Themis: advancing precision oncology through comprehensive molecular subtyping and optimization.

Recent advances in tumor molecular subtyping have revolutionized precision oncology, offering novel avenues for patient-specific treatment strategies. However, a comprehensive and independent comparison of these subtyping methodologies remains unexplored. This study introduces 'Themis' (Tumor HEterogeneity analysis on Molecular subtypIng System), an evaluation platform that encapsulates a few representative tumor molecular subtyping methods, including Stemness, Anoikis, Metabolism, and pathway-based classifications, utilizing 38 test datasets curated from The Cancer Genome Atlas (TCGA) and significant studies. Our self-designed quantitative analysis uncovers the relative strengths, limitations, and applicability of each method in different clinical contexts. Crucially, Themis serves as a vital tool in identifying the most appropriate subtyping methods for specific clinical scenarios. It also guides fine-tuning existing subtyping methods to achieve more accurate phenotype-associated results. To demonstrate the practical utility, we apply Themis to a breast cancer dataset, showcasing its efficacy in selecting the most suitable subtyping methods for personalized medicine in various clinical scenarios. This study bridges a crucial gap in cancer research and lays a foundation for future advancements in individualized cancer therapy and patient management.

Breast cancer in northern Peru: molecular subtypes and HER2 low. / Cáncer de mama en el norte del Perú: subtipos moleculares y HER2 low.

This study aimed to understand the immunohistochemical profile of breast cancer and to identify the HER2 low subgroup in the northern macro-region of Peru. A cross-sectional study was conducted in 1176 patients from the Regional Institute of Neoplastic Diseases Northern Peru, from January 2016 to December 2023. We analyzed the data (age, histological type, grade and complementary results), with frequencies and percentages. The profile corresponded to: luminal B (45.6%); luminal A (24.7%); triple negative (18.2%); and HER2 positive non luminal (11.5%). In addition, 215 patients presented HER2 low (25.1% of those previously considered negative). This study provides evidence that the subtyping of breast cancer has changed, being luminal B the most frequent. It is essential to involve health policies to acquire targeted therapies considering HER2 low patients. Motivation for the study. Molecular classification of breast cancer allows the use of targeted treatments. Information on this profile in the northern macroregion of Peru is unknown. In addition, new therapies have appeared for a subgroup of patients. Main findings. In this study, the most frequent molecular subtypes were: luminal B, luminal A, triple negative and non-luminal HER2. Also, 18.3% of patients had low HER2 expression. Implications. Health policies should be aligned with scientific advances, to guarantee targeted therapies and to update the information in health manuals or protocols.

El objetivo del estudio fue conocer el perfil inmunohistoquímico del cáncer de mama e identificar el subgrupo HER2 low en la macrorregión norte del Perú. Se realizó un estudio transversal con una muestra de 1176 pacientes atendidos en el Instituto Regional de Enfermedades Neoplásicas Norte del Perú desde enero de 2016 a diciembre de 2023. Los datos recolectados (edad, tipo histológico, grado y resultados complementarios), se analizaron con frecuencias y porcentajes. El perfil correspondió a: luminal B (45,6%); luminal A (24,7%); triple negativo (18,2%); y HER2 positivo no luminal (11,5%). Además, HER2 low fueron 215 pacientes (25,1% de los considerados previamente negativos). Este estudio proporciona evidencia que la subtipificación de cáncer de mama ha cambiado, siendo luminal B más frecuente, y es esencial involucrar a políticas de salud para adquirir terapias dirigidas considerando a pacientes HER2 low. Motivación para realizar el estudio. La clasificación molecular del cáncer de mama permite utilizar tratamientos dirigidos. La información de este perfil en la macrorregión norte del Perú es desconocida. Además, han aparecido nuevas terapias para un subgrupo de pacientes. Principales hallazgos. En este estudio, los subtipos moleculares por orden de frecuencia fueron: luminal B, luminal A, triple negativo y HER2 no luminal. Asimismo, 18,3% de pacientes tuvieron expresión HER2 low. Implicancias. Las políticas de salud deben corresponderse con los avances científicos, para garantizar terapias dirigidas y actualizar la información de los manuales o protocolos de salud.

