Predictor Of Prognosis In Breast Cancer Research Articles

Genome-wide identification and analysis of epithelial-mesenchymal transition-related RNA-binding proteins and alternative splicing in a human breast cancer cell line

Exploring the mechanism of breast cancer metastasis and searching for new drug therapeutic targets are still the focuses of current research. RNA-binding proteins (RBPs) may affect breast cancer metastasis by regulating alternative splicing (AS) during epithelial-mesenchymal transition (EMT). We hypothesised that during EMT development in breast cancer cells, the expression level of RBPs and the gene AS pattern in the cell were significantly changed on a genome-wide scale. Using GEO database, this study identified differentially expressed RBPs and differential AS events at different stages of EMT in breast cancer cells. By establishing the correlation network of differential RBPs and differential AS events, we found that RBM47, PCBP3, FRG1, SRP72, RBMS3 and other RBPs may regulate the AS of ITGA6, ADGRE5, TNC, COL6A3 and other cell adhesion genes. By further analysing above EMT-related RBPs and AS in breast cancer tissues in TCGA, it was found that the expression levels of ADAT2, C2orf15, SRP72, PAICS, RBMS3, APOBEC3G, NOA1, ACO1 and the AS of TNC and COL6A3 were significantly correlated with the prognosis of breast cancer patients. The expression levels of all 8 RBPs were significantly different in breast cancer tissues without metastasis compared with normal breast tissues. Conclusively, eight RBPs such as RBMS3 and AS of TNC and COL6A3 could be used as predictors of breast cancer prognosis. These findings need to be further explored as possible targets for breast cancer treatment.

Integrative analyses identified gap junction beta‐2 as a prognostic biomarker and therapeutic target for breast cancer

AbstractBackgroundIncreasing evidence has shown that connexins are involved in the regulation of tumor development, immune escape, and drug resistance. This study investigated the gene expression patterns, prognostic values, and potential mechanisms of connexins in breast cancer.MethodsWe conducted a comprehensive analysis of connexins using public gene and protein expression databases and clinical samples from our institution. Connexin mRNA expressions in breast cancer and matched normal tissues were compared, and multiomics studies were performed.ResultsGap junction beta‐2 mRNA was overexpressed in breast cancers of different pathological types and molecular subtypes, and its high expression was associated with poor prognosis. The tumor membrane of the gap junction beta‐2 mutated group was positive, and the corresponding protein was expressed. Somatic mutation and copy number variation of gap junction beta‐2 are rare in breast cancer. The gap junction beta‐2 transcription level in the p110α subunit of the phosphoinositide 3‐kinase mutant subgroup was higher than that in the wild‐type subgroup. Gap junction beta‐2 was associated with the phosphoinositide 3‐kinase‐Akt signaling pathway, extracellular matrix–receptor interaction, focal adhesion, and proteoglycans in cancer. Furthermore, gap junction beta‐2 overexpression may be associated with phosphoinositide 3‐kinase and histone deacetylase inhibitor resistance, and its expression level correlated with infiltrating CD8+ T cells, macrophages, neutrophils, and dendritic cells.ConclusionsGap junction beta‐2 may be a promising therapeutic target for targeted therapy and immunotherapy and may be used to predict breast cancer prognosis.

Abstract PO1-07-11: Radiogenomic-based imaging intratumor heterogeneity model predicts breast cancer prognosis and unveils therapeutic targets

