Breast Cancer Outcomes Research Articles

Role of oedema and shrinkage patterns for prediction of response to neoadjuvant chemotherapy and survival outcomes in luminal breast cancer

To explore the independent and additional value of oedema and shrinkage patterns for predicting the disease-free survival (DFS) and neoadjuvant chemotherapy (NAC) response in luminal breast cancer (BC). Patients with luminal BC who underwent NAC were enrolled in this study from 2017 to 2022. Traditional MRI features include BI-RADS-based MRI descriptors, tumor size, and ADC values, while emerging MRI features include oedema and shrinkage patterns, all of which were evaluated before, early, and after NAC. The changes in features during NAC were also evaluated. The value of features was evaluated through univariate, multivariate analyses. A total of 258 patients were enrolled in this study, of which 77 responded to NAC. Diffuse oedema, stable or increased oedema during early NAC were adverse predictors for treatment response, while a greater reduction in tumor size and increase in ADC value were favorable predictors (all P<0.05). Furthermore, 20 of 60 patients who were followed up experienced recurrence. Diffuse oedema, pre-pectoral or subcutaneous oedema, and non-concentric shrinkage patterns after NAC were risk factors for DFS, whereas a greater increase in ADC value was a protective factor. Incorporating oedema and shrinkage patterns into traditional MRI features improved the predictive performance for treatment response (AUC from 0.76-0.78 to 0.80-0.83) and DFS (C-index from 0.67-69 to 0.75-0.80). Oedema is an unfavorable predictor for treatment response and survival outcomes, while shrinkage patterns contribute more to the prognostic value, both of which could offer supplementary benefits for clinical outcomes in luminal BC.

Non-apoptotic regulatory cell death scoring system to predict the clinical outcome and drug choices in breast cancer

BackgroundBreast cancer (BC), the most common cancer among women globally, has been shown by numerous studies to significantly involve non-apoptotic regulatory cell death (RCD) in its pathogenesis and progression. MethodsWe obtained the RNA sequences and clinical data of BC patients from The Cancer Genome Atlas (TCGA) database for the training set, while datasets GSE96058, GSE86166, and GSE20685 from The Gene Expression Omnibus (GEO) database were utilized as validation cohorts. Initially, we performed non-negative matrix factorization (NMF) clustering analysis on the BC samples from the TCGA database to discern non-apoptotic RCD-related molecular subtypes. To identify prognostically-relevant non-apoptotic RCD genes (NRGs) and construct a prognostic model, we implemented three machine learning algorithms: lasso regression, random forest, and XGBoost analysis. The expression of selected genes was verified using real-time quantitative polymerase chain reaction (RT-qPCR), single-cell RNA-sequencing (scRNA-seq) analysis, and The Human Protein Atlas (HPA) database. The risk signature was evaluated concerning clinical characteristics and drug sensitivity. Furthermore, we developed a nomogram to predict BC patient survival. ResultsThe NMF method successfully compartmentalized patients from the TCGA database into three distinct non-apoptotic RCD-related subtypes, with significant variations observed in immune characteristics and prognostic stratification across these subtypes. We identified 5 differentially expressed NRGs used in establishing the risk signature. Patients with different risk groups exhibited distinct clinicopathological features, drug sensitivity, and prognostic outcomes. A nomogram was subsequently developed, incorporating the NRGs-related risk signature, age, T stage, and N stage, to aid clinical decision-making. ConclusionWe identified a novel NRGs-related risk signature, which was expected to become a potential prognostic marker in BC.

Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature.

