Unraveling the causal links and novel molecular classification of Crohn's disease in breast Cancer: a two-sample mendelian randomization and transcriptome analysis with prognostic modeling.

BACKGROUND: Crohn's disease (CD), a prominent manifestation of chronic gastrointestinal inflammation, and breast cancer (BC), seemingly disparate in the medical domain, exhibit a shared characteristic. This convergence arises from their involvement in chronic inflammation and immune responses, an aspect that has progressively captivated the attention of investigators but remain controversial. METHODS: We used two-sample Mendelian Randomization (MR) and transcriptomics to explore the relationship between CD and BC. MR assessed causality of CD on different BC subtypes and reverse causality of BC on CD. We identified CD-related differentially expressed genes and their prognostic impact on BC, and developed a new molecular BC classification based on these key genes. RESULTS: MR revealed a causal link between CD and increased BC risk, especially in estrogen receptor-positive (ER+) patients, but not in ER-negative (ER-) cases. BC showed no causal effect on CD. Transcriptomics pinpointed genes like B4GALNT2 and FGF19 that affected BC prognosis in CD patients. A nomogram based on these genes predicted BC outcomes with high accuracy. Using these genes, a new molecular classification of BC patients was proposed. CONCLUSIONS: CD is a risk factor for ER + BC but not for ER- BC. BC does not causally affect CD. Our prognostic model and new BC molecular classifications offer insights for personalized treatment strategies.

Non-invasive prediction of axillary lymph node dissection exemption in breast cancer patients post-neoadjuvant therapy: A radiomics and deep learning analysis on longitudinal DCE-MRI data.

PURPOSE: In breast cancer (BC) patients with clinical axillary lymph node metastasis (cN+) undergoing neoadjuvant therapy (NAT), precise axillary lymph node (ALN) assessment dictates therapeutic strategy. There is a critical demand for a precise method to assess the axillary lymph node (ALN) status in these patients. MATERIALS AND METHODS: A retrospective analysis was conducted on 160 BC patients undergoing NAT at Fujian Medical University Union Hospital. We analyzed baseline and two-cycle reassessment dynamic contrast-enhanced MRI (DCE-MRI) images, extracting 3668 radiomic and 4096 deep learning features, and computing 1834 delta-radiomic and 2048 delta-deep learning features. Light Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), RandomForest, and Multilayer Perceptron (MLP) algorithms were employed to develop risk models and were evaluated using 10-fold cross-validation. RESULTS: Of the patients, 61 (38.13 %) achieved ypN0 status post-NAT. Univariate and multivariable logistic regression analyses revealed molecular subtypes and Ki67 as pivotal predictors of achieving ypN0 post-NAT. The SVM-based "Data Amalgamation" model that integrates radiomic, deep learning features, and clinical data, exhibited an outstanding AUC of 0.986 (95 % CI: 0.954-1.000), surpassing other models. CONCLUSION: Our study illuminates the challenges and opportunities inherent in breast cancer management post-NAT. By introducing a sophisticated, SVM-based "Data Amalgamation" model, we propose a way towards accurate, dynamic ALN assessments, offering potential for personalized therapeutic strategies in BC.

Efficacy and safety of neoadjuvant therapy for HR-positive/HER2-negative early breast cancer: a Bayesian network meta-analysis.

