Population-based validation of the prognostic model ADJUVANT! for early breast cancer

The impact of extensive intraductal component (EIC) on the genomic risk of recurrence in early hormone receptor positive breast cancer

BACKGROUND

Early invasive ductal carcinoma (IDC) breast cancer often presents with a coexisting ductal carcinoma in situ (DCIS) component, while about 5 % of cases present with an extensive (>25 %) intraductal component (EIC). The impact of EIC on the genomic risk of recurrence is unclear.

METHODS

Patients with early hormone receptor-positive HER2neu-negative (HR + HER2-) IDC breast cancer and a known OncotypeDX Breast Recurrence Score® (RS) who underwent breast surgery at our institute were included. Using a rule-based text-analysis algorithm, we analyzed pathological reports and categorized patients into three groups: EIC, non-extensive DCIS (DCIS-L), and pure-IDC (NO-DCIS). Genomic risk was determined using OncotypeDX RS.

RESULTS

A total of 33 (4.6 %) EIC cases, 377 (57.2 %) DCIS-L cases and 307 (42.8 %) NO-DCIS cases were identified. Patients in the EIC group were younger and had lower tumor grades than other groups. The distribution of genomic risk varied between the groups, with EIC tumors significantly less likely to have a high RS (>25) compared to DCIS-L and No-DCIS tumors (3 % vs 20 % and 20 %, respectively; p = 0.03). When adjusted to age, tumor size, grade and LNs involvement, both DCIS-L and NO-DCIS groups were significantly correlated with a higher probability of high RS compared to the EIC group (OR 12.3 and OR 13.1, respectively; p < 0.02). Moreover, patients with EIC had a lower likelihood for adjuvant chemotherapy recommendation.

CONCLUSIONS

In early HR + HER2- IDC, an EIC correlates with a reduced genomic recurrence risk. The impact on genomic risk seems to be influenced by the extent, not merely the presence, of DCIS.

Prosigna Assay for Treatment Decisions in Early Breast Cancer: A Decision Impact Study

Introduction: Therapeutic decisions in early breast cancer are based on clinico-pathological features which are subject to intra- and inter-observer variability. This single-center decision impact study aimed to evaluate the effects of the Prosigna assay on physicians' adjuvant treatment choices. Methods: Between 09/2017 and 02/2018, formalin-fixed tumor samples from 52 newly diagnosed, postmenopausal, hormone receptor-positive, HER2-negative breast cancer (T1-T2; pN0-N1a) patients were analyzed. Pre-test clinical judgements and Prosigna test results were compared. Results: The mean age was 59 (42-77). Invasive ductal carcinoma (79.2%), grade 2 (52.8%) and T1c-N0 tumors (43.4%) were represented. There was 40.4% discordance between the pre- and post-test risk of recurrences. No significant change was observed in the clinical intermediate risk category, while there was a net reclassification of low-risk patients into a high Prosigna recurrence risk group. In addition, clinically determined intrinsic subtypes were 34.6% discordant with the Prosigna results, which is largely driven by the reclassification of the luminal A tumors into the Prosigna-assessed luminal B group. Before the Prosigna test, endocrine treatment was the primary choice in 20 patients (39.2%), and chemotherapy was recommended to 31 patients (60.8%). Overall, the Prosigna assay led to a change in treatment choice for one patient. Conclusions: Although conventional risk assessment methods are relatively inexpensive with shorter turnaround times, their accuracy and value for risk reduction are suboptimal. According to our results, the Prosigna assay was found to be a relevant tool for the clinical decision making process. Long-term follow-up of these patients will elucidate the potential benefits of using multigene molecular tests as biomarkers for treatment.

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.

Locoregional Breast Cancer Recurrence in the European Organisation for Research and Treatment of Cancer 10041/BIG 03-04 MINDACT Trial: Analysis of Risk Factors Including the 70-Gene Signature

PURPOSE

A number of studies are currently investigating de-escalation of radiation therapy in patients with a low risk of in-breast relapses on the basis of clinicopathologic factors and molecular tests. We evaluated whether 70-gene risk score is associated with risk of locoregional recurrence (LRR) and estimated 8-year cumulative incidences for LRR in patients with early-stage breast cancer treated with breast conservation.

METHODS

In this exploratory substudy of European Organisation for Research and Treatment of Cancer 10041/BIG 03-04 MINDACT trial, we evaluated women with a known clinical and genomic 70-gene risk score test result and who had breast-conserving surgery (BCS). The primary end point was LRR at 8 years, estimated by cumulative incidences. Distant metastasis and death were considered competing risks.

RESULTS

Among 6,693 enrolled patients, 5,470 (81.7%) underwent BCS, of whom 98% received radiotherapy. At 8-year follow-up, 189 patients experienced a LRR, resulting in an 8-year cumulative incidence of 3.2% (95% CI, 2.7 to 3.7). In patients with a low-risk 70-gene signature, the 8-year LRR incidence was 2.7% (95% CI, 2.1 to 3.3). In univariable analysis, adjusted for chemotherapy, five of 12 variables were associated with LRR, including the 70-gene signature. In multivariable modeling, adjuvant endocrine therapy and to a lesser extent tumor size and grade remained significantly associated with LRR.

CONCLUSION

This exploratory analysis of the MINDACT trial estimated an 8-year low LRR rate of 3.2% after BCS. The 70-gene signature was not independently predictive of LRR perhaps because of the low number of events observed and currently cannot be used in clinical decision making regarding LRR. The overall low number of events does provide an opportunity to design trials toward de-escalation of local therapy.

Agreement on risk assessment and chemotherapy recommendations among breast cancer specialists: A survey within the MINDACT cohort

PURPOSE

Tailored recommendation for adjuvant chemotherapy in breast cancer patients is of great importance. This survey assessed agreement among oncologists on risk assessment and chemotherapy recommendation, the impact of adding the 70-gene signature to clinical-pathological characteristics, and changes over time.

METHODS

A survey consisting of 37 discordant patient cases from the MINDACT trial (T1-3N0-1M0) was sent to European breast cancer specialists for assessment of risk (high or low) and chemotherapy administration (yes or no). In 2015 the survey was sent twice (survey 1 and 2), several weeks apart, and in 2021 a third time (survey 3). Only the second and third surveys included the 70-gene signature result.

RESULTS

41 breast cancer specialists participated in all three surveys. Overall agreement between respondents decreased slightly between survey 1 and 2, but increased again in survey 3. Over time there was an increase in agreement with the 70-gene signature result on risk assessment, 23% in survey 2 versus 1 and 11% in survey 3 versus 2. With information available indicating a low risk 70-gene signature (n = 25 cases), 20% of risk assessments changed from high to low and 19% of recommendations changed from yes to no chemotherapy in survey 2 versus 1, further increasing with 18% and 21%, respectively, in survey 3 versus 2.

CONCLUSION

There is a variability in risk assessment of early breast cancer patients among breast cancer specialists. The 70-gene signature provided valuable information, resulting in fewer patients being assessed as high risk and fewer recommendations for chemotherapy, increasing over time.

Validation of PREDICT Version 2.2 in a Retrospective Cohort of Indian Women With Operable Breast Cancer

PURPOSE

Online prediction models that use known prognostic factors in breast cancer (BC) are routinely used to assist in decisions for adjuvant therapy. PREDICT Version 2.2 (P2.2) is one such online tool, which uses tumor size, lymph node involvement, grade, age, hormone receptor status, human epidermal growth factor receptor 2 (HER2) status, and Ki67. We performed an external validation in a retrospective cohort of patients treated at a tertiary center in India.

