Effects of interval between age at first pregnancy and age at diagnosis on breast cancer survival according to menopausal status: a register-based study in Korea

Reproductive history and breast cancer survival: Findings from the African breast cancer-Disparities in outcomes cohort and implications of Africa's fertility transition on breast cancer prognosis

Reproductive characteristics are known risk factors for breast cancer but, other than recent birth, their role as prognostic factors is less clear, and has not been studied in Sub-Saharan Africa (SSA). In this setting, we examined whether reproductive factors independently influence breast cancer survival in a subset of the African Breast Cancer-Disparities in Outcomes cohort study. In 1485 women with incident breast cancer recruited between 2014 and 2017, we examined birth cohort changes in reproductive factors, and used Cox models to examine whether reproductive characteristics were associated with all-cause mortality after adjusting for confounders (age, stage, treatment, HIV, and social factors). Four years after diagnosis, 822 (56%) women had died. Median parity was 4 (IQR = 2, 6) and 209 (28%) of premenopausal women had had a recent birth (<3 years prior to cancer diagnosis). Each pregnancy was associated with a 5% increase (95% CI: 2%, 8%) in mortality rates, which held among postmenopausal women (5%, [1%-9%]). Pre-menopausal women with a recent birth had 52% (20%, 92%) higher mortality rates. Fertility trends by birth cohort showed declining parity, increasing age at first birth and declining age at last birth, however the impact of these population-level changes on future average survival was predicted to be very small (<3% absolute gain).

A Historical Misconception in Clinical Trials of Drugs for Cancer-Age Grouping

In clinical trials of cancer drugs, grouping by age is a very common grouping method, as it can allow for a visual comparison of the different pharmaceutical responses in patients at different age stages. Under the guidance of this thinking, many researchers use age grouping when studying clinical cancer drugs. However, even people at the same age may be at different stages in their lives, such as individuals who are going through puberty, menopause/andropause, or intermediate transition, as well as childhood and old age, affected by factors such as hormone levels, immune responses, ethnic groups, and regions. Every individual has different cancer symptoms and responses to drugs; therefore, the experimental effect of life stage grouping will be more obvious and clearer. Not only does this conclusion apply to cancer drugs, but it also applies to clinical trials for other diseases. In addition, this does not mean that age grouping should be completely abandoned. Life stage is a more general interval that can be further divided into life stage groups according to the age of the patients. Based on the principal law of lifespan (PLOSP), age trends in life stages also need to be updated from time to time. To date, life stage grouping has not been discussed systematically and has not been used as a grouping method for cancer patients. In this paper, life stage grouping is discussed as one of the important grouping categories in cancer clinical trials.

Comparative proteomic analysis of different stages of breast cancer tissues using ultra high performance liquid chromatography tandem mass spectrometer

BACKGROUND

Breast cancer is the fifth most prevalent cause of death among women worldwide. It is also one of the most common types of cancer among Malaysian women. This study aimed to characterize and differentiate the proteomics profiles of different stages of breast cancer and its matched adjacent normal tissues in Malaysian breast cancer patients. Also, this study aimed to construct a pertinent protein pathway involved in each stage of cancer.

METHODS

In total, 80 samples of tumor and matched adjacent normal tissues were collected from breast cancer patients at Seberang Jaya Hospital (SJH) and Kepala Batas Hospital (KBH), both in Penang, Malaysia. The protein expression profiles of breast cancer and normal tissues were mapped by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The Gel-Eluted Liquid Fractionation Entrapment Electrophoresis (GELFREE) Technology System was used for the separation and fractionation of extracted proteins, which also were analyzed to maximize protein detection. The protein fractions were then analyzed by tandem mass spectrometry (LC-MS/MS) analysis using LC/MS LTQ-Orbitrap Fusion and Elite. This study identified the proteins contained within the tissue samples using de novo sequencing and database matching via PEAKS software. We performed two different pathway analyses, DAVID and STRING, in the sets of proteins from stage 2 and stage 3 breast cancer samples. The lists of molecules were generated by the REACTOME-FI plugin, part of the CYTOSCAPE tool, and linker nodes were added in order to generate a connected network. Then, pathway enrichment was obtained, and a graphical model was created to depict the participation of the input proteins as well as the linker nodes.