Abstract Background: Intratumor heterogeneity (ITH) refers to variations observed among cancer cells within a single tumor, posing significant challenges in clinical practice due to its contribution to treatment resistance and unfavorable outcome. However, the inconvenience of multi-region biopsy and limitations of genomic sequencing based on limited tissue impede the practical detection of ITH. Consequently, there is an urgent need for a non-invasive method that comprehensively captures ITH from a holistic perspective of the entire tumor. Methods: We utilized a large multicenter dataset comprising dynamic contrast-enhanced magnetic resonance images from breast cancer patients (n = 1423) along with matched multiomics data (n = 468) to develop a non-invasive machine learning approach for measuring ITH. We extracted quantitative radiomic features from both the entire tumor and peritumor regions, with a specific focus on features associated with imaging heterogeneity. Using these radiomic features, we established an imaging ITH (IITH) model. The robustness of IITH evaluation was determined by assessing its correlation with genomic ITH (employing the mutant-allele tumor heterogeneity [MATH] algorithm) and pathological cellular ITH (analyzing variation in quantitative nuclear features extracted through digital pathology). Prognostic power was evaluated using Kaplan-Meier and multivariate Cox proportional hazards analyses. Integrated multiomics analyses were performed to investigate the molecular basis of different IITH subgroups. Results: We developed a non-invasive radiomic signature to quantify imaging ITH (IITH) in the FUSCC cohort. Breast cancer patients were retrospectively categorized into high and low IITH groups. Multivariate Cox analysis identified high IITH as an independent predictor of poor prognosis in breast cancer patients, even after adjusting for clinical risk factors such as tumor size, positive lymph nodes, clinical subtype and lymphovascular invasion status. These findings were further validated in the DUKE cohort, confirming the prognostic value of IITH. Moreover, we substantiated the robustness of IITH by demonstrating its association with genomic and pathological ITH. Multiomics analysis revealed the activation of oncogenic pathways and metabolic dysregulation in high-IITH tumors. Intriguingly, our investigation also highlighted ferroptosis as a vulnerability and potential therapeutic target in high-IITH tumors, which was further supported by evidence from the TCGA cohort. Conclusion: Radiomic-based assessment of ITH provides a non-invasive approach to comprehensively capture ITH and predict the prognosis of breast cancer patients. Targeting ferroptosis may hold promise as a treatment strategy for patients with high IITH. Citation Format: Guan-Hua Su, Yi Xiao, Chao You, Ren-Cheng Zheng, Shen Zhao, He Wang, Yi-Zhou Jiang, Ya-Jia Gu, Zhi-Ming Shao. Radiogenomic-based imaging intratumor heterogeneity model predicts breast cancer prognosis and unveils therapeutic targets [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-11.

Proximity-Guaranteed DNA Machine for Accurate Identification of Breast Cancer Extracellular Vesicles.

Breast cancer is one of the most diagnosed cancers worldwide. Precise diagnosis and subtyping have important significance for targeted therapy and prognosis prediction of breast cancer. Herein, we design a proximity-guaranteed DNA machine for accurate identification of breast cancer extracellular vesicles (EVs), which is beneficial to explore the subtype features of breast cancer. In our design, two proximity probes are located close on the same EV through specific recognition of coexisting surface biomarkers, thus being ligated with the help of click chemistry. Then, the ligated product initiates the operation of a DNA machine involving catalytic hairpin assembly and clusters of regularly interspaced short palindromic repeats (CRISPR)-Cas12a-mediated trans-cleavage, which finally generates a significant response that enables the identification of EVs expressing both biomarkers. Principle-of-proof studies are performed using EVs derived from the breast cancer cell line BT474 as the models, confirming the high sensitivity and specificity of the DNA machine. When further applied to clinical samples, the DNA machine is shown to be capable of not only distinguishing breast cancer patients with special subtypes but also realizing the tumor staging regarding the disease progression. Therefore, our work may provide new insights into the subtype-based diagnosis of breast cancer as well as identification of more potential therapeutic targets in the future.

Prognostic and clinicopathological value of systemic inflammation response index (SIRI) in patients with breast cancer: a meta-analysis

Background Many studies have explored the value of the systemic inflammation response index (SIRI) in predicting the prognosis of patients with breast cancer (BC); however, their findings remain controversial. Consequently, we performed the present meta-analysis to accurately identify the role of SIRI in predicting BC prognosis. Methods PubMed, Embase, Cochrane Library, and Web of Science databases were comprehensively searched between their inception and February 10, 2024. The significance of SIRI in predicting overall survival (OS) and disease-free survival (DFS) in BC patients was analyzed by calculating pooled hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). Results Eight articles involving 2,997 patients with BC were enrolled in the present study. According to our combined analysis, a higher SIRI was markedly associated with dismal OS (HR = 2.43, 95%CI = 1.42–4.15, p < 0.001) but not poor DFS (HR = 2.59, 95%CI = 0.81–8.24, p = 0.107) in patients with BC. Moreover, based on the pooled results, a high SIRI was significantly related to T3–T4 stage (OR = 1.73, 95%CI = 1.40–2.14, p < 0.001), N1–N3 stage (OR = 1.61, 95%CI = 1.37–1.91, p < 0.001), TNM stage III (OR = 1.63, 95%CI = 1.34–1.98, p < 0.001), and poor differentiation (OR = 1.25, 95%CI = 1.02–1.52, p = 0.028). Conclusion According to our results, a high SIRI significantly predicted poor OS in patients with BC. Furthermore, elevated SIRI was also remarkably related to increased tumor size and later BC tumor stage. The SIRI can serve as a novel prognostic biomarker for patients with BC.

