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95.13948 Rev A ∙ 04/2016

Routine integration of fully automated image analysis of Kreatech CLL FISH Probes, using the CytoVision GSL Scanning System(Fully automated FISH Analysis for B-cell Chronic Lymphocytic Leukemia: a faster alternative to manual double scoring)

Leica Biosystems White Paper Series Routine integration of fully automated image analysis of Kreatech CLL FISH Probes, using the Cytovision GSL Scanning System.

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Executive Summary

The automation of FISH slide scoring in a high throughput fashion has long been a goal for cytogenetic analysis. The development of CytoVision automated scanning and image analysis has enabled realization of this aspiration. In the current study, automation was utilized across seven assays, in combination with a CLL menu of Kreatech FISH probes. The results demonstrate that the combination of Kreatech FISH probes with a fully automated, unsupervised analysis can successfully be used to classify patient samples according to known CLL-associated genomic abnormalities. The overall comparative analysis per CLL probe set, between manually scored slides and slides analyzed using automation and unsupervised analysis, showed at least 94% concordance, which increased to 100% following subsequent review. Overall, the hands-on time was drastically reduced by 88% (48 hr 45 min) and a permanent record for each sample automatically created. The full automation of the image analysis process using the CytoVision GSL-120 scanning system demonstrates the versatility and effectiveness of the system to be easily integrated into high throughput, routine FISH analysis.


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Summary

B-cell chronic lymphocytic leukemia (CLL) is the most common leukemia in adults, with a median survival of ~9 years (1). The disease is characterized by a highly variable clinical course.

Fluorescence in situ hybridization (FISH) has been shown to detect genomic abnormalities in over 80% of CLL cases (2, 3). A menu of seven specific molecular biomarkers detectable using FISH assays has been established within the Leica Biosystems’ repeat-free Kreatech FISH portfolio to predict clinical outcomes per patient. These seven DNA FISH probes have been selected to detect prevalent genetic aberrations found in B-cell CLL with known diagnostic value.

The most common recurrent chromosomal abnormalities in CLL include deletion of 13q14 in 40 - 55% of cases, trisomy 12 in 16 - 20% of cases and deletion of 11q22 and deletion of 17p13 in 14 - 20% and 7 - 11% of cases respectively (4, 5, and 6). These genomic aberrations are used to group patients into the Döhner categories, providing prognostic information on each patient subgroup (7, 8, 9, 10). Five prognostic categories have been defined in a hierarchical model. Those with 17p13 deletions show the worst prognosis, followed by those with 11q22 deletions. Patients with trisomy 12 and those with normal karyotypes show intermediate survival. Patients with 13q14 deletions as the sole abnormality had the longest estimated survival times (7, 8).

The automated analysis of common FISH tests in routine cytogenetic laboratories has many potential benefits. Obvious benefits include a more efficient workflow, freeing up valuable laboratory staff time and increased consistency of scoring. In addition, the ability to work more ergonomically and without the need for a darkroom would be attractive for many laboratories and personnel. Moving the analysis away from manual microscopes also has the potential to lower costs, and utilizing on the screen analysis could simplify staff training. Automated routine FISH analysis requires slide preparation to be of a consistent high quality. At the Leica Biosystems’ Amsterdam site a range of high-quality FISH probes are manufactured for commercial sale. During manufacture, the CytoVision GSL-120 platform is used as part of the quality control process.

In this study we investigated the use of CytoVision GSL-120 for a set of common genomic aberrations associated with CLL, compared with typical manual dual scoring in order to investigate the potential for use of such analysis in a routine laboratory workflow.

This study was designed and conducted to compare fully automated scanning and unsupervised image analysis of patient samples versus manual signal enumeration per probe set within the CLL menu of Kreatech FISH probes.


