A simple digital image analysis system for automated Ki67 assessment in primary breast cancer.

AIMS: Ki67 is a well-established immunohistochemical marker associated with cell proliferation that has prognostic and predictive value in breast cancer. Quantitative evaluation of Ki67 is traditionally performed by assessing stained tissue slides with light microscopy. Automated image analysis systems have become available and, if validated, could provide greater standardisation and improved precision of Ki67 scoring. Here, we aimed to evaluate the use of the Cognition Master Professional Suite (CogM) image analysis software, which is a simple system for scoring Ki67 in primary breast cancer samples. METHODS AND RESULTS: Sections from 94 core-cut biopsies, 20 excision specimens and 29 pairs of core-cut biopsies and excision specimens were stained for Ki67 with MIB1 antibody and the Dako EnVision FLEX Detection System. Stained slides were scanned to convert them to digital data. Computer-based Ki67 scoring was performed with CogM. Manual Ki67 scoring assessment was conducted on previously stained sections from the same biopsies with a clinically validated system that had been calibrated against the risk of recurrence. A high correlation between manual and digital scores was observed [rCores  = 0.92, 95% confidence interval (CI) 0.87-0.94, P < 0.0001; rExcisions  = 0.95, 95% CI 0.86-0.98, P < 0.0001] and there was no significant bias between them (P = 0.45). There was also a high correlation of Ki67 scores between paired core-cut biopsies and excision specimens when CogM was used (r = 0.78, 95% CI 0.59-0.89, P < 0.0001). CONCLUSIONS: CogM image analysis allows for standardised automated Ki67 scoring that accurately replicates previously clinically validated and calibrated manual scores.

Development and validation for research assessment of Oncotype DX® Breast Recurrence Score, EndoPredict® and Prosigna®.

Multi-gene prognostic signatures including the Oncotype® DX Recurrence Score (RS), EndoPredict® (EP) and Prosigna® (Risk Of Recurrence, ROR) are widely used to predict the likelihood of distant recurrence in patients with oestrogen-receptor-positive (ER+), HER2-negative breast cancer. Here, we describe the development and validation of methods to recapitulate RS, EP and ROR scores from NanoString expression data. RNA was available from 107 tumours from postmenopausal women with early-stage, ER+, HER2- breast cancer from the translational Arimidex, Tamoxifen, Alone or in Combination study (TransATAC) where previously these signatures had been assessed with commercial methodology. Gene expression was measured using NanoString nCounter. For RS and EP, conversion factors to adjust for cross-platform variation were estimated using linear regression. For ROR, the steps to perform subgroup-specific normalisation of the gene expression data and calibration factors to calculate the 46-gene ROR score were assessed and verified. Training with bootstrapping (n = 59) was followed by validation (n = 48) using adjusted, research use only (RUO) NanoString-based algorithms. In the validation set, there was excellent concordance between the RUO scores and their commercial counterparts (rc(RS) = 0.96, 95% CI 0.93-0.97 with level of agreement (LoA) of -7.69 to 8.12; rc(EP) = 0.97, 95% CI 0.96-0.98 with LoA of -0.64 to 1.26 and rc(ROR) = 0.97 (95% CI 0.94-0.98) with LoA of -8.65 to 10.54). There was also a strong agreement in risk stratification: (RS: κ = 0.86, p < 0.0001; EP: κ = 0.87, p < 0.0001; ROR: κ = 0.92, p < 0.001). In conclusion, the calibrated algorithms recapitulate the commercial RS and EP scores on individual biopsies and ROR scores on samples based on subgroup-centreing method using NanoString expression data.

Assessment of Ki67 in Breast Cancer: Updated Recommendations From the International Ki67 in Breast Cancer Working Group.

