Quantitative analysis of estrogen receptor heterogeneity in breast cancer

Reproducibility and intratumoral heterogeneity of the PAM50 breast cancer assay

BACKGROUND

The PAM50 assay is used routinely in clinical practice to determine breast cancer prognosis and management; however, research assessing how technical variation and intratumoral heterogeneity contribute to misclassification and reproducibility of these tests is limited.

METHODS

We evaluated the impact of intratumoral heterogeneity on the reproducibility of results for the PAM50 assay by testing RNA extracted from formalin-fixed paraffin embedded breast cancer blocks sampled at distinct spatial locations. Samples were classified according to intrinsic subtype (Luminal A, Luminal B, HER2-enriched, Basal-like, or Normal-like) and risk of recurrence with proliferation score (ROR-P, high, medium, or low). Intratumoral heterogeneity and technical reproducibility (replicate assays on the same RNA) were assessed as percent categorical agreement between paired intratumoral and replicate samples. Euclidean distances between samples, calculated across the PAM50 genes and the ROR-P score, were compared for concordant vs. discordant samples.

RESULTS

Technical replicates (N = 144) achieved 93% agreement for ROR-P group and 90% agreement on PAM50 subtype. For spatially distinct biological replicates (N = 40 intratumoral replicates), agreement was lower (81% for ROR-P and 76% for PAM50 subtype). The Euclidean distances between discordant technical replicates were bimodal, with discordant samples showing higher Euclidian distance and biologic heterogeneity.

CONCLUSION

The PAM50 assay achieved very high technical reproducibility for breast cancer subtyping and ROR-P, but intratumoral heterogeneity is revealed by the assay in a small proportion of cases.

Estrogen regulates divergent transcriptional and epigenetic cell states in breast cancer

Breast cancers are known to be driven by the transcription factor estrogen receptor and its ligand estrogen. While the receptor's cis-binding elements are known to vary between tumors, heterogeneity of hormone signaling at a single-cell level is unknown. In this study, we systematically tracked estrogen response across time at a single-cell level in multiple cell line and organoid models. To accurately model these changes, we developed a computational tool (TITAN) that quantifies signaling gradients in single-cell datasets. Using this approach, we found that gene expression response to estrogen is non-uniform, with distinct cell groups expressing divergent transcriptional networks. Pathway analysis suggested the two most distinct signatures are driven separately by ER and FOXM1. We observed that FOXM1 was indeed activated by phosphorylation upon estrogen stimulation and silencing of FOXM1 attenuated the relevant gene signature. Analysis of scRNA-seq data from patient samples confirmed the existence of these divergent cell groups, with the FOXM1 signature predominantly found in ER negative cells. Further, multi-omic single-cell experiments indicated that the different cell groups have distinct chromatin accessibility states. Our results provide a comprehensive insight into ER biology at the single-cell level and potential therapeutic strategies to mitigate resistance to therapy.

An integrative approach of digital image analysis and transcriptome profiling to explore potential predictive biomarkers for TGFβ blockade therapy

Increasing evidence suggests that the presence and spatial localization and distribution pattern of tumor infiltrating lymphocytes (TILs) is associate with response to immunotherapies. Recent studies have identified TGFβ activity and signaling as a determinant of T cell exclusion in the tumor microenvironment and poor response to PD-1/PD-L1 blockade. Here we coupled the artificial intelligence (AI)-powered digital image analysis and gene expression profiling as an integrative approach to quantify distribution of TILs and characterize the associated TGFβ pathway activity. Analysis of T cell spatial distribution in the solid tumor biopsies revealed substantial differences in the distribution patterns. The digital image analysis approach achieves 74% concordance with the pathologist assessment for tumor-immune phenotypes. The transcriptomic profiling suggests that the TIL score was negatively correlated with TGFβ pathway activation, together with elevated TGFβ signaling activity observed in excluded and desert tumor phenotypes. The present results demonstrate that the automated digital pathology algorithm for quantitative analysis of CD8 immunohistochemistry image can successfully assign the tumor into one of three infiltration phenotypes: immune desert, immune excluded or immune inflamed. The association between “cold” tumor-immune phenotypes and TGFβ signature further demonstrates their potential as predictive biomarkers to identify appropriate patients that may benefit from TGFβ blockade.

Clinical utility of circulating tumor DNA as a response and follow-up marker in cancer therapy

Response evaluation for cancer treatment consists primarily of clinical and radiological assessments. In addition, a limited number of serum biomarkers that assess treatment response are available for a small subset of malignancies. Through recent technological innovations, new methods for measuring tumor burden and treatment response are becoming available. By utilization of highly sensitive techniques, tumor-specific mutations in circulating DNA can be detected and circulating tumor DNA (ctDNA) can be quantified. These so-called liquid biopsies provide both molecular information about the genomic composition of the tumor and opportunities to evaluate tumor response during therapy. Quantification of tumor-specific mutations in plasma correlates well with tumor burden. Moreover, with liquid biopsies, it is also possible to detect mutations causing secondary resistance during treatment. This review focuses on the clinical utility of ctDNA as a response and follow-up marker in patients with non-small cell lung cancer, melanoma, colorectal cancer, and breast cancer. Relevant studies were retrieved from a literature search using PubMed database. An overview of the available literature is provided and the relevance of ctDNA as a response marker in anti-cancer therapy for clinical practice is discussed. We conclude that the use of plasma-derived ctDNA is a promising tool for treatment decision-making based on predictive testing, detection of resistance mechanisms, and monitoring tumor response. Necessary steps for translation to daily practice and future perspectives are discussed.

Hormone Receptor Loss in Breast Cancer: Molecular Mechanisms, Clinical Settings, and Therapeutic Implications

Hormone receptor-positive breast cancer (HR+ BC) accounts for approximately 75% of new BC diagnoses. Despite the undisputable progresses obtained in the treatment of HR+ BC in recent years, primary or acquired resistance to endocrine therapies still represents a clinically relevant issue, and is largely responsible for disease recurrence after curative surgery, as well as for disease progression in the metastatic setting. Among the mechanisms causing primary or acquired resistance to endocrine therapies is the loss of estrogen/progesterone receptor expression, which could make BC cells independent of estrogen stimulation and, consequently, resistant to estrogen deprivation or the pharmacological inhibition of estrogen receptors. This review aims at discussing the molecular mechanisms and the clinical implications of HR loss as a result of the therapies used in the neoadjuvant setting or for the treatment of advanced disease in HR+ BC patients.

Generation of in situ sequencing based OncoMaps to spatially resolve gene expression profiles of diagnostic and prognostic markers in breast cancer

BACKGROUND

Gene expression analysis of breast cancer largely relies on homogenized tissue samples. Due to the high degree of cellular and molecular heterogeneity of tumor tissues, bulk tissue-based analytical approaches can only provide very limited system-level information about different signaling mechanisms and cellular interactions within the complex tissue context.

METHODS

We describe an analytical approach using in situ sequencing (ISS), enabling highly multiplexed, spatially and morphologically resolved gene expression profiling. Ninety-one genes including prognostic and predictive marker profiles, as well as genes involved in specific cellular pathways were mapped within whole breast cancer tissue sections, covering luminal A/B-like, HER2-positive and triple negative tumors. Finally, all these features were combined and assembled into a molecular-morphological OncoMap for each tumor tissue.

