Calibration and discriminatory accuracy of prognosis calculation for breast cancer with the online Adjuvant! program: a hospital-based retrospective cohort study

Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer

BACKGROUND

The histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients.

METHODS

In this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs.

RESULTS

Statistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648-3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3.

CONCLUSION

Our results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

Real-World Analysis of the Clinical and Economic Impact of the 21-Gene Recurrence Score (RS) in Invasive Lobular Early-Stage Breast Carcinoma in Ireland

Background: This study, using real-world data, assesses the impact of RS testing on treatment pathways and the associated economic consequences of such testing. This paper pertains to lobular breast cancer. Methods: A retrospective, observational study was undertaken between 2011 and 2019 on a cross-section of hormone receptor-positive (HR+), HER2-negative, lymph node-negative, early-stage breast cancer patients. All patients had ILC and had RS testing in Ireland. The patient population is representative of the national population. Patients were classified as low (RS ≤ 25) or high (RS > 25) risk. Patients aged ≤50 were stratified as low (RS 0-15), intermediate (RS 16-25), or high risk (RS > 25). Results: A total of 168 patients were included, most of whom had grade 2 (G2) tumors (n = 154, 92%). Overall, 155 patients (92.3%) had low RS (≤25), 12 (7.1%) had high RS (>25), and 1 (0.6%) had unknown RS status. In 29 (17.5%) patients aged ≤50 at diagnosis, RS was ≤15 in 16 (55%), 16-20 in 6 (21%), 21-25 in 5 (17%), >25 in 1 (3.5%), and unknown in 1 (3.5%). Post RS testing, 126 patients (78%) had a change in chemotherapy recommendation; all to hormone therapy. In total, only 35 patients (22%) received chemotherapy. RS testing achieved a 75% reduction in chemotherapy use, resulting in savings of €921,543.84 in treatment costs, and net savings of €387,283.84. Conclusions: The use of this test resulted in a 75% reduction in chemotherapy and a significant cost savings in our publicly funded health system.

Current views on in vivo models for breast cancer research and related drug development

INTRODUCTION

Animal models play a crucial role in breast cancer research, in particular mice and rats, who develop mammary tumors that closely resemble their human counterparts. These models allow the study of mechanisms behind breast carcinogenesis, as well as the efficacy and safety of new, and potentially more effective and advantageous therapeutic approaches. Understanding the advantages and disadvantages of each model is crucial to select the most appropriate one for the research purpose.

AREA COVERED

This review provides a concise overview of the animal models available for breast cancer research, discussing the advantages and disadvantages of each one for searching new and more effective approaches to treatments for this type of cancer.

EXPERT OPINION

Rodent models provide valuable information on the genetic alterations of the disease, the tumor microenvironment, and allow the evaluation of the efficacy of chemotherapeutic agents. However, in vivo models have limitations, and one of them is the fact that they do not fully mimic human diseases. Choosing the most suitable model for the study purpose is crucial for the development of new therapeutic agents that provide better care for breast cancer patients.

A vicious circle in breast cancer: The interplay between inflammation, reactive oxygen species, and microRNAs

Breast cancer (BC) is the most common cancer in women worldwide. This highly heterogeneous disease is molecularly stratified into luminal A, luminal B, HER2, triple-negative/basal-like, and normal-like subtypes. An important aspect in BC progression is the activation of inflammatory processes. The activation of CD8+/Th1, NK, and M1 tumor associated macrophages (TAMs), leads to tumor destruction. In contrast, an anti-inflammatory response mediated by CD4+/Th2 and M2 TAMs will favor tumor progression. Inflammation also stimulates the production of inflammatory mediators like reactive oxygen species (ROS). In chronic inflammation, ROS activates oxidative stress and endothelial dysfunction. In cancer, ROS plays a dual role with anti-tumorigenic and pro-tumorigenic effects in cell signaling pathways that control proliferation, survival, apoptosis, and inflammation. MicroRNAs (miRNAs), which are known to be involved in BC progression and inflammation, can be regulated by ROS. At the same time, miRNAs regulate the expression of genes modulating oxidative stress. In this review, we will discuss the interplay between inflammation, ROS, and miRNAs as anticancer and tumor promoter molecules in BC. A clear understanding of the role of miRNAs in the regulation of ROS production and inflammation, may lead to new opportunities for therapy in BC.

A comparative analysis of recurrence risk predictions in ER+/HER2− early breast cancer using NHS Nottingham Prognostic Index, PREDICT, and CanAssist Breast

Aims

Clinicians use multi-gene/biomarker prognostic tests and free online tools to optimize treatment in early ER+/HER2− breast cancer. Here we report the comparison of recurrence risk predictions by CanAssist Breast (CAB), Nottingham Prognostic Index (NPI), and PREDICT along with the differences in the performance of these tests across Indian and European cohorts.

Methods

Current study used a retrospective cohort of 1474 patients from Europe, India, and USA. NPI risk groups were categorized into three prognostic groups, good (GPG-NPI index ≤ 3.4) moderate (MPG 3.41–5.4), and poor (PPG  > 5.4). Patients with chemotherapy benefit of < 2% were low-risk and ≥ 2% high-risk by PREDICT. We assessed the agreement between the CAB and NPI/PREDICT risk groups by kappa coefficient.

Results

Risk proportions generated by all tools were: CAB low:high 74:26; NPI good:moderate:poor prognostic group- 38:55:7; PREDICT low:high 63:37. Overall, there was a fair agreement between CAB and NPI[κ = 0.31(0.278–0.346)]/PREDICT [κ = 0.398 (0.35–0.446)], with a concordance of 97%/88% between CAB and NPI/PREDICT low-risk categories. 65% of NPI-MPG patients were called low-risk by CAB. From PREDICT high-risk patients CAB segregated 51% as low-risk, thus preventing over-treatment in these patients. In cohorts (European) with a higher number of T1N0 patients, NPI/PREDICT segregated more as LR compared to CAB, suggesting that T1N0 patients with aggressive biology are missed out by online tools but not by the CAB.

Conclusion

Data shows the use of CAB in early breast cancer overall and specifically in NPI-MPG and PREDICT high-risk patients for making accurate decisions on chemotherapy use. CAB provided unbiased risk stratification across cohorts of various geographies with minimal impact by clinical parameters.

The 21-Gene Recurrence Score in Clinically High-Risk Lobular and Ductal Breast Cancer: A National Cancer Database Study

Objective

The aim of this study was to evaluate whether patients with invasive lobular carcinoma (ILC) are more likely to have discordant clinical and genomic risk than those with invasive ductal carcinoma (IDC) when using the 21-gene recurrence score (RS), and to assess overall survival outcomes of patients with 1–3 positive nodes and RS ≤25 with and without chemotherapy, stratified by histology.

Methods

We performed a cohort study using the National Cancer Database and included patients with hormone receptor-positive, HER2-negative, stage I–III invasive breast cancer who underwent 21-gene RS testing. Our primary outcome was rate of discordant clinical and genomic risk status by histologic subtype. Propensity score matching was used to compare 60-month overall survival in individuals with 1–3 positive nodes and RS ≤25 who did and did not receive chemotherapy.

Results

Overall, 186,867 patients were included in our analysis, including 37,685 (20.2%) patients with ILC. There was a significantly higher rate of discordant clinical and genomic risk in patients with ILC compared with IDC. Among patients with 1–3 positive nodes and RS ≤25, there was no significant difference in survival between those who did and did not receive chemotherapy in the IDC or ILC cohorts. Unadjusted exploratory analyses of patients under age 50 years with 1–3 positive nodes and RS ≤25 showed improved overall survival in IDC patients who received chemotherapy, but not among those with ILC.

Conclusion

Our findings highlight the importance of lobular-specific tools for stratifying clinical and genomic risk, as well as the need for histologic subtype-specific analyses in randomized trials.

Supplementary Information

The online version contains supplementary material available at 10.1245/s10434-022-12065-3.

Progress on deep learning in digital pathology of breast cancer: a narrative review

BACKGROUND AND OBJECTIVE

Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis. However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology.

METHODS

A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied.

KEY CONTENT AND FINDINGS

DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches.

CONCLUSIONS

Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.

