Concordance of RT-qPCR with immunohistochemistry and its beneficial role in breast cancer subtyping

This study was to compare the concordance of transcription-quantitative polymerase chain reaction (RT-qPCR) with immunohistochemistry (IHC) in determining estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and tumor proliferation index (Ki67) status in breast cancer, and to assess the prognosis based on different subtypes. Totally 323 breast cancer patients were selected, including 216 in the training set and 107 in the validation set. Logistic regression models were constructed using 5-fold cross-validation with the mRNA expression of each biomarker as the predictor and the corresponding IHC expression level as the binary response variable. Receiver operating characteristic curve was used to determine the cutoff value. When the thresholds of ER, PR, HER2, and Ki67 were 0.764, 0.709, 0.161, and 0.554, there existed high concordance rates between IHC and RT-qPCR in ER (94.4%), PR (88.0%) and HER2 (89.4%) and a medium concordance rate in Ki67 (67.8%), which were further confirmed in the validation set (ER: 81.3%, PR: 78.3%, HER2: 80.4%, and Ki67: 69.1%). Based on the subtyping stratified by RT-qPCR, the 5-year recurrence-free interval rates of patients with luminal, HER2-enriched, and triple-negative subtypes were 88% (95% CI: 0.84-0.93), 82% (95% CI: 0.73-0.92) and 58% (95% CI: 0.42-0.80), respectively, which were similar to those assessed by IHC (88%, 78% and 47%). RT-qPCR may be a complementary method to IHC, which can not only provide additional useful information in clinic, but also show more advantages over IHC in determining certain subtypes of breast cancer.

Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape

The cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal breast cancer. We also confirmed that non-matching subgroup classification affected the survival of breast cancer patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.

T1-2N1M0 triple-negative breast cancer patients from the SEER database showed potential benefit from post-mastectomy radiotherapy

The effects of post-mastectomy radiotherapy (PMRT) on different subtypes of T1-2N1M0 breast cancer remain controversial. Patients with T1-2N1M0 breast cancer treated by mastectomy or mastectomy and PMRT were identified from the 2010–2013 dataset from the Surveillance, Epidemiology and End Results (SEER) registry. A total of 7,466 patients with the 7th American Joint Committee on Cancer stage (Tumor-Node-Metastasis stages 1–2, 1 and 0, respectively) including 2,760 cases (36.97%) treated by mastectomy and PMRT and 4,706 cases (63.03%) treated by mastectomy alone were analyzed in this study. The follow-up time for patients in the dataset used from the SEER registry was 0–59 months. The breast cancer-specific survival (BCSS) of the patients was derived from the SEER dataset and stratified by treatment approach. A propensity score matching (PSM) analysis (experimental group: Control group ratio, 1:1) was conducted. Using univariate and multivariate analyses Cox proportional hazards analyses, PMRT was identified as an independent prognostic factor for triple-negative breast cancer (TNBC). Before PSM analysis, the BCSS favored PMRT in the hormone receptor (HR)⁺/human epidermal growth factor receptor 2 (HER2)⁺ (P=0.025) and HR⁻/HER2⁻ groups (P=0.010) but not in the HR⁺/HER2⁻ (P=0.346) and HR⁻/HER2⁺ (P=0.288) groups. Following PSM analysis, BCSS favored PMRT alone in the TNBC (HR⁻/HER2⁻) group (P=0.025). Patients with T1-2N1M0 TNBC may benefit from radiotherapy post-mastectomy.

Comparison of Residual Risk-Based Eligibility vs Tumor Size and Nodal Status for Power Estimates in Adjuvant Trials of Breast Cancer Therapies

Importance

Many large adjuvant clinical trials end up underpowered because of fewer than expected events in the control arm. Ensuring a minimum number of events would result in more informative trials.

Objective

To calculate individualized residual risk estimates using residual risk prediction software and assess whether defining eligibility based on a minimum residual risk threshold could increase the reliability of clinical trial power calculations compared with eligibility criteria based on tumor size and nodal status.