FINDINGS

Our in situ approach spatially revealed intratumoral heterogeneity with regard to tumor subtype as well as to the OncotypeDX recurrence score and even uncovered areas of minor cellular subpopulations. Since ISS-resolved molecular profiles are linked to their histological context, a deeper analysis of the core and periphery of tumor foci enabled identification of specific gene expression patterns associated with these morphologically relevant regions.

INTERPRETATION

ISS generated OncoMaps represent useful tools to extend our general understanding of the biological processes behind tumor progression and can further support the identification of novel therapeutical targets as well as refine tumor diagnostics. FUND: Swedish Cancerfonden, UCAN, Vetenskapsrådet, Cancer Genomics Netherlands, Iris, Stig och Gerry Castenbäcks Stiftelse, BRECT, PCM Program, King Gustaf V Jubilee Fund, BRO, KI and Stockholm County Council, Alice Wallenberg Foundation.

Multiregion gene expression profiling reveals heterogeneity in molecular subtypes and immunotherapy response signatures in lung cancer

Intra-tumor heterogeneity may be present at all molecular levels. Genomic intra-tumor heterogeneity at the exome level has been reported in many cancer types, but comprehensive gene expression intra-tumor heterogeneity has not been well studied. Here, we delineated the gene expression intra-tumor heterogeneity by exploring gene expression profiles of 35 tumor regions from 10 non-small cell lung cancer tumors (three or four regions/tumor), including adenocarcinoma, squamous cell carcinoma, large-cell carcinoma, and pleomorphic carcinoma of the lung. Using Affymetrix Gene 1.0 ST arrays, we generated the gene expression data for every sample. Inter-tumor heterogeneity was generally higher than intra-tumor heterogeneity, but some tumors showed a substantial level of intra-tumor heterogeneity. The analysis of various clinically relevant gene expression signatures including molecular subtype, epithelial-to-mesenchymal transition, and anti-PD-1 resistance signatures also revealed heterogeneity between different regions of the same tumor. The gene expression intra-tumor heterogeneity we observed was associated with heterogeneous tumor microenvironments represented by stromal and immune cells infiltrated. Our data suggest that RNA-based prognostic or predictive molecular tests should be carefully conducted in consideration of the gene expression intra-tumor heterogeneity.

Estrogen receptor quantitative measures and breast cancer survival

PURPOSE

While the estrogen receptor (ER) is the single most widely used biomarker to evaluate breast cancer outcomes, aspects of ER marker biology remain poorly understood. We sought to determine whether quantitative measures of ER, such as protein expression and intensity, were associated with survival, or with survival disparities experienced by Hispanic women.

METHODS

A case-cohort study included a 15% random sample of invasive breast cancer cases diagnosed from 1997 to 2009 in six New Mexico counties and all deaths due to breast cancer-related causes. Pathology reports and tissue microarrays served as sources of ER information. Analyses were restricted to women with ≥1% ER immunohistochemical staining. Hazard ratios (HR) and 95% confidence intervals (CI) for breast cancer death were estimated using Cox proportional hazards models.

RESULTS

Included women represented 4336 ER+ breast cancer cases and 448 deaths. Median follow-up was 93 months. ER percent expression was not associated with breast cancer survival after adjustment for standard prognostic factors (p trend = 0.76). ER intensity remained a strong and independent risk factor for breast cancer survival in multivariate analyses: Women whose tumors expressed ER at intensity = 2 (HR 0.6; 95% CI 0.4-1.0) or 3 (HR 0.5; 95% CI 0.2-0.9) had a reduced risk of breast cancer mortality, compared to ER intensity = 1 (p trend = 0.02). Neither ER protein expression nor intensity influenced Hispanic survival disparities.

CONCLUSIONS

Estrogen receptor percent positive staining is not independently related to breast cancer survival after adjustment for other survival-related factors. ER intensity, in contrast, demonstrates promise for prognostic utility.

Intratumoral heterogeneity and subclonal diversification of early breast cancer

Heterogeneity has long been recognized as a feature of some primary breast cancers manifesting as mixed histopathological subtypes or variable expression of the therapeutic targets ER, PgR and HER2. The recent emergence of next generation sequencing (NGS) technologies has revolutionized our understanding of the extent and nature of subclonal diversification. Careful examination of primary breast cancers often reveals multiple genomically distinct subclones that may contain driver alterations that follow spatial patterns of segregation. Subclonality is of clinical relevance as it forms the substrate of selection and can give rise to aggressive clinical features such as invasiveness, metastasis and treatment resistance. However, spatial and temporal intra-tumoral heterogeneity pose fundamental challenges to representative sampling and consequently the feasibility of a personalized medicine approach. Fundamental clinical and biological questions are starting to be addressed by applying NGS to the study of intra-tumoral heterogeneity and the insights that it provides should be used to better inform the prospective design of clinico-genomics trials.

Biologically Relevant Heterogeneity: Metrics and Practical Insights

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.

Discordance between original and central laboratories in ER and HER2 results in a diverse, population-based sample

PURPOSE

To investigate the discordance between original and central laboratories in estrogen receptor (ER) status, in tumors originally deemed to be ER-negative, and in HER2 status in a diverse population-based sample.

METHODS

In a follow-up study of 1785 women with Stage I-III breast cancer diagnosed between 2005 and 2007 in the Detroit and Los Angeles County SEER registry catchment areas, participants were asked to consent to reassessment of ER (in tumors originally deemed to be ER-negative) and HER2 status on archival tumor samples approximately four years after diagnosis. Blocks were centrally prepared and analyzed for ER and HER2 using standardized methods and the guidelines of the American Society of Clinical Oncology and the College of American Pathologists. Analyses determined the discordance between original and central laboratories.

RESULTS

132 (31%) of those eligible for ER reassessment and 367 (21%) eligible for HER2 reassessment had archival blocks reassessed centrally. ER discordance was only 6%. HER2 discordance by immunohistochemistry (IHC) was 26%, but final HER2 results-employing FISH in tumors that were IHC 2+ at the central laboratory-were discordant in only 6%. Half of the original laboratories did not perform their own assays.

CONCLUSIONS

Discordance between original and central laboratories in two large metropolitan areas was low in this population-based sample compared to previously reported patient samples. Centralization of testing for key pathology variables appears to be occurring in many hospitals. In addition, quality improvement efforts may have preceded the publication and dissemination of specialty society guidelines.

Immunophenotyping and Transcriptomic Outcomes in PDX-Derived TNBC Tissue

Cancer tissue functions as an ecosystem of a diverse set of cells that interact in a complex tumor microenvironment. Genomic tools applied to biopsies in bulk fail to account for this tumor heterogeneity, whereas single-cell imaging methods limit the number of cells which can be assessed or are very resource intensive. The current study presents methods based on flow cytometric analysis and cell sorting using known cell surface markers (CXCR4/CD184, CD24, THY1/CD90) to identify and interrogate distinct groups of cells in triple-negative breast cancer clinical biopsy specimens from patient-derived xenograft (PDX) models. The results demonstrate that flow cytometric analysis allows a relevant subgrouping of cancer tissue and that sorting of these subgroups provides insights into cancer cell populations with unique, reproducible, and functionally divergent gene expression profiles. The discovery of a drug resistance signature implies that uncovering the functional interaction between these populations will lead to deeper understanding of cancer progression and drug response.Implications: PDX-derived human breast cancer tissue was investigated at the single-cell level, and cell subpopulations defined by surface markers were identified which suggest specific roles for distinct cellular compartments within a solid tumor. Mol Cancer Res; 15(4); 429-38. ©2016 AACR.

Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers

BACKGROUND

Measures of spatial intratumor heterogeneity are potentially important diagnostic biomarkers for cancer progression, proliferation, and response to therapy. Spatial relationships among cells including cancer and stromal cells in the tumor microenvironment (TME) are key contributors to heterogeneity.

METHODS

We demonstrate how to quantify spatial heterogeneity from immunofluorescence pathology samples, using a set of 3 basic breast cancer biomarkers as a test case. We learn a set of dominant biomarker intensity patterns and map the spatial distribution of the biomarker patterns with a network. We then describe the pairwise association statistics for each pattern within the network using pointwise mutual information (PMI) and visually represent heterogeneity with a two-dimensional map.

RESULTS

We found a salient set of 8 biomarker patterns to describe cellular phenotypes from a tissue microarray cohort containing 4 different breast cancer subtypes. After computing PMI for each pair of biomarker patterns in each patient and tumor replicate, we visualize the interactions that contribute to the resulting association statistics. Then, we demonstrate the potential for using PMI as a diagnostic biomarker, by comparing PMI maps and heterogeneity scores from patients across the 4 different cancer subtypes. Estrogen receptor positive invasive lobular carcinoma patient, AL13-6, exhibited the highest heterogeneity score among those tested, while estrogen receptor negative invasive ductal carcinoma patient, AL13-14, exhibited the lowest heterogeneity score.

CONCLUSIONS

This paper presents an approach for describing intratumor heterogeneity, in a quantitative fashion (via PMI), which departs from the purely qualitative approaches currently used in the clinic. PMI is generalizable to highly multiplexed/hyperplexed immunofluorescence images, as well as spatial data from complementary in situ methods including FISSEQ and CyTOF, sampling many different components within the TME. We hypothesize that PMI will uncover key spatial interactions in the TME that contribute to disease proliferation and progression.

ESR1 and PGR polymorphisms are associated with estrogen and progesterone receptor expression in breast tumors

Hormone receptor-positive (HR+) breast cancers express the estrogen (ERα) and/or progesterone (PgR) receptors. Inherited single nucleotide polymorphisms (SNPs) in ESR1, the gene encoding ERα, have been reported to predict tamoxifen effectiveness. We hypothesized that these associations could be attributed to altered tumor gene/protein expression of ESR1/ERα and that SNPs in the PGR gene predict tumor PGR/PgR expression. Formalin-fixed paraffin-embedded breast cancer tumor specimens were analyzed for ESR1 and PGR gene transcript expression by the reverse transcription polymerase chain reaction based Oncotype DX assay and for ERα and PgR protein expression by immunohistochemistry (IHC) and an automated quantitative immunofluorescence assay (AQUA). Germline genotypes for SNPs in ESR1 (n = 41) and PGR (n = 8) were determined by allele-specific TaqMan assays. One SNP in ESR1 (rs9322336) was significantly associated with ESR1 gene transcript expression (P = 0.006) but not ERα protein expression (P > 0.05). A PGR SNP (rs518162) was associated with decreased PGR gene transcript expression (P = 0.003) and PgR protein expression measured by IHC (P = 0.016), but not AQUA (P = 0.054). There were modest, but statistically significant correlations between gene and protein expression for ESR1/ERα and PGR/PgR and for protein expression measured by IHC and AQUA (Pearson correlation = 0.32-0.64, all P < 0.001). Inherited ESR1 and PGR genotypes may affect tumor ESR1/ERα and PGR/PgR expression, respectively, which are moderately correlated. This work supports further research into germline predictors of tumor characteristics and treatment effectiveness, which may someday inform selection of hormonal treatments for patients with HR+ breast cancer.

Quantitative comparison and reproducibility of pathologist scoring and digital image analysis of estrogen receptor β2 immunohistochemistry in prostate cancer

Background

Digital image analysis offers advantages over traditional pathologist visual scoring of immunohistochemistry, although few studies examining the correlation and reproducibility of these methods have been performed in prostate cancer. We evaluated the correlation between digital image analysis (continuous variable data) and pathologist visual scoring (quasi-continuous variable data), reproducibility of each method, and association of digital image analysis methods with outcomes using prostate cancer tissue microarrays (TMAs) stained for estrogen receptor-β2 (ERβ2).

Methods

Prostate cancer TMAs were digitized and evaluated by pathologist visual scoring versus digital image analysis for ERβ2 staining within tumor epithelium. Two independent analysis runs were performed to evaluate reproducibility. Image analysis data were evaluated for associations with recurrence-free survival and disease specific survival following radical prostatectomy.

Results

We observed weak/moderate Spearman correlation between digital image analysis and pathologist visual scores of tumor nuclei (Analysis Run A: 0.42, Analysis Run B: 0.41), and moderate/strong correlation between digital image analysis and pathologist visual scores of tumor cytoplasm (Analysis Run A: 0.70, Analysis Run B: 0.69). For the reproducibility analysis, there was high Spearman correlation between pathologist visual scores generated for individual TMA spots across Analysis Runs A and B (Nuclei: 0.84, Cytoplasm: 0.83), and very high correlation between digital image analysis for individual TMA spots across Analysis Runs A and B (Nuclei: 0.99, Cytoplasm: 0.99). Further, ERβ2 staining was significantly associated with increased risk of prostate cancer-specific mortality (PCSM) when quantified by cytoplasmic digital image analysis (HR 2.16, 95 % CI 1.02–4.57, p = 0.045), nuclear image analysis (HR 2.67, 95 % CI 1.20–5.96, p = 0.016), and total malignant epithelial area analysis (HR 5.10, 95 % CI 1.70–15.34, p = 0.004). After adjusting for clinicopathologic factors, only total malignant epithelial area ERβ2 staining was significantly associated with PCSM (HR 4.08, 95 % CI 1.37–12.15, p = 0.012).

Conclusions

Digital methods of immunohistochemical quantification are more reproducible than pathologist visual scoring in prostate cancer, suggesting that digital methods are preferable and especially warranted for studies involving large sample sizes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13000-016-0511-5) contains supplementary material, which is available to authorized users.

Intratumoral heterogeneity as a source of discordance in breast cancer biomarker classification

BACKGROUND

Spatial heterogeneity in biomarker expression may impact breast cancer classification. The aims of this study were to estimate the frequency of spatial heterogeneity in biomarker expression within tumors, to identify technical and biological factors contributing to spatial heterogeneity, and to examine the impact of discordant biomarker status within tumors on clinical record agreement.

METHODS

Tissue microarrays (TMAs) were constructed using two to four cores (1.0 mm) for each of 1085 invasive breast cancers from the Carolina Breast Cancer Study, which is part of the AMBER Consortium. Immunohistochemical staining for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) was quantified using automated digital imaging analysis. The biomarker status for each core and for each case was assigned using clinical thresholds. Cases with core-to-core biomarker discordance were manually reviewed to distinguish intratumoral biomarker heterogeneity from misclassification of biomarker status by the automated algorithm. The impact of core-to-core biomarker discordance on case-level agreement between TMAs and the clinical record was evaluated.

