Prognostic Factor In Breast Cancer Research Articles

Collagen signature adds prognostically significant information to staging for breast cancer

Tumor-associated collagen signature (TACS) is an independent prognostic factor for breast cancer. However, it is unclear whether the complete collagen signature, including TACS, the TACS-based collagen microscopic features (TCMF1), and the TACS-based nuclear features (TCMF2), can provide additional prognostic information for the current tumor-node-metastasis (TNM) staging system. We included 941 patients with breast cancer from three cohorts: the training (n= 355), internal (n= 334), and external validation cohorts (n= 252). TACS and TCMF1 were obtained by multiphoton microscopy (MPM). TCMF2 was extracted on the hematoxylin and eosin images colocated with MPM images. They were linearly combined to establish a complete collagen signature score for reclassifying current TNM staging into stage Ⅰ (II and Ⅲ)/low risk and stage Ⅰ (II and Ⅲ)/high risk. The low-risk collagen signatures 'downstaged' patients in stage II or Ⅲ, while the high-risk collagen signatures 'upstaged' patients with stage Ⅰ tumors. After incorporating the complete collagen signature into the current TNM staging system, the modified staging system had a higher ability to stratify patients [referent, Ⅰ-new; Ⅱ-new, hazard ratio (HR) 8.655, 6.136, and 4.699 in the training, internal validation, and external validation cohorts, respectively; Ⅲ-new, HR 14.855, 11.201, and 13.245 in the corresponding three cohorts, respectively] than the current TNM staging system (referent, Ⅰ; Ⅱ, HR 1.642, 1.853, and 1.371 in the corresponding three cohorts, respectively; Ⅲ, HR 4.131, 4.283, and 3.711 in the corresponding three cohorts, respectively). Furthermore, the modified staging system showed a higher area under the curve than the current TNM staging system (training cohort: 0.843 versus 0.683; internal validation cohort: 0.792 versus 0.661; and external validation cohort: 0.793 versus 0.646). The complete collagen signature is an independent predictor of survival outcomes in breast cancer. It adds significant information about the biological behavior of the disease to staging for breast cancer.

Identification of a novel prognostic signature for breast cancer derived from post-translational ubiquitin and ubiquitin-like modification-related genes.

Ubiquitin and ubiquitin-like (UUL) modifications play pleiotropic functions and are subject to fine regulatory mechanisms frequently altered in cancer. However, the comprehensive impact of UUL modification on breast cancer remains unclear. Transcriptomic and clinical data of breast cancer were downloaded from TCGA and GEO databases. Molecular subtyping of breast cancer was conducted using the NMF and CIBERSORT algorithms. Prognostic genes were identified via univariate, lasso and multivariate Cox regression analyses. Clinical pathological features, immune cell infiltration, immune therapeutic response and chemotherapy drug sensitivity were compared between groups using the Wilcoxon test. Survival analysis was performed using the Kaplan-Meier method and log-rank test. A total of 63 UUL modification-related genes were differentially expressed, with 29 up-regulated and 34 down-regulated genes. These genes were used to generate two UUL modification patterns that exhibited significant differences in prognostic features and immune cell infiltration. The UUL modification patterns were associated with 2038 differentially expressed genes that were significantly enriched in nuclear division, chromosome segregation, neuroactive ligand-receptor interaction, cell cycle, and other biological processes. Of these genes, 425 were associated with breast cancer prognosis, which enabled the classification of breast cancer into two clusters with significantly distinct prognoses. We developed a prognostic model, UULscore, which comprised nine genes and showed a significant correlation with partial immune cell infiltration. Furthermore, UULscore demonstrated potential predictive value in breast cancer overall survival prediction, immune therapeutic response, and chemotherapy drug sensitivity. UULscore, stage, radiotherapy, and chemotherapy were identified as independent prognostic factors for breast cancer. Based on these factors, a nomogram model was constructed, which demonstrated exceptional prognostic predictive performance. The present study identified two UUL modification-derived molecular subtypes in breast cancer, and have successfully constructed a risk-scoring model that holds potential value in prognosis, immune infiltration, immune therapeutic response, and chemotherapy sensitivity.

