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Objective: To establish and validate a new clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer. Methods: A total of 593 breast cancer patients who underwent preoperative breast MRI from Jan 2017 to Dec 2020 were enrolled, which were randomly divided into the training (n=474) and test sets (n=119) at the ratio of 8:2. Intratumoral region (ITR) of interest were manually delineated, and peritumoral regions of 3 mm and 5 mm (PTR-3 mm and PTR-5 mm) were automatically obtained by dilating the ITR. Intratumoral and peritumoral radiomics features were extracted from the fat-suppressed T2-weighted images, including first-order statistical features, shape features, texture features, and filtered features. The Mann-Whitney U Test, Z score normalization, K-best method, and least absolute shrinkage and selection operator (LASSO) algorithm were applied to select key features to construct radscores based on ITR, PTR-3 mm, PTR-5 mm, ITR+PTR-3 mm and ITR+ PTR-5 mm. Risk factors were selected by univariate and multivariate logistic regressions and were used to construct a clinical model and a clinical-radiomics model that presented as a nomogram. The performance of models was assessed by sensitivity, specificity, accuracy, the area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA). Results: ITR+PTR-3 mm radsore and histological grade were selected as risk factors. A clinical-radiomics model was constructed by adding ITR+PTR-3mm radscore to the clinical factor, which was presented as a nomogram. The clinical-radiomics nomogram showed the highest AUC (0.873), sensitivity (72.3%), specificity (78.9%) and accuracy (77.0%) in the training set and the highest AUC (0.851), sensitivity (71.4%), specificity (79.8%) and accuracy (77.3%) in the test set. DCA showed that the clinical-radiomics nomogram had the greatest net clinical benefit compared to the other models. Conclusion: The clinical-radiomics nomogram showed promising clinical application value in differentiating luminal and non-luminal breast cancer. [ABSTRACT FROM AUTHOR]

ObjectiveTo establish and validate a new clinical-radiomics nomogram based on the fat-suppressed T2 sequence for differentiating luminal and non-luminal breast cancer.MethodsA total of 593 breast cancer patients who underwent preoperative breast MRI from Jan 2017 to Dec 2020 were enrolled, which were randomly divided into the training (n=474) and test sets (n=119) at the ratio of 8:2. Intratumoral region (ITR) of interest were manually delineated, and peritumoral regions of 3 mm and 5 mm (PTR-3 mm and PTR-5 mm) were automatically obtained by dilating the ITR. Intratumoral and peritumoral radiomics features were extracted from the fat-suppressed T2-weighted images, including first-order statistical features, shape features, texture features, and filtered features. The Mann-Whitney U Test, Z score normalization, K-best method, and least absolute shrinkage and selection operator (LASSO) algorithm were applied to select key features to construct radscores based on ITR, PTR-3 mm, PTR-5 mm, ITR+PTR-3 mm and ITR+ PTR-5 mm. Risk factors were selected by univariate and multivariate logistic regressions and were used to construct a clinical model and a clinical-radiomics model that presented as a nomogram. The performance of models was assessed by sensitivity, specificity, accuracy, the area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA).ResultsITR+PTR-3 mm radsore and histological grade were selected as risk factors. A clinical-radiomics model was constructed by adding ITR+PTR-3mm radscore to the clinical factor, which was presented as a nomogram. The clinical-radiomics nomogram showed the highest AUC (0.873), sensitivity (72.3%), specificity (78.9%) and accuracy (77.0%) in the training set and the highest AUC (0.851), sensitivity (71.4%), specificity (79.8%) and accuracy (77.3%) in the test set. DCA showed that the clinical-radiomics nomogram had the greatest net clinical benefit compared to the other models.ConclusionThe clinical-radiomics nomogram showed promising clinical application value in differentiating luminal and non-luminal breast cancer.

Breast cancer is the most frequently diagnosed cancer in women worldwide. Actually CDK4/6 inhibitor Ribociclib is approved for the treatment of metastatic hormone-positive and human epidermal growth factor receptor 2 (HER 2)- negative breast cancer, but comorbidities like infectious or cardiovascular diseases may limit its use. Case report: A 45-year-old woman was diagnosed with metastatic breast cancer in September 2021; also, her hepatitis screening resulted positive for hepatitis B infection. Patient assumed eradicative therapy for hepatitis and bit after started oncological therapy with Ribociclib. Outcome: Frequent check of hepatological function was observed since start of eradicative therapy; liver transaminases and bilirubin kept to not rise despite start of oncological treatment with Ribociclib. Patient's Performance Status was also not compromised and revaluation at 4, 9 and 13 months showed partial response and then stable disease. Discussion: hepatotoxicity of Ribociclib is reported as a possible side effect, and often positivity for hepatitis is cause of exclusion from therapy; in our case, no hepatotoxicity was noted and patient obtained response in terms of control of both infectious and oncological diseases. [ABSTRACT FROM AUTHOR]

Gene co-expression networks are a useful tool in the study of interactions that have allowed the visualization and quantification of diverse phenomena, including the loss of co-expression over long distances in cancerous samples. This characteristic, which could be considered fundamental to cancer, has been widely reported in various types of tumors. Since copy number variations (CNVs) have previously been identified as causing multiple genetic diseases, and gene expression is linked to them, they have often been mentioned as a probable cause of loss of co-expression in cancerous networks. In order to carry out a comparative study of the validity of this statement, we took 477 proteincoding genes from chromosome 8, and the CNVs of 101 genes, also proteincoding, belonging to the 8q24.3 region, a cytoband that is particularly active in the appearance of breast cancer. We created CNVS-conditioned co-expression networks of each of the 101 genes in the 8q24.3 region using conditional mutual information. The study was carried out using the four molecular subtypes of breast cancer (Luminal A, Luminal B, Her2, and Basal), as well as a case corresponding to healthy samples. We observed that in all cancer cases, the measurement of the Kolmogorov-Smirnov statistic shows that there are no significant differences between one and other values of the CNVs for any case. Furthermore, the co-expression interactions are stronger in all cancer subtypes than in the control networks. However, the control network presents a homogeneously distributed set of co-expression interactions, while for cancer networks, the highest interactions are more confined to specific cytobands, in particular 8q24.3 and 8p21.3. With this approach, we demonstrate that despite copy number alterations in the 8q24 region being a common trait in breast cancer, the loss of long-distance co-expression in breast cancer is not determined by CNVs. [ABSTRACT FROM AUTHOR]

Breast cancer is the most frequently diagnosed cancer in women worldwide. Actually CDK4/6 inhibitor Ribociclib is approved for the treatment of metastatic hormone-positive and human epidermal growth factor receptor 2 (HER 2)-negative breast cancer, but comorbidities like infectious or cardiovascular diseases may limit its use.Case reportA 45-year-old woman was diagnosed with metastatic breast cancer in September 2021; also, her hepatitis screening resulted positive for hepatitis B infection. Patient assumed eradicative therapy for hepatitis and bit after started oncological therapy with Ribociclib.OutcomeFrequent check of hepatological function was observed since start of eradicative therapy; liver transaminases and bilirubin kept to not rise despite start of oncological treatment with Ribociclib. Patient’s Performance Status was also not compromised and revaluation at 4, 9 and 13 months showed partial response and then stable disease.Discussionhepatotoxicity of Ribociclib is reported as a possible side effect, and often positivity for hepatitis is cause of exclusion from therapy; in our case, no hepatotoxicity was noted and patient obtained response in terms of control of both infectious and oncological diseases.

Gene co-expression networks are a useful tool in the study of interactions that have allowed the visualization and quantification of diverse phenomena, including the loss of co-expression over long distances in cancerous samples. This characteristic, which could be considered fundamental to cancer, has been widely reported in various types of tumors. Since copy number variations (CNVs) have previously been identified as causing multiple genetic diseases, and gene expression is linked to them, they have often been mentioned as a probable cause of loss of co-expression in cancerous networks. In order to carry out a comparative study of the validity of this statement, we took 477 protein-coding genes from chromosome 8, and the CNVs of 101 genes, also protein-coding, belonging to the 8q24.3 region, a cytoband that is particularly active in the appearance of breast cancer. We created CNVS-conditioned co-expression networks of each of the 101 genes in the 8q24.3 region using conditional mutual information. The study was carried out using the four molecular subtypes of breast cancer (Luminal A, Luminal B, Her2, and Basal), as well as a case corresponding to healthy samples. We observed that in all cancer cases, the measurement of the Kolmogorov-Smirnov statistic shows that there are no significant differences between one and other values of the CNVs for any case. Furthermore, the co-expression interactions are stronger in all cancer subtypes than in the control networks. However, the control network presents a homogeneously distributed set of co-expression interactions, while for cancer networks, the highest interactions are more confined to specific cytobands, in particular 8q24.3 and 8p21.3. With this approach, we demonstrate that despite copy number alterations in the 8q24 region being a common trait in breast cancer, the loss of long-distance co-expression in breast cancer is not determined by CNVs.

Introduction: Circular RNAs (circRNAs) regulatory network is important in human cancer. We, therefore, mapped the regulatory networks driven by circRNA in luminal-subtype breast cancer.Methods: Breast cancer-related microarray datasets from GEO database were analyzed for the differentially expressed circRNAs, miRNAs, and mRNAs. The potential downstream RNAs were collected using Circular RNA Interactome or Targetscan database. Protein-protein interaction (PPI) analysis was performed for the filtered genes to identify hub genes. The functions were annotated by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. CircRNA-miRNA-mRNA networks were mapped using Cytoscape software. Hsa_circ_0086735-miR-1296-5p-STAT1 axis was used for verification. The expression levels of hsa_circ_0086735, miR-1296-5p, and STAT1 mRNA were confirmed by qRT-PCR in luminal-subtype tissues and cell lines. The interactions among them were verified by Luciferase reporter assay and RNA pull-down assay. Cell proliferation and apoptosis were assayed. Overall and distant metastasis-free survival was analyzed.Results: A total of 70 genes were finally targeted and enriched in multi-process and multi-pathway. Networks containing 96 circRNA-miRNA-mRNA axes were constructed. Hsa_circ_0086735 and STAT1 mRNA was upregulated in luminal breast cancer, while miR-1296-5p was downregulated. Hsa_circ_0086735-miR-1296-5p-STAT1 axis promotes breast cancer progression and contributes to tamoxifen resistance. High hsa_circ_0086735 was associated with poor overall and distant metastasis-free survival.Discussion: This study identified the hsa_circ_0086735-miR-1296-5p-STAT1 as an important regulatory axis in luminal-subtype breast cancer, aiding to determine potential therapeutic targets.

Prolactin coordinates with the ovarian steroids to orchestrate mammary development and lactation, culminating in nourishment and an increasingly appreciated array of other benefits for neonates. Its central activities in mammary epithelial growth and differentiation suggest that it plays a role(s) in breast cancer, but it has been challenging to identify its contributions, essential for incorporation into prevention and treatment approaches. Large prospective epidemiologic studies have linked higher prolactin exposure to increased risk, particularly for ER+ breast cancer in postmenopausal women. However, it has been more difficult to determine its actions and clinical consequences in established tumors. Here we review experimental data implicating multiple mechanisms by which prolactin may increase the risk of breast cancer. We then consider the evidence for role(s) of prolactin and its downstream signaling cascades in disease progression and treatment responses, and discuss how new approaches are beginning to illuminate the biology behind the seemingly conflicting epidemiologic and experimental studies of prolactin actions across diverse breast cancers. [ABSTRACT FROM AUTHOR]

Prolactin coordinates with the ovarian steroids to orchestrate mammary development and lactation, culminating in nourishment and an increasingly appreciated array of other benefits for neonates. Its central activities in mammary epithelial growth and differentiation suggest that it plays a role(s) in breast cancer, but it has been challenging to identify its contributions, essential for incorporation into prevention and treatment approaches. Large prospective epidemiologic studies have linked higher prolactin exposure to increased risk, particularly for ER+ breast cancer in postmenopausal women. However, it has been more difficult to determine its actions and clinical consequences in established tumors. Here we review experimental data implicating multiple mechanisms by which prolactin may increase the risk of breast cancer. We then consider the evidence for role(s) of prolactin and its downstream signaling cascades in disease progression and treatment responses, and discuss how new approaches are beginning to illuminate the biology behind the seemingly conflicting epidemiologic and experimental studies of prolactin actions across diverse breast cancers.

GATA3 is known to be one of the most frequently mutated genes in breast cancer. More than 10% of breast tumors carry mutations in this gene. However, the functional consequence of GATA3 mutations is still largely unknown. Clinical data suggest that different types of GATA3 mutations may have distinct roles in breast cancer characterization. In this study, we have established three luminal breast cancer cell lines that stably express different truncation mutants (X308 splice site deletion, C321 frameshift, and A333 frameshift mutants) found in breast cancer patients. Transcriptome analysis identified common and distinct gene expression patterns in these GATA3 mutant cell lines. In particular, the impacts on epithelial-to-mesenchymal transition (EMT) related genes are similar across these mutant cell lines. Chromatin localization of the mutants is highly overlapped and exhibits non-canonical motif enrichment. Interestingly, the A333 frameshift mutant expressed cells displayed the most significant impact on the GATA3 binding compared to X308 splice site deletion and C321fs mutants expressed cells. Our results suggest the common and different roles of GATA3 truncation mutations during luminal breast cancer development.

GATA3 is known to be one of the most frequently mutated genes in breast cancer. More than 10% of breast tumors carry mutations in this gene. However, the functional consequence of GATA3 mutations is still largely unknown. Clinical data suggest that different types of GATA3 mutations may have distinct roles in breast cancer characterization. In this study, we have established three luminal breast cancer cell lines that stably express different truncation mutants (X308 splice site deletion, C321 frameshift, and A333 frameshift mutants) found in breast cancer patients. Transcriptome analysis identified common and distinct gene expression patterns in these GATA3 mutant cell lines. In particular, the impacts on epithelial-to-mesenchymal transition (EMT) related genes are similar across these mutant cell lines. Chromatin localization of the mutants is highly overlapped and exhibits non-canonical motif enrichment. Interestingly, the A333 frameshift mutant expressed cells displayed the most significant impact on the GATA3 binding compared to X308 splice site deletion and C321fs mutants expressed cells. Our results suggest the common and different roles of GATA3 truncation mutations during luminal breast cancer development. [ABSTRACT FROM AUTHOR]

As a new-generation CDK inhibitor, a CDK4/6 inhibitor combined with endocrine therapy has been successful in the treatment of advanced estrogen receptor–positive (ER+) breast cancer. Although there has been overall progress in the treatment of cancer, drug resistance is an emerging cause for breast cancer–related death. Overcoming CDK4/6 resistance is an urgent problem. Overactivation of the cyclin-CDK-Rb axis related to uncontrolled cell proliferation is the main cause of CDK4/6 inhibitor resistance; however, the underlying mechanisms need to be clarified further. We review various resistance mechanisms of CDK4/6 inhibitors in luminal breast cancer. The cell signaling pathways involved in therapy resistance are divided into two groups: upstream response mechanisms and downstream bypass mechanisms. Finally, we discuss possible strategies to overcome CDK4/6 inhibitor resistance and identify novel resistance targets for future clinical application.

Background: The tumor microenvironment (TME) has been reported to have significant value in the diagnosis and prognosis of cancers. This study aimed to identify key biomarkers in the TME of luminal breast cancer (BC).Methods: We obtained immune scores (ISs) and stromal scores (SSs) for The Cancer Genome Atlas (TCGA) luminal BC cohort from the online ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) portal. The relationships between ISs and SSs and the overall survival of luminal BC patients were assessed by the Kaplan-Meier method. The differentially expressed messenger RNAs (DEmRNAs) related to the ISs and SSs were subjected to functional enrichment analysis. Additionally, a competing endogenous RNA (ceRNA) network was constructed with differentially expressed microRNAs (DEmiRNAs) and long noncoding RNAs (DElncRNAs). Furthermore, a protein–protein interaction (PPI) network was established to analyze the DEmRNAs in the ceRNA network. Then, survival analysis of biomarkers involved in the ceRNA network was carried out to explore their prognostic value. Finally, these biomarkers were validated using the luminal BC dataset from the Gene Expression Omnibus (GEO) database.Results: The results showed that ISs were significantly associated with longer survival times of luminal BC patients. Functional enrichment analysis showed that the DEmRNAs were mainly associated with immune response, antigen binding, and the extracellular region. In the PPI network, the top 10 DEmRNAs were identified as hub genes that affected the TME of luminal BC. Finally, two DEmiRNAs, two DElncRNAs, and 17 DEmRNAs of the ceRNA network associated with the TME were shown to have prognostic value. Subsequently, the expression of 15 prognostic biomarkers was validated in one additional dataset (GSE81002). In particular, one lncRNA (GVINP1) and five mRNAs (CCDC69, DOCK2, IKZF1, JCHAIN, and NCKAP1L) were novel biomarkers.Conclusions: Our studies demonstrated that ISs were associated with the survival of luminal BC patients, and a set of novel biomarkers that might play a prognostic role in the TME of luminal BC was identified.

Background: The tumor microenvironment (TME) has been reported to have significant value in the diagnosis and prognosis of cancers. This study aimed to identify key biomarkers in the TME of luminal breast cancer (BC). Methods: We obtained immune scores (ISs) and stromal scores (SSs) for The Cancer Genome Atlas (TCGA) luminal BC cohort from the online ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) portal. The relationships between ISs and SSs and the overall survival of luminal BC patients were assessed by the Kaplan-Meier method. The differentially expressed messenger RNAs (DEmRNAs) related to the ISs and SSs were subjected to functional enrichment analysis. Additionally, a competing endogenous RNA (ceRNA) network was constructed with differentially expressed microRNAs (DEmiRNAs) and long noncoding RNAs (DElncRNAs). Furthermore, a protein–protein interaction (PPI) network was established to analyze the DEmRNAs in the ceRNA network. Then, survival analysis of biomarkers involved in the ceRNA network was carried out to explore their prognostic value. Finally, these biomarkers were validated using the luminal BC dataset from the Gene Expression Omnibus (GEO) database. Results: The results showed that ISs were significantly associated with longer survival times of luminal BC patients. Functional enrichment analysis showed that the DEmRNAs were mainly associated with immune response, antigen binding, and the extracellular region. In the PPI network, the top 10 DEmRNAs were identified as hub genes that affected the TME of luminal BC. Finally, two DEmiRNAs, two DElncRNAs, and 17 DEmRNAs of the ceRNA network associated with the TME were shown to have prognostic value. Subsequently, the expression of 15 prognostic biomarkers was validated in one additional dataset (GSE81002). In particular, one lncRNA (GVINP1) and five mRNAs (CCDC69, DOCK2, IKZF1, JCHAIN, and NCKAP1L) were novel biomarkers. Conclusions: Our studies demonstrated that ISs were associated with the survival of luminal BC patients, and a set of novel biomarkers that might play a prognostic role in the TME of luminal BC was identified. [ABSTRACT FROM AUTHOR]

Background: Multigene signatures refine the risk of recurrence and guide adjuvant chemotherapy decision in luminal breast cancers. The decision to perform the assay is highly variable among oncologists. In order to guide the appropriate clinical group in whom to perform a genomic signature, our study analyzed in a homogeneous cohort which clinical risk groups triggered the use of the PAM50-based signature and their concordance with the genomic risk.Methods: A real life cohort of 222 early breast cancer patients with hormone receptor positive and HER2 negative disease had a commercial PAM50-based assay (Prosigna®) performed at our institution. The assay provided the risk group, the 10-year risk of distant recurrence and the intrinsic molecular subtype of breast cancer.Results: Based on nodal involvement, Ki67, tumor grade, mitotic index, and tumor size, no clinical pattern could identify a specific genomic risk group. The discordance with the genomic risk was high in patients with clinical low risk tumors, both in node negative and node positive patients. Up to 60% of them had a 10% or more risk of distant recurrence. Moreover, we identified a subgroup of luminal A tumors with a high genomic risk of recurrence. Genomic risk and intrinsic subtype were strong determinants of chemotherapy decision.Conclusions: Clinical profiles could not reliably identify genomic risk groups and guide the decision to use a multigene signature. Significant discordance with the genomic risk was observed within low clinical risk and luminal A tumors.

Background: Multigene signatures refine the risk of recurrence and guide adjuvant chemotherapy decision in luminal breast cancers. The decision to perform the assay is highly variable among oncologists. In order to guide the appropriate clinical group in whom to perform a genomic signature, our study analyzed in a homogeneous cohort which clinical risk groups triggered the use of the PAM50-based signature and their concordance with the genomic risk. Methods: A real life cohort of 222 early breast cancer patients with hormone receptor positive and HER2 negative disease had a commercial PAM50-based assay (Prosigna®) performed at our institution. The assay provided the risk group, the 10-year risk of distant recurrence and the intrinsic molecular subtype of breast cancer. Results: Based on nodal involvement, Ki67, tumor grade, mitotic index, and tumor size, no clinical pattern could identify a specific genomic risk group. The discordance with the genomic risk was high in patients with clinical low risk tumors, both in node negative and node positive patients. Up to 60% of them had a 10% or more risk of distant recurrence. Moreover, we identified a subgroup of luminal A tumors with a high genomic risk of recurrence. Genomic risk and intrinsic subtype were strong determinants of chemotherapy decision. Conclusions: Clinical profiles could not reliably identify genomic risk groups and guide the decision to use a multigene signature. Significant discordance with the genomic risk was observed within low clinical risk and luminal A tumors. [ABSTRACT FROM AUTHOR]

As a new-generation CDK inhibitor, a CDK4/6 inhibitor combined with endocrine therapy has been successful in the treatment of advanced estrogen receptor–positive (ER+) breast cancer. Although there has been overall progress in the treatment of cancer, drug resistance is an emerging cause for breast cancer–related death. Overcoming CDK4/6 resistance is an urgent problem. Overactivation of the cyclin-CDK-Rb axis related to uncontrolled cell proliferation is the main cause of CDK4/6 inhibitor resistance; however, the underlying mechanisms need to be clarified further. We review various resistance mechanisms of CDK4/6 inhibitors in luminal breast cancer. The cell signaling pathways involved in therapy resistance are divided into two groups: upstream response mechanisms and downstream bypass mechanisms. Finally, we discuss possible strategies to overcome CDK4/6 inhibitor resistance and identify novel resistance targets for future clinical application. [ABSTRACT FROM AUTHOR]