METHODS

Women with operable BC between 2008 and 2016 with nonmetastatic, T1-T2 invasive, and HER2 receptor-negative BC and with available 5-year overall survival (OS) data were selected. Median predicted 5-year OS rates were used to calculate predicted events for the whole cohort and subgroups. The chi-square test was used to evaluate the goodness of fit of the tool.

RESULTS

Of 11,760 cases registered between 2008 and 2016, 2,783 (23.66%) eligible patients with a median age of 50 (26-70) years and a median pT size of 2.5 (0.1-5) cm, 2,037 (73.19%) with grade 3 tumors, 1,172 (42.11%) with node-positive disease, 817 (29.35%) with triple-negative breast cancer, and 1,966 (70.64%) with HR-positive BC were included in the analysis. The observed 5-year OS and predicted 5-year OS in the whole cohort were 94.8% and 90.00%, respectively, with an absolute difference of 4.8% (95% CI, 3.417 to 6.198, P < .001). The observed 5-year OS and predicted 5-year OS were also different in various subgroups.

CONCLUSION

PREDICT version 2.2 overestimated the number of deaths, with lower predicted 5-year OS compared with the observed value, in this retrospective Indian cohort. The reasons for this discrepancy could be differing biologic characteristics and possible selection bias in our cohort. We recommend a prospective validation of PREDICT in Indian patients and advocate caution in its use until such validation is achieved.

Detection method has independent prognostic significance in the PLCO lung screening trial

Prognostic models in cancer use patient demographic and tumor characteristics to predict survival and dynamic disease prognosis. Past work in breast cancer has shown that cancer detection method, screen-detected or symptom-detected, has prognostic significance. We investigate this phenomenon in the lung component of the Prostate, Lung, Colorectal, and Ovarian (PLCO) screening trial. Patients were randomized to intervention, receiving four annual chest x-rays (CXRs), or to control, receiving usual care. Patients were followed for a total of approximately 13 years. In PLCO, lung cancer detection method has independent prognostic value exceeding that of variables commonly used in lung cancer prognostic models, including sex, histology, and age. Results are robust to cohort selection and type of predictive model. These results imply that detection method should be considered when developing prognostic models in lung cancer studies, and cancer registries should routinely collect cancer detection method.

Prognostic models for breast cancer: based on logistics regression and Hybrid Bayesian Network

BACKGROUND

To construct two prognostic models to predict survival in breast cancer patients; to compare the efficacy of the two models in the whole group and the advanced human epidermal growth factor receptor-2-positive (HER2+) subgroup of patients; to conclude whether the Hybrid Bayesian Network (HBN) model outperformed the logistics regression (LR) model.

METHODS

In this paper, breast cancer patient data were collected from the SEER database. Data processing and analysis were performed using Rstudio 4.2.0, including data preprocessing, model construction and validation. The L_DVBN algorithm in Julia0.4.7 and bnlearn package in R was used to build and evaluate the HBN model. Data with a diagnosis time of 2018(n = 23,384) were distributed randomly as training and testing sets in the ratio of 7:3 using the leave-out method for model construction and internal validation. External validation of the model was done using the dataset of 2019(n = 8128). Finally, the late HER2 + patients(n = 395) was selected for subgroup analysis. Accuracy, calibration and net benefit of clinical decision making were evaluated for both models.

RESULTS

The HBN model showed that seventeen variables were associated with survival outcome, including age, tumor size, site, histologic type, radiotherapy, surgery, chemotherapy, distant metastasis, subtype, clinical stage, ER receptor, PR receptor, clinical grade, race, marital status, tumor laterality, and lymph node. The AUCs for the internal validation of the LR and HBN models were 0.831 and 0.900; The AUCs for the external validation of the LR and HBN models on the whole population were 0.786 and 0.871; the AUCs for the external validation of the two models on the subgroup population were 0.601 and 0.813.

CONCLUSION

The accuracy, net clinical benefit, and calibration of the HBN model were better than LR model. The predictive efficacy of both models decreased and the difference was greater in advanced HER2 + patients, which means the HBN model had higher robustness and more stable predictive performance in the subgroup.

A pharmacometrics model to define docetaxel target in early breast cancer

AIMS

We aimed to study the relation between pharmacokinetics (PK) and pharmacodynamics (PD) of docetaxel in early breast cancer and recommend a target exposure.

METHODS

A PK/PD study was performed in 27 early breast cancer patients treated with doxorubicin and cyclophosphamide for 4 cycles followed by 4 cycles of docetaxel 75-100 mg/m² infused every 21 days. Individual Bayesian estimates of docetaxel PK parameters were obtained using a nonparametric population PK model developed with data from patients with metastatic breast cancer who received dose-intensified docetaxel (300-350 mg/m² ). Docetaxel area under the curve (AUC) and maximum concentration (Cmax) in each cycle and total cumulative AUC (AUCcum) were calculated and related to the incidence of adverse effects and tumour recurrence.

RESULTS

Docetaxel clearance showed no change over the 4 treatment cycles, but a gradual increase in the volume of distribution was observed. One third of the patients had at least 1 dose reduction of docetaxel due to toxicity. The mean AUC, AUCcum and Cmax in patients showing docetaxel-associated adverse events were significantly higher than in patients free of toxicity (P < .05). Fatigue and decrease in haemoglobin and haematocrit levels were related to docetaxel AUC and Cmax and pain to AUC. AUC and Cmax >4.5 mg*h/L and 3.5 mg/L, respectively, were risk factors for docetaxel toxicity, while an AUC <4.5 mg*h/L was associated with tumour recurrence.

CONCLUSION

We report for the first time a relation between docetaxel exposure and toxicity and recommend specific targets of drug exposure with implications for the clinical management of early breast cancer patients.

Cost-effectiveness analysis of Oncotype DX from a Brazilian private medicine perspective: a GBECAM multicenter retrospective study

Background

Oncotype DX (ODX) is a validated assay for the prediction of risk of recurrence and benefit of chemotherapy (CT) in both node negative (N0) and 1-3 positive nodes (N1), hormone receptor positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) early breast cancer (eBC). Due to limited access to genomic assays in Brazil, treatment decisions remain largely driven by traditional clinicopathologic risk factors. ODX has been reported to be cost-effective in different health system, but limited data are available considering the reality of middle-income countries such as Brazil. We aim to evaluate the cost-effectiveness of ODX across strata of clinical risk groups using data from a dataset of patients from Brazilian institutions.

Methods

Clinicopathologic and ODX information were analyzed for patients with T1-T3, N0-N1, HR+/HER2- eBC who had an ODX performed between 2005 and 2020. Projections of CT indication by clinicopathologic criteria were based on binary clinical risk categorization based on the Adjuvant! Algorithm. The ODX score was correlated with the indication of CT according to TAILORx and RxPONDER data. Two decision-tree models were developed. In the first model, low and high clinical risk patients were included while in the second, only high clinical risk patients were included. The cost for ODX and CT was based on the Brazilian private medicine perspective.

