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• W2000996935 abstract "A recent Nature Medicine study by Eppert et al., 2011Eppert K. Takenaka K. Lechman E.R. Waldron L. Nilsson B. van Galen P. Metzeler K.H. Poeppl A. Ling V. Beyene J. et al.Nat. Med. 2011; 17 (in press. Published online August 28, 2011)https://doi.org/10.1038/nm.2415Crossref PubMed Scopus (728) Google Scholar describes analyses of functionally defined leukemia stem cell populations that provide new insights on the biology of human tumor populations and the potential use of stem cell-associated gene signatures for prognosis. A recent Nature Medicine study by Eppert et al., 2011Eppert K. Takenaka K. Lechman E.R. Waldron L. Nilsson B. van Galen P. Metzeler K.H. Poeppl A. Ling V. Beyene J. et al.Nat. Med. 2011; 17 (in press. Published online August 28, 2011)https://doi.org/10.1038/nm.2415Crossref PubMed Scopus (728) Google Scholar describes analyses of functionally defined leukemia stem cell populations that provide new insights on the biology of human tumor populations and the potential use of stem cell-associated gene signatures for prognosis. With the advent of comprehensive gene expression analysis platforms, transcriptional profiles of almost any cell type can be readily obtained. This approach has a variety of applications, including that of a prognostic tool for human disease. In the cancer field, numerous studies have employed genome-wide transcriptional signatures to determine the biological state of a tumor and to make predictions on the efficacy of conventional therapy. Retrospective analyses have shown that various signatures can identify good- and poor-prognosis patients (Liu et al., 2007Liu R. Wang X. Chen G.Y. Dalerba P. Gurney A. Hoey T. Sherlock G. Lewicki J. Shedden K. Clarke M.F. N. Engl. J. Med. 2007; 356: 217-226Crossref PubMed Scopus (845) Google Scholar), and in conjunction with more conventional methods (e.g., cytogenetics, mutational analyses, etc.) may allow optimization of therapy. One challenge in applying transcriptional analyses to cancer studies is that most tumors, like the tissues from which they are derived, are composed of heterogeneous cell populations (Rosen and Jordan, 2009Rosen J.M. Jordan C.T. Science. 2009; 324: 1670-1673Crossref PubMed Scopus (582) Google Scholar). Such cellular diversity is of course frequently accompanied by distinct expression profiles. Thus, a limitation of bulk tissue profiling is that it may not be representative of the expression profile of a specific subpopulation. In particular, if tumor-initiating cells, or so-called “cancer stem cells” (CSCs) are present as a relatively minor subpopulation, their transcriptional status is likely to be obscured by the presence of bulk tumor cells with differing expression profiles. An obvious means to address this issue is to enrich for stem cell populations using standard methods, which typically rely on cell surface antigens and antibody-based cell sorting. In normal tissues this strategy has been very effective because of the presence of a consistently maintained hierarchical organization and conserved expression of specific stem cell antigens, features that permit enrichment of stem cells to a high degree. Thus, genome-wide expression studies for normal stem cell populations have facilitated the identification of the many genes involved in the regulation of stem cell function or the determinants of “stemness” (Zon, 2008Zon L.I. Nature. 2008; 453: 306-313Crossref PubMed Scopus (230) Google Scholar). In leukemia, this approach has proven to be much more challenging than originally envisioned due to the interpatient and intrapatient variability of leukemia stem cell (LSC) phenotypes. Indeed, it seems that cell surface antigens expressed on LSCs can vary from patient to patient, and may change within an individual patient during the course of disease pathogenesis (Taussig et al., 2010Taussig D.C. Vargaftig J. Miraki-Moud F. Griessinger E. Sharrock K. Luke T. Lillington D. Oakervee H. Cavenagh J. Agrawal S.G. et al.Blood. 2010; 115: 1976-1984Crossref PubMed Scopus (279) Google Scholar). Recent data relying on cell surface antigen expression indicates that in some cases LSC functional activity may be present in more than one population (Sarry et al., 2011Sarry J.E. Murphy K. Perry R. Sanchez P.V. Secreto A. Keefer C. Swider C.R. Strzelecki A.C. Cavelier C. Récher C. et al.J. Clin. Invest. 2011; 121: 384-395Crossref PubMed Scopus (289) Google Scholar). Despite these limitations, prior studies that performed genome-wide expression profiling of LSCs have led to the identification of several pathways involved in regulating LSC function (Krivtsov et al., 2006Krivtsov A.V. Twomey D. Feng Z. Stubbs M.C. Wang Y. Faber J. Levine J.E. Wang J. Hahn W.C. Gilliland D.G. et al.Nature. 2006; 442: 818-822Crossref PubMed Scopus (1163) Google Scholar, Majeti et al., 2009Majeti R. Becker M.W. Tian Q. Lee T.L. Yan X. Liu R. Chiang J.H. Hood L. Clarke M.F. Weissman I.L. Proc. Natl. Acad. Sci. USA. 2009; 106: 3396-3401Crossref PubMed Scopus (237) Google Scholar). Many of these genes and pathways have been shown to be important to malignant stem cell activity using gain- or loss-of-function analyses. In addition, the results of such studies have been shown to provide prognostic information for the segregation of patients into high-risk and low-risk groups when applied retrospectively to established microarray data sets (Gentles et al., 2010Gentles A.J. Plevritis S.K. Majeti R. Alizadeh A.A. JAMA. 2010; 304: 2706-2715Crossref PubMed Scopus (282) Google Scholar). In an intriguing new study by Eppert et al., 2011Eppert K. Takenaka K. Lechman E.R. Waldron L. Nilsson B. van Galen P. Metzeler K.H. Poeppl A. Ling V. Beyene J. et al.Nat. Med. 2011; 17 (in press. Published online August 28, 2011)https://doi.org/10.1038/nm.2415Crossref PubMed Scopus (728) Google Scholar in Nature Medicine, the authors report the results of a genome-wide expression analysis of enriched LSC populations derived from acute myeloid