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Summary Continued

Two analysis methods evaluated the performance of Kreatech FISH probe sets designed to detect the following genetic aberrations that are features of CLL:

• Deletions of the target regions of TP53 (17p13) and/or ATM (11q22)

• Deletions of DLEU1 (13q14) in combination with probes to detect a gain of centromere 12 (D12Z3) and a control probe for region (13q34)

• Translocation of CCND1 (11q13) / IgH (14q32) (specific for mantle cell lymphoma; plasma cell lymphoma)

In total, 34 CLL patient samples were hybridized using the CLL Kreatech FISH probes and enumerated manually by at least two cytogeneticists before submitting the same slides to fully automated scanning and image analysis using the CytoVision GSL-120 scanning and analysis system. Upon comparative analysis of all results we demonstrated that:

• CytoVision GSL-120 scanning and analysis software can be successfully used for the analysis of archived (sub-optimal) patient samples hybridized using all Kreatech FISH probes within the CLL menu

• We demonstrate a concordance of results between CytoVision GSL-120 and manually scored slides of 100 % for the menu of CLL FISH probes

• Slide scanning and image analysis was fully automated and unsupervised, without operator intervention outside the working hours of the laboratory staff

• In only three cases, a manual re-analysis of the captured frames of borderline false positive cases was warranted, resulting in an increased concordance to the manual results of 100% (two of the manual cases required a third reader because of one false positive and one false negative case)

• Algorithms for CLL-specific genetic aberrations can easily be established and demonstrate efficient and reliable image analysis, simultaneously creating a permanent record for each case with the option to remotely review cases

• Fully automated FISH analysis of CLL FISH probes using the CytoVision GSL-120 reduced hands-on time for laboratory personnel by 88% (48hrs 45min)

• Implementation of automated CLL FISH analysis as a total, or partial replacement to dual manual review could significantly reduce time to results


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Method

Repeat-free (RF) DNA FISH probes

The probes were constructed using the repeat-free technology that is based on subtractive hybridization, which specifically removes all repetitive elements from the probe, which are dispersed throughout the target area of interest. Eliminating these repeat sequences leads to more specific binding kinetics and makes the need for blocking DNA obsolete. This results in brighter signals and reduced background noise.

CLL menu of probes

The Kreatech FISH probes that are part of the CLL menu are listed with associated genomic localization, fluorescent label, type of detection and anticipated signal pattern in Table 1.

Table 1: Kreatech CLL DNA FISH probes used within the studyProbe Name Label Type of

Detection Sample Type Normal Pattern

Common CLL Pattern

DLEU1 (13q14) / SE12 (D12Z3) / 13q34 DLEU1 / SE 12 / 13q34

13q14 RED

D12Z3 GREEN

13q34 BLUE

Copy Number Variation

Fixed Cells (CLL / leukemia)

2 RED 1 RED

2 GREEN

2 GREEN 2 BLUE

Or

2 BLUE2 RED

3 GREEN

2 BLUE

TP53 (17p13) / ATM (11q22) TP53 / ATM

17p13 RED

11q22 GREENDeletion Fixed Cells

(CLL / leukemia)

2 RED 1 RED

2 GREEN

2 GREENOr

2 RED

1 GREEN

CCND1 / IGH t(11;14) CCND1 / IGH t(11;14)

CCND1 GREEN

IGH REDTranslocation Fixed Cells

(CLL / MCL/PCL)

2 RED MCL

1 RED

2 GREEN1 GREEN

2 YELLOW

Or

PCL

3 RED

2 GREEN


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CLL patient samples

For this study, archival cell preparations that had been stored at -20°C for longer than three years were utilized.

In total, 34 CLL patient-cultured lymphocyte samples containing eight normal cell preparations were part of this analysis. Two of the samples contained insufficient numbers of cells and were eliminated from our study. For one case the FISH assay using the DLEU1/SE12/13q34 probe set failed (no signal). The data for the other probe sets of this case was collected and considered in our comparison. 16 out of the 32 final samples (50%) showed a single abnormality, 8 out of the 32 final samples (25%) showed 2 abnormalities. The most common aberration within our sample pool is the 13q deletion. The second and third most common aberrations are trisomy 12 and 17p deletion, respectively. The CCND1/IGH translocation represents the least common aberration.

Table 2 shows a summary of the final set of samples used for the current study. The genotyping represents the clinical manual scoring.

Table 2: Final CLL patient samples included in this study

Genotype Number of Cases * CLL Prognosis**

Normal 8 intermediate

13q Deletion 14 favorable

11q Deletion 4 poor

12 Trisomy 7 intermediate

17p Deletion 4 poor

CCND1 / IGH translocation

1 (mantle cell lymphoma)

IGH translocation 2 (plasma cell lymphoma)

* based on manual signal enumeration of each probe after review by two cytogeneticists

** based on Döhner classification of CLL prognosis (4)

FISH assays

Fixative-refreshed samples were processed manually or using a ThermoBrite Elite (TBE) using a recommended automated FISH protocol for each probe set (Ref. Kreatech IFU “Man_KBI_Kreatech_FISH_probes_D7.0.pdf). The TBE was utilized specifically for the denaturation, hybridization and post-hybridization wash steps.