BACKGROUND: Neoadjuvant treatment for hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) breast cancer is controversial and requires a comprehensive analysis for optimal therapy assessment. Therefore, a two-step Bayesian network meta-analysis (NMA) was performed to compare the efficacy and safety of different neoadjuvant regimens. RESEARCH DESIGN AND METHODS: Phase II/III randomized clinical trials comparing various neoadjuvant therapies for HR+/HER2- breast cancer were included. NMA and pairwise meta-analyses were conducted using Stata (version 14), R (version 4.2.3), and Review Manager 5.4. RESULTS: Twenty-eight studies (5,625 patients) were eligible. NMA of objective response rate (ORR) indicated the highest SUCRA for chemotherapy (CT) and chemotherapy with anthracycline (CT(A)). Pathologic complete response (PCR) NMA demonstrated significant PCR improvement with chemotherapy regimens containing programmed cell death protein-1 and programmed cell death ligand-1 inhibitors (PD-1i/PD-L1i) and poly ADP-ribose polymerase inhibitors (PARPi). Combined analysis considering both the ORR and safety highlighted CT(A)'s efficacy and toxicity balance. CONCLUSIONS: CT(A) and CT showed improved ORR compared with alternative regimens. CT(A) combined with PD-1/PD-L1 or PARP inhibitors significantly increased PCR rates. Comprehensive assessment of both ORR and safety indicated that CT(A) represents an optimal neoadjuvant therapy for HR+/HER2- breast cancer, whereas AI + CDK4/6 inhibitors rank solely behind chemotherapy. REGISTRATION: PROSPERO Registration: CRD42024538948. International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY) registration number INPLASY202440092.

Adjuvant chemotherapy and survival in males aged 70 years or older with breast cancer: a population-based retrospective study.

BACKGROUND: Male breast cancer constitutes a minority of breast cancer diagnoses, yet its incidence has been on the rise in recent decades. However, elderly male breast cancer patients have been inadequately represented in clinical trials, posing challenges in treatment decisions. This study seeks to clarify the efficacy of chemotherapy in this demographic and identify the population most likely to benefit from such intervention. METHODS: We conducted a retrospective analysis using the Surveillance, Epidemiology, and End Results (SEER) database, encompassing a total of 1900 male breast cancer patients aged 70 years or older. Among them, 1652 were categorized in the no-chemotherapy group, while 248 were in the chemotherapy group. A multifactorial logistic regression model was employed to investigate the determinants influencing the administration of chemotherapy in elderly male breast cancer patients. Additionally, the multivariate Cox proportional hazards regression model was applied to identify factors associated with outcomes, with overall survival (OS) as the primary endpoint. RESULTS: Multivariate logistic regression analysis revealed that grade, tumor size, and nodal status were robust predictors for elderly male breast cancer patients receiving chemotherapy. Furthermore, the multivariate analysis demonstrated that chemotherapy conferred benefits compared to the no-chemotherapy group (HR = 0.822, 95% CI: 0.682-0.991, p = 0.040). Stratified analyses indicated that individuals with N+, poorly/undifferentiated grade, and stage II/III disease could derive benefits from chemotherapy. Upon further investigation of progesterone receptor (PR) positive patients, it was found that only stage III patients experienced significant benefits from chemotherapy (HR = 0.571, 95% CI: 0.372-0.875, p = 0.010). Conversely, in PR negative patients, both stage II (HR = 0.201, 95% CI: 0.051-0.792, p = 0.022) and stage III patients (HR = 0.242, 95% CI: 0.060-0.972, p = 0.046) derived benefits from chemotherapy. CONCLUSION: Adjuvant chemotherapy may benefit certain elderly male breast cancer patients, specifically those with positive lymph node status, poorly/undifferentiated grade, and PR-positive in stage III, as well as PR-negative expression in stage II/III. Given favorable physical tolerance, it is advisable not to hastily dismiss chemotherapy for these elderly male breast cancer patients.

The value of oral selective estrogen receptor degraders in patients with HR-positive, HER2-negative advanced breast cancer after progression on ≥ 1 line of endocrine therapy: systematic review and meta-analysis.

