Short-term and long-term clinical outcomes of uncommon types of invasive breast cancer

Special Types of Breast Cancer: Clinical Behavior and Radiological Appearance

Breast cancer is a complex disease that includes entities with different characteristics, behaviors, and responses to treatment. Breast cancers are categorized into subgroups based on histological type and grade, and these subgroups affect clinical presentation and oncological outcomes. The subgroup of "special types" encompasses all those breast cancers with insufficient features to belong to the subgroup "invasive ductal carcinoma not otherwise specified". These cancers account for around 25% of all cases, some of them having a relatively good prognosis despite high histological grade. The purpose of this paper is to review and illustrate the radiological appearance of each special type, highlighting insights and pitfalls to guide breast radiologists in their routine work.

Changing Trends in the Proportional Incidence and Five-year Net Survival of Screened and Non-screened Breast Cancers among Women During 1995-2011 in England

BACKGROUND AND OBJECTIVES

Uptake of breast cancer screening has been decreasing in England since 2007. However, the associated factors are unclear. On the other hand, survival among breast cancer patients have recently increased. We conducted a quasi-experimental analysis to test whether the trend-change in proportional incidence of non-screened cancers coincided with that in five-year net-survival.

METHODS

We extracted population-based proportional incidence and age-standardized five-year net-survival data from Public Health England that included English women with invasive breast cancer diagnosed during 1995-2011 (linked to death certificates, followed through 2016). Piece-wise log-linear models with change-point/joinpoint were used to estimate temporal trends.

RESULTS

Among 254,063 women in England with invasive breast cancer diagnosed during 1995-2011, there was downward-to-upward trend-change in proportional incidence of non-screened breast cancers (annual percent change [APC]=5.6 after 2007 versus APC=-3.5 before 2007, p<0.001) in diagnosis-year 2007, when a steeper upward-trend in age-standardized five-year net survival started (APC=5.7 after 2007/2008 versus APC=0.3 before 2007/2008, p<0.001). Net-survival difference of screened versus non-screened cancers also significantly narrowed (18% in 2007/2008 versus 5% in 2011). Similar associations were found in all strata of race, cancer stage, grade, and histology, except in Black patients or patients with stage I, stage III, or grade I cancer.

CONCLUSIONS

There was a downward-to-upward trend-change in proportional incidence of non-screened breast cancers in 2007 that coincided with a steeper upward-trend in age-standardized five-year net survival among English women in 2007. Survival benefits of breast cancer screening decreased during 2007-2011. The data support reduction of breast cancer screening in some patients, but future validation studies are warranted.

Survival analysis in patients with invasive lobular cancer and invasive ductal cancer according to hormone receptor expression status in the Korean population

Background

We compared the clinicopathological characteristics and survival outcomes of invasive lobular carcinoma (ILC) cases with those of invasive ductal carcinoma (IDC) cases in various hormone receptor expression subgroups.

Methods

We compared clinicopathological characteristics, overall survival (OS), and breast cancer-specific survival (BCSS) between patients with IDC (n = 95,486) and ILC (n = 3,023). In addition, we analyzed the effects of different hormone receptor expression subgroups on survival.

Results

The ILC group had more instances of advanced stage and hormonal receptor positivity than did the IDC group (p < 0.001), but the IDC group had higher histological grade and nuclear grade, as well as higher frequency of human epidermal growth factor receptor 2 and Ki67 expression than did the ILC group (p < 0.001). The OS and BCSS were not significantly different between the IDC and ILC groups. The 5-year OS of the IDC group was 88.8%, while that of the ILC group was 90.6% (p = 0.113). The 5-year BCSS of the IDC group was 94.8%, while that of the ILC group was 95.0% (p = 0.552). When analyzing each hormone receptor expression subgroup, there were no significant differences in survival between the IDC and ILC groups. However, the estrogen receptor (ER) negative/progesterone receptor (PR) negative subgroup showed differences in survival between the IDC and ILC groups. Moreover, the hazard ratio of ILC in the ER negative/PR negative subgroup was 1.345 (95% confidence interval: 1.012–1.788; p = 0.041).

Conclusions

Hormone receptor expression should be considered when determining prognosis and treatment regimen for IDC and ILC. Researchers should further study the ER negative/PR negative population to identify treatment and prognostic models that will facilitate the development of individualized therapy for these patients, which is needed for good outcomes.

Altona Prognostic Index: A New Prognostic Index for ER-Positive and Her2-Negative Breast Cancer of No Special Type

Simple Summary

Breast cancer is the most common tumor-related cause of death in women in Europe and worldwide. The aim of our retrospective study, including 6654 women, was on the one hand to verify the validity of the worldwide known Nottingham prognostic index (NPI), and on the other hand to create a new model with even more prognostic validity. Our newly developed Altona prognostic index (API) shows significantly superior outcome in calculating progression free survival. In contrast to the NPI, the API considers characteristics such as subtypes of breast cancer, as this disease is heterogenous involving different entities, and patient’s age. Evaluating progression free survival in different subgroups, our study shows that both these prognostic indices should only be applied on a patient collective that is ≤70 years old with first primary, unifocal, unilateral breast cancer that is of no special type (NST), estrogen receptor-positive and Her2-negative to get valid prediction data.