This study aims to identify precise biomarkers for breast cancer to improve patient outcomes, addressing the limitations of traditional staging in predicting treatment responses. Our analysis encompassed data from over 7,000 breast cancer patients across 14 datasets, which included in-house clinical data and single-cell data from 8 patients (totaling 43,766 cells). We utilized an integrative approach, applying 10 machine learning algorithms in 54 unique combinations to analyze 100 existing breast cancer signatures. Immunohistochemistry assays were performed for empirical validation. The study also investigated potential immunotherapies and chemotherapies. Our research identified five consistent glutamine metabolic reprogramming (GMR)-related genes from multi-center cohorts, forming the foundation of a novel GMR-model. This model demonstrated superior accuracy in predicting recurrence and mortality risks compared to existing clinical and molecular features. Patients classified as high-risk by the model exhibited poorer outcomes. IHC validation in 30 patients reinforced these findings, suggesting the model's broad applicability. Intriguingly, the model indicates a differential therapeutic response: low-risk patients may benefit more from immunotherapy, whereas high-risk patients showed sensitivity to specific chemotherapies like BI-2536 and ispinesib. The GMR-model marks a significant leap forward in breast cancer prognosis and the personalization of treatment strategies, offering vital insights for the effective management of diverse breast cancer patient populations.

Toward More Equitable Breast Cancer Outcomes

Toward More Equitable Breast Cancer Outcomes

PD-1/PD-L1 pathway: Current research in breast cancer.

Immunotherapy has shown encouraging outcomes in breast cancer (BC) treatment in recent years. The programmed cell death ligand 1 (PD-L1) transmembrane protein is suggested to function as a co-inhibitory factor in the immune response, where it collaborates with programmed cell death protein 1 (PD-1) to stimulate apoptosis, suppress cytokine release from PD-1 positive cells, and limit the growth of PD-1 positive cells. Furthermore, in many malignancies, PD-L1 reduces the immune system's response to neoplastic cells. These observations suggest that the PD-1/PD-L1 axis plays a vital role in cancer therapy and the regulation of cancer immune escape mechanisms. This review aimed to provide an overview of the functions of PD-1 and PD-L1 in BC cancer therapy. This research design is a literature review. The style is a traditional review on topics or variables relating to the PD-1/PD-L1 pathway. A literature search was carried out using three online databases. The search using the keywords yielded a total of 248 studies. Each result was filtered again according to the inclusion and exclusion criteria, resulting in a final total of 4 studies to be included in the literature review. The combination of PD-1/PD-L1 is essential for many malignancies. According to the evidence presented, this combination presents both an opportunity and a challenge in cancer treatment. Since many solid cancers, especially BC, express high levels of PD-1/PD-L1, cancer treatment mainly involves targeted therapies.

Bridging frontiers: The role of a Global Breast Research Working Group.

Breast cancer remains a significant cause of death for women globally, despite advancements in detection and treatment, low- and middle-income countries face unique obstacles. Role of Research Working Group (RWG) can expedite research progress by fostering collaboration between scientists, clinicians, and stakeholders. Benefits of a Global RWG include pooling resources and expertise to develop new research ideas, addressing disparities, and building local research capacity, with the potential to improve breast cancer research and outcomes.

Early Detection of Breast Cancer using Deep Learning in Mammograms

Breast cancer still poses a serious threat to world health, needing creative approaches to early identification in order to improve patient outcomes. This study investigates the potential of deep learning methods to improve the precision and effectiveness of mammography interpretation for the identification of breast cancer. In this paper proposed, a convolutional neural network (CNN) architecture, ResNet50, is created and trained on a sizable data set of annotated mammograms. The CNN is made to automatically identify and extract pertinent elements, such as microcalcifications, masses, and architectural distortions,that may be symptomatic of possible cancers. The model develops the ability to distinguish between benign and malignant instances through an iterative process of training and validation, finally displaying a high level of discriminatory accuracy. The paper findings show that the deep learning model outperforms conventional mammography interpretations in terms of sensitivity and specificity for detecting breast cancer. Furthermore, the model's potential for use in actual clinical settings is highlighted by its generalizability across a range of patient demographics and imaging technologies.This study represents a big step in improving radiologists' capacity for breast cancer early detection. Our deep learning-based architecture has promise for improving the screening procedure and potentially decreasing the difficulties brought on by radiologist shortages by lowering false positives, improving accuracy, and offering quick analysis. By utilising cutting-edge technology to enable prompt and efficient detection, this study contributes to continuing efforts by the international healthcare community to improve breast cancer outcomes.