RESULTS

On the basis of automated analysis, discordant biomarker status between TMA cores occurred in 9 %, 16 %, and 18 % of cases for ER, PR, and HER2, respectively. Misclassification of benign epithelium and/or ductal carcinoma in situ as invasive carcinoma by the automated algorithm was implicated in discordance among cores. However, manual review of discordant cases confirmed spatial heterogeneity as a source of discordant biomarker status between cores in 2 %, 7 %, and 8 % of cases for ER, PR, and HER2, respectively. Overall, agreement between TMA and clinical record was high for ER (94 %), PR (89 %), and HER2 (88 %), but it was reduced in cases with core-to-core discordance (agreement 70 % for ER, 61 % for PR, and 57 % for HER2).

CONCLUSIONS

Intratumoral biomarker heterogeneity may impact breast cancer classification accuracy, with implications for clinical management. Both manually confirmed biomarker heterogeneity and misclassification of biomarker status by automated image analysis contribute to discordant biomarker status between TMA cores. Given that manually confirmed heterogeneity is uncommon (<10 % of cases), large studies are needed to study the impact of heterogeneous biomarker expression on breast cancer classification and outcomes.

Translation of New Molecular Imaging Approaches to the Clinical Setting: Bridging the Gap to Implementation

Molecular imaging with PET is a rapidly emerging technique. In breast cancer patients, more than 45 different PET tracers have been or are presently being tested. With a good rationale, after development of the tracer and proven feasibility, it is of interest to evaluate whether there is a potential meaningful role for the tracer in the clinical setting-such as in staging, in the (early) prediction of a treatment response, or in supporting drug choices. So far, only (18)F-FDG PET has been incorporated into breast cancer guidelines. For proof of the clinical relevance of tracers, especially for analysis in a multicenter setting, standardization of the technology and access to the novel PET tracer are required. However, resources for PET implementation research are limited. Therefore, next to randomized studies, novel approaches are required for proving the clinical value of PET tracers with the smallest possible number of patients. The aim of this review is to describe the process of the development of PET tracers and the level of evidence needed for the use of these tracers in breast cancer. Several breast cancer trials have been performed with the PET tracers (18)F-FDG, 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT), and (18)F-fluoroestradiol ((18)F-FES). We studied them to learn lessons for the implementation of novel tracers. After defining the gap between a good rationale for a tracer and implementation in the clinical setting, we propose solutions to fill the gap to try to bring more PET tracers to daily clinical practice.

Intratumor Heterogeneity Affects Gene Expression Profile Test Prognostic Risk Stratification in Early Breast Cancer

PURPOSE

To examine the effect of intratumor heterogeneity (ITH) on detection of genes within gene expression panels (GEPs) and the subsequent ability to predict prognostic risk.

EXPERIMENTAL DESIGN

Multiplexed barcoded RNA analysis was used to measure the expression of 141 genes from five GEPs (Oncotype Dx, MammaPrint, PAM50, EndoPredict, and Breast Cancer Index) in breast cancer tissue sections and tumor-rich cores from 71 estrogen receptor (ER)-positive node-negative tumors, on which clinical Oncotype Dx testing was previously performed. If the tumor had foci of high Ki67 (n = 26), low/negative progesterone receptor (PR; n = 13), or both (n = 5), additional cores were obtained. In total, 181 samples were processed. Oncotype Dx recurrence scores were calculated from NanoString nCounter gene expression data.

RESULTS

Hierarchical clustering using all GEP genes showed that majority (61 of 71) of tumor samples clustered by patient, indicating greater interpatient heterogeneity (IPH) than ITH. We found a strikingly high correlation between Oncotype Dx recurrence scores obtained from whole sections versus tumor-rich cores (r = 0.94). However, high Ki67 and low PR cores had slightly higher but not statistically significant recurrence scores. For 18 of 71 (25%) patients, scores were divergent between sections and cores and crossed the boundaries for low, intermediate, and high risk.

CONCLUSIONS

Our study indicates that in patients with highly heterogeneous tumors, GEP recurrence scores from a single core could under- or overestimate prognostic risk. Hence, it may be a useful strategy to assess multiple samples (both representative and atypical cores) to fully account for the ITH-driven variation in risk prediction. Clin Cancer Res; 22(21); 5362-9. ©2016 AACR.

Role of positron emission tomography for the monitoring of response to therapy in breast cancer

This review considers the potential utility of positron emission tomography (PET) tracers in the setting of response monitoring in breast cancer, with a special emphasis on glucose metabolic changes assessed with (18)F-fluorodeoxyglucose (FDG). In the neoadjuvant setting of breast cancer, the metabolic response can predict the final complete pathologic response after the first cycles of chemotherapy. Because tumor metabolic behavior highly depends on cancer subtype, studies are ongoing to define the optimal metabolic criteria of tumor response in each subtype. The recent multicentric randomized AVATAXHER trial has suggested, in the human epidermal growth factor 2-positive subtype, a clinical benefit of early tailoring the neoadjuvant treatment in women with poor metabolic response after the first course of treatment. In the bone-dominant metastatic setting, there is increasing clinical evidence that FDG-PET/computed tomography (CT) is the most accurate imaging modality for assessment of the tumor response to treatment when both metabolic information and morphologic information are considered. Nevertheless, there is a need to define standardized metabolic criteria of response, including the heterogeneity of response among metastases, and to evaluate the costs and health outcome of FDG-PET/CT compared with conventional imaging. New non-FDG radiotracers highlighting specific molecular hallmarks of breast cancer cells have recently emerged in preclinical and clinical studies. These biomarkers can take into account the heterogeneity of tumor biology in metastatic lesions. They may provide valuable clinical information for physicians to select and monitor the effectiveness of novel therapeutics targeting the same molecular pathways of breast tumor.

Upregulation of Claudin-4, CAIX and GLUT-1 in distant breast cancer metastases

BACKGROUND

Several studies have shown that the immunophenotype of distant breast cancer metastases may differ significantly from that of the primary tumor, especially with regard to differences in the level of hormone receptor protein expression, a process known as receptor conversion. This study aimed to compare expression levels of several membrane proteins between primary breast tumors and their corresponding distant metastases in view of their potential applicability for molecular imaging and drug targeting.

METHODS

Expression of Claudin-4, EGFR, CAIX, GLUT-1 and IGF1R was assessed by immunohistochemistry on tissue microarrays composed of 97 paired primary breast tumors and their distant (non-bone) metastases.

RESULTS

In both the primary cancers and the metastases, Claudin-4 was most frequently expressed, followed by GLUT-1, CAIX and EGFR.From primary breast cancers to their distant metastases there was positive to negative conversion, e.g. protein expression in the primary tumor with no expression in its paired metastasis, in 6%, 19%, 12%, 38%, and 0% for Claudin-4 (n.s), GLUT-1 (n.s), CAIX (n.s), EGFR (n.s) and IGF1R (n.s) respectively. Negative to positive conversion was seen in 65%, 47%, 43%, 9% and 0% of cases for Claudin-4 (p = 0.049), GLUT-1 (p = 0.024), CAIX (p = 0.002), EGFR (n.s.) and IGF1R (n.s.) respectively. Negative to positive conversion of Claudin-4 in the metastasis was significantly associated with tumor size (p = 0.015), negative to positive conversion of EGFR with negative PR status (p = 0.046) and high MAI (p = 0.047) and GLUT-1 negative to positive conversion with (neo)adjuvant chemotherapy (p = 0.039) and time to metastasis formation (p = 0.034). CAIX and GLUT-1 expression in the primary tumor were significantly associated with high MAI (p = 0.008 and p = 0.038 respectively).