RESULTS

This study identified 12 proteins that were detected in stage 2 tumor tissues, and 17 proteins that were detected in stage 3 tumor tissues, related to their normal counterparts. It also identified some proteins that were present in stage 2 but not stage 3 and vice versa. Based on these results, this study clarified unique proteins pathways involved in carcinogenesis within stage 2 and stage 3 breast cancers.

CONCLUSIONS

This study provided some useful insights about the proteins associated with breast cancer carcinogenesis and could establish an important foundation for future cancer-related discoveries using differential proteomics profiling. Beyond protein identification, this study considered the interaction, function, network, signaling pathway, and protein pathway involved in each profile. These results suggest that knowledge of protein expression, especially in stage 2 and stage 3 breast cancer, can provide important clues that may enable the discovery of novel biomarkers in carcinogenesis.

Associations of reproductive factors with breast cancer prognosis and the modifying effects of menopausal status

Reproductive factors associated with breast cancer risk may also affect the prognosis. This study aimed to evaluate the associations of multiple reproductive factors with breast cancer prognosis and the modifying effects of menopausal status. We obtained data from 3805 breast cancer patients recruited between October 2008 and June 2016 in Guangzhou. The subjects were followed up until 30 June 2018. The hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated using multivariate Cox models to estimate the associations. It was found that there were U‐shaped patterns for the associations of age at first birth and durations from first/last birth to diagnosis with breast cancer prognosis. The adverse effects of old age at first birth [>30 years vs 23‐30 years, HR (95% CI): 1.59 (1.01‐2.50)] and long intervals from first [≥20 years vs 10‐19 years, HR (95% CI): 1.55 (1.07‐2.27)] or last [≥20 years vs 10‐19 years, HR (95% CI): 1.63 (1.08‐2.46)] birth to diagnosis on progression‐free survival (PFS) were significantly more pronounced among premenopausal women. Additionally, long interval (>5 years) between first and second birth was associated with a better PFS [HR (95% CI): 0.64 (0.42‐0.97)]. These results suggested that age at first birth, durations from first/last birth to diagnosis, and intervals between first and second birth should be taken into account when following the patients and assessing the prognosis of breast cancer, particularly for premenopausal patients. These findings would also have implications for further insight into the mechanisms of breast cancer development.

Predicting Invasive Disease-Free Survival for Early-stage Breast Cancer Patients Using Follow-up Clinical Data

OBJECTIVE

Chinese women are seriously threatened by breast cancer with high morbidity and mortality. The lack of robust prognosis models results in difficulty for doctors to prepare an appropriate treatment plan that may prolong patient survival time. An alternative prognosis model framework to predict Invasive Disease-Free Survival (iDFS) for early-stage breast cancer patients, called MP4Ei, is proposed. MP4Ei framework gives an excellent performance to predict the relapse or metastasis breast cancer of Chinese patients in 5 years.

METHODS

MP4Ei is built based on statistical theory and gradient boosting decision tree framework. 5246 patients, derived from the Clinical Research Center for Breast (CRCB) in West China Hospital of Sichuan University, with early-stage (stage I-III) breast cancer are eligible for inclusion. Stratified feature selection, including statistical and ensemble methods, is adopted to select 23 out of the 89 patient features about the patient' demographics, diagnosis, pathology and therapy. Then 23 selected features as the input variables are imported into the XGBoost algorithm, with Bayesian parameter tuning and cross validation, to find out the optimum simplified model for 5-year iDFS prediction.

RESULTS

For eligible data, with 4196 patients (80%) for training, and with 1050 patients (20%) for testing, MP4Ei achieves comparable accuracy with AUC 0.8451, which has a significant advantage (p < 0.05).

