Validity of Adjuvant! Online program in older patients with breast cancer: a population-based study

Improving shared decision-making about cancer treatment through design-based data-driven decision-support tools and redesigning care paths: an overview of the 4D PICTURE project

Background

Patients with cancer often have to make complex decisions about treatment, with the options varying in risk profiles and effects on survival and quality of life. Moreover, inefficient care paths make it hard for patients to participate in shared decision-making. Data-driven decision-support tools have the potential to empower patients, support personalized care, improve health outcomes and promote health equity. However, decision-support tools currently seldom consider quality of life or individual preferences, and their use in clinical practice remains limited, partly because they are not well integrated in patients' care paths.

Aim and objectives

The central aim of the 4D PICTURE project is to redesign patients' care paths and develop and integrate evidence-based decision-support tools to improve decision-making processes in cancer care delivery. This article presents an overview of this international, interdisciplinary project.

Design methods and analysis

In co-creation with patients and other stakeholders, we will develop data-driven decision-support tools for patients with breast cancer, prostate cancer and melanoma. We will support treatment decisions by using large, high-quality datasets with state-of-the-art prognostic algorithms. We will further develop a conversation tool, the Metaphor Menu, using text mining combined with citizen science techniques and linguistics, incorporating large datasets of patient experiences, values and preferences. We will further develop a promising methodology, MetroMapping, to redesign care paths. We will evaluate MetroMapping and these integrated decision-support tools, and ensure their sustainability using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework. We will explore the generalizability of MetroMapping and the decision-support tools for other types of cancer and across other EU member states.

Ethics

Through an embedded ethics approach, we will address social and ethical issues.

Discussion

Improved care paths integrating comprehensive decision-support tools have the potential to empower patients, their significant others and healthcare providers in decision-making and improve outcomes. This project will strengthen health care at the system level by improving its resilience and efficiency.

A nomogram for individually predicting overall survival for elderly patients with early breast cancer: a consecutive cohort study

Background

Elderly patients with breast cancer are highly heterogeneous, and tumor load and comorbidities affect patient prognosis. Prediction models can help clinicians to implement tailored treatment plans for elderly patients with breast cancer. This study aimed to establish a prediction model for breast cancer, including comorbidities and tumor characteristics, in elderly patients with breast cancer.

Methods

All patients were ≥65 years old and admitted to the Peking Union Medical College Hospital. The clinical and pathological characteristics, recurrence, and death were observed. Overall survival (OS) was analyzed using the Kaplan-Meier curve and a prediction model was constructed using Cox proportional hazards model regression. The discriminative ability and calibration of the nomograms for predicting OS were tested using concordance (C)-statistics and calibration plots. Clinical utility was demonstrated using decision curve analysis (DCA).

Results

Based on 2,231 patients, the 5- and 10-year OS was 91.3% and 78.4%, respectively. We constructed an OS prediction nomogram for elderly patients with early breast cancer (PEEBC). The C-index for OS in PEEBC in the training and validation cohorts was 0.798 and 0.793, respectively. Calibration of the nomogram revealed a good predictive capability, as indicated by the calibration plot. DCA demonstrated that our model is clinically useful.

Conclusion

The nomogram accurately predicted the 3-year, 5-year, and 10-year OS in elderly patients with early breast cancer.

Gene expression signatures in older patients with breast cancer: A systematic review

BACKGROUND

Gene expression signatures have emerged to predict prognosis and guide the use of adjuvant therapy in patients with hormone receptor-positive breast cancer. The objective of this systematic review was to evaluate the prognostic and predictive value of commercially available gene expression signatures as a tool in adjuvant treatment decision-making in older patients with breast cancer.

METHODS

PubMed, MEDLINE, Embase, Web of Science, Cochrane Library, and Emcare were reviewed for relevant articles published before December 2021. Eligible studies were randomised trials and cohort studies that externally validated commercially available gene expression signatures in patients aged 65 years and older, including studies that presented subanalyses of this age group. Data extraction and risk of bias assessment was performed independently by two investigators.

RESULTS

Fifteen studies were included. Most studies investigated Oncotype DX, while results from other gene expression signatures were limited. Several studies underlined the prognostic performance of Oncotype DX and Prosigna Risk of Recurrence in older patients. Moreover, Oncotype DX was predictive for older patients with an intermediate-risk recurrence score; chemotherapy could be spared in both lymph node-positive and lymph node-negative disease.

CONCLUSIONS

Prognostic performance has been demonstrated in older patients for several gene expression signatures. However, additional validation in patients with high-risk tumours is needed before gene expression signatures can be implemented in clinical practice as a prediction tool for adjuvant chemotherapy decision-making in the older age group.

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

Background

Objective

Methods

Results

Conclusions

Is the oncotype DX test useful in elderly breast cancer patients: a subgroup analysis of real-life Italian PONDx study

INTRODUCTION

The 21-gene Oncotype DX Breast Recurrence Score® test, (Genomic Health, Redwood City CA) has not been formally evaluated in an older cohort with estrogen receptor (ER)-positive breast cancer (BC) in term of physicians' treatment decisions. We determine the utility of Recurrence Score® (RS) result on adjuvant therapy prescription in elderly patients with resected early BC.

MATERIAL AND METHODS

PONDx was a multicenter, prospective, observational study, and which investigated the real-life use of the Oncotype DX® test by physicians treating early BC patients in clinical practice.

RESULTS

Data from the elderly extracted from 1724 BC patients who underwent Oncotype DX testing were available from 27 reference centers located in 6 regions of Italy (Lombardia, Lazio, Emilia Romagna, Campania, Abruzzo, and Marche). A total of 230 patients (13% of the total population) aged > 70 years were analyzed. The study primarily evaluated the impact of the Oncotype DX test on adjuvant treatment decisions. Physicians chosen chemotherapy plus endocrine therapy in 36% of elderly patients and 46% of those 50-70 years before the Oncotype DX test. After knowing the RS data, these rates fell to 23 and 33% (38 and 28% relative reduction, respectively).