Results

In all, 645 patients were analyzed; 411 patients (63.7%) had low clinical risk and 234 patients (36.3%) had high clinical risk disease. The ODX indicated low (<11), intermediate (11-25), and high (>25) risk in 119 (18.4%), 415 (64.3%), and 111 (17.2%) patients, respectively. Among 645 patients analyzed in the first model, ODX was effective (5.6% reduction in CT indication) though with an incremental cost of United States Dollar (US$) 2288.87 per patient. Among 234 patients analyzed in the second model (high clinical risk only), ODX led to a 57.7% reduction in CT indication and reduced costs by US$ 4350.66 per patient.

Conclusions

Our study suggests that ODX is cost-saving for patients with high clinical risk HR+/HER2- eBC and cost-attractive for the overall population in the Brazilian private medicine perspective. Its incorporation into routine practice should be strongly considered by healthcare providers.

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.

Improving outcomes for women aged 70 years or above with early breast cancer: research programme including a cluster RCT

Background

In breast cancer management, age-related practice variation is widespread, with older women having lower rates of surgery and chemotherapy than younger women, based on the premise of reduced treatment tolerance and benefit. This may contribute to inferior outcomes. There are currently no age- and fitness-stratified guidelines on which to base treatment recommendations.

Aim

We aimed to optimise treatment choice and outcomes for older women (aged ≥ 70 years) with operable breast cancer.

Objectives

Our objectives were to (1) determine the age, comorbidity, frailty, disease stage and biology thresholds for endocrine therapy alone versus surgery plus adjuvant endocrine therapy, or adjuvant chemotherapy versus no chemotherapy, for older women with breast cancer; (2) optimise survival outcomes for older women by improving the quality of treatment decision-making; (3) develop and evaluate a decision support intervention to enhance shared decision-making; and (4) determine the degree and causes of treatment variation between UK breast units.

Design

A prospective cohort study was used to determine age and fitness thresholds for treatment allocation. Mixed-methods research was used to determine the information needs of older women to develop a decision support intervention. A cluster-randomised trial was used to evaluate the impact of this decision support intervention on treatment choices and outcomes. Health economic analysis was used to evaluate the cost–benefit ratio of different treatment strategies according to age and fitness criteria. A mixed-methods study was used to determine the degree and causes of variation in treatment allocation.

Main outcome measures

The main outcome measures were enhanced age- and fitness-specific decision support leading to improved quality-of-life outcomes in older women (aged ≥ 70 years) with early breast cancer.

Results

(1) Cohort study: the study recruited 3416 UK women aged ≥ 70 years (median age 77 years). Follow-up was 52 months. (a) The surgery plus adjuvant endocrine therapy versus endocrine therapy alone comparison: 2854 out of 3416 (88%) women had oestrogen-receptor-positive breast cancer, 2354 of whom received surgery plus adjuvant endocrine therapy and 500 received endocrine therapy alone. Patients treated with endocrine therapy alone were older and frailer than patients treated with surgery plus adjuvant endocrine therapy. Unmatched overall survival and breast-cancer-specific survival were higher in the surgery plus adjuvant endocrine therapy group (overall survival: hazard ratio 0.27, 95% confidence interval 0.23 to 0.33; p < 0.001; breast-cancer-specific survival: hazard ratio 0.41, 95% confidence interval 0.29 to 0.58; p < 0.001) than in the endocrine therapy alone group. In matched analysis, surgery plus adjuvant endocrine therapy was still associated with better overall survival (hazard ratio 0.72, 95% confidence interval 0.53 to 0.98; p = 0.04) than endocrine therapy alone, but not with better breast-cancer-specific survival (hazard ratio 0.74, 95% confidence interval 0.40 to 1.37; p = 0.34) or progression-free-survival (hazard ratio 1.11, 95% confidence interval 0.55 to 2.26; p = 0.78). (b) The adjuvant chemotherapy versus no chemotherapy comparison: 2811 out of 3416 (82%) women received surgery plus adjuvant endocrine therapy, of whom 1520 (54%) had high-recurrence-risk breast cancer [grade 3, node positive, oestrogen receptor negative or human epidermal growth factor receptor-2 positive, or a high Oncotype DX® (Genomic Health, Inc., Redwood City, CA, USA) score of > 25]. In this high-risk population, there were no differences according to adjuvant chemotherapy use in overall survival or breast-cancer-specific survival after propensity matching. Adjuvant chemotherapy was associated with a lower risk of metastatic recurrence than no chemotherapy in the unmatched (adjusted hazard ratio 0.36, 95% confidence interval 0.19 to 0.68; p = 0.002) and propensity-matched patients (adjusted hazard ratio 0.43, 95% confidence interval 0.20 to 0.92; p = 0.03). Adjuvant chemotherapy improved the overall survival and breast-cancer-specific survival of patients with oestrogen-receptor-negative disease. (2) Mixed-methods research to develop a decision support intervention: an iterative process was used to develop two decision support interventions (each comprising a brief decision aid, a booklet and an online tool) specifically for older women facing treatment choices (endocrine therapy alone or surgery plus adjuvant endocrine therapy, and adjuvant chemotherapy or no chemotherapy) using several evidence sources (expert opinion, literature and patient interviews). The online tool was based on models developed using registry data from 23,842 patients and validated on an external data set of 14,526 patients. Mortality rates at 2 and 5 years differed by < 1% between predicted and observed values. (3) Cluster-randomised clinical trial of decision support tools: 46 UK breast units were randomised (intervention, n = 21; usual care, n = 25), recruiting 1339 women (intervention, n = 670; usual care, n = 669). There was no significant difference in global quality of life at 6 months post baseline (difference –0.20, 95% confidence interval –2.7 to 2.3; p = 0.90). In women offered a choice of endocrine therapy alone or surgery plus adjuvant endocrine therapy, knowledge about treatments was greater in the intervention arm than the usual care arm (94% vs. 74%; p = 0.003). Treatment choice was altered, with higher rates of endocrine therapy alone than of surgery in the intervention arm. Similarly, chemotherapy rates were lower in the intervention arm (endocrine therapy alone rate: intervention sites 21% vs. usual-care sites 15%, difference 5.5%, 95% confidence interval 1.1% to 10.0%; p = 0.02; adjuvant chemotherapy rate: intervention sites 10% vs. usual-care site 15%, difference 4.5%, 95% confidence interval 0.0% to 8.0%; p = 0.013). Survival was similar in both arms. (4) Health economic analysis: a probabilistic economic model was developed using registry and cohort study data. For most health and fitness strata, surgery plus adjuvant endocrine therapy had lower costs and returned more quality-adjusted life-years than endocrine therapy alone. However, for some women aged > 90 years, surgery plus adjuvant endocrine therapy was no longer cost-effective and generated fewer quality-adjusted life-years than endocrine therapy alone. The incremental benefit of surgery plus adjuvant endocrine therapy reduced with age and comorbidities. (5) Variation in practice: analysis of rates of surgery plus adjuvant endocrine therapy or endocrine therapy alone between the 56 breast units in the cohort study demonstrated significant variation in rates of endocrine therapy alone that persisted after adjustment for age, fitness and stage. Clinician preference was an important determinant of treatment choice.

Conclusions

This study demonstrates that, for older women with oestrogen-receptor-positive breast cancer, there is a cohort of women with a life expectancy of < 4 years for whom surgery plus adjuvant endocrine therapy may offer little benefit and simply have a negative impact on quality of life. The Age Gap decision tool may help make this shared decision. Similarly, although adjuvant chemotherapy offers little benefit and has a negative impact on quality of life for the majority of older women with oestrogen-receptor-positive breast cancer, for women with oestrogen-receptor-negative breast cancer, adjuvant chemotherapy is beneficial. The negative impacts of adjuvant chemotherapy on quality of life, although significant, are transient. This implies that, for the majority of fitter women aged ≥ 70 years, standard care should be offered.