The probes were used in a Ready-to-Use (RTU) format. After the post-hybridization wash, each slide was dehydrated using an ethanol series and a DAPI counterstain applied. The processed slides were stored at room temperature in the dark.

FISH review

Each slide was manually reviewed by two independent cytogeneticists using a Leica DM5500B microscope fitted with appropriate filters. If the scores of the two reviewers did not show alignment a third reader was consulted.

After the manual analysis the slides were automatically scanned and analyzed using a Leica DM6000 CytoVision GSL-120. This was done in a fully automated and unsupervised fashion.

In total >100 informative nuclei were analyzed per probe per review (including CytoVision GSL-120).

As we were using seven different single FISH probes our final analysis utilized >221 data points.

For the automated scanning and image analysis a custom-defined classifier was created to specifically select cell nuclei (set to pick up medium and large round nuclei). The image analysis was based on assays created to identify classic abnormalities and common variants associated with each single probe part of the CLL probe menu (example: 2 Red 2 Green; 1 Red 3 Green; 0 Red 2 Green).


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Data Analysis

Analysis of manual scoring:

The results of the two readers were recorded and examined. If one reader had an abnormal signal pattern count at or below the normal cut-off value and the other reader had an abnormal signal pattern count above the normal cut-off value, a third reader scored 100 nuclei within the hybridization target. Of the three scores, the two scores closest to one another were combined to generate the count for 200 nuclei. If all three scores were equidistant, the median value was doubled and used as the score. The scores for each probe individually (total >200 nuclei) were then averaged and cut-offs applied as previously determined during probe validation. Based on these cut-off values each patient sample was grouped into positive and negative for each individual probe with its associated abnormality. This grouping for all probes represents the predicate result for each individual patient sample and was used for comparison with the data generated using automation.

Analysis of CytoVision GSL-120 scoring:

Each slide was automatically scanned and the signal pattern determined for each individual probe (total >100 nuclei). The percentage of aberrant nuclei per individual probe was determined using probe-specific assays created using CytoVision. To determine cut-offs for each probe using the CytoVision GSL-120 the mean percentage was determined for all negative samples (normal : 8) based on the manual scores (i.e manual score below cut-off value) and the cut-off per probe was then calculated as mean percentage value plus 2 x SD using Excel. All cases were then grouped into positive and negative for all individual probes based on these cut-off values. Generally, higher cut-off values for all probes were noticed when using the CytoVision GSL-120 as compared to the corresponding value for the manual analysis.

Comparison of CytoVision GSL-120 and manual results:

A side-by-side comparison of both results was conducted and the overall concordance between the positive and negative groups per probe was determined.


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Results

Manual analysis

In two cases, a third review of the slide was warranted because of a discrepancy between the scores of the initial two readers (case 20 for probe TP53; case 28 for probe 13q; Figure 1). The scores of these third reads were subsequently used, as described in the method section, to ascribe a final genetic aberration to a case. A representative image of the TP53 /ATM probe set using manual review is shown in Figure 3B.

The two cases requiring a third manual review, cases 20 and 28 (see below), showed a discrepancy among the first two readers. After taking the third reviews of these slides into account the score values decreased below threshold and both cases determined to represent a negative result for the utilized probes. As a result, the concordance between the manual scores for TP53 and 13q and the fully automated, unsupervised analysis of the same slides reached 100% (Figure 2A).

TP53 TP53 ATM ATM SE12 SE12 13q 13q t(11;14) t(11;14) t(14;x) t(14;x)

