Trends in presentation, management and survival of patients with de novo metastatic breast cancer in a Southeast Asian setting

Incidence and survival of primary metastatic breast cancer in Denmark; implication of breast cancer screening, classification, and staging practice

BACKGROUND

Primary metastatic breast cancer (pMBC) accounts for 5-10% of annual breast cancers with a median survival of 3-4 years, varying among subtypes. In Denmark, the incidence of breast cancer increased until 2010, followed by a stabilisation. Several factors influencing pMBC incidence and survival, including screening prevalence, staging methods, and classification standards, remain pivotal but inadequately documented.

MATERIAL AND METHOD

This retrospective observational study involving pMBC patients diagnosed between 2000 and 2020 encompassed all Danish oncology departments. Data from the Danish Breast Cancer Group database and the National Patient Register included diagnosis specifics, demographics, treatment, and follow-up.

RESULTS

Between 2000 and 2020, 3,272 patients were diagnosed with pMBC, a rise from 355 patients in 2000-2004 to 1,323 patients in 2015-2020. The increase was particularly observed in patients aged 70 years or older. Changes in tumour subtypes were observed, notably with a rise in human epidermal growth factor receptor 2 (HER2)-positive cases but a steady distribution of estrogen receptor (ER) status. Diagnostic practices changed over the two decades, with 6% evaluated with PET/CT (positron emission tomography-computed tomography) or CT (computed tomography) with a bone evaluation in 2000-2004 and 65% in 2015-2020. Overall survival (OS) improved from 23 months in 2000-2004 to 33 months in 2015-2020. In patients with ER-positive and HER2-positive disease, the multivariable model showed improved survival by year of diagnosis, and further, patients with ER-negative/HER2-negative disease fared worse the first 2 years after diagnosis.

INTERPRETATION

Our study delineates changes in the treatment and survival of pMBC over two decades. Stage migration, screening introduction, and changes in registration practice, however, prevent a valid assessment of a possible causal relationship.

A Nomogram and Risk Classification System Predicting the Prognosis of Patients with De Novo Metastatic Breast Cancer Undergoing Immediate Breast Reconstruction: A Surveillance, Epidemiology, and End Results Population-Based Study

Background The lifespan of patients diagnosed with de novo metastatic breast cancer (dnMBC) has been prolonged. Nonetheless, there remains substantial debate regarding immediate breast reconstruction (IBR) for this particular subgroup of patients. The aim of this study was to construct a nomogram predicting the breast cancer-specific survival (BCSS) of dnMBC patients who underwent IBR. Methods A total of 682 patients initially diagnosed with metastatic breast cancer (MBC) between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. All patients were randomly allocated into training and validation groups at a ratio of 7:3. Univariate Cox hazard regression, least absolute shrinkage and selection operator (LASSO), and best subset regression (BSR) were used for initial variable selection, followed by a backward stepwise multivariate Cox regression to identify prognostic factors and construct a nomogram. Following the validation of the nomogram with concordance indexes (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCAs), risk stratifications were established. Results Age, marital status, T stage, N stage, breast subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, radiotherapy, and chemotherapy were independent prognostic factors for BCSS. The C-indexes were 0.707 [95% confidence interval (CI), 0.666-0.748] in the training group and 0.702 (95% CI, 0.639-0.765) in the validation group. In the training group, the AUCs for BCSS were 0.857 (95% CI, 0.770-0.943), 0.747 (95% CI, 0.689-0.804), and 0.700 (95% CI, 0.643-0.757) at 1 year, 3 years, and 5 years, respectively, while in the validation group, the AUCs were 0.840 (95% CI, 0.733-0.947), 0.763 (95% CI, 0.677-0.849), and 0.709 (95% CI, 0.623-0.795) for the same time points. The calibration curves for BCSS probability prediction demonstrated excellent consistency. The DCA curves exhibited strong discrimination power and yielded substantial net benefits. Conclusions The nomogram, constructed based on prognostic risk factors, has the ability to provide personalized predictions for BCSS in dnMBC patients undergoing IBR and serve as a valuable reference for clinical decision making.

Survival nomogram for patients with de novo metastatic breast cancer based on the SEER database and an external validation cohort

Background

On average, 5-10% of patients are diagnosed with metastatic breast cancer (MBC) at the initial diagnosis. This study aimed to develop a nomogram to predict the overall survival (OS) in these patients.