Development of a Prognostic Application to Predict Survival for Chinese Women with Breast Cancer (Preprint)

Background

Objective

Methods

Results

Conclusions

Advancement of prognostic models in breast cancer: a narrative review

Objective

To provide a reference for clinical work and guide the decision-making of healthcare providers and end-users, we systematically reviewed the development, validation and classification of classical prognostic models for breast cancer.

Background

Patients suffering from breast cancer have different prognosis for its high heterogeneity. Accurate prognosis prediction and risk stratification for breast cancer are crucial for individualized treatment. There is a lack of systematic summary of breast cancer prognostic models.

Methods

We conducted a PubMed search with keywords "breast neoplasm", "prognostic model", "recurrence" and "metastasis", and screened the retrieved publications at three levels: title, abstract and full text. We identified the articles presented the development and/or validation of models based on clinicopathological factors, genomics, and machine learning (ML) methods to predict survival and/or benefits of adjuvant therapy in female breast cancer patients.

Conclusions

Combining prognostic-related variables with long-term clinical outcomes, researchers have developed a series of prognostic models based on clinicopathological parameters, genomic assays, and medical figures. The discrimination, calibration, overall performance, and clinical usefulness were validated by internal and/or external verifications. Clinicopathological models integrated the clinical parameters, including tumor size, histological grade, lymph node status, hormone receptor status to provide prognostic information for patients and doctors. Gene-expression assays deeply revealed the molecular heterogeneity of breast cancer, some of which have been cited by AJCC and National Comprehensive Cancer Network (NCCN) guidelines. In addition, the models based on the ML methods provided more detailed information for prognosis prediction by increasing the data dimension. Combined models incorporating clinical variables and genomics information are still required to be developed as the focus of further researches.

Nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to predict the histologic grade in breast cancer

The purpose of this study is to develop and validate a new nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to differentiate tumor grade in patients with invasive breast cancer. A total of 543 patients were included in this study. We used computer-generated random numbers to assign 328 of these patients to the training cohort and 215 patients to the validation cohort. Macroscopic tumor-associated collagen signatures (TACS1-8) were obtained by multiphoton microscopy at the invasion front and inside of the breast primary tumor. TACS corresponding microscopic features (TCMF) including morphology and texture features were extracted from the segmented regions of interest using Matlab 2016b. Using ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8, and the least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust TCMF features to build a TCMF-score. Univariate logistic regression analysis demonstrates that the TACS-score and TCMF-score are significantly associated with histologic grade (odds ratio, 2.994; 95% CI, 2.013-4.452; P < 0.001; 4.245, 2.876-6.264, P < 0.001 in the training cohort). The nomogram (collagen) model combining the TACS-score and TCMF-score could stratify patients into Grade1 and Grade2/3 groups with the AUC of 0.859 and 0.863 in the training and validation cohorts. The predictive performance can be further improved by combining the clinical factors, achieving the AUC of 0.874 in both data cohorts. The nomogram model combining the TACS-score and TCMF-score can be useful in differentiating breast tumor patients with Grade1 and Grade2/3.

The impact of tumor detection method on genomic and clinical risk and chemotherapy recommendation in early hormone receptor positive breast cancer

BACKGROUND

Symptomatic breast cancers share aggressive clinico-pathological characteristics compared to screen-detected breast cancers. We assessed the association between the method of cancer detection and genomic and clinical risk, and its effect on adjuvant chemotherapy recommendations.

PATIENTS AND METHODS

Patients with early hormone receptor positive (HR+) HER2neu-negative (HER2-) breast cancer, and known OncotypeDX Breast Recurrence Score test were included. A natural language processing (NLP) algorithm was used to identify the method of cancer detection. The clinical and genomic risks of symptomatic and screen-detected tumors were compared.

RESULTS

The NLP algorithm identified the method of detection of 401 patients, with 216 (54%) diagnosed by routine screening, and the remainder secondary to symptoms. The distribution of OncotypeDX recurrence score (RS) varied between the groups. In the symptomatic group there were lower proportions of low RS (13% vs 23%) and higher proportions of high RS (24% vs. 13%) compared to the screen-detected group. Symptomatic tumors were significantly more likely to have a high clinical risk (59% vs 40%). Based on genomic and clinical risk and current guidelines, we found that women aged 50 and under, with a symptomatic cancer, had an increased probability of receiving adjuvant chemotherapy recommendation compared to women with screen-detected cancers (60% vs. 37%).

CONCLUSIONS

We demonstrated an association between the method of cancer detection and both genomic and clinical risk. Symptomatic breast cancer, especially in young women, remains a poor prognostic factor that should be taken into account when evaluating patient prognosis and determining adjuvant treatment plans.

Application of the 21-Gene Recurrence Score in Patients with Early HR-Positive/HER2-Negative Breast Cancer: Chemotherapy and Survival Rate According to Clinical Risk

Simple Summary

It is important to address the influence of 21-gene Recurrence Score (RS) on chemotherapy decision-making stratified by clinical risk in patients with hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative early breast cancer. Our study presented that the application of the 21-gene RS assay significantly reduced the chemotherapy rate in patients with high clinical risk. Meanwhile, there was no significant difference in the chemotherapy rate according to the implementation of the 21-gene RS assay in those with low clinical risk. Furthermore, we observed no difference in prognosis according to the application of 21-gene RS for either clinical risk. These results suggest that the 21-gene RS could be considered more positively in HR+/HER2- patients with high clinical risk to reduce chemotherapy rates without increasing the occurrence of relapse.

Abstract

We assessed the impact of 21-gene Recurrence Score (RS) assay on chemotherapy decision-making according to binary clinical risk stratification in patients with hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative early breast cancer. We included patients with tumors measuring 1–5 cm, N0-1, and HR+/HER2- breast cancer who underwent surgery followed by adjuvant treatment. The clinical risk was determined by a modified version of Adjuvant! Online. We performed propensity score matching (PSM) according to the application of 21-gene RS separately in the low and high clinical risk groups. Before PSM, 342 (39.0%) of 878 patients were classified as having high clinical risk. In the high clinical risk group, 21-gene RS showed a significantly reduced chemotherapy rate of 39.3%, without increasing the recurrence. After PSM, the 21-gene RS application significantly reduced chemotherapy rate by 34.0% in 200 patients with high clinical risk (21-gene RS application, 32.0% vs. no 21-gene RS application, 66.0%, p < 0.001). There was also no significant difference in RFS according to 21-gene RS status in the high clinical risk group (log-rank test, p = 0.467). These results support the usefulness of the 21-gene RS to reduce the chemotherapy rate without adversely affecting prognosis in a high clinical risk group.

Real-world analysis of clinical and economic impact of 21-gene recurrence score (RS) testing in early-stage breast cancer (ESBC) in Ireland

PURPOSE

Results from TAILOR-X suggest that up to 70% of hormone receptor-positive (HR+) node-negative (N0) ESBC patients (pts) may avoid chemotherapy (CT) with RS ≤ 25. We assess clinical and economic impacts of RS testing on treatment using real-world data.

METHODS

From October 2011 to February 2019, a retrospective, cross-sectional observational study was conducted of HR+ N0 ESBC pts who had RS testing in Ireland. Pts were classified low risk (RS ≤ 25) and high risk (RS > 25). Clinical risk was calculated. Data were collected via electronic patient records. Cost data were supplied by the National Healthcare Pricing Regulatory Authority.

RESULTS

963 pts. Mean age is 56 years. Mean tumour size is 1.7 cm. 114 (11.8%), 635 (66%), 211 (22%), 3 (0.2%) pts had G1, G2, G3 and unknown G, respectively. 796 pts (82.8%) low RS, 159 (16.5%) high RS and 8 pts (0.7%) unknown RS. 263 pts (26%) were aged ≤ 50 at diagnosis; 117 (45%) had RS 0-15, 63 (24.5%) 16-20, 39 (15.3%) 21-25 and 40 (15.2%) RS 26-100. 4 pts (1.5%) had unknown RS. Post-RS testing, 602 pts (62.5%) had a change in CT decision; 593 changed to hormone therapy (HT) alone. In total, 262 pts received CT. Of pts receiving CT; 138 (53%) had RS > 25, 124 (47%) had RS ≤ 25. Of pts aged ≤ 50, 153 (58%) had high clinical risk, of whom 28 had RS 16-20. Assay use achieved a 62.5% change in treatment with 73% of pts avoiding CT. This resulted in savings of €4 million in treatment costs. Deducting assay costs, savings of €1.9 million were achieved.