CONCLUSIONS

21-gene test may be helpful even in a relatively low-risk group as elderly patients and may avoid the toxicity of adjuvant chemotherapy in a significant amount. If the Oncotype DX test is currently adopted on a large scale among the elderly and may impact the general prognosis of elderly BC patients, it is challenging and still unproven.

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.

Development and validation of the PORTRET tool to predict recurrence, overall survival, and other-cause mortality in older patients with breast cancer in the Netherlands: a population-based study

BACKGROUND

Current prediction tools for breast cancer outcomes are not tailored to the older patient, in whom competing risk strongly influences treatment effects. We aimed to develop and validate a prediction tool for 5-year recurrence, overall mortality, and other-cause mortality for older patients (aged ≥65 years) with early invasive breast cancer and to estimate individualised expected benefits of adjuvant systemic treatment.

METHODS

We selected surgically treated patients with early invasive breast cancer (stage I-III) aged 65 years or older from the population-based FOCUS cohort in the Netherlands. We developed prediction models for 5-year recurrence, overall mortality, and other-cause mortality using cause-specific Cox proportional hazard models. External validation was performed in a Dutch Cancer registry cohort. Performance was evaluated with discrimination accuracy and calibration plots.

FINDINGS

We included 2744 female patients in the development cohort and 13631 female patients in the validation cohort. Median age was 74·8 years (range 65-98) in the development cohort and 76·0 years (70-101) in the validation cohort. 5-year follow-up was complete for more than 99% of all patients. We observed 343 and 1462 recurrences, and 831 and 3594 deaths, of which 586 and 2565 were without recurrence, in the development and validation cohort, respectively. The area under the receiver-operating-characteristic curve at 5 years in the external dataset was 0·76 (95% CI 0·75-0·76) for overall mortality, 0·76 (0·76-0·77) for recurrence, and 0·75 (0·74-0·75) for other-cause mortality.

INTERPRETATION

The PORTRET tool can accurately predict 5-year recurrence, overall mortality, and other-cause mortality in older patients with breast cancer. The tool can support shared decision making, especially since it provides individualised estimated benefits of adjuvant treatment.

FUNDING

Dutch Cancer Foundation and ZonMw.

Bayesian Framework to Augment Tumor Board Decision Making

PURPOSE

Ideally, specific treatment for a cancer patient is decided by a multidisciplinary tumor board, integrating prior clinical experience, published data, and patient-specific factors to develop a consensus on an optimal therapeutic strategy. However, many oncologists lack access to a tumor board, and many patients have incomplete data descriptions so that tumor boards must act on imprecise criteria. We propose these limitations to be addressed through a flexible but rigorous mathematical tool that can define the probability of success of given therapies and be made readily available to the oncology community.

METHODS

We present a Bayesian approach to tumor forecasting using a multimodel framework to predict patient-specific response to different targeted therapies even when historical data are incomplete.

RESULTS

We demonstrate that the Bayesian decision theory's integrative power permits the simultaneous assessment of a range of therapeutic options.

CONCLUSION

This methodology proposed, built upon a robust and well-established mathematical framework, can play a crucial role in supporting patient-specific clinical decisions by individual oncologists and multispecialty tumor boards.

Updated recommendations regarding the management of older patients with breast cancer: a joint paper from the European Society of Breast Cancer Specialists (EUSOMA) and the International Society of Geriatric Oncology (SIOG)

Breast cancer is increasingly prevalent in older adults and is a substantial part of routine oncology practice. However, management of breast cancer in this population is challenging because the disease is highly heterogeneous and there is insufficient evidence specific to older adults. Decision making should not be driven by age alone but should involve geriatric assessments plus careful consideration of life expectancy, competing risks of mortality, and patient preferences. A multidisciplinary taskforce, including members of the European Society of Breast Cancer Specialists and International Society of Geriatric Oncology, gathered to expand and update the previous 2012 evidence-based recommendations for the management of breast cancer in older individuals with the endorsement of the European Cancer Organisation. These guidelines were expanded to include chemotherapy toxicity prediction calculators, cultural and social considerations, surveillance imaging, genetic screening, gene expression profiles, neoadjuvant systemic treatment options, bone-modifying drugs, targeted therapies, and supportive care. Recommendations on geriatric assessment, ductal carcinoma in situ, screening, primary endocrine therapy, surgery, radiotherapy, adjuvant systemic therapy, and secondary breast cancer were updated.

Influence of the competing risk of death on estimates of disease recurrence in trials of adjuvant endocrine therapy for early-stage breast cancer: A secondary analysis of MA.27, MA.17 and MA.17R

BACKGROUND

Many women diagnosed with early-stage hormone-sensitive breast cancer die of causes other than their breast cancer. These competing risks can create challenges in analysing and clearly communicating data on risk of breast cancer recurrence or death. Here, we quantify the impact of competing risks on estimates of disease recurrence and benefit from therapy.

PATIENTS AND METHODS

Using data from the MA.27, MA.17 and MA.17R trials of adjuvant endocrine therapy in early breast cancer, we compared Kaplan-Meier (KM) and competing risk methods for disease-free survival (DFS) and distant recurrence-free survival (DRFS). Each trial was analysed separately. In KM analyses, participants were censored at the time of non-breast cancer death. Competing risk analyses comprised cumulative incidence functions in which non-breast cancer death was a competing risk.

RESULTS

Non-breast cancer deaths were observed more often in older participants, in those with lower risk of breast cancer and after longer follow-up. Compared with conventional analyses, estimates of the proportion of participants with DFS or DRFS events were lower in competing risk analyses, with this difference increasing over the course of follow-up. The absolute treatment benefit was similar or modestly lower in competing risk analyses.

CONCLUSION

Compared with KM methods, competing risk analyses result in lower estimates of DFS and DRFS events and similar or modestly lower absolute benefit from experimental endocrine therapy. Over a long time horizon, competing risk methods may be preferable to KM methods when estimating future risk of recurrence in early-stage hormone-sensitive breast cancer.

CLINICAL TRIALS REGISTRATION

Clinicaltrials.gov; NCT00003140, NCT00754845, NCT00066573.