Development and validation of a reliable DNA copy-number-based machine learning algorithm (CopyClust) for breast cancer integrative cluster classification.

The Integrative Cluster subtypes (IntClusts) provide a framework for the classification of breast cancer tumors into 10 distinct groups based on copy number and gene expression, each with unique biological drivers of disease and clinical prognoses. Gene expression data is often lacking, and accurate classification of samples into IntClusts with copy number data alone is essential. Current classification methods achieve low accuracy when gene expression data are absent, warranting the development of new approaches to IntClust classification. Copy number data from 1980 breast cancer samples from METABRIC was used to train multiclass XGBoost machine learning algorithms (CopyClust). A piecewise constant fit was applied to the average copy number profile of each IntClust and unique breakpoints across the 10 profiles were identified and converted into ~ 500 genomic regions used as features for CopyClust. These models consisted of two approaches: a 10-class model with the final IntClust label predicted by a single multiclass model and a 6-class model with binary reclassification in which four pairs of IntClusts were combined for initial multiclass classification. Performance was validated on the TCGA dataset, with copy number data generated from both SNP arrays and WES platforms. CopyClust achieved 81% and 79% overall accuracy with the TCGA SNP and WES datasets, respectively, a nine-percentage point or greater improvement in overall IntClust subtype classification accuracy. CopyClust achieves a significant improvement over current methods in classification accuracy of IntClust subtypes for samples without available gene expression data and is an easily implementable algorithm for IntClust classification of breast cancer samples with copy number data.

Pseudo-class part prototype networks for interpretable breast cancer classification.

Interpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key to the successful adoption of machine learning models. However, using confounding/irrelevant information in making predictions by deep learning models, even the interpretable ones, poses critical challenges to their clinical acceptance. That has recently drawn researchers' attention to issues beyond the mere interpretation of deep learning models. In this paper, we first investigate application of an inherently interpretable prototype-based architecture, known as ProtoPNet, for breast cancer classification in digital pathology and highlight its shortcomings in this application. Then, we propose a new method that uses more medically relevant information and makes more accurate and interpretable predictions. Our method leverages the clustering concept and implicitly increases the number of classes in the training dataset. The proposed method learns more relevant prototypes without any pixel-level annotated data. To have a more holistic assessment, in addition to classification accuracy, we define a new metric for assessing the degree of interpretability based on the comments of a group of skilled pathologists. Experimental results on the BreakHis dataset show that the proposed method effectively improves the classification accuracy and interpretability by respectively 8 % and 18 % . Therefore, the proposed method can be seen as a step toward implementing interpretable deep learning models for the detection of breast cancer using histopathology images.

Fine tuning deep learning models for breast tumor classification.

This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training and testing sets), followed by data augmentation techniques. Both feature extraction and classification tasks are employed by a Custom CNN. The experimental results show that the proposed approach using the Custom CNN model exhibits better performance with an accuracy of 84% than applying the same approach using other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, and ResNet50V2, that present relatively lower accuracies, ranging from 74 to 82%; these four models are used as both feature extractors and classifiers. To increase the accuracy and other performance metrics, Grey Wolf Optimization (GWO), and Modified Gorilla Troops Optimization (MGTO) metaheuristic optimizers are applied to each model separately for hyperparameter tuning. In this case, the experimental results show that the Custom CNN model, refined with MGTO optimization, reaches an exceptional accuracy of 93.13% in just 10 iterations, outperforming the other state-of-the-art methods, and the other four used pretrained models based on the BreakHis dataset.

Research on breast cancer pathological image classification method based on wavelet transform and YOLOv8.