Ki67 immunohistochemistry (IHC), commonly used as a proliferation marker in breast cancer, has limited value for treatment decisions due to questionable analytical validity. The International Ki67 in Breast Cancer Working Group (IKWG) consensus meeting, held in October 2019, assessed the current evidence for Ki67 IHC analytical validity and clinical utility in breast cancer, including the series of scoring studies the IKWG conducted on centrally stained tissues. Consensus observations and recommendations are: 1) as for estrogen receptor and HER2 testing, preanalytical handling considerations are critical; 2) a standardized visual scoring method has been established and is recommended for adoption; 3) participation in and evaluation of quality assurance and quality control programs is recommended to maintain analytical validity; and 4) the IKWG accepted that Ki67 IHC as a prognostic marker in breast cancer has clinical validity but concluded that clinical utility is evident only for prognosis estimation in anatomically favorable estrogen receptor-positive and HER2-negative patients to identify those who do not need adjuvant chemotherapy. In this T1-2, N0-1 patient group, the IKWG consensus is that Ki67 5% or less, or 30% or more, can be used to estimate prognosis. In conclusion, analytical validity of Ki67 IHC can be reached with careful attention to preanalytical issues and calibrated standardized visual scoring. Currently, clinical utility of Ki67 IHC in breast cancer care remains limited to prognosis assessment in stage I or II breast cancer. Further development of automated scoring might help to overcome some current limitations.

Assessment of Molecular Relapse Detection in Early-Stage Breast Cancer.

Importance: Current treatment cures most cases of early-stage, primary breast cancer. However, better techniques are required to identify which patients are at risk of relapse. Objective: To assess the clinical validity of molecular relapse detection with circulating tumor DNA (ctDNA) analysis in early-stage breast cancer. Design, Setting, and Participants: This prospective, multicenter, sample collection, validation study conducted at 5 United Kingdom medical centers from November 24, 2011, to October 18, 2016, assessed patients with early-stage breast cancer irrespective of hormone receptor and ERBB2 (formerly HER2 or HER2/neu) status who were receiving neoadjuvant chemotherapy followed by surgery or surgery before adjuvant chemotherapy. The study recruited 170 women, with mutations identified in 101 patients forming the main cohort. Secondary analyses were conducted on a combined cohort of 144 patients, including 43 patients previously analyzed in a proof of principle study. Interventions: Primary tumor was sequenced to identify somatic mutations, and personalized tumor-specific digital polymerase chain reaction assays were used to monitor these mutations in serial plasma samples taken every 3 months for the first year of follow-up and subsequently every 6 months. Main Outcomes and Measures: The primary end point was relapse-free survival analyzed with Cox proportional hazards regression models. Results: In the main cohort of 101 female patients (mean [SD] age, 54 [11] years) with a median follow-up of 35.5 months (interquartile range, 27.9-43.0 months), detection of ctDNA during follow-up was associated with relapse (hazard ratio, 25.2; 95% CI, 6.7-95.6; P < .001). Detection of ctDNA at diagnosis, before any treatment, was also associated with relapse-free survival (hazard ratio, 5.8; 95% CI, 1.2-27.1; P = .01). In the combined cohort, ctDNA detection had a median lead time of 10.7 months (95% CI, 8.1-19.1 months) compared with clinical relapse and was associated with relapse in all breast cancer subtypes. Distant extracranial metastatic relapse was detected by ctDNA in 22 of 23 patients (96%). Brain-only metastasis was less commonly detected by ctDNA (1 of 6 patients [17%]), suggesting relapse sites less readily detectable by ctDNA analysis. Conclusions and Relevance: The findings suggest that detection of ctDNA during follow-up is associated with a high risk of future relapse of early-stage breast cancer. Prospective studies are needed to assess the potential of molecular relapse detection to guide adjuvant therapy.

Assessment of the Spatial Heterogeneity of Breast Cancers: Associations Between Computed Tomography and Immunohistochemistry.