Case ID manual class GSL class manual

class GSL class manual class GSL class manual

class GSL class manual class GSL class manual

class GSL class

1 neg neg neg neg neg neg pos pos neg neg neg neg

2 pos pos neg neg neg neg pos pos neg neg neg neg

3 pos pos neg neg neg neg neg neg neg neg neg neg

4 neg neg neg pos pos pos neg neg neg neg neg neg

5 neg neg pos pos neg neg pos pos neg neg neg neg

6 pos pos neg neg pos pos neg neg

7 neg neg neg neg pos pos neg neg neg neg neg neg

8 neg neg neg neg neg neg pos pos neg neg pos pos




12 neg neg neg neg neg neg neg neg neg neg neg neg








20 pos neg pos pos neg neg pos pos neg neg neg neg

21 pos pos neg neg neg neg neg neg neg neg neg neg




25 neg neg neg neg neg pos neg neg neg neg neg neg



28 neg neg neg neg neg pos pos neg neg neg neg neg



31 neg neg neg neg pos pos neg neg neg neg pos pos


concordance 97% 97% 94% 97% 100% 100%

Figure 1: The raw data for the fully automated, unsupervised analysis of all results side-by-side (manual and CytoVision GSL-120 collected data) is shown. The concordance of these results for each identified CLL-associated genomic aberration using either manual signal enumeration for each probe set or using the CytoVision GSL-120 in combination with algorithms for probe patterns is shown below each probe. Green fields indicate a negative score for the genetic aberration. Red fields indicate a positive score for each genetic aberration. Overall, five scores out of 224 (2.2%) required additional review, indicated by a reduced color intensity.

1


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Automated image capture and unsupervised analysis

After the manual reviews all slides were loaded onto the CytoVision GSL-120 and scanned automatically. A classifier to specifically detect B-cell nuclei was set to select medium to large, very round nuclei. As described in the method section for the analysis of each individual probe, assays were created that identified classic probe-associated abnormalities. This analysis was performed unsupervised without selective training of the software or repeat-runs. Representative screenshots of such analysis for the TP53 / ATM probe set are shown in Figure 3A.

The primary results for each case were then compared directly to the case classifications obtained using manual signal enumeration per probe.

The comparison yielded three instances out of > 200 for which a manual review of the acquired images/frames was necessary. The initial score for the hybridization of case 4 using the ATM probe yielded a positive result, meaning deletion of the 11q region was scored above the calculated cut-off value for the CytoVision GSL-120 (0.8 % above cut-off). A review of the capture frames revealed scoring of poorly hybridized nuclei, which may explain the slightly increased percentage of one signal for the green part of this probe set (Figure 2B ; intermediate to poor group).

For another two cases, 25 and 28, the unsupervised result analysis using the CytoVision GSL-120 assay resulted in positive results for both, meaning a gain of centromere 12 was detected above the calculated threshold for each case (case 25 would have been assigned to the same prognostic group without re-analysis of the CytoVision GSL-120 results and case 28 stay within intermediate prognostic group). The CytoVision GSL-120 scores were above the threshold by 0.1% and 0.9% respectively. The frames for this particular probe were reviewed for these two cases manually. A tendency for the CytoVision GSL-120 to enumerate split-signals as two individual signals for the centromeric probe was noted. This appears to be a phenomenon associated with centromeric probes and was also seen during manual signal enumeration as the variability of these repeats among individuals is known to cause a low degree of dispersed signals. These results suggest that particularly for probes designed to hybridize to the centromere, the CytoVision GSL-120 needs to be trained and if the scores for any centromeric probe are close to the threshold value, the results are carefully checked.

To show that a manual re-analysis of the CytoVision GSL-120 scores does not generally result in the reclassification of a case, we chose two positive cases, one for ATM and one for SE12, and reviewed the frames of the CytoVision GSL-120. The overall percentage of the aberrant pattern was then compared to the original generated percentage. As shown in Figure 2C, this analysis did not result in a re-classification of the two cases. For the review of the ATM deletion for case 5 the total score of aberrant cells changed from 65.9% to 84.6%, remaining positive. Review of case 11 for the trisomy of centromere 12 resulted in 71.2% of aberrant nuclei compared to the original 66.0%, confirming the original classification (Figure 2C).

A side-by-side comparison of all raw data generated in our study is summarized in Figure 1. Overall, for all probes (including the 13q34 probe; data incorporated into the total 13q analysis) the concordance between the results of the manual signal analysis versus the fully automated, unsupervised analysis using the CytoVision GSL-120 is between 94% and 100%.