Limitations

As with any observational study, despite detailed propensity score matching, residual bias cannot be excluded. Follow-up was at median 52 months for the cohort analysis. Longer-term follow-up will be required to validate these findings owing to the slow time course of oestrogen-receptor-positive breast cancer.

Future work

The online algorithm is now available (URL: https://agegap.shef.ac.uk/; accessed May 2022). There are plans to validate the tool and incorprate quality-of-life and 10-year survival outcomes.

Trial registration

This trial is registered as ISRCTN46099296.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Programme Grants for Applied Research programme and will be published in full in Programme Grants for Applied Research; Vol. 10, No. 6. See the NIHR Journals Library website for further project information.

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.

Abemaciclib: The First FDA-Approved CDK4/6 Inhibitor for the Adjuvant Treatment of HR+ HER2- Early Breast Cancer

OBJECTIVE

To review the new indication of cyclin-dependent kinase (CDK4/6) inhibitor abemaciclib for the adjuvant treatment of hormone receptor positive (HR+), human epidermal growth factor receptor 2 negative (HER2-), axillary lymph node (LN) positive early breast cancer (EBC) at high risk of recurrence and a Ki-67 ≥20%.

DATA SOURCES

A literature search was performed through PubMed, ClinicalTrials.gov, and Food and Drug Administration (FDA) website (February 1, 2018, to December 23, 2021) to identify relevant information.

STUDY SELECTION AND DATA EXTRACTION

Human and animal studies related to pharmacology, pharmacokinetics, efficacy, and safety of abemaciclib were identified.

DATA SYNTHESIS

Addition of abemaciclib to standard of care endocrine therapy (ET) for patients with high-risk clinicopathologic features and Ki-67 ≥20% demonstrated 30% reduction in the risk of developing invasive disease and distant recurrence. At 15.5 months, abemaciclib + ET demonstrated a significant improvement in invasive disease-free survival (IDFS) vs ET alone (hazard ratio [HR], 0.75; 95% confidence interval [CI], 0.60-0.93, P = 0.01). At 27 months, IDFS benefit was maintained (HR, 0.70; 95% CI, 0.59-0.82, P < 0.0001). Diarrhea occurred in more than 80% of patients in the abemaciclib arm.

RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE

This review describes the clinical applicability of adjuvant abemaciclib for patients with HR+, HER2- EBC at high risk for recurrence.

CONCLUSION

Adjuvant abemaciclib significantly reduces the risk for early development of invasive disease and distant recurrence in patients with HR+, HER2- node positive EBC. Longer follow-up is needed to determine the impact of adjuvant abemaciclib on late disease recurrence and survival outcomes.

Transferring MINDACT to Daily Routine: Implementation of the 70-Gene Signature in Luminal Early Breast Cancer - Results from a Prospective Registry of the Austrian Group Medical Tumor Therapy (AGMT)

Background

For hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative early breast cancer (EBC), adjuvant chemotherapy (ACT) is recommended in the case of high-risk features only. The MINDACT trial showed that patients with high clinical risk (CR) but low genomic risk (GR) defined by the 70-gene signature (MammaPrint®; 70-GS) did not benefit from ACT. In this registry, we investigated the frequency and feasibility of 70-GS and concurrent 80-gene subtyping (BluePrint®) use in HR-positive, HER2-negative EBC. Furthermore, we recorded the frequency of ACT recommendation and the adherence to it when the "MINDACT strategy" was used.

Methods

This prospective registry included patients from 2 Austrian cancer centers. Similar to MINDACT, a modified version of Adjuvant!Online was used to determine CR, and 70-GC was used to determine GR in high-CR patients. ACT was recommended to patients with high CR and high GR.

Results

Of 224 enrolled patients, 76 (33.9%) had high CR and 67 (88.2%) received genomic testing. Of those, 43 (64.2%) were classified as low and 24 (35.8%) as high GR, respectively. All 24 patients with high CR and GR (10.7% of all patients) received the recommendation for ACT, but ACT was started in only 15 patients (62.5%). The median time from surgery to the start of ACT was 45 days (range 32-68), and the median time from test decision to the test result was 15 days (range 9-56).

Conclusion

We showed that the results of the MINDACT trial are reproducible in an Austrian population. Incorporating 70-GS into the daily clinical routine is feasible and mostly accepted by physicians for the guidance of treatment recommendations.

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

Background

Objective

Methods

Results

Conclusions

Association between regional nodal irradiation and breast cancer recurrence-free interval for patients with low-risk, node-positive breast cancer

PURPOSE/OBJECTIVE(S)

Randomized clinical trials have shown that regional nodal irradiation (RNI) in patients with unselected N1 breast cancer improves breast cancer-specific survival. However, the benefit of RNI in women with biologically low risk, N1 breast cancer is uncertain. We conduct a population-based study to determine if RNI is associated with improved breast cancer recurrence-free interval (BCRFI) in this population.

MATERIALS/METHODS

Patients aged 40-79 with pT1-2pN1 (node-positive) breast cancers diagnosed from 2005 to 2014 were identified. Inclusion criteria were modeled off the TAILOR RT study, which is a randomized non-inferiority clinical trial designed to assess the value of RNI in low-risk N1 patients. Eligible patients had BCS (breast-conserving surgery) or mastectomy & axillary lymph node dissection (ALND) with 1-3 positive nodes, BCS and sentinel lymph node biopsy (SLNB) with 1-2 positive nodes, or mastectomy and SLNB with 1 positive node. Additionally, patients had Luminal A breast cancers, as approximated by: estrogen receptor positive (Allred 6-8/8), progesterone receptor positive (Allred 6-8/8), human epidermal growth factor receptor 2 (HER2)-negative, and grade 1-2 immunohistochemical testing. All patients were prescribed hormonal treatment. The primary endpoint of BCRFI, which was the time to any breast cancer recurrence or breast cancer-related death, was analyzed using multivariate competing risks analysis.

RESULTS

The cohort included 1,169 women with a median follow-up of 9.2 years. Radiation treatments were: none (151 treated with mastectomy alone), breast-only (133) and locoregional (885). Patients undergoing RNI were younger (median 58 versus 62 years), more likely to have 2-3 macroscopic lymph nodes involved and more often received chemotherapy (all p<0.05). The 10-year estimate of BCRFI was 90% without RNI versus 90% with RNI (p=0.5). On multivariable analysis, RNI was not a significant predictor of BCRFI (HR=1.0, p=0.9).

CONCLUSION

In this retrospective analysis, RNI was not associated with improved BCRFI for women with biologically low risk, N1 breast cancer. We advocate accrual to the ongoing TAILOR RT study.

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.

Differences in baseline risk estimated by physicians and patients with early breast cancer

BACKGROUND

Physicians recommend adjuvant therapy to patients based on baseline risk. A common recognition for baseline risk between patients and physicians is critical for successful adjuvant therapy. We prospectively investigated the differences in estimated baseline risk between physicians and patients with early breast cancer.

