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Cytogenetic follow-up by karyotyping and fluorescence in situ hybridization: implications for monitoring patients with MDS
By Dross at 2010-11-30 02:12
Cytogenetic follow-up by karyotyping and fluorescence in situ hybridization: implications for monitoring patients with MDS

 In patients with low and intermediate risk myelodysplastic syndrome and deletion 5q (del(5q)) treated with lenalidomide, monitoring of cytogenetic response is mandatory, since patients without cytogenetic response have a significantly increased risk of progression. Therefore, we have reviewed cytogenetic data of 302 patients. Patients were analyzed by karyotyping and fluorescence in situ hybridization. In 85 patients, del(5q) was only detected by karyotyping. In 8 patients undergoing karyotypic evolution, the del(5q) and additional chromosomal aberrations were only detected by karyotyping. In 3 patients, del(5q) was only detected by fluorescence in situ hybridization, but not by karyotyping due to a low number of metaphases. Karyotyping was significantly more sensitive than fluorescence in situ hybridization in detecting the del(5q) clone. In conclusion, to optimize therapy control of myelodysplastic syndrome patients with del(5q) treated with lenalidomide and to identify cytogenetic non-response or progression as early as possible, fluorescence in situ hybridization alone is inadequate for evaluation. Karyotyping must be performed to optimally evaluate response.(clinicaltrials.gov identifier: NCT01099267 and NCT00179621).

 

http://www.haematologica.org/cgi/pmidlookup?view=long&pmid=21109690 

 



2 comments | 1846 reads

by gdpawel on Wed, 2010-12-08 05:37
The cytogenetic assay as a measure of genetic instability induced by genotoxic agents

Human genetic integrity is compromised by the intense industrial activity, which emphasizes the importance to determine an "acceptable" genetic damage level and to carry out routine genotoxicity assays in the populations at risk. Micronuclei are cytoplasmatic bodies of nuclear origin which correspond to genetic material that is not correctly incorporated in the daughter cells in the cellular division; they reflect the existence of chromosomal aberrations and are originated by chromosomal breaks, replication errors followed by cellular division of the DNA and/or exposure to genotoxic agents. There are several factors able to modify the number of micronuclei present in a given cell, among them are age, gender, vitamins, medical treatments, daily exposure to genotoxic agents, etc. The cytogenetic assay for the detection of micronuclei (CBMN: cytokinesis-block micronucleus) is based on the use of a chemical agent, cytochalasin-B, which is able to block cytocinesis but allowing the nuclear division, therefore yielding binucleated and monodivided cells. The micronuclei scoring is performed on 1000 binucleated cells and the starting sample may vary, although most studies are performed on peripheral blood lymphocytes. The micronuclei assay is considered a practical, universally validated and technically feasible protocol which is useful to evaluate the genetic instability induced by genotoxic agents.

Lymphoid aggregates (LA) are a common finding in bone marrow biopsies but little is known about their clinical implications and biological significance. We found LA in 51/206 patients with myelodysplastic syndromes (MDS). There was no correlation with age, disease progression or overall survival. The group with LA had lower hemoglobin values (P=0.03), and was associated with an increase in reticulin fibres (P=0.01). Although they were more frequent in RAEB, this did not reach statistical significance. Most LA had a benign morphology and showed CD20 expression in three distinct patterns: central, perinodular or diffuse. No evidence of an association with lymphoproliferative disease was observed. LA probably represent an ongoing immune stimulation and are probably related to an altered bone marrow microenvironment, with no impact on prognosis.

by gdpawel on Thu, 2012-03-15 06:23
The concept of "statistical significance" is so difficult to understand that misunderstandings are forgivable, suggests Donald Berry, a biostatistician at the University of Texas M.D. Anderson Cancer Center. Carl Bialik of the WSJ asked several statisticians to offer definitions of "statistical significance."