Improving Breast Cancer Outcomes for Indigenous Women in Australia.

In Australia, the incidence rate of breast cancer is lower in Indigenous* women than non-Indigenous women; however, the mortality rate is higher, with Indigenous women 1.2 times more likely to die from the disease. This paper provides practical and achievable solutions to improve health outcomes for Indigenous women with breast cancer in Australia. This research employed the Context-Mechanism-Outcome (CMO) framework to reveal potential mechanisms and contextual factors that influence breast cancer outcomes for Indigenous women, stratified into multiple levels, namely, micro (interpersonal), meso (systemic) and macro (policy) levels. The CMO framework allowed us to interpret evidence regarding Indigenous women and breast cancer and provides nine practical ways to improve health outcomes and survival rates.

Impact of Low BMI and Nutritional Status on Quality of Life and Disease Outcome in Breast Cancer Patients: Insights From a Tertiary Cancer Center in India

This study investigates the impact of Body Mass Index (BMI) on Quality of Life (QoL) and treatment outcomes in breast cancer (BC) patients, particularly focusing on underweight individuals with compromised nutritional status. A nonrandomized prospective study comprising 121 newly diagnosed patients across various BMI categories utilized FACT-B & FACIT-Sp-12 questionnaires. Follow-ups occurred at baseline, during (3rd and 6th), and after (12th month) anthracycline-taxane chemotherapy, either sequentially or concomitantly. Patients with low BMI (<18.5 kg/m2; 53.7%) exhibited significantly poorer QoL, marked by compromised nutritional indicators (low MUAC and SFT). Repeated measures ANOVA identified significant correlations between BMI groups in functional, social, and emotional QoL aspects (p < 0.05), with no notable differences in other domains. A Chi-square (ꭓ2) test underscored a significant link between BMI and treatment response (p < 0.0001), showing higher rates of non-responders among underweight patients (p = 4.259e−14). The study advocates pretreatment consultation with a dietitian as standard care for Indian BC patients, offering complimentary nutritional support for improved QoL outcomes and treatment responses.

Clinicopathological importance of Bcl-2 and p53 in postmenopausal triple-negative breast carcinoma and association with age.

Appropriate biomarkers are required to predict the clinical outcome of triple-negative breast cancer (TNBC). In this study, we focused on the clinical importance of two representative tumor-associated proteins, Bcl-2 and p53. Bcl-2 expression is usually related to estrogen receptor expression and a favorable outcome in breast cancer. TNBC has been reported to show a high frequency of p53 positivity suggesting TP53 mutations. The expressions of Bcl-2 and p53 were immunohistochemically examined in TNBC involving two age groups of postmenopausal women (≥75 y/o, n = 75; 55-64 y/o, n = 47), who underwent surgery without neoadjuvant therapy. We examined their associations with each other, or with clinicopathological factors including the outcome. Bcl-2 expression was inversely correlated with androgen receptor, apocrine morphology, and p53 expressions, and was an independent predictor of a poor outcome in total or in younger women. p53 positivity was associated with a more favorable outcome than p53 negativity in the younger group. In combined analyzes, none of the twenty Bcl-2-negative/p53-positive cases in the younger group exhibited recurrence, resulting in the independent favorable predictive value of Bcl-2-negative/p53-positive. The anti-apoptotic nature of Bcl-2 may be apparent in TNBC. The excellent outcome of Bcl-2-negative/p53-positive cases in the younger group warrants further combined investigation of Bcl-2/p53 in TNBC.