CONCLUSION

This work demonstrates the complete iDFS prognosis model with very competitive performance.

SIGNIFICANCE

The proposed method in this paper could be used in clinical practice to predict patients' prognosis and future surviving state, which may help doctors make treatment plan.

Comparison Cure Rate Models by DIC Criteria in Breast Cancer Data

Background: One of the malignant tumors is Breast Cancer (BC) that starts in the cells of breast. There is many models for survival analysis of patients such as Cox PH model, Parametric models etc. But some disease are that all of patients will not experience main event then usual survival model is inappropriate. In addition, In the presence of cured patients, if researcher can specify distribution of survival time, usually cure rate models are preferable to parametric models. Distribution of Survival time can be Weibull, Log normal, Logistic, Gamma and so. Comparison of Weibull, Log normal and Logistic distribution for finding the best distribution of survival time is purpose of this study. Material and Methods: Among 787 patients with BC by Cancer Research Center recognized and followed from 1985 until 2013. Variables stage of cancer, age at diagnosis, tumor size and Number of Removed Positive Lymph Nodes (NRPLN) for fitting Cure rate model were selected. The best model selected with DIC criteria. All analysis were performed using SAS 9.2. Results: Mean (SD) of age was 48.47 (11.49) years and Mean of survival time and Maximum follow up time was 326 and 55.12 months respectively. During following patients, 145 (18.4%) patients died from BC and others survived (censored). Also, 1-year, 5-year and 10-year survival rate was 94, 77 and 56 percent respectively. Log normal model with smaller DIC were selected and fitted. All of mentioned variables in the model were significant on cure rate. Conclusion: This study indicated that survival time of BC followed from Log normal distribution in the best way.

Breast cancer subtype and survival by parity and time since last birth

BACKGROUND

Pregnancy affects breast cancer risk but how it affects the subtype and prognosis remain controversial. We studied the effect of parity and time since last birth on breast cancer subtype and outcome.

PATIENTS AND METHODS

We conducted a retrospective multivariate cohort study including all premenopausal women with early breast cancer aged ≤ 50 years (N = 1306) at diagnosis at the University Hospitals Leuven (Jan. 2000-Dec. 2009). Primary study endpoints were the breast cancer subtype, disease-free survival, and distant disease-free survival by parity and time since last birth. Statistical methods used were baseline-category logits models and Cox proportional hazard models. Multivariable models were used to correct for possible confounders.

RESULTS

Breast cancer subtypes did not differ between nulliparous (N = 266) and parous women (N = 1040) but subtypes differed significantly in parous women by time since last birth (p < 0.001). Tumors within 5 years of last birth were proportionally more likely triple negative and HER-2 like, even when corrected for age at diagnosis. After a mean follow-up period of 10 years, parous women had a better disease-free survival compared to nulliparous women (HR 0.733; CI 0.560-0.961; p = 0.025, HR 0.738; CI 0.559-0.974; p = 0.032 before and after correction for known prognostic factors, respectively). In parous women, a longer time since last birth was correlated with a longer disease-free survival compared to patients with a recent pregnancy (HR 0.976; CI 0.957-0.996; p = 0.018). However, after correction, this association completely disappeared (HR 1.010; CI 0.982-1.040; p = 0.480).

CONCLUSION

We observed a better disease-free survival for parous than nulliparous women. The influence of recent birth on disease-free survival is probably due to tumor and patient characteristics, as recent birth is associated with more aggressive subtypes.

The Effect of Reproductive Factors on Breast Cancer Presentation in Women Who Are BRCA Mutation Carrier

PURPOSE

Germline mutations in the BRCA1 and BRCA2 genes confer increased risks for breast cancers. However, the clinical presentation of breast cancer among women who are carriers of the BRCA1 or BRCA2 (BRCA1/2 carriers) mutations is heterogenous. We aimed to identify the effects of the reproductive histories of women with the BRCA1/2 mutations on the clinical presentation of breast cancer.