Design, Setting, and Participants

We estimated residual risk in 443 consecutive patients with early-stage breast cancer and assessed clinical trial power as a function of residual risk distribution among the accrued patients. We defined residual risk as the risk of recurrence that remains despite receipt of standard-of-care therapy; this risk is determined by baseline prognostic risk and by the improvement from adjuvant therapy. We performed trial simulations to examine how the power of a 2-arm, 1:1 randomized clinical trial would change as the residual risk distribution of the trial population that met eligibility criteria based on tumor size and nodal status changes. We also simulated trials that use a minimum residual risk value as eligibility criterion.

Main Outcomes and Measures

Residual risk; clinical trial power as a function of residual risk distribution among the patients.

Results

In the 443 patients (mean [SD] age, 56.1 [12.3] years; range, 23-89 years), baseline prognostic and residual risks differed substantially: 328 (74%) patients had more than 20% baseline risk of recurrence; however, after adjustment for treatment effect only 12 (27%) had more than 20% residual risk. We assessed residual risk distribution in patient cohorts that met tumor size- and nodal status-based eligibility criteria for 3 currently accruing randomized adjuvant trials; the median residual risks were 28% (interquartile range [IQR], 25%-31%), 22% (IQR, 15%-28%), and 22% (IQR, 15%-28%), respectively, indicating that the power of these trials could vary unpredictably. Simulations showed that trials that use anatomical risk-based eligibility criteria can become underpowered if they accrue patients with low residual risk despite all participants meeting eligibility requirements. Using a minimum required residual risk threshold as eligibility criterion produced more reliable power calculations.

Conclusions and Relevance

When tumor size and nodal status are used to determine trial eligibility, the residual risk of recurrence can vary broadly, leading to unstable power estimates. The success of future adjuvant trials could be improved by defining patient eligibility based on a minimal residual risk of recurrence, and these trials can achieve a prespecified power with smaller sample sizes.

Molecular Subtypes and Local-Regional Control of Breast Cancer

In the era of personalized medicine, there has been significant progress regarding the molecular analysis of breast cancer subtypes. Research efforts have focused on how classification of subtypes could provide information on prognosis and influence treatment planning. Although much is known about the impact of different molecular subtypes on disease-specific survival, more recent studies have investigated the role of the different molecular subtypes on local-regional recurrence. This is an area of active study, and in recent years there has been significant progress. This article describes outcomes among disease subtypes to aid in optimal surgical decision-making to improve local-regional control.

Exploring the intrinsic differences among breast tumor subtypes defined using immunohistochemistry markers based on the decision tree

Exploring the intrinsic differences among breast cancer subtypes is of crucial importance for precise diagnosis and therapeutic decision-making in diseases of high heterogeneity. The subtypes defined with several layers of information are related but not consistent, especially using immunohistochemistry markers and gene expression profiling. Here, we explored the intrinsic differences among the subtypes defined by the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 based on the decision tree. We identified 30 mRNAs and 7 miRNAs differentially expressed along the tree's branches. The final signature panel contained 30 mRNAs, whose performance was validated using two public datasets based on 3 well-known classifiers. The network and pathway analysis were explored for feature genes, from which key molecules including FOXQ1 and SFRP1 were revealed to be densely connected with other molecules and participate in the validated metabolic pathways. Our study uncovered the differences among the four IHC-defined breast tumor subtypes at the mRNA and miRNA levels, presented a novel signature for breast tumor subtyping, and identified several key molecules potentially driving the heterogeneity of such tumors. The results help us further understand breast tumor heterogeneity, which could be availed in clinics.

Testing violations of the exponential assumption in cancer clinical trials with survival endpoints

Personalized cancer therapy requires clinical trials with smaller sample sizes compared to trials involving unselected populations that have not been divided into biomarker subgroups. The use of exponential survival modeling for survival endpoints has the potential of gaining 35% efficiency or saving 28% required sample size (Miller, 1983), making personalized therapy trials more feasible. However, the use of exponential survival has not been fully accepted in cancer research practice due to uncertainty about whether or not the exponential assumption holds. We propose a test for identifying violations of the exponential assumption using a reduced piecewise exponential approach. Compared with an alternative goodness-of-fit test, which suffers from inflation of type I error rate under various censoring mechanisms, the proposed test maintains the correct type I error rate. We conduct power analysis using simulated data based on different types of cancer survival distribution in the SEER registry database, and demonstrate the implementation of this approach in existing cancer clinical trials.