 Breast cancer is one of the cancers with high morbidity and mortality in the world, which is a serious threat to the health of women. With the development of deep learning, the recognition about computer-aided diagnosis technology is getting higher and higher. And the traditional data feature extraction technology has been gradually replaced by the feature extraction technology based on convolutional neural network which helps to realize the automatic recognition and classification of pathological images. In this paper, a novel method based on deep learning and wavelet transform is proposed to classify the pathological images of breast cancer. Firstly, the image flip technique is used to expand the data set, then the two-level wavelet decomposition and reconfiguration technology is used to sharpen and enhance the pathological images. Secondly, the processed data set is divided into the training set and the test set according to 8:2 and 7:3, and the YOLOv8 network model is selected to perform the eight classification tasks of breast cancer pathological images. Finally, the classification accuracy of the proposed method is compared with the classification accuracy obtained by YOLOv8 for the original BreaKHis dataset, and it is found that the algorithm can improve the classification accuracy of images with different magnifications, which proves the effectiveness of combining two-level wavelet decomposition and reconfiguration with YOLOv8 network model.

Omitting Radiotherapy after Breast-Conserving Surgery in Luminal A Breast Cancer.

BACKGROUND: Adjuvant radiotherapy is prescribed after breast-conserving surgery to reduce the risk of local recurrence. However, radiotherapy is inconvenient, costly, and associated with both short-term and long-term side effects. Clinicopathologic factors alone are of limited use in the identification of women at low risk for local recurrence in whom radiotherapy can be omitted. Molecularly defined intrinsic subtypes of breast cancer can provide additional prognostic information. METHODS: We performed a prospective cohort study involving women who were at least 55 years of age, had undergone breast-conserving surgery for T1N0 (tumor size <2 cm and node negative), grade 1 or 2, luminal A-subtype breast cancer (defined as estrogen receptor positivity of ≥1%, progesterone receptor positivity of >20%, negative human epidermal growth factor receptor 2, and Ki67 index of ≤13.25%), and had received adjuvant endocrine therapy. Patients who met the clinical eligibility criteria were registered, and Ki67 immunohistochemical analysis was performed centrally. Patients with a Ki67 index of 13.25% or less were enrolled and did not receive radiotherapy. The primary outcome was local recurrence in the ipsilateral breast. In consultation with radiation oncologists and patients with breast cancer, we determined that if the upper boundary of the two-sided 90% confidence interval for the cumulative incidence at 5 years was less than 5%, this would represent an acceptable risk of local recurrence at 5 years. RESULTS: Of 740 registered patients, 500 eligible patients were enrolled. At 5 years after enrollment, recurrence was reported in 2.3% of the patients (90% confidence interval [CI], 1.3 to 3.8; 95% CI, 1.2 to 4.1), a result that met the prespecified boundary. Breast cancer occurred in the contralateral breast in 1.9% of the patients (90% CI, 1.1 to 3.2), and recurrence of any type was observed in 2.7% (90% CI, 1.6 to 4.1). CONCLUSIONS: Among women who were at least 55 years of age and had T1N0, grade 1 or 2, luminal A breast cancer that were treated with breast-conserving surgery and endocrine therapy alone, the incidence of local recurrence at 5 years was low with the omission of radiotherapy. (Funded by the Canadian Cancer Society and the Canadian Breast Cancer Foundation; LUMINA ClinicalTrials.gov number, NCT01791829.).

A study of clinical and molecular characteristics in bilateral primary breast cancer.

BACKGROUND: Bilateral primary breast cancer (BPBC) is a rare type of breast cancer. Studies on the clinicopathologic and molecular characteristics of BPBC in a metastatic context are very limited. METHODS: A total of 574 unselected metastatic breast cancer patients with clinical information were enrolled in our next-generation sequencing (NGS) database. Patients with BPBC from our NGS database were regarded as the study cohort. In addition, 1467 patients with BPBC and 2874 patients with unilateral breast cancer (UBC) from the Surveillance, Epidemiology, and End Results (SEER) public database were also analyzed to determine the characteristics of BPBC. RESULTS: Among the 574 patients enrolled in our NGS database, 20 (3.5%) patients had bilateral disease, comprising 15 (75%) patients with synchronous bilateral disease and 5 (25%) patients with metachronous bilateral disease. Eight patients had bilateral hormone receptor-positive (HR+)/human epidermal growth factor receptor-negative (HER2-) tumors, and three had unilateral HR+/HER2- tumors. More HR+/HER2- tumors and lobular components were found in BPBC patients than in UBC patients. The molecular subtype of the metastatic lesions in three patients was inconsistent with either side of the primary lesions, which suggested the importance of rebiopsy. Strong correlations in clinicopathologic features between the left and right tumors in BPBC were exhibited in the SEER database. In our NGS database, only one BPBC patient was found with a pathogenic germline mutation in BRCA2. The top mutated somatic genes in BPBC patients were similar to those in UBC patients, including TP53 (58.8% in BPBC and 60.6% in UBC) and PI3KCA (47.1% in BPBC and 35.9% in UBC). CONCLUSIONS: Our study suggested that BPBC may tend to be lobular carcinoma and have the HR+/HER2- subtype. Although our study did not find specific germline and somatic mutations in BPBC, more research is needed for verification.