Methods

The nomogram was based on a retrospective study of 9435 patients with de novo MBC from the Surveillance, Epidemiology, and End Results (SEER) database. The predictive accuracy and discriminative ability of the nomogram were determined using the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), and calibration curve. Decision curve analysis (DCA) was employed to evaluate the benefits and advantages of our new predicting model over the 8th edition of the American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) staging system. The results were validated in a retrospective study of 103 patients with de novo MBC from January 2013 to June 2022 at an institution in northwest China.

Results

Multivariate analysis of the primary cohort revealed that independent factors for survival were age at diagnosis, pathological type, histological grade, T stage, N stage, molecular subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, surgery, chemotherapy, and radiotherapy. The nomogram achieved a C-index of 0.688 (95% confidence interval [CI], 0.682-0.694) in the training cohort and 0.875 (95% CI, 0.816-0.934) in the validation cohort. The AUC of the nomograms indicated good specificity and sensitivity in the training and validation cohorts, respectively. Calibration curves showed favorable consistency between the predicted and actual survival probabilities. Additionally, the DCA curve produced higher net gains than by the AJCC-TNM staging system. Finally, risk stratification can accurately identify groups of patients with de novo MBC at different risk levels.

Conclusions

The nomogram showed favorable predictive and discriminative abilities for OS in patients with de novo MBC. Other populations from different countries or prospective studies are needed to further validate the nomogram.

De novo metastatic breast cancer in men vs women: a Swedish population-based cohort study

Current evidence on de novo metastatic breast cancer is based on data from women. This Swedish population-based cohort study compared the incidence over time and prognosis of de novo metastatic breast cancer between sexes using data from the Swedish National Quality Register for Breast Cancer. Joinpoint regression analysis was used to compare incidence trends in all stages (104 733 women, 648 men) and multivariate Cox regression analysis to investigate potential sex disparities in de novo metastatic breast cancer prognosis (6005 women, 41 men). For both sexes, increased trends were evident for cancer stages I and II, with a stabilizing trend at the later years for women, while stage III incidence remained stable. An increased trend for de novo metastatic breast cancer in women, and to a lesser extent in men, was observed. No difference in de novo metastatic breast cancer overall survival between sexes was observed (hazard ratio = 1.24; 95% confidence interval = 0.85 to 1.81). The comparable features in terms of incidence and prognosis of de novo metastatic breast cancer between sexes imply similarities, supporting the adoption of common treatment strategies.

Pan-Asian adapted ESMO Clinical Practice Guidelines for the diagnosis, staging and treatment of patients with metastatic breast cancer

The most recent version of the European Society for Medical Oncology (ESMO) Clinical Practice Guidelines for the diagnosis, staging and treatment of patients with metastatic breast cancer (MBC) was published in 2021. A special, hybrid guidelines meeting was convened by ESMO and the Korean Society of Medical Oncology (KSMO) in collaboration with nine other Asian national oncology societies in May 2022 in order to adapt the ESMO 2021 guidelines to take into account the differences associated with the treatment of MBC in Asia. These guidelines represent the consensus opinions reached by a panel of Asian experts in the treatment of patients with MBC representing the oncological societies of China (CSCO), India (ISMPO), Indonesia (ISHMO), Japan (JSMO), Korea (KSMO), Malaysia (MOS), the Philippines (PSMO), Singapore (SSO), Taiwan (TOS) and Thailand (TSCO). The voting was based on the best available scientific evidence and was independent of drug access or practice restrictions in the different Asian countries. The latter were discussed when appropriate. The aim of these guidelines is to provide guidance for the harmonisation of the management of patients with MBC across the different regions of Asia, drawing from data provided by global and Asian trials whilst at the same time integrating the differences in genetics, demographics and scientific evidence, together with restricted access to certain therapeutic strategies.

Clinical Characteristics and Survival Outcomes of Patients with De Novo Metastatic Breast Cancer

Purpose

Though less than 5% of patients with breast cancer present with De Novo Metastasis (dnMBC) in Western societies, this percentage may reach 30% in developing countries. In this study, we present survival outcomes of patients diagnosed with dnMBC treated at a tertiary center in a developing country.

Patients and Methods

Using hospital-based database, consecutive patients with dnMBC diagnosed between 2013 and 2017 were identified. Demographic data, tumor characteristics, types of treatment, and survival data were retrospectively collected.