CONCLUSION

Claudin-4 is frequently expressed in primary breast cancers but especially in their metastases and is thereby an attractive membrane bound molecular imaging and drug target. Conversion in expression of the studied proteins from the primary tumor to metastases was fairly frequent, except for IGF1R, implying that the expression status of metastases cannot always be reliably predicted from the primary tumor, thereby necessitating biopsy for reliable assessment.

Evaluation of Ki67 expression across distinct categories of breast cancer specimens: a population-based study of matched surgical specimens, core needle biopsies and tissue microarrays

Introduction

Tumor cell proliferation in breast cancer is strongly prognostic and may also predict response to chemotherapy. However, there is no consensus on counting areas or cut-off values for patient stratification. Our aim was to assess the matched level of proliferation by Ki67 when using different tissue categories (whole sections, WS; core needle biopsies, CNB; tissue microarrays, TMA), and the corresponding prognostic value.

Methods

We examined a retrospective, population-based series of breast cancer (n = 534) from the Norwegian Breast Cancer Screening Program. The percentage of Ki67 positive nuclei was evaluated by visual counting on WS (n = 534), CNB (n = 154) and TMA (n = 459).

Results

The median percentage of Ki67 expression was 18% on WS (hot-spot areas), 13% on CNB, and 7% on TMA, and this difference was statistically significant in paired cases. Increased Ki67 expression by all evaluation methods was associated with aggressive tumor features (large tumor diameter, high histologic grade, ER negativity) and reduced patient survival.

Conclusion

There is a significant difference in tumor cell proliferation by Ki67 across different sample categories. Ki67 is prognostic over a wide range of cut-off points and for different sample types, although Ki67 results derived from TMA sections are lower compared with those obtained using specimens from a clinical setting. Our findings indicate that specimen specific cut-off values should be applied for practical use.

Obstacles to precision oncology: confronting current factors affecting the successful introduction of biomarkers to the clinic

BACKGROUND

Tailoring treatment strategies to individual patients requires the availability of reliable biomarkers. Despite important investment in biomarker research, few examples of successful biomarker-drug co-development are currently seen in clinical practice. The validity of a biomarker measurement may be affected by different pre-analytical, analytical and post-analytical factors. The lack of control or oversight of any of these factors may ultimately lead to failure in translating a promising research finding into clinical practice. In the present review, we put into perspective some of the obstacles to "precision" oncology, focusing on the technical and biological hurdles that may affect the validity of a biomarker result and, ultimately, the likelihood of a new targeted agent to reach the clinic.

CONCLUSION

Biomarker application in precision oncology must consider the evolution of neoplastic disease, evaluate strengths and limitations of the platform used for the determination, and efficiently address specimen type and handling issues. In-depth analytical validation of a new biomarker test that includes evaluation of target stability should be performed before the test is used in clinical samples. More efficient sampling and use of high-sensitivity methodologies may overcome the influence of tumor heterogeneity on biomarker measurement. Clinical trials with biomarker endpoints may only be successful when multidisciplinary academic study teams are involved and results meet the highest quality standards.

Modeling luminal breast cancer heterogeneity: combination therapy to suppress a hormone receptor-negative, cytokeratin 5-positive subpopulation in luminal disease

Introduction

Many Luminal breast cancers are heterogeneous, containing substantial numbers of estrogen (ER) and progesterone (PR) receptor-negative cells among the ER+ PR+ ones. One such subpopulation we call “Luminobasal” is ER-, PR- and cytokeratin 5 (CK5)-positive. It is not targeted for treatment.

Methods

To address the relationships between ER+PR+CK5– and ER–PR–CK5+ cells in Luminal cancers and tightly control their ratios we generated isogenic pure Luminal (pLUM) and pure Luminobasal (pLB) cells from the same parental Luminal human breast cancer cell line. We used high-throughput screening to identify pLB-specific drugs and examined their efficacy alone and in combination with hormone therapy in mixed-cell tumor models.

Results

We show that pLUM and MCF7 cells suppress proliferation of pLB cells in mixed-cell 3D colonies in vitro and that pLUM cells suppress growth of pLB cells in mixed-cell xenografts in vivo. High-throughput screening of 89 FDA-approved oncology drugs shows that pLB cells are sensitive to monotherapy with the epidermal growth factor receptor (EGFR) inhibitors gefitinib and erlotinib. By exploiting mixed-cell 3D colonies and mixed-cell solid mouse tumors models we demonstrate that combination therapy with gefitinib plus the anti-estrogen fulvestrant constitutes a robust treatment strategy.

Conclusions

We propose that response to combination endocrine/EGFR inhibitor therapies in heterogeneous Luminal cancers may improve long-term survival in patients whose primary tumors have been preselected for appropriate biomarkers, including ER, PR, CK5 and EGFR.

Electronic supplementary material

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Digital pathology and image analysis in tissue biomarker research

Digital pathology and the adoption of image analysis have grown rapidly in the last few years. This is largely due to the implementation of whole slide scanning, advances in software and computer processing capacity and the increasing importance of tissue-based research for biomarker discovery and stratified medicine. This review sets out the key application areas for digital pathology and image analysis, with a particular focus on research and biomarker discovery. A variety of image analysis applications are reviewed including nuclear morphometry and tissue architecture analysis, but with emphasis on immunohistochemistry and fluorescence analysis of tissue biomarkers. Digital pathology and image analysis have important roles across the drug/companion diagnostic development pipeline including biobanking, molecular pathology, tissue microarray analysis, molecular profiling of tissue and these important developments are reviewed. Underpinning all of these important developments is the need for high quality tissue samples and the impact of pre-analytical variables on tissue research is discussed. This requirement is combined with practical advice on setting up and running a digital pathology laboratory. Finally, we discuss the need to integrate digital image analysis data with epidemiological, clinical and genomic data in order to fully understand the relationship between genotype and phenotype and to drive discovery and the delivery of personalized medicine.

IHC Profiler: an open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples

In anatomic pathology, immunohistochemistry (IHC) serves as a diagnostic and prognostic method for identification of disease markers in tissue samples that directly influences classification and grading the disease, influencing patient management. However, till today over most of the world, pathological analysis of tissue samples remained a time-consuming and subjective procedure, wherein the intensity of antibody staining is manually judged and thus scoring decision is directly influenced by visual bias. This instigated us to design a simple method of automated digital IHC image analysis algorithm for an unbiased, quantitative assessment of antibody staining intensity in tissue sections. As a first step, we adopted the spectral deconvolution method of DAB/hematoxylin color spectra by using optimized optical density vectors of the color deconvolution plugin for proper separation of the DAB color spectra. Then the DAB stained image is displayed in a new window wherein it undergoes pixel-by-pixel analysis, and displays the full profile along with its scoring decision. Based on the mathematical formula conceptualized, the algorithm is thoroughly tested by analyzing scores assigned to thousands (n = 1703) of DAB stained IHC images including sample images taken from human protein atlas web resource. The IHC Profiler plugin developed is compatible with the open resource digital image analysis software, ImageJ, which creates a pixel-by-pixel analysis profile of a digital IHC image and further assigns a score in a four tier system. A comparison study between manual pathological analysis and IHC Profiler resolved in a match of 88.6% (P<0.0001, CI = 95%). This new tool developed for clinical histopathological sample analysis can be adopted globally for scoring most protein targets where the marker protein expression is of cytoplasmic and/or nuclear type. We foresee that this method will minimize the problem of inter-observer variations across labs and further help in worldwide patient stratification potentially benefitting various multinational clinical trial initiatives.