The prognostic value of histological grade determined after neoadjuvant chemotherapy of breast cancer

Histological grade is a validated prognostic factor of breast cancer but may show alterations following neoadjuvant chemotherapy (NACT). Its reporting after NACT is recommended by several guidelines, but evidence of its retained prognostic impact is scarce. Patients treated with NACT followed by surgery and having sufficient residual tumour for the determination of grade were analysed for the survival effects of posttreatment grade (yG). Kaplan-Meier analyses and the log-rank test were applied, followed by the univariable and multivariable Cox proportional hazards models. The cohort comprised 355 patients with known yG, and 320 of them had also a pretreatment grade available. Pretreatment grade changed in 99/320 (31 %) cases following NACT, and downgrading was more common (n=78/320, 24 %) than upgrading (21/320, 7 %). Among 355 breast cancer patients, those with yG3 (poorly differentiated) tumours (n=155) had worse 5-year relapse-free and overall survival estimates than those with yG2 (n=169) or yG1 (n=31) tumours. This was also substantiated by univariable analysis; however, yG lost its significance in the multivariable model. Post-NACT histological grade has a prognostic impact, but does not seem to be an independent prognosticator in the post-NACT setting; however, these results lend support for its reporting by pathologists after primary systemic treatment.

Abstract 4135712: Mortality and Cardiovascular Complications Associated with Weight Loss Drugs among Breast Cancer Females Under Cardiotoxic Therapies: A Real-World Populational Study in the United States

Background: Obesity is an adverse prognostic factor for breast cancer (BC). While liraglutide and semaglutide, derived from antidiabetic glucagon-like peptide-1 receptor agonists (GLP1RA), are established weight loss drugs known for their cardiovascular safety in the general obese population, their safety in BC patients undergoing cardiotoxic antineoplastic therapies remains less understood. Objective: To investigate the mortality and cardiovascular outcomes related to GLP1RA used for weight loss among the highly susceptible BC females. Methods: This retrospective cohort study was based on the TrinetX network, comprising real-time health records from over 60 US healthcare organizations. The study population included obese females (≥18 years old) who received at least three prescriptions of either exclusive GLP1RA (liraglutide or semaglutide) or non-GLP1RA weight loss drugs (phentermine, bupropion-naltrexone, orlistat) upon BC diagnosis between 01/01/2015 and 01/01/2022. The index date was the initiation of systemic anti-cancer treatment, including chemotherapy (anthracycline, taxane, or cyclophosphamide), and/or anti-HER2 agents (trastuzumab, pertuzumab, or lapatinib). We used 1:1 propensity score-matching (PSM) to balance demographics, BMI, comorbidities, concurrent medications for cardiovascular diseases and metabolic disorders, BC receptor status and prior treatment, and levels of serum cholesterol (LDL, HDL) and hemoglobin A1c. Cox regression models were applied to compute hazard ratios (HR) and 95% confidence intervals (CIs). Follow-up continued until 05/24/2024, or until adverse cardiovascular events or death occurred. Results: Among 380 PSM-balanced pairs, the GLP1RA group (mean age: 57.6±11) showed significantly lower risks of all-cause mortality (HR 0.593, 95% CI 0.457-0.77), acute heart failure (HR 0.496, 95% CI 0.286-0.859), angina pectoris (HR 0.566, 95% CI 0.343-0.936), and acute stroke (HR 0.661, 95% CI 0.46-0.951) than the non-GLP1RA group (mean age: 57.5±12). Otherwise, no significant difference was shown in acute myocardial infarction (HR 0.787, 95% CI 0.495-1.251), pulmonary hypertension (HR 0.777, 95% CI 0.55-1.099), or arrhythmias (HR 0.829, 95% CI 0.566-1.215). Conclusion: Compared to non-GLP1RA weight loss agents, GLP1RA offers better survival and cardiovascular protection for BC patients undergoing cardiotoxic therapies. Future studies are warranted to validate the safety of these weight loss drugs during the BC treatment continuum.

Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics

The axillary lymph node status remains an important prognostic factor in breast cancer, and nodal staging using sentinel lymph node biopsy (SLNB) is routine. Randomized clinical trials provide evidence supporting de-escalation of axillary surgery and omission of SLNB in patients at low risk. However, identifying sentinel lymph node macrometastases (macro-SLNMs) is crucial for planning treatment tailored to the individual patient. This study is the first to explore the capacity of deep learning (DL) models to identify macro-SLNMs based on preoperative clinicopathological characteristics. We trained and validated five multivariable models using a population-based cohort of 18,185 patients. DL models outperform logistic regression, with Transformer showing the strongest results, under the constraint that the sensitivity is no less than 90%, reflecting the sensitivity of SLNB. This highlights the feasibility of noninvasive macro-SLNM prediction using DL. Feature importance analysis revealed that patients with similar characteristics exhibited different nodal status predictions, indicating the need for additional predictors for further improvement.