METHODS

We retrospectively analyzed clinical data on women with proven BRCA1 and BRCA2 mutations who were recruited to the Korean Hereditary Breast Cancer study, from 2007 to 2014.

RESULTS

Among the 736 women who were BRCA1/2 mutation carriers, a total of 483 women had breast cancers. Breast cancer diagnosis occurred at significantly younger ages in women who experienced menarche at ≤14 years of age, compared to those who experienced menarche at >14 years of age (37.38±7.60 and 43.30±10.11, respectively, p<0.001). Additionally, the number of full-term pregnancies was significantly associated with the age of diagnosis, especially in women with the BRCA2 mutation. The prevalence of advanced stages (stage II or III vs. stage I) of disease in parous women was higher than in nulliparous women (68.5% vs. 55.2%, p=0.043). This association was more pronounced in women with the BRCA2 mutation (hazard ratio, 2.67; p=0.014).

CONCLUSION

Our results suggest that reproductive factors, such as the age of onset of menarche and the presence of parity, are associated with the clinical presentation patterns of breast cancer in BRCA1/2 mutation carriers.

Human Chorionic Gonadotropin and Breast Cancer

Breast cancer is well known as a malignancy being strongly influenced by female steroids. Pregnancy is a protective factor against breast cancer. Human chorionic gonadotropin (HCG) is a candidate hormone which could mediate this antitumoral effect of pregnancy. For this review article, all original research articles on the role of HCG in breast cancer were considered, which are listed in PubMed database and were written in English. The role of HCG in breast cancer seems to be a paradox. Placental heterodimeric HCG acts as a protective agent by imprinting a permanent genomic signature of the mammary gland determining a refractory condition to malignant transformation which is characterized by cellular differentiation, apoptosis and growth inhibition. On the other hand, ectopic expression of β-HCG in various cancer entities is associated with poor prognosis due to its tumor-promoting function. Placental HCG and ectopically expressed β-HCG exert opposite effects on breast tumorigenesis. Therefore, mimicking pregnancy by treatment with HCG is suggested as a strategy for breast cancer prevention, whereas targeting β-HCG expressing tumor cells seems to be an option for breast cancer therapy.

Breast cancer-specific mortality in small-sized tumor with node-positive breast cancer: a nation-wide study in Korean breast cancer society

Tumor size and number of lymph node (LN) metastases are well known as the most important prognostic factors of breast cancer. We hypothesized that very small breast cancers with LN metastasis represent a progressive biologic behavior and evaluated tumor size stratified by LN metastasis. Data between 1990 and 2010 were obtained retrospectively from the Korean Breast Cancer Society Registry with inclusion criteria of female, non-metastatic, unilateral, and T1/2 breast cancer. We collected the following variables: age at surgery, tumor size, number of LN metastases, nuclear grade (NG), lymphovascular invasion (LVI), estrogen receptor status, progesterone receptor status, and epidermal growth factor receptor-2 status. Patient characteristics were compared by means of independent t-tests for continuous variables and the Chi-square or Fisher's exact test for categorical variables. Kaplan-Meier curves, with corresponding results of log-rank tests, were constructed for breast cancer-specific survival (BCSS). Five- and eight-year breast cancer-specific mortality (BCSM) was obtained in groups of 300 patients, followed by smoothing according to the confidence interval using the lowess method. We identified 39,826 breast cancer patients who met the inclusion criteria. Among them, 1433 (3.6 %) patients died due to breast cancer. The median follow-up duration was 63.4 (3-255) months. In the multivariate analysis, age at surgery, NG, LVI, subtype, and tumor size-nodal interactions were independently associated with BCSM. The N1 group had lower BCSS for T1a than T1b. The N2+ group also had lower BCSS for T1b than T1c or T2. In the N1 group of tumors smaller than 10 mm, 5- and 8-year BCSM decreased with larger tumor size. Patients with very small tumors with LN metastasis have decreased BCSM according to increase tumor size. Small tumors with LN metastasis could have aggressive biological behavior.