Focal Spot, Winter 2006/2007

https://digitalcommons.wustl.edu/focal_spot_archives/1104/thumbnail.jp

Improving biomarker assessment in breast pathology

The accuracy of prognostic and therapy-predictive biomarker assessment in breast tumours is crucial for management and therapy decision in patients with breast cancer. In this thesis, biomarkers used in clinical practice with emphasise on Ki67 and HER2 were studied using several methods including immunocytochemistry, in situ hybridisation, gene expression assays and digital image analysis, with the overall aim to improve routine biomarker evaluation and clarify the prognostic potential in early breast cancer. In paper I, we reported discordances in biomarker status from aspiration cytology and paired surgical specimens from breast tumours. The limited prognostic potential of immunocytochemistry-based Ki67 scoring demonstrated that immunohistochemistry on resected specimens is the superior method for Ki67 evaluation. In addition, neither of the methods were sufficient to predict molecular subtype. Following this in paper II, biomarker agreement between core needle biopsies and subsequent specimens was investigated, both in the adjuvant and neoadjuvant setting. Discordances in Ki67 and HER2 status between core biopsies and paired specimens suggested that these biomarkers should be re-tested on all surgical breast cancer specimens. In paper III, digital image analysis using a virtual double staining software was used to compare methods for assessment of proliferative activity, including mitotic counts, Ki67 and the alternative marker PHH3, in different tumour regions (hot spot, invasive edge and whole section). Digital image analysis using virtual double staining of hot spot Ki67 outperformed the alternative markers of proliferation, especially in discriminating luminal B from luminal A tumours. Replacing mitosis in histological grade with hot spot-scored Ki67 added significant prognostic information. Following these findings, the optimal definition of a hot spot for Ki67 scoring using virtual double staining in relation to molecular subtype and outcome was investigated in paper IV. With the growing evidence of global scoring as a superior method to improve reproducibility of Ki67 scoring, a different digital image analysis software (QuPath) was also used for comparison. Altogether, we found that automated global scoring of Ki67 using QuPath had independent prognostic potential compared to even the best virtual double staining hot spot algorithm, and is also a practical method for routine Ki67 scoring in breast pathology. In paper V, the clinical value of HER2 status was investigated in a unique trastuzumab-treated HER2-positive cohort, on the protein, mRNA and DNA levels. The results demonstrated that low levels of ERBB2 mRNA but neither HER2 copy numbers, HER2 ratio nor ER status, was associated with risk of recurrence among anti-HER2 treated breast cancer patients. In conclusion, we have identified important clinical aspects of Ki67 and HER2 evaluation and provided methods to improve the prognostic potential of Ki67 using digital image analysis. In addition to protein expression of routine biomarkers, mRNA levels by targeted gene expression assays may add further prognostic value in early breast cance

Application of image analysis in external and internal quality assurance for diagnostic clinical immunohistochemistry

Clinical immunohistochemistry (IHC) techniques are not yet fully standardized. In this project, a standardization method was developed and tested for proficiency testing (PT) in external quality assurance (EQA) and quality control (QC) in clinical IHC laboratories. The breast cancer markers estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor 2 (HER2) were used as a model system. Digital image analysis (IA) was used in conjunction with new calibrated and standardized cell line microarrays (CLMA). CLMAs built from nine formalin-fixed paraffin-embedded (FFPE) breast cancer cell lines were used for both QC controls and PT samples, instead of traditionally used FFPE tissues, in the standardization of breast cancer IHC. IA was used for measurement of IHC results, and compared to evaluation by the traditional expert-assessment method. Laboratory Score: Reference Score Ratio (LSRSR) was derived from Histo-Scores (HScores) determined by IA. HScores and LSRSRs were examined statistically and evaluated as histograms and boxplots to summarize and rank participant laboratory EQA results, in comparison to a reference sample or reference laboratories in two consecutive Canada-wide EQA runs. LSRSR-derived reference ranges were highly sensitive in evaluating laboratory EQA performance in PT as well as for monitoring of controls for QC. Laboratory on-slide tissue and cell-line IHC QA controls were assessed using IA and Levey Jennings QC charts. These charts were determined to be an excellent way to observe trending in laboratory IHC staining over time, particularly when cell line controls were used. This approach also reduced the time and labor costs for PT evaluation. Overall, cell line calibration controls were functionally equivalent or better than tissue-based controls in QC and PT mainly because of cell line biological homogeneity and sample availability. This study identified an optimal design for preparation of IHC cell line controls and PT samples for breast cancer markers. Optimal, intermediate staining cell line IHC controls were identified for all three breast cancer markers. Using IA with LSRSR and cell line samples is recommended for standardization of IHC methodology. This approach advances QA for diagnostic IHC and when implemented will improve patient car