Cáncer de mama: nueva clasificación molecular / Breast cancer: New molecular classification

A pesar de utilizar criterios histológicos e inmunohistoquímicos, no somos capaces de reflejar la heterogeneidad del cáncer de mama. En 2012 se realiza el estudio Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), el cual analiza la arquitectura genómica y de transcripción en 2000 cánceres de mama. Aparecieron subtipos moleculares con gran implicación. Tal es la importancia de la biología molecular que, en el AJCC-TNM8 (2017), se incorporaron grupos pronósticos con base en la expresión de biomarcadores (RE, RP, HER2, Ki67). Estos grupos complementan a la clasificación tradicional y añade un enfoque biológico al puramente anatómico existente. Hemos analizado el estudio METABRIC, haciendo hincapié en la nueva línea de investigación que aportó. Realizamos una exhaustiva búsqueda bibliográfica en las principales bases de datos, obteniendo los artículos que exponen los resultados del METABRIC. Desglosamos los 10 grupos integradores descubiertos recientemente, sus variaciones genéticas y su implicación para nuestra práctica clínica. Comprobamos que la clasificación actual del cáncer de mama no es lo suficientemente precisa, cuyas incongruencias se explican por los grupos integradores. Sientan los cimientos para una nueva clasificación o para refinar los subtipos existentes. (AU)

Despite using histological and immunohistochemical criteria, we are unable to reflect the heterogeneity of breast cancer. In 2012 METABRIC analyzed the genomic and transcriptional architecture of 2000 breast cancers. Molecular subtypes were found to be highly implicated. Such is the importance of molecular biology that, in AJCC-TNM8 (2017), prognostic groups based on biomarker expression (ER, PR, HER2, and Ki67) were incorporated. These groups complement the traditional classification and add a biological approach to the existing purely anatomical one. We have analyzed the METABRIC study, emphasizing the new line of research it contributed. We did an exhaustive literature search in the main databases, obtaining the articles presenting the METABRIC results. We broke down the 10 recently discovered integrative clusters, their genetic variations and their implication for our clinical practice. We found that the current classification of breast cancer is not enough accurate, the inconsistencies of which are explained by the integrative clusters. They lay the foundation for a new classification or for refining existing subtypes. (AU)

Perfil imuno-histoquímico dos casos de carcinomas mamários da microrregião de catanduva-sp / Immunohistochemical profile of breast carcinoma cases from the microregion of catanduva-sp / Perfil inmunohistoquímico de los casos de carcinomas de mama en la microrregión de catanduva-sp