PET imaging of oestrogen receptors in patients with breast cancer

Oestrogen receptors are overexpressed in around 70% of all breast cancers, and are a target for endocrine therapy. These receptors can be visualised on PET with use of 16α-[(18)F]-fluoro-17β-oestradiol ((18)F-FES) as a tracer. Compared with biopsy, which enables assessment of individual sites, whole-body (18)F-FES-PET enables quantification of oestrogen-receptor expression in all metastases. In several studies, measurement of tumour protein expression in oestrogen receptors by (18)F-FES-PET, concurrent with biopsy, detected oestrogen-receptor-positive tumour lesions with a sensitivity of 84% and specificity of 98%. Roughly 45% of patients with metastatic breast cancer have discordant oestrogen-receptor expression across lesions (ie, (18)F-FES-positive and (18)F-FES-negative metastases). Low tumour (18)F-FES uptake in metastases can predict failure of hormonal therapy in patients with oestrogen-receptor-positive primary tumours. Finally, (18)F-FES-PET has shown that oestrogen-receptor binding capacity changes after intervention with hormonal drugs, but findings need to be confirmed. Factors other than oestrogen-receptor expression, including menopausal status and concomitant therapies, that can affect tumour (18)F-FES uptake must be taken into account.

A novel, automated technology for multiplex biomarker imaging and application to breast cancer

AIMS

Multiplexed immunofluorescence is a powerful tool for validating multigene assays and understanding the complex interplay of proteins implicated in breast cancer within a morphological context. We describe a novel technology for imaging an extended panel of biomarkers on a single, formalin-fixed paraffin-embedded breast sample and evaluating biomarker interaction at a single-cell level, and demonstrate proof-of-concept on a small set of breast tumours, including those which co-express hormone receptors with Her2/neu and Ki-67.

METHODS AND RESULTS

Using a microfluidic flow cell, reagent exchange was automated and consisted of serial rounds of staining with dye-conjugated antibodies, imaging and chemical deactivation. A two-step antigen retrieval process was developed to satisfy all epitopes simultaneously, and key parameters were optimized. The imaging sequence was applied to seven breast tumours, and compared with conventional immunohistochemistry. Single-cell correlation analysis was performed with automated image processing.

CONCLUSIONS

We have described a novel platform for evaluating biomarker co-localization. Expression in multiplexed images is consistent with conventional immunohistochemistry. Automation reduces inconsistencies in staining and positional shifts, while the fluorescent dye cycling approach dramatically expands the number of biomarkers which can be visualized and quantified on a single tissue section.

TOP2A protein by quantitative immunofluorescence as a predictor of response to epirubicin in the neoadjuvant treatment of breast cancer

Aim: Anthracyclines are commonly used in breast cancer, although they lack validated predictive biomarkers. We explored the interaction between TOP2A protein by quantitative immunofluorescence (QIF) and anthracycline sensitivity. Patients & methods: Patients with estrogen receptor-negative breast cancer received neoadjuvant epirubicin. Pretreatment biopsies were analyzed using AQUA®. Total, cytoplasmic (C) and nuclear (N) TOP2A protein concentrations were expressed as QIF scores and compared with pathologic complete response (pCR), TOP2A by immunohistochemistry, TOP2A mRNA, TOP2A and HER2 gene status, and Ki-67 level. Results: In total, 76 cases were assessable. C, N, and total scores did not correlate with pCR, or other markers. The N:C ratio differed significantly by HER2 status. No pCRs occurred in patients in the lowest N:C quartile. Conclusion: Although no relevant correlation between TOP2A QIF scores and pCR was found, N:C ratio may have a negative predictive role, and may merit further exploration in a multifactorial predictive model that includes tumor and host factors.

Performance comparison of machine learning methods for prognosis of hormone receptor status in breast cancer tissue samples

We examined the classification and prognostic scoring performances of several computer methods on different feature sets to obtain objective and reproducible analysis of estrogen receptor status in breast cancer tissue samples. Radial basis function network, k-nearest neighborhood search, support vector machines, naive bayes, functional trees, and k-means clustering algorithm were applied to the test datasets. Several features were employed and the classification accuracies of each method for these features were examined. The assessment results of the methods on test images were also experimentally compared with those of two experts. According to the results of our experimental work, a combination of functional trees and the naive bayes classifier gave the best prognostic scores indicating very good kappa agreement values (κ=0.899 and κ=0.949, p<0.001) with the experts. This combination also gave the best dichotomization rate (96.3%) for assessment of estrogen receptor status. Wavelet color features provided better classification accuracy than Laws texture energy and co-occurrence matrix features.

The effect of prolonged cold ischemia time on estrogen receptor immunohistochemistry in breast cancer

To facilitate accurate detection of estrogen receptor (ER) expression in breast tumors, the American Society of Clinical Oncology/College of American Pathologists recommends that cold ischemia time be kept under 1 h. However, data to address the upper threshold of cold ischemia time are limited. Although it is our routine practice to keep cold ischemia time under 1 h for breast core biopsy specimens, this is difficult for surgical specimens because of the comprehensive intraoperative assessment performed at our institution. In this retrospective study, we compared ER immunohistochemical staining results in paired breast tumor core biopsy specimens and resection specimens with cold ischemia times ranging from 64 to 357 min in 97 patients. The staining category (≥10%, positive; 1-9%, low positive; <1%, negative) between the core biopsy and resection specimens changed for five patients (5%). The weighted Kappa statistic for ER staining category between the two specimen types was 0.86 (95% confidence interval, 0.74-0.99), indicating good concordance. The difference in the percentage of ER staining between core biopsy and resection was not significantly associated with cold ischemia time (P=0.81, Spearman correlation). Although we did not observe significant associations between the difference in ER staining in the two specimen types and cold ischemia time after placing the patients in three groups of 'increase', 'decrease' and 'no change' using a difference of 25% in ER staining percentage as the cutoff, a trend of decreased ER staining with cold ischemia time >2 h was detected. No statistically significant association was found between the change of ER staining and the history of neoadjuvant chemotherapy. Our findings indicate that prolonged cold ischemia time up to 4 h (97% of our cohort) in the practice setting of our institution has minimal clinical impact on ER immunohistochemical expression in breast tumors.

Quantitative assessment of effect of preanalytic cold ischemic time on protein expression in breast cancer tissues

BACKGROUND

Companion diagnostic tests can depend on accurate measurement of protein expression in tissues. Preanalytic variables, especially cold ischemic time (time from tissue removal to fixation in formalin) can affect the measurement and may cause false-negative results. We examined 23 proteins, including four commonly used breast cancer biomarker proteins, to quantify their sensitivity to cold ischemia in breast cancer tissues.