BACKGROUND: Currently, the value of oral selective estrogen receptor degraders (SERDs) for hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (aBC) after progression on ≥ 1 line of endocrine therapy (ET) remains controversial. We conducted a meta-analysis to evaluate progression-free survival (PFS) and safety benefits in several clinical trials. MATERIALS AND METHODS: Cochrane Library, Embase, PubMed, and conference proceedings (SABCS, ASCO, ESMO, and ESMO Breast) were searched systematically and comprehensively. Random effects models or fixed effects models were used to assess pooled hazard ratios (HRs) and 95% confidence intervals (CIs) for treatment with oral SERDs versus standard of care. RESULTS: A total of four studies involving 1,290 patients were included in our analysis. The hazard ratio (HR) of PFS showed that the oral SERD regimen was better than standard of care in patients with HR+/HER2- aBC after progression on ≥ 1 line of ET (HR: 0.75, 95% CI: 0.62-0.91, p = 0.004). In patients with ESR1 mutations, the oral SERD regimen provided better PFS than standard of care (HR: 0.58, 95% CI: 0.47-0.71, p < 0.00001). Regarding patients with disease progression following previous use of CDK4/6 inhibitors, PFS benefit was observed in oral SERD-treatment arms compared to standard of care (HR: 0.75, 95% CI: 0.64-0.87, p = 0.0002). CONCLUSIONS: The oral SERD regimen provides a significant PFS benefit compared to standard-of-care ET in patients with HR+/HER2- aBC after progression on ≥ 1 line of ET. In particular, we recommend oral SERDs as a preferred choice for those patients with ESR1m, and it could be a potential replacement for fulvestrant. The oral SERD regimen is also beneficial after progression on CDK4/6 inhibitors combined with endocrine therapy.

Efficacy and safety of first-line therapy in patients with HER2-positive advanced breast cancer: a network meta-analysis of randomized controlled trials.

PURPOSE: The numerous first-line treatment regimens for human epidermal growth factor receptor 2 (HER2)-positive advanced breast cancer (ABC) necessitate a comprehensive evaluation to inform clinical decision-making. We conducted a Bayesian network meta-analysis (NMA) to compare the efficacy and safety of different interventions. METHODS: We systematically searched for relevant randomized controlled trials (RCTs) in Pubmed, Embase, Cochrane Library and online abstracts from inception to June 1, 2023. NMA was performed to calculate and analyze progression-free survival (PFS), overall survival (OS), objective response rate (ORR), and adverse events of grade 3 or higher (≥ 3 AEs). RESULTS: Out of the 10,313 manuscripts retrieved, we included 28 RCTs involving 11,680 patients. Regarding PFS and ORR, the combination of trastuzumab with tyrosine kinase inhibitors (TKIs) was more favorable than dual-targeted therapy. If only using trastuzumab, combination chemotherapy is superior to monochemotherapy in terms of PFS. It is important to note that the addition of anthracycline did not result in improved PFS. For patients with hormone receptor-positive HER2-positive diseases, dual-targeted combined with endocrine therapy showed better benefit in terms of PFS compared to dual-targeted alone, but it did not reach statistical significance. The comprehensive analysis of PFS and ≥ 3 AEs indicates that monochemotherapy combined with dual-targeted therapy still has the optimal balance between efficacy and safety. CONCLUSION: Monochemotherapy (Docetaxel) plus dual-target (Trastuzumab and Pertuzumab) therapy remains the optimal choice among all first-line treatment options for ABC. The combination of trastuzumab with TKIs (Pyrotinib) demonstrated a significant improvement in PFS and ORR, but further data are warranted to confirm the survival benefit.

Epidemiology and a Predictive Model of Prognosis Index Based on Machine Learning in Primary Breast Lymphoma: Population-Based Study.