METHODS

This analysis was performed at a single institution in Japan. Early breast cancer patients over 18 years old were enrolled after surgery. After explaining the pathological results, physicians asked each patient about an estimated baseline risk. Differences in estimated baseline risk were defined as the baseline risk estimated by patients minus the baseline risk estimated by physicians. The primary endpoint was that the number of patients who estimate baseline risk higher than physicians was higher than those who estimate a lower baseline risk. The secondary endpoints were differences in estimated baseline risk by stage, subtype and the influence of patient factors to differences in estimated baseline risk.

RESULTS

From July 2017 to December 2018, 262 patients were enrolled. Among the 262 patients, 190 estimated a higher baseline risk than physicians, 53 estimated a lower baseline risk and 19 estimated the same. Overall, patients estimated a significantly higher baseline risk than physicians (P < 0.001). Differences in estimated baseline risk was significantly smaller in patients who knew the term 'baseline risk' than patients who did not (P = 0.0037). Differences in estimated baseline risk were also significantly smaller in patients with stage II breast cancer than patients with stage I (P = 0.0239). However, there were no statistically significant differences of differences in estimated baseline risk according to other factors.

CONCLUSIONS

Patients with early breast cancer estimated a significantly higher baseline risk than physicians. Physicians should accurately explain baseline risk to patients for shared decision making.

Using Breast Cancer Gene Expression Signatures in Clinical Practice: Unsolved Issues, Ongoing Trials and Future Perspectives

The development of gene expression signatures since the early 2000's has offered standardized assays to evaluate the prognosis of early breast cancer. Five signatures are currently commercially available and recommended by several international guidelines to individualize adjuvant chemotherapy decisions in hormone receptors-positive/HER2-negative early breast cancer. However, many questions remain unanswered about their predictive ability, reproducibility and external validity in specific populations. They also represent a new hope to tailor (neo)adjuvant systemic treatment, adjuvant radiation therapy, hormone therapy duration and to identify a subset of patients who might benefit from CDK4/6 inhibitor adjuvant treatment. This review will highlight these particular issues, address the remaining questions and discuss the ongoing and future trials.

Overexpression of the lncRNA AC012213.3 Promotes Proliferation, Migration and Invasion of Breast Cancer via RAD54B/PI3K/AKT Axis and is Associated with Worse Patient Prognosis

Background

Long noncoding RNA (LncRNA) has been shown to mediate the development of human malignancies. However, data on its role in breast cancer remains scant. We aimed to evaluate the prognostic potential of lncRNAs in breast cancer.

Methods

We downloaded data on breast cancer patients from The Cancer Genome Atlas (TCGA) database. Tissues were obtained from The Fifth People's Hospital of Chengdu. We then used the DESeq2 package to profile the expression of the lncRNAs between the patients and normal samples. Besides, we performed prognosis and survival analysis using survival tools in R package. We then assayed the role of the differentially expressed lncRNAs, AC012213.3 (ENSG00000266289), in cancer cell lines. Quantitative real-time PCR and Western blot analysis were performed to evaluate the expression of the gene in the cell lines and then assessed its role in the progression of breast cancer using cell proliferation (CCK8 and colony formation assays), migration, invasion (transwell and wound-healing assays) and apoptotic (flow cytometry) assays.

Results

Our data showed high expression of lncRNA AC012213.3 in breast cancer tissues and cell lines. The high expression of the AC012213.3 was associated with the worse prognosis and clinical features. Besides, in vitro assays demonstrated that downregulation of AC012213.3 suppresses the proliferation and invasion of breast cancer cells. Further analysis showed that RAD54B is a downstream AC012213.3 target gene and was upregulated in breast cancer. Interestingly, RAD54B expression was associated with shorter survival in breast cancer. In addition, AC012213.3 was shown to facilitate breast cancer progression through the RAD54B/PI3K/AKT axis.

Conclusion

Taken together, our data demonstrated that lncRNA AC012213.3 is upregulated in breast cancer and could enhance breast cancer progression through RAD54B/PI3K/AKT axis.

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.

Concordance between results of inexpensive statistical models and multigene signatures in patients with ER+/HER2- early breast cancer

Multigene signatures (MGS) are used to guide adjuvant chemotherapy (aCT) decisions in patients diagnosed with estrogen receptor (ER)-positive HER2-negative early breast cancer. We used results from three MGS (Oncotype DX® (ODX), MammaPrint® (MP) or Prosigna®) and assessed the concordance between high or low risk of recurrence and the predicted risk of recurrence based on statistical models. In addition, we looked at the impact of MGS results on final aCT administration during the multidisciplinary meeting (MDM). We retrospectively included 129 patients with ER-positive HER2-negative early breast cancer for which MGS testing was performed after MDM at University Hospitals Leuven between May 2013 and April 2019 in case there was doubt about aCT recommendation. Tumor tissue was analyzed either by ODX (N = 44), MP (N = 28), or Prosigna® (N = 57). Eight statistical models were computed: Magee equations (ME), Memorial Sloan Kettering simplified risk score (MSK-SRS), Breast Cancer Recurrence Score Estimator (BCRSE), OncotypeDXCalculator (ODXC), new Adjuvant! Online (nAOL), Mymammaprint.com (MyMP), PREDICT, and SiNK. Concordance, negative percent agreement, and positive percent agreement were calculated. Of 129 cases, 53% were MGS low and 47% MGS high risk. Concordances of 100.0% were observed between risk results obtained by ODX and ME. For MP, BCRSE demonstrated the best concordance, and for Prosigna® the average of ME. Concordances of <50.0% were observed between risk results obtained by ODX and nAOL, ODX and MyMP, ODX and SiNK, MP and MSK-SRS, MP and nAOL, MP and MyMP, MP and SiNK, and Prosigna® and ODXC. Integration of MGS results during MDM resulted in change of aCT recommendation in 47% of patients and a 15% relative and 9% absolute reduction. In conclusion, statistical models, especially ME and BCRSE, can be useful in selecting ER-positive HER2-negative early breast cancer patients who may need MGS testing resulting in enhanced cost-effectiveness and reduced delay in therapeutic decision-making.

Machine learning applications in radiation oncology

Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.

Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis

Background

Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice.

Results

In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation.

Conclusions

The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04215-3.

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.

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.

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).

Challenges in Adjuvant Therapy for Premenopausal Women Diagnosed With Luminal Breast Cancers

More than 90% of women with newly diagnosed breast cancer present with stage I to III disease and, with optimal multidisciplinary therapy, are likely to survive their disease. Of these patients, 70% are hormone receptor-positive and candidates for adjuvant endocrine therapy. The adoption of cumulatively better adjuvant treatments contributed to improved outcomes in patients with hormone receptor-positive, early-stage breast cancer. Premenopausal women with hormone receptor-positive breast cancer often present with complex disease and have inferior survival outcomes compared with their postmenopausal counterparts. Risk stratification strategies, including classic clinicopathologic features and newer gene expression assays, can assist in treatment decisions, including adjuvant chemotherapy use and type or duration of endocrine therapy. Gene expression assays may help identify patients who can safely forgo chemotherapy, although to a lesser extent among premenopausal patients, in whom they may play a role only in node-negative disease. Patients at lower risk of recurrence can be adequately treated with tamoxifen alone, whereas higher-risk patients benefit from ovarian function suppression with tamoxifen or an aromatase inhibitor. The role of adding newer therapies such as CDK4/6 inhibitors to adjuvant endocrine therapy is not yet clear. Breast cancer treatments are associated with several side effects, with major impact on patients' quality of life and treatment adherence, particularly in premenopausal women for whom these side effects may be more prominent as the result of the abrupt decrease in estrogen concentrations. Personalized management of treatment side effects, addressing patients' concerns, and health promotion should be an integral part of the care of premenopausal women diagnosed with luminal breast cancers.