Quantification of Estrogen Receptor-Alpha Expression in Human Breast Carcinomas With a Miniaturized, Low-Cost Digital Microscope : A Comparison with a High-End Whole Slide- Scanner

Introduction: A significant barrier to medical diagnostics in low-resource environments is the lack of medical care and equipment. Here we present a low-cost, cloud-connected digital microscope for applications at the point-of-care. We evaluate the performance of the device in the digital assessment of estrogen receptor-alpha (ER) expression in breast cancer samples. Studies suggest computer-assisted analysis of tumor samples digitized with whole slide-scanners may be comparable to manual scoring, here we study whether similar results can be obtained with the device presented. Materials and methods: A total of 170 samples of human breast carcinoma, immunostained for ER expression, were digitized with a high-end slide-scanner and the point-of-care microscope. Corresponding regions from the samples were extracted, and ER status was determined visually and digitally. Samples were classified as ER negative (<1% ER positivity) or positive, and further into weakly (1-10% positivity) and strongly positive. Interobserver agreement (Cohen's kappa) was measured and correlation coefficients (Pearson's product-momentum) were calculated for comparison of the methods. Results: Correlation and interobserver agreement (r = 0.98, p < 0.001, kappa = 0.84, CI95% = 0.75-0.94) were strong in the results from both devices. Concordance of the point-of-care microscope and the manual scoring was good (r = 0.94, p < 0.001, kappa = 0.71, CI95% = 0.61-0.80), and comparable to the concordance between the slide scanner and manual scoring (r = 0.93, p < 0.001, kappa = 0.69, CI95% = 0.60-0.78). Fourteen (8%) discrepant cases between manual and device-based scoring were present with the slide scanner, and 16 (9%) with the point-of-care microscope, all representing samples of low ER expression. Conclusions: Tumor ER status can be accurately quantified with a low-cost imaging device and digital image-analysis, with results comparable to conventional computer-assisted or manual scoring. This technology could potentially be expanded for other histopathological applications at the point-of-care

Development of Multigene Expression Signature Maps at the Protein Level from Digitized Immunohistochemistry Slides

Molecular classification of diseases based on multigene expression signatures is increasingly used for diagnosis, prognosis, and prediction of response to therapy. Immunohistochemistry (IHC) is an optimal method for validating expression signatures obtained using high-throughput genomics techniques since IHC allows a pathologist to examine gene expression at the protein level within the context of histologically interpretable tissue sections. Additionally, validated IHC assays may be readily implemented as clinical tests since IHC is performed on routinely processed clinical tissue samples. However, methods have not been available for automated n-gene expression profiling at the protein level using IHC data. We have developed methods to compute expression level maps (signature maps) of multiple genes from IHC data digitized on a commercial whole slide imaging system. Areas of cancer for these expression level maps are defined by a pathologist on adjacent, co-registered H&E slides, allowing assessment of IHC statistics and heterogeneity within the diseased tissue. This novel way of representing multiple IHC assays as signature maps will allow the development of n-gene expression profiling databases in three dimensions throughout virtual whole organ reconstructions

Use of quantitative pathology to improve grading and predict prognosis in tumours of the gastrointestinal tract