Introdução: O câncer de mama compreende a principal neoplasia maligna que acomete as mulheres brasileiras, com destaque nos índices de mortalidade. O diagnóstico precoce é preconizado através da mamografia, a qual, quando alterada, sugere realizar biópsia para estudo histopatológico e, caso confirmado um carcinoma mamário, acrescenta-se o estudo imuno-histoquímico para determinação de fatores prognósticos e preditivos para o tumor. Objetivos: Levantar os resultados de análise imuno-histoquímica dos carcinomas mamários diagnosticados pelo Serviço de Patologia do Hospital Emílio Carlos (Catanduva-SP) e estabelecer os principais subtipos moleculares do câncer de mama encontrados nessa população. Material e Método: O estudo foi transversal e retrospectivo, a partir dos relatórios de imunohistoquímica dos carcinomas. Foram relatados idade, sexo, subtipo histológico do tumor e positividade imunohistoquímica para receptor de estrogênio, receptor de progesterona, fator de crescimento epidérmico humano 2, índice de proliferação celular e E-caderina. Os casos foram classificados conforme os critérios estabelecidos pelo Consenso de St. Gallen e os dados apresentados por meio de gráficos e tabelas. Resultados: A amostra foi constituída por n=210 casos de carcinomas mamários, com idade média de 58 anos e predominantemente do sexo feminino. O tipo histológico predominante foi o carcinoma mamário invasivo do tipo não especial. A expressão de receptor de estrogênio ocorreu em 92,86%, progesterona 80,48%, HER2 32,38% e Ki67 alto em 70%. O principal subtipo molecular foi o luminal B. Conclusão: Os casos de carcinomas mamários da microrregião de Catanduva apresentam diferenças quando comparados com estudos nacionais, porém similares a outros de caráter regional

Introduction: Breast cancer is the main malignant neoplasm that affects Brazilian women, especially in mortality rates. Early diagnosis is recommended through mammography, which, when altered, suggests biopsy for histopathological study and, if a breast carcinoma is confirmed, the immunohistochemical study is added for determination of prognostic and predictive factors for the tumor. Objectives: To survey the results of immunohistochemical analysis of breast carcinomas diagnosed by the Pathology Service of Hospital Emílio Carlos (Catanduva-SP) and to establish the main molecular subtypes of breast cancer found in this population. Material and Method: The study was cross-sectional and retrospective, from the reports of immunohistochemistry of carcinomas (CEP/UNIFIPA number 4737142). Age, sex, histological subtype of the tumor and immunohistochemical positivity for estrogen receptor, progesterone receptor, human epidermal growth factor 2, cell proliferation index and E-cadherin were reported. The cases were classified according to the criteria established by the St. Gallen Consensus and the data presented by means of graphs and tables. Results: The sample consisted of n=210 cases of breast carcinomas, with a mean age of 58 years and predominantly female. The predominant histological type was invasive breast carcinoma of the non-special type. Estrogen receptor expression occurred in 92.86%, progesterone 80.48%, HER2 32.38% and high Ki67 in 70%. The main molecular subtype was luminal B. Conclusion: The cases of breast carcinomas in the microregion of Catanduva present differences when compared to national studies, but similar to other regional studies

Introduction: Breast cancer comprises the main malignant neoplasm that affects Brazilian women, especially in mortality rates. Early diagnosis is recommended through mammography, which, when altered, suggests biopsy for histopathological study and, if confirmed a breast carcinoma, the immuno-study is addedto determine prognostic and predictive factors for the tumor. Objectives: To collect the results of immunohistochemical analysis of breast carcinomas diagnosed by the Pathology Service of the Emílio Carlos Hospital (Catanduva-SP) and to establish the main molecular subtypes of breast cancer found in this population. Methods: The study was cross-sectional and retrospective, from the reports of immunohistochemistry of carcinomas. Age, sex, tumor histological subtype and immunohistochemical positivity for estrogen receptor, progesterone receptor, human epidermal growth factor 2, cell proliferation index and E-cadherin were reported. The cases were classified according to the criteria established by the St. Gallen Consensus and the data presented through graphs and tables. Results: The sample consisted of n = 210 cases of breast carcinomas, with a mean age of 58 years and predominantly female. The predominant histological type was nonspecial invasive breast carcinoma. The expression of estrogen receptor occurred in 92.86%, progesterone 80.48%, HER2 32.38% and Ki67 high in 70%. The main molecular subtype was luminal B. Conclusion: The cases of breast carcinomas in the Catanduva microregion show differences when compared to national studies, but similar to others of regional character

The Value of 320-Slice Spiral Computed Tomography Perfusion Imaging in Staging and Long-Term Dynamic Evaluation of Breast Cancer.