CONCLUSION

Over the 8 years of the study, a 62.5% reduction in CT use was achieved with savings of over €1,900,000.

Prediction models for breast cancer prognosis among Asian women

BACKGROUND

Robust and reliable prognosis prediction models have not been developed and validated for Asian patients with breast cancer, a rapidly growing yet understudied population in the United States.

METHODS

We used longitudinal data from the Shanghai Breast Cancer Survival Study, a population-based prospective cohort study (n = 5042), to develop prediction models for 5- and 10-year disease-free survival (DFS) and overall survival (OS). The initial models considered age at diagnosis, tumor grade, tumor size, number of positive nodes, TNM stage, chemotherapy, tamoxifen therapy, and estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status. We then evaluated whether the addition of modifiable lifestyle factors (physical activity, soy isoflavones intake, and postdiagnostic weight change) improved the models. All final models have been validated internally and externally in the National Cancer Database when applicable.

RESULTS

Our final models included age at diagnosis, tumor grade, tumor size, number of positive nodes, TNM stage, chemotherapy, tamoxifen therapy, ER status, PR status, 6-month postdiagnostic weight change, interaction between ER status and tamoxifen therapy, and interaction between age and TNM stage. The internal validation yielded C-statistics of 0.76, 0.74, 0.78, and 0.75 for 5-year DFS, 10-year DFS, 5-year OS, and 10-year OS, respectively. The external validation yielded C-statistics of 5- and 10-year OS both at 0.78 for Chinese ethnicity, 0.79 for East Asian ethnicity, and 0.75 and 0.76 for all ethnic groups combined.

CONCLUSION

We developed prediction models for breast cancer prognosis from a large prospective study. Our prognostic models performed very well in women from the United States-particularly in Asian American women-and demonstrated high prediction accuracy and generalizability.

The IHC4+C score: an affordable and reproducible non-molecular decision-aid in hormone receptor-positive breast cancer. Does it still hold value for patients in 2020?

AIM

The majority of women diagnosed with early breast cancer have hormone-receptor positive (HR+)/HER2-negative disease. Adjuvant endocrine therapy provides substantial risk reduction benefits in virtually all patients. The role of adjuvant chemotherapy in certain subsets of patients is equivocal. This paper sought to evaluate the role of the IHC4+C score to aid this clinical decision in addition to providing an overview of the current molecular and non- molecular tools available in the adjuvant setting.

METHODS

This prospective study included 53 post-operative HR+/HER2- negative early breast cancer patients selected from the multidiscipliniary team meeting between August 2017 and January 2020. IHC4+C testing was requested by clinicians for patients in whom the availability of the score may have impacted adjuvant decision-making. Adjuvant treatment decisions were recorded at three time points (prior and post IHC4+C scoring as well as the patient's final decision). The primary goal was the proportion of patients who were spared chemotherapy following the availability of IHC4+C scores to impact on clinicians' recommendations for adjuvant systemic therapy.

RESULTS

A total of 34 patients (64%) were initially recommended to undergo chemotherapy or to consider chemotherapy. With the availability of the IHC4+C score, only 17 patients (32%) underwent chemotherapy, demonstrating a substantial reduction in the frequency of chemotherapy prescribing.

CONCLUSION

This study demonstrates that when utilized appropriately in a multidisciplinary setting, the IHC4+C algorithm is an alternative, reproducible and affordable tool with a proven capacity to stratify risk and to spare a large proportion of patients from undergoing chemotherapy.

Histological Aspects and Quantitative Assessment of Ki67 as Prognostic Factors in Breast Cancer Patients: Result from a Single-Center, Cross Sectional Study

Background and objectives: Our aim is to explore the relationship between the levels of protein encoded by Ki67 and the histopathological aspects regarding the overall survival and progression-free survival in a single university center. A secondary objective was to examine other factors that can influence these endpoints. New approaches to the prognostic assessment of breast cancer have come from molecular profiling studies. Ki67 is a nuclear protein associated with cell proliferation. Together with the histological type and tumor grade, it is used to appreciate the aggressiveness of the breast tumors. Materials and Methods: We conducted a retrospective single-institution study, at Elias University Emergency Hospital, Bucharest, Romania, in which we enrolled women with stage I to III breast cancer. The protocol was amended to include the immunohistochemistry determination of Ki67 and the histological aspects. The methodology consisted in using a Kaplan-Meier analysis for the entire sample and restricted mean survival time up to 36 months. Results: Both lower Ki67 and low tumor grade are associated with better prognosis in terms of overall survival (OS) and progression-free survival (PFS) for our patients' cohort. In our group, the histological type did not impact the time to progression or survival. Conclusions: Both overall survival and progression-free survival may be influenced by the higher value of Ki67 and less differentiated tumors. Further studies are needed in order to establish if the histologic type may impact breast cancer prognostic, probably together with other histologic and molecular markers.

Multidimensional Machine Learning Personalized Prognostic Model in an Early Invasive Breast Cancer Population-Based Cohort in China: Algorithm Validation Study

Background

Current online prognostic prediction models for breast cancer, such as Adjuvant! Online and PREDICT, are based on specific populations. They have been well validated and widely used in the United States and Western Europe; however, several validation attempts in non-European countries have revealed suboptimal predictions.

Objective

We aimed to develop an advanced breast cancer prognosis model for disease progression, cancer-specific mortality, and all-cause mortality by integrating tumor, demographic, and treatment characteristics from a large breast cancer cohort in China.

Methods

This study was approved by the Clinical Test and Biomedical Ethics Committee of West China Hospital, Sichuan University on May 17, 2012. Data collection for this project was started in May 2017 and ended in March 2019. Data on 5293 women diagnosed with stage I to III invasive breast cancer between 2000 and 2013 were collected. Disease progression, cancer-specific mortality, all-cause mortality, and the likelihood of disease progression or death within a 5-year period were predicted. Extreme gradient boosting was used to develop the prediction model. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUROC), and the model was calibrated and compared with PREDICT.

Results

The training, test, and validation sets comprised 3276 (499 progressions, 202 breast cancer-specific deaths, and 261 all-cause deaths within 5-year follow-up), 1405 (211 progressions, 94 breast cancer-specific deaths, and 129 all-cause deaths), and 612 (109 progressions, 33 breast cancer-specific deaths, and 37 all-cause deaths) women, respectively. The AUROC values for disease progression, cancer-specific mortality, and all-cause mortality were 0.76, 0.88, and 0.82 for training set; 0.79, 0.80, and 0.83 for the test set; and 0.79, 0.84, and 0.88 for the validation set, respectively. Calibration analysis demonstrated good agreement between predicted and observed events within 5 years. Comparable AUROC and calibration results were confirmed in different age, residence status, and receptor status subgroups. Compared with PREDICT, our model showed similar AUROC and improved calibration values.

Conclusions

Our prognostic model exhibits high discrimination and good calibration. It may facilitate prognosis prediction and clinical decision making for patients with breast cancer in China.

Machine Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast Cancer

PURPOSE

For patients with early-stage breast cancer, predicting the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (eg, Cox regression). Here we define and evaluate the predictive ability of a mechanistic model for time to distant metastatic relapse.

METHODS

The data we used for our model consisted of 642 patients with 21 clinicopathologic variables. A mechanistic model was developed on the basis of two intrinsic mechanisms of metastatic progression: growth (parameter α) and dissemination (parameter μ). Population statistical distributions of the parameters were inferred using mixed-effects modeling. A random survival forest analysis was used to select a minimal set of five covariates with the best predictive power. These were further considered to individually predict the model parameters by using a backward selection approach. Predictive performances were compared with classic Cox regression and machine learning algorithms.

RESULTS

The mechanistic model was able to accurately fit the data. Covariate analysis revealed statistically significant association of Ki67 expression with α (P = .001) and EGFR expression with μ (P = .009). The model achieved a c-index of 0.65 (95% CI, 0.60 to 0.71) in cross-validation and had predictive performance similar to that of random survival forest (95% CI, 0.66 to 0.69) and Cox regression (95% CI, 0.62 to 0.67) as well as machine learning classification algorithms.

CONCLUSION

By providing informative estimates of the invisible metastatic burden at the time of diagnosis and forward simulations of metastatic growth, the proposed model could be used as a personalized prediction tool for routine management of patients with breast cancer.

A Comparison Between the Online Prediction Models CancerMath and PREDICT as Prognostic Tools in Thai Breast Cancer Patients

Background and Purpose

Web-based prognostic calculators have been developed to inform about the use of adjuvant systemic treatments in breast cancer. CancerMath and PREDICT are two examples of web-based prognostic tools that predict patient survival up to 15 years after an initial diagnosis of breast cancer. The aim of this study is to validate the use of CancerMath and PREDICT as prognostic tools in Thai breast cancer patients.

Patients and Methods

A total of 615 patients who underwent surgical treatment for stage I to III breast cancer from 2003 to 2011 at the Division of Head Neck and Breast Surgery, Department of Surgery, Siriraj Hospital, Mahidol University, Thailand were recruited. A model-predicted overall survival rate (OS) and the actual OS of the patients were compared. The efficacy of the model was evaluated using receiver-operating characteristic (ROC) analysis.

Results

For CancerMath, the predicted 5-year OS was 88.9% and the predicted 10-year OS was 78.3% (p<0.001). For PREDICT, the predicted 5-year OS was 83.1% and the predicted 10-year OS was 72.0% (p<0.001). The actual observed 5-year OS was 90.8% and the observed 10-year OS was 82.6% (p<0.001). CancerMath demonstrated better predictive performance than PREDICT in all subgroups for both 5- and 10-year OS. In addition, there was a marked difference between CancerMath and observed survival rates in patients who were older as well as patients who were stage N3. The area under the ROC curve for 5-year OS in CancerMath and 10-year OS was 0.74 (95% CI; 0.65-0.82) and 0.75 (95% CI; 0.68-0.82). In the PREDICT group, the area under the ROC curve for 5-year OS was 0.78 (95% CI; 0.71-0.85) and for 10-year OS, it was 0.78 (95% CI; 0.71-0.84).

Conclusion

CancerMath and PREDICT models both underestimated the OS in Thai breast cancer patients. Thus, a novel prognostic model for Thai breast cancer patients is required.

The prognostic and predictive role of 21-gene recurrence scores in hormone receptor-positive early-stage breast cancer

Over the past two decades, gene expression profiling of breast cancer has emerged as an important tool in early-stage breast cancer management. The approach provides important information on underlying biological mechanisms, breast cancer classification, future risk potential of developing recurrent metastatic disease, and provides beneficial clues for adjuvant chemotherapy in hormone receptor (HR) positive breast cancer. Of the commercially available genomic tests for breast cancer, the prognostic and predictive value of 21-gene recurrence score tests have been validated using both retrospective data and prospective clinical trials. In this paper, we reviewed the current evidence on 21-gene expression profiles for HR-positive HER2-negative early-stage breast cancer management. We show that current evidence supports endocrine therapy alone as an appropriate adjuvant systemic therapy for approximately 70% of women with HR-positive, HER2-negative, node-negative breast cancer. Evolving evidence also suggests that 21-gene recurrence scores have predictive values for node-positive breast cancer and that chemotherapy can be avoided in more than half of women with nodes 1 to 3 positive HR-positive breast cancer. Furthermore, retrospective data also supports the predictive role of 21-gene recurrence scores for adjuvant radiation therapy. A prospective trial in this area is ongoing.

Molecular Classification of Breast Cancer

Cancer classification aims to provide an accurate diagnosis of the disease and prediction of tumor behavior to facilitate oncologic decision making. Traditional breast cancer classification, mainly based on clinicopathologic features and assessment of routine biomarkers, may not capture the varied clinical courses of individual breast cancers. The underlying biology in cancer development and progression is complicated. Recent findings from high-throughput technologies added important information with regard to the underlying genetic alterations and the biological events in breast cancer. The information provides insights into new treatment strategies and patient stratifications that impact on the management of breast cancer patients. This review provides an overview of recent data on high throughput analysis of breast cancers, and it analyzes the relationship of these findings with traditional breast cancer classification and their clinical potentials.

Decision of Adjuvant Systemic Treatment in HR+ HER2- Early Invasive Breast Cancer: Which Biomarkers Could Help?

The decision to administer adjuvant chemotherapy in treatment of early invasive breast cancer (EBC) is often complex, particularly for hormone receptor-positive (HR+) diseases, and current guidelines often classify these patients in an intermediate-risk group. Several biomarkers are currently available in this indication, in order to obtain additional and more accurate prognostic information compared to classic clinicopathological characteristics and guide the indication of adjuvant chemotherapy, optimizing the efficacy/toxicity ratio. We conducted a systematic review to evaluate the clinical validity and clinical utility of five biomarkers (uPA/PAI-1, OncotypeDX^®, MammaPrint^®, PAM50, and EndoPredict^®) in HR+/HER2- EBC, whatever the nodal status. A total of 89 studies met the inclusion criteria. Even though data currently available confirm the clinical validity of these biomarkers, there is a lack of data regarding clinical utility for most of them. Prospective studies in well-defined populations are needed to integrate these biomarkers in a decision strategy.

A Nomogram To Predict The Overall Survival Of Breast Cancer Patients And Guide The Postoperative Adjuvant Chemotherapy In China

Purpose

We aim to construct a nomogram to predict breast cancer survival and guide postoperative adjuvant chemotherapy in China.

Patients and methods

A total of 5,504 breast cancer patients from the Tianjin Breast Cancer Cases Cohort were included. Multivariable Cox regression was used to investigate the factors associated with overall survival (OS) and a nomogram was constructed based on these prognostic factors. The nomogram was internal and external validated and the performance was evaluated by area under the curve (AUC) and calibration curve. The partial score was also constructed and stratified them into low, moderate and high-risk subgroups for death according to the tripartite grouping method. Multivariate Cox regression analysis and the propensity score matching method were respectively used to test the association between adjuvant chemotherapy and OS in different risk subgroups.

Results

Age, diameter, histological differentiation, lymph node metastasis, estrogen, and progesterone receptor were incorporated into the nomogram and validation results showed this nomogram was well-calibrated to predict the 3-year [AUC =74.1%; 95% confidence interval (CI): 70.1–78.0%] and 5-year overall survival [AUC =72.3%; 95% CI: 69.6–75.1%]. Adjuvant chemotherapy was negatively associated with death in high risk subgroup [Hazard Ratio (HR) = 0.54; 95% CI: 0.37–0.77; P<0.001]. However, no significant association were found in groups with low (HR=1.47; 95% CI: 0.52–4.19; P=0.47) and moderate risk (HR=0.78; 95% CI: 0.42–1.48; P=0.45). The 1:1 PSM generated 822 pairs of well-matched patients and Kaplan-Meier showed the high-risk patients could benefit from chemotherapy, whereas low risk and moderate risk subjects did not appear to benefit from chemotherapy.

Conclusion

Not all of the breast cancer patients benefit equally from chemotherapy. The nomogram could be used to evaluate the overall survival of breast cancer patients and predict the magnitude of benefit and guide adjuvant chemotherapy for breast cancer patients after surgery.

Computer-Assisted Nuclear Atypia Scoring of Breast Cancer: a Preliminary Study

Inter-pathologist agreement for nuclear atypia scoring of breast cancer is poor. To address this problem, previous studies suggested some criteria for describing the variations appearance of tumor cells relative to normal cells. However, these criteria were still assessed subjectively by pathologists. Previous studies used quantitative computer-extracted features for scoring. However, application of these tools is limited as further improvement in their accuracy is required. This study proposes COMPASS (COMputer-assisted analysis combined with Pathologist's ASSessment) for reproducible nuclear atypia scoring. COMPASS relies on both cytological criteria assessed subjectively by pathologists as well as computer-extracted textural features. Using machine learning, COMPASS combines these two sets of features and output nuclear atypia score. COMPASS's performance was evaluated using 300 images for which expert-consensus derived reference nuclear pleomorphism scores were available, and they were scanned by two scanners from different vendors. A personalized model was built for three pathologists who gave scores to six atypia-related criteria for each image. Leave-one-out cross validation (LOOCV) was used. COMPASS was trained and tested for each pathologist separately. Percentage agreement between COMPASS and the reference nuclear scores was 93.8%, 92.9%, and 93.1% for three pathologists. COMPASS's performance in nuclear grading was almost identical for both scanners, with Cohen's kappa ranging from 0.80 to 0.86 for different pathologists and different scanners. Independently, the images were also assessed by two experienced senior pathologists. Cohen's kappa of COMPASS was comparable to the Cohen's kappa for two senior pathologists (0.79 and 0.68).