BACKGROUND: Primary breast lymphoma (PBL) is a rare disease whose epidemiological features, treatment principles, and factors used for the patients' prognosis remain controversial. OBJECTIVE: The aim of this study was to explore the epidemiology of PBL and to develop a better model based on machine learning to predict the prognosis for patients with primary breast lymphoma. METHODS: The annual incidence of PBL was extracted from the surveillance, epidemiology, and end results database between 1975 and 2019 to examine disease occurrence trends using Joinpoint software (version 4.9; National Cancer Institute). We enrolled data from 1251 female patients with primary breast lymphoma from the surveillance, epidemiology, and end results database for survival analysis. Univariable and multivariable analyses were performed to explore independent prognostic factors for overall survival and disease-specific survival of patients with primary breast lymphoma. Eight machine learning algorithms were developed to predict the 5-year survival of patients with primary breast lymphoma. RESULTS: The overall incidence of PBL increased drastically between 1975 and 2004, followed by a significant downward trend in incidence around 2004, with an average annual percent change (AAPC) of -0.8 (95% CI -1.1 to -0.6). Disparities in trends of PBL exist by age and race. The AAPC of the 65 years or older cohort was about 1.2 higher than that for the younger than 65 years cohort. The AAPC of White patients is 0.9 (95% CI 0.0-1.8), while that of Black patients was significantly higher at 2.1 (95% CI -2.5 to 6.9). We also identified that the risk of death from PBL is multifactorial and includes patient factors and treatment factors. Survival analysis revealed that the patients diagnosed between 2007 and 2015 had a significant risk reduction of mortality compared to those diagnosed between 1983 and 1990. The gradient booster model outperforms other models, with 0.752 for sensitivity and 0.817 for area under the curve. The important features established with the gradient booster model were the year of diagnosis, age, histologic type, and primary site, which were the 4 most relevant variables to explain 5-year survival status. CONCLUSIONS: The incidence of PBL started demonstrating a tendency to decrease after 2004, which varied by age and race. In recent years, the prognosis of patients with primary breast lymphoma has been remarkably improved. The gradient booster model had a promising performance. This model can help clinicians identify the early prognosis of patients with primary breast lymphoma and therefore improve the clinical outcome by changing management strategies and patient health care.

Tyrosine kinase inhibitors in HER2-positive breast cancer brain metastases: A systematic review and meta-analysis.

BACKGROUND: Small tyrosine kinase inhibitors (TKIs) show activity against breast cancer brain metastases (BCBM) of the human epidermal growth factor receptor 2 (HER2)-positive subtype. This meta-analysis aimed to objectively explore the efficacy and safety of TKIs. METHODS: Electronic databases were searched for relevant clinical trials. We conducted a pairwise meta-analysis, pooled analysis, and estimated summary survival curves to compare survival outcomes following TKIs therapy for BCBM patients using Stata version 16.0 or R x64 4.0.5. RESULTS: Thirteen clinical trials involving 987 HER2-positive BCBM patients were analyzed. A trend of longer progression-free survival (PFS) was observed in the TKI-containing arm compared to the non-TKI-containing arm (hazard ratio = 0.64, 95% confidence interval [CI]: 0.35-1.15, p = 0.132), although the difference is not statistically significant. Summary survival curves reported the summary median PFS and overall survival were 7.9 months and 12.3 months. Subgroup analysis revealed that TKIs combined with capecitabine (TKI + Cap) regimens resulted in improved survival outcomes. Tucatinib may be more effective in BCBM patients. The main grade 3-5 adverse events (AEs) were diarrhea (22%, 95% CI: 14%-32%), neutropenia (11%, 95% CI: 5%-18%), hepatic toxicity (7%, 95% CI: 1%-16%), and sensory neuropathy (6%, 95% CI: 2%-12%). CONCLUSION: TKIs therapy improved the survival outcomes of HER2-positive BCBM patients, especially when combined with capecitabine and tolerable AEs. We also identified the clinical value of tucatinib, which appears to be the most favorable TKI drug for BCBM patients.

Efficacy and safety of neoadjuvant therapy for triple-negative breast cancer: a Bayesian network meta-analysis.

BACKGROUND: Numerous studies have concentrated on neoadjuvant therapies for treating triple-negative breast cancer (TNBC) that improve the pathological complete response (pCR) rate but remain controversial. We conducted a network meta-analysis (NMA) to objectively explore the efficacy and safety of different neoadjuvant regimens. METHODS: Phase II/III randomized clinical trials that compared different neoadjuvant therapies for TNBC were included. NMA and pairwise meta-analysis were performed using WinBUGS (version 1.4.3) and Review Manager 5.3. RESULTS: Forty-four studies with 8459 patients met the eligibility criteria. The NMA of pCR showed that programmed cell death Protein-1 and programmed cell death Ligand-1 inhibitors (PD-1/PD-L1), bevacizumab (Bev), zoledronic acid (ZOL), and platinum salts plus poly polymerase inhibitors (Pt+PARPi) may be favorable for TNBC neoadjuvant therapy. Chemotherapy combined with platinum salts or nanoparticle albumin-bound paclitaxel (Nab-p) has additional beneficial effects. However, neo-type drugs may also have increased toxicity. CONCLUSION: PD-1/PD-L1, Bev, ZOL, and Pt+ PARPi-containing regimens improved the pCR rate compared to traditional chemotherapy, including anthracyclines and taxanes. Chemotherapy with platinum salts or Nab-p improved the pCR rate. Nevertheless, the balance between efficacy and toxicity should be evaluated rigorously. PD-1/PD-L1-containing regimens appear to be the most favorable for TNBC neoadjuvant therapy, with good efficacy and tolerance.