The value of 320-slice spiral computed tomography (CT) perfusion imaging in staging and long-term dynamic evaluation of breast cancer was explored. 120 breast cancer patients who underwent preoperative CT examination and were confirmed by surgery and pathology were selected. All patients underwent preoperative TNM staging of breast cancer, with 120 cases in each stage. According to the results of 320-slice spiral CT, the postoperative pathology and surgical methods were compared and analyzed. CT diagnosis of breast cancer showed that T1 sensitivity was 71% and accuracy was 61%, T2 sensitivity was 74% and accuracy was 64%, T3 sensitivity was 94% and the accuracy was 84%, and the T4 sensitivity was 100% and the accuracy was 91%. The sensitivity of N1 stage was 71%, and the accuracy was 61%; and the sensitivity of N2 ~ N3 stage was 81%, and the accuracy was 76%. There were 7 cases of M1 with distant metastasis, the sensitivity was 71%, and the accuracy was 71%. At T1 stage, blood flow (BF) was 39.2 ± 16.7 mL/min/100 g, blood volume (BV) was 2.66 ± 1.4 mL/100 g, mean transit time (MTT) was 8.16 ± 2.7 s, and permeability surface (PS) was 16.6 ± 9.7 mL/min/100 g. 320-slice spiral CT perfusion imaging technology provided a new diagnostic mode for everyone, which can quantitatively identify breast cancer with multiple parameters, which was of great significance for clinical auxiliary diagnosis.

Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors.

Breast cancer is a global epidemic, responsible for one of the highest mortality rates among women. Ultrasound imaging is becoming a popular tool for breast cancer screening, and quantitative ultrasound (QUS) techniques are being increasingly applied by researchers in an attempt to characterize breast tissue. Several different quantitative descriptors for breast cancer have been explored by researchers. This study proposes a breast tumor classification system using the three major types of intratumoral QUS descriptors which can be extracted from ultrasound radiofrequency (RF) data: spectral features, envelope statistics features, and texture features. A total of 16 features were extracted from ultrasound RF data across two different datasets, of which one is balanced and the other is severely imbalanced. The balanced dataset contains RF data of 100 patients with breast tumors, of which 48 are benign and 52 are malignant. The imbalanced dataset contains RF data of 130 patients with breast tumors, of which 104 are benign and 26 are malignant. Holdout validation was used to split the balanced dataset into 60% training and 40% testing sets. Feature selection was applied on the training set to identify the most relevant subset for the classification of benign and malignant breast tumors, and the performance of the features was evaluated on the test set. A maximum classification accuracy of 95% and an area under the receiver operating characteristic curve (AUC) of 0.968 was obtained on the test set. The performance of the identified relevant features was further validated on the imbalanced dataset, where a hybrid resampling strategy was firstly utilized to create an optimal balance between benign and malignant samples. A maximum classification accuracy of 93.01%, sensitivity of 94.62%, specificity of 91.4%, and AUC of 0.966 were obtained. The results indicate that the identified features are able to distinguish between benign and malignant breast lesions very effectively, and the combination of the features identified in this research has the potential to be a significant tool in the noninvasive rapid and accurate diagnosis of breast cancer.

Ultrasonic Omics Based on Intelligent Classification Algorithm in Hormone Receptor Expression and Efficacy Evaluation of Breast Cancer.