Prediction of Other-Cause Mortality in Older Patients with Breast Cancer Using Comorbidity

Simple Summary

Selecting older patients for adjuvant breast cancer treatments is challenging as its benefits can be diminished by shorter life expectancies. In addition to age, comorbidity increases the risk of dying from other causes than breast cancer. Available prediction tools have either not adjusted for individual comorbidities or have shown inaccurate predictions when a higher number of comorbidities are present. Up to now, an optimal comorbidity score to be used in prediction tools has not been established. Therefore, this study aimed to assess the predictive value of the Charlson comorbidity index for other-cause mortality and to compare these predictions with using a simple comorbidity count. We found that the Charlson index performed similarly as comorbidity count. The use of comorbidity count in the development of new prediction tools for older patients with breast cancer is recommended as its simplicity enhances the tool’s applicability in clinical practice.

Abstract

Background: Individualized treatment in older patients with breast cancer can be improved by including comorbidity and other-cause mortality in prediction tools, as the other-cause mortality risk strongly increases with age. However, no optimal comorbidity score is established for this purpose. Therefore, this study aimed to compare the predictive value of the Charlson comorbidity index for other-cause mortality with the use of a simple comorbidity count and to assess the impact of frequently occurring comorbidities. Methods: Surgically treated patients with stages I-III breast cancer aged ≥70 years diagnosed between 2003 and 2009 were selected from the Netherlands Cancer Registry. Competing risk analysis was performed to associate 5-year other-cause mortality with the Charlson index, comorbidity count, and specific comorbidities. Discrimination and calibration were assessed. Results: Overall, 7511 patients were included. Twenty-nine percent had no comorbidities, and 59% had a Charlson score of 0. After five years, in 1974, patients had died (26%), of which 1450 patients without a distant recurrence (19%). Besides comorbidities included in the Charlson index, the psychiatric disease was strongly associated with other-cause mortality (sHR 2.44 (95%-CI 1.70–3.50)). The c-statistics of the Charlson index and comorbidity count were similar (0.65 (95%-CI 0.64–0.65) and 0.64 (95%-CI 0.64–0.65)). Conclusions: The predictive value of the Charlson index for 5-year other-cause mortality was similar to using comorbidity count. As it is easier to use in clinical practice, our findings indicate that comorbidity count can aid in improving individualizing treatment in older patients with breast cancer. Future studies should elicit whether geriatric parameters could improve prediction.

Bridging the age gap in breast cancer: cluster randomized trial of two decision support interventions for older women with operable breast cancer on quality of life, survival, decision quality, and treatment choices

BACKGROUND

Rates of surgery and adjuvant therapy for breast cancer vary widely between breast units. This may contribute to differences in survival. This cluster RCT evaluated the impact of decision support interventions (DESIs) for older women with breast cancer, to ascertain whether DESIs influenced quality of life, survival, decision quality, and treatment choice.

METHODS

A multicentre cluster RCT compared the use of two DESIs against usual care in treatment decision-making in older women (aged at least ≥70 years) with breast cancer. Each DESI comprised an online algorithm, booklet, and brief decision aid to inform choices between surgery plus adjuvant endocrine therapy versus primary endocrine therapy, and adjuvant chemotherapy versus no chemotherapy. The primary outcome was quality of life. Secondary outcomes included decision quality measures, survival, and treatment choice.

RESULTS

A total of 46 breast units were randomized (21 intervention, 25 usual care), recruiting 1339 women (670 intervention, 669 usual care). There was no significant difference in global quality of life at 6 months after the baseline assessment on intention-to-treat analysis (difference -0.20, 95 per cent confidence interval (C.I.) -2.69 to 2.29; P = 0.900). In women offered a choice of primary endocrine therapy versus surgery plus endocrine therapy, knowledge about treatments was greater in the intervention arm (94 versus 74 per cent; P = 0.003). Treatment choice was altered, with a primary endocrine therapy rate among women with oestrogen receptor-positive disease of 21.0 per cent in the intervention versus 15.4 per cent in usual-care sites (difference 5.5 (95 per cent C.I. 1.1 to 10.0) per cent; P = 0.029). The chemotherapy rate was 10.3 per cent at intervention versus 14.8 per cent at usual-care sites (difference -4.5 (C.I. -8.0 to 0) per cent; P = 0.013). Survival was similar in both arms.

CONCLUSION

The use of DESIs in older women increases knowledge of breast cancer treatment options, facilitates shared decision-making, and alters treatment selection. Trial registration numbers: EudraCT 2015-004220-61 (https://eudract.ema.europa.eu/), ISRCTN46099296 (http://www.controlled-trials.com).

Predicting disease-related and patient-reported outcomes in older patients with breast cancer - a systematic review

The number of older patients with breast cancer has increased due to the aging of the general population. The use of a geriatric assessment in this population has been advocated in many studies and guidelines as it can be used to identify high risk populations for early mortality and toxicity. Additionally, geriatric parameters could predict relevant outcome measures. This systematic review summarizes all available evidence on predictive factors for various outcomes (disease-related and survival, toxicity, and patient-reported outcomes), with a special focus on geriatric parameters and patient-reported outcomes, in older patients with breast cancer. Studies were identified through systematic review of the literature published up to September 1st 2019 in the PubMed database and EMBASe. A total of 173 studies were included. Most studies investigated disease-related and survival outcomes (n = 123, 71%). Toxicity was investigated in 40 studies (23%) and a mere 15% (n = 26) investigated patient-reported outcomes. Various measures that can be derived from a geriatric assessment were predictive for survival endpoints. Furthermore, geriatric parameters were among the most frequently found predictors for toxicity and patient-reported outcomes. In conclusion, this study shows that geriatric parameters can predict survival, toxicity, and patient-reported outcomes in older patients with breast cancer. These findings can be used in daily clinical practice to identify patients at risk of early mortality, high risk of treatment toxicity or poor functional outcome after treatment. A minority of studies used relevant outcome measures for older patients, showing the need for studies that are tailored to the older population.

Geriatric principles for patients with cancer

Cancer is primarily a disease of older persons. Given the heterogeneity of aging, physiological age, rather than chronological age, better expresses the cumulative effect of environmental, medical, and psychosocial stressors, which modifies life expectancy. Comprehensive geriatric assessment, a tool that helps ascertain the physiological age of older individuals, is the gold standard for assessing older adults with cancer. Several international organizations recommend using the geriatric assessment domains to identify unrecognized health problems that can interfere with treatment and predict adverse health-related outcomes, aiding complex treatment decision making. More recently, it has been shown that geriatric assessment-guided interventions improve quality of life and mitigate treatment toxicity without compromising survival. In this review, we discuss the role of comprehensive geriatric assessment in cancer care for older adults and provide the reader with useful information to assess potential treatment risks and benefits, anticipate complications, and plan interventions to better care for older people with cancer.

The Evolving Complexity of Treating Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor-2 (HER2)-Negative Breast Cancer: Special Considerations in Older Breast Cancer Patients-Part I: Early-Stage Disease

The median age for breast cancer diagnosis is 62 years, but a disproportionate number of patients are over the age of 75 years and the majority of those have hormone receptor-positive, human epidermal growth factor receptor-2 (HER2)-negative cancers. This review provides a logical algorithm to guide providers through the many complicated issues involved in adjuvant systemic therapy decisions in older patients with hormone receptor-positive, HER2-negative breast cancer. For this subtype of breast cancer, the mainstay of treatment is surgery and adjuvant endocrine therapy with tamoxifen or an aromatase inhibitor (AI). Adjuvant chemotherapy is added to the treatment regimen when the benefits of treatment are deemed to outweigh the risks, making the risk-benefit discussion particularly important in older women. Traditional tools for cancer risk assessment and genomic expression profiles (GEPs) are under-utilized in older patients, but yield equally useful information about cancer prognosis as they do in younger patients. Additionally, there are tools that estimate life-limiting toxicity risk from chemotherapy and life expectancy, which are both important issues in the risk-benefit discussion. For very low-risk cancers, such as non-invasive and small lymph node (LN)-negative cancers, the benefits of any adjuvant therapy is likely outweighed by the risks, but endocrine therapy might be considered to prevent future new breast cancers. For invasive tumors that are > 5 mm (T1b or larger) or involve LNs, adjuvant endocrine therapy is recommended. Generally, AIs should be included, though tamoxifen is effective and should be offered when AIs are not tolerated. Bone-preserving agents and high-dose vitamin D are options to preserve bone density or treat osteoporosis, especially in older women who are taking AIs. Where the risk-reducing benefit from adjuvant chemotherapy outweighs the toxicity risk, adjuvant chemotherapy should be considered. Adjuvant chemotherapy has similar benefits in older and younger patients and standard regimens are preferred. Several exciting clinic trials are underway and have included older patients, including those adding molecularly targeted agents, cyclin-dependent kinase (CDK) 4/6 inhibitors and everolimus, to endocrine therapy in the adjuvant setting. The high incidence of breast cancer in older women should drive us to design clinical trials for this population and emphasize their inclusion in ongoing trials as much as possible.

The prevalence of luminal B subtype is higher in older postmenopausal women with ER+/HER2- breast cancer and is associated with inferior outcomes

OBJECTIVES

To establish whether clinicopathologic and genomic characteristics may explain the poor prognosis associated with advanced age in ER+/HER2- breast cancer.

MATERIALS AND METHODS

The cohort included 271 consecutive post-menopausal patients with ER+/HER2- invasive breast cancer ages 55 years and older. Patients were categorized as "younger" (ages 55- < 75) and "older" (ages ≥75). The Kaplan-Meier method was used to estimate locoregional recurrence (LRR), recurrence-free interval (RFi), and overall survival (OS). Gene expression of tumor samples was assessed with Affymetrix Rosetta/Merck Human RSTA microarray platform. Differential gene expression analysis of tumor samples was performed using R package Limma.

RESULTS

271 breast cancer patients were identified, including 186 younger and 85 older patients. Older patients had higher rates of Luminal B subtype (53% vs 34%) and lower rates of Luminal A subtype (42% vs 58%, p = 0.02). Older patients were less likely to receive chemotherapy (9% vs 40%, p < 0.001) and hormone therapy (71% vs 89%, p < 0.001). For cases of grade 1-2 disease, older patients had a higher proportion of the luminal B subtype (49% vs. 30%, p = 0.014). Age ≥ 75 predicted for inferior OS (HR = 3.06, p < 0.001). The luminal B subtype predicted for inferior OS (HR = 2.12, p = 0.014), RFi (HR 5.02, p < 0.001), and LRR (HR = 3.12, p = 0.045). There were no significant differences in individual gene expression between the two groups.

CONCLUSION

Women with ER+/HER2- breast cancer ≥75 years old had higher rates of the more aggressive luminal B subtype and inferior outcomes. Genomic testing of these patients should be strongly considered, and treatment should be intensified when appropriate.

Adjuvant Chemotherapy for Low-Clinical-Risk Breast Cancer Defined by Modified Version of Adjuvant! Online: A Propensity Score Matched SEER Analysis

Background

The purpose of this research was to investigate whether the modified version of Adjuvant! Online was able to omit chemotherapy (CT) for patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, and axillary node-negative breast cancer, who are defined as low clinical risk.

Methods

From 2010 to 2014, HR-positive, HER2-negative, and node-negative breast cancer patients aged 50 years and older were retrieved from the Surveillance, Epidemiology, and End Results (SEER) 18 database. The propensity score matching method was applied between the no-CT and CT groups. Overall survival (OS) was evaluated using Kaplan-Meier analysis and compared across groups using a log-rank test.

Results

A total of 48,857 patients were enrolled. After propensity score matching, the numbers of patients in the no-CT and CT groups were both 3,102. The median follow-up period was 37 months. The 5-year OS rates in the no-CT and CT groups were 92 and 91%, respectively (p = 0.066). In the subgroup with a tumor score (tumor size added to tumor grade) of 2-3, OS was significantly higher in the no-CT group than in the CT group (93 vs. 90%, p < 0.001). In the subgroup with a tumor score of 4, OS was not different between these 2 groups (92 vs. 93%, p = 0.47).

