This study defines and analyzes the stability radii of stochastic descriptor systems. We utilize generalized Lyapunov
techniques to establish necessary and sufficient conditions for exponential stability. Additionally, the paper aims
to explore robust stability by characterizing the stability radius through generalized Lyapunov equations. To the best of our knowledge, this research is the first to investigate robust stability using the infinite-dimensional
generalized Lyapunov equation.  

This study defines and analyzes the stability radii of stochastic descriptor systems. We utilize generalized Lyapunov
techniques to establish necessary and sufficient conditions for exponential stability. Additionally, the paper aims
to explore robust stability by characterizing the stability radius through generalized Lyapunov equations. To the best of our knowledge, this research is the first to investigate robust stability using the infinite-dimensional
generalized Lyapunov equation.  

This study defines and analyzes the stability radii of stochastic descriptor systems. We utilize generalized Lyapunov
techniques to establish necessary and sufficient conditions for exponential stability. Additionally, the paper aims
to explore robust stability by characterizing the stability radius through generalized Lyapunov equations. To the best of our knowledge, this research is the first to investigate robust stability using the infinite-dimensional
generalized Lyapunov equation.  

This study evaluates the inhibition mechanisms of CaCO₃ scaling in reverse osmosis using Zn⁺⁺ which has been superficially investigated in RO, with only three papers published. In order to do that a novel experimental approach was used; this approach involved a synthetic calco-carbonic solution, with an initial hardness of 60 °F (240 mg/l Ca++) and saturated with CO₂, The CO₂ leakage through the RO membrane allowed the interfacial pH to increase and, thus, accelerated the scaling occurrence. The condition for CaCO₃ precipitation is the solubility product verification: (Ca⁺⁺)* (CO₃⁼) ≥ K_S. In saturation pH, the CO₃⁼ concentration remained at ppm level; masking the CO₃⁼ ligand by Zn⁺⁺, for scaling prevention, was the main research hypothesis. This approach is very different from conventional kinetic and crystallographic theories. Furthermore, the synthetic solution was desalted in batch mode using various Zn⁺⁺ concentrations (0–1.5 ppm); the results showed a 288 minutes induction time without Zn⁺⁺ and 402 mn with Zn⁺⁺ at 1.5 ppm, the saturation pH increased from 7.33 to 8.18, confirming the Zn⁺⁺ efficiency. Also, the pH–time, conductivity–time, [Ca⁺⁺]–time and turbidity–time plotting allowed scaling detection in the fluid bulk; their comparison showed a good correlation. SEM and EDS spectro were used.

This study evaluates the inhibition mechanisms of CaCO₃ scaling in reverse osmosis using Zn⁺⁺ which has been superficially investigated in RO, with only three papers published. In order to do that a novel experimental approach was used; this approach involved a synthetic calco-carbonic solution, with an initial hardness of 60 °F (240 mg/l Ca++) and saturated with CO₂, The CO₂ leakage through the RO membrane allowed the interfacial pH to increase and, thus, accelerated the scaling occurrence. The condition for CaCO₃ precipitation is the solubility product verification: (Ca⁺⁺)* (CO₃⁼) ≥ K_S. In saturation pH, the CO₃⁼ concentration remained at ppm level; masking the CO₃⁼ ligand by Zn⁺⁺, for scaling prevention, was the main research hypothesis. This approach is very different from conventional kinetic and crystallographic theories. Furthermore, the synthetic solution was desalted in batch mode using various Zn⁺⁺ concentrations (0–1.5 ppm); the results showed a 288 minutes induction time without Zn⁺⁺ and 402 mn with Zn⁺⁺ at 1.5 ppm, the saturation pH increased from 7.33 to 8.18, confirming the Zn⁺⁺ efficiency. Also, the pH–time, conductivity–time, [Ca⁺⁺]–time and turbidity–time plotting allowed scaling detection in the fluid bulk; their comparison showed a good correlation. SEM and EDS spectro were used.

The coupling system of two different sources has always been an important subject of research in the field of electrical grids of any voltage range. In particular, after the connection of the photovoltaic and the public grids, the voltages cannot be distinguished from each other, because after their coupling there is one voltage across the output load. In this article, we take into account the variation of the current when the load varies in order to establish the relationship between the measured current and the output AC voltage, which can be regulated using only the current. For this purpose, we employ two types of controllers, the Proportional-Integral-Derivative (PID) controller and the Artificial Neural Network (ANN) controller,using Matlab/Simulink. Despite the connection of aninverter, which increases the loss rate and the error,the results are encouraging considering that the error rate obtained for the ANN controller, which is 1.49%, is much lower compared to that of the PID controller, which is 2.4%. Based on the results obtained, it can be concluded that the ANN controller is the best choice to perform this simulation.

The coupling system of two different sources has always been an important subject of research in the field of electrical grids of any voltage range. In particular, after the connection of the photovoltaic and the public grids, the voltages cannot be distinguished from each other, because after their coupling there is one voltage across the output load. In this article, we take into account the variation of the current when the load varies in order to establish the relationship between the measured current and the output AC voltage, which can be regulated using only the current. For this purpose, we employ two types of controllers, the Proportional-Integral-Derivative (PID) controller and the Artificial Neural Network (ANN) controller,using Matlab/Simulink. Despite the connection of aninverter, which increases the loss rate and the error,the results are encouraging considering that the error rate obtained for the ANN controller, which is 1.49%, is much lower compared to that of the PID controller, which is 2.4%. Based on the results obtained, it can be concluded that the ANN controller is the best choice to perform this simulation.

The coupling system of two different sources has always been an important subject of research in the field of electrical grids of any voltage range. In particular, after the connection of the photovoltaic and the public grids, the voltages cannot be distinguished from each other, because after their coupling there is one voltage across the output load. In this article, we take into account the variation of the current when the load varies in order to establish the relationship between the measured current and the output AC voltage, which can be regulated using only the current. For this purpose, we employ two types of controllers, the Proportional-Integral-Derivative (PID) controller and the Artificial Neural Network (ANN) controller,using Matlab/Simulink. Despite the connection of aninverter, which increases the loss rate and the error,the results are encouraging considering that the error rate obtained for the ANN controller, which is 1.49%, is much lower compared to that of the PID controller, which is 2.4%. Based on the results obtained, it can be concluded that the ANN controller is the best choice to perform this simulation.

Our paper focuses on the discovery and analysisof a recently identified three-dimensional chaotic model. Thisresearch presents a remarkable system characterised by its easeof implementation, but which exhibits a more complex dynamicbehaviour, exceeding that of many similar chaotic systems. Byunravelling the underlying mechanisms of this system throughthe analysis of eigenvalues, bifurcation diagrams and Lyapunovexponents, its chaotic behaviour is verified by building anelectronic circuit. The experimental behaviour is in agreementwith the numerical studies. This paper paves the way for furtherexploitation of the unique interplay between simplicity andcomplexity in chaotic systems, promising applications in variousscientific disciplines.

Our paper focuses on the discovery and analysisof a recently identified three-dimensional chaotic model. Thisresearch presents a remarkable system characterised by its easeof implementation, but which exhibits a more complex dynamicbehaviour, exceeding that of many similar chaotic systems. Byunravelling the underlying mechanisms of this system throughthe analysis of eigenvalues, bifurcation diagrams and Lyapunovexponents, its chaotic behaviour is verified by building anelectronic circuit. The experimental behaviour is in agreementwith the numerical studies. This paper paves the way for furtherexploitation of the unique interplay between simplicity andcomplexity in chaotic systems, promising applications in variousscientific disciplines.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.