Outcomes According to Breast Cancer Subtype in Patients Treated With Accelerated Partial Breast Irradiation

BACKGROUND

The purpose of the study was to determine outcomes for patients treated with accelerated partial breast irradiation (APBI) on the basis of breast cancer subtype (BCST).

PATIENTS AND METHODS

Our single-institution, institutional review board-approved APBI database was queried for patients who had complete testing results for the estrogen (ER), progesterone (PR), and HER2/neu receptors to determine outcomes for each BCST. Women were assigned as luminal A (LA), luminal B (LB), HER2, and basal BCST using their ER, PR, and HER2/neu receptor status. Degree of ER expression supplemented the receptor-based luminal BCST assignment. Two hundred seventy-eight patients had results for all 3 receptors (LA = 164 [59%], LB = 81 [29%], HER2 = 5 [2%], basal = 28 [10%]), which were submitted for analysis (ipsilateral breast tumor recurrence [IBTR], regional nodal failure, distant metastasis [DM], disease-free survival [DFS], cause-specific survival [CSS], and overall survival [OS]).

RESULTS

Median follow-up was 5.4 years (range, 0.1-12.4 years). Basal and HER2 subtype patients had higher histologic grades (Grade 3 = 75% vs. 10% LA/LB; P < .001), larger tumors (13.0 mm basal vs. 10.7 mm LA/LB; P = .059), and were more likely to receive chemotherapy (68% vs. 15% LA/LB; P < .001). Margin and nodal status were similar among BCSTs. At 5 years, IBTR rates were similar (1.8%, 2.9%, 0%, and 4.8%) for LA, LB, HER2, and basal subtypes, respectively (P = .62). DM was only seen in LA (2.9%) and LB (1.3%) (P = .83). DFS (95%-100%), CSS (97%-100%), and OS (80%-100%) were not statistically different (P = .97, .87, .46, respectively).

CONCLUSION

Five-year local control rates after breast-conserving surgery, APBI, and appropriate systemic therapy are excellent for luminal, HER2, and basal phenotypes of early-stage breast cancer; however, further study of receptor subtype effect on risk stratification in early-stage breast cancer is needed.

Ki67 expression in the primary tumor predicts for clinical benefit and time to progression on first-line endocrine therapy in estrogen receptor-positive metastatic breast cancer

We examined whether baseline Ki67 expression in estrogen receptor-positive (ER+) primary breast cancer correlates with clinical benefit and time to progression on first-line endocrine therapy and survival in metastatic disease. Ki67 values and outcome information were retrieved from a prospectively maintained clinical database and validated against the medical records; 241 patients with metastatic breast cancer were included--who had ER+ primary cancer with known Ki67 expression level--and received first-line endocrine therapy for metastatic disease. Patients were assigned to low (<10 %), intermediate (10-25 %), or high (>25 %) Ki67 expression groups. Kaplan-Meier survival curves were plotted and multivariate analysis was performed to assess association between clinical and immunohistochemical variables and outcome. The clinical benefit rates were 81, 65, and 55 % in the low (n = 32), intermediate (n = 103), and high (n = 106) Ki67 expression groups (P = 0.001). The median times to progression on first-line endocrine therapy were 20.3 (95 % CI, 17.5-38.5), 10.8 (95 % CI, 8.9-18.8), and 8 (95 % CI, 6.1-11.1) months, respectively (P = 0.0002). The median survival times after diagnosis of metastatic disease were also longer for the low/intermediate compared to the high Ki67 group, 52 versus 30 months (P < 0.0001). In multivariate analysis, high Ki67 expression in the primary tumor remained an independent adverse prognostic factor in metastatic disease (P = 0.001). Low Ki67 expression in the primary tumor is associated with higher clinical benefit and longer time to progression on first-line endocrine therapy and longer survival after metastatic recurrence.

A three-gene model to robustly identify breast cancer molecular subtypes

BACKGROUND

Single sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression-based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets.

