The Relevance of External Quality Assessment for Molecular Testing for ALK Positive Non-Small Cell Lung Cancer: Results from Two Pilot Rounds Show Room for Optimization

Background and Purpose

Molecular profiling should be performed on all advanced non-small cell lung cancer with non-squamous histology to allow treatment selection. Currently, this should include EGFR mutation testing and testing for ALK rearrangements. ROS1 is another emerging target. ALK rearrangement status is a critical biomarker to predict response to tyrosine kinase inhibitors such as crizotinib. To promote high quality testing in non-small cell lung cancer, the European Society of Pathology has introduced an external quality assessment scheme. This article summarizes the results of the first two pilot rounds organized in 2012–2013.

Materials and Methods

Tissue microarray slides consisting of cell-lines and resection specimens were distributed with the request for routine ALK testing using IHC or FISH. Participation in ALK FISH testing included the interpretation of four digital FISH images.

Results

Data from 173 different laboratories was obtained. Results demonstrate decreased error rates in the second round for both ALK FISH and ALK IHC, although the error rates were still high and the need for external quality assessment in laboratories performing ALK testing is evident. Error rates obtained by FISH were lower than by IHC. The lowest error rates were observed for the interpretation of digital FISH images.

Conclusion

There was a large variety in FISH enumeration practices. Based on the results from this study, recommendations for the methodology, analysis, interpretation and result reporting were issued. External quality assessment is a crucial element to improve the quality of molecular testing.     

The influence of variations in the measuring procedure on quantitative nuclear image features in histologic sections of lung tissue

The qualitative and quantitative features of cell nuclei in tissue sections play an important role in diagnostic histopathology; variations in staining intensity and measuring procedures may interfere with their proper evaluation. To identify nuclear features that are relatively insensitive to these technical variables, the influence of critical steps in a scanning-stage densitometer measuring system was studied on 87 quantitative nuclear image (QNI) features in histologic sections of lung tissue. The influences of the following measuring variations were evaluated: interactive segmentation (with and without median filtering; with and without 5% uniform distributed noise added); scanning (with and without median filtering); calibration of the photomultiplier (different background localizations and different intensity levels); and time. In addition, the influence of artificially changed intensity variations was investigated. The results showed that, while the coefficient of variation (CV) induced by variations in the measuring system was usually low (below 10%), for some QNI features the CV can be high (up to 216%). The influence of artificial variations in intensity was restricted: only a minority of the QNI features showed a significant difference. Of the 87 QNI features, 35 had a CV of less than 10%, and 25 of these were significantly correlated with each other. Thus, only ten uncorrelated, low-CV QNI features remained; these belonged to all of the different QNI feature categories used. These features may be diagnostically important since they may best describe the morphologic properties of the nuclei. The results of this study should help in selecting quantitative nuclear image features that are less sensitive to variations in the measuring procedure and staining intensity.     

The effect of sampling on quantitative nuclear image features in histologic sections of lung carcinomas

A feasibility study showed that quantitative nuclear image (QNI) analysis, in which the morphology of the nucleus is described by a number of mathematical parameters, can be used to make the therapeutically and prognostically important distinction between small cell lung carcinoma (SCLC) and non-SCLC, which can be difficult to make with subjective histologic typing. In the present study, the effects of sample size and sample site on the QNI features were investigated. For all sample sites in a given tumor, comparison was made between the histologic classification, the ultrastructural findings and the classification based on the QNI features. Using a running mean, it was found that a sample size of 25 nuclei is sufficiently large. Histologic and quantitative classifications of samples from different sites of the same tumors were in agreement with regard to the separation of SCLC and non-SCLC in 19 of 20 sections. In the case in which disagreement occurred in one section, the ultrastructural findings supported the quantitative classification. These data indicate that sampling from different sites has no essential influence on the QNI classification of lung carcinomas.