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TP53 / case 20 ATM / case 4 ATM / case 5

manual class GSL class manual class GSL class manual class GSL class

pos neg neg pos pos pos

12% 12.7% 65.9%

7% 6.8% 84.2%

6%

re-class neg neg re-class neg neg pos pos

13q / case 28 SE12 / case 25 SE12 / case 11

manual class GSL class manual class GSL class manual class GSL class

pos neg neg pos pos pos

12% 19.4% 66.0%

6% 3.5% 71.2%

1%

re-class neg neg re-class neg neg pos pos

SE12 / case 28

manual class GSL class

neg pos

20.2%

1.1%

re-class neg neg

Figure 2A: The original manually generated scores and the corresponding reanalyzed scores are shown for the slides that showed a discrepancy in our comparative study. For two cases the manual results needed to be reanalyzed using a third reader. The scores of each reader are listed below the manual classification for TP53 and 13q. Figure 2B: For three cases, the CytoVision GSL-120 generated scores had to be manually reviewed because of a classification discrepancy. In all three cases this resulted in a change of the scores below threshold and re-classification for ATM/case 4 and SE12 cases 25 and 28. Figure 2C: Two additional positive cases were manually reviewed (case 5 and 11) to show that a review of the CytoVision GSL-120 scores does not generally result in a re-classification of cases.

The unsupervised data was also analyzed in view of possible prognostic differences that would appear without any reanalysis of the data. This summary is shown Table 3. Overall, for two cases the prognostic grouping would change if the raw data (without review) had been used to determine such a prognosis.

Table 3: Comparison of Classification of Cases (before re-analysis of scores)Genotype Manual

analysisGSL analysis (unsupervised)

Non-concordant cases based on raw scores Associated prognostic change

Normal 7 6 GSL score for case 25

13q Deletion 15 14 Manual score for case 28 (3rd reader confirmed negative score)

Intermediate to favorable

11q Deletion 4 5 GSL score for case 4 (borderline; 0.8% above threshold)

Intermediate to poor

12 Trisomy 7 9 GSL score for case 25 and 28 (borderline ; 0.1% and 0.9% above threshold because of low number of split signals for SE12)

No change for both cases (intermediate)

17p Deletion 5 4 Manual score for case 20 (3rd reader confirmed negative score)

No change (poor)

CCND1 / IGH translocation 1 1

IGH translocation 2 2

2A 2B 2C


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Image acquisition and analysis

An advantage of image scanning and analysis using the CytoVision GSL-120 system is that image libraries specific for each case and probe set will automatically be generated, establishing a permanent record for each patient. An example screenshot is presented in Figure 3A. A frame-by-frame review of single nuclei is easily performed and the resulting percentage for each pre set signal pattern per probe is visible for further analysis and classification of each case. In Figure 3B a representative image is shown for the same probe set as in 3A using the DM5500B for manual reading of each slide. Appendix 1 shows additional representative images for the DLEU/SE12/13q34 and CCND1/IGH probe sets.

Figure 3A: Representative screenshot of CytoVision GSL-120 analysis, set up for the TP53/ATM probe set (negative case). The pie chart shows the scoring summary for this slide as percentages of the different signal pattern that were detected (negative case - refer to the green part of the chart depicting normal signal pattern). This is part of the permanent record automatically created during scanning and analysis using the CytoVision GSL-120. Figure 3B: Image of the same slide taken during the manual enumeration of the FISH results using the DM5500B.

2R2G

2R2G

2R2G

3A

3B


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Hands-on time comparison

The automated image capture and analysis using the CytoVision GSL-120 was performed either during normal work hours freeing up staff time, or during non-work hours (scanning of slides overnight). On average, to capture 100 informative nuclei per slide (multi-color probe sets/ DAPI / use of archival samples containing highly variable numbers of cells) took 35 minutes using fixed camera settings and the user-determined classifier in the current study. The number of frames captured using this setup varied between 35 and 187 because of the highly variable number of cells for each sample.

After refining our classifier, the time to capture and analyze each slide in the same manner as with the previous set-up took approximately 19 minutes per slide. On average 29 frames were captured for the same slides as with the previous setting.

In summary, this highlights the adaptability of the CytoVision GSL-120 to customer-defined settings, and easy integration into an existing workflow. In Table 4 a summary of the hands-on time for the overall scoring and analysis of the 102 slides as it relates to our present study is given, highlighting the drastically reduced direct hands-on time for laboratory staff. If integrated into a workflow that would utilize the CytoVision GSL-120 as a replacement for a second manual read, the hands-on time difference would be ~22 hours (Appendix 2).

