumors generated i.m and i.t. </p

Principal component analysis (PCA) and hierarchical clustering of data sets.

<p><i>A</i>, The PCA results are provided as two-dimensional representations based on contribution scores for the first two components. Discrimination between cancer cell subpopulations (CCSPs) C12 and C13 samples is shown as indicated in the color capture. <i>B</i>, Hierarchical clustering of the samples using all 48,803 probe elements on the Illumina bead chip demonstrated variability between CCSPs C12 and C13 samples.</p

Validation of the gene expression microarray data.

<p>Total RNA extracted from CCSPs C12 and C13 <i>in </i><i>vitro</i> grown cells and from C12 and C13 – derived tumors generated intramuscular (i.m) and intarteratoma (i.t) were analyzed by quantitative real-time RT-PCR using specific primers as indicated (each in 2 independent RNA samples). <i>A</i>, DNA products were separated on 2% agarose gel and <i>B</i>, the bars demonstrate the relative fold change in expression levels of 10 differentially expressed genes. ACTB and GAPDH were used for internal controls. Asterisk indicates that no KISS1R expression was observed in the C13 samples. These experiments were performed twice, each sample in quadruplicates. <i>C</i>, Bisulfit sequencing analysis of GPX3, MX1, TACSTD2, and KISS1R promoter regions in CCSPs C12 and C13. Open circles represent unmethylated CpG dinucleotides and closed circles represent methylated CpG dinucleotides. Each row is derived from an individual subclone. These experiments were performed twice, each sample in quadruplicates. </p

Gene Ontology (GO) annotations for cancer cell subpopulations (CCSPs) C12 and C13-derived tumors generated intramuscular (i.m) and intrateratoma (i.t).

<p>Statistically significant GSEA gene sets were subjected to leading edge analysis (LEA) and the resulting genes were grouped in ontological annotations of biological process categories. The pie charts present the enriched GO annotations and their matching enrichment scores (ES) for: <i>A</i>, GO annotations upregulated in C12 tumors generated i.m compared with C12 tumors generated i.t. <i>B</i>, GO annotations upregulated in C12 tumors generated i.t compared with C12 tumors generated i.m (the 20 GO annotations with the highest enrichment scores are shown). <i>C</i>, GO annotations up regulated in C13 tumors generated i.m compared with C13 tumors generated i.t. <i>D</i>, GO annotations upregulated in C13 tumors generated i.t compared with C13 tumors generated i.m.</p

WNT, Notch, and Hedgehog signaling pathway arrays analyses.

<p>A comparison analysis of gene expression was performed using the RT² Profiler PCR SuperArrays technique. <i>A</i>, Using a threshold value of 2-fold expression change and a statistical significance of p-value < 0.05, 69 and 54 differentially expressed genes in these 3 pathways were identified between CCSPs C12 and C13 tumors generated intramuscular (i.m) and intrateratoma (i.t) as indicated. <i>B</i>, Ingenuity Pathways Analysis (IPA) demonstrates 10 statistically significant biological functions with the highest significance score common but ranked differently by p-values in C12 and C13 tumors generated i.t as indicated. The upper X-axis is the reciprocal of the p-values and the lower X-axis and orange squares indicate the ratio between altered genes and the total number of genes in the particular pathway. The threshold line marks p-value = 0.05. </p