Abstract

Breast cancer is a heterogeneous disease representing a number of different histopathologic and molecular types which should be taken into consideration if prognostic or predictive models are to be developed. The aim of the present study was to demonstrate the validity of the long-known Nottingham prognostic index (NPI) in a large retrospective study (n = 6654 women with a first primary unilateral and unifocal invasive breast cancer diagnosed and treated between April 1996 and October 2018; median follow-up time of breast cancer cases was 15.5 years [14.9–16.8]) from a single pathological institution. Furthermore, it was intended to develop an even superior risk stratification model considering an additional variable, namely the patient’s age at the time of diagnosis. Heterogeneity of these cases was addressed by focusing on estrogen receptor-positive as well as Her2-negative cases and taking the WHO-defined different tumor types into account. Calculating progression free survival Cox-regression and CART-analysis revealed significantly superior iAUC as well as concordance values in comparison to the NPI based stratification, leading to an alternative, namely the Altona prognostic index (API). The importance of the histopathological tumor type was corroborated by the fact that when calculated separately and in contrast to the most frequent so-called “No Special Type” (NST) carcinomas, neither NPI nor API could show valid prognostic stratification.

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

Most biomedical datasets, including those of 'omics, population studies, and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms. Machine learning (ML) algorithms have been used to classify outcomes in biomedical datasets, including random forests (RF), decision tree (DT), artificial neural networks (ANN), and support vector machine (SVM). However, their performance and efficiency in classifying multi-category outcomes of rectangular data are poorly understood. Therefore, we compared these metrics among the 4 ML algorithms. As an example, we created a large rectangular dataset using the female breast cancers in the surveillance, epidemiology, and end results-18 database, which were diagnosed in 2004 and followed up until December 2016. The outcome was the five-category cause of death, namely alive, non-breast cancer, breast cancer, cardiovascular disease, and other cause. We analyzed the 54 dichotomized features from ~45,000 patients using MatLab (version 2018a) and the tenfold cross-validation approach. The accuracy in classifying five-category cause of death with DT, RF, ANN, and SVM was 69.21%, 70.23%, 70.16%, and 69.06%, respectively, which was higher than the accuracy of 68.12% with multinomial logistic regression. Based on the features' information entropy, we optimized dimension reduction (i.e., reduce the number of features in models). We found 32 or more features were required to maintain similar accuracy, while the running time decreased from 55.57 s for 54 features to 25.99 s for 32 features in RF, from 12.92 s to 10.48 s in ANN, and from 175.50 s to 67.81 s in SVM. In summary, we here show that RF, DT, ANN, and SVM had similar accuracy for classifying multi-category outcomes in this large rectangular dataset. Dimension reduction based on information gain will increase the model's efficiency while maintaining classification accuracy.

Effect of adjuvant radiotherapy in elderly patients with breast cancer

BACKGROUND

Radiotherapy (RT) is of critical importance in the locoregional management of early breast cancer. Although RT is routinely used following breast conserving surgery (BCS), patients may occasionally be effectively treated with BCS alone. Currently, the selection of patients undergoing BCS who do not need breast irradiation is under investigation. With the advancement of personalized medicine, there is an increasing interest in reduction of aggressive treatments especially in older women. The primary objective of this study was to identify elderly patients who may forego breast irradiation after BCS without measurable consequences on local tumor growth and survival.

METHODS

We analyzed 2384 early breast cancer patients aged 70 and older who were treated in 17 German certified breast cancer centers between 2001 and 2009. We compared RT versus no RT after guideline adherent (GA) BCS. The outcomes studied were breast cancer recurrence (RFS) and breast cancer-specific survival (BCSS). Low-risk patients were defined by luminal A, tumor size T1 or T2 and node-negative whereas higher-risk patients were defined by patients with G3 or T3/T4 or node-positive or other than Luminal A tumors. To test if there is a difference between two or more survival curves, we used the Gp family of tests of Harrington and Fleming.

RESULTS

The median age was 77 yrs (mean 77.6±5.6 y) and the median observation time 46 mths (mean 48.9±24.8 mths). 950 (39.8%) patients were low-risk and 1434 (60.2%) were higher-risk. 1298 (54.4%) patients received GA BCS of which 85.0% (1103) received GA-RT and only 15% (195) did not. For low-risk patients with GA-BCS there were no significant differences in RFS (log rank p = 0.651) and in BCSS (p = 0.573) stratified by GA-RT. 5 years RFS in both groups were > 97%. For higher-risk patients with GA-BCS we found a significant difference (p<0.001) in RFS and tumor-associated OS stratified by GA-RT. The results remain the same after adjusting by adjuvant systemic treatment (AST) and comorbidity (ASA and NYHA).