Table 4: Manual Review of Slides Automated GSL-120 Review of Slides

Slide Number 102 slides 102 slides

Time / Slide 15 min 19 min

Total Time 51 hours review time (dual read) 32 hours - (+ 1 hour loading/ set-up)

3rd Review plus 2 x 15 min (third review) plus 3 x 15 min (review of results)

Data Collection creating a record of results ~2 hours

exporting data to file ~1 hours

Data Analysis data analysis / score calculations ~2 hours

data analysis / score calculation ~2 hours

Hands-on Time 55 hours 30 min 4 hours 45 min

Images /Record Created No Yes

Table 4: A side-by-side comparison of the hands-on time as calculated during our study is shown. A difference resulting in a reduction of ~50 hours hands-on time was observed. A notable advantage was the generation of a record and image library when using the CytoVision GSL-120, which enabled easy review of the same nuclei per case and allowed for review of the case remotely.

To demonstrate potential shorter Time to Results (TTR) we established a comparison model demonstrated in Appendix 2. This model shows how the automated workflow can be integrated in the clinical workflow, resulting in faster TTR.


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Conclusion

The high quality of Kreatech FISH probes used throughout our study resulted in successful FISH assays that delivered confident prognostic grouping for all patient samples.

Automated FISH signal enumeration and pattern analysis technology is rapidly advancing but its implementation into routine clinical reporting remains uncommon. One reason for the reluctance of laboratories to adopt this technology is the lack of clear guidelines on how to validate automated FISH analysis and the subsequent implementation into routine work.

A first key step towards implementation is validating that the automated FISH analysis is consistent with expert manual scoring. Most regulatory submissions compare automated analysis to manual scoring. Often this comparison is between supervised and reviewed results that are “cleaned up” by manual review of the image galleries and creation of an artificial comparison of the scoring methods. This time consuming step removes many of the potential efficacy gains, so it was interesting for us to postulate on a fully automated analysis without any manual review.

Our current comparative study using the CLL menu of Kreatech FISH probes, demonstrates that the fully automated and unsupervised analysis of hybridized slides from 32 different patient samples is highly concordant to manual interpretation with levels of concordance between 94% and 100%. After the additional review of three CytoVision GSL-120 scored cases and two manually scored cases we achieved a 100% concordance for all FISH probes within the CLL menu. These results highlight the reliability of the automated system in the prognostic classification of patient samples. All CLL patient samples were stored over three years and varied drastically in their cell numbers. Combining these suboptimal samples with the automated analysis of the CytoVision GSL-120 for FISH signal pattern recognition resulted in highly concordant prognostic classification to the manual analysis. Also, the overall hands-on time remained drastically lower with the CytoVision GSL-120 system, demonstrating the versatility of the system to score the highly variable nuclei numbers observed between patient samples.

During our study we recognized the value of generating a permanent record per patient case with an associated image/frame library that could potentially be integrated into an electronic medical record. More importantly, the primary as well as a re-analysis of each case (FISH probe) can be performed remotely, leaving the CytoVision GSL-120 free to process a next batch. Review of manual results is not possible by another person or by a physician not located in the laboratory which eliminates a quality control point that is present with the automated system.

A second question we tried to address was the implementation of the automated system into routine FISH workflow. We noted that a significantly reduced hands-on time was required to score all slides. The initial set-up of the CytoVision GSL-120 with a defined relevant classifier was easily achieved. Our results demonstrate that the CytoVision GSL-120 can be used unsupervised, and hence during the non-working hours of the lab staff. Potentially, this would enable the lab to expand the number of cases it handles and could be positioned as a state-of-the-art processor of patient samples, but also time is freed up to carefully study difficult cases to improve diagnostic confidence.

The results of our study also lead us to speculate that in the future an optimal workflow with reduced FISH assay time would allow a same day FISH assay, ready to be scanned overnight using automated image capture and scoring. For such an improved workflow we could envision the CytoVision GSL-120 to be an essential tool in the lab to drastically reduce TTR.


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References

1. Rai KR, Sawitsky A, Cronkite EP, et al. Clinical staging of chronic lymphocytic leukemia. Blood. 1975;46(2):219-34.

2. Shanafelt TD, Byrd JC, Call TG, et al. Narrative review: initial management of newly diagnosed, early-stage chronic lymphocytic leukemia. Ann Intern Med. 2006;145:435-7.

3. Reddy, KS. Chronic lymphocytic leukaemia (CLL). Atlas Genet Cytogenet Oncol Haematol. 2005; 9(3):238-40.

4. Méhes G. Chromosome abnormalities with prognostic impact in B-cell chronic lymphocytic leukemia. Pathol Oncol Res. 2005;11(4):205-10.