Risk of Incident Cancer in Patients with Inflammatory Bowel Disease with Prior Breast Cancer: A Multicenter Cohort Study

Background and AimsBreast cancer is the most common malignancy observed in patients with inflammatory bowel diseases (IBD). The aim of our study was to evaluate incident cancer rate (recurrence or new-onset cancer) in a cohort of IBD patients with a history of breast cancer according to the subsequent IBD treatment provided. MethodsA multicenter retrospective study included consecutive IBD patients with prior breast cancer. The inclusion date corresponded to the diagnosis of index malignancy. Follow-up lasted from cancer diagnosis until the occurrence of incident cancer. ResultsAmong 207 patients included (median disease duration: 13 years [IQR 6 - 21]), first line treatment (median interval of 28 months [IQR 7 - 64]) was a conventional immunosuppressant in 19.3 % of patients, anti-TNF in 19.8 %, vedolizumab in 7.2 % and ustekinumab in 1.9 %.After a median follow-up of 71 months [IQR, 34 - 148], 42 (20%) incident cancers were observed (34 breast cancer recurrences). Adjusted incidence rates per 1000 person-years were 10.2 (95%CI 6.0- 16.4) for the untreated arm and 28.9 (95%CI 11.6-59.6) for exposed patients (p= 0.0519). There was no significant difference between treated patients and controls regarding incident-cancer free survival rates (p=0.4796). In multivariable analysis, factors associated with incident cancer were stage T4d (p=0.036), triple negative tumor (p=0.016) and follow-up of less than 71 months (p=0.005). ConclusionWe did not find a statistically significant increase in incident breast cancer related to IBD treatment beyond the already known poor prognostic factors of breast cancer.

Radiomics for Predicting Prognostic Factors in Breast Cancer: Insights from Contrast-Enhanced Mammography (CEM).

Objectives: To evaluate the correlation between radiomic features extracted from contrast-enhanced mammography (CEM) tumor lesions and peritumoral background with prognostic factors in breast cancer (BC). Methods: In this retrospective, single-center study, 134 women with histologically confirmed breast cancer underwent CEM examination. Radiomic features were extracted from manually segmented lesions and lesion contours were automatically delineated using PyRadiomics. The extracted features were categorized into seven classes: First-order Features, Shape Features (2D), Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM). Histological examination assessed tumor type, grade, receptor structure (ER, PgR, HER2), Ki67 index, and lymph node involvement. Pearson correlation and multivariate regression were applied to evaluate associations between radiomic features and prognostic factors. Results: Significant correlations were found between First-order Features and prognostic factors such as ER, PgR, and Ki67 (p < 0.05). GLCM-based texture features showed strong associations with Ki67 and HER2 (p < 0.01). Radiomic features from peritumoral regions, especially shape and GLSZM metrics, were significantly correlated with Ki67 and lymph node involvement. Conclusions: Radiomic analysis of both tumor and peritumoral regions offers significant insights into BC prognosis. These findings support the integration of radiomics into personalized diagnostic and therapeutic strategies, potentially improving clinical decision making in BC management.

Comprehensive single-cell and bulk transcriptomic analyses to develop an NK cell-derived gene signature for prognostic assessment and precision medicine in breast cancer.