Association between estrogen receptor alpha and aryl hydrocarbon receptor gene polymorphisms in the prognosis of breast cancer in Egypt

Breast cancer is the most malignant tumor among women in the world. Single nucleotide polymorphisms (SNPs) might better predict breast cancer prognosis. PvuII (T/C substitution), XbaI (A/G substitution), and aryl hydrocarbon (AhR) (G/A substitution) were evaluated as possible genetic prognostic factors for breast cancer. The aim of the current study was to assess the relation between PvuII (rs2234693), XbaI (rs9340799), and aryl hydrocarbon receptor gene polymorphisms AhR (rs2066853) in breast cancer prognosis. This was a case-control study that included 120 breast cancer patients classified into two groups. The first group included 60 patients with good prognostic factors, and the second group included 60 patients with poor prognostic factors. Blood samples were taken from all study participants to perform the genotyping assay. We found that positive genotypes of PvuII, XbaI, and AhR polymorphisms were strongly associated with better prognostic factors for breast cancer patients, while negative genotypes of PvuII and XbaI were more and significantly prevalent in poor prognostic breast cancer patients. We conclude that PvuII T/C, XbaI G/A, and AhR G/A alleles may be prognostic for breast cancer progression. ‎

ЗАСТОСУВАННЯ ГЛИБОКИХ ШТУЧНИХ НЕЙРОННИХ МЕРЕЖ ДЛЯ КЛАСИФІКАЦІЇ МУЛЬТИМОДАЛЬНИХ ДАНИХ

Multimodal data analysis is gaining attention in recent research. Pu Liang et al. (2023) provide a comprehensive overview on multimodal machine learning, highlighting its founda-tions, challenges and achievements in recent years. More problem-oriented works propose new methods and applications for multimodal ML, such a Ngiam et al. (2011) propose to use joint audio and video data to improve speech recognition accuracy; Sun, Wand and Li (2018) describe application of multimodal classification for breast cancer prognosis prediction; Mao et al. (2014) propose an architecture of multimodal recurrent network to generate text de-scription of images and so on. However, such works usually focus on the task itself and meth-ods therein, and not on integrating multimodal data processing into other software systems. The goal of this research is to propose a way to conduct multimodal data processing, specifically as a part of a digital twin systems, thus efficiency and near-real-time operation are required. The paper presents an approach to conduct parallel multimodal data classification, adapting to available computing power. The method is modular and scalable and intended for in digital twin application as a part of analysis and modeling tools. Later, the detailed example of such a software module is discussed. It uses multimodal data from open datasets to detect and classify the behavior of pets using deep learning mod-els. Videos are processed using two artificial neural networks: YOLOv3 object detection net-work to process individual frames of the video and a relatively simple convolutional network to classify sounds based on their frequency spectra. Constructed module uses a producer-consumer parallel processing pattern and allows processing 5 frames per second of a video on available hardware, which can be sufficiently improved by using GPU acceleration or more paralleled processing threads.

Correction for: A novel six-microRNA-based model to improve prognosis prediction of breast cancer.

Correction for: A novel six-microRNA-based model to improve prognosis prediction of breast cancer.

Independent Validation of the BRENDA-Score Breast Cancer Prognosis Prediction Tool In Chinese Patients

BackgroundThe BRENDA-Score was developed and used to predict the prognosis of patients with breast cancer (BC). This study was performed to validate the use of this tool in Chinese patients with primary invasive BC patients. MethodsPatients underwent surgery for BC from January 2009 to December 2016. Discrimination was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Calibrations were assessed by comparing predicted and observed 5-year and 10-year metastasis-free survival (MFS) in the overall cohort and patient subgroups. ResultsA total of 2029 BC patients were enrolled. Kaplan–Meier analysis revealed significant differences in MFS risk groups (log-rank test P < .01). ROC analysis showed good accuracy for 5-year MFS (AUC 0.779) and fair accuracy for 10-year MFS (AUC 0.728). The BRENDA-Score accurately predicted 5-year and 10-year MFS in the entire cohort and in all other predefined subgroups, except for the 5-year MFS in the subgroup aged<40 years, which was overestimated (differences between the predicted and observed MFS were 6.7%, P < .05). The 5-year MFS rates of ER- positive and ER-negative patients were 90.9% and 80.6%, respectively (P < .05). The 10-year MFS rates of ER-positive and ER-negative patients were 78.0% and 73.7%, respectively (P = .25). ConclusionsThe BRENDA-Score accurately predicted 5-year and 10-year MFS. The results showed good validity, transportability, and potential clinical value. However, the results for 5-years MFS should be interpreted carefully in patients aged <40 years. After 10 years the value of the ER as a prognostic factor was less important.

The Correlation between Morpho-Dynamic Contrast-Enhanced Mammography (CEM) Features and Prognostic Factors in Breast Cancer: A Single-Center Retrospective Analysis.

Breast cancer, a major contributor to female mortality globally, presents challenges in detection, prompting exploration beyond digital mammography. Contrast-Enhanced Mammography (CEM), integrating morphological and functional information, emerges as a promising alternative, offering advantages in cost-effectiveness and reduced anxiety compared to MRI. This study investigates CEM's correlation with breast cancer prognostic factors, encompassing histology, grade, and molecular markers. In a retrospective analysis involving 114 women, CEM revealed diverse lesion characteristics. Statistical analyses identified correlations between specific CEM features, such as spiculated margins and irregular shape, and prognostic factors like tumor grade and molecular markers. Notably, spiculated margins predicted lower grade and HER2 status, while irregular shape correlated with PgR and Ki-67 status. The study emphasizes CEM's potential in predicting breast cancer prognosis, shedding light on tumor behavior. Despite the limitations, including sample size and single-observer analysis, the findings advocate for CEM's role in stratifying breast cancers based on biological characteristics. CEM features, particularly spiculated margins, irregular shape, and enhancement dynamics, may serve as valuable indicators for personalized treatment decisions. Further research is crucial to validate these correlations and enhance CEM's clinical utility in breast cancer assessment.

A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis.

Breast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients. BRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the 'limma' package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function. Our study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells. Our 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.

Correlation Between Quantitative Parameters Obtained by Dual Energy Spectral CT and Prognostic Histopathological Factors and Biomarkers in Breast Cancer

Rationale and ObjectivesTo investigate the correlation between quantitative parameters obtained by dual energy spectral computed tomography (DESCT) and various histopathological factors and biomarkers associated with the prognosis of breast cancer. Materials and MethodsQuantitative parameters such as iodine content (IC), normalized IC (nIC), iodine enhancement (IE) and normalized IE (nIE) were measured on virtual monochromatic images and iodine mapping images obtained from DESCT in 116 female breast cancer patients. The relationship between these parameters and prognostic biomarkers such as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) status and Ki67 levels, as well as the correlation with histological grade (HG), lymphovascular invasion (LVI), and metastatic axillary lymphadenopathy (LAP) were evaluated. ResultsER-negative tumors had significantly higher values of IC, nIC, IE, and nIE compared to ER-positive tumors. PR-negative tumors had significantly higher values of IE and nIEc compared to PR-positive tumors. HER2 overexpressed and Ki-67 high proliferation tumors showed significantly higher values of all quantitative parameters compared to HER2 negative and Ki-67 low proliferation tumors. All quantitative parameters were significantly higher in HG 3 tumors, tumors with detected LVI, and tumors with metastatic axillary LAP compared to low-grade tumors, LVI-negative tumors and tumors without metastatic axillary lymph nodes, respectively. ConclusionQuantitative parameters of IC and IE obtained from DESCT have shown potential for predicting prognosis in breast cancer patients. Higher values of these parameters have been found to correlate with poor prognostic biomarkers and histopathological features. These results suggest that quantitative DESCT imaging may offer an additional benefit in the noninvasive prediction of breast cancer prognosis.

Electrochemical aptasensor fabricated by anchoring recognition aptamers and immobilizing redox probes on bipolar silica nanochannel array for reagentless detection of carbohydrate antigen 15-3.

Timely, convenient, and efficient detection of carbohydrate antigen 15-3 (CA15-3) levels in serum holds significant importance in early screening, diagnostic assistance and prognosis prediction of breast cancer. The development of efficient and convenient electrochemical aptasensors with immobilized redox probes for label-free detection of CA15-3 is highly desirable. In this work, a bipolar silica nanochannel array film (bp-SNA) with two distinct functional domains including nanochannels and an outer surface was employed for the immobilization of recognition ligands and electrochemical redox probes, enabling the construction of a probe-integrated aptasensor for reagentless electrochemical detection of CA15-3. Cost-effective and readily available indium tin oxide (ITO) was used as the supporting electrode for sequential growth of a negatively charged inner layer (n-SNA) followed by a positively charged outer layer (p-SNA). The preparation process of bp-SNA is convenient. Functionalization of amino groups on the outer surface of bp-SNA was modified by aldehyde groups for covalent immobilization of recognition aptamers, further establishing the recognition interface. Within the nanochannels of bp-SNA, the electrochemical redox probe, tri (2,2'-dipyridyl) cobalt (II) (Co(bpy)3 2+) was immobilized, which experienced a dual effect of electrostatic attraction from n-SNA and electrostatic repulsion from p-SNA, resulting in high stability of the immobilized probes. The constructed aptasensor allowed for reagentless electrochemical detection of CA15-3 ranged from 0.001U/mL to 500 U/mL with a low detection limit (DL), 0.13mU/mL). The application of the constructed aptasensor for CA15-3 detection in fetal bovine serum was also validated. This sensor offers advantages of a simple and readily obtainable supporting electrode, easy bp-SNA fabrication, high probe stability and good stability.

Construction of a ferroptosis-based prognostic model for breast cancer helps to discriminate high/low risk groups and treatment priority.

Breast cancer is a common malignant tumor associated with high morbidity and mortality. The role of ferroptosis, a regulated form of cell death, in breast cancer development and prognosis remains unclear. This study aims to investigate the relationship between ferroptosis-related genes and breast cancer and develop a prognostic model. RNA-seq expression datasets and clinical samples of breast cancer patients were obtained from public databases. Immunity- and drug resistance-related data were integrated. A preliminary screening was performed, resulting in the identification of 73 candidate ferroptosis factors. Univariate Cox regression analysis was conducted to select 12 genes, followed by LASSO Cox regression analysis to construct a prognostic risk prediction model consisting of 10 ferroptosis-related genes. The model was further characterized by immune cell infiltration. The expression levels of ferroptosis-related genes were validated in human breast cancer cell lines, and immunohistochemical (IHC) analysis was conducted on cancer specimens to assess ferroptosis-related protein expression. The study identified 10 ferroptosis-related genes that were significantly associated with breast cancer prognosis. The constructed prognostic risk prediction model showed potential for predicting the prognostic value of these genes. In addition, the infiltration of immune cells was observed to be a characteristic of the model. The expression levels of ferroptosis-related genes were confirmed in human breast cancer cell lines, and IHC analysis provided evidence of ferroptosis-related protein expression in cancer specimens. This study provides a novel prognostic model for breast cancer, incorporating 10 ferroptosis-related genes. The model demonstrates the potential for predicting breast cancer prognosis and highlights the involvement of immune cell infiltration. The expression levels of ferroptosis-related genes and proteins further support the association between ferroptosis and breast cancer development.

Multi-modal fusion network with intra- and inter-modality attention for prognosis prediction in breast cancer

Accurate breast cancer prognosis prediction can help clinicians to develop appropriate treatment plans and improve life quality for patients. Recent prognostic prediction studies suggest that fusing multi-modal data, e.g., genomic data and pathological images, plays a crucial role in improving predictive performance. Despite promising results of existing approaches, there remain challenges in effective multi-modal fusion. First, albeit a powerful fusion technique, Kronecker product produces high-dimensional quadratic expansion of features that may result in high computational cost and overfitting risk, thereby limiting its performance and applicability in cancer prognosis prediction. Second, most existing methods put more attention on learning cross-modality relations between different modalities, ignoring modality-specific relations that are complementary to cross-modality relations and beneficial for cancer prognosis prediction. To address these challenges, in this study we propose a novel attention-based multi-modal network to accurately predict breast cancer prognosis, which efficiently models both modality-specific and cross-modality relations without bringing in high-dimensional features. Specifically, two intra-modality self-attentional modules and an inter-modality cross-attentional module, accompanied by latent space transformation of channel affinity matrix, are developed to successfully capture modality-specific and cross-modality relations for efficient integration of genomic data and pathological images, respectively. Moreover, we design an adaptive fusion block to take full advantage of both modality-specific and cross-modality relations. Comprehensive experiment demonstrates that our method can effectively boost prognosis prediction performance of breast cancer and compare favorably with the state-of-the-art methods.