Conclusion

This retrospective study provides evidence that CT may not be beneficial to patients 50 years of age or older with HR-positive, HER2-negative, axillary node-negative breast cancer and additionally defined as low clinical risk by a modified version of Adjuvant! Online.

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.

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.

The Warwick Experience of the Oncotype DX® Breast Recurrence Score® Assay as a Predictor of Chemotherapy Administration

Introduction

Oncotype DX® analyses the expression of 21 genes within tumour tissue to determine a Recurrence Score® (RS). RS is a marker of risk for distant recurrence in oestrogen receptor-positive early breast cancer, allowing patient-specific benefit of chemotherapy to be evaluated. Our aim was to determine whether the introduction of Oncotype DX led to a net reduction in chemotherapy use.

Methods

Consecutive patients that underwent Oncotype DX at Warwick Hospital were reviewed. Patients were anonymised and re-discussed at a multidisciplinary team meeting (MDM; without RS), and treatment recommendations were recorded. This was compared to the original MDM outcome (recommendations made with RS). Differences were analysed using Wilcoxon signed-rank test.

Results

67 patients were identified. Proportions of high, intermediate and low risk were 28, 33 and 39% (n = 19/22/26), respectively. Without RS, 56 (84%) patients were recommended for chemotherapy and 3 were not. The remaining 8 patients were deemed borderline for requiring chemotherapy and referred for discussion with an oncologist. With availability of RS, 34 (50%) patients were recommended for chemotherapy, and 24 (43%) patients were spared chemotherapy (p < 0.0005). The net reduction in chemotherapy was 33%.

Conclusion

There has been a significant reduction in chemotherapy usage in patients at Warwick since the introduction of Oncotype DX.

Development and Application of a Machine Learning Approach to Assess Short-term Mortality Risk Among Patients With Cancer Starting Chemotherapy

IMPORTANCE

Patients with cancer who die soon after starting chemotherapy incur costs of treatment without the benefits. Accurately predicting mortality risk before administering chemotherapy is important, but few patient data-driven tools exist.

OBJECTIVE

To create and validate a machine learning model that predicts mortality in a general oncology cohort starting new chemotherapy, using only data available before the first day of treatment.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study of patients at a large academic cancer center from January 1, 2004, through December 31, 2014, determined date of death by linkage to Social Security data. The model was derived using data from 2004 through 2011, and performance was measured on nonoverlapping data from 2012 through 2014. The analysis was conducted from June 1 through August 1, 2017. Participants included 26 946 patients starting 51 774 new chemotherapy regimens.

MAIN OUTCOMES AND MEASURES

Thirty-day mortality from the first day of a new chemotherapy regimen. Secondary outcomes included model discrimination by predicted mortality risk decile among patients receiving palliative chemotherapy, and 180-day mortality from the first day of a new chemotherapy regimen.

RESULTS

Among the 26 946 patients included in the analysis, mean age was 58.7 years (95% CI, 58.5-58.9 years); 61.1% were female (95% CI, 60.4%-61.9%); and 86.9% were white (95% CI, 86.4%-87.4%). Thirty-day mortality from chemotherapy start was 2.1% (95% CI, 1.9%-2.4%). Among the 9114 patients in the validation set, the most common primary cancers were breast (21.1%; 95% CI, 20.2%-21.9%), colorectal (19.3%; 95% CI, 18.5%-20.2%), and lung (18.0%; 95% CI, 17.2%-18.8%). Model predictions were accurate for all patients (area under the curve [AUC], 0.940; 95% CI, 0.930-0.951). Predictions for patients starting palliative chemotherapy (46.6% of regimens; 95% CI, 45.8%-47.3%), for whom prognosis is particularly important, remained highly accurate (AUC, 0.924; 95% CI, 0.910-0.939). To illustrate model discrimination, patients were ranked initiating palliative chemotherapy by model-predicted mortality risk, and observed mortality was calculated by risk decile. Thirty-day mortality in the highest-risk decile was 22.6% (95% CI, 19.6%-25.6%); in the lowest-risk decile, no patients died. Predictions remained accurate across all primary cancers, stages, and chemotherapies, even for clinical trial regimens that first appeared in years after the model was trained (AUC, 0.942; 95% CI, 0.882-1.000). The same model also performed well for prediction of 180-day mortality (AUC for all patients, 0.870 [95% CI, 0.862-0.877]; highest- vs lowest-risk decile mortality, 74.8% [95% CI, 72.7%-77.0%] vs 0.2% [95% CI, 0.01%-0.4%]). Predictions were more accurate than estimates from randomized clinical trials of individual chemotherapies or the Surveillance, Epidemiology, and End Results data set.

CONCLUSIONS AND RELEVANCE

A machine learning algorithm using electronic health record data accurately predicted short-term mortality among patients starting chemotherapy. Further research is necessary to determine the generalizability and feasibility of applying this algorithm in clinical settings.

Metabolomics in breast cancer: A decade in review

Breast cancer (BC) is a heterogeneous disease which has been characterised and stratified by many platforms such as clinicopathological risk factors, genomic assays, computer generated models, and various "-omic" technologies. Genomic, proteomic and transcriptomic analysis in breast cancer research is well established, and metabolomics, which can be considered a downstream manifestation of the former disciplines, is of growing interest. The past decade has seen significant progress made within the field of clinical metabolomic BC research, with several groups demonstrating results with significant promise in the setting of BC screening and biological characterisation, as well as future potential for prognostic metabolomic biomarkers.

Health-Related Quality of Life in a Predictive Model for Mortality in Older Breast Cancer Survivors

OBJECTIVES

To develop a predictive model and risk score for 10-year mortality using health-related quality of life (HRQOL) in a cohort of older women with early-stage breast cancer.

DESIGN

Prospective cohort.

SETTING

Community.

PARTICIPANTS

U.S. women aged 65 and older diagnosed with Stage I to IIIA primary breast cancer (N=660).

MEASUREMENTS

We used medical variables (age, comorbidity), HRQOL measures (10-item Physical Function Index and 5-item Mental Health Index from the Medical Outcomes Study (MOS) 36-item Short-Form Survey; 8-item Modified MOS Social Support Survey), and breast cancer variables (stage, surgery, chemotherapy, endocrine therapy) to develop a 10-year mortality risk score using penalized logistic regression models. We assessed model discriminative performance using the area under the receiver operating characteristic curve (AUC), calibration performance using the Hosmer-Lemeshow test, and overall model performance using Nagelkerke R² (NR).

RESULTS

Compared to a model including only age, comorbidity, and cancer stage and treatment variables, adding HRQOL variables improved discrimination (AUC 0.742 from 0.715) and overall performance (NR 0.221 from 0.190) with good calibration (p=0.96 from HL test).

CONCLUSION

In a cohort of older women with early-stage breast cancer, HRQOL measures predict 10-year mortality independently of traditional breast cancer prognostic variables. These findings suggest that interventions aimed at improving physical function, mental health, and social support might improve both HRQOL and survival.

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.

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.

Bridging the age gap in breast cancer: evaluation of decision support interventions for older women with operable breast cancer: protocol for a cluster randomised controlled trial

INTRODUCTION

While breast cancer outcomes are improving steadily in younger women due to advances in screening and improved therapies, there has been little change in outcomes among the older age group. It is inevitable that comorbidities/frailty rates are higher, which may increase the risks of some breast cancer treatments such as surgery and chemotherapy, many older women are healthy and may benefit from their use. Adjusting treatment regimens appropriately for age/comorbidity/frailty is variable and largely non-evidence based, specifically with regard to rates of surgery for operable oestrogen receptor-positive disease and rates of chemotherapy for high-risk disease.

METHODS AND ANALYSIS

This multicentre, parallel group, pragmatic cluster randomised controlled trial (RCT) (2015-18) reported here is nested within a larger ongoing 'Age Gap Cohort Study' (2012-18RP-PG-1209-10071), aims to evaluate the effectiveness of a complex intervention of decision support interventions to assist in the treatment decision making for early breast cancer in older women. The interventions include two patient decision aids (primary endocrine therapy vs surgery/antioestrogen therapy and chemotherapy vs no chemotherapy) and a clinical treatment outcomes algorithm for clinicians.

ETHICS AND DISSEMINATION

National and local ethics committee approval was obtained for all UK participating sites. Results from the trial will be submitted for publication in international peer-reviewed scientific journals.

IRAS REFERENCE

115550.

TRIAL REGISTRATION NUMBER

European Union Drug Regulating Authorities Clinical Trials (EudraCT) number 2015-004220-61;Pre-results. Sponsor's Protocol Code Number Sheffield Teaching Hospitals STH17086. ISRCTN 32447*.

Controversial issues in the management of older adults with early breast cancer

It is well recognized that the incidence of breast cancer increases significantly with age. Despite this, older people remain under-represented in many clinical trials and their management relies on extrapolation of data from younger patients. Providing an aggressive intervention can be challenging, particularly in less fit older patients where a conservative approach is commonly perceived to be more appropriate. The optimal management of this population is unknown and treatment decision should be personalized. This review article will discuss several controversial issues in managing older adults with early breast cancer in a multidisciplinary setting, including the role of surgical treatment of the axilla in clinically node negative disease, radiotherapy after breast conservation surgery in low-risk tumours, personalizing adjuvant systemic therapy, and geriatric assessments in breast cancer treatment decisions.

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.

4th symposium on primary breast cancer in older women. Theme: putting personalized care into practice (Held: 3 March 2017)

Following the inception in 2010, the University of Nottingham hosted the 4th Symposium on Primary Breast Cancer in Older Women, under the auspices of the International Society of Geriatric Oncology, in March 2017, at East Midlands Conference Centre.  This is the only meeting of its kind in the UK, now held biennially, aiming at a multidisciplinary audience, including patients, their carers and advocates.  With a theme on ‘Putting personalising care into practice’, this Symposium included sessions on ‘local and systemic therapies’, ‘new ideas’, ‘patients and carers’, and ‘challenging areas’, covered by an international and local faculty, interviewing patients and carers, and abstract presentations.  Topics covered were practical and wide-ranging, including selectng for chemotherapy, radiotherapy and breast reconstruction, treating HER2-positive disease, and the roles of the geriatrician and geriatric oncology nurse.

Using ePrognosis to estimate 2-year all-cause mortality in older women with breast cancer: Cancer and Leukemia Group B (CALGB) 49907 and 369901 (Alliance A151503)

PURPOSE

Tools to estimate survival, such as ePrognosis ( http://eprognosis.ucsf.edu/carey2.php ), were developed for general, not cancer, populations. In older patients with breast cancer, accurate overall survival estimates would facilitate discussions about adjuvant therapies.

METHODS

Secondary analyses were performed of data from two parallel breast cancer studies (CALGB/Alliance 49907/NCT000224102 and CALGB/Alliance 369901/NCT00068328). We included patients (n = 971) who were age 70 years and older with complete baseline quality of life data (194 from 49907; 777 from 369901). Estimated versus observed all-cause two-year mortality rates were compared. ePrognosis score was calculated based on age, sex, and daily function (derived from EORTC QLQ-C30). ePrognosis scores range from 0 to 10, with higher scores indicating worse prognosis based on mortality of community-dwelling elders and were categorized into three groups (0-2, 3-6, 7-10). Observed mortality rates were estimated using Kaplan-Meier methods.

RESULTS

Patient mean age was 75.8 years (range 70-91) and 73% had stage I-IIA disease. Most patients were classified by ePrognosis as good prognosis (n = 562, 58% 0-2) and few (n = 18, 2% 7-10) poor prognosis. Two-year observed mortality rates were significantly lower than ePrognosis estimates for patients scoring 0-2 (2% vs 5%, p = 0.001) and 3-6 (8% vs 12%, p = 0.01). The same trend was seen with scores of 7-10 (23% vs 36%, p = 0.25).

CONCLUSIONS

ePrognosis tool only modestly overestimates mortality rate in older breast cancer patients enrolled in two cooperative group studies. This tool, which estimates non-cancer mortality risk based on readily available clinical information may inform adjuvant therapy decisions but should be validated in non-clinical trial populations.

Adjuvant chemotherapy and HER-2-directed therapy for early-stage breast cancer in the elderly

There is a lack of sufficient evidence-based data defining the optimal adjuvant systemic therapies in older women. Recommendations are mainly based on retrospective studies, subgroup analyses within larger randomised trials and expert opinion. Treatment decisions should consider the functional fitness of the patient, co-morbidities, in addition to chronological age with the aim to balance risks and potential benefits from treatment(s). In this review, we discuss assessment tools to aid clinicians to select elderly patients who are ‘fit' for chemotherapy, and review the literature on the use of chemotherapy and of the anti-HER 2 antibody trastuzumab in this population. We will also review two commonly used prediction models to assess their accuracy in predicting survival outcomes in elderly patients. Ongoing clinical trials specifically focusing on older patients may help to clarify the absolute benefits and risks of adjuvant systemic therapy in this age group.

Breast Cancer in Women Older Than 80 Years

Breast cancer is the most common cancer in women, with an incidence that rises dramatically with age. The average age at diagnosis of breast cancer is 61 years, and the majority of woman who die of breast cancer are age 65 years and older. Major improvements in public health and medical care have resulted in dramatic increases in longevity. The oldest old (those age 80 years and older) are a rapidly expanding group and now comprise 9 million members of the US population. The treatment of individuals who are age 80 years and older is complex and involves clearly defining the goals and value of treatment while also weighing risks, such as the potential effects of treatment on functional loss and quality of life. Limited evidence-based treatment guidelines exist for the caring of this older cohort of patients with breast cancer. Data from clinical trials that enroll primarily younger patients lack the information needed to estimate the likelihood of toxicities that can be life changing in older adults. Clinicians who make treatment recommendations should place the available evidence in the context of the patient's life expectancy and geriatric assessment results that include an evaluation of a patient's functional status, comorbidities, cognition, social support, nutritional status, and psychological state. Furthermore, these decisions should be placed in the context of the patient's goals for treatment, preferences, and values. This review summarizes the current literature and focuses on the role of geriatric assessment in treatment recommendations for patients age 80 years and older with early and metastatic breast cancer.

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.

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.

Validity of Adjuvant! Online in older patients with stage III colon cancer based on 2967 patients from the ACCENT database

BACKGROUND

Adjuvant! Online is a tool used for clinical decision making in patients with early stage colon cancer. As details of the tool's construction are not published, the ability of Adjuvant! Online to accurately predict outcomes for older patients (age 70+) with node positive colon cancer receiving adjuvant chemotherapy is unclear.

METHODS

Individual data from older patients with stage III colon cancer who enrolled into multiple trials within the ACCENT database were entered into the Adjuvant! Online program to obtain predicted probabilities of 5-year overall survival (OS) and recurrence-free survival (RFS). Median predictions were compared with known rates. As co-morbidities were not known for ACCENT patients, but required for calculator entry, patients were assumed to have either "minor" or "average for age" co-morbidities.

RESULTS

2967 older patients from 10 randomized studies were included. When "minor" co-morbidities were assumed, the median predicted 5-year OS rate of 64% nearly matched the actual rate of 65%; when "average for age" co-morbidities were assumed, the median prediction dropped to 58%, outside the CI for the actual rate. On the other hand, assuming "minor" co-morbidities gave a median 5-year RFS prediction of 62%, outside the 95% CI for the actual rate of 58%, while assuming "average for age" co-morbidities yielded a better median prediction of 57%.

CONCLUSION

Adjuvant! Online is reasonably accurate overall for predicting outcomes in older trial patients with stage III colon cancer, though accuracy may differ between 5-year RFS and 5-year OS predictions when a fixed degree of co-morbidities is assumed.

Internet tools to enhance breast cancer care

Internet tools have become a great aid in the daily practice of physicians who treat breast cancer patients. In cancer care there are frequent and important intersections where major decisions need to be made; these include (1) whether or not to give chemotherapy; (2) how much toxicity to expect, and (3) the life expectancy of the patient, considering non-breast cancer comorbidities. These decisions can be made more accurately using calculators based on data sets of thousands of patients as opposed to physician intuition. Such tools also help patients and caregivers in optimal decision making, as they estimate the absolute benefits and risks of treatment. In this perspective we describe selected internet sites that are useful across several domains of care, including the potential benefits of different adjuvant regimens for early breast cancer, prognosis after neoadjuvant therapy, prognosis for ductal carcinoma in situ, and toxicity and life expectancy estimates. We review the variables required to use the tools, the results obtained, the methods of validation, and the advantages and disadvantages of each tool.

Adjuvant chemotherapy in elderly patients with breast cancer: key challenges

Elderly women with early breast cancer (BC) form a heterogeneous and large subgroup (41.8% of women with BC are over 65). Decision making in this subgroup is made more difficult by lack of familiarity with their physical, cognitive and social issues. Adequate management depends on biological factors and accurate clinical evaluation through comprehensive geriatric assessment (CGA). CGA can help to better select and determine potential risks factors for patients who are candidates for adjuvant chemotherapy. It is still recently introduced in geriatric oncology and there is a lack of awareness of its importance. Available data on adjuvant chemotherapy for BC is limited but suggests it can be of benefit for well selected patients, though the risk of short and long-term toxicity is significant. Here we provide a discussion of the key practical issues in decision making in the setting of adjuvant chemotherapy for elderly BC patients.

Validity of the online PREDICT tool in older patients with breast cancer: a population-based study

Background:

Predicting breast cancer outcome in older patients is challenging, as it has been shown that the available tools are not accurate in older patients. The PREDICT tool may serve as an alternative tool, as it was developed in a cohort that included almost 1800 women aged 65 years or over. The aim of this study was to assess the validity of the online PREDICT tool in a population-based cohort of unselected older patients with breast cancer.