METHODS

A series of 93 breast cancer specimens with known time-to-fixation represented in a tissue microarray and a second series of 25 matched pairs of core needle biopsies and breast cancer resections were used to evaluate changes in antigenicity as a function of cold ischemic time. Estrogen receptor (ER), progesterone receptor (PgR), HER2 or Ki67, and 19 other antigens were tested. Each antigen was measured using the AQUA method of quantitative immunofluorescence on at least one series. All statistical tests were two-sided.

RESULTS

We found no evidence for loss of antigenicity with time-to-fixation for ER, PgR, HER2, or Ki67 in a 4-hour time window. However, with a bootstrapping analysis, we observed a trend toward loss for ER and PgR, a statistically significant loss of antigenicity for phosphorylated tyrosine (P = .0048), and trends toward loss for other proteins. There was evidence of increased antigenicity in acetylated lysine, AKAP13 (P = .009), and HIF1A (P = .046), which are proteins known to be expressed in conditions of hypoxia. The loss of antigenicity for phosphorylated tyrosine and increase in expression of AKAP13, and HIF1A were confirmed in the biopsy/resection series.

CONCLUSIONS

Key breast cancer biomarkers show no evidence of loss of antigenicity, although this dataset assesses the relatively short time beyond the 1-hour limit in recent guidelines. Other proteins show changes in antigenicity in both directions. Future studies that extend the time range and normalize for heterogeneity will provide more comprehensive information on preanalytic variation due to cold ischemic time.

Conversion of epidermal growth factor receptor 2 and hormone receptor expression in breast cancer metastases to the brain

Introduction

We investigated the status of estrogen receptor alpha (ERα), progesterone receptor (PR), and epidermal growth factor receptor 2 (HER2) in primary tumor and in the corresponding brain metastases in a consecutive series of breast cancer patients. Additionally, we studied factors potentially influencing conversion and evaluated its association with survival.

Methods

The study group included 120 breast cancer patients. ERα, PR, and HER2 status in primary tumors and in matched brain metastases was determined centrally by immunohistochemistry and/or fluorescence in situ hybridization.

Results

Using the Allred score of ≥ 3 as a threshold, conversion of ERα and PR in brain metastases occurred in 29% of cases for both receptors, mostly from positive to negative. Conversion of HER2 occurred in 14% of patients and was more balanced either way. Time to brain relapse and the use of chemotherapy or trastuzumab did not influence conversion, whereas endocrine therapy induced conversion of ERα (P = 0.021) and PR (P = 0.001), mainly towards their loss. Receptor conversion had no significant impact on survival.

Conclusions

Receptor conversion, particularly loss of hormone receptors, is a common event in brain metastases from breast cancer, and endocrine therapy may increase its incidence. Receptor conversion does not significantly affect survival.

Systematic review on hormone receptor testing in breast cancer

Assessment of hormone receptors (estrogen and progesterone) helps to direct therapy for women with breast cancer. Immunohistochemistry is most commonly used to assess hormone receptor status and it is essential that these tests are performed accurately and reliably within and across laboratories. The overall purpose of this guideline is to improve the quality and accuracy of hormone receptor testing and its utility as a prognostic and predictive marker for invasive and in situ breast cancer. Medline, EMBASE, the Cochrane Database of Systematic Reviews, and abstracts from the San Antonio Breast Cancer Symposium were searched. An environmental scan of the internet and of international guideline developers and key organizations was performed. Preanalytic elements such as the collection, fixation, and storage of samples, and analytic elements such as selection of antibodies and scoring methods that seem to offer the best results for immunohistochemical assessment of hormone receptors are presented. Proficiency testing or quality assurance of immunohistochemistry is described.

Tapping the potential of quantum dots for personalized oncology: current status and future perspectives

Cancer is one of the most serious health threats worldwide. Personalized oncology holds potential for future cancer care in clinical practice, where each patient could be delivered individualized medicine on the basis of key biological features of an individual tumor. One of the most urgent problems is to develop novel approaches that incorporate the increasing molecular information into the understanding of cancer biological behaviors for personalized oncology. Quantum dots are a heterogeneous class of engineered fluorescent nanoparticles with unique optical and chemical properties, which make them promising platforms for biomedical applications. With the unique optical properties, the utilization of quantum dot-based nanotechnology has been expanded into a wide variety of attractive biomedical applications for cancer diagnosis, monitoring, pathogenesis, treatment, molecular pathology and heterogeneity in combination with cancer biomarkers. Here, we focus on the clinical application of quantum dot-based nanotechnology in personalized oncology, covering topics on individualized cancer diagnosis and treatment by in vitro and in vivo molecular imaging technologies, and in-depth understanding of the biological behaviors of tumors from a nanotechnology perspective. In addition, the major challenges in translating quantum dot-based nanotechnology into clinical application and promising future directions in personalized oncology are also discussed.

Cancer care Ontario guideline recommendations for hormone receptor testing in breast cancer

Hormone receptor testing (oestrogen and progesterone) in breast cancer at the time of primary diagnosis is used to guide treatment decisions. Accurate and standardised testing methods are critical to ensure the proper classification of the patient's hormone receptor status. Recommendations were developed to improve the quality and accuracy of hormone receptor testing based on a systematic review conducted jointly by the American Society of Clinical Oncology/College of American Pathologists and Cancer Care Ontario's Program in Evidence-Based Care. Evidence-based recommendations were formulated to set standards for optimising immunohistochemistry in assessing hormone receptor status, as well as assuring quality and proficiency between and within laboratories. A formal external review was conducted to validate the relevance of these recommendations. It is anticipated that widespread adoption of these guidelines will further improve the accuracy of hormone receptor testing in Canada.

Dark pixel intensity determination and its applications in normalizing different exposure time and autofluorescence removal

The purpose of this study is to investigate how to scale pixel intensity acquired from one exposure time to another. This is required when comparing grayscale images acquired at different exposure times and other image processing such as autofluorescence removal. Pixel intensity is linear to exposure time as long as images are acquired at the linear range of a camera, but importantly there exists an intercept, which is set by the camera. We termed this intercept as dark pixel intensity, as it is the pixel intensity under conditions of no light and zero exposure time. Dark pixel intensity is determined by camera's readout noise (electron/pixel), gain, and DC offset. Knowing dark pixel intensity, image acquired from one exposure time can be linearly scaled to an image at a different exposure time. Dark pixel intensity can be directly measured by obtaining an image at no light and zero (or minimum) exposure time. It can be also indirectly calculated by capturing images at a series of exposure times. Finally, the prestained and poststained images were acquired at their optimal exposures and autofluorescence was completely removed by normalizing images with the exposure time ratio and dark pixel intensity followed by subtraction.

Cytoplasmic estrogen receptor in breast cancer

PURPOSE

In addition to genomic signaling, it is accepted that estrogen receptor-α (ERα) has nonnuclear signaling functions, which correlate with tamoxifen resistance in preclinical models. However, evidence for cytoplasmic ER localization in human breast tumors is less established. We sought to determine the presence and implications of nonnuclear ER in clinical specimens.