5 Ripollés L, Ortega M, Ortuño F, et al. Genetic abnormalities and clinical outcome in chronic lymphocytic leukemia. Cancer Genet Cytogenet. 2006;171(1):57-64.

6. Stilgenbauer S, Bullinger L, Lichter P, et al. Genetics of chronic lymphocytic leukemia: genomic aberrations and V(H) gene mutation status in pathogenesis and clinical course. Leukemia. 2002;16(6):993-1007.

7. Döhner H, Stilgenbauer S, Benner A, et al. Genomic aberrations and survival in chronic lymphocytic leukemia. N Eng J Med. 2000;343(26):1910-6.

8. Kröber A, Seiler T, Benner A, et al. V(H) mutation status, CD38 expression level, genomic aberrations, and survival in chronic lymphocytic leukemia. Blood. 2002;100(4):1410-1416.

9. Oscier DG, Gardiner AC, Mould SJ, et at. Multivariate analysis of prognostic factors in CLL: clinical stage, IGVH gene mutational status, and loss or mutation of the p53 gene are independent prognostic factors. Blood. 2002;100(4):1177-84.

10. Shanafelt TD, Witzig TE, Fink SR, et al. Prospective evaluation of clonal evolution during long-term follow-up of patients with untreated early-stage chronic lymphocytic leukemia. J Clin Oncol. 2006;24(28):4634-41.


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Appendix 1

Additional images of CytoVision GSL-120 scoring and images of manually generated images for the remaining probe sets (DLEU1/SE12/13q34; and CCND1/IGH)

Figure 4A: Representative screenshot from CytoVision GSL-120 analysis, set up for the DLEU1/SE12/13q34 triple color probe set (positive case). The chart shows the scoring summary for this slide as percentages of the different signal pattern (refer to red part of the chart depicting 13q deletion). Figure 4B: Representative image for the same slide using the DM5500B that was used for manual signal enumeration.

Figure 5A: Representative screenshot from CytoVision GSL-120 analysis, set up for the CCND1/IGH probe set (PCL positive case). The chart shows the scoring summary for this slide as percentages of the different signal pattern (refer to purple part of the chart depicting an IGH translocation pattern). Figure 5B: Representative image for the same slide using the DM5500B that was used for manual signal enumeration.

5A 5B

2R2G

3R2G

4A 4B

1R2G2B

1R2G2B

2R2G2B


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Appendix 2

Comparison of TTR using manual scoring vs. automated analysis (based on the presented 32 cases as an example)

In the first example, using the fully automated workflow for FISH assay scoring, the time to results is three days. At the end of the fifth day the clinical outcome is ready to be sent to the requesting physician.

In the second example the time to results is three days, when using the fully automated workflow for FISH-assay scoring. At the end of the third day the clinical outcome is ready to be sent to the requesting party.

The last example integrates manual scoring and automated scoring into a semi-automated workflow. The example depicts two reviewers simultaneously reviewing the slides manually, and reviewed slides being scanned and scored using the CytoVision GSL-120. Turnaround time is similar to the second example, with the advantage of having the same slides scored both manually and automatically.

Loading2hr

Review45min

Analysis2hr

Export 2hr

Semi-automatedWorkflow (ALT) Hands-on time

36hr 15minTTR3 days

Review45min

Export 2hr

Results 2hr

Analysis2hr

Time To Results (TTR) Comparison

Reviewer 1

Reviewer 2

CytoVision GSL-120 Auto scan & score 32hrs

Reviewer 1

Loading1hr


Loading1hr


Reviewer 2

Fully AutomatedWorkflow Hands-on time

6hr 45minTTR3 days

Results 2hr

Analysis2hr

Day 1 Day 2 Day 3 Day 4 Day 5Manual

Workflow Hands-on time55hr 30minTTR5 days

Reviewer 30.5hr

Assumptions:

• 8.5hr work day

• 1hr for breaks - i.e. 7.5hrs per day work

• 6hr slide review per day

• Reviewers 1&2 work in parallel

• Slide loading / CytoVision set-up 2hr

• In semi-automatic mode, would not need 30 mins for review 3 as per manual workflow

• In semi-automatic mode, would scan 60 slides on days 1 and 2

routine integration of fully automated image analysis of ...€¦ · after the manual analysis the...

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