Methods:

Patients were included from the population-based FOCUS-cohort. Observed 5- and 10-year overall survival were estimated using the Kaplan–Meier method, and compared with predicted outcomes. Calibration was tested by composing calibration plots and Poisson Regression. Discriminatory accuracy was assessed by composing receiver-operator-curves and corresponding c-indices.

Results:

In all 2012 included patients, observed and predicted overall survival differed by 1.7%, 95% confidence interval (CI)=−0.3–3.7, for 5-year overall survival, and 4.5%, 95% CI=2.3–6.6, for 10-year overall survival. Poisson regression showed that 5-year overall survival did not significantly differ from the ideal line (standardised mortality ratio (SMR)=1.07, 95% CI=0.98–1.16, P=0.133), but 10-year overall survival was significantly different from the perfect calibration (SMR=1.12, 95% CI=1.05–1.20, P=0.0004). The c-index for 5-year overall survival was 0.73, 95% CI=0.70–0.75, and 0.74, 95% CI=0.72–0.76, for 10-year overall survival.

Conclusions:

PREDICT can accurately predict 5-year overall survival in older patients with breast cancer. Ten-year predicted overall survival was, however, slightly overestimated.

Effect of age on drug metabolism in women with breast cancer

INTRODUCTION

The aging of the population will increase the number of breast cancer patients requiring treatment in both the adjuvant and metastatic setting. Hormones, chemotherapy and targeted drugs all have a role in treatment. Older patients have been underrepresented in clinical trials making evidence-based decisions difficult. The increase in comorbidity and aging, polypharmacy and changes in function make pharmacotherapy decisions more complicated. Knowledge of the issues is critical in the prescribing of effective and safe therapy. There are factors associated with advancing age that can result in pharmacokinetic and pharmacodynamic variations in processing of hormonal agents, chemotherapy and targeted drugs.

AREAS COVERED

A review of the literature pertaining to pharmacokinetic changes in aging in breast cancer was untaken. Studies are reviewed involving single agents and some combinations.

EXPERT OPINION

Older patients should be considered for standard therapies. Their specific problems need to be evaluated by geriatric-specific assessment including functional status, end organ dysfunction and polypharmacy. There are few instances for age-related changes in pharmacokinetics and when present are usually not clinically significant. When changes are present, they are often the result of comorbidity, drug interactions and drug scheduling issues. The older patients may be more sensitive to certain toxicities such as cardiac toxicity, neuropathy and myelosuppression.

Performing Survival Analyses in the Presence of Competing Risks: A Clinical Example in Older Breast Cancer Patients

An important consideration in studies that use cause-specific endpoints such as cancer-specific survival or disease recurrence is that risk of dying from another cause before experiencing the event of interest is generally much higher in older patients. Such competing events are of major importance in the design and analysis of studies with older patients, as a patient who dies from another cause before the event of interest cannot reach the endpoint. In this Commentary, we present several clinical examples of research questions in a population-based cohort of older breast cancer patients with a high frequency of competing events and discuss implications of choosing models that deal with competing risks in different ways. We show that in populations with high frequency of competing events, it is important to consider which method is most appropriate to estimate cause-specific endpoints. We demonstrate that when calculating absolute cause-specific risks the Kaplan-Meier method overestimates risk of the event of interest and that the cumulative incidence competing risks (CICR) method, which takes competing risks into account, should be used instead. Two approaches are commonly used to model the association between prognostic factors and cause-specific survival: the Cox proportional hazards model and the Fine and Gray model. We discuss both models and show that in etiologic research the Cox Proportional Hazards model is recommended, while in predictive research the Fine and Gray model is often more appropriate. In conclusion, in studies with cause-specific endpoints in populations with a high frequency of competing events, researchers should carefully choose the most appropriate statistical method to prevent incorrect interpretation of results.

Cancer and Aging: General Principles, Biology, and Geriatric Assessment

Cancer is a disease of aging as older adults are much more likely to develop cancer compared with their younger counterparts. Understanding the biology of cancer and aging remains complex, and numerous theories regarding the relationship between the two have been proposed. Cancer treatment decisions in older patients are particularly challenging, because the evidence is scarce and the risk of toxicity increases with age. Determination of biologic age is essential due to heterogeneity of functional status, comorbidity, and physiologic reserves between patients of the same chronologic age.

The predictive accuracy of PREDICT: a personalized decision-making tool for Southeast Asian women with breast cancer

Web-based prognostication tools may provide a simple and economically feasible option to aid prognostication and selection of chemotherapy in early breast cancers. We validated PREDICT, a free online breast cancer prognostication and treatment benefit tool, in a resource-limited setting. All 1480 patients who underwent complete surgical treatment for stages I to III breast cancer from 1998 to 2006 were identified from the prospective breast cancer registry of University Malaya Medical Centre, Kuala Lumpur, Malaysia. Calibration was evaluated by comparing the model-predicted overall survival (OS) with patients' actual OS. Model discrimination was tested using receiver-operating characteristic (ROC) analysis. Median age at diagnosis was 50 years. The median tumor size at presentation was 3 cm and 54% of patients had lymph node-negative disease. About 55% of women had estrogen receptor-positive breast cancer. Overall, the model-predicted 5 and 10-year OS was 86.3% and 77.5%, respectively, whereas the observed 5 and 10-year OS was 87.6% (difference: -1.3%) and 74.2% (difference: 3.3%), respectively; P values for goodness-of-fit test were 0.18 and 0.12, respectively. The program was accurate in most subgroups of patients, but significantly overestimated survival in patients aged <40 years, and in those receiving neoadjuvant chemotherapy. PREDICT performed well in terms of discrimination; areas under ROC curve were 0.78 (95% confidence interval [CI]: 0.74-0.81) and 0.73 (95% CI: 0.68-0.78) for 5 and 10-year OS, respectively. Based on its accurate performance in this study, PREDICT may be clinically useful in prognosticating women with breast cancer and personalizing breast cancer treatment in resource-limited settings.