CONCLUSIONS

Patients aged 70 years and older suffering from low-risk early breast cancer with GA-BCS can avoid breast irradiation with <3% chance of relapse. In the case of higher-risk, breast irradiation should be used routinely following GA-BCS. As a side effect of these results, removing the entire breast of elderly low risk patients to spare them from breast irradiation seems to be not necessary.

Increased number of intratumoral IL-17+ cells, a harbinger of the adverse prognosis of triple-negative breast cancer

INTRODUCTION

Triple negative breast cancer (TNBC) is an aggressive cancer subtype and lack of effective targeted therapies. It has been recently reported that Interleukin 17 (IL-17), a family of cytokines secreted in tumor microenvironment, affects tumor progression through a variety of molecular pathways. Its role in TNBC is so far still poorly explored.

MATERIALS AND METHODS

We employed immunohistochemistry to evaluate the distribution of IL-17+ cells in TNBC with no special type features (TNBC-NST), their association with tumor microangiogenesis, as well as their impact on prognosis of the patients.

RESULTS

In comparison to medullary carcinoma with triple-negative molecular features (TNBC-MC), we found a significant increase in IL-17+ cell infiltrates in intratumoral stroma and extratumoral stroma of TNBC-NST. Similarly, stromal cells with co-expression of CD4 and IL-17 were noted in intratumoral and extratumoral stroma in both TNBC-NST and TNBC-MC. In addition, intratumoral IL-17+ cells were positively associated with tumor cell expression of vascular endothelial growth factor A (VEGFA) and with intratumoral tumor microvascular density (MVD). Multivariate analysis identified that intratumoral IL-17+ cells (P = 0.018), MVD (P = 0.039), and TNM stage (P = 0.002) were independent prognostic factors for predicting poor PFS.

CONCLUSION

The study indicates that IL-17 is overexpressed in intratumoral stromal cells of TNBC-NST. The overexpression of IL-17 might engage in active tumor microangiogenesis through its signal transduction pathways resulting in increased tumor secretion of VEGFA, and then promote tumor progression. IL-17 might serve as a potential new target for individualized therapy to TNBC-NST patients by development of specific antibodies. Additional study is deemed to further explore the role of IL-17+ stromal cells in breast cancer.

The Role of Axillary Lymph Node Dissection in Tubular Carcinoma of the Breast: A Population Database Study

Background

The aim of this study was to investigate the role of axillary lymph node dissection on the outcome of patients with tubular carcinoma of the breast.

Material/Methods

Patients diagnosed with tubular carcinoma of the breast between 2000–2013 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Statistical analysis of the data was undertaken, including analysis of breast cancer-specific survival (BCSS).

Results

Of the 5,645 patients identified on the SEER database with tubular carcinoma of the breast, 5,032 (89.4%) patients had undergone axillary lymph node dissection, with significantly increased rates after 2002 compared with rates between 2000–2001 (p <0.001), which stabilized between 2002–2013 (p=0.330). Axillary lymph node metastases were present in 6.1% of all patients and in 5.3% of patients with a tumor size ≤2 cm. Lymph node-positive disease was associated with patient age ≤65 years, intermediate-grade or high-grade tumors, and tumor size >2.0 cm. Axillary lymph node dissection was an independent prognostic indicator. The 10-year BCSS was 97.3% and 96.6% in patients with and without axillary lymph node dissection, respectively (p=0.002). The number of removed lymph nodes was not related to breast cancer-specific survival.

Conclusions

In patients with tubular carcinoma of the breast, lymph node status was not associated with significant breast cancer-specific survival. However, axillary lymph node dissection may still be considered for patients with for tubular carcinoma of the breast even in patients with a small tumor size.

Tubular carcinomas of the breast: an epidemiologic study

AIM

We explored the clinicopathologic characteristics, prognostic factors and outcomes in tubular carcinoma (TC) of the breast.

METHODS

We retrospectively assessed 8091 TC patients using the SEER database from 2000 to 2013.

RESULTS

Most patients were non-Hispanic white, well-differentiated disease, tumor size ≤2 cm, node-negative, nonmetastatic, hormone receptor-positive and HER2-negative status. The 10-year breast cancer-specific survival and overall survival were 98.1 and 82.0%, respectively. Multivariate analysis indicated that age, ethnicity, surgery procedures, radiotherapy and chemotherapy were independent predictors affecting survival outcomes. There was comparable breast cancer-specific survival between surgery and nonsurgery groups.

CONCLUSION

The patients with TC has excellent survival outcomes, which may in part be due to the favorable tumor characteristics.