EXPERIMENTAL DESIGN

A panel of ERα-specific antibodies (SP1, MC20, F10, 60c, and 1D5) was validated by Western blot and quantitative immunofluorescent (QIF) analysis of cell lines and patient controls. Then eight retrospective cohorts collected on tissue microarrays were assessed for cytoplasmic ER. Four cohorts were from Yale (YTMA 49, 107, 130, and 128) and four others (NCI YTMA 99, South Swedish Breast Cancer Group SBII, NSABP B14, and a Vietnamese Cohort) from other sites around the world.

RESULTS

Four of the antibodies specifically recognized ER by Western and QIF analysis, showed linear increases in amounts of ER in cell line series with progressively increasing ER, and the antibodies were reproducible on YTMA 49 with Pearson correlations (r(2) values) ranging from 0.87 to 0.94. One antibody with striking cytoplasmic staining (MC20) failed validation. We found evidence for specific cytoplasmic staining with the other four antibodies across eight cohorts. The average incidence was 1.5%, ranging from 0 to 3.2%.

CONCLUSIONS

Our data show ERα is present in the cytoplasm in a number of cases using multiple antibodies while reinforcing the importance of antibody validation. In nearly 3,200 cases, cytoplasmic ER is present at very low incidence, suggesting its measurement is unlikely to be of routine clinical value.

Molecular classification of nonsmall cell lung cancer using a 4-protein quantitative assay

BACKGROUND

The importance of definitive histological subclassification has increased as drug trials have shown benefit associated with histology in nonsmall-cell lung cancer (NSCLC). The acuity of this problem is further exacerbated by the use of minimally invasive cytology samples. Here we describe the development and validation of a 4-protein classifier that differentiates primary lung adenocarcinomas (AC) from squamous cell carcinomas (SCC).

METHODS

Quantitative immunofluorescence (AQUA) was employed to measure proteins differentially expressed between AC and SCC followed by logistic regression analysis. An objective 4-protein classifier was generated to define likelihood of AC in a training set of 343 patients followed by validation in 2 independent cohorts (n = 197 and n = 235). The assay was then tested on 11 cytology specimens.

RESULTS

Statistical modeling selected thyroid transcription factor 1 (TTF1), CK5, CK13, and epidermal growth factor receptor (EGFR) to generate a weighted classifier and to identify the optimal cutpoint for differentiating AC from SCC. Using the pathologist's final diagnosis as the criterion standard, the molecular test showed a sensitivity of 96% and specificity of 93%. Blinded analysis of the validation sets yielded sensitivity and specificity of 96% and 97%, respectively. Our assay classified the cytology specimens with a specificity of 100% and sensitivity of 87.5%.

CONCLUSIONS

Molecular classification of NSCLC using an objective quantitative test can be highly accurate and could be translated into a diagnostic platform for broad clinical application.

Quantitative assessment shows loss of antigenic epitopes as a function of pre-analytic variables

Pre-analytic variables, specifically cold ischemic time, have been implicated as key variables in the measurement of proteins by immunohistochemistry. To determine the significance and magnitude of antigenic loss due to pre-analytic variables, we have compared protein antigenicity in core needle biopsies, with essentially no cold ischemic time, with that in routinely processed tumor resection specimens. Two cohorts of matched core needle biopsies and tumor resections were collected with 20 matched pairs and 14 matched pairs, respectively. Both series were analyzed by quantitative immunofluorescence using the AQUA® method. Epitopes phospho-ERK, total ERK, phospho-AKT, total AKT, phospho-S6K1, total S6K1, estrogen receptor (ER), Ki67, cytokeratin and GAPDH were assessed. Detection levels for all phospho-epitopes were significantly decreased in tumor resections compared with biopsies while no significant change was seen in the corresponding total proteins. Of the other four proteins examined, ER and cytokeratin showed significant loss of antigenicity. This data suggest that measurement of phospho-protein antigenicity in formalin-fixed tissue by immunological methods is dramatically affected by pre-analytic variables. This study suggests that core needle biopsies are more accurate for assessment of tissue biomarkers.

Optimal tumor sampling for immunostaining of biomarkers in breast carcinoma

Introduction

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 aim of this study was 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: ER, HER-2, AKT, ERK, S6K1, GAPDH, Cytokeratin, and MAP-Tau.

Methods

Two collections of whole tissue sections of breast carcinoma were immunostained for biomarkers. Expression was quantified using the Automated Quantitative Analysis (AQUA) method of quantitative immunofluorescence. Simulated sampling of various numbers of fields (ranging from one to thirty five) 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.

Results

The optimal number of 20X fields varied by biomarker, ranging between three to fourteen fields. More heterogeneous markers, such as MAP-Tau protein, required a larger sample of 20X fields to produce representative measurement.

Conclusions

The optimal number of 20X fields that must be sampled to produce a representative measurement of biomarker expression varies by marker with more heterogeneous markers requiring a larger number. The clinical implication of these findings is that breast biopsies consisting of a small number of fields may be inadequate to represent whole tumor biomarker expression for many markers. Additionally, 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.

Inflammation and thrombosis in diabetes

Patients with diabetes mellitus are at increased risk of cardiovascular morbidity and mortality. Atherothrombosis, defined as atherosclerotic lesion disruption with superimposed thrombus formation, is the most common cause of death among these patients. Following plaque rupture, adherence of platelets is followed by local activation of coagulation, the formation of a cross-linked fibrin clot and the development of an occlusive platelet rich fibrin mesh. Patients with diabetes exhibit a thrombotic risk clustering which is composed of hyper-reactive platelets, up regulation of pro-thrombotic markers and suppression of fibrinolysis. These changes are mainly mediated by the presence of insulin resistance and dysglycaemia and an increased inflammatory state which directly affects platelet function, coagulation factors and clot structure. This prothrombotic state is related to increased cardiovascular risk and may account for the reduced response to antithrombotic therapeutic approaches, underpinning the need for adequate antithrombotic therapy in patients with diabetes to reduce their cardiovascular mortality.

Automation of immunohistochemical evaluation in breast cancer using image analysis

AIM: To automate breast cancer diagnosis and to study the inter-observer and intra-observer variations in the manual evaluations.

METHODS: Breast tissue specimens from sixty cases were stained separately for estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER-2/neu). All cases were assessed by manual grading as well as image analysis. The manual grading was performed by an experienced expert pathologist. To study inter-observer and intra-observer variations, we obtained readings from another pathologist as the second observer from a different laboratory who has a little less experience than the first observer. We also took a second reading from the second observer to study intra-observer variations. Image analysis was carried out using in-house developed software (TissueQuant). A comparison of the results from image analysis and manual scoring of ER, PR and HER-2/neu was also carried out.

RESULTS: The performance of the automated analysis in the case of ER, PR and HER-2/neu expressions was compared with the manual evaluations. The performance of the automated system was found to correlate well with the manual evaluations. The inter-observer variations were measured using Spearman correlation coefficient r and 95% confidence interval. In the case of ER expression, Spearman correlation r = 0.53, in the case of PR expression, r = 0.63, and in the case of HER-2/neu expression, r = 0.68. Similarly, intra-observer variations were also measured. In the case of ER, PR and HER-2/neu expressions, r = 0.46, 0.66 and 0.70, respectively.

CONCLUSION: The automation of breast cancer diagnosis from immunohistochemically stained specimens is very useful for providing objective and repeatable evaluations.