Natural killer (NK) cells play crucial roles in mediating anti-cancer activity in breast cancer (BRCA). However, the potential of NK cell-related molecules in predicting BRCA outcomes and guiding personalized therapy remains largely unexplored. This study focused on developing a prognostic and therapeutic prediction model for BRCA by incorporating NK cell-related genes. The data analyzed primarily originated from the TCGA and GEO databases. The prognostic role of NK cells was evaluated, and marker genes of NK cells were identified via single-cell analysis. Module genes closely associated with immunotherapy resistance were identified by bulk transcriptome-based weighted correlation network analysis (WGCNA). Following taking intersection and LASSO regression, NK-related genes (NKRGs) relevant to BRCA prognosis were screened, and the NK-related prognostic signature was subsequently constructed. Analyses were further expanded to clinicopathological relevance, GSEA, tumor microenvironment (TME) analysis, immune function, immunotherapy responsiveness, and chemotherapeutics. Key NKRGs were screened by machine learning and validated by spatial transcriptomics (ST) and immunohistochemistry (IHC). Tumor-infiltrating NK cells are a favorable prognostic factor in BRCA. By combining scRNA-seq and bulk transcriptomic analyses, we identified 7 NK-related prognostic NKRGs (CCL5, EFHD2, KLRB1, C1S, SOCS3, IRF1, and CCND2) and developed an NK-related risk scoring (NKRS) system. The prognostic reliability of NKRS was verified through survival and clinical relevance analyses across multiple cohorts. NKRS also demonstrated robust predictive power in various aspects, including TME landscape, immune functions, immunotherapy responses, and chemotherapeutic sensitivity. Additionally, KLRB1 and CCND2 emerged as key prognostic NKRGs identified through machine learning and external validation, with their expression correlation with NK cells confirmed in BRCA specimens by ST and IHC. We developed a novel NK-related gene signature that has proven valuable for evaluating prognosis and treatment response in BRCA, expecting to advance precision medicine of BRCA.

Comparison of Sentinel Lymph Node Sampling with Positron Emission Tomography in the Evaluation of Axillary Lymph Node Involvement in Breast Cancer Patients

Objectives Axillary lymph node status is the most important prognostic factor in breast cancer patients.: This study was carried out to evaluate the diagnostic accuracy of FDG-PET/CT as a noninvasive technique and intraoperative frozen biopsy of sentinel lymphadenectomy (SLNB) in detecting axillary lymph node metastasis. Materials and Methods This study was carried out retrospectively on 44 patients diagnosed with breast cancer, who underwent preoperative FDGPET/ CT imaging and intraoperative SLNB, at the General Surgery Clinic of Istanbul Medeniyet University Göztepe Training and Research Hospital. The axilla was clinically negative in all patients. Preopative FDG-PET/CT imaging and intraoperative SLNB were performed. FDG-PET/CT results were compared with the histopathological results of SLNB and axillary lymph node dissection (ALND). Results According to the pathology results of axillary dissection, metastatic nodes were detected in 22 of 44 cases, and FDG-PET/ CT imaging gave false-negative results in 10. The number of false negative cases of SLNB was 3; Axillary involvement was detected as a result of pathology in one of them, while the others were evaluated as skip metastases. The sensitivity of SLNB and FDG-PET/CT imaging was measured as 86.3% and 54.5%. The specificity values were 95.4% and 100%. FDG-PET/CT imaging has low sensitivity; specificity and positive predictive value were at acceptable levels Conclusion: The sensitivity of FDG-PET/CT imaging is low in detecting axillary involvement, and SLNB is needed in those with negative axillary involvement in FDG-PET/ CT imaging. In our study, SLNB examination was superior to FDG-PET/CT imaging in detecting axillary nodal status. Bangladesh Journal of Medical Science Vol. 23 No. 04 October’24 Page : 1083-1094

ABO Blood Type and Pretreatment Systemic Inflammatory Response Index Associated with Lymph Node Metastasis in Patients with Breast Cancer.

Lymph node metastasis (LNM) is an important prognostic factor for breast cancer. Inflammatory stimulation can change tumor microenvironment and lead to LNM, but the relationship between LNM and peripheral immunoinflammatory indices has not been clarified in breast cancer. The clinical information of 1918 patients with breast cancer admitted to Meizhou People's Hospital from October 2017 to December 2023 were retrospectively analyzed. The relationship of clinicopathological features (age, body mass index (BMI), ABO blood types, family history of cancer, tumor site, disease stage, LNM, distant metastasis, and molecular subtypes) and peripheral immunoinflammatory indices (pan-immune inflammation value (PIV), systemic immune inflammation index (SII), and system inflammation response index (SIRI)) were analyzed. There were 935 (48.7%) patients had no LNM and 983 (51.3%) had LNM. There were statistically significant differences in the distributions of ABO blood groups (p=0.022) and molecular subtypes (p<0.001) between the two groups. PIV, SII, and SIRI levels in patients with LNM were significantly higher than those without LNM (all p<0.05). The proportions of LNM in patients with high PIV, SII, and SIRI levels were higher than those with low PIV, SII, and SIRI levels, respectively. Logistic regression analysis showed that non-O blood type (non-O blood type vs O blood type, odds ratio (OR): 1.327, 95% confidence interval (CI): 1.056-1.667, p=0.015), luminal B subtype (luminal B vs luminal A, OR: 2.939, 95% CI: 2.147-4.022, p<0.001), HER2+ subtype (HER2+ vs luminal A, OR: 2.044, 95% CI: 1.388-3.009, p<0.001), and high SIRI level (≥0.875 vs <0.875, OR: 1.572, 95% CI: 1.092-2.265, p=0.015) were independently associated with LNM. Non-O blood type, luminal B and HER2+ subtypes, and high SIRI level (≥0.875) have potential role in predicting the status of LNM in breast cancer patients.