The impact of chemotherapy and survival prediction by machine learning in early Elderly Triple Negative Breast Cancer (eTNBC): a population based study from the SEER database.

PURPOSE: We aimed to analysis the impact of chemotherapy and establish prediction models of prognosis in early elderly triple negative breast cancer (eTNBC) by using machine learning. METHODS: We enrolled 4,696 patients in SEER Database who were 70 years or older, diagnosed with primary early TNBC(larger than 5 mm), from 2010 to 2016. The propensity-score matched method was utilized to reduce covariable imbalance. Univariable and multivariable analyses were used to compare breast cancer-specific survival(BCSS) and overall survival(OS). Nine models were developed by machine learning to predict the 5-year OS and BCSS for patients received chemotherapy. RESULTS: Compared to matched patients in no-chemotherapy group, multivariate analysis showed a better survival in chemotherapy group. Stratified analyses by stage demonstrated that patients with stage II and stage III other than stage I could benefit from chemotherapy. Further investigation in stage II found that chemotherapy was a better prognostic indicator for patients with T2N0M0 and stage IIb, but not in T1N1M0. Patients with grade III could achieve a better survival by receiving chemotherapy, but those with grade I and II couldn't. With 0.75 in 5-year BCSS and 0.81 in 5-year OS for AUC, the LightGBM outperformed other algorithms. CONCLUSION: For early eTNBC patients with stage I, T1N1M0 and grade I-II, chemotherapy couldn't improve survival. Therefore, de-escalation therapy might be appropriate for selected patients. The LightGBM is a trustful model to predict the survival and provide precious systemic treatment for patients received chemotherapy.

Efficacy and safety of neoadjuvant therapy for HER2-positive early breast cancer: a network meta-analysis.

AIMS: Currently, there are many approaches available for neoadjuvant therapy for human epidermal growth factor receptor 2 (HER2)-positive breast cancer that improve therapeutic efficacy but are also controversial. We conducted a two-step Bayesian network meta-analysis (NMA) to compare odds ratios (ORs) for pathologic complete response (PCR) and safety endpoints. METHODS: The Cochrane Central Register of Controlled Trials, PubMed, Embase, and online abstracts from the American Society of Clinical Oncology and San Antonio Breast Cancer Symposium were searched comprehensively and systematically. Phase II/III randomised clinical trials for targeted therapy in at least one arm were included. RESULTS: A total of 9779 published manuscripts were identified, and 36 studies including 10,379 patients were finally included in our analysis. The NMA of PCR showed that dual-target therapy is better than single-target therapy and combination chemotherapy is better than monochemotherapy. However, anthracycline did not bring extra benefits, whether combined with dual-target therapy or single-target therapy. On the other hand, the addition of endocrine therapy in the HER2-positive, hormone receptor (HR)-positive subgroup might have additional beneficial effects but without significant statistical difference. By performing a conjoint analysis of the PCR rate and safety endpoints, we found that 'trastuzumab plus pertuzumab' and 'T-DM1 containing regimens' were well balanced in terms of efficacy and toxicity in all target regimens. CONCLUSION: In summary, trastuzumab plus pertuzumab-based dual-target therapy with combination chemotherapy regimens showed the highest efficacy of all optional regimens. They also achieved the best balance between efficacy and toxicity. As our study showed that anthracycline could be replaced by carboplatin, we strongly recommended TCbHP as the preferred choice for neoadjuvant treatment of HER2-positive breast cancer. We also look forward to the potential value of T-DM1 in improving outcomes, which needs further study in future trials.