Results

A total of 435 patients were included; median age (range) at time of diagnosis was 51 (24-85) years. Most of the tumors expressed hormone receptors (81% Estrogen Receptor positive, 77% Progesterone Receptor positive). Human epidermal growth factor receptor-2 (HER2) overexpression was reported in 134 (30.9%) patients, while only 24 (5.5%) had Triple Negative (TN) disease. Bone, lung and liver were the most common sites of metastasis involved in 70.6%, 36.1%, and 32.0%, respectively. The median Overall Survival (OS) for all patients was 38 months, and 5-year OS was 32.6%. On univariate analysis, high tumor grade, advanced T-stage, TN-disease and metastasis to multiple sites, but not HER2 status, were associated with poor OS. On multivariate analysis, high tumor grade (Hazard Ratio =1.6, p=0.002), advanced T-stage (Hazard Ratio=1.6, p=0.003), and triple negative status (Hazard Ratio= 2.1, p=0.008) predicted poor OS.

Conclusion

The overall survival of patients with dnMBC remains poor. Better understanding of the disease behavior and factors affecting survival is required for optimal utilization of available regimens and new drugs to hopefully improve patients' outcomes.

Theory and Practice of Integrating Machine Learning and Conventional Statistics in Medical Data Analysis

The practice of medical decision making is changing rapidly with the development of innovative computing technologies. The growing interest of data analysis with improvements in big data computer processing methods raises the question of whether machine learning can be integrated with conventional statistics in health research. To help address this knowledge gap, this paper presents a review on the conceptual integration between conventional statistics and machine learning, focusing on the health research. The similarities and differences between the two are compared using mathematical concepts and algorithms. The comparison between conventional statistics and machine learning methods indicates that conventional statistics are the fundamental basis of machine learning, where the black box algorithms are derived from basic mathematics, but are advanced in terms of automated analysis, handling big data and providing interactive visualizations. While the nature of both these methods are different, they are conceptually similar. Based on our review, we conclude that conventional statistics and machine learning are best to be integrated to develop automated data analysis tools. We also strongly believe that machine learning could be explored by health researchers to enhance conventional statistics in decision making for added reliable validation measures.

Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches

Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute's SEER Program's November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients' survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K-nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression (accuracy = 80.57 percent) and the greatest acquired from the random forest (accuracy = 94.64 percent). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area.

Targeting metabolism: A potential strategy for hematological cancer therapy

Most hematological cancer-related relapses and deaths are caused by metastasis; thus, the importance of this process as a target of therapy should be considered. Hematological cancer is a type of cancer in which metabolism plays an essential role in progression. Therefore, we are required to block fundamental metastatic processes and develop specific preclinical and clinical strategies against those biomarkers involved in the metabolic regulation of hematological cancer cells, which do not rely on primary tumor responses. To understand progress in this field, we provide a summary of recent developments in the understanding of metabolism in hematological cancer and a general understanding of biomarkers currently used and under investigation for clinical and preclinical applications involving drug development. The signaling pathways involved in cancer cell metabolism are highlighted and shed light on how we could identify novel biomarkers involved in cancer development and treatment. This review provides new insights into biomolecular carriers that could be targeted as anticancer biomarkers.

Filamin A Is a Potential Driver of Breast Cancer Metastasis via Regulation of MMP-1

Recurrent metastasis is a major fatal cause of breast cancer. Regretfully, the driving force and the molecular beneath have not been fully illustrated yet. In this study, a cohort of breast cancer patients with locoregional metastasis was recruited. For them, we collected the matched samples of the primary tumor and metastatic tumor, and then we determined the mutation profiles with whole-exome sequencing (WES). On basis of the profiles, we identified a list of deleterious variants in eight susceptible genes. Of them, filamin A (FLNA) was considered a potential driver gene of metastasis, and its low expression could enhance 5 years’ relapse survival rate by 15%. To prove the finding, we constructed a stable FLNA knockout tumor cell line, which manifested that the cell abilities of proliferation, migration, and invasion were significantly weakened in response to the gene knockout. Subsequently, xenograft mouse experiments further proved that FLNA knockout could inhibit local or distal metastasis. Putting all the results together, we consolidated that FLNA could be a potential driver gene to metastasis of breast cancer, in particular triple-negative breast cancer. Additional experiments also suggested that FLNA might intervene in metastasis via the regulation of MMP-1 expression. In summary, this study demonstrates that FLNA may play as a positive regulator in cancer proliferation and recurrence. It provides new insight into breast cancer metastasis and suggests a potential new therapeutic target for breast cancer therapy.

