Comparison of Nottingham Prognostic Index and Adjuvant Online prognostic tools in young women with breast cancer: review of a single-institution experience

Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer

Approximately 30% of breast cancer (BC) patients suffer from disease relapse after definitive treatment. Monitoring BC at baseline and disease progression using comprehensive genomic profiling would facilitate the prediction of prognosis. We retrospectively studied 101 BC patients ultimately experiencing relapse and/or metastases. The baseline and circulating tumor DNA‐monitoring cohorts included patients with baseline tumor tissue and serial plasma samples, respectively. Samples were analyzed with targeted next‐generation sequencing of 425 cancer‐relevant genes. Of 35 patients in the baseline cohort, patients with TP53 mutations (P < 0.01), or CTCF/GNAS mutations (P < 0.01) displayed inferior disease‐free survival, and patients harboring TP53 (P = 0.06) or NOTCH1 (P = 0.06) mutations showed relatively poor overall survival (OS), compared to patients with wild‐type counterparts. Of the 59 patients with serial plasma samples, 11 patients who were newly detected with TP53 mutations had worse OS than patients whose TP53 mutational status remained negative (P < 0.01). These results indicate that an inferior prognosis of advanced breast cancer was potentially associated with baseline TP53, CTCF, and NOTCH1 alterations. Newly identified TP53 mutations after relapse and/or metastasis was another potential prognostic biomarker of poor prognosis.

Systemic Treatment of Breast Cancer. 1st Central-Eastern European Professional Consensus Statement on Breast Cancer

This text is based on the recommendations accepted by the 4th Hungarian Consensus Conference on Breast Cancer, modified based on the international consultation and conference within the frames of the Central-Eastern European Academy of Oncology. The professional guideline primarily reflects the resolutions and recommendations of the current ESMO, NCCN and ABC5, as well as that of the St. Gallen Consensus Conference statements. The recommendations cover classical prognostic factors and certain multigene tests, which play an important role in therapeutic decision-making. From a didactic point of view, the text first addresses early and then locally advanced breast cancer, followed by locoregionally recurrent and metastatic breast cancer. Within these, we discuss each group according to the available therapeutic options. At the end of the recommendations, we summarize the criteria for treatment in certain rare clinical situations.

Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers

Recently, a significant number of breast cancer (BC) patients have been diagnosed at an early stage. It is therefore critical to accurately predict the risk of recurrence and distant metastasis for better management of BC in this setting. Clinicopathologic patterns, particularly lymph node status, tumor size, and hormonal receptor status are routinely used to identify women at increased risk of recurrence. However, these factors have limitations regarding their predictive ability for late metastasis risk in patients with early BC. Emerging molecular signatures using gene expression-based approaches have improved the prognostic and predictive accuracy for this indication. However, the use of their based-scores for risk assessment has provided contradictory findings. Therefore, developing and using newly emerged alternative predictive and prognostic biomarkers for identifying patients at high- and low-risk is of great importance. The present review discusses some serum biomarkers and multigene profiling scores for predicting late recurrence and distant metastasis in early-stage BC based on recently published studies and clinical trials.

Mammographic density changes following BC treatment

BACKGROUND

Mammographic density (MD) reduction is associated with lower risk of breast cancer (BC) recurrence and may be used as a marker of treatment outcome; however, trends in MD following BC therapies and the factors associated with such trends are poorly understood. The aim of this study was to investigate MD changes following BC treatment and the factors associated with these changes.

METHODS

A total of 226 BC-affected patients who received BC treatments were examined. MD was assessed by the Laboratory for individualized Radiodensity Assessment (LIBRA) software. A Wilcoxon ranked signed test was used to investigate the differences in MD before and after treatment and median independent test to assess the associated factors.

RESULTS

Significant differences in MD between baseline and follow-up mammograms were observed for all MD measures: percent density (p ≤ 0.005), dense area (p ≤ 0.004), and nondense area (p ≤ 0.02). After adjustment, these differences were more pronounced among younger at BC diagnosis (p ≤ 0.001), premenopausal (p ≤ 0.003), and obese women (p ≤ 0.05). Changes in MD were evident regardless of the treatment regimen. MD reduction was observed among patients with high baseline MD (p < 0.001), younger at BC diagnosis (p ≤ 0.04), premenopausal (p < 0.001), and normal body mass index (p = 0.04). Patients who experienced an increase in nondense area had high percent density at baseline (p ≤ 0.001).

CONCLUSION

Two different MD changes were observed over time: MD increase and decrease. Baseline MD, menopausal status, age at BC diagnosis, and body mass index influenced these changes.