Practice Changing Potential of TAILORx: A Retrospective Review of the National Cancer Data Base from 2010 to 2015

BACKGROUND

Uncertainty regarding chemotherapy benefit among breast cancer patients with intermediate Oncotype Dx^® recurrence scores (RS; 11-25) led to the TAILORx study. We evaluated chemotherapy use in patients with intermediate RS to determine practice change potential based on the TAILORx results.

METHODS

National Cancer Data Base patients with hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative, N0 breast cancer were identified and were divided into three groups: Group A, ≤ 50 years of age (RS 11-15); Group B, ≤ 50 years of age (RS 16-25); and Group C, > 50 years of age (RS 11-25). Demographic and clinical factors were compared using Chi square tests and Poisson regression models to determine predictors of chemotherapy receipt.

RESULTS

Overall, 37,087 patients met the inclusion criteria, with 6.3% in Group A and 11.7% in Group C having received chemotherapy that may have been avoided based on TAILORx. The majority of Group B (64.7%) did not receive chemotherapy, whereas TAILORx showed potential benefit from treatment. Chemotherapy use decreased over time for all intermediate RS patients. T2 tumors, high grade, and treatment before 2012 increased the likelihood of chemotherapy receipt among both groups. Younger patients with the lower intermediate RS (Group A) were more likely to receive chemotherapy if they had treatment at community or comprehensive centers, whereas moderate grade was also a significant factor to receive chemotherapy in Group B. Significant factors in older patients (Group C) were Black race, estrogen receptor-positive/progesterone receptor-negative, and moderate/high grade.

CONCLUSIONS

The most potential impact of TAILORx findings on practice change is for patients ≤ 50 years of age with RS of 16-25 who did not receive chemotherapy but may benefit. These findings may serve as a baseline for future analysis of practice patterns related to TAILORx.

Clinical and Genomic Risk to Guide the Use of Adjuvant Therapy for Breast Cancer

BACKGROUND

The use of adjuvant chemotherapy in patients with breast cancer may be guided by clinicopathological factors and a score based on a 21-gene assay to determine the risk of recurrence. Whether the level of clinical risk of breast cancer recurrence adds prognostic information to the recurrence score is not known.

METHODS

We performed a prospective trial involving 9427 women with hormone-receptor-positive, human epidermal growth factor receptor 2-negative, axillary node-negative breast cancer, in whom an assay of 21 genes had been performed, and we classified the clinical risk of recurrence of breast cancer as low or high on the basis of the tumor size and histologic grade. The effect of clinical risk was evaluated by calculating hazard ratios for distant recurrence with the use of Cox proportional-hazards models. The initial endocrine therapy was tamoxifen alone in the majority of the premenopausal women who were 50 years of age or younger.

RESULTS

The level of clinical risk was prognostic of distant recurrence in women with an intermediate 21-gene recurrence score of 11 to 25 (on a scale of 0 to 100, with higher scores indicating a worse prognosis or a greater potential benefit from chemotherapy) who were randomly assigned to endocrine therapy (hazard ratio for the comparison of high vs. low clinical risk, 2.73; 95% confidence interval [CI], 1.93 to 3.87) or to chemotherapy plus endocrine (chemoendocrine) therapy (hazard ratio, 2.41; 95% CI, 1.66 to 3.48) and in women with a high recurrence score (a score of 26 to 100), all of whom were assigned to chemoendocrine therapy (hazard ratio, 3.17; 95% CI, 1.94 to 5.19). Among women who were 50 years of age or younger who had received endocrine therapy alone, the estimated (±SE) rate of distant recurrence at 9 years was less than 5% (≤1.8±0.9%) with a low recurrence score (a score of 0 to 10), irrespective of clinical risk, and 4.7±1.0% with an intermediate recurrence score and low clinical risk. In this age group, the estimated distant recurrence at 9 years exceeded 10% among women with a high clinical risk and an intermediate recurrence score who received endocrine therapy alone (12.3±2.4%) and among those with a high recurrence score who received chemoendocrine therapy (15.2±3.3%).

CONCLUSIONS

Clinical-risk stratification provided prognostic information that, when added to the 21-gene recurrence score, could be used to identify premenopausal women who could benefit from more effective therapy. (Funded by the National Cancer Institute and others; ClinicalTrials.gov number, NCT00310180.).

Facilitating validation of prediction models: a comparison of manual and semi-automated validation using registry-based data of breast cancer patients in the Netherlands

Background

Clinical prediction models are not routinely validated. To facilitate validation procedures, the online Evidencio platform (https://www.evidencio.com) has developed a tool partly automating this process. This study aims to determine whether semi-automated validation can reliably substitute manual validation.

Methods

Four different models used in breast cancer care were selected: CancerMath, INFLUENCE, Predicted Probability of Axillary Metastasis, and PREDICT v.2.0. Data were obtained from the Netherlands Cancer Registry according to the inclusion criteria of the original development population. Calibration (intercepts and slopes) and discrimination (area under the curve (AUC)) were compared between semi-automated and manual validation.

Results

Differences between intercepts and slopes of all models using semi-automated validation ranged from 0 to 0.03 from manual validation, which was not clinically relevant. AUCs were identical for both validation methods.

Conclusions

This easy to use semi-automated validation option is a good substitute for manual validation and might increase the number of validations of prediction models used in clinical practice. In addition, the validation tool was considered to be user-friendly and to save a lot of time compared to manual validation. Semi-automated validation will contribute to more accurate outcome predictions and treatment recommendations in the target population.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0761-5) contains supplementary material, which is available to authorized users.

Communicating prognosis to women with early breast cancer - overview of prediction tools and the development and pilot testing of a decision aid

Background

Shared decision-making in oncology requires information on individual prognosis. This comprises cancer prognosis as well as competing risks of dying due to age and comorbidities. Decision aids usually do not provide such information on competing risks. We conducted an overview on clinical prediction tools for early breast cancer and developed and pilot-tested a decision aid (DA) addressing individual prognosis using additional chemotherapy in early, hormone receptor-positive breast cancer as an example.

Methods

Systematic literature search on clinical prediction tools for the effects of drug treatment on survival of breast cancer. The DA was developed following criteria for evidence-based patient information and International Patient Decision Aids Standards. We included data on the influence of age and comorbidities on overall prognosis. The DA was pilot-tested in focus groups. Comprehension was additionally evaluated through an online survey with women in breast cancer self-help groups.

Results

We identified three prediction tools: Adjuvant!Online, PREDICT and CancerMath. All tools consider age and tumor characteristics. Adjuvant!Online considers comorbidities, CancerMath displays age-dependent non-cancer mortality. Harm due to therapy is not reported.

Twenty women participated in focus groups piloting the DA until data saturation was achieved. A total of 102 women consented to participate in the online survey, of which 86 completed the survey. The rate of correct responses was 90.5% and ranged between 84 and 95% for individual questions.

Conclusions

None of the clinical prediction tools fulfilled the requirements to provide women with all the necessary information for informed decision-making. Information on individual prognosis was well understood and can be included in patient decision aids.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-3988-2) contains supplementary material, which is available to authorized users.

Prognostic models for breast cancer: a systematic review

BACKGROUND

Breast cancer is the most common cancer in women worldwide, with a great diversity in outcomes among individual patients. The ability to accurately predict a breast cancer outcome is important to patients, physicians, researchers, and policy makers. Many models have been developed and tested in different settings. We systematically reviewed the prognostic models developed and/or validated for patients with breast cancer.

METHODS

We conducted a systematic search in four electronic databases and some oncology websites, and a manual search in the bibliographies of the included studies. We identified original studies that were published prior to 1st January 2017, and presented the development and/or validation of models based mainly on clinico-pathological factors to predict mortality and/or recurrence in female breast cancer patients.