Epidemiology of De Novo Metastatic Breast Cancer

Most cases of metastatic breast cancer (MBC) arise as a recurrence of a previously treated early breast cancer. Distinct from recurrent MBC is de novo MBC (dnMBC), which describes patients who present with distant sites of disease at initial diagnosis and is reviewed here. dnMBC represents approximately 3% to 6% of new breast cancer diagnoses in high-income countries. This incidence has not declined despite decades of widespread use of population-based mammography screening. Overrepresentation of both biologically aggressive tumors and patients negatively impacted by social determinants of health are characteristics of dnMBC. Survival has generally been superior for patients with dnMBC compared with those with recurrent MBC, although it is similar to that for patients with recurrent MBC with long disease-free intervals. Subgroups of patients with dnMBC who experience prolonged survival include those with human epidermal growth factor receptor-2-positive disease or hormone receptor-positive bone-only disease. Opportunities to decrease dnMBC presentation may include novel screening modalities suited for biologically aggressive breast tumors and improved access to health care. Recognizing that there will remain some women diagnosed with dnMBC, refining our ability to identify those likely to be long-term survivors could allow for appropriate escalation or de-escalation of care. Finally, evaluation of tumor genomics in robust sample sizes has the potential to advance our knowledge of the biology of dnMBC as an entity distinct from recurrent MBC.

Understanding the Psychosocial Needs of Women who Present with Advanced Breast Cancer

INTRODUCTION

Advanced breast cancer (ABC) remains common in Singapore. In 2019, 22.1% of breast cancer patients presented with ABC in our institution. Despite increasing affluence and the advent of national mammographic screening, the incidence of ABC has not changed significantly. This suggests inherent differences in women who present late. We aim to explore the socio-economic background, knowledge and attitudes of women who present with ABC.

METHODS

Between December 2013 and July 2015, 100 patients who presented consecutively with ABC in a tertiary institution in Singapore were recruited to participate in an interviewer-led questionnaire exploring psychosocial and economic issues.

RESULTS

Among the 100 patients, 63 and 37 presented with stages 3 and 4 breast cancer respectively. Median age was 57 (27-86), 52% had at least secondary education, 53% had no formal employment and 71% were married; 88% were aware of breast cancer symptoms, 82% were aware that mammography can help detect cancer, 82% believed that current treatment modality for breast cancer is effective, 96% had never undergone a mammography and 52.9% felt mammograms were unnecessary. A total of 64% presented symptomatic from the breast tumour, with a median duration of 3 months. Many of the patients were aware of breast cancer symptoms and the utility of mammography. However, a group of patients did not comply with screening. This may be due to poor understanding about breast screening and detection in its asymptomatic phase.

CONCLUSION

Further public education to improve understanding of breast cancer and screening mammography may help to improve rates for earlier detection of breast cancer.

A multidisciplinary expert opinion on CINV and RINV, unmet needs and practical real-life approaches

Introduction: A range of combination chemotherapy regimens are currently used in clinical practice. However, international antiemetic guidelines often only categorize the emetogenic potential of single agents rather than the emetogenicity of combination chemotherapy regimens. To manage the nausea and vomiting induced by antineoplastic combinations, guidelines suggest antiemetics that are appropriate for the component drug with the highest emetogenic potential. Furthermore, antiemetic guidelines generally do not consider the influence of other factors, including individual patient characteristics, on the emetic effects of cancer treatments. Similarly, the emetogenic potential of radiotherapy is stratified only according to the site of radiation, while other factors contributing to emetic risk are overlooked.Areas covered: An Expert Panel was convened to examine unresolved issues and summarize the current clinical research on managing nausea and vomiting associated with combination chemotherapy and radiotherapy.Expert opinion: The panel identified the incidence of nausea and vomiting induced by multi-drug combination therapies currently used to treat cancer at different anatomic sites and by radiotherapy in the presence of other risk factors. Based on these data and the clinical experience of panel members, several suggestions are made for a practical approach to prevent or manage nausea and vomiting due to chemotherapy regimens and radiation therapy.

A review of the influence of mammographic density on breast cancer clinical and pathological phenotype

PURPOSE

It is well established that high mammographic density (MD), when adjusted for age and body mass index, is one of the strongest known risk factors for breast cancer (BC), and also associates with higher incidence of interval cancers in screening due to the masking of early mammographic abnormalities. Increasing research is being undertaken to determine the underlying histological and biochemical determinants of MD and their consequences for BC pathogenesis, anticipating that improved mechanistic insights may lead to novel preventative or treatment interventions. At the same time, technological advances in digital and contrast mammography are such that the validity of well-established relationships needs to be re-examined in this context.

METHODS

With attention to old versus new technologies, we conducted a literature review to summarise the relationships between clinicopathologic features of BC and the density of the surrounding breast tissue on mammography, including the associations with BC biological features inclusive of subtype, and implications for the clinical disease course encompassing relapse, progression, treatment response and survival.