Recommendations from the European Commission Initiative on Breast Cancer for multigene testing to guide the use of adjuvant chemotherapy in patients with early breast cancer, hormone receptor positive, HER-2 negative

Background

Predicting the risk of recurrence and response to chemotherapy in women with early breast cancer is crucial to optimise adjuvant treatment. Despite the common practice of using multigene tests to predict recurrence, existing recommendations are inconsistent. Our aim was to formulate healthcare recommendations for the question “Should multigene tests be used in women who have early invasive breast cancer, hormone receptor-positive, HER2-negative, to guide the use of adjuvant chemotherapy?”

Methods

The European Commission Initiative on Breast Cancer (ECIBC) Guidelines Development Group (GDG), a multidisciplinary guideline panel including experts and three patients, developed recommendations informed by systematic reviews of the evidence. Grading of Recommendations Assessment, Development and Evaluation (GRADE) Evidence to Decision frameworks were used. Four multigene tests were evaluated: the 21-gene recurrence score (21-RS), the 70-gene signature (70-GS), the PAM50 risk of recurrence score (PAM50-RORS), and the 12-gene molecular score (12-MS).

Results

Five studies (2 marker-based design RCTs, two treatment interaction design RCTs and 1 pooled individual data analysis from observational studies) were included; no eligible studies on PAM50-RORS or 12-MS were identified and the GDG did not formulate recommendations for these tests.

Conclusions

The ECIBC GDG suggests the use of the 21-RS for lymph node-negative women (conditional recommendation, very low certainty of evidence), recognising that benefits are probably larger in women at high risk of recurrence based on clinical characteristics. The ECIBC GDG suggests the use of the 70-GS for women at high clinical risk (conditional recommendation, low certainty of evidence), and recommends not using 70-GS in women at low clinical risk (strong recommendation, low certainty of evidence).

Development and internal validation of a predictive risk model for anxiety after completion of treatment for early stage breast cancer

Objective

To develop a predictive risk model (PRM) for patient-reported anxiety after treatment completion for early stage breast cancer suitable for use in practice and underpinned by advances in data science and risk prediction.

Methods

Secondary analysis of a prospective survey of > 800 women at the end of treatment and again 6 months later using patient reported outcome (PRO) the hospital anxiety and depression scale-anxiety (HADS-A) and > 20 candidate predictors. Multiple imputation using chained equations (for missing data) and least absolute shrinkage and selection operator (LASSO) were used to select predictors. Final multivariable linear model performance was assessed (R²) and bootstrapped for internal validation.

Results

Five predictors of anxiety selected by LASSO were HADS-A (Beta 0.73; 95% CI 0.681, 0.785); HAD-depression (Beta 0.095; 95% CI 0.020, 0.182) and having caring responsibilities (Beta 0.488; 95% CI 0.084, 0.866) increased risk, whereas being older (Beta − 0.010; 95% CI -0.028, 0.004) and owning a home (Beta 0.432; 95% CI -0.954, 0.078) reduced the risk. The final model explained 60% of variance and bias was low (− 0.006 to 0.002).

Conclusions

Different modelling approaches are needed to predict rather than explain patient reported outcomes. We developed a parsimonious and pragmatic PRM. External validation is required prior to translation to digital tool and evaluation of clinical implementation. The routine use of PROs and data driven PRM in practice provides a new opportunity to target supportive care and specialist interventions for cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41687-020-00267-w.

How to Develop Statistical Predictive Risk Models in Oncology Nursing to Enhance Psychosocial and Supportive Care

OBJECTIVES

Predictive risk models are advocated in psychosocial oncology practice to provide timely and appropriate support to those likely to experience the emotional and psychological consequences of cancer and its treatments. New digital technologies mean that large scale and routine data collection are becoming part of everyday clinical practice. Using these data to try to identify those at greatest risk for late psychosocial effects of cancer is an attractive proposition in a climate of unmet need and limited resource. In this paper, we present a framework to support the development of high-quality predictive risk models in psychosocial and supportive oncology. The aim is to provide awareness and increase accessibility of best practice literature to support researchers in psychosocial and supportive care to undertake a structured evidence-based approach.

DATA SOURCES

Statistical prediction risk model publications.

CONCLUSION

In statistical modeling and data science different approaches are needed if the goal is to predict rather than explain. The deployment of a poorly developed and tested predictive risk model has the potential to do great harm. Recommendations for best practice to develop predictive risk models have been developed but there appears to be little application within psychosocial and supportive oncology care.

IMPLICATIONS FOR NURSING PRACTICE

Use of best practice evidence will ensure the development and validation of predictive models that are robust as these are currently lacking. These models have the potential to enhance supportive oncology care through harnessing routine digital collection of patient-reported outcomes and the targeting of interventions according to risk characteristics.

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.

Compliance with multidisciplinary team recommendations and disease outcomes in early breast cancer patients: An analysis of 4501 consecutive patients

Background

Multidisciplinary team (MDT) discussions are widely held to facilitate the diagnosis and treatment of breast cancer, but patient compliance with the MDT recommendations and the impact of compliance on disease outcome are uncertain.

Methods

We conducted a retrospective review of data from a prospective database of breast cancer patients treated at Shanghai Ruijin Hospital between April 2013 and August 2018. MDT discussions were held for all patients before they started adjuvant therapy. The patients were classified into compliant and non-compliant groups according to whether they received the MDT-recommended regimens. We also analyzed which clinicopathological factors were associated with compliance and prognosis.

Results

Of 4501 breast cancer patients, 3681 (81.8%) and 820 (18.2%) were included in the compliant and non-compliant groups, respectively. Age >70 years (P < 0.001), invasive ductal carcinoma (P < 0.001), and histological grade III (P = 0.011) were independently associated with higher risk of non-compliance, whereas Ki-67 labeling index ≥14% and history of benign breast disease were independently associated with compliance. Disease-free survival (hazard ratio [HR] 1.813, 95% confidence interval [CI] 1.367–2.405, P < 0.001) and overall survival (HR 2.478, 95% CI 1.431–4.291, P < 0.001) were worse in the non-compliant group.

Conclusions

Several clinicopathological factors were associated with non-compliance with MDT recommendations for early breast cancer patients. Non-compliance was associated with worse disease outcome.

•

A large consecutive breast cancer cohort with MDT-based treatment recommendation.

•

Factors identified associated with non-compliance with MDT recommendations.

•

A significantly better survival in patients compliant with MDT recommendation.

Overview of Breast Cancer and Implications of Overtreatment of Early-Stage Breast Cancer: An Indian Perspective

The prevalence and mortality of breast cancer is increasing in Asian countries, including India. With advances in medical technology leading to better detection and characterization of the disease, it has been possible to classify breast cancer into various subtypes using markers, which helps predict the risk of distant recurrence, response to therapy, and prognosis using a combination of molecular and clinical parameters. Breast cancer and its therapy, mainly surgery, systemic therapy (anticancer chemotherapy, hormonal therapy, targeted therapy, and immunotherapy), and radiation therapy, are associated with significant adverse influences on physical and mental health, quality of life, and the economic status of the patient and her family. The fear of recurrence and its devastating effects often leads to overtreatment, with a toxic cost to the patient financially and physically in cases in which this is not required. This article discusses some aspects of a breast cancer diagnosis and its impact on the various facets of the life of the patient and her family. It further elucidates the role of prognostic factors, the currently available biomarkers and prognostic signatures, and the importance of ethnically validating biomarkers and prognostic signatures.