Cancer represents a formidable health burden and was the second leading cause of death globally in 2018. In Norway, almost 35000 new cancer cases were reported in 2019. For colon cancer, the incidence and mortality rates in Norway are among the highest in the world. Furthermore, the tumour-node-metastasis (TNM) system used today is not optimal for selecting which patients should receive adjuvant therapy or not. With the implementation of digital pathology in different pathology departments, there will be better opportunities for digital image analysis, a tool aimed at giving a more reproducible and objective diagnosis than subjective evaluation in a microscope. In digital image analysis, a computer programme is used for the quantification of different biomarkers. This can improve cancer diagnostics because several biases in manual evaluation can be reduced or avoided. One of the challenges in pathology is intra-and inter-observer variability of prognostic and predictive biomarkers. This especially applies for gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs), in which the proliferation marker Ki67 is important for grading (1–3), prognosis and treatment of patients. Several studies have shown interand intra-observer variations in the manual evaluation of Ki67 positivity, which can be improved with digital image analysis. This is important because the interpretation of the immunohistochemical staining of different biomarkers, such as Ki67, often influences patient prognosis and treatment. The immune system, especially the number of T-cells in and around the tumour, has been investigated as a promising biomarker for predicting prognosis and survival in colorectal cancer (CRC). The immune system is closely linked to microsatellite instability (MSI) in CRC, and MSI-high CRC has been shown to respond well to immune therapy. A TNM-immune is suggested based on scoring of the number of T-cells in the tumour centre and the invasive margin using digital image analysis. In this study, we explored the correlation between T-cells in presurgical blood samples and T-cells in the invasive margins and the tumour centres in CRC with digital image analysis in a feasibility study and found a correlation. Furthermore, we used digital image analysis to calculate the immune score in colon cancer patients based on immunohistochemical (IHC) staining of cluster of differentiation (CD)3+ and CD8+ T-cells in invasive margins and tumour centres in a prospective cohort. This immune score corresponded strongly with known clinicopathological features, such as stage and MSI status. Also, we evaluated digital image analysis as an objective assessment tool for two different proliferation markers in GEP-NENs: Ki67 and Phosphohistone 3 (PHH3). We compared manual (visual) evaluation of Ki67 from pathology reports with digital image analysis of Ki67 and found excellent agreement, but there is a tendency to upgrade cases from grade 1 to grade 2 with digital image analysis. For the digital image analysis of PHH3, the measurements were more diverging. The data presented show the use of digital image analysis in two settings: developing an immune score as a prognostic marker in colon cancer and providing an objective and reproducible evaluation of proliferation in neuroendocrine neoplasms. With the transition to digital pathology, digital image analysis can be implemented in daily diagnostics. This implementation requires more research for the validation of the different methods. With time, digital image analysis is expected to be utilized for tasks performed by pathologists today.Doktorgradsavhandlin

Optimal Tumor Sampling For Immunostaining Of Biomarkers In Breast Carcinoma

OPTIMAL TUMOR SAMPLING FOR IMMUNOSTAINING OF BIOMARKERS IN BREAST CARCINOMA. Juliana Tolles, Yalai Bai, Maria Baquero, Lyndsay N. Harris, David L. Rimm, Annette M. Molinaro. Division of Biostatistics, Yale University School of Public Health, New Haven, CT. Biomarkers, such as estrogen receptor, are used to determine therapy and prognosis in breast carcinoma. Immunostaining assays of biomarker expression have a high rate of inaccuracy, for example estimates are as high as 20% for estrogen receptor. Biomarkers have been shown to be heterogeneously expressed in breast tumors and this heterogeneity may contribute to the inaccuracy of immunostaining assays. Currently, no evidence-based standards exist for the amount of tumor that must be sampled in order to correct for biomarker heterogeneity. The purpose of this study is to determine the optimal number of 20X fields that are necessary to estimate a representative measurement of expression in a whole tissue section for selected biomarkers: estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), AKT, extracellular signal-regulated kinase (ERK), ribosomal protein S6 kinase 1 (S6K1), glyceraldehyde 3-phosphate dehydrogenase (GAPDH), cytokeratin, and microtubule-associated protein-Tau (MAP-Tau). Two collections of whole tissue sections of breast carcinoma were immunostained for biomarkers. Expression was quantified using Automated Quantitative Analysis (AQUA). Simulated sampling of various numbers of fields (ranging from 1-35) was performed for each marker. The optimal number was selected for each marker via resampling techniques and minimization of prediction error over an independent test set. The optimal number of 20X fields varied by marker, ranging between 3-14 fields. More heterogeneous markers, such as MAP-Tau, required a larger sample of 20X fields to produce representative measurement. The clinical implication of these findings is that small core needle breast biopsies may be inadequate to represent whole tumor biomarker expression for many markers. Also, for biomarkers newly introduced into clinical use, especially if therapeutic response is dictated by level of expression, the optimal size of tissue sample must be determined on a marker-by-marker basis

Focal Spot, Fall/Winter 2010/2011

https://digitalcommons.wustl.edu/focal_spot_archives/1115/thumbnail.jp