Deviation-support based fuzzy ensemble of multi-modal deep learning classifiers for breast cancer prognosis prediction

Breast cancer is the fifth leading cause of death in females worldwide. Early detection and treatment are crucial for improving health outcomes and preventing more serious conditions. Analyzing diverse information from multiple sources without errors, particularly with the growing burden of cancer cases, is a daunting task for humans. In this study, our main objective is to improve the accuracy of breast cancer survival prediction using a novel ensemble approach. It is novel due to the consideration of deviation (closeness between predicted classes and actual classes) and support (sparsity between predicted classes and actual classes) of the predicted class with respect to the actual class, a feature lacking in traditional ensembles. The ensemble uses fuzzy integrals on support and deviation scores from base classifiers to calculate aggregated scores while considering how confident or uncertain each classifier is. The proposed ensemble mechanism has been evaluated on a multi-modal breast cancer dataset of breast tumors collected from participants in the METABRIC trial. The proposed architecture proves its efficiency by achieving the accuracy, sensitivity, F1-score, and balanced accuracy of 82.88%, 58.64%, 62.94%, and 74.75% respectively. The obtained results are superior to the performance of individual classifiers and existing ensemble approaches.

Proliferation Index in “HER2 Low” Metastatic Breast Cancer

Abstract Introduction/Objective There is considerable genetic heterogeneity between primary and metastatic breast cancer. The new classification of the "HER2-low" category includes tumors characterized by a low level of HER2 expression (immunohistochemistry (IHC) score 1+ or 2+ without in situ hybridization (ISH) amplification), in addition to the known HER2 negative (HER2-0) and HER2+ (amplified) cases. The Ki67 proliferation index (PI) can predict breast cancer prognosis. Proliferation changes in metastatic breast cancer and in different HER2 categories are not well delineated in clinical cohorts. In this study, we delineate proliferation characteristics of primary and metastatic breast cancer in association with HER2 status. Methods/Case Report We retrospectively reviewed the biological data from 98 metastatic breast cancer specimens for 90 patients from 2018-2021 (see Table 1). As part of the clinical workup, 72 metastatic cancer specimens had PI/Ki67, estrogen receptor (ER), and progesterone receptor (PR) testing by (IHC). HER2 was tested by IHC and reflexed ISH. We identified the overall frequency of breast cancer patients with different HER2 subtypes and compared the PI between primary and metastatic breast cancer within different HER2 categories. Results (if a Case Study enter NA) The median age at primary breast diagnosis was 51 (IQR; 42 – 58), and the median for metastatic cancer was 64 (IQR;57-70). Average PI/Ki67 was lower in metastatic cancers than in primary breast cancer samples, 38% and 46%, respectively (t-test, p &amp;lt; .001, two-tailed). Most metastatic and primary tumors were HR-positive, 24 (56%) and 16 (64%). The triple-negative subtype HR-HER2- has the highest PI/Ki67 in primary and metastatic tumors. Among the metastatic specimens, we found “HER2 low” expression in 45 (62.5%); 19 (26.4%) were HER2-positive, and 8 (6.3%) were HER2-0. 85% of traditionally HER2-negative samples were “HER2 low”. There were no statistically significant differences between the Ki67 proliferation index of “HER2 low”, HER2+, and HER2-0 patients (ANOVA, p= 0.488). Five patients (7%) showed different HER2 expressions between primary and metastatic cancer. Conclusion The average PI in our cohort is lower in metastatic breast cancers compared to primary breast cancers. This is an unexpected finding considering the pathophysiology of the metastatic cascade. Including "HER2 low" criteria increases the number of patients with metastatic breast cancer potentially eligible for HER2 targeted treatment from 26.4% (HER2+ / amplified) to over 60% (HER2 1+/2+ not amplified).