BACKGROUND: Tumour heterogeneity is considered an important mechanism of treatment failure. Imaging-based assessment of tumour heterogeneity is showing promise but the relationship between these mathematically derived measures and accepted 'gold standards' of tumour biology such as immunohistochemical measures is not established. METHODS: A total of 20 women with primary breast cancer underwent a research dynamic contrast-enhanced computed tomography prior to treatment with data being available for 15 of these. Texture analysis was performed of the primary tumours to extract 13 locoregional and global parameters. Immunohistochemical analysis associations were assessed by the Spearman rank correlation. RESULTS: Hypoxia-inducible factor-1α was correlated with first-order kurtosis (r = -0.533, P = .041) and higher order neighbourhood grey-tone difference matrix coarseness (r = 0.54, P = .038). Vascular maturity-related smooth muscle actin was correlated with higher order grey-level run-length long-run emphasis (r = -0.52, P = .047), fractal dimension (r = 0.613, P = .015), and lacunarity (r = -0.634, P = .011). Micro-vessel density, reflecting angiogenesis, was also associated with lacunarity (r = 0.547, P = .035). CONCLUSIONS: The associations suggest a biological basis for these image-based heterogeneity features and support the use of imaging, already part of standard care, for assessing intratumoural heterogeneity.

An international multicenter study to evaluate reproducibility of automated scoring for assessment of Ki67 in breast cancer.

The nuclear proliferation biomarker Ki67 has potential prognostic, predictive, and monitoring roles in breast cancer. Unacceptable between-laboratory variability has limited its clinical value. The International Ki67 in Breast Cancer Working Group investigated whether Ki67 immunohistochemistry can be analytically validated and standardized across laboratories using automated machine-based scoring. Sets of pre-stained core-cut biopsy sections of 30 breast tumors were circulated to 14 laboratories for scanning and automated assessment of the average and maximum percentage of tumor cells positive for Ki67. Seven unique scanners and 10 software platforms were involved in this study. Pre-specified analyses included evaluation of reproducibility between all laboratories (primary) as well as among those using scanners from a single vendor (secondary). The primary reproducibility metric was intraclass correlation coefficient between laboratories, with success considered to be intraclass correlation coefficient >0.80. Intraclass correlation coefficient for automated average scores across 16 operators was 0.83 (95% credible interval: 0.73-0.91) and intraclass correlation coefficient for maximum scores across 10 operators was 0.63 (95% credible interval: 0.44-0.80). For the laboratories using scanners from a single vendor (8 score sets), intraclass correlation coefficient for average automated scores was 0.89 (95% credible interval: 0.81-0.96), which was similar to the intraclass correlation coefficient of 0.87 (95% credible interval: 0.81-0.93) achieved using these same slides in a prior visual-reading reproducibility study. Automated machine assessment of average Ki67 has the potential to achieve between-laboratory reproducibility similar to that for a rigorously standardized pathologist-based visual assessment of Ki67. The observed intraclass correlation coefficient was worse for maximum compared to average scoring methods, suggesting that maximum score methods may be suboptimal for consistent measurement of proliferation. Automated average scoring methods show promise for assessment of Ki67 scoring, but requires further standardization and subsequent clinical validation.

Breast cancer biomarkers in clinical testing: analysis of a UK national external quality assessment scheme for immunocytochemistry and in situ hybridisation database containing results from 199 300 patients.

We describe a collated data set of results from clinical testing of breast cancers carried out between 2009 and 2016 in the United Kingdom and Republic of Ireland. More than 199 000 patient biomarker data sets, together with clinicopathological parameters were collected. Our analyses focused on human epidermal growth factor receptor-2 (HER2), oestrogen receptor (ER) and progesterone receptor (PR), with the aim of the study being to provide robust confirmatory evidence on known associations in these biomarkers and to uncover new data on previously undescribed or unconfirmed associations, thus strengthening the evidence-base in clinical breast cancer testing. Overall, 13.1% of tumours were HER2-positive; 10.6% in ER-positive tumours, and 25.5% in ER-negative tumours. Higher rates of HER2 positivity were significantly associated with patient age <56 years versus age ≥56 years, symptomatic versus screen-detected tumours, testing of involved axillary node versus primary breast cancer, invasive ductal carcinoma (not otherwise specified) versus other histological types, higher histological grade, increasing tumour size, increasing nodal involvement, ER-negative versus ER-positive tumour status, PR-negative versus PR-positive tumour status. Where ER status was known, 82.7% of tumours were ER-positive; 80.9% in women age <56 years, and 83.6% in those age ≥56 years (ER-positive cut-off ≥1.0% positive tumour cells or equivalent). Where PR status was known, 64.9% of tumours were PR-positive; 65.8% in women age <56 years, and 64.4% in women age ≥56 years (PR-positive cut off ≥10.0% or equivalent). These analyses of clinical test results provide contemporary benchmarking data for HER2, ER and PR positive rates.