Vitamin D receptor is associated with prognostic characteristics of breast cancer after neoadjuvant chemotherapy-an observational study.

Breast cancer (BC) is the most commonly diagnosed malignant tumor in women. The disease and its subsequent treatment pose a serious burden on the quality of life of patients. Neoadjuvant chemotherapy (NAC) has become one of the crucial strategies for the management of BC. Since the identification of the vitamin D receptor (VDR) in mammary tissues, extensive mechanistic research has been conducted on its function. The expression of VDR in BC cells and the tumor microenvironment could be a new prognostic factor for BC after NAC. This observational, single-center study compared data from clinical and histopathological records of 111 female subjects with the expression of VDR in different cellular and tissue components of breast specimens obtained from surgery after NAC. VDR expression was evaluated using an immunoreactive score assigned after immunohistochemistry. Intergroup comparisons and logistic regression were used to identify associations between VDR expression and clinicopathological features of BC. We found that the expression of VDR is associated with various clinical features (i.e., age, menopausal status, and NAC cycle number) and characteristics of prognostic significance, such as residual cancer burden class. Logistic regression analysis revealed that the expression of VDR in the nuclei and cytoplasm of surrounding normal mammary cells predicted vascular invasion and lymph node involvement. The expression of VDR in tumor cells and their microenvironment is related to the clinicopathological characteristics of BC after NAC.

Do elastography values correlate with molecular subtype of breast cancer or is morphology the key player?

Objectives We evaluated the association between shear wave elastography (SWE) parameters and the molecular subtypes and other prognostic factors of breast cancer. We also examined the influence of morphological characteristics and other prognostic factors of breast cancers on elastography values. Materials and Methods This is a retrospective observational study on 148 patients with biopsy-proven 152 breast cancers who underwent both B-mode ultrasonography and SWE. The analysis involved calculating the correlation between the five quantitative parameters on SWE (mean, minimum, maximum, standard deviation, and ratio) and the molecular subtyping and other prognostic factors of breast cancer. We analyzed the influence of morphology and other prognostic factors on SWE parameters. Results The mean, minimum, and maximum values in SWE were significantly lower (P&lt;0.05) in cases where the lesion displayed non-mass abnormality, circumscribed margins, posterior acoustic enhancement, and non-ductal histopathology. Hormone positive tumors frequently exhibited spiculated margins and higher (P &lt; 0.05) elastography values, although within this category, circumscribed masses demonstrated lower elastography values compared to the remaining cases. Triple negative breast cancers often displayed circumscribed margins and lower (P &lt; 0.05) elastography values; however, among this subgroup, masses with spiculated margins exhibited higher values compared to the rest. Conclusion Our findings indicate that elastography independently do not correlate with molecular subtypes in breast cancer. Morphological features and histopathological subtypes appear to have strong influence on elastography values.

Factors influencing pathological complete response and tumor regression in neoadjuvant radiotherapy and chemotherapy for high-risk breast cancer