The changes of hormone expression and efficacy of breast cancer (BC) were investigated under the VGG19FCN algorithm and ultrasound omics. 120 patients with BC were selected, of which 90 were positive for hormone receptor and 30 were negative. The VGG19FCN model algorithm and classifier were selected to classify the features of ultrasound breast map, and reliable ultrasound feature data were obtained. The evaluation and analysis of BC hormone receptor expression and clinical efficacy in patients with BC were realized by using ultrasonic omics. The evaluation of the results of the VGG19FCN algorithm was DSC (Dice similarity coefficient) = 0.9626, MPA (mean pixel accuracy) = 0.9676, and IOU (intersection over union) = 0.9155. When the classifier was used to classify the lesion features of BC image, the sensitivity of classification was improved to a certain extent. Compared with the classification of radiologists, when classifying whether patients had BC lesions, the sensitivity increased by 22.7%, the accuracy increased from 71.9% to 79.7%, and the specific evaluation index increased by 0.8%. No substantial difference was indicated between RT (arrive time), WIS (wash in slope), and TTP (time to peak) before and after chemotherapy, P > 0.05. After chemotherapy, the AUC (area under curve) and PI (peak intensity) of ultrasonographic examination were substantially lower than those before chemotherapy, and there were substantial differences in statistics (P < 0.05). In summary, the VGG19FCN algorithm effectively reduces the subjectivity of traditional ultrasound images and can effectively improve the value of ultrasound image features in the accurate diagnosis of BC. It provides a theoretical basis for the subsequent treatment of BC and the prediction of biological behavior. The VGG19FCN algorithm had a good performance in ultrasound image processing of BC patients, and hormone receptor expression changed substantially after chemotherapy treatment.

Assessment of Ki-67 proliferation index with deep learning in DCIS (ductal carcinoma in situ).

The proliferation index (PI) is crucial in histopathologic diagnostics, in particular tumors. It is calculated based on Ki-67 protein expression by immunohistochemistry. PI is routinely evaluated by a visual assessment of the sample by a pathologist. However, this approach is far from ideal due to its poor intra- and interobserver variability and time-consuming. These factors force the community to seek out more precise solutions. Virtual pathology as being increasingly popular in diagnostics, armed with artificial intelligence, may potentially address this issue. The proposed solution calculates the Ki-67 proliferation index by utilizing a deep learning model and fuzzy-set interpretations for hot-spots detection. The obtained region-of-interest is then used to segment relevant cells via classical methods of image processing. The index value is approximated by relating the total surface area occupied by immunopositive cells to the total surface area of relevant cells. The achieved results are compared to the manual calculation of the Ki-67 index made by a domain expert. To increase results reliability, we trained several models in a threefold manner and compared the impact of different hyper-parameters. Our best-proposed method estimates PI with 0.024 mean absolute error, which gives a significant advantage over the current state-of-the-art solution.

Gini's mean difference and the long-term prognostic value of nodal quanta classes after pre-operative chemotherapy in advanced breast cancer.

Gini's mean difference (GMD, mean absolute difference between any two distinct quantities) of the restricted mean survival times (RMSTs, expectation of life at a given time limit) has been proposed as a new metric where higher GMD indicates better prognostic value. GMD is applied to the RMSTs at 25 years time-horizon to evaluate the long-term overall survival of women with breast cancer who received neoadjuvant chemotherapy, comparing a classification based on the number (pN) versus a classification based on the ratio (LNRc) of positive nodes found at axillary surgery. A total of 233 patients treated in 1980-2009 with documented number of positive nodes (npos) and number of nodes examined (ntot) were identified. The numbers were categorized into pN0, npos = 0; pN1, npos = [1,3]; pN2, npos = [4,9]; pN3, npos ≥ 10. The ratios npnx = npos/ntot were categorized into Lnr0, npnx = 0; Lnr1, npnx = (0,0.20]; Lnr2, npnx = (0.20,0.65]; Lnr3, npnx > 0.65. The GMD for pN-classification was 5.5 (standard error: ± 0.9) years, not much improved over a simple node-negative vs. node-positive that showed a GMD of 5.0 (± 1.4) years. The GMD for LNRc-classification was larger, 6.7 (± 0.8) years. Among other conventional metrics, Cox-model LNRc's c-index was 0.668 vs. pN's c = 0.641, indicating commensurate superiority of LNRc-classification. The usability of GMD-RMSTs warrants further investigation.

Investigation of genes and pathways involved in breast cancer subtypes through gene expression meta-analysis.

BACKGROUND: Molecular-based studies have revealed heterogeneity in Breast cancer BC while also improving classification and treatment. However, efforts are underway to distinguish between distinct subtypes of breast cancer. In this study, the results of several microarray studies were combined to identify genes and pathways specific to each BC subtype. METHODS: Meta-analysis of multiple gene expression profