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Quality assessment and correlation of microsatellite instability and immunohistochemical markers among population- and clinic-based colorectal tumors results from the Colon Cancer Family Registry.

ABSTRACT: The detection of defective mismatch repair (MMR), as assessed by the presence of tumor microsatellite instability (MSI) and/or loss of MMR protein expression by IHC, has been useful for risk assessment, prognosis, and prediction of treatment in patients with colorectal cancer. We analyzed tumors for the presence of defective MMR from 5927 Colorectal Cancer Family Registry patients recruited at six international consortium sites. We evaluated the appropriate percentage instability cutoff used to distinguish the three MSI phenotypes [ie, stable (MSS), low instability (MSI-L), and high instability (MSI-H)]; the sensitivity, specificity, and performance characteristics of individual markers; and the concordance between MSI and IHC phenotypes. Guided by the results of the IHC testing, our findings indicate that the distinction between an MSI-H phenotype from a low-instability or MSS phenotype can best be accomplished by using a cutoff of 30% or greater of the markers showing instability. The sensitivity and specificity of the mononucleotide markers were higher than those of the dinucleotide markers. Specifically, BAT26 and BAT25 had the highest sensitivity (94%) and specificity (98%), and the use of mononucleotide markers alone identified 97% of the MSI-H cases correctly. As expected, the presence of MSI-H correlated with an older age of diagnosis, the presence of tumor in the proximal colon, and female sex.

SUBMITTER: Cicek MS

PROVIDER: S-EPMC3077724 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

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Quality assessment and correlation of microsatellite instability and immunohistochemical markers among population- and clinic-based colorectal tumors results from the Colon Cancer Family Registry.

Cicek Mine S MS Lindor Noralane M NM Gallinger Steven S Bapat Bharati B Hopper John L JL Jenkins Mark A MA Young Joanne J Buchanan Daniel D Walsh Michael D MD Le Marchand Loic L Burnett Terrilea T Newcomb Polly A PA Grady William M WM Haile Robert W RW Casey Graham G Plummer Sarah J SJ Krumroy Lisa A LA Baron John A JA Thibodeau Stephen N SN

The Journal of molecular diagnostics : JMD 20110501 3

The detection of defective mismatch repair (MMR), as assessed by the presence of tumor microsatellite instability (MSI) and/or loss of MMR protein expression by IHC, has been useful for risk assessment, prognosis, and prediction of treatment in patients with colorectal cancer. We analyzed tumors for the presence of defective MMR from 5927 Colorectal Cancer Family Registry patients recruited at six international consortium sites. We evaluated the appropriate percentage instability cutoff used to ...[more]

PMID: 21497289

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Project description:Background & aimsMicrosatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and less expensively than molecular assays. However, clinical application of this technology requires high performance and multisite validation, which have not yet been performed.MethodsWe collected H&E-stained slides and findings from molecular analyses for MSI and dMMR from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (N = 6406 specimens) and validated in an external cohort (n = 771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC).ResultsThe deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound, 0.91; upper bound, 0.93) and an AUPRC of 0.63 (range, 0.59-0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC of 0.95 (range, 0.92-0.96) without image preprocessing and an AUROC of 0.96 (range, 0.93-0.98) after color normalization.ConclusionsWe developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using H&E-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens.

Dataset Information

Quality assessment and correlation of microsatellite instability and immunohistochemical markers among population- and clinic-based colorectal tumors results from the Colon Cancer Family Registry.

Publications

Quality assessment and correlation of microsatellite instability and immunohistochemical markers among population- and clinic-based colorectal tumors results from the Colon Cancer Family Registry.

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