Factors influencing breast cancer outcomes in Australia: A systematic review

Risk of first recurrence after treatment in a population-based cohort of young women with breast cancer

PURPOSE

Breast cancer (BC) in women under 45 is rare yet often aggressive. We aim to analyze loco-regional recurrences (LR), distant recurrences (DR), second breast cancers, and mortality in young BC patients.

METHODS

We enrolled 776 women with non-metastatic BC ≤45 years diagnosed from 1970 to 2012. Variables included age, family history, tumor stage/grade, and treatment. We used multivariate Cox regression and competing risk models.

RESULTS

Among the participants, 37.0% were diagnosed before the age of 40. Most had stage I or II, grade II, ER- and PR-positive, HER2-negative tumors. Over a median follow-up of 8.7 years, 10.1% experienced LR, 13.7% developed DR, and 10.8% died, primarily due to BC. The majority of recurrences occurred within the first five years. Older age (>40) significantly reduced the risk of LR and DR. Advanced disease stage, certain surgical strategies, and positive margins increased DR risk. In the cohort diagnosed between 2001 and 2012, recent diagnosis, triple-negative cancer, and hormonal therapy were associated with reduced LR risk. Breast-conserving surgery appeared to offer protective effects against DR.

CONCLUSION

This study highlights that BC in young women carries a significant risk of early recurrence, with age, tumor characteristics, and treatment modalities influencing outcomes. The findings emphasize the need for tailored treatment strategies for young BC patients, focusing on surgical precision and aggressive adjuvant therapy for high-risk cases. This research contributes valuable insights into managing BC in younger patients, aiding in improving long-term outcomes.

The contribution of prognostic factors to socio-demographic inequalities in breast cancer survival in Victoria, Australia

BACKGROUND

Breast cancer survival in Australia varies according to socio-economic status (SES) and between rural and urban places of residence. Part of this disparity may be due to differences in prognostic factors at the time of diagnosis.

METHODS

Women with invasive breast cancer diagnosed from 2008 until 2012 (n = 14,165) were identified from the Victorian Cancer Registry and followed up for 5 years, with death from breast cancer or other causes recorded. A prognostic score, based on stage at diagnosis, cancer grade, whether the cancer was detected via screening, reported comorbidities and age at diagnosis, was constructed for use in a mediation analysis.

RESULTS

Five-year breast cancer mortality for women with breast cancer who were in the lowest quintile of SES (10.3%) was almost double that of those in the highest quintile (5.7%). There was a small survival advantage (1.7% on average, within each socio-economic quintile) of living in inner-regional areas compared with major cities. About half of the socio-economic disparity was mediated by prognostic factors, particularly stage at diagnosis and the presence of comorbidities. The inner-regional survival advantage was not due to differences in prognostic factors.

CONCLUSIONS

Part of the socio-economic disparity in breast cancer survival could be addressed by earlier detection in, and improved general health for, more disadvantaged women. Further research is required to identify additional causes of socio-economic disparities as well as the observed inner-regional survival advantage.

Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models

Background

Breast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up.

Materials and methods

This study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant prognostic factors. Time-to-event deep-learning-based models, including Nnet-survival, DeepHit, DeepSurve, NMLTR and Cox-time, were developed using a grid search approach with all variables initially and then with only the most important variables selected from feature importance. The performance metrics used to determine the best-performing model were C-index and IBS. Additionally, the dataset was clustered based on molecular receptor status (i.e., luminal A, luminal B, HER2-enriched, and triple-negative), and the best-performing prediction model was used to estimate survival probability for each molecular subtype.

Results

The random forest method identified tumor state, age at diagnosis, and lymph node status as the best subset of variables for predicting breast cancer (BC) survival probabilities. All models yielded very close performance, with Nnet-survival (C-index=0.77, IBS=0.13) slightly higher using all 18 variables or the three most important variables. The results showed that the Luminal A had the highest predicted BC survival probabilities, while triple-negative and HER2-enriched had the lowest predicted survival probabilities over time. Additionally, the luminal B subtype followed a similar trend as luminal A for the first five years, after which the predicted survival probability decreased steadily in 10- and 15-year intervals.

Conclusion

This study provides valuable insight into the survival probability of patients based on their molecular receptor status, particularly for HER2-positive patients. This information can be used by healthcare providers to make informed decisions regarding the appropriateness of medical interventions for high-risk patients. Future clinical trials should further explore the response of different molecular subtypes to treatment in order to optimize the efficacy of breast cancer treatments.

Prognostic analysis of breast cancer in Xinjiang based on Cox proportional hazards model and two-step cluster method

Objective

To examine the factors that affect the prognosis and survival of breast cancer patients who were diagnosed at the Affiliated Cancer Hospital of Xinjiang Medical University between 2015 and 2021, forecast the overall survival (OS), and assess the clinicopathological traits and risk level of prognosis of patients in various subgroups.

Method

First, nomogram model was constructed using the Cox proportional hazards models to identify the independent prognostic factors of breast cancer patients. In order to assess the discrimination, calibration, and clinical utility of the model, additional tools such as the receiver operating characteristic (ROC) curve, calibration curve, and clinical decision curve analysis (DCA) were used. Finally, using two-step cluster analysis (TCA), the patients were grouped in accordance with the independent prognostic factors. Kaplan-Meier survival analysis was employed to compare prognostic risk among various subgroups.

Result

T-stage, N-stage, M-stage, molecular subtyping, type of operation, and involvement in postoperative chemotherapy were identified as the independent prognostic factors. The nomogram was subsequently constructed and confirmed. The area under the ROC curve used to predict 1-, 3-, 5- and 7-year OS were 0.848, 0.820, 0.813, and 0.791 in the training group and 0.970, 0.898, 0.863, and 0.798 in the validation group, respectively. The calibration curves of both groups were relatively near to the 45° reference line. And the DCA curve further demonstrated that the nomogram has a higher clinical utility. Furthermore, using the TCA, the patients were divided into two subgroups. Additionally, the two groups' survival curves were substantially different. In particular, in the group with the worse prognosis (the majority of patients did not undergo surgical therapy or postoperative chemotherapy treatment), the T-, N-, and M-stage were more prevalent in the advanced, and the total points were likewise distributed in the high score side.

Conclusion

For the survival and prognosis of breast cancer patients in Xinjiang, the nomogram constructed in this paper has a good prediction value, and the clustering results further demonstrated that the selected factors were important. This conclusion can give a scientific basis for tailored treatment and is conducive to the formulation of focused treatment regimens for patients in practical practice.

Adherence to Mediterranean Diet and Nutritional Status in Women with Breast Cancer: What Is Their Impact on Disease Progression and Recurrence-Free Patients' Survival?

Introduction: Nutritional status impacts the survival of patients with cancer. There are few studies that investigate the role of nutritional status on breast cancer survival in women with breast cancer, and even fewer regarding the impact of adhering to the Mediterranean diet (MD). The present study aims to assess the nutritional status, MD adherence, physical activity levels and health-related quality of life (HRQOL) in women diagnosed with breast cancer and evaluate these parameters regarding recurrence-free survival. Methods: A total of 114 women, aged 35-87 years old, diagnosed with breast cancer in Larissa, Greece, participated in the study. Tumor histopathology was reported, and anthropometric indices were measured by a trained nurse, while questionnaires regarding nutritional status (via mini nutritional assessment), HRQOL via EORTC QLQ-C30, physical activity levels via IPAQ and Mediterranean diet adherence via MedDietScore were administered. The participants were followed-up for a maximum time interval of 42 months or until recurrence occurred. Results: A total of 74% of patients were overweight or obese, while 4% of women were undernourished, and 28% were at risk of malnutrition. After 42 months of follow-up, 22 patients (19.3%) had relapsed. The median time to recurrence was 38 months (IQR: 33-40 months) and ranged between 23 to 42 months. Higher levels of MD adherence were significantly associated with lower body mass index (BMI) values, earlier disease stage, smaller tumor size, absence of lymph node metastases and better physical activity levels (p < 0.05). Normal nutritional status was significantly associated with higher BMI values and better health-related quality of life (p ≤ 0.05). In univariate analysis, patients with higher levels of MD adherence and well-nourished patients had significantly longer recurrence-free survival (p < 0.05). In multivariate analysis, MD adherence and nutritional status were independently associated with recurrence-free patients' survival after adjustment for several confounding factors (p < 0.05). Conclusions: The impact of MD on time to recurrence is still under investigation, and future interventional studies need to focus on the role of adhering to the MD before and after therapy in survival and breast cancer progression. Furthermore, the present study also highlights the importance of an adequate nutritional status on disease progression, and the need for nutritional assessment, education and intervention in women with breast cancer.

Survival of Breast Cancer by Stage, Grade and Molecular Groups in Mallorca, Spain

The aims of this study are: (1) to determine cause-specific survival by stage, grade, and molecular groups of breast cancer, (2) to identify factors which explain and predict the likelihood of survival and the risk of dying from this cancer; and (3) to find out the distribution of breast cancer cases by stage, grade, and molecular groups in females diagnosed in the period 2006–2012 in Mallorca (Spain). We collected data regarding age, date and diagnostic method, histology, laterality, sublocation, pathological or clinical tumor size (T), pathological or clinical regional lymph nodes (N), metastasis (M) and stage, histologic grade, estrogen and progesterone receptors status, HER-2 expression, Ki67 level, molecular classification, date of last follow-up or date of death, and cause of death. We identified 2869 cases. Cause-specific survival for the entire sample was 96% 1 year after diagnosis, 91% at 3 years and 87% at 5 years. Relative survival was 96.9% 1 year after diagnosis, 92.6% at 3 years and 88.5% at 5 years. The competing-risks regression model determined that patients over 65 years of age and patients with triple negative cancer have worse prognoses, and as stages progress, the prognosis for breast cancer worsens, especially from stage III.

Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factors

We developed an artificial intelligence (AI) model that can predict five-year survival in patients with stage IV metastatic breast cancer, mainly based on host factors and sarcopenia. From a prospectively built breast cancer registry, a total of 210 metastatic breast cancer patients were selected in a consecutive manner using inclusion/exclusion criteria. The patients’ data were divided into two categories: a group that survived for more than 5 years and a group that did not survive for 5 years. For the AI model input, 11 features were considered, including age, body mass index, skeletal muscle area (SMA), height-relative SMA (H-SMI), height square-relative SMA (H2-SMA), weight-relative SMA (W-SMA), muscle mass, anticancer chemotherapy, radiation therapy, and comorbid diseases such as hypertension and mellitus. For the feature importance analysis, we compared classifiers using six different machine learning algorithms and found that extreme gradient boosting (XGBoost) provided the best accuracy. Subsequently, we performed the feature importance analysis based on XGBoost and proposed a 4-layer deep neural network, which considered the top 10 ranked features. Our proposed 4-layer deep neural network provided high sensitivity (75.00%), specificity (78.94%), accuracy (78.57%), balanced accuracy (76.97%), and an area under receiver operating characteristics of 0.90. We generated a web application for anyone to easily access and use this AI model to predict five-year survival. We expect this web application to be helpful for patients to understand the importance of host factors and sarcopenia and achieve survival gain.

Autophagy: A promising target for triple negative breast cancers

Triple negative breast cancer (TNBC) is the most aggressive type of breast cancers which constitutes about 15% of all breast cancer cases and characterized by negative expression of hormonal receptors and human epidermal growth factor receptor 2 (HER2). Thus, endocrine and HER2 targeted therapies are not effective toward TNBCs, and they mainly rely on chemotherapy and surgery for treatment. Despite recent advances in chemotherapy, 40% of TNBC patients develop a metastatic relapse and recurrence. Therefore, understanding the molecular profile of TNBC is warranted to identify targets that can be selected for the development of a new and effective therapeutic approach. Autophagy is an internal defensive mechanism that allows the cells to survive under different stressors. It has been well known that autophagy exerts a crucial role in cancer progression. The critical role of autophagy in TNBC progression is emerging in recent years. This review will discuss autophagic pathway, how autophagy affects TNBC progression and recent therapeutic approaches that can target autophagy as a new treatment modality.

Current Resources for Evidence-Based Practice, May 2020

An extensive review of new resources to support the provision of evidence-based care for women and infants. The current column includes a discussion of a new National Academy of Medicine report on planned place of birth and implications during the SARS-CoV-2 pandemic and commentaries on reviews focused on anorectal sexually transmitted infections and feeding methods following cleft lip repair in infants.

Prognostic Value of Skeletal Muscle Depletion Measured on Computed Tomography for Overall Survival in Patients with Non-Metastatic Breast Cancer

Purpose

The purpose of this study was to evaluate the prognostic value of skeletal muscle depletion measured on computed tomography (CT) in patients with non-metastatic invasive breast cancer.

Methods

This retrospective study included 577 consecutive women (mean age ± standard deviation: 48.9 ± 10.2 years with breast cancer who underwent a preoperative positron-emission tomography (PET)/CT scan and curative surgery between January 2012 and August 2014. The total abdominal muscle area (TAMA), subcutaneous fat area (SFA), and visceral fat area (VFA) were measured on CT images at the L3 vertebral level. Univariate and multivariate Cox proportional-hazard regression analyses were performed to evaluate whether there was an association between sarcopenia and overall survival (OS) outcome.

Results

Of the 577 women, 49 (8.5%) died after a mean of 46 months. The best TAMA threshold for predicting OS was 83.7 cm². The multivariate Cox proportional-hazard analysis revealed that sarcopenia (TAMA ≤ 83.70 cm²) was a strong prognostic biomarker (hazard ratio [HR], 1.951; 95% confidence interval [CI], 1.061–3.586), along with large tumor size, axillary lymph node metastasis, high nuclear grade, estrogen receptor status, and adjuvant radiation therapy. In the subgroup analysis of patients aged ≥ 50 years, TAMA (≤ 77.14 cm²) was a significant independent factor (HR, 2.856; 95% CI, 1.218–6.695).

Conclusion

Skeletal muscle depletion measured on CT was associated with worse OS outcome in patients with non-metastatic breast cancer.