Fluorescence in situ hybridization BCR/ABL fusion signal rate in interphase nuclei of healthy volunteer donors: a test study for establishing false positive rate

Applications of Fluorescence In Situ Hybridization Technology in Malignancies

The molecular characterization of nonrandom recurrent cytogenetic abnormalities has identified numerous disease-related genes involved in hematologic and lymphoid malignancies. Cytogenetic analysis has become essential for disease diagnosis, classification, prognostic stratification, and treatment guidance. Fluorescence in situ hybridization (FISH) has greatly enhanced the field and enabled a more precise determination of the presence and frequency of genetic abnormalities. The advantages of FISH compared to standard cytogenetic analysis are that FISH can be used to identify genetic changes that are too small to be detected under a microscope, does not require cell culture, and can be applied directly on fresh or paraffin-embedded tissues for rapid evaluation of interphase nuclei. The application of FISH with a variety of chromosome-specific DNA probes helps to further define molecular subclasses and cytogenetic risk categories for patients with particular hematologic malignancies. FISH analysis is useful in identifying genetic abnormalities undetectable by conventional chromosomal analysis and monitoring residual disease during treatment and follow-up. Therefore, FISH has become an indispensable tool in the management of hematologic malignancies.

State-of-the-art FISHing: automated analysis of cytogenetic aberrations in interphase nuclei

Interphase fluorescence in situ hybridization (i-FISH) is a powerful tool for visualizing various molecular targets in non-dividing cells. Manual scoring of i-FISH signals is a labor intensive, time-consuming, and error-prone process liable to subjective interpretation. Automated evaluation of signal patterns provides the opportunity to overcome these difficulties. The first report on automated i-FISH analysis has been published 20 years ago and since then several applications have been introduced in the fields of oncology, and prenatal and fertility screening. In this article, we provide an insight into the automated i-FISH analysis including its course, brief history, clinical applications, and advantages and challenges. The lack of guidelines for describing new automated i-FISH methods hampers the precise comparison of performance of various applications published, thus, we make a proposal for a panel of parameters essential to introduce and standardize new applications and reproduce previously described technologies.

Automated detection of residual cells after sex-mismatched stem-cell transplantation - evidence for presence of disease-marker negative residual cells

BACKGROUND

A new chimerism analysis based on automated interphase fluorescence in situ hybridization (FISH) evaluation was established to detect residual cells after allogene sex-mismatched bone marrow or blood stem-cell transplantation.Cells of 58 patients were characterized as disease-associated due to presence of a bcr/abl-gene-fusion or a trisomy 8 and/or a simultaneous hybridization of gonosome-specific centromeric probes. The automatic slide scanning platform Metafer with its module MetaCyte was used to analyse 3,000 cells per sample.

RESULTS

Overall 454 assays of 58 patients were analyzed. 13 of 58 patients showed residual recipient cells at one stage of more than 4% and 12 of 58 showed residual recipient cells less than 4%, respectively. As to be expected, patients of the latter group were associated with a higher survival rate (48 vs. 34 month). In only two of seven patients with disease-marker positive residual cells between 0.1-1.3% a relapse was observed. Besides, disease-marker negative residual cells were found in two patients without relapse at a rate of 2.8% and 3.3%, respectively.

CONCLUSION

The definite origin and meaning of disease-marker negative residual cells is still unclear. Overall, with the presented automatic chimerism analysis of interphase FISH slides, a sensitive method for detection of disease-marker positive residual cells is on hand.

Automated detection of residual leukemic cells by consecutive immunolabeling for CD10 and fluorescence in situ hybridization for ETV6/RUNX1 rearrangement in childhood acute lymphoblastic leukemia

Among the various methods available for analyzing minimal residual disease, a new procedure for the cell-based approaches using consecutive phenotypic and genotypic analysis as revealed by immunofluorescent labeling and subsequent fluorescent in situ hybridization (FISH) has been developed. We are introducing a fluorescent microscopy-based technique by which not only cellular targets and immunological marker positivity, but also the FISH pattern was identified by automated scanning. For the latter one translocation-specific FISH pattern recognition was accomplished by using an automated scanning mode for the 3D determination of valid distances between FISH signals, to define the cutoff value for the shortest green-red spot distance differentiating positive cells from negative ones. The procedure was tested with CD10(+) acute lymphoblastic leukemia cell line harboring the t(12;21)(p13;q22) resulting in the ETV6/RUNX1 rearrangement (formerly TEL/AML1), as well as peripheral blood lymphocytes of healthy individuals. Using the combined, automated method, a sensitivity of 98.67% and a specificity of 99.97% were obtained. The mean false positivity + 2 standard deviations cutoff level (0.09%) allows detection of leukemic cells with high accuracy, even a bit below the tumor load dilution of 10(-3), a value reported to be critical in clinical decision making.

Pitfalls in the detection of t(11;22) translocation by fluorescence in situ hybridization and RT-PCR: a single-blinded study

The t(11;22) translocation is a diagnostic hallmark of various small round-cell tumors. This study correlates the performance of fluorescence in situ hybridization (FISH) and reverse transcription polymerase chain reaction (RT-PCR) in the detection of this translocation analyzing paraffin-embedded tissue specimens. As negative control samples, 10 cases of normal colon mucosa and 10 cases of colon carcinoma tissue were analyzed by FISH to determine a valid cutoff value for the diagnosis of a t(11;22) translocation. The mean number of false-positive nuclei differed significantly between disomic and polysomic control group cases (P=0.002). Therefore, the cutoff value was determined considering the pitfall polysomy. The analysis group consisted of 20 cases from the University of Düsseldorf and 10 cases from the University of Bonn. These cases were analyzed using PCR (Düsseldorf) and FISH (Bonn) using a single-blinded approach. Twenty-two cases (73.3%) were concordant in both methods. Five cases (16.7%) were discrepant, showing a positive result in FISH whereas PCR was negative. Three cases (10.0%) were analyzed by FISH, and PCR failed for nonoptimized tissue preparation. In conclusion, the detection of t(11;22) translocation is critically dependent on a thoroughly defined cutoff value for FISH and on appropriate tissue preparation for both methods. We recommend FISH as a sensitive screening tool in the detection of t(11;22) followed by subsequent PCR amplification of the specific chimeric transcript.

Automated fluorescent in situ hybridization (FISH) analysis of t(9;22)(q34;q11) in interphase nuclei

BACKGROUND

For chronic myeloid leukemia, the FISH detection of t(9;22)(q34;q11) in interphase nuclei of peripheral leukocytes is an alternative method to bone marrow karyotyping for monitoring treatment. With automation, several drawbacks of manual analysis may be circumvented. In this article, the capabilities of a commercially available automated image acquisition and analysis system were determined by detecting t(9;22)(q34;q11) in interphase nuclei of peripheral leukocytes.

METHODS

Three peripheral blood samples of normal adults, 21 samples of CML patients, and one sample of a t(9;22)(q34;q11) positive cell-line were used.

RESULTS

Single nuclei with correctly detected signals amounted to 99.6% of nuclei analyzed after exclusion of overlapping nuclei and nuclei with incorrect signal detection. A cut-off value of 0.84 mum was defined to discriminate between translocation positive and negative nuclei based on the shortest distance between signals. Using this value, the false positive rate of the automated analysis for negative samples was 7.0%, whereas that of the manual analysis was 5.8%. Automated and manual results showed strong correlation (R(2) = 0.985), the mean difference of results was only 3.7%.

CONCLUSIONS

A reliable and objective automated analysis of large numbers of cells is possible, avoiding interobserver variability and producing statistically more accurate results than manual evaluation.