BackgroundPathological complete response (pCR) is a well-established prognostic factor in breast cancer treated with neoadjuvant systemic therapy (naST). The determining factors of pCR are known to be intrinsic subtype, proliferation index, grading, clinical tumor and nodal stage as well as type of systemic therapy. The addition of neoadjuvant radiotherapy (naRT) to this paradigm might improve response, freedom from disease, toxicity and cosmetic outcome compared to adjuvant radiotherapy. The factors for pCR and primary tumor regression when neoadjuvant radiation therapy is added to chemotherapy have not been thoroughly described.MethodsWe performed a retrospective analysis of 341 patients (cT1-cT4/cN0-N+) treated with naRT and naST between 1990 and 2003. Patients underwent naRT to the breast and mostly to the supra-/infraclavicular lymph nodes combined with an electron or brachytherapy boost. NaST was given either sequentially or simultaneously to naRT using different regimens. We used the univariate and multivariate regression analysis to estimate the effect of different subgroups and treatment modalities on pCR (ypT0/Tis and ypN0) as well as complete primary tumor response (ypT0/Tis; bpCR) in our cohort. Receiver operating characteristic (ROC) analysis was performed to evaluate the interval between radiotherapy (RT) and resection (Rx) as well as radiotherapy dose.ResultsOut of 341 patients, pCR and pbCR were achieved in 31% and 39%, respectively. pCR rate was influenced by resection type, breast cancer subtype, primary tumor stage and interval from radiation to surgery in the multivariate analysis. Univariate analysis of bpCR showed age, resection type, breast cancer subtype, clinical tumor stage and grading as significant factors. Resection type, subtype and clinical tumor stage remained significant in multivariate analysis. Radiation dose to the tumor and interval from radiation to surgery were not significant factors for pCR. However, when treatment factors were added to the model, a longer interval from radiotherapy to resection was a significant predictor for pCR.ConclusionsThe factors associated with pCR following naST and naRT are similar to known factors after naST alone. Longer interval to surgery might to be associated with higher pCR rates. Dose escalation beyond 60 Gy did not result in higher response rates.

TP53 signature predicts pathological complete response after neoadjuvant chemotherapy for breast cancer: Observational and confirmational study using prospective study cohorts

The TP53 signature is considered a predictor of neoadjuvant chemotherapy (NAC) response and prognostic factor in breast cancer. The objective of this study was to confirm TP53 signature can predict pathological complete response (pCR) and prognosis in cohorts of breast cancer patients who received NAC in prospective studies.Development cohorts (retrospective [n = 37] and prospective [n = 216] cohorts) and validation cohorts (NAC administered prospective study cohorts [n = 407] and retrospective perioperative chemotherapy (PC)-naïve, hormone receptor (HrR)-positive cohort [PC-naïve_HrR+ cohort] [n = 322]) were used. TP53 signature diagnosis kit was developed using the development cohorts. TP53 signature predictability for pCR and the relationship between recurrence-free survival (RFS), overall survival (OS), and the TP53 signature were analyzed.The pCR rate of the mutant (mt) signature group was significantly higher than that of the wild-type (wt) signature group (odds ratio, 5.599; 95 % confidence interval = 1.876–16.705; P = 0.0008). The comparison of the RFS and OS between the HrR+ and HER2− subgroup of the NAC cohort and of the PC-naïve_HrR+ cohort indicated that the RFS and OS benefit of NAC was greater in the mt signature group than in the wt signature group. From post hoc analyses, the RFS and OS benefit from adding capecitabine to FEC+T as NAC might be observed only in the mt signature group.The TP53 signature can predict the pCR after NAC, and the RFS and OS benefit from NAC may be greater in the mt signature group than in the wt signature group.

Combination of IVIM with DCE-MRI for diagnostic and prognostic evaluation of breast cancer

PurposeTo identify the most effective combination of DCE-MRI (Ktrans,Kep) and IVIM (D,f) and analyze the correlations of these parameters with prognostic indicators (ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size) to improve the diagnostic and prognostic efficiency in breast cancer. MethodsThis is a prospective study. We performed T1WI, T2WI, IVIM, DCE-MRI at 3 T MRI examinations on benign and malignant breast lesions that met the inclusion criteria. We also collected pathological results of corresponding lesions, including ER, PR, and HER2, Ki-67 index, axillary lymph node (ALN) and tumor size. The diagnostic efficacy of DCE-MRI, IVIM imaging, and their combination for benign and malignant breast lesions was assessed. Correlations between the DCE-MRI and IVIM parameters and prognostic indicators were assessed. ResultsOverall,59 female patients with 62 lesions (22 benign lesions and 40 malignant lesions) were included in this study. The malignant group showed significantly lower D values (p < 0.05) and significantly higher Ktrans, Kep, and f values (p < 0.05). The AUC values of DCE, IVIM, DCE + IVIM were 0.828, 0.882, 0.901. Ktrans, Kep, D and f values were correlated with the pathological grade (p < 0.05); Ktrans was negatively correlated with ER expression (r = −0.519, p < 0.05); Kep was correlated with PR expression and the Ki-67 index (r = −0.489, 0.330, p < 0.05); the DCE and IVIM parameters showed no significant correlations with the HER2 and ALN (p > 0.05). Tumor diameter was correlated with the Kep, D and f values (r = 0.246, −0.278, 0.293; p < 0.05). ConclusionIVIM and DCE-MRI allowed differential diagnosis of benign and malignant breast lesions, and their combination showed significantly better diagnostic efficiency. DCE- and IVIM-derived parameters showed correlations with some prognostic factors for breast cancer.