Patient Navigation Improved Breast Cancer Outcomes Among Women in the Public System in Rio de Janeiro, Brazil

Patient Navigation Improved Breast Cancer Outcomes Among Women in the Public System in Rio de Janeiro, Brazil

Assessment of body composition in breast cancer patients: concordance between transverse computed tomography analysis at the fourth thoracic and third lumbar vertebrae.

Specific body composition markers derived from L3 axial computed tomography (CT) images predict clinical cancer outcomes, including chemotherapy toxicity and survival. However, this method is only applicable to those undergoing lumbar (L3) CT scanning, which is not universally conducted in early breast cancer cases. This study aimed to evaluate CT analysis at T4 as a feasible alternative marker of body composition in breast cancer. All patients participated in the Investigating Outcomes from Breast Cancer: Correlating Genetic, Immunological, and Nutritional (BeGIN) Predictors observational cohort study (REC reference number: 14/EE/1297). Staging chest-abdomen-pelvic CT scan images from 24 women diagnosed with early breast cancer at University Hospital Southampton were analysed. Adipose tissue, skeletal muscle, and muscle attenuation were measured from the transverse CT slices' cross-sectional area (CSA) at T4 and L3. Adipose tissue and skeletal muscle area measurements were adjusted for height. Spearman's rank correlation coefficient analysis was used to determine concordance between body composition measurements using CT analysis at L3 and T4 regions. Derived estimates for total adipose tissue, subcutaneous adipose tissue, and intramuscular adipose tissue mass following adjustment for height were highly concordant when determined from CSAs of CT slices at T4 and L3 (Rs = 0.821, p < 0.001; Rs = 0.816, p < 0.001; and Rs = 0.830, p < 0.001). In this cohort, visceral adipose tissue (VAT) and skeletal muscle estimates following height adjustment were less concordant when measured by CT at T4 and L3 (Rs = 0.477, p = 0.039 and Rs = 0.578, p = 0.003). The assessment of muscle attenuation was also highly concordant when measured by CT at T4 and L3 (Rs = 0.840, p < 0.001). These results suggest that the CT analysis at T4 and L3 provides highly concordant markers for total adipose, subcutaneous adipose, and intramuscular adipose estimation, but not VAT, in this breast cancer population. High concordance between T4 and L3 was also found when assessing skeletal muscle attenuation. Lower concordance was observed for the estimates of skeletal muscle area, potentially explained by differences in the quantity and proportions of axial and appendicular muscle between the thorax and abdomen. Future studies will determine the value of T4 metrics as predictive tools for clinical outcomes in breast cancer.

Racial disparities in outcomes of patients with stage I-III triple-negative breast cancer after adjuvant chemotherapy: a post-hoc analysis of the E5103 randomized trial.

Breast cancer mortality is higher in Black women than other racial groups. This difference has been partially attributed to a higher proportion of triple-negative breast cancer (TNBC). However, it is uncertain if survival disparities exist in racially diverse TNBC patients receiving similar treatments. Here, we examine racial differences in disease-related outcomes in TNBC patients treated on the E5103 clinical trial. From 2007 to 2011, 4,994 patients with stage I-III HER2-negative breast cancer were randomized to adjuvant chemotherapy with or without bevacizumab. This analysis was limited to the subset of 1,742 TNBC patients with known self-reported race. Unadjusted Kaplan-Meier curves and adjusted Cox-Proportional Hazards models were used to determine breast cancer events and survival outcomes. Of the analysis population, 51 (2.9%) were Asian, 269 (15.4%) Black, and 1422 (81.6%) White. Median age was 51 years. Patient characteristics, treatment arm, and local therapies were similar across racial groups. White women were more commonly node-negative (56% vs. 49% and 44% in Asian and Black women, respectively; p < 0.01). At a median follow-up of 46 months, unadjusted Kaplan-Meier locoregional and distant recurrence, and disease-free and overall survival, did not differ significantly by race. In Cox models adjusted for patient and tumor characteristics and treatment arm, race was not associated with any disease event. Larger tumor size and nodal involvement were consistently associated with breast cancer events. This clinical trial population of similarly treated TNBC patients showed no racial differences in breast cancer outcomes. Disease extent, rather than race, was associated with disease events.

Systematic Review of Survival Outcomes of Pregnancy-Associated Breast Cancer in Asian Countries.

Pregnancy-associated breast cancer (PABC) is a rare type of gestational cancer. It poses a significant challenge in diagnosis and management, especially in Asian countries with limited resources. We carried out a systematic literature review and narrative synthesis to identify survival outcomes for women with PABC in Asia. We searched MEDLINE, PubMed, Cochrane Library and the reference lists of the included English language articles for those conducted between January 2010 and August 2022. The search terms were pregnancy-associated breast cancer, breast cancer AND pregnancy, survival of PABC and prognosis of PABC patients. PABC is defined as breast cancer diagnosed either during pregnancy or 1 year-5 years postpartum. This review included observational studies conducted in Asian countries. The final 11 articles met the selection criteria and were analysed. Five of the studies had high quality methods as assessed using the Joanna Briggs Institute (JBI) checklist. We reported study design, year of diagnosis, country, definition of PABC, control group, age of participants, median follow-up time, survival outcomes and pregnancy as prognostic factors. Only five studies reported that PABC patients had a poor overall or disease-free survival rate compared to the control. Pregnancy was a significant independent prognostic factor of breast cancer in only two studies. This review highlights that pregnancy has an unconfirmed association with breast cancer survival in Asia. Most studies that found a non-significant association had small samples, thus there is a need for large-scale multinational epidemiological studies in Asia to establish the survival outcomes in PABC patients.

Racial disparities in disease-specific mortality and surgical management of patients with ductal carcinoma in situ with microinvasion.

Given persistent racial disparities in breast cancer outcomes, this study explores racial differences in disease-specific mortality and surgical management among patients with microinvasive ductal carcinoma in situ (DCIS-MI). The Surveillance, Epidemiology, and End Results Program was queried for patients aged 18+ years with DCIS-MI between January 1, 2010 and December 31, 2018. The study cohort was divided into non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients. Disease-specific mortality was evaluated using Cox proportional hazards models. A total of 3400 patients were identified, of which 569 (16.7%) were NHB and 2831 (83.3%) were NHW. Compared with NHW patients, NHB patients had more positive lymph nodes (7.6% vs. 3.9% p < 0.001). In addition, NHB women were more likely to undergo axillary lymph node dissection (6.0% vs. 3.8%, p = 0.044) and receive chemotherapy (11.8% vs. 7.2%, p < 0.001). There were no racial differences in breast surgery type (p = 0.168), reconstructive surgery (p = 0.362), or radiation therapy (p = 0.342). Overall, NHB patients had worse disease-specific mortality (adjusted hazard ratio 2.13, 95% confidence interval [CI]: 1.10-4.14) with mortality risks diverging from NHW women after 3 years (6 years rate ratio [RR] 2.12, 95% CI: 1.13-4.34; 9 years RR 2.32, 95% CI: 1.24-4.35). NHB women with DCIS-MI present with higher nodal disease burden and experience worse disease-specific mortality than NHW women.

Survival outcomes in HER2-low versus HER2-zero breast cancer after neoadjuvant chemotherapy: a meta-analysis

BackgroundThe survival outcomes in HER2-low versus HER2-zero breast cancer (BC) after neoadjuvant chemotherapy (NACT) remain unclear. The meta-analysis was conducted to summarize current evidence about the survival outcomes in HER2-low versus HER2-zero BC.MethodsWe conducted a systematic search in PubMed and EMBASE databases to identify relevant studies.ResultsA total of 14 studies with 53,714 patients were included. Overall, 34,037 patients (63.37%) were HER2-low, and 19,677 patients (36.63%) were HER2-zero. Patients with HER2-low tumors had a significantly lower pathological complete response (pCR) rate than patients with HER2-zero tumors, regardless of the hormone receptor status. Compared with HER2-zero breast cancer, the overall survival (OS) and disease-free survival (DFS) of HER2-low BC were longer in the overall cohort (HR = 0.72; 95% CI = 0.61–0.85; P < 0.0001; HR = 0.83; 95% CI = 0.75–0.92; P = 0.0002); however, no differences were observed in terms of OS and DFS between HER2-low and HER2-zero BC in the HR-negative group. In the HR-positive group, HER2-low status had no significant impact on OS, while significantly associated with increased DFS (HR = 0.85; 95% CI = 0.76–0.96; P = 0.007).ConclusionThese results suggest that although HER2-low BC has a poor response to NACT, it is correlated with favorable OS and DFS after NACT in the overall cohort as well as longer DFS in the HR-positive group.

Assessing the Value of Incorporating a Polygenic Risk Score with Nongenetic Factors for Predicting Breast Cancer Diagnosis in the UK Biobank.

Previous studies have demonstrated that incorporating a polygenic risk score (PRS) to existing risk prediction models for breast cancer improves model fit, but to determine its clinical utility the impact on risk categorization needs to be established. We add a PRS to two well-established models and quantify the difference in classification using the net reclassification improvement (NRI). We analyzed data from 126,490 post-menopausal women of "White British" ancestry, aged 40 to 69 years at baseline from the UK Biobank prospective cohort. The breast cancer outcome was derived from linked registry data and hospital records. We combined a PRS for breast cancer with 10-year risk scores from the Tyrer-Cuzick and Gail models, and compared these to the risk scores from the models using phenotypic variables alone. We report metrics of discrimination and classification, and consider the importance of the risk threshold selected. The Harrell's C statistic of the 10-year risk from the Tyrer-Cuzick and Gail models was 0.57 and 0.54, respectively, increasing to 0.67 when the PRS was included. Inclusion of the PRS gave a positive NRI for cases in both models [0.080 (95% confidence interval (CI), 0.053-0.104) and 0.051 (95% CI, 0.030-0.073), respectively], with negligible impact on controls. The addition of a PRS for breast cancer to the well-established Tyrer-Cuzick and Gail models provides a substantial improvement in the prediction accuracy and risk stratification. These findings could have important implications for the ongoing discussion about the value of PRS in risk prediction models and screening.

Additional prognostic value of polymorphisms within the 3'-untranslated region of programmed cell death pathway genes in early-stage breast cancer.

The programmedcell death (PCD) pathway plays an important role in restricting cancer cell survival and proliferation. However, limited studies have investigated the association between genetic variants in the 3'-untranslated region of the PCD pathway genes and breast cancer outcomes. In this study, we genotyped 28 potentially functional single nucleotide polymorphisms (SNPs)in 23 PCD pathwaygenesin 1,177patients withearly-stagebreast cancer(EBC)from a Han Chinese population.The median follow-up period was 174 months. Among all the candidateSNPs, fourindependent SNPs (rs4900321 and rs7150025 in ATG2B, rs6753785 in BCL2L11, and rs2213181 in c-Kit) were associated with invasive disease-free survival (iDFS), distant disease-free survival (DDFS), breast cancer-specific survival (BCSS)and overall survival (OS), respectively. Further combined genotypes of these four SNPs revealed that the survival decreasedas the number of unfavorable genotypes increased(Ptrend= 1.0 × 10-6, 8.5 × 10-8, 3.6 × 10-4, and 1.3 × 10-4for iDFS, DDFS, BCSS, and OS, respectively). Receiver operating characteristiccurve analysis demonstrated that incorporating unfavorable genotypes and clinicopathological variables improved the ability to predict EBC survival (P= 0.006, 0.004, 0.029, and 0.019 for iDFS, DDFS, BCSS, and OS, respectively).Additionally, rs6753785 and rs2213181 were associated with BCL2L11and c-KitmRNA expression, respectively. Our results suggest that these four SNPs may act as novel biomarkers for EBC survival, possibly by modulating the expression of the corresponding genes.

Subcellular expression of MTA1, HIF1A and p53 in primary tumor predicts aggressive triple negative breast cancers: a meta-analysis based study.

The prevalence of TNBC in India is higher compared to western countries. There is a multitude of biomarkers associated with different clinical outcomes of TNBC with contradictory reports. Identification of a set of specific biomarkers from the very many number of proteins reported in the literature to predict prognosis of TNBC is an urgent clinical need. A systematic review of key molecular biomarkers in cohort studies that have been investigated for their role in breast cancer prognosis was conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was followed. A meta-analysis was used to evaluate their pooled hazard ratio (HR) and the corresponding 95% confidence interval (95% CI) statistically. Immunohistochemical characterization of the meta-analyzed markers were performed in a cohort of 200 retrospective TNBC and 100 non TNBC patient tissues. Kaplan-Meier plot were used to evaluate disease free survival (DFS), and overall survival (OS). Cox regression models were used to evaluate predictors of DFS and OS. Using a meta-analytical approach, we consolidated the biomarker signatures associated with survival outcomes in breast cancers. The promising markers that emerged for the prediction of DFS and OS included E-Cadherin, Survivin, p53, MTA1, HIF1A, CD133, Vimentin and CK5/6. Evaluation of these markers in tumor tissue revealed that subcellular localization of p53, MTA1 and HIF1A had a significant association in predicting TNBC prognosis. Kaplan Meier plot revealed that p53 (OS p = 0.007, DFS p = 0.004), HIF 1A (OS p = 0.054, DFS p = 0.009) and MTA1 (OS p = 0.043, DFS = p = 0.001) expression in the primary tumor tissue were found to be significantly correlated with poor OS and DFS, whereas expression of Survivin (DFS p = 0.024) and E Cadherin (DFS p = 0.027) correlated with DFS alone in TNBC. Univariate analysis revealed that p53, HIF1A and MTA1 could be independent prognostic markers. Our study suggests cytoplasmic over expression of HIF1A, nuclear over expression of MTA1 and mutated p53 in the primary tumor tissue of TNBC have significance as markers predicting survival of TNBC patients.

Implementation and Evaluation of a Breast Cancer Disease Model Using Real-World Claims Data in Germany from 2010 to 2020.

Breast cancer is the leading cause of cancer-related mortality among women in Germany and worldwide. This retrospective claims data analysis utilizing data from AOK Baden-Wuerttemberg, a major statutory German health insurance provider, aimed to construct and assess a real-world data breast cancer disease model. The study included 27,869 female breast cancer patients and 55,738 age-matched controls, analyzing data from 2010 to 2020. Three distinct breast cancer stages were analyzed: Stage A (early breast cancer without lymph node involvement), Stage B (early breast cancer with lymph node involvement), and Stage C (primary distant metastatic breast cancer). Tumor subtypes were estimated based on the prescription of antihormonal or HER2-targeted therapy. The study established that 77.9% of patients had HR+ breast cancer and 9.8% HER2+; HR+/HER2- was the most common subtype (70.9%). Overall survival (OS) analysis demonstrated significantly lower survival rates for stages B and C than for controls, with 5-year OS rates ranging from 79.3% for stage B to 35.4% for stage C. OS rates were further stratified by tumor subtype and stage, revealing varying prognoses. Distant recurrence-free survival (DRFS) analysis showed higher recurrence rates in stage B than in stage A, with HR-/HER2- displaying the worst DRFS. This study, the first to model breast cancer subtypes, stages, and outcomes using German claims data, provides valuable insights into real-world breast cancer epidemiology and demonstrates that this breast cancer disease model has the potential to be representative of treatment outcomes.