METHODS

Thirty-six publicly available breast cancer datasets (n = 5715) were subjected to molecular subtyping using five published classifiers (three SSPs and two SCMs) and SCMGENE, the new three-gene (ER, HER2, and AURKA) SCM. We used the prediction strength statistic to estimate robustness of the classification models, defined as the capacity of a classifier to assign the same tumors to the same subtypes independently of the dataset used to fit it. We used Cohen κ and Cramer V coefficients to assess concordance between the subtype classifiers and association with clinical variables, respectively. We used Kaplan-Meier survival curves and cross-validated partial likelihood to compare prognostic value of the resulting classifications. All statistical tests were two-sided.

RESULTS

SCMs were statistically significantly more robust than SSPs, with SCMGENE being the most robust because of its simplicity. SCMGENE was statistically significantly concordant with published SCMs (κ = 0.65-0.70) and SSPs (κ = 0.34-0.59), statistically significantly associated with ER (V = 0.64), HER2 (V = 0.52) status, and histological grade (V = 0.55), and yielded similar strong prognostic value.

CONCLUSION

Our results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.

Gene expression profiling in breast cancer: classification, prognostication, and prediction

Microarray-based gene expression profiling has had a major effect on our understanding of breast cancer. Breast cancer is now perceived as a heterogeneous group of different diseases characterised by distinct molecular aberrations, rather than one disease with varying histological features and clinical behaviour. Gene expression profiling studies have shown that oestrogen-receptor (ER)-positive and ER-negative breast cancers are distinct diseases at the transcriptomic level, that additional molecular subtypes might exist within these groups, and that the prognosis of patients with ER-positive disease is largely determined by the expression of proliferation-related genes. On the basis of these principles, a molecular classification system and prognostic multigene classifiers based on microarrays or derivative technologies have been developed and are being tested in randomised clinical trials and incorporated into clinical practice. In this review, we focus on the conceptual effect and potential clinical use of the molecular classification of breast cancer, and discuss prognostic and predictive multigene predictors.

Ethics of mandatory research biopsy for correlative end points within clinical trials in oncology

Clinical investigators in oncology are increasingly interested in using molecular analysis of cancer tissue to understand the biologic bases of response or resistance to novel interventions and to develop prognostic and predictive biomarkers that will guide clinical decision making. Some scientific questions of this nature can only be addressed, or may best be addressed, through the conduct of a clinical trial in which research biopsies are obtained from all participants. However, trial designs with mandatory research biopsies have raised ethical concerns related to the risk of harm to participants, the adequacy of voluntary informed consent, and the potential for misunderstanding among research participants when access to an experimental intervention is linked to the requirement to undergo a research biopsy. In consideration of the ethical and scientific issues at stake in this debate, the Cancer and Leukemia Group B Ethics Committee proposes guidelines for clinical trials involving mandatory research biopsies. Any cancer clinical trial that requires research biopsies of participants must be well designed to address the scientific question, obtain the biopsy in a way that minimizes risk, and ensure that research participants are fully informed of the risks, rationale, and requirements of the study, as well as of treatment alternatives. Further guidelines and discussions of this issue are specified in this position paper. We feel that if these principles are respected, an informed adult with cancer can both understand and voluntarily consent to participation in a clinical trial involving mandatory research biopsy for scientific end points.

Estrogen receptor expression and docetaxel efficacy in patients with metastatic breast cancer: a pooled analysis of four randomized trials

BACKGROUND

Differences in the efficacy of various chemotherapies in patients with estrogen receptor (ER)(+) metastatic breast cancer are not well understood. In the present study, we assessed the efficacy of docetaxel in patients with metastatic breast cancer according to ER expression.

METHODS

The efficacy of docetaxel in terms of the response rate and progression-free survival (PFS) time was analyzed according to ER expression in four randomized trials comparing a docetaxel-based regimen with a nontaxane regimen that included a total of 1,631 patients. The odds ratio for tumor response was estimated with logistic regression and a hazard ratio (HR) for PFS was estimated with Cox proportional hazards models.

FINDINGS

ER expression was assessable in 1,037 patients included in these trials (64%). ER was expressed in 601 tumors (58%). Docetaxel was associated with a similarly higher response rate in both patients with ER(+) (odds ratio, 2.90; 95% confidence interval [CI], 1.72-4.87) and patients with ER(-) (odds ratio, 2.55; 95% CI, 1.44-4.51) disease. The lower hazard for disease progression with docetaxel was also similar in ER(+) (HR, 0.82; 95% CI, 0.67-1.00) and ER(-) (HR, 0.86; 95% CI, 0.70-1.07) cancers. The effect of docetaxel was not different in ER(+) and ER(-) disease, in terms of both the response rate and PFS time (interaction test, p = .77 and p = .93).