Time-varying effect in older patients with early-stage breast cancer: a model considering the competing risks based on a time scale.

Patients with early-stage breast cancer may have a higher risk of dying from other diseases, making a competing risks model more appropriate. Considering subdistribution hazard ratio, which is used often, limited to model assumptions and clinical interpretation, we aimed to quantify the effects of prognostic factors by an absolute indicator, the difference in restricted mean time lost (RMTL), which is more intuitive. Additionally, prognostic factors of breast cancer may have dynamic effects (time-varying effects) in long-term follow-up. However, existing competing risks regression models only provide a static view of covariate effects, leading to a distorted assessment of the prognostic factor. To address this issue, we proposed a dynamic effect RMTL regression that can explore the between-group cumulative difference in mean life lost over a period of time and obtain the real-time effect by the speed of accumulation, as well as personalized predictions on a time scale. A simulation validated the accuracy of the coefficient estimates in the proposed regression. Applying this model to an older early-stage breast cancer cohort, it was found that 1) the protective effects of positive estrogen receptor and chemotherapy decreased over time; 2) the protective effect of breast-conserving surgery increased over time; and 3) the deleterious effects of stage T2, stage N2, and histologic grade II cancer increased over time. Moreover, from the view of prediction, the mean C-index in external validation reached 0.78. Dynamic effect RMTL regression can analyze both dynamic cumulative effects and real-time effects of covariates, providing a more comprehensive prognosis and better prediction when competing risks exist.

Association of Breast Cancer Subtypes and Clinicopathological Factors with Axillary Lymph Node Positivity Amongst Women with Breast Cancer in Rajasthan: An Observational Analytical Study.

Prognostic factors by definition, are capable of providing information on clinical outcomes at the time of diagnosis, independent of therapy. The number of positive lymph nodes (number of ipsilateral axillary nodes with metastatic tumour deposits) is a strong and independent prognostic factor in breast cancer. In a meta-analysis (New England Journal of Medicine, 2017) of over 62,000 patients, the risk of distant recurrence over years 5 to 20 for those with T1 tumours was 13% in the absence of lymph node involvement, 20% among those with one to three involved lymph nodes, and 34% among those with four to nine involved nodes. In this study, we analyzed the association of clinicopathological factors and breast cancer subtypes with axillary lymph node (ALN) positivity in women with breast cancer in Rajasthan. A multivariate Logistic (Ordinal) Regression Model was used to predict the number of positive lymph nodes based on independent variables that showed 90% significance in bivariate analysis, such as total number of lymph nodes dissected, tumour necrosis, and lymphovascular invasion. The Wald criterion indicated that only LVI had a significant impact on the prediction (p < 0.05), while tumour necrosis and the total number of lymph nodes dissected were not significant predictors (p > 0.05). Patients with LVI had a 43.47 times higher risk of having positive lymph nodes (p < 0.05). Early prediction of lymph node metastasis through LVI testing can help in prognostication. Breast cancer subtypes should not be a criterion while deciding lymph nodal management.

Electrical lymph node scanning (ELS) system for real-time intra-operative detection of involved axillary lymph nodes in adjuvant breast cancer patients