RESULTS

From the 96 articles selected from 4095 citations found, we identified 58 models, which predicted mortality (n = 28), recurrence (n = 23), or both (n = 7). The most frequently used predictors were nodal status (n = 49), tumour size (n = 42), tumour grade (n = 29), age at diagnosis (n = 24), and oestrogen receptor status (n = 21). Models were developed in Europe (n = 25), Asia (n = 13), North America (n = 12), and Australia (n = 1) between 1982 and 2016. Models were validated in the development cohorts (n = 43) and/or independent populations (n = 17), by comparing the predicted outcomes with the observed outcomes (n = 55) and/or with the outcomes estimated by other models (n = 32), or the outcomes estimated by individual prognostic factors (n = 8). The most commonly used methods were: Cox proportional hazards regression for model development (n = 32); the absolute differences between the predicted and observed outcomes (n = 30) for calibration; and C-index/AUC (n = 44) for discrimination. Overall, the models performed well in the development cohorts but less accurately in some independent populations, particularly in patients with high risk and young and elderly patients. An exception is the Nottingham Prognostic Index, which retains its predicting ability in most independent populations.

CONCLUSIONS

Many prognostic models have been developed for breast cancer, but only a few have been validated widely in different settings. Importantly, their performance was suboptimal in independent populations, particularly in patients with high risk and in young and elderly patients.

Clinical implication of subcategorizing T2 category into T2a and T2b in TNM staging of breast cancer

Regarding TNM staging in breast cancer, T2 category is currently not divided into subcategories even though it covers a wider range of tumor sizes than T1 category. Using Korean Breast Cancer Registry database, data of 41 071 women diagnosed as non-metastatic T2 breast cancer between 2001 and 2014 were analyzed. Cutoff value for optimal tumor size was approximated by receiver operating characteristic (ROC) curve to subcategorize T2 tumors. Overall survival (OS) was compared between two subcategories. Median follow-up period was 65 months. Of 41 071 patients, 4504 (11.0%) died. Based on ROC curve analysis, 3.0 cm was selected as the cutoff value. Five-year OS rate was 91% in patients with breast tumors ≤3.0 cm (T2a) and 86% in patients with breast tumors >3.0 cm (T2b) (log-rank P < 0.001). T2b subcategory showed worse OS than T2a subcategory regardless of node status (log-rank P < 0.001 for all node categories). Within every subgroup defined by primary OS analysis covariates, T2b subcategory consistently showed worse outcome compared to T2a subcategory. By multivariate analysis, T2b subcategory was a significant independent prognostic factor of OS (hazard ratio: 1.26, 95% CI = 1.18-1.34). T2 category of breast cancer could be subcategorized into T2a and T2b with a cutoff value of 3 cm. These subcategories definitely showed different OSs even after adjusted for known prognostic factors. Subcategorization of T2 category might be useful for predicting prognosis more accurately and tailoring adjuvant therapy.

Clinical decision making in cancer care: a review of current and future roles of patient age

Background

Patient age is among the most controversial patient characteristics in clinical decision making. In personalized cancer medicine it is important to understand how individual characteristics do affect practice and how to appropriately incorporate such factors into decision making. Some argue that using age in decision making is unethical, and how patient age should guide cancer care is unsettled. This article provides an overview of the use of age in clinical decision making and discusses how age can be relevant in the context of personalized medicine.

Methods

We conducted a scoping review, searching Pubmed for English references published between 1985 and May 2017. References concerning cancer, with patients above the age of 18 and that discussed age in relation to diagnostic or treatment decisions were included. References that were non-medical or concerning patients below the age of 18, and references that were case reports, ongoing studies or opinion pieces were excluded. Additional references were collected through snowballing and from selected reports, guidelines and articles.

Results

Three hundred and forty-seven relevant references were identified. Patient age can have many and diverse roles in clinical decision making: Contextual roles linked to access (age influences how fast patients are referred to specialized care) and incidence (association between increasing age and increasing incidence rates for cancer); patient-relevant roles linked to physiology (age-related changes in drug metabolism) and comorbidity (association between increasing age and increasing number of comorbidities); and roles related to interventions, such as treatment (older patients receive substandard care) and outcome (survival varies by age).

Conclusions

Patient age is integrated into cancer care decision making in a range of ways that makes it difficult to claim age-neutrality. Acknowledging this and being more transparent about the use of age in decision making are likely to promote better clinical decisions, irrespective of one’s normative viewpoint. This overview also provides a starting point for future discussions on the appropriate role of age in cancer care decision making, which we see as crucial for harnessing the full potential of personalized medicine.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4456-9) contains supplementary material, which is available to authorized users.

Long-term prognosis of young breast cancer patients (≤40 years) who did not receive adjuvant systemic treatment: protocol for the PARADIGM initiative cohort study

INTRODUCTION

Currently used tools for breast cancer prognostication and prediction may not adequately reflect a young patient's prognosis or likely treatment benefit because they were not adequately validated in young patients. Since breast cancers diagnosed at a young age are considered prognostically unfavourable, many treatment guidelines recommend adjuvant systemic treatment for all young patients. Patients cured by locoregional treatment alone are, therefore, overtreated. Lack of prognosticators for young breast cancer patients represents an unmet medical need and has led to the initiation of the PAtients with bReAst cancer DIaGnosed preMenopausally (PARADIGM) initiative. Our aim is to reduce overtreatment of women diagnosed with breast cancer aged ≤40 years.

METHODS AND ANALYSIS

All young, adjuvant systemic treatment naive breast cancer patients, who had no prior malignancy and were diagnosed between 1989 and 2000, were identified using the population based Netherlands Cancer Registry (n=3525). Archival tumour tissues were retrieved through linkage with the Dutch nationwide pathology registry. Tissue slides will be digitalised and placed on an online image database platform for clinicopathological revision by an international team of breast pathologists. Immunohistochemical subtype will be assessed using tissue microarrays. Tumour RNA will be isolated and subjected to next-generation sequencing. Differences in gene expression found between patients with a favourable and those with a less favourable prognosis will be used to establish a prognostic classifier, using the triple negative patients as proof of principle.

ETHICS AND DISSEMINATION

Observational data from the Netherlands Cancer Registry and left over archival patient material are used. Therefore, the Dutch law on Research Involving Human Subjects Act (WMO) is not applicable. The PARADIGM study received a 'non-WMO' declaration from the Medical Ethics Committee of the Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, waiving individual patient consent. All data and material used are stored in a coded way. Study results will be presented at international (breast cancer) conferences and published in peer-reviewed, open-access journals.

Validation of the online prediction tool PREDICT v. 2.0 in the Dutch breast cancer population

BACKGROUND

PREDICT version 2.0 is increasingly used to estimate prognosis in breast cancer. This study aimed to validate this tool in specific prognostic subgroups in the Netherlands.

METHODS

All operated women with non-metastatic primary invasive breast cancer, diagnosed in 2005, were selected from the nationwide Netherlands Cancer Registry (NCR). Predicted and observed 5- and 10-year overall survival (OS) were compared for the overall cohort, separated by oestrogen receptor (ER) status, and predefined subgroups. A >5% difference was considered as clinically relevant. Discriminatory accuracy and goodness-of-fit were determined using the area under the receiver operating characteristic curve (AUC) and the Chi-squared-test.

RESULTS

We included 8834 patients. Discriminatory accuracy for 5-year OS was good (AUC 0.80). For ER-positive and ER-negative patients, AUCs were 0.79 and 0.75, respectively. Predicted 5-year OS differed from observed by -1.4% in the entire cohort, -0.7% in ER-positive and -4.9% in ER-negative patients. Five-year OS was accurately predicted in all subgroups. Discriminatory accuracy for 10-year OS was good (AUC 0.78). For ER-positive and ER-negative patients AUCs were 0.78 and 0.76, respectively. Predicted 10-year OS differed from observed by -1.0% in the entire cohort, -0.1% in ER-positive and -5.3 in ER-negative patients. Ten-year OS was overestimated (6.3%) in patients ≥75 years and underestimated (-13.%) in T3 tumours and patients treated with both endocrine therapy and chemotherapy (-6.6%).

CONCLUSIONS

PREDICT predicts OS reliably in most Dutch breast cancer patients, although results for both 5-year and 10-year OS should be interpreted carefully in ER-negative patients. Furthermore, 10-year OS should be interpreted cautiously in patients ≥75 years, T3 tumours and in patients considering endocrine therapy and chemotherapy.

Preoperative indication for systemic therapy extended to patients with early-stage breast cancer using multiparametric 7-tesla breast MRI

Purpose

To establish a preoperative decision model for accurate indication of systemic therapy in early-stage breast cancer using multiparametric MRI at 7-tesla field strength.