INTERPRETATION

Docetaxel produces a higher response rate and lower risk for disease progression to a statistically similar extent in both patients with ER(+) and patients with ER(-) metastatic breast cancer.

Breast cancer molecular profiling with single sample predictors: a retrospective analysis

BACKGROUND

Microarray expression profiling classifies breast cancer into five molecular subtypes: luminal A, luminal B, basal-like, HER2, and normal breast-like. Three microarray-based single sample predictors (SSPs) have been used to define molecular classification of individual samples. We aimed to establish agreement between these SSPs for identification of breast cancer molecular subtypes.

METHODS

Previously described microarray-based SSPs were applied to one in-house (n=53) and three publicly available (n=779) breast cancer datasets. Agreement was analysed between SSPs for the whole classification system and for the five molecular subtypes individually in each cohort.

FINDINGS

Fair-to-substantial agreement between every pair of SSPs in each cohort was recorded (kappa=0.238-0.740). Of the five molecular subtypes, only basal-like cancers consistently showed almost-perfect agreement (kappa>0.812). The proportion of cases classified as basal-like in each cohort was consistent irrespective of the SSP used; however, the proportion of each remaining molecular subtype varied substantially. Assignment of individual cases to luminal A, luminal B, HER2, and normal breast-like subtypes was dependent on the SSP used. The significance of associations with outcome of each molecular subtype, other than basal-like and luminal A, varied depending on SSP used. However, different SSPs produced broadly similar survival curves.

INTERPRETATION

Although every SSP identifies molecular subtypes with similar survival, they do not reliably assign the same patients to the same molecular subtypes. For molecular subtype classification to be incorporated into routine clinical practice and treatment decision making, stringent standardisation of methodologies and definitions for identification of breast cancer molecular subtypes is needed.

FUNDING

Breakthrough Breast Cancer, Cancer Research UK.

Ki67 Expression and Docetaxel Efficacy in Patients With Estrogen Receptor–Positive Breast Cancer

Purpose

The indications of adjuvant chemotherapy for patients with estrogen receptor (ER) –positive breast cancer are controversial. We analyzed the predictive value of Ki67, HER2, and progesterone receptor (PR) expression for the efficacy of docetaxel in patients with ER-positive, node-positive breast cancer.

Patients and Methods

Expression of Ki67, HER2, and PR was measured by immunohistochemistry in tumor samples from 798 patients with ER-positive breast cancer who participated in PACS01, a randomized trial that evaluated the efficacy of docetaxel. Risk reduction was evaluated using a Cox model adjusted for age, tumor size, nodal involvement, treatment arm, and biomarkers. The predictive value of biomarkers was assessed by an interaction test. Disease-free survival (DFS) was the primary end point.

Results

Ki67, HER2, and PR were expressed in 21%, 9%, and 62% of samples, respectively. Hazard ratios for relapse associated with docetaxel were 0.51 (95% CI, 0.26 to 1.01) in ER-positive/Ki67-positive tumors and 1.03 (95% CI, 0.69 to 1.55) in ER-positive/Ki67-negative tumors (ratio for interaction: 0.53; 95% CI, 0.24 to 1.16; P = .11). Five-year DFS rates were 81% (95% CI, 76% to 86%) and 84% (95% CI, 75% to 93%) in patients with ER-positive/Ki67-negative and ER-positive/Ki67-positive tumors treated with docetaxel and 81% (95% CI, 76% to 86%) and 62% (95% CI, 52% to 72%) in patients with ER-positive/Ki67-negative and ER-positive/Ki67-positive tumors treated with fluorouracil, epirubicin, and cisplatin. No trend for interaction was observed between docetaxel and HER2 (ratio for interaction: 0.83; 95% CI, 0.35 to 1.94; P = .66), nor between docetaxel and PR (ratio for interaction: 0.89; 95% CI, 0.47 to 1.66; P = .71).

Conclusion

Ki67 expression identifies a subset of patients with ER-positive breast cancer who could be sensitive to docetaxel treatment in the adjuvant setting.