Dataset Information

Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium.

ABSTRACT: Breast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large-scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65-70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa?=?0.76), followed by human epidermal growth factor receptor 2 (Kappa?=?0.69) and progesterone receptor (Kappa?=?0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose-response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96-98%), but yielded many false positives (positive predictive value?=?30-32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large-scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker-specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.

SUBMITTER: Howat WJ

PROVIDER: S-EPMC4858117 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Publications

Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium.

Howat William J WJ Blows Fiona M FM Provenzano Elena E Brook Mark N MN Morris Lorna L Gazinska Patrycja P Johnson Nicola N McDuffus Leigh-Anne LA Miller Jodi J Sawyer Elinor J EJ Pinder Sarah S van Deurzen Carolien H M CH Jones Louise L Sironen Reijo R Visscher Daniel D Caldas Carlos C Daley Frances F Coulson Penny P Broeks Annegien A Sanders Joyce J Wesseling Jelle J Nevanlinna Heli H Fagerholm Rainer R Blomqvist Carl C Heikkilä Päivi P Ali H Raza HR Dawson Sarah-Jane SJ Figueroa Jonine J Lissowska Jolanta J Brinton Louise L Mannermaa Arto A Kataja Vesa V Kosma Veli-Matti VM Cox Angela A Brock Ian W IW Cross Simon S SS Reed Malcolm W MW Couch Fergus J FJ Olson Janet E JE Devillee Peter P Mesker Wilma E WE Seyaneve Caroline M CM Hollestelle Antoinette A Benitez Javier J Perez Jose Ignacio Arias JI Menéndez Primitiva P Bolla Manjeet K MK Easton Douglas F DF Schmidt Marjanka K MK Pharoah Paul D PD Sherman Mark E ME García-Closas Montserrat M

The journal of pathology. Clinical research 20141204 1

Breast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large-scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sec ...[more]

PMID: 27499890

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Project description:Automated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37-0.87) and study (kappa range = 0.39-0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p-value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000-4,500 cells: kappa = 0.78) than those with lower counts (50-500 cells: kappa = 0.41; p-value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactory performance.

Project description:BackgroundGenetic alterations in cellular signaling networks are a hallmark of cancer, however, effective methods to discover them are lacking. A novel form of abnormality called acquired uniparental disomy (aUPD) was recently found to pinpoint the region of mutated genes in various cancers, thereby identifying the region for next-generation sequencing.Methods/principal findingsWe retrieved large genomic data sets from the Gene Expression Omnibus database to perform genome-wide analysis of aUPD in breast tumor samples and cell lines using approaches that can reliably detect aUPD. aUPD was identified in 52.29% of the tumor samples. The most frequent aUPD regions were located at chromosomes 2q, 3p, 5q, 9p, 9q, 10q, 11q, 13q, 14q and 17q. We evaluated the data for any correlation between the most frequent aUPD regions and HER2/neu, ER, and PR status, and found a statistically significant correlation between the recurrent regions of aUPD and triple negative (TN) breast cancers. aUPD at chromosome 17q (VEZF1, WNT3), 3p (SUMF1, GRM7), 9p (MTAP, NFIB) and 11q (CASP1, CASP4, CASP5) are predictors for TN. The frequency of aUPD was found to be significantly higher in TN breast cancer cases compared to HER2/neu-positive and/or ER or PR-positive cases. Furthermore, using previously published mutation data, we found TP53 homozygously mutated in cell lines having aUPD in that locus.Conclusions/significanceWe conclude that aUPD is a common and non-random molecular feature of breast cancer that is most prominent in triple negative cases. As aUPD regions are different among the main pathological subtypes, specific aUPD regions may aid the sub-classification of breast cancer. In addition, we provide statistical support using TP53 as an example that identifying aUPD regions can be an effective approach in finding aberrant genes. We thus conclude that a genome-wide scale analysis of aUPD regions for homozygous sequence alterations can provide valuable insights into breast tumorigenesis.

Project description:Although most women with luminal breast cancer do well on endocrine therapy alone, some will develop fatal recurrence thereby necessitating the need to prospectively determine those for whom additional cytotoxic therapy will be beneficial. Categorical combinations of immunohistochemical measures of ER, PR, HER2, and KI67 are traditionally used to classify patients into luminal A-like and B-like subtypes for chemotherapeutic reasons, but this may lead to the loss of prognostically relevant information. Here, we compared the prognostic value of quantitative measures of these markers, combined in the IHC4-score, to categorical combinations in subtypes. Using image analysis-based scores for all four markers, we computed the IHC4-score for 2498 patients with luminal breast cancer from two European study populations. We defined subtypes (A-like (ER + and PR + : and HER2- and low KI67) and B-like (ER + and/or PR + : and HER2 + or high KI67)) by combining binary categories of these markers. Hazard ratios and 95% confidence intervals for associations with 10-year breast cancer-specific survival were estimated in Cox proportional-hazard models. We accounted for clinical prognostic factors, including grade, tumor size, lymph-nodal involvement, and age, by using the PREDICT-score. Overall, Subtypes [hazard ratio (95% confidence interval) B-like vs. A-like = 1.64 (1.25-2.14); P-value < 0.001] and IHC4-score [hazard ratio (95% confidence interval)/1 standard deviation = 1.32 (1.20-1.44); P-value < 0.001] were prognostic in univariable models. However, IHC4-score [hazard ratio (95% confidence interval)/1 standard deviation = 1.24 (1.11-1.37); P-value < 0.001; likelihood ratio chi-square (LRχ2) = 12.5] provided more prognostic information than Subtype [hazard ratio (95% confidence interval) B-like vs. A-like = 1.38 (1.02-1.88); P-value = 0.04; LRχ2 = 4.3] in multivariable models. Further, higher values of the IHC4-score were associated with worse prognosis, regardless of subtype (P-heterogeneity = 0.97). These findings enhance the value of the IHC4-score as an adjunct to clinical prognostication tools for aiding chemotherapy decision-making in luminal breast cancer patients, irrespective of subtype.

Dataset Information

Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium.

Publications

Performance of automated scoring of ER, PR, HER2, CK5/6 and EGFR in breast cancer tissue microarrays in the Breast Cancer Association Consortium.

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