Chemotherapy in a Breast Cancer Patient Heterozygous Carrier of Ornithine Transcarbamylase Deficiency

Urea cycle disorders (UCDs) are an unusual genetic condition that may lead to hyperammonemia in catabolic situations such as surgery, infections or chemotherapy administration. Without specific treatment, it causes life-threatening encephalopathy. We present the case of a young woman, heterozygous carrier of ornithine transcarbamylase deficiency (OTCD) with breast cancer, who was treated with surgery, chemotherapy, radiotherapy and hormone therapy while following a protocol to minimize the risk of metabolic decompensation due to her condition.

The concordance of treatment decision guided by OncotypeDX and the PREDICT tool in real-world early-stage breast cancer

Background

Decision‐making regarding adjuvant chemotherapy for early‐stage breast cancer can be guided by genomic assays such as OncotypeDX. The concordance of expected clinical decisions guided by OncotypeDX and prognostication online tools such as PREDICT is unknown.

Methods

We performed a retrospective single‐center cohort study comprising all women with estrogen receptor (ER) positive, human epidermal growth factor receptor 2 (HER2) negative, node negative disease, whose tumors were sent for OncotypeDX analysis. Expected decision on adjuvant chemotherapy was evaluated using OncotypeDX and using PREDICT. The concordance between these two tools was calculated. The impact on concordance of prespecified features was assessed, including age, tumor size, intensity of ER and progesterone receptor (PR), grade, Ki67 and perineural and lymphovascular invasion.

Results

A total of 445 women were included. Overall concordance was 75% (K = 0.284). The concordance was significantly higher for grade 1 disease compared to grade 2‐3 (93% vs 72%, P < .001), tumor ≤ 1 cm compared to >1 cm (85% vs 72%, P = .009), PR positive compared to PR negative (78% vs 58%, P < .001) and ki67 < 10% compared to ≥10% (92% vs 63%, P < .001). The intensity of ER and the presence of perineural or lymphovascular invasion had no significant impact on concordance.

Conclusions

Compared to PREDICT, using OncotypeDx in node negative, ER positive disease is expected to change the clinical decision in a quarter of patients. The concordance between OncotypeDx and PREDICT is influenced by pathological features. In patients with very low risk, treatment decisions may be made based solely on clinical risk assessment.

Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be differences in how particular CDSSs are developed, the information they include, the decisions they recommend, and how they are used in practice. This systematic review examines various CDSSs to determine their availability, intended use, medical characteristics, and expected outputs concerning breast cancer therapeutic decisions, an area that is known to have varying degrees of subjectivity in clinical practice. Utilising the methodology of Kitchenham and Charter, a systematic search of the literature was performed in Springer, Science Direct, Google Scholar, PubMed, ACM, IEEE, and Scopus. An overview of CDSS which supports decision-making in breast cancer treatment is provided along with a critical appraisal of their benefits, limitations, and opportunities for improvement.

Prognostic DNA methylation markers for hormone receptor breast cancer: a systematic review

BACKGROUND

In patients with hormone receptor-positive breast cancer, differentiating between patients with a low and a high risk of recurrence is an ongoing challenge. In current practice, prognostic clinical parameters are used for risk prediction. DNA methylation markers have been proven to be of additional prognostic value in several cancer types. Numerous prognostic DNA methylation markers for breast cancer have been published in the literature. However, to date, none of these markers are used in clinical practice.

METHODS

We conducted a systematic review of PubMed and EMBASE to assess the number and level of evidence of published DNA methylation markers for hormone receptor-positive breast cancer. To obtain an overview of the reporting quality of the included studies, all were scored according to the REMARK criteria that were established as reporting guidelines for prognostic biomarker studies.

RESULTS

A total of 74 studies were identified reporting on 87 different DNA methylation markers. Assessment of the REMARK criteria showed variation in reporting quality of the studies. Eighteen single markers and one marker panel were studied in multiple independent populations. Hypermethylation of the markers RASSF1, BRCA, PITX2, CDH1, RARB, PCDH10 and PGR, and the marker panel GSTP1, RASSF1 and RARB showed a statistically significant correlation with poor disease outcome that was confirmed in at least one other, independent study.

CONCLUSION

This systematic review provides an overview on published prognostic DNA methylation markers for hormone receptor-positive breast cancer and identifies eight markers that have been independently validated. Analysis of the reporting quality of included studies suggests that future research on this topic would benefit from standardised reporting guidelines.

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.

Counseling young women with early breast cancer on fertility preservation

PURPOSE

Women with early-stage breast cancer may still have a future child wish, while chemotherapy may impair fertility. To pursue on fertility preservation shortly after breast cancer diagnosis is complex. This review holds a critical reflection on all topics that need to be counseled to give them the opportunity to make a well-informed decision before starting any oncological treatment.

METHODS

A comprehensive literature review was performed on papers published in English language on breast cancer in young women, risk of chemotherapy-induced infertility, fertility preservation techniques, impact of possible mutation carriership, and future pregnancy outcome.

RESULTS

Below 40 years of age, the risk of permanent chemotherapy-induced ovarian function failure is approximately 20%, where taxanes do not significantly add to this risk. Overall, 23% of reported women who performed fertility preservation by cryopreserving oocytes or embryos returned for embryo transfer. Of these, 40% gave live birth. Both fertility preservation in women diagnosed with breast cancer and pregnancy after treatment seem safe with respect to breast cancer survival. Women who have a genetic predisposition for breast cancer like BRCA gene mutation should also be informed about the possibility of pre-implantation genetic diagnosis.

CONCLUSIONS

Women with an early stage of breast cancer and a possible future child wish should be referred to an expertise center in breast cancer, fertility preservation, and genetics in this complex decision-making process, shortly after diagnosis.

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.

Axillary lymph node dissection in node-positive breast cancer: are ten nodes adequate and when is enough, enough?

PURPOSE

National guidelines define adequate axillary lymph node dissections as those yielding ≥ 10 lymph nodes (LNs). We aimed to identify the optimal LN yield among node-positive patients.

METHODS

Using the National Cancer Data Base (2010-2015), we categorized node-positive patients as follows: (1) neoadjuvant chemotherapy (NAC, cN1-3 or ypN1mi-3) or (2) upfront surgery (pN1-3). A restricted cubic splines model was used to estimate LN retrieval thresholds associated with change in overall survival (OS).

RESULTS

129,685 patients were identified: 21.2% NAC, 78.8% upfront surgery. Low, moderate, and high retrieval thresholds were estimated to be 1-6, 7-21, and > 21 LNs (upfront surgery), and 1-7, 8-22, and > 22 LNs (NAC). In an adjusted model, high versus low LN yield was associated with greater receipt of adjuvant chemotherapy (upfront surgery OR 1.96, p < 0.001) and greater use of adjuvant radiation (upfront surgery OR 1.08, p = 0.02; NAC OR 1.23, p = 0.002). After adjustment, high versus low LN retrieval was associated with improved OS (upfront surgery HR 0.86, p < 0.001; NAC HR 0.77, p < 0.001). Worse OS was associated with retrieving fewer LNs, likely as a result of an under-staged axilla and missed opportunity for adjuvant therapy, while better OS was independently associated with retrieval of up to approximately 20 LNs, after which survival did not improve.

CONCLUSION

In node-positive breast cancer, the number of nodes retrieved is significantly associated with an increased positive nodal count and greater use of adjuvant therapy. Removal of approximately 20 LNs may improve survival by both more accurate nodal staging and increased adjuvant therapy use.

External validation of the PREDICT tool in Spanish women with breast cancer participating in population-based screening programmes

RATIONALE, AIMS, AND OBJECTIVES

To externally validate the PREDICT tool in a cohort of women participating in a population-based breast cancer screening programme who were diagnosed with breast cancer between 2000 and 2008 in Spain.

METHODS

A total of 535 women were included in the validation study. We calculated predicted 5-year survival using the beta values from the development of the PREDICT model and predicted and observed events for a given risk groups. Model fit, discrimination, and calibration were evaluated. Seeking to improve the model, we also explored the impact on discrimination of the inclusion of additional variables, not in the PREDICT algorithm.

RESULTS

In patients who were oestrogen receptor (ER) positive (negative), PREDICT overestimated (underestimated) the 5-year overall survival in all the subgroups studied. Analysis of model performance showed good calibration but modest discrimination (C-index, 0.697 [ER negative] and 0.768 [ER positive]). When updating the model, no additional variables were found to be significant in ER-negative patients, but for ER-positive patients, concurrent liver disease was a significant factor, its inclusion improving model discrimination (C-index, 0.817).

CONCLUSIONS

The PREDICT tool does not discriminate well in our population considering only the variables of the original algorithm. More accurate tools are needed to obtain a better discrimination.

Multigene expression signatures in early hormone receptor positive HER 2 negative breast cancer

Background The standard treatment of hormone receptor positive, HER2 negative early breast cancer (BC) is surgery followed by adjuvant systemic therapy either with endocrine therapy alone or with the addition of chemotherapy followed by endocrine therapy. Adjuvant systemic therapy reduces the risk of recurrence and death from BC. Whether an individual patient will benefit from adjuvant chemotherapy is an important clinical decision. Decisions that rely solely on clinical-pathological factors can often lead to overtreatment. Multigene signatures represent an important progress in optimal selection of high risk patients that might benefit from the addition of chemotherapy to adjuvant endocrine therapy. Conclusions Several signatures are already commercially available and also accepted by international guidelines. Oncotype DX and MammaPrint have been most extensively validated and supported by level IA evidence.

Nomogram Identifies Age as the Most Important Predictor of Overall Survival in Oral Cavity Squamous Cell Cancer After Primary Surgery

Our goal was to determine the most important predictors and construct a nomogram for overall survival (OS) in oral cavity squamous cell cancer (OCSCC) treated with primary surgery followed by observation, adjuvant radiation or chemoradiation. Multivariable analysis was performed using Cox Proportional Hazard model of 9258 OCSCC patients from Surveillance, Epidemiology and End Results Program (SEER) database treated with surgery from 2003 to 2009. Potential predictors of OS were age, gender, race, tobacco use, oral cancer sub-sites, pathologic tumor stage and grade, pathologic nodal stage, extra-capsular invasion, clinical levels IV and V involvement, and adjuvant treatment selection. Weighted propensity scores for treatment were used to balance observed baseline characteristics between three treatment groups in order to reduce bias. Following primary surgery, patients underwent observation (56%), radiation alone (31%) or chemoradiation (13%). All tested predictors were statistically significant and included in our final nomogram. Most important predictor of OS was age, followed by pathologic tumor stage. SEER based-survival nomogram for OCSCC patients differs from published models derived from patients treated in a single or few academic treatment centers. An unexpected finding of patient age being the best OS predictor suggests that this factor may be more critical for the outcome than previously anticipated.

Role of Patient and Disease Factors in Adjuvant Systemic Therapy Decision Making for Early-Stage, Operable Breast Cancer: Update of the ASCO Endorsement of the Cancer Care Ontario Guideline

PURPOSE

To update the American Society of Clinical Oncology endorsement of the Cancer Care Ontario recommendations on the Role of Patient and Disease Factors in Adjuvant Systemic Therapy Decision Making for Early-Stage, Operable Breast Cancer.

METHODS

Two phase III trials—the Trial Assigning Individualized Options for Treatment (TAILORx) in women with hormone receptor–positive, node-negative tumors and the Microarray in Node-Negative and 1 to 3 Positive Lymph Node Disease May Avoid Chemotherapy (MINDACT) trial—provided the evidence for this update.

UPDATED RECOMMENDATIONS

Shared decision making between clinicians and patients is appropriate for adjuvant systemic therapy for breast cancer. For patients older than age 50 years and whose tumors have Onco type DX recurrence scores less than 26, and for patients age 50 years or younger whose tumors have Onco type DX recurrence scores less than 16, there is little to no benefit from chemotherapy. Clinicians may offer endocrine therapy alone for these patients. For patients age 50 years or younger with recurrence scores of 16 to 25, clinicians may offer chemoendocrine therapy. Patients with recurrence scores greater than 30 should be considered candidates for chemoendocrine therapy. Based on informal consensus, the Panel recommends that oncologists may offer chemoendocrine therapy to patients with Onco type DX scores of 26 to 30. The MammaPrint assay could be used to guide decisions on withholding adjuvant systemic chemotherapy in patients with hormone receptor–positive lymph node–negative breast cancer and in select patients with lymph node–positive cancers. In both patients with node-positive and node-negative disease, evidence of clinical utility of the MammaPrint assay was only apparent in those determined to be at high clinical risk; the Panel thus did not recommend use of MammaPrint assay in patients determined to be at low clinical risk. Remaining recommendations from the 2016 ASCO guideline endorsement are unchanged. Additional information is available at www.asco.org/breast-cancer-guidelines .

Improved survival of older patients with advanced breast cancer due to an increase in systemic treatments: a population-based study

Purpose

The number of older patients with breast cancer is rapidly increasing. A previous study showed that between 1990 and 2005, the survival of older patients with breast cancer did not improve in contrast to younger patients. In recent years, scientific evidence in the older age group has increased and specific guidelines for older women with breast cancer have been developed. The aim of this study was to assess changes in survival outcomes of older patients with breast cancer.

Patients and methods

All patients with breast cancer between 2000 and 2017 were included from the Netherlands cancer registry. We assessed changes in treatments using logistic regression. We calculated changes in relative survival as proxy for breast cancer mortality, stratified by age and stage.

Results

We included 239,992 patients. Relative survival improved for patients < 65 for all stages. In patients aged 65–75 years, relative survival did not improve in stage I–II but did improve in stage III breast cancer (RER 0.98, 95% CI 0.96–1.00, p = 0.046). Concurrently, prescription of systemic treatments increased. In patients > 75, relative survival did not improve in patients with stage I/II or stage III disease, nor did treatment strategies change.

Conclusions

This study shows that relative survival of patients aged 65–75 years with advanced breast cancer has improved, and concurrently, prescription of systemic treatment increased. To improve survival of patients > 75 as well, future studies should focus on individualizing treatments based on concomitant comorbidity, geriatric parameters and the risk of competing mortality and toxicity of treatments.