Are mammographic density phenotypes associated with breast cancer treatment response and clinical outcomes? A systematic review and meta-analysis

Mammographic density (MD) increases breast cancer (BC) risk, however, its association with patient outcomes is unclear. We examined the association of baseline MD (BMD), and MD reduction (MDR) following BC treatment with patient outcomes. Six databases (CINAHL, Scopus, PubMed, Web of Science, MEDLINE, and Embase) were used to identify relevant articles. The PRISMA strategy was used to extract relevant details. Study quality and risk of bias were assessed using the "Quality In Prognosis Studies" (QUIPS) tool. A Meta-analysis and pooled risk estimates were performed. Results showed that BMD is associated with contralateral breast cancer (CBC) risk (HR = 1.9; 95%CI: 1.3-3.0, p = 0.0007), recurrence (HR = 2.0; 95%CI: 1.0-4.0, p = 0.04), and mortality (HR = 1.4; 95%CI: 1.1-1.9, p = 0.003). No association was found between BMD and prognosis (HR = 3.2; 95%CI: 0.9-11.2, p = 0.06). Data on risk estimates (95%CI) from BMD for survival [RR: 1.75; 0.99-3.1 to 2.4; 1.4-4.1], ipsilateral BC [HR: 1; 0.6-1.6 to 3; 1.2-7.5], and treatment response (OR, 1.8; 0.98-3.3) are limited. MDR showed no association with mortality (HR = 0.5; 95%CI: 0.2-1.2, p = 0.13). MDR is associated with a reduced risk of recurrence [HR/RR: 0.35; 0.17-0.68 to 1.33; 0.67-2.65)], however data on MDR and outcomes such as mortality [HR/RR: 0.5; 0.27-0.93 to 0.59; 0.22-0.88], and CBC risk [RR/HR: 0.53; 0.24-0.84 to 1.3; 0.6-2.7] are limited. Evidence, although sparse, demonstrates that high BMD is associated with an increased risk of recurrence, CBC, and mortality. Conversely, MDR is associated with a reduced risk of BC recurrence, CBC, and BC-related mortality.

High WDR34 mRNA expression as a potential prognostic biomarker in patients with breast cancer as determined by integrated bioinformatics analysis

The WD-repeat domain (WDR) family is distributed in the majority of eukaryotes and has several unique biological functions. It serves important roles in signal transduction, cytoskeleton assembly, protein transport, RNA processing, chromatin modification and transcription mechanisms. WD repeat domain 34 (WDR34) has been recently identified as a member of the WDR family. Overexpression of WDR34 was accompanied by the presence of multiple centrioles in the cell, suggesting that it was associated with tumor occurrence. However, its association with breast cancer was unclear. To the best of our knowledge, it has not yet been confirmed whether WDR34 gene expression is associated with breast cancer. Therefore, the current study attempted to clarify this by performing a comprehensive study using multiple datasets in the Oncomine, Breast Cancer Gene-Expression Miner and Kaplan-Meier Plotter databases. The analysis indicated that the mRNA expression levels of WDR34 were increased in breast cancer tissues compared with normal tissues. Consistent with this result, the Broad-Novartis Cancer Cell Line Encyclopedia revealed that WDR34 mRNA expression levels were upregulated in breast cancer cell lines compared with other cancer cells. It was noted that high WDR34 mRNA expression was associated with forkhead box M1 and PTTG1 regulator of sister chromatid separation, securing in co-expression analysis. Expression profile characteristics of WDR34 mRNA were identified in different molecular subtypes of breast cancer. Furthermore, survival analysis revealed that increased expression levels of WDR34 mRNA were associated with poor overall survival in patients with breast cancer, particularly in luminal B, lymph node status-positive and estrogen receptor (ER)-negative subgroups. Additionally, Kaplan-Meier curves revealed that high WDR34 mRNA expression was associated with shorter relapse-free survival in patients with breast cancer, particularly in ER-positive, human epidermal growth factor receptor 2-negative and progesterone receptor-positive subgroups. These results suggested that WDR34 may be used as a prognosis predictor in breast cancer and may provide a novel target for the diagnosis and treatment of breast cancer.