Materials and methods

Patients eligible for breast-conserving therapy were consecutively included. Patients underwent conventional diagnostic workup and one preoperative multiparametric 7-tesla breast MRI. The postoperative (gold standard) indication for systemic therapy was established from resected tumor and lymph-node tissue, based on 10-year risk-estimates of breast cancer mortality and relapse using Adjuvant! Online. Preoperative indication was estimated using similar guidelines, but from conventional diagnostic workup. Agreement was established between preoperative and postoperative indication, and MRI-characteristics used to improve agreement. MRI-characteristics included phospomonoester/phosphodiester (PME/PDE) ratio on 31-phosphorus spectroscopy (³¹P-MRS), apparent diffusion coefficients on diffusion-weighted imaging, and tumor size on dynamic contrast-enhanced (DCE)-MRI. A decision model was built to estimate the postoperative indication from preoperatively available data.

Results

We included 46 women (age: 43-74yrs) with 48 invasive carcinomas. Postoperatively, 20 patients (43%) had positive, and 26 patients (57%) negative indication for systemic therapy. Using conventional workup, positive preoperative indication agreed excellently with positive postoperative indication (N = 8/8; 100%). Negative preoperative indication was correct in only 26/38 (68%) patients. However, ³¹P-MRS score (p = 0.030) and tumor size (p = 0.002) were associated with the postoperative indication. The decision model shows that negative indication is correct in 21/22 (96%) patients when exempting tumors larger than 2.0cm on DCE-MRI or with PME>PDE ratios at ³¹P-MRS.

Conclusions

Preoperatively, positive indication for systemic therapy is highly accurate. Negative indication is highly accurate (96%) for tumors sized ≤2,0cm on DCE-MRI and with PME≤PDE ratios on ³¹P-MRS.

Are online prediction tools a valid alternative to genomic profiling in the context of systemic treatment of ER-positive breast cancer?

Background

Clinicians use clinical and pathological parameters, such as tumour size, grade and nodal status, to make decisions on adjuvant treatments for breast cancer. However, therapeutic decisions based on these features tend to vary due to their subjectivity. Computational and mathematical algorithms were developed using clinical outcome data from breast cancer registries, such as Adjuvant! Online and NHS PREDICT. More recently, assessments of molecular profiles have been applied in the development of better prognostic tools.

Methods

Based on the available literature on online registry-based tools and genomic assays, we evaluated whether these online tools could be valid and accurate alternatives to genomic and molecular profiling of the individual breast tumour in aiding therapeutic decisions, particularly in patients with early ER-positive breast cancer.

Results and conclusions

Early breast cancer is currently considered a systemic disease and a complex ecosystem with behaviour determined by the complex genetic and molecular signatures of the tumour cells, mammary stem cells, microenvironment and host immune system. We anticipate that molecular profiling will continue to evolve, expanding beyond the primary tumour to include the tumour microenvironment, cancer stem cells and host immune system. This should further refine therapeutic decisions and optimise clinical outcome.

This article was specially invited by the editors and represents work by leading researchers.

Interaction of mammographic breast density with menopausal status and postmenopausal hormone use in relation to the risk of aggressive breast cancer subtypes

PURPOSE

We examined the associations of mammographic breast density with breast cancer risk by tumor aggressiveness and by menopausal status and current postmenopausal hormone therapy.

METHODS

This study included 2596 invasive breast cancer cases and 4059 controls selected from participants of four nested case-control studies within four established cohorts: the Mayo Mammography Health Study, the Nurses' Health Study, Nurses' Health Study II, and San Francisco Mammography Registry. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were assessed from digitized film-screen mammograms using a computer-assisted threshold technique and standardized across studies. We used polytomous logistic regression to quantify the associations of breast density with breast cancer risk by tumor aggressiveness (defined as presence of at least two of the following tumor characteristics: size ≥2 cm, grade 2/3, ER-negative status, or positive nodes), stratified by menopausal status and current hormone therapy.

RESULTS

Overall, the positive association of PD and borderline inverse association of NDA with breast cancer risk was stronger in aggressive vs. non-aggressive tumors (≥51 vs. 11-25% OR 2.50, 95% CI 1.94-3.22 vs. OR 2.03, 95% CI 1.70-2.43, p-heterogeneity = 0.03; NDA 4th vs. 2nd quartile OR 0.54, 95% CI 0.41-0.70 vs. OR 0.71, 95% CI 0.59-0.85, p-heterogeneity = 0.07). However, there were no differences in the association of DA with breast cancer by aggressive status. In the stratified analysis, there was also evidence of a stronger association of PD and NDA with aggressive tumors among postmenopausal women and, in particular, current estrogen+progesterone users (≥51 vs. 11-25% OR 3.24, 95% CI 1.75-6.00 vs. OR 1.93, 95% CI 1.25-2.98, p-heterogeneity = 0.01; NDA 4th vs. 2nd quartile OR 0.43, 95% CI 0.21-0.85 vs. OR 0.56, 95% CI 0.35-0.89, p-heterogeneity = 0.01), even though the interaction was not significant.

CONCLUSION

Our findings suggest that associations of mammographic density with breast cancer risk differ by tumor aggressiveness. While there was no strong evidence that these associations differed by menopausal status or hormone therapy, they did appear more prominent among current estrogen+progesterone users.

Early health technology assessment of magnetic resonance-guided high intensity focused ultrasound ablation for the treatment of early-stage breast cancer

BACKGROUND

Magnetic resonance-guided high intensity focused ultrasound (MR-HIFU) ablation is in development for minimally invasive treatment of breast cancer. Cost-effectiveness has not been assessed yet. An early health technology assessment was performed to estimate costs of MR-HIFU ablation, compared to breast conserving treatment (BCT).

METHODS

An MR-HIFU treatment model using the dedicated MR-HIFU breast system (Sonalleve, Philips Healthcare) was developed. Input parameters (treatment steps and duration) were based on the analysis of questionnaire data from an expert panel. MR-HIFU experts assessed face validity of the model. Data collected by questionnaires were compared to published data of an MR-HIFU breast feasibility study. Treatment costs for tumours of 1 to 3 cm were calculated.

RESULTS

The model structure was considered of acceptable face validity by consulted experts, and questionnaire data and published data were comparable. Costs of MR-HIFU ablation were higher than BCT costs. MR-HIFU best-case scenario costs exceeded BCT costs with approximately €1000. Cooling times and breathing correction contributed most to treatment costs.

CONCLUSIONS

MR-HIFU ablation is currently not a cost-effective alternative for BCT. MR-HIFU experience is limited, increasing uncertainty of estimations. The potential for cost-effectiveness increases if future research reduces treatment durations and might substantiate equal or improved results.

Do online prognostication tools represent a valid alternative to genomic profiling in the context of adjuvant treatment of early breast cancer? A systematic review of the literature

INTRODUCTION

Decision-making regarding adjuvant chemotherapy has been based on clinical and pathological features. However, such decisions are seldom consistent. Web-based predictive models have been developed using data from cancer registries to help determine the need for adjuvant therapy. More recently, with the recognition of the heterogenous nature of breast cancer, genomic assays have been developed to aid in the therapeutic decision-making.

METHODS

We have carried out a comprehensive literature review regarding online prognostication tools and genomic assays to assess whether online tools could be used as valid alternatives to genomic profiling in decision-making regarding adjuvant therapy in early breast cancer.

RESULTS AND CONCLUSIONS

Breast cancer has been recently recognized as a heterogenous disease based on variations in molecular characteristics. Online tools are valuable in guiding adjuvant treatment, especially in resource constrained countries. However, in the era of personalized therapy, molecular profiling appears to be superior in predicting clinical outcome and guiding therapy.

A Comprehensive Model for Predicting Recurrence and Survival in Cases of Chinese Postoperative Invasive Breast Cancer

We investigated relationships between clinical pathologic data, molecular biomarkers and prognosis of invasive breast cancer based on a Chinese population. Immunohistochemistry (IHC) was used to assess the status of ER, PR, HER-2 and Ki-67, with fluorescence in situ hybridization (FISH) performed to further confirm HER-2 positivity with an equivocal result (IHC 2+). Subsequently, Kaplan-Meier univariate and multivariate COX regression analyses of ER, PR, HER-2, Ki-67, clinical features, therapeutic status and follow-up data were performed according to the establishment principle of the Nottingham prognostic index (NPI). From this study, age, tumor size, lymph node status, ER, HER-2, Ki-67 status were found to be associated with prognosis. Eventually, a prognostic model of (PI= (1.5×age) - size + (0.1×lymph node status) - (0.5×ER) + (2×HER-2) - (0.2×Ki-67)) was established with 288 randomly selected patients and verified with another 100 cases with invasive breast cancer. Pearson correlation analysis demonstrated a significant positive correlation index of 0.376 (P=0.012<0.05) between the prognostic index (PI) and actual prognosis. Remarkably, the consistency with the model predicted recurrence was 93% in the validation set. Therefore, it appears feasible to predict the prognosis of individuals with invasive breast cancer and to determine optimal therapeutic strategy with this model.