An ensemble-based serial cascaded attention network and improved variational auto encoder for breast cancer prognosis prediction using data

Breast cancer is one of the most common types of cancer in women and it produces a huge amount of death rate in the world. Early recognition is lessening its impact. The early recognition of breast cancer could convince patients to receive surgical therapy, which will significantly improve the chance of restoration. This information is used by the machine learning technique to find links between them and appraise our forecasts of fresh occurrences. Later recognition of breast cancer can lead to death. An accurate prescient framework for breast cancer prediction is urgently needed in the current era. In order to accomplish the objective, an adaptive ensemble model is proposed for breast cancer prognosis prediction using data. At the initial stage, the raw data are fetched from benchmark datasets. It is then followed by data cleaning and preprocessing. Subsequently, the pre-processed data is fed into the Improved Variational Autoencoder (IVAE), where the deep features are extracted. Finally, the resultant features are given as input to the Ensemble-based Serial Cascaded Attention Network (ESCANet), which is built with Deep Temporal Convolution Network (DTCN), Bi-directional Long Short-Term Memory (BiLSTM), and Recurrent Neural Network (RNN). The effectiveness of the model is validated and compared with conventional methodologies. Therefore, the results elucidate that the proposed methodology achieves extensive results; thus, it increases the system’s efficiency.

Predictive Significance of Systemic Immune-Inflammation Index in Patients with Breast Cancer: A Retrospective Cohort Study.

Peripheral blood inflammation indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), have become research hotspots in the diagnosis, treatment, and prognosis prediction of breast cancer, whereas existing research findings remain controversial. Data pertaining to 1808 breast cancer patients were collected retrospectively to analyze the predictive value of NLR/PLR/SII for breast cancer clinicopathological characteristics, chemotherapy response, and relapse. 1489, 258, and 53 eligible breast cancer patients entered into the three analyses, respectively. Logistic regression analyses were used to assess the correlation between these indices and poor response to chemotherapy. A predictive scoring model was established to predict chemotherapeutic responses based upon the odds ratio values of significant variables identified in logistic regression analyses. Higher pretherapeutic NLR/PLR/SII values were significantly correlated with higher tumor stage, triple-negative breast cancer, premenopausal status, and younger age. Logistic regression analyses indicated that pretherapeutic high SII (as a continuous variable or with a cut-off value of 586.40) and HER2-negative status were independent predictors of poor response to neoadjuvant chemotherapy. A first-in-class SII-based predictive scoring model well distinguished patients who might not benefit from neoadjuvant chemotherapy, with an area under the curve of 0.751. In HR-positive cancers, SII was more strongly associated with clinicopathological features and chemotherapy response. In addition, a receiver operating characteristic curve analysis indicated that the specificity of follow-up SII in identifying cancer relapse was greater than 98.0% at a cut-off value of 900. As a predictor of breast cancer, especially in the HR-positive subtype, SII may eclipse NLR/PLR. SII-high patients are more likely to have a worse chemotherapy response and a higher risk of recurrence.

Integrative landscape analysis of prognostic model biomarkers and immunogenomics of disulfidptosis-related genes in breast cancer based on LASSO and WGCNA analyses

BackgroundDisulfidptosis is a novel type of programmed cell death. However, the value of disulfidptosis-related genes (DRGs) in the prediction of breast cancer prognosis is unclear.MethodsRNA-seq data of 1231 patients, together with information on patient clinical characteristics and prognosis, were downloaded from TCGA. DRGs were identified between cancerous and non-cancerous tissues. The LASSO algorithm was used to assign half of the samples to the training set. Risk scores were used for construction of a prognostic model for risk stratification and prognosis prediction, and the clinical applicability was examined using a line diagram. The relationships between risk scores, immune cell infiltration, molecular subtypes, and responses to immunotherapy and chemotherapy were examined.ResultsWe identified and obtained four DRG-related prognostic lncRNAs (AC009097.2, AC133552.5, YTHDF3-AS1, and AC084824.5), which were used for establishing the risk model. Longer survival was associated with low risk. The DRG-associated lncRNAs were found to independently predict patient prognosis. The AUCs under the ROCs for one-, three-, and 5-year survival in the training cohort were 0.720, 0.687, and 0.692, respectively. The model showed that the high-risk patients had reduced overall survival as well as high tumor mutation burdens. Furthermore, high-risk patients showed increased sensitivity to therapeutic drugs, including docetaxel, paclitaxel, and oxaliplatin.ConclusionThe risk score model was effective for predicting both prognosis and sensitivity to therapeutic drugs, suggesting its possible usefulness for the management of patients with breast cancer.