Invasive Breast Carcinoma Patients Research Articles

Identification of Potential Predictive Transcript Isoform-Biomarkers for the Early Diagnosis of Breast Cancer Using Bioinformatics Tools

Background: Several studies have demonstrated that the expression status of isoforms is more informative as a biomarker than overall gene expression. This study aimed to determine highly but significantly expressed transcript isoforms and evaluate their prognostic and diagnostic impact in breast invasive carcinoma (BRCA) stage I patients. Methods: The differentially expressed genes and their transcript isoforms in BRCA stage I were determined using the Cancer Differentially Expressed Isoform and gene (Cancer DEIso) platform based on The Cancer Genome Atlas (TCGA) data. The prognostic and diagnostic impact of significantly upregulated top 10 genes and their transcripts were revealed using the Cancer DEIso tool, the Kaplan-Meier (KM) method, and the Receiver Operating Characteristic Curve (ROC) approach, respectively. Isoform-level protein-protein interactions (PPI) were constructed using the Domain Interaction Graph Guided ExploreR (DIGGER) database. ConsensusPathDB was used to perform pathway enrichment analysis based on the constructed interactions. Results: This study revealed that NM_024037, NM_001143782, and NM_021619 transcript isoforms have significant diagnostic ability with AUC values 93.2%, 77.1% and 75.3%, respectively to distinguish stage I BRCA patients from normal. KM-plot analysis showed that these three isoforms have no prognostic significance in stage I patients, but their upregulation was correlated with decreased survival in BRCA patients regardless of stage. Isoform-based pathway enrichment analyses indicated that these three isoforms involved in chromatin organization, senescense, DNA damage and several signalling pathways which promotes cancer when there is misregulation. . Conclusion: NM_024037, NM_001143782, and NM_021619 transcript isoforms are potential biomarkers for detecting early-stage BRCA. Thus, it is essential to find out how these three isoforms contribute to the development of breast carcinogenesis and evolve a new approach for capturing breast tumors at an earlier stage of the clinical landscape.

The expression of Ki-67 and clinicohistopathological characteristics of breast cancer in Southern Sumatra, Indonesia

Background: Breast cancer is the most frequently diagnosed cancer in the world, approximately 2.3 million new cases (11.7% of all cancer cases) per year in 2020. The Ki-67 expression is clinically used to classify molecular subtypes of breast cancer into luminal A and luminal B groups.Objectives: This study aimed to investigate relationships between clinicohistopathological characteristics (ages, histopathological types, histopathological grades, molecular subtypes) of breast carcinoma patients and the Ki-67 proliferation index at dr. Moh. Hoesin General Hospital (RSMH) Palembang in 2019–2021.Method: This study applied an observational analytic study with a cross-sectional approach. 521 samples were included in this study based on inclusion and exclusion criteria. The samples in this study included invasive breast carcinoma patients who underwent histopathological examination and immunohistochemistry (IHC), recorded in the medical records at RSMH Palembang for 2019–2021 period. Then a statistical analysis was performed by using the chi-square test, which was analysed in the SPSS application.Results: The correlation between Ki-67 proliferation index and histopathological grades was statistically significant (p=0.018). The Ki-67 proliferation index was also statistically significantly associated with the molecular subtypes (p=0.000). Neither age (p=0.315) nor histopathological types (p=0.417) were significantly associated with the Ki-67 proliferation index.Conclusion: The Ki-67 expression was significantly associated with histopathological value and molecular subtype in breast carcinoma patients at RSMH Palembang in 2019–2021. The Ki-67 expression was not associated with the clinic-histopathological characteristics of ages and histopathological types.

Development and validation of survival nomograms in elder triple-negative invasive ductal breast carcinoma patients

ABSTRACT Background We aimed to develop a nomogram to predict the overall survival of elderly patients with Triple-negative invasive ductal breast carcinoma (TNIDC). Research design and methods 12165 elderly patients with nonmetastatic TNIDC were retrieved from the SEER database from 2010 to 2019 and were randomly assigned to training and validation cohorts. Stepwise Cox regression analysis was used to select variables for the nomogram based on the training cohort. Univariate and multivariate Cox analyses were used to calculate the correlation between variables and prognosis of the patients. Survival analysis was performed for high- and low-risk subgroups based on risk score. Results Eleven predictive factors were identified to construct our nomograms. Compared with the TNM stage, the discrimination of the nomogram revealed good prognostic accuracy and clinical applicability as indicated by C-index values of 0.741 (95% CI 0.728–0.754) against 0.708 (95% CI 0.694–0.721) and 0.765 (95% CI 0.747–0.783) against 0.725 (95% CI 0.705–0.744) for the training and validation cohorts, respectively. Differences in OS were also observed between the high- and low-risk groups (p < 0.001). Conclusion The proposed nomogram provides a convenient and reliable tool for individual evaluations for elderly patients with M0_stage TNIDC. However, the model may only for Americans.

Abstract 4105: Immune-modulating vaccines targeting CD73

Abstract Background: To realize the generalizable regulation of the tumor microenvironment (TME), immune-modulating vaccines are developed to target shared antigens expressed by immunosuppressive cells and cancer cells. CD73, also known as ecto-5'-nucleotidase, is a pivotal enzyme in the adenosine metabolic pathway and is expressed on the surface of multiple cancer cells and immunosuppressive cells in the TME. In this study, we aim to develop a CD73-targeting immune modulatory peptide vaccine by predicting antigen sequences, screening immunogenic antigen peptides, and assessing their anti-tumor efficacy and capacity to improve the TME. Methods: The expression level of CD73 on various cancer cell lines were evaluated by flow cytometry. Several online software were used to predict CD73-derived peptides that bind to the mouse MHC-I (major histocompatibility complex class I) molecules. Cells from the draining lymph nodes of healthy mice and peptide-immunized mice were harvested and stimulated by the synthetic peptides in vitro. T cell activation level, IFN-γrelease degree and their cytolytic capability towards cancer cells was measured. CD73-derived peptides were mixed with CpG to treat 4T1 tumor-bearing mice and the changes in the TME were investigated by flow cytometry. The online website (www.camoip.net) was used to analyze the correlation between CD73 expression and patient survival in the TCGA Breast invasive carcinoma (BRCA) cohort.Results: 4T1 exhibited a strikingly elevated surface expression of CD73 compared to other mouse cancer cell lines. Three CD73-derived peptides (EP1, EP2, EP3) consisting of 8-10 amino acids were synthesized. In vitro stimulation experiments indicated that EP1 and EP3 significantly elicited T-cell responses in draining lymph nodes. After stimulation, the proportions of CD25+, CD69+, CD107a+, and CD137+ cells markedly increased in CD8+ T cell populations. These cells also displayed enhanced cytotoxicity against 4T1 tumor cells. In vivo experiments demonstrated that EP3 induced a pronounced anti-tumor response in an established 4T1 tumor model without evident toxicity. Moreover, subcutaneous injection of EP3 improves the TME by promoting antigen-specific CD8+ T-cell responses, reducing M2 macrophages, and inducing neutrophil maturation. High tumor CD73 expression was also found to be associated with poor outcomes among BRCA patients, which further indicated clinical relevance. Conclusions: In this study, we successfully identified an immunogenic vaccine targeting CD73. It expressed dual functionality by effectively triggering cytotoxic T-cell responses that directly killing CD73-highexpressing tumor cells and regulating the tumor microenvironment through eliminating immunosuppressive cells with elevated CD73 expression. These findings hold promise for a novel immune-modulating vaccine and suggest a potential therapeutic avenue for the treatment of breast cancer and other types of cancer. Citation Format: Yuting Luo, Tao Shi, Hanbing Wang, Baorui Liu, Jia Wei. Immune-modulating vaccines targeting CD73 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4105.

Establishing the role of BRCA1 in the diagnosis, prognosis and immune infiltrates of breast invasive cancer by bioinformatics analysis and experimental validation.

Breast cancer susceptibility gene 1 (BRCA1) is a well-known gene that acts a vital role in suppressing the growth of tumors. Previous studies have primarily focused on the genetic mutations of BRCA1 and its association with hereditary breast invasive carcinoma (BRCA). However, little research has been done to investigate the relationship between BRCA1 and immune infiltrates and prognosis in BRCA. We obtained the expression profiles and clinical information of patients with BRCA from the Cancer Genome Atlas (TCGA) database. The levels of the BRCA1 gene between BRCA tissues and normal breast tissues were compared through the Wilcoxon rank-sum test. Additionally, we performed WB and RT-qPCR techniques to detect the expression of BRCA1. We conducted functional enrichment analyses. Furthermore, we assessed immune cell infiltration using a single-sample gene set enrichment analysis. The methylation status of the BRCA1 gene was analyzed using the UALCAN and MethSurv databases. The Cox regression analysis and (KM) Kaplan-Meier method were employed to determine the prognostic value of BRCA1. In order to provide a practical tool for predicting the overall survival rates at different time points, we also constructed a nomogram. Our analysis revealed that the expression of BRCA1 was significantly higher in BRCA tissues compared to normal tissues. Furthermore, this increased level of BRCA1 was found to be associated with specific BRCA subtypes, including T2, stage II, ER positive, ect. Importantly, the overexpression of BRCA1 was shown to be a negative prognostic marker for the overall survival rates of BRCA patients. Moreover, low methylation status of the BRCA1 gene was related to a poorer prognosis. Furthermore, our results indicated that high levels of BRCA1 are related to a decrease in level of killer immune cells, such as natural killer (NK) cells, macrophages, CD8+ T cells, and plasma-like dendritic cells (pDCs) within the tumor microenvironment. Our study is the first to provide evidence indicating that the presence of BRCA1 can serve as a reliable marker for both diagnosing and determining the prognosis of BRCA. Moreover, BRCA1 acts as a crucial indicator of the cancer's potential to infiltrate and invade the immune system, which has important implications for developing targeted therapies in BRCA.

Polo-like kinase 1 as a biomarker predicts the prognosis and immunotherapy of breast invasive carcinoma patients.

Invasive breast carcinoma (BRCA) is associated with poor prognosis and high risk of mortality. Therefore, it is critical to identify novel biomarkers for the prognostic assessment of BRCA. The expression data of polo-like kinase 1 (PLK1) in BRCA and the corresponding clinical information were extracted from TCGA and GEO databases. PLK1 expression was validated in diverse breast cancer cell lines by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Single sample gene set enrichment analysis (ssGSEA) was performed to evaluate immune infiltration in the BRCA microenvironment, and the random forest (RF) and support vector machine (SVM) algorithms were used to screen for the hub infiltrating cells and calculate the immunophenoscore (IPS). The RF algorithm and COX regression model were applied to calculate survival risk scores based on the PLK1 expression and immune cell infiltration. Finally, a prognostic nomogram was constructed with the risk score and pathological stage, and its clinical potential was evaluated by plotting calibration charts and DCA curves. The application of the nomogram was further validated in an immunotherapy cohort. PLK1 expression was significantly higher in the tumor samples in TCGA-BRCA cohort. Furthermore, PLK1 expression level, age and stage were identified as independent prognostic factors of BRCA. While the IPS was unaffected by PLK1 expression, the TMB and MATH scores were higher in the PLK1-high group, and the TIDE scores were higher for the PLK1-low patients. We also identified 6 immune cell types with high infiltration, along with 11 immune cell types with low infiltration in the PLK1-high tumors. A risk score was devised using PLK1 expression and hub immune cells, which predicted the prognosis of BRCA patients. In addition, a nomogram was constructed based on the risk score and pathological staging, and showed good predictive performance. PLK1 expression and immune cell infiltration can predict post-immunotherapy prognosis of BRCA patients.

Glioma-associated oncogene homolog 1 in breast invasive carcinoma: a comprehensive bioinformatic analysis and experimental validation.

Breast cancer, despite significant advancements in treatment, remains a major cause of cancer-related deaths among women. Immunotherapy, an emerging therapeutic strategy, offers promise for better outcomes, particularly through the modulation of immune functions. Glioma-Associated Oncogene Homolog 1 (GLI1), a transcription factor implicated in cancer biology, has shown varying roles in different cancers. However, its immunoregulatory functions in breast invasive carcinoma (BRCA) remain elusive. The current study aimed to unravel the expression patterns and immune-regulatory roles of GLI1 in BRCA. Utilizing multiple bioinformatic platforms (TIMER2.0, GEPIA2, and R packages) based on The Cancer Genome Atlas (TCGA) and/or Genotype-Tissue Expression (GTEx) databases, we analyzed the expression of GLI1 in BRCA and its pan-cancer expression profiles. We further validated these findings by conducting qPCR and immunohistochemical staining on clinical BRCA samples. Kaplan-Meier analysis and Cox proportional hazards regression were performed to assess the prognostic value of GLI1. Additionally, the association between GLI1 expression and immune infiltration within the tumor immune microenvironment (TMIE) was examined. The findings reveal dysregulated expression of GLI1 in numerous cancers, with a significant decrease observed in BRCA. High GLI1 expression indicated better survival outcomes and was correlated with the age and stage of BRCA patients. GLI1 was involved in immune status, as evidenced by its strong correlations with immune and stromal scores and the infiltration levels of multiple immune cells. Meanwhile, GLI1 was co-expressed with multiple immune-related genes, and high GLI1 expression was associated with the activation of immune-related pathways, such as binding to proteasome and mismatch repair and retinol metabolism signaling pathways. Additionally, the differential expression of GLI1 may be related to the effect of immunotherapy on CTLA-4, PD-1, and other signals, and can effectively predict the immune efficacy. Our study underscores the critical role of GLI1 in BRCA, both as a potential tumor suppressor and an immune regulator. The association between GLI1 expression and favorable prognosis suggests its potential as a prognostic biomarker and immunotherapeutic target in BRCA.

THEM6 is a prognostic biomarker for breast cancer and is associated with immune infiltration

To characterize the implications of lipid metabolism-related gene thioesterase superfamily member 6 (THEM6) in breast cancer. Several databases including The Cancer Genome Atlas (TCGA) were utilized for our meticulous bioinformatics analysis. We further performed qRT-PCR, immunoblotting and IHC assays to validate the expression of THEM6 in various breast cancer cells and tissues. In addition, we have carried out relevant functional experiments to explore the regulatory role of THEM6 in vitro. Lipid metabolism-related genes are independent factors for overall survival. According to several databases, THEM6 was significantly more expressed in cancerous tissues of breast invasive carcinoma (BRCA) compared to its paracancerous tissues. Furthermore, THEM6 overexpression was correlated with poorer overall survival of BRCA patients, serving as a separate prognostic factor for BRCA. Biological functional analyses revealed that THEM6 was associated with tumor progression and pathogenesis. Finally, we discovered that in BRCA, THEM6 expression was linked to multiple immune cell types. qRT-PCR and Western blotting experiments demonstrated a general upregulation of THEM6 expression in breast carcinoma cells. IHC showed that THEM6 was expressed in both breast cancer tissues and para-cancer tissues, but its expression level was significantly higher in carcinoma tissues. In vitro studies indicated that THEM6 increased proliferation, invasion, and inhibited apoptosis of breast carcinoma cells, while also affecting the cell cycle and promoting cancer progression. Furthermore, THEM6 may influence macrophage recruitment and polarization in the tumor microenvironment by regulating CCL2 secretion, which in turn affects macrophage recruitment in the tumor microenvironment. Our findings indicate that the overexpression of THEM6, which is linked to the development of breast cancer, is a predictor of a poor prognosis and has an impact on the degree of immune cell infiltration. Therefore, THEM6 has the potential to be a valuable target for BRCA.

Inhibiting KLRB1 expression is associated with impairing cancer immunity and leading to cancer progression and poor prognosis in breast invasive carcinoma patients.

The association between Killer cell lectin like receptor B1 (KLRB1) and cancer has been reported, but the roles of KLRB1 in breast invasive carcinoma (BRCA) has not been fully revealed. Our study utilized the Cancer Genome Atlas (TCGA), Kaplan-Meier (K-M) Plotter, and TIMER databases to investigate the expression and clinical relevance of KLRB1 in BRCA and to explore its roles and mechanism in BRCA progression using gene set enrichment analysis, CCK-8, migration, apoptosis, and western blotting. We examined the relationship between KLRB1 expression and the BRCA immune microenvironment, using data from TCGA, and Gene Expression Profiling Interactive Analysis (GEPIA) databases and validated these findings in K-M Plotter databases. A significant decrease of KLRB1 expression was observed in BRCA patients. BRCA patients with low KLRB1 levels were associated with older age, advanced disease stage, HER2-positivity, poor prognosis, and a decreased survival probability compared to the high-expression group. Increased KLRB1 expression levels were correlated with inhibition of breast cancer cell proliferation, migration, and invasion, as well as promotion of cell apoptosis, possible through regulation of the NF-κB, PI3K/AKT, and TNF signaling pathways. Moreover, the study also indicated that decreased KLRB1 expression correlated with tumor purity, immune score, and immune cell infiltration (B cells, CD8+ T cells, CD4+ T cells, neutrophils, dendritic cells, among others), cell markers, and immunotherapy. Decreased KLRB1 expression in BRCA is associated with poor prognosis and immune microenvironment. This study also highlights KLRB1 as a potential molecular marker for poor prognosis in BRCA patients, and therefore, it may provide clinical implications for the management of patients with BRCA.

Evaluation of the relationship between Ki67 expression level and neoadjuvant treatment response and prognosis in breast cancer based on the Neo-Bioscore staging system

BackgroundNeoadjuvant chemotherapy (NAC) is widely used in the treatment of primary breast cancer. Different staging systems have been developed to evaluate the residual tumor after NAC and classify patients into different prognostic groups. Ki67, a proliferation marker, has been shown to be useful in predicting treatment response and prognosis. We aimed to investigate the prognostic importance Neo-Bioscore stage and pretreatment and posttreatment Ki67 levels in breast cancer patients who received NAC and correlations between Neo-Bioscore stage and pretreatment and posttreatment Ki67 levels.MethodsA total of 176 invasive breast carcinoma patients who underwent NAC were included in the study. Ki67 levels were evaluated by immunohistochemical methods in Trucut biopsy and surgical excision specimens. Patients were classified into prognostic groups using the Neo-Bioscore staging system.ResultsPatients with high pretreatment Ki67 score were more likely to be in the higher Neo-Bioscore risk group (p < 0.001). Patients with a high posttreatment Ki67 score were more likely to be in the higher Neo-Bioscore prognostic risk group (p < 0.001). Overall survival (OS) and disease-free survival (DFS) were shorter in patients with high posttreatment Ki67 scores and in patients in the higher Neo-Bioscore risk group. We also determined a cutoff 37% for pathological complete response.ConclusionNeo-Bioscore staging system is found to be important in predicting survival. The posttreatment Ki67 level is more important than pretreatment Ki67 level in predicting survival.

The Relationship between Body Mass Index (BMI) and Clinical Pathological Profile of Invasive Breast Carcinoma Patients in the Department of Anatomic Pathology FKUI/RSCM from 2019 to 2022

Background&#x0D; Breast cancer is the most prevalent malignancy among women and is the second largest contributor to cancer-related deaths globally. Women with a high body mass index (BMI) are at a greater risk of developing breast cancer and an increased risk of lymph node metastasis. This retrospective study aimed to determine the relationship between BMI and the clinical pathological profile of invasive breast carcinoma (IBC) patients.&#x0D; Method&#x0D; This study was conducted from 2019 to 2022. Data were collected through the archives of the Department of Anatomic Pathology FKUI/RSCM (Faculty of Medicine, University of Indonesia/Cipto Mangunkusumo Hospital). Clinical pathological data were collected. The histological type, grade, lymphovascular invasion, and lymph node metastasis of the tumors were re-evaluated. The chi-square hypothesis test was used to determine the relationship between BMI and clinical pathological profile. The results were considered statistically significant with a p &lt;0.05.&#x0D; Result&#x0D; A total of 151 cases of IBC with lymph node dissection from 2019 to 2022 were included in this study, all of which were female, with the majority being &gt;50 years old (55%). The most common tumor size and histological type were T2 (39,7%) and invasive ductal carcinoma NST (74,8%), 49% of cases were grade 3 tumors. Most IBC cases had an obese BMI (49%), 63.1% with lymph node metastasis had an overweight-obese BMI. Statistical analysis was performed between BMI and lymph node metastasis status, and p-value=0.025 was obtained. The relationship between tumor size and lymphovascular invasion with lymph node metastasis was also found to be statistically significant, as indicated by a p-value of 0.000.&#x0D; Conclusion&#x0D; Cases with lymph node metastasis had the highest rate of overweight-obesity, and a significant relationship was found between BMI and lymph node metastasis status. A significant relationship was also found between tumor size and lymphovascular invasion with lymph node metastasis.

Complex Analysis of Endothelial Markers as Potential Prognostic Indicators in Luminal Invasive Breast Carcinoma Patients: Outcomes of a Six-Year Observational Study.

(1) Background: Metastasis is a complex process in which the primary cancer cells spread to a distant organ or organs, creating a secondary tumor location, which in many patients leads to treatment failure and death. The aim of the present study was to assess the association of endothelial markers (i.e., sP-selectin, sE-selectin and von Willebrand factor) with the leptin-to-adiponectin ratio (LAR) and to perform an analysis of the predictive value on the survival of patients with luminal A and B invasive breast cancer (IBrC). (2) Methods: The trial included 70 treatment-naïve early-stage IBrC patients with a median age of 54.5 years and a median tumor diameter of 1.5 cm. The median duration of follow-up was 5.7 years, with a relapse rate of 15.71%. Specific immunoenzymatic kits were used to determine pre- and post-treatment concentrations of analyzed factors. (3) Results: Regardless of the treatment pattern, endothelial marker concentrations and the LAR increased after adjuvant treatment. The follow-up showed a significantly higher relapse rate in patients with IBrC who had higher pre-treatment sP-selectin and post-treatment LAR levels. According to receiver operating characteristic (ROC) analysis, a post-treatment LAR with a sensitivity of 88.9% and specificity of 57.9% discriminating cases with or without disease relapse. Additionally, a higher risk of breast cancer relapse was associated with a lower post-treatment sP-selectin concentration. (4) Conclusions: Our results showed mainly that pre-treatment sP-selectin levels and post-treatment LAR may have value as prognostic indicators and may contribute to predicting the future outcomes in patients with early-stage IBrC.

The mRNA stability of NCAPG2, a novel contributor to breast invasive carcinoma, is enhanced by the RNA-binding protein PCBP2

Non-SMC condensin II complex subunit G2 (NCAPG2) is one of the three non-SMC subunits in condensin II, which plays a vital role in regulating chromosome condensation and segregation. Although the tumor-promoting role of NCAPG2 has been reported in several solid malignancies, its function in breast invasive carcinoma (BRCA) remains unknown. Data both from GEPIA and GSE36295 indicated that NCAPG2 mRNA expression was abnormally upregulated in cancer tissues, which was further verified in 40 paired BRCA and para-carcinoma samples. Kaplan-Meier Plotter further illustrated that BRCA patients with higher NCAPG2 expression have a poorer prognosis. Functional experiments carried out in two BRCA cell lines (MCF-7 and T-47D) showed that NCAPG2-silenced BRCA cells acquired less aggressive behavior — weakened growth and metastasis both in vitro and in vivo. Label-free proteomics quantified the protein expression patterns in MCF-7 cells, and the results revealed 684 differentially expressed proteins (|log2FC| > 1 and P < 0.05) downstream to NCAPG2. Interestingly, poly(C)-binding protein 2 (PCBP2), an RNA binding protein previously known to increase RNA stability of its target genes, was found to directly bind to and protect NCAPG2 mRNA from degradation—PCBP2 knockdown accelerated the degradation half-life time of NCAPG2 mRNA from approximately 8 h to 5 h. Taken together, our study indicates that NCAPG2 acts as a novel contributor to BRCA growth and metastasis under the regulation of PCBP2, providing insights into BRCA treatment.

Machine Learning-Based Prediction of Distant Recurrence in Invasive Breast Carcinoma Using Clinicopathological Data: A Cross-Institutional Study.

Breast cancer is the most common type of cancer worldwide. Alarmingly, approximately 30% of breast cancer cases result in disease recurrence at distant organs after treatment. Distant recurrence is more common in some subtypes such as invasive breast carcinoma (IBC). While clinicians have utilized several clinicopathological measurements to predict distant recurrences in IBC, no studies have predicted distant recurrences by combining clinicopathological evaluations of IBC tumors pre- and post-therapy with machine learning (ML) models. The goal of our study was to determine whether classification-based ML techniques could predict distant recurrences in IBC patients using key clinicopathological measurements, including pathological staging of the tumor and surrounding lymph nodes assessed both pre- and post-neoadjuvant therapy, response to therapy via standard-of-care imaging, and binary status of adjuvant therapy administered to patients. We trained and tested four clinicopathological ML models using a dataset (144 and 17 patients for training and testing, respectively) from Duke University and validated the best-performing model using an external dataset (8 patients) from Dartmouth Hitchcock Medical Center. The random forest model performed better than the C-support vector classifier, multilayer perceptron, and logistic regression models, yielding AUC values of 1.0 in the testing set and 0.75 in the validation set (p < 0.002) across both institutions, thereby demonstrating the cross-institutional portability and validity of ML models in the field of clinical research in cancer. The top-ranking clinicopathological measurement impacting the prediction of distant recurrences in IBC were identified to be tumor response to neoadjuvant therapy as evaluated via SOC imaging and pathology, which included tumor as well as node staging.

The Role of Diffusion Kurtosis MR Imaging in the Evaluation of Invasive Breast Cancer Classification and its Correlation with Prognostic Factors: A Prospective Study of Primarily Advanced Stage Cancers

Background: The present exploration is aimed to determine whether diffusion kurtosis imaging (DKI)-derived mean diffusivity (MD) and mean kurtosis (MK) are possible parameters for the invasive breast cancer grading and whether MD/MK is related to breast cancer clinical-pathologic factors including estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2) and Ki-67. Methods: Tumors from 108 invasive breast carcinoma patients (45.6 ± 11.2 years old; range, 20–84 years), diagnosed by pathological examination between January 2016 and August 2017, were included. DKI data (with b values of 0, 1000, 2000 sec/mm2) and MD/MK were measured. The expression of ER, PR, HER-2, and Ki-67 was evaluated by immunohistochemistry. Results: It was found that MD was significantly lower in grade III breast cancer than in grade II breast cancer (0.82 ± 0.21 vs 1.17 ± 0.24, p &lt; 0.0001), while MK was significantly higher in grade III breast cancer than that in grade II breast cancer (1.00 ± 0.31 vs 0.85 ± 0.21, respectively; p &lt; 0.05). In addition, MD was negatively associated with Ki-67 level (r = –0.39, p &lt; 0.05), while MK was positively associated with Ki-67 level (r = 0.56, p &lt; 0.05). Furthermore, MD was significantly increased in ER and PR positive expression group compared with ER and PR negative expression group, while there is no significant difference of MK in ER and PR positive expression group compared with ER and PR negative expression group. Additionally, there is no significant difference of MD and MK in HER-2 positive expression group compared with HER-2 negative expression group. Conclusions: These results demonstrate that DKI has value in the evaluation on the classification of invasive breast carcinoma. MD values were associated with breast cancer clinical-pathologic factors. DKI can provide useful information in the assessment of tumor proliferation activity.

Prognostic and immune implications of a novel 7-methylguanosine-related microRNA signature in breast invasive carcinoma: from exploration to validation.

This study aims to develop and validate a prognostic signature based on 7-methylguanosine-related (M7G-related) miRNAs for predicting prognosis and immune implications in breast invasive carcinoma (BRCA). M7G-related miRNA data of BRCA were obtained from The Cancer Genome Atlas (TCGA). Least absolute shrinkage and selection operator (LASSO)-penalized, univariate, and multivariate Cox regression analyses were used to construct the prognostic signature. Furthermore, the predictive validity was verified using Kaplan-Meier (KM) survival risk and receiver operating characteristic (ROC) plots. Internal random sampling verification was used to simplify and validate the signature. RT-qPCR was used to quantify the expression level of transcriptional profiles. The independent prognostic role of the risk score was validated using univariate and multivariate regression. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used for functional and immune enrichment analysis. A total of 18 M7G-related miRNAs were identified to construct the prognostic signature in BRCA. The low-risk group exhibited significantly higher overall survival than the high-risk group in the KM survival plot (P < 0.001). The area under the curve (AUC) for 1-, 3-, and 5-year survivals in the ROC curve were 0.737, 0.724, and 0.702, respectively. The survival significance in the training and testing cohorts was confirmed by random sampling verification. The most prominent miRNAs in the signature were the miR-7, miR-139, miR-10b, and miR-4728. Furthermore, immune scores for B, mast, and Th1 cells varied between risk groups. Our research demonstrated that CD52 was the most positively correlated gene with immune cells and functions in BRCA. Our study presents a comprehensive and systematic analysis of M7G-related miRNAs to construct a prognostic signature in BRCA. The signature demonstrated excellent prognostic validity, with the risk score as an independent prognostic factor. These results provide critical evidence for further investigation of M7G miRNAs and offer new insights for BRCA patients in the context of effective immunotherapy.

Abstract LB070: Predicting distant recurrences in invasive breast carcinoma patients using their clinicopathological profiles

Abstract Purpose: To determine whether the classification-based machine learning (ML) or artificially intelligent (AI) techniques can predict distant recurrence flags (yes or no) in invasive breast cancer patients using the data comprising several clinicopathological measurements such as pathological staging of tumor and surrounding nodes deemed both pre and post neoadjuvant (i.e. either chemo, radiation or hormone based) therapy including imaging based therapy-response as well and the status of adjuvant therapy (Chemo or Anti-Her2/Neu antibody) therapy were administered to patients post resection of their tumor to minimize the possibility of diseases-recurrence in them. Method: The clinicopathological data from a retrospective study of 900 breast cancer patients at Duke university (posted at The Cancer Imaging Archive, i.e. TCIA) was tapped for our study. These patients received neoadjuvant therapies such as Chemo-, Radiation or Endocrine Hormone based therapy, had their responses evaluated through imaging and pathological staging (both tumor and nodal), and received Adjuvant therapies (such as Chemo or Anti-Her2/Neu antibody therapy) post tumor resection. The patient entries pertaining to ungraded tumor responses from therapies or those labelled as ‘Not applicable (NA)’ for any of the aforementioned clinicopathological parameters were filtered out retaining 161 patient-entries which were further split into train and test sets in 90:10 ratio where 90% were used for training (ntrain=144) and 10% for testing (ntest=17) of several ML models. Classification based machine learning models such as Random Forest (RF), C-Support Vector Classification (SVC) and Supervised Neural Network (aka Multi-Layer Perceptron, i.e. MLP) were employed to train and test aforementioned clinicopathological parameters to predict distant recurrence flags or labels (i.e. Yes or No). The training of the model was conducted using ImaGene software. Out of the three aforementioned models, RF seemed to have performed best with grid search activated over tree-depth hyperparameter to predict distant recurrence flags in test patients (ntest=17) at AUC=1.0 (p&amp;lt;0.002). Further, the validation of the model was conducted using external clinicopathological dataset of 20 patients from Dartmouth-Hitchcock (DH) Medical center which yielded AUC&amp;gt;0.75. Conclusion: Random Forest model trained using grid search through tree-depth hyperparameter can predict distant recurrence flags (Yes or No) in invasive breast cancer patients at AUC in the range of 0.75 1.0 across multiple institutions, thereby aiding portability of models and advancing the idea of multi-instutional ML/AI model validations in Invasive Breast Carcinoma subtype of Breast Cancer. Citation Format: Shrey S. Sukhadia, Kristen Muller, Adrienne Workman, Shivashankar Nagaraj, Olivier Gevaert. Predicting distant recurrences in invasive breast carcinoma patients using their clinicopathological profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB070.

Integrated pancancer analysis reveals the oncogene characteristics and prognostic value of DIP2B in breast cancer

BackgroundDisco-interaction protein 2 homologue B (DIP2B) plays an important role in DNA methylation. There have been many reports on DIP2B in various diseases, but neither the diagnostic value nor the prognostic value of DIP2B across cancer types has been deeply explored.MethodsThe expression levels of DIP2B in 33 cancer types were analysed based on data sets from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. The relationships of DIP2B expression with immune cell infiltration and immune-related gene expression were studied via the CIBERSORT, ESTIMATE and TISIDB tools. Gene set variation analysis (GSVA) was performed to identify pathways related to DIP2B. DIP2B knockdown by siRNA was performed in breast cancer cell lines to investigate the effect on proliferation, apoptosis and migration. The relationships of DIP2B expression with clinicopathological features and prognosis were analysed based on immunohistochemistry.ResultsDIP2B was highly expressed in 26 of 33 cancer types and was significantly associated with poor overall survival (OS) in breast invasive carcinoma (BRCA), mesothelioma and chromophobe renal cell carcinoma (each P < 0.05). DIP2B showed a negative correlation with the immune score, the infiltration levels of key immune killer cells (CD8 + T cells, activated NK cells and plasma cells), and the expression of major histocompatibility complex–related genes and chemokine-related genes in BRCA. Subtype analysis showed that DIP2B expression was associated with poor OS in Her-2 + BRCA patients (P < 0.05). DIP2B showed a negative correlation with immune killer cell infiltration and immune regulatory genes in BRCA subtypes. In BRCA, the GSVA results revealed that genes correlating positively with DIP2B were enriched in cancer-related pathways (PI3K-AKT) and cell-cycle-related pathways (MITOTIC_SPINDLE, G2M_CHECKPOINT and E2F_TARGETS), while genes correlating negatively with DIP2B were enriched in DNA_REPAIR. Knockdown of the DIP2B gene induced a reduction in proliferation and migration and an increase in apoptosis in breast cancer cell lines. DIP2B expression was associated with lymph node metastasis and poor histological grade in BRCA according to immunohistochemistry (each P < 0.05). DIP2B expression predicted reduced disease-free survival and OS in BRCA patients (each P < 0.05), especially those with the Her-2 + subtype (P = 0.023 and P = 0.069).ConclusionsDIP2B may be a prognostic biomarker for BRCA, especially for the Her-2 + subtype. DIP2B is associated with a “cold” tumour immune microenvironment in BRCA and might serve as a future target for immunotherapy.

The prognostic gene CRABP2 affects drug sensitivity by regulating docetaxel-induced apoptosis in breast invasive carcinoma: A pan-cancer analysis

Cellular retinoic acid-binding protein 2 (CRABP2), a specific transporter of retinoic acid, has been shown to have an important biological role in human cancers. However, due to the substantial variability among different tumors, the role of CRABP2 remains uncertain and has not yet been subjected to systematic analysis. Utilizing The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Clinical Proteomic Tumor Analysis Consortium (CPTAC), Human Protein Atlas (HPA), Gene Expression Profiling Interactive Analysis 2 (GEPIA2), Kaplan-Meier Plotter, Biomarker Exploration of Solid Tumors (BEST), Cancer Cell Line Encyclopedia (CCLE), Receiver Operating Characteristic plotter (ROC plotter), and other online public tools, expression levels of CRABP2 in breast invasive carcinoma (BRCA), lung adenocarcinoma (LUAD), and ovarian serous cystadenocarcinoma (OV) were found to be significantly greater than those in adjacent normal tissues, suggesting a correlation to poor prognosis. Among the three, CRABP2 expression in BRCA was most closely associated with clinical prognosis. In a study of docetaxel-treated BRCA patients, CRABP2 expression was significantly higher in the drug-resistant group. Colony formation and flow cytometry analysis were used to further investigate the relationship between CRABP2 and docetaxel sensitivity in BRCA cells MDA-MB-231and BT549. The knockdown of CRABP2 expression significantly reduced cell growth and increased sensitivity to the chemotherapeutic agent docetaxel in BRCA cells. Furthermore, CRABP2 knockdown augmented docetaxel-induced apoptosis. Molecular docking using SwissDock tool revealed that CRABP2 had a greater binding affinity to docetaxel than docetaxel-targeted proteins. This research provides an insight into the expression and prognostic potential of CRABP2 in cancers and suggests that CRABP2 may control docetaxel sensitivity in BRCA cells through apoptosis, warranting further investigation.

Comprehensive Analysis of RNA-Binding Protein-Related lncRNA in Breast Invasive Carcinoma.

RNA-binding proteins (RBPs), which form complexes or single/multiple RNA-binding domains, have a functional role in regulating and determining the function or stability of the bound RNAs in various cancers, including breast invasive carcinoma (BRCA). However, the biological functions and clinical implications of RBP-related long noncoding RNAs (lncRNAs) in BRCA remain largely unknown. Herein, we first identified and characterized RBP-related lncRNAs in BRCA. Then we built an RBP-related lncRNA signature (RBPLSig) and explored the clinical evaluation and prediction performance of the RBPLSig by bioinformatic analysis. In addition, to optimize treatment plans, prediction online tools was developed to predict the patient survival rate. Lastly, to verify the function of lncRNA WAC antisense RNA 1 (WAC-AS1), the experiments such as Quantitative real-time PCR (qRT-PCR), lncRNA knockdown, CCK-8, and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) staining were performed. We also gained the potential mechanisms of the druggable compounds of the WAC-AS1 related RBP gene, putative NSUN6, using molecular docking. The results showed that RBPLSig, as an independent prognostic factor for BRCA patients, was involved in numerous malignancy-associated immunoregulatory pathways. We found different immune statuses and responses to immunotherapy, chemotherapy, and targeted therapy between the high- and low-risk groups stratified by RBPLSig. Our data broaden the comprehensive understanding of the biological functions of RBP-related lncRNAs, and demonstrate a novel and independent RBPLSig to assess prognosis and the immune microenvironment, thus helping to guide treatment decisions for BRCA.