The prognostic performance of Adjuvant! Online and Nottingham Prognostic Index in young breast cancer patients

Background:

Limited data are available on the prognostic performance of Adjuvant! Online (AOL) and Nottingham Prognostic Index (NPI) in young breast cancer patients.

Methods:

This multicentre hospital-based retrospective cohort study included young (⩽40 years) and older (55–60 years) breast cancer patients treated from January 2000 to December 2004 at four large Belgian and Italian institutions. Predicted 10-year overall survival (OS) and disease-free survival (DFS) using AOL and 10-year OS using NPI were calculated for every patient. Tools ability to predict outcomes (i.e., calibration) and their discriminatory accuracy was assessed.

Results:

The study included 1283 patients, 376 young and 907 older women. Adjuvant! Online accurately predicted 10-year OS (absolute difference: 0.7% P=0.37) in young cohort, but overestimated 10-year DFS by 7.7% (P=0.003). In older cohort, AOL significantly underestimated both 10-year OS and DFS by 7.2% (P<0.001) and 3.2% (P=0.04), respectively. Nottingham Prognostic Index significantly underestimated 10-year OS in both young (8.5% P<0.001) and older (4.0% P<0.001) cohorts. Adjuvant! Online and NPI had comparable discriminatory accuracy.

Conclusions:

In young breast cancer patients, AOL is a reliable tool in predicting OS at 10 years but not DFS, whereas the performance of NPI is sub-optimal.

Validation of the CancerMath prognostic tool for breast cancer in Southeast Asia

BACKGROUND

CancerMath is a set of web-based prognostic tools which predict nodal status and survival up to 15 years after diagnosis of breast cancer. This study validated its performance in a Southeast Asian setting.

METHODS

Using Singapore Malaysia Hospital-Based Breast Cancer Registry, clinical information was retrieved from 7064 stage I to III breast cancer patients who were diagnosed between 1990 and 2011 and underwent surgery. Predicted and observed probabilities of positive nodes and survival were compared for each subgroup. Calibration was assessed by plotting observed value against predicted value for each decile of the predicted value. Discrimination was evaluated by area under a receiver operating characteristic curve (AUC) with 95 % confidence interval (CI).

RESULTS

The median predicted probability of positive lymph nodes is 40.6 % which was lower than the observed 43.6 % (95 % CI, 42.5 %-44.8 %). The calibration plot showed underestimation for most of the groups. The AUC was 0.71 (95 % CI, 0.70-0.72). Cancermath predicted and observed overall survival probabilities were 87.3 % vs 83.4 % at 5 years after diagnosis and 75.3 % vs 70.4 % at 10 years after diagnosis. The difference was smaller for patients from Singapore, patients diagnosed more recently and patients with favorable tumor characteristics. Calibration plot also illustrated overprediction of survival for patients with poor prognosis. The AUC for 5-year and 10-year overall survival was 0.77 (95 % CI: 0.75-0.79) and 0.74 (95 % CI: 0.71-0.76).

CONCLUSIONS

The discrimination and calibration of CancerMath were modest. The results suggest that clinical application of CancerMath should be limited to patients with better prognostic profile.

Comparison of the Ability of Different Clinical Treatment Scores to Estimate Prognosis in High-Risk Early Breast Cancer Patients: A Hellenic Cooperative Oncology Group Study

BACKGROUND-AIM

Early breast cancer is a heterogeneous disease, and, therefore, prognostic tools have been developed to evaluate the risk for distant recurrence. In the present study, we sought to develop a risk for recurrence score (RRS) based on mRNA expression of three proliferation markers in high-risk early breast cancer patients and evaluate its ability to predict risk for relapse and death. In addition the Adjuvant! Online score (AOS) was also determined for each patient, providing a 10-year estimate of relapse and mortality risk. We then evaluated whether RRS or AOS might possibly improve the prognostic information of the clinical treatment score (CTS), a model derived from clinicopathological variables.

METHODS

A total of 1,681 patients, enrolled in two prospective phase III trials, were treated with anthracycline-based adjuvant chemotherapy. Sufficient RNA was extracted from 875 samples followed by multiplex quantitative reverse transcription-polymerase chain reaction for assessing RACGAP1, TOP2A and Ki67 mRNA expression. The CTS, slightly modified to fit our cohort, integrated the prognostic information from age, nodal status, tumor size, histological grade and treatment. Patients were also classified to breast cancer subtypes defined by immunohistochemistry. Likelihood ratio (LR) tests and concordance indices were used to estimate the relative increase in the amount of information provided when either RRS or AOS is added to CTS.

RESULTS

The optimal RRS, in terms of disease-free survival (DFS) and overall survival (OS), was based on the co-expression of two of the three evaluated genes (RACGAP1 and TOP2A). CTS was prognostic for DFS (p<0.001), while CTS, AOS and RRS were all prognostic for OS (p<0.001, p<0.001 and p = 0.036, respectively). The use of AOS in addition to CTS added prognostic information regarding DFS (LR-Δχ2 8.7, p = 0.003), however the use of RRS in addition to CTS did not. For estimating OS, the use of either AOS or RRS in addition to CTS added significant prognostic information. Specifically, the use of both CTS and AOS had significantly better prognostic value vs. CTS alone (LR-Δχ2 20.8, p<0.001), as well as the use of CTS and RRS vs. CTS alone (LR-Δχ2 4.8, p = 0.028). Additionally, more patients were scored as high-risk by AOS than CTS. According to immunohistochemical subtypes, prognosis was improved in the Luminal A (LR-Δχ2 7.2, p = 0.007) and Luminal B (LR-Δχ2 8.3, p = 0.004) subtypes, in HER2-negative patients (LR-Δχ2 23.4, p<0.001) and in patients with >3 positive nodes (LR-Δχ2 23.9, p<0.001) when AOS was added to CTS.

CONCLUSIONS

The current study has shown a clear benefit in predicting overall survival of high-risk early breast cancer patients when combining CTS with either AOS or RRS. The combination of CTS and AOS adds significant prognostic information compared to CTS alone for DFS, while the combination of CTS with either AOS or RRS has better prognostic value than CTS alone for OS. These findings could possibly add on the information needed for the best risk prediction strategy in high-risk early breast cancer patients in a rather simple and inexpensive way, especially in Luminal A and B subtypes, HER2-negative patients and those with >3 positive nodes.

Prognostic models in male breast cancer

PURPOSE

Breast cancer in men is uncommon; it accounts for 1 % of all patients with primary breast cancer. Its treatment is mostly extrapolated from its female counterpart. Accurate predictions are essential for adjuvant systemic treatment decision-making and informing patients. Several predictive models are available for female breast cancer (FBC) including the Morphometric Prognostic Index (MPI), Nottingham Prognostic Index (NPI), Adjuvant! Online and Predict. The aim of this study was to examine and compare the prognostic performance of these models for male breast cancer (MBC).

METHODS

The population of this study consists of 166 MBC patients. The prognostic scores of the patients are categorized by good, (moderate) and poor, defined by the test itself (MPI and NPI) or based on tertiles (Adjuvant! Online and Predict). Survival according to prognostic score was compared by Kaplan-Meier analysis and differences were tested by logRank. The prognostic performances were evaluated with C-statistics. Calibration was done with the aim to estimate to what extent the survival rates predicted by Predict were similar to the observed survival rates.

RESULTS

All prediction models were capable of discriminating between good, moderate and poor survivors. P-values were highly significant. Comparison between the models using C-statistics (n = 88) showed equal performance of MPI (0.67), NPI (0.68), Adjuvant! Online (0.69) and Predict (0.69). Calibration of Predict showed overestimation for MBC patients.

CONCLUSION

In conclusion, MPI, NPI, Adjuvant! and Predict prognostic models, originally developed and validated for FBC patients, also perform quite well for MBC patients.