Lymph node (LN) status is an essential prognostic factor in breast cancer (BC) patients, with an important role in the surgical and therapeutic plan. Recently, we have been developed a novel system for real-time intra-operative electrical LN scanning in BC patients. The ELS scores were calibrated by pathological evaluation of the LNs. Herein, we evaluated the efficacy of ELS in a prospective study for non-chemo-treated breast cancer patients. This is a prospective study in which ELS scores are blind for pathologists who declare the clearance or involvement of LNs based on permanent pathology as the gold standard. ELS and frozen-section (FS) pathology results were achieved intra-operatively, and samples were sent for the permanent pathology. The score of ELS did not affect the surgeons’ decision, and the treatment approach was carried out based on FS pathology and pre-surgical data, such as imaging and probable biopsies. Patients were recruited from October 2021 through November 2022, and 381 lymph nodes of 97 patients were included in the study. In this study we recruited 38 patients (39.2%) with sentinel lymph node biopsy (SLNB) and 59 patients (60.8%) with ALND. Of the 381 LNs scored by ELS, 329 sentinel LNs underwent routine pathology, while others (n = 52) underwent both FS and permanent pathology. ELS showed a sensitivity of 91.4% for node-positive patients, decreasing to 84.8% when considering all LNs. Using ROC analysis, ELS diagnosis showed a significant AUC of 0.878 in relation to the permanent pathology gold standard. Comparison of ELS diagnosis for different tumor types and LN sizes demonstrated no significant differences, while increasing LN size correlated with enhanced ELS sensitivity. This study confirmed ELS’s efficacy in real-time lymph node detection among non-chemo-treated breast cancer patients. The use of ELS’s pathological scoring for intra-operative LN diagnosis, especially in the absence of FS pathology or for non-sentinel LN involvement, could improve prognosis and reduce complications by minimizing unnecessary dissection.

The specific shapes of capillaries are associated with worse prognosis in patients with invasive breast cancer.

Angiogenesis is considered essential for tumor progression; however, whether histological counting of blood vessel numbers, expressed as microvessel density (MVD), can be a prognostic factor in breast cancer remains controversial. It has been suggested that the specific morphology of blood vessels such as glomeruloid microvascular proliferation (GMP) is associated with clinical parameters. Here, we aimed to clarify the significance of MVD with revised immunohistochemistry and to identify new blood vessel shapes that predict prognosis in breast cancer. Four hundred and eleven primary breast cancer specimens were collected, and the sections were immunohistochemically stained with CD31 (single staining) and CD31 and Collagen IV (double staining). The prognosis of patients was examined based on the MVD value, and the presence of GMP and other blood vessels with other specific shapes. As a result, high MVD value and the presence of GMP were not associated with worse prognosis. By contrast, patients with deep-curved capillaries surrounding tumor cell nests (C-shaped) or excessively branched capillaries near tumor cell nests showed a significantly poor prognosis. The presence of these capillaries was also correlated with clinicopathological parameters such as Ki-67 index. Thus, the morphology of capillaries rather than MVD can be a better indicator of tumor aggressiveness.

Identification of SMC2 and SMC4 as prognostic markers in breast cancer through bioinformatics analysis.

Breast cancer (BRCA) is one of the most common malignant tumors. The structural maintenance of chromosome (SMC) gene family has been shown to play an important role in human cancers. However, the role of SMC families in BRCA is unclear. This study aimed to explore the role and potential clinical value of whole SMCs in BRCA. TIMER and UALCAN database were used to analysis the expression level. Genetic variations were analyzed by cBioPortal. Promoter methylation and protein level were analyzed by UCLCAN. GO and KEGG were analyzed by Metascape database. Prognostic value of SMCs was analyzed by Kaplan-Meier and multivariate cox regression analyses. Immune infiltration analysis was conducted by CIBERSORT. Immunotherapy outcome prediction was conducted by Cancer Immunome Atlas. Targeted drug therapy outcome prediction was taken by GDSC and R language. The cell viability was tested by CCK8 and migration was tested by wound healing assay. Xenograft model was used to investigate the in vivo role of SMC2. Expression levels of SMC1A, SMC2, SMC4, SMC5 and SMC6 mRNA were increased in BRCA tissues, and negatively correlated with promoter methylation. Overexpression of SMC2 and SMC4 was negatively correlated with survival. Function of SMCs family regulatory genes was mainly related to ATPase activity. Expression of most SMCs was negatively correlated with immunotherapy and drug therapy outcomes. Interfere SMC2 and SMC4 decreased IC50 values of 5-fluorouracil and oxaliplatin and inhibited the migration of MCF7 cells. Tumor growth and weights were significantly decreased in si-SMC2 groups. Combined bioinformatics and clinical specimen analysis verified SMC2 and SMC4 as independent prognostic factors in BRCA, suggesting their significance for the diagnosis and treatment of BRCA.