RESULTS AND CONCLUSIONS

There is reasonable evidence to support positive relationships between high MD (HMD) and tumour size, lymph node positivity and local relapse in the absence of radiotherapy, but not between HMD and LVI, regional relapse or distant metastasis. Conflicting data exist for associations of HMD with tumour location, grade, intrinsic subtype, receptor status, second primary incidence and survival, which need further confirmatory studies. We did not identify any relationships that did not hold up when data involving newer imaging techniques were employed in analysis.

Predicting factors for survival of breast cancer patients using machine learning techniques

BACKGROUND

Breast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an alternative, this study used machine learning techniques to build models for detecting and visualising significant prognostic indicators of breast cancer survival rate.

METHODS

A large hospital-based breast cancer dataset retrieved from the University Malaya Medical Centre, Kuala Lumpur, Malaysia (n = 8066) with diagnosis information between 1993 and 2016 was used in this study. The dataset contained 23 predictor variables and one dependent variable, which referred to the survival status of the patients (alive or dead). In determining the significant prognostic factors of breast cancer survival rate, prediction models were built using decision tree, random forest, neural networks, extreme boost, logistic regression, and support vector machine. Next, the dataset was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry to perform advanced modelling using random forest. Subsequently, the important variables were ranked via variable selection methods in random forest. Finally, decision trees were built and validation was performed using survival analysis.

RESULTS

In terms of both model accuracy and calibration measure, all algorithms produced close outcomes, with the lowest obtained from decision tree (accuracy = 79.8%) and the highest from random forest (accuracy = 82.7%). The important variables identified in this study were cancer stage classification, tumour size, number of total axillary lymph nodes removed, number of positive lymph nodes, types of primary treatment, and methods of diagnosis.

CONCLUSION

Interestingly the various machine learning algorithms used in this study yielded close accuracy hence these methods could be used as alternative predictive tools in the breast cancer survival studies, particularly in the Asian region. The important prognostic factors influencing survival rate of breast cancer identified in this study, which were validated by survival curves, are useful and could be translated into decision support tools in the medical domain.

Metastatic and triple-negative breast cancer: challenges and treatment options

The major current conventional types of metastatic breast cancer (MBC) treatments include surgery, radiation, hormonal therapy, chemotherapy, or immunotherapy. Introducing biological drugs, targeted treatment and gene therapy can potentially reduce the mortality and improve the quality of life in patients with MBC. However, combination of several types of treatment is usually recommended. Triple negative breast cancer (TNBC) accounts for 10-20% of all cases of breast carcinoma and is characterized by the low expression of progesterone receptor (PR), estrogen receptor (ER), and human epidermal growth factor receptor 2 (HER2). Consequently, convenient treatments used for MBC that target these receptors are not effective for TNBC which therefore requires special treatment approaches. This review discusses the occurrence of MBC, the prognosis and predictive biomarkers of MBC, and focuses on the novel advanced tactics for treatment of MBC and TNBC. Nanotechnology-based combinatorial approach for the suppression of EGFR by siRNA and gifitinib is described.

Survival analysis according to period and analysis of the factors influencing changes in survival in patients with recurrent breast cancer: a large-scale, single-center study

BACKGROUND

We performed this study to analyze changing survival patterns regarding recurrent breast cancer in Korea during the last 16 years (1993-2008). We also sought to determine factors possibly influencing outcomes and changes over time in the duration of survival after recurrence.

METHODS

We retrospectively analyzed 9671 patients with breast cancer treated between January 1993 and December 2008, comparing the periods 1993-2002 and 2003-2008.We retrospectively reviewed the collected database including the age at diagnosis, clinical manifestations, pathology report, surgical methods, types of adjuvant treatment modalities, type of recurrence, and follow-up period.

RESULTS

There were 1944 cases (20.1%) of recurrence. Median age at the first recurrence was 49.5 years (range 21.8-92.9). Median follow-up was 28.8 months (range 0-228.0) from the time of relapse. Median survival time was 35.0 months. Survival after recurrence (SAR) significantly improved in 2003-2008 compared to that in 1993-2002. Median survival time increased from 27.6 months in the period I to 42.3 months in period II (p = 0.001). Independent prognostic factors after the first recurrence by multivariate analysis were age at diagnosis, tumor size, nodal status, tumor grade, subtype, anti-hormonal therapy, time at diagnosis, and disease-free interval.

CONCLUSIONS

Outcomes of breast cancer have been improving recently, and survival time after the first recurrence of breast cancer has steadily increased in recent decades. We confirmed that advances in treatments have contributed to this improvement in survival after the first recurrence.