Impact of type of full-field digital image on mammographic density assessment and breast cancer risk estimation: a case-control study.

BACKGROUND: Full-field digital mammography, which is gradually being introduced in most clinical and screening settings, produces two types of images: raw and processed. However, the extent to which mammographic density measurements, and their ability to predict breast cancer risk, vary according to type of image is not fully known. METHODS: We compared the performance of the semi-automated Cumulus method on digital raw, "analogue-like" raw and processed images, and the performance of a recently developed method - Laboratory for Breast Radiodensity Assessment (LIBRA) - on digital raw and processed images, in a case-control study (414 patients (cases) and 684 controls) by evaluating the extent to which their measurements were associated with breast cancer risk factors, and by comparing their ability to predict breast cancer risk. RESULTS: Valid Cumulus and LIBRA measurements were obtained from all available images, but the resulting distributions differed according to the method and type of image used. Both Cumulus and LIBRA percent density were inversely associated with age, body mass index (BMI), parity and postmenopausal status, regardless of type of image used. Cumulus percent density was strongly associated with breast cancer risk, but with the magnitude of the association slightly stronger for processed (risk increase per one SD increase in percent density (95 % CI): 1.55 (1.29, 1.85)) and "analogue-like" raw (1.52 (1.28, 1.80)) than for raw (1.35 (1.14, 1.60)) images. LIBRA percent density produced weaker associations with risk, albeit stronger for processed (1.32 (1.08, 1.61)) than raw images (1.17 (0.99, 1.37)). The percent density values yielded by the various density assessment/type of image combinations had similar ability to discriminate between patients and controls (area under the receiving operating curve values for percent density, age, BMI, parity and menopausal status combined ranged from 0.61 and 0.64). CONCLUSIONS: The findings showed that Cumulus can be used to measure density on all types of digital images. They also indicate that LIBRA may provide a valid fully automated alternative to the more labour-intensive Cumulus. However, the same digital image type and assessment method should be used when examining mammographic density across populations, or longitudinal changes in density within a single population.

Cross-Stratification and Differential Risk by Breast Cancer Index and Recurrence Score in Women with Hormone Receptor-Positive Lymph Node-Negative Early-Stage Breast Cancer.

PURPOSE: Previous results from the TransATAC study demonstrated that both the Breast Cancer Index (BCI) and the OncotypeDX Recurrence Score (RS) added significant prognostic information to clinicopathologic factors over a 10-year period. Here, we examined cross-stratification between BCI and RS to directly compare their prognostic accuracy at the individual patient level. EXPERIMENTAL DESIGN: A total of 665 patients with hormone receptor-positive (HR+) and lymph node-negative disease were included in this retrospective analysis. BCI and RS risk groups were determined using predefined clinical cut-off points. Kaplan-Meier estimates of 10-year risk of distant recurrence (DR) and log-rank tests were used to examine cross-stratification between BCI and RS. RESULTS: As previously reported, both RS and BCI were significantly prognostic in years 0 to 10. BCI provided significant additional prognostic information to the Clinical Treatment Score (CTS) plus RS (ΔLR-χ2 = 11.09; P < 0.001), whereas no additional prognostic information was provided by RS to CTS plus BCI (ΔLR-χ2 = 2.22; P = 0.1). Restratification by BCI of the low and intermediate RS risk groups led to subgroups with significantly different DR rates (P < 0.001 and P = 0.003, respectively). In contrast, restratified subgroups created by RS of BCI risk groups did not differ significantly. CONCLUSIONS: In this retrospective analysis, BCI demonstrated increased prognostic accuracy versus RS. Notably, BCI identified subsets of RS low and RS intermediate risk patients with significant and clinically relevant rates of DR. These results indicate that additional subsets of women with HR+, lymph node-negative breast cancer identified by BCI may be suitable candidates for adjuvant chemotherapy or extended endocrine therapy. Clin Cancer Res; 22(20); 5043-8. ©2016 AACRSee related commentary by Brufsky and Davidson, p. 4963.

Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods.

INTRODUCTION: Mammographic density is a strong breast cancer risk factor and a major determinant of screening sensitivity. However, there is currently no validated estimation method for full-field digital mammography (FFDM). METHODS: The performance of three area-based approaches (BI-RADS, the semi-automated Cumulus, and the fully-automated ImageJ-based approach) and three fully-automated volumetric methods (Volpara, Quantra and single energy x-ray absorptiometry (SXA)) were assessed in 3168 FFDM images from 414 cases and 685 controls. Linear regression models were used to assess associations between breast cancer risk factors and density among controls, and logistic regression models to assess density-breast cancer risk associations, adjusting for age, body mass index (BMI) and reproductive variables. RESULTS: Quantra and the ImageJ-based approach failed to produce readings for 4% and 11% of the participants. All six density assessment methods showed that percent density (PD) was inversely associated with age, BMI, being parous and postmenopausal at mammography. PD was positively associated with breast cancer for all methods, but with the increase in risk per standard deviation increment in PD being highest for Volpara (1.83; 95% CI: 1.51 to 2.21) and Cumulus (1.58; 1.33 to 1.88) and lower for the ImageJ-based method (1.45; 1.21 to 1.74), Quantra (1.40; 1.19 to 1.66) and SXA (1.37; 1.16 to 1.63). Women in the top PD quintile (or BI-RADS 4) had 8.26 (4.28 to 15.96), 3.94 (2.26 to 6.86), 3.38 (2.00 to 5.72), 2.99 (1.76 to 5.09), 2.55 (1.46 to 4.43) and 2.96 (0.50 to 17.5) times the risk of those in the bottom one (or BI-RADS 1), respectively, for Volpara, Quantra, Cumulus, SXA, ImageJ-based method, and BI-RADS (P for trend <0.0001 for all). The ImageJ-based method had a slightly higher ability to discriminate between cases and controls (area under the curve (AUC) for PD = 0.68, P = 0.05), and Quantra slightly lower (AUC = 0.63; P = 0.06), than Cumulus (AUC = 0.65). CONCLUSIONS: Fully-automated methods are valid alternatives to the labour-intensive "gold standard" Cumulus for quantifying density in FFDM. The choice of a particular method will depend on the aims and setting but the same approach will be required for longitudinal density assessments.

Biomarkers for the clinical management of breast cancer: international perspective.

The higher incidence of breast cancer in developed countries has been tempered by reductions in mortality, largely attributable to mammographic screening programmes and advances in adjuvant therapy. Optimal systemic management requires consideration of clinical, pathological and biological parameters. Oestrogen receptor alpha (ERα), progesterone receptor (PgR) and human epidermal growth factor receptor 2 (HER2) are established biomarkers evaluated at diagnosis, which identify cardinal subtypes of breast cancer. Their prognostic and predictive utility effectively guides systemic treatment with endocrine, anti-HER2 and chemotherapy. Hence, accurate and reliable determination remains of paramount importance. However, the goals of personalized medicine and targeted therapies demand further information regarding residual risk and potential benefit of additional treatments in specific circumstances. The need for biomarkers which are fit for purpose, and the demands placed upon them, is therefore expected to increase. Technological advances, in particular high-throughput global gene expression profiling, have generated multi-gene signatures providing further prognostic and predictive information. The rational integration of routinely evaluated clinico-pathological parameters with key indicators of biological activity, such as proliferation markers, also provides a ready opportunity to improve the information available to guide systemic therapy decisions. The additional value of such information and its proper place in patient management is currently under evaluation in prospective clinical trials. Expanding the utility of biomarkers to lower resource settings requires an emphasis on cost effectiveness, quality assurance and possible international variations in tumor biology; the potential for improved clinical outcomes should be justified against logistical and economic considerations.

Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor 2 immunohistochemical score and comparison with the Genomic Health recurrence score in early breast cancer.

PURPOSE: We recently reported that the mRNA-based, 21-gene Genomic Health recurrence score (GHI-RS) provided additional prognostic information regarding distant recurrence beyond that obtained from classical clinicopathologic factors (age, nodal status, tumor size, grade, endocrine treatment) in women with early breast cancer, confirming earlier reports. The aim of this article is to determine how much of this information is contained in standard immunohistochemical (IHC) markers. PATIENTS AND METHODS: The primary cohort comprised 1,125 estrogen receptor-positive (ER-positive) patients from the Arimidex, Tamoxifen, Alone or in Combination (ATAC) trial who did not receive adjuvant chemotherapy, had the GHI-RS computed, and had adequate tissue for the four IHC measurements: ER, progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki-67. Distant recurrence was the primary end point, and proportional hazards models were used with sample splitting to control for overfitting. A prognostic model that used classical variables and the four IHC markers (IHC4 score) was created and assessed in a separate cohort of 786 patients. RESULTS: All four IHC markers provided independent prognostic information in the presence of classical variables. In sample-splitting analyses, the information in the IHC4 score was found to be similar to that in the GHI-RS, and little additional prognostic value was seen in the combined use of both scores. The prognostic value of the IHC4 score was further validated in the second separate cohort. CONCLUSION: This study suggests that the amount of prognostic information contained in four widely performed IHC assays is similar to that in the GHI-RS. Additional studies are needed to determine the general applicability of the IHC4 score.

Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group.

Uncontrolled proliferation is a hallmark of cancer. In breast cancer, immunohistochemical assessment of the proportion of cells staining for the nuclear antigen Ki67 has become the most widely used method for comparing proliferation between tumor samples. Potential uses include prognosis, prediction of relative responsiveness or resistance to chemotherapy or endocrine therapy, estimation of residual risk in patients on standard therapy and as a dynamic biomarker of treatment efficacy in samples taken before, during, and after neoadjuvant therapy, particularly neoadjuvant endocrine therapy. Increasingly, Ki67 is measured in these scenarios for clinical research, including as a primary efficacy endpoint for clinical trials, and sometimes for clinical management. At present, the enormous variation in analytical practice markedly limits the value of Ki67 in each of these contexts. On March 12, 2010, an international panel of investigators with substantial expertise in the assessment of Ki67 and in the development of biomarker guidelines was convened in London by the co-chairs of the Breast International Group and North American Breast Cancer Group Biomarker Working Party to consider evidence for potential applications. Comprehensive recommendations on preanalytical and analytical assessment, and interpretation and scoring of Ki67 were formulated based on current evidence. These recommendations are geared toward achieving a harmonized methodology, create greater between-laboratory and between-study comparability, and allow earlier valid applications of this marker in clinical practice.

Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model.

The majority of candidates for breast cancer prevention have not accepted tamoxifen because of the perception of an unfavorable risk/benefit ratio and the acceptance of raloxifene remains to be determined. One means of improving this ratio is to identify women at very high risk of breast cancer. Family history, age, atypia in a benign biopsy, and reproductive factors are the main parameters currently used to determine risk. The most powerful risk factor, mammographic density, is not presently employed routinely. Other potentially important factors are plasma estrogen and androgen levels, bone density, weight gain, age of menopause, and fracture history, which are also not currently used in a comprehensive risk prediction model because of lack of prospective validation. The Breast Cancer Prevention Collaborative Group (BCPCG) met to critically examine and prioritize risk factors that might be selected for further testing by multivariate analysis using existing clinical material. The BCPCG reached a consensus that quantitative breast density, state of the art plasma estrogen and androgen measurements, history of fracture and height loss, BMI, and waist-hip ratio had sufficient priority for further testing. As a practical approach, these parameters could be added to the existing Tyrer-Cuzick model which encompasses factors included in both the Claus and Gail models. The BCPCG analyzed potentially available clinical material from previous prospective studies and determined that a large case/control study to evaluate these new factors might be feasible at this time.