Shane Reese had the briefest one, tailored for a clinical trial for a drug: “It is unlikely that chance alone could have produced the improvement shown in our clinical trial. Because it seems unlikely that chance produced the improvements, we logically conclude that the improvement is due to the drug.” Reese and other statisticians noted that this definition is backwards: It is based on assuming there is no link, then finding the probability that chance alone could have produced the experimental results seen.

Reese and Brad Carlin, who also offered a definition, suggest that Bayesian statistics are a better alternative, because they tackle the probability that the hypothesis is true head-on, and incorporate prior knowledge about the variables involved.

There are other problems with "statistical significance." It can be ill-suited to cases where it is unclear if all data is being collected, such as with the reporting of adverse events experienced by users of a drug that is past the clinical-trial stage, or never had to go through clinical trials, and is now on the market. In such a situation, “you have to make a lot of assumptions in order to do any statistical test, and all of those are questionable,” said Susan Ellenberg, a biostatistician at the University of Pennsylvania’s medical school.

“Every statistical test relies on half a dozen assumptions,” echoed Aris Spanos, an economist at Virginia Tech. “Before you use that test, you have to check your assumptions.”

Spanos wishes the Supreme Court had gone further in its recent ruling, in which it determined that a lack of statistical significance didn’t always provide drug companies with enough cover to avoid disclosing reports of adverse events from users of their drugs. Spanos would have liked to see more guidance for how to proceed without relying strictly on statistical significance. “It was a move in the right direction but then you open the system to different kinds of abuses,” Spanos said.

The U.S. FDA also doesn’t use such a black-and-white rule. In January the FDA warned women who have gotten breast implants or might get breast implants, because of an elevated risk of the rare cancer anaplastic large-cell lymphoma. The FDA did so even though the link wasn’t statistically significant, in part because the agency reasoned that perhaps not all such incidents were reported. “It underscores the importance of not solely relying on a statistical test to tell you there is a public-health issue,” said William Maisel, the chief scientist for the agency’s Center for Devices and Radiological Health.

There also are cases where seemingly statistically significant results aren’t, statisticians say. For example, a very large sample size reduces the effects of statistical noise, so it can yield very high levels of significance for fairly minor relationships, or roughly speaking, a large degree of confidence in the existence of a very small effect.

Checking for lots of potential effects can also lead to results that appear to be statistically significant, but aren’t. “In the early days of clinical trials, it wasn’t unusual for people to keep looking at data as they go along,” said Ellenberg. “It was a fishing expedition, completely subverting the whole notion of chance findings.”

Also, a "statistically significant" effect may not matter much in practice. “Statistics and value judgment belong to different domains,” Siu L. Chow, professor emeritus in psychology at the University of Regina in Saskatchewan, wrote in a written response. “It follows that statistical decision and assessment of substantive impact have their own respective metrics. Hence, it is incorrect to use "statistical significance" or any other statistical indices (e.g., effect size) to index real-life importance.”

Stephen Ziliak, an economist at Roosevelt University in Chicago, and co-author of the 2008 book “The Cult of Statistical Significance” with Deirdre McCloskey, an economist at the University of Illinois at Chicago, said he would like to see large effect sizes reported even when they are not statistically significant. Researchers “probably ought to go ahead and report what happened anyway,” Ziliak said. “There’s probably a lot of stuff out there that didn’t see the light of day.”

Stephen Stigler, a statistician at the University of Chicago, agrees with the general premise that “you can have a real effect which is nonetheless trivial in the practical sense.” He doesn’t think this is widely misunderstood, though: “I don’t think in science we generally sanction the unequivocal acceptance of significance tests.”

Ziliak disagrees, saying in his book: “It is passionate in the sense that we do reveal anger. We had collectively been working on this issue in a calm fashion for 45 years. We deserved to open up the conversation a little more widely in this way.”

[url]http://blogs.wsj.com/numbersguy/a-statistical-test-gets-its-closeup-1050/

For an amusing take on statistical significance.

[url]http://xkcd.com/882/

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