leukemia (AML) patients. The major technical feature that sets this study apart from previous reports is the exclusive use of functionally defined LSC populations. The authors employed antibodies for CD34 and CD38, the most common antigens used in the analysis of primitive human hematopoietic stem and progenitor populations, and isolated the four distinct subpopulations that can be obtained with these two markers (CD34+/CD38+, CD34+/CD38−, CD34−/CD38+, and CD34−/CD38−) from 16 primary AML patient specimens. Each sorted fraction was then independently evaluated for stem cell potential by transplantation into immune-deficient mice. The data shows that in over 90% of the patient samples tested, LSCs are present in the CD34+/CD38− compartment. In nearly 60% of samples, the CD34+/CD38+ population also possessed functional LSCs, while in less than one-fourth of the samples, LSC activity was identified in the remaining CD34−/CD38+ and CD34−/CD38− populations. Limiting-dilution studies also examined the frequency of LSCs in each fraction and found that in most samples the CD34+/CD38− cells were the most highly enriched for LSCs, with frequencies on the order of 1/1,000 to 1/100,000. Using 25 sorted fractions containing functionally validated LSCs versus 26 fractions with no detectable LSCs, the authors then perform comprehensive gene expression studies and identify 42 genes preferentially expressed in tumor-forming cell-containing populations, termed the LSC-R signature. Notably, 18 genes from the LSC-R signature are implicated in regulation of normal or malignant stem cells (e.g., MEIS1, ERG, HLF, EVI1, etc.), indicating a clear association with stem cell function. Using a similar strategy, they also identify a gene set consisting of 121 genes that represent the profile of normal hematopoietic stem cells, the HSC-R signature. By comparing the LSC-R and HSC-R signatures, they found that the HSC-R gene signature was enriched in the LSC-R profile, and conversely, the LSC-R signature was enriched in the HSC-R signature. They conclude that the extensive overlap of these two signatures highlights a stemness profile shared between both populations despite their normal and malignant origins. Finally, by comparing the HSC-R and LSC-R signatures, they also identified a set of 134 genes preferentially upregulated in LSCs versus HSCs that may represent targets for therapy. Next, to evaluate the prognostic utility of their gene expression profiles, the authors examined data from cohorts of AML patients treated according to the protocols of the Dutch–Belgian Hematology–Oncology Cooperative group, for which gene expression and linked clinical outcome data is available. They found that expression of either the LSC-R or HSC-R profiles correlated with poor prognosis and worse clinical outcomes. Subsequent studies investigated AML patients that presented with normal cytogenetics. In this cohort, the LSC-R signature was examined using a multivariate analysis with standard risk factors (Flt3, NPM1, CEBPα mutation status, age, and white blood cell counts). Again, the LSC-R profile was independently able to identify subsets of patients at increased risk of relapse and poor overall survival. Predictive and prognostic biomarkers offer the potential to personalize cancer medicine, and the growth in this research area in the last decade has been tremendous. In AML, the use of molecular features of the disease to assign treatment is part of everyday practice. As novel mutations and recurrent chromosomal abnormalities are identified, each undergoes rigorous evaluation and validation using multiple tissue and data repositories. Thus, a next step for the signatures defined by Eppert et al. will be independent validation by other groups, followed by clinical trials in which treatment is assigned based the individual patient's stemness signature so as to determine the clinical impact of this approach. As yet, gene profiling in AML has not reached the status it has for other malignancies, such as breast cancer and colorectal cancer. The LSC signature described by Eppert et al. will hopefully provide a clinical tool that will allow the patients at highest risk to be identified at initial diagnosis. Such patients can then be treated with more aggressive regimens that may provide improved outcomes. Finally, the findings from this study support the fact that primary tumor populations demonstrate clear biological heterogeneity. Moreover, both molecular (gene expression) and functional (xenograft transplantation) assays link current definitions of stemness with clinical behavior and prognosis for leukemia patients. Going forward, it remains an intriguing challenge to elucidate how various aspects of stem cell biology are related to clinical outcome. Two clues come from previous studies that demonstrate that normal and leukemia stem cells naturally reside in a more quiescent cell cycle state and also express relatively high levels of proteins that function as drug efflux pumps (ABCG2 and related multidrug resistance proteins) (reviewed in Jordan and Guzman, 2004Jordan C.T. Guzman M.L. Oncogene. 2004; 23: 7178-7187Crossref PubMed Scopus (114) Google Scholar). It is possible that programs controlling basic stem cell properties are intimately linked to either cycle status or expression of ABC family transporters, thereby explaining why stemness is associated with resistance to treatment with conventional chemotherapy. If true, then the key objectives in creating improved therapeutic regimens should be to identify agents that are (1) not dependent on cycle status, (2) not substrates for efflux pump proteins, and (3) have preferentially increased activity in primitive leukemic cell types. The data reported by Eppert et al. provide exciting opportunities to design new therapies that fulfill these criteria." @default.
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• W2000996935 title "Leukemia Stemness Signatures Step toward the Clinic" @default.
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