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

Background

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

Methods

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

Results

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

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

Conclusions

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

Electronic supplementary material

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

Prognostic models for breast cancer: a systematic review

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Use of clinical nomograms for predicting survival outcomes in young women with breast cancer

Early-onset breast cancer (BC) has been recognized to be more aggressive compared with its later counterparts. Survival models of BC in young patients have rarely been reported in previous studies. The current study aimed to establish and validate prediction models with clinicopathological variables for visceral metastasis-free survival (VFS), disease-free-survival (DFS) and overall survival (OS) time in young patients with BC. Clinicopathological data were obtained for 351 patients with primary breast tumors who were ≤40 years old. Univariate and multivariate analyses were performed and nomograms were established to screen and illustrate the prognostic factors. Risk scores were calculated based on coefficients from the Cox regression analysis. Internal validation of the prediction models was conducted by predicting the prognosis of cases randomly sampled from the cohort used in the current study. Multivariate analysis demonstrated that N stage (P=0.004), molecular subtype (P=0.007) and age (P=0.005) were significant independent prognostic factors for VFS. Similarly, N stage (P=0.002) and molecular subtype (P=0.001) were significantly associated with DFS. In addition, N stage (P=0.006), molecular subtype (P=0.006) and the presence of an initially inoperable tumor (P=0.005) were significant independent prognostic factors for OS. According to the Cox regression analysis, nomograms were generated to illustrate the effect of independent prognostic factors on VFS, DFS and OS. Risk scores were calculated and internal validation demonstrated the reliability of the prediction models. In conclusion, N stage and molecular subtype were identified as predictors for VFS, DFS and OS in early-onset BC. Furthermore, an age of <35 years at diagnosis was revealed to be unfavorable for VFS and the presence of an initially inoperable tumor was identified to reduce OS time.

Women's Perceptions of Journeying Toward an Unknown Future With Breast Cancer: The "Lives at Risk Study"

Breast cancer risk classifications are useful for prognosis, yet little is known of their effect on patients. This study clarified women's understandings of risk as they "journeyed" through the health care system. Breast cancer patients and women undergoing genetic investigation were recruited ( N = 25) from a large UK Health Board, 2014-2015, completing a "Book of Experience," and Bio-photographic elicitation interviews. Stakeholder and Participant Feedback Forums were undertaken with key stakeholders, including patients, oncologists, funders, and policy developers, to inform team understanding. Thematic and visual frameworks from multidisciplinary analysis workshops uncovered two themes: "Subjective Understandings of Risk" and "Journeying Toward an Unknown Future." Breast cancer patients and women undergoing investigation experienced risk intuitively. Statistical formulations were often perplexing, diverting attention away from concrete life-and-death facts. Following risk classification, care must be co-defined to reduce patients' foreboding about an unknown future, taking into consideration personal risk management strategies and aspirations for a cancer-free future.

The BIG 2.04 MRC/EORTC SUPREMO Trial: pathology quality assurance of a large phase 3 randomised international clinical trial of postmastectomy radiotherapy in intermediate-risk breast cancer

Introduction

SUPREMO is a phase 3 randomised trial evaluating radiotherapy post-mastectomy for intermediate-risk breast cancer. 1688 patients were enrolled from 16 countries between 2006 and 2013. We report the results of central pathology review carried out for quality assurance.

Patients and methods

A single recut haematoxylin and eosin (H&E) tumour section was assessed by one of two reviewing pathologists, blinded to the originally reported pathology and patient data. Tumour type, grade and lymphovascular invasion were reviewed to assess if they met the inclusion criteria. Slides from potentially ineligible patients on central review were scanned and reviewed online together by the two pathologists and a consensus reached. A subset of 25 of these cases was double-reported independently by the pathologists prior to the online assessment.

Results

The major contributors to the trial were the UK (75%) and the Netherlands (10%). There is a striking difference in lymphovascular invasion (LVi) rates (41.6 vs. 15.1% (UK); p = <0.0001) and proportions of grade 3 carcinomas (54.0 vs. 42.0% (UK); p = <0.0001) on comparing local reporting with central review. There was no difference in the locally reported frequency of LVi rates in node-positive (N+) and node-negative (N−) subgroups (40.3 vs. 38.0%; p = 0.40) but a significant difference in the reviewed frequency (16.9 vs. 9.9%; p = 0.004). Of the N− cases, 104 (25.1%) would have been ineligible by initial central review by virtue of grade and/or lymphovascular invasion status. Following online consensus review, this fell to 70 cases (16.3% of N− cases, 4.1% of all cases).

Conclusions

These data have important implications for the design, powering and interpretation of outcomes from this and future clinical trials. If critical pathology criteria are determinants for trial entry, serious consideration should be given to up-front central pathology review.

Electronic supplementary material

The online version of this article (doi:10.1007/s10549-017-4145-4) contains supplementary material, which is available to authorized users.

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

Background:

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

Methods:

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

Results:

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

Conclusions:

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

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

BACKGROUND-AIM

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

METHODS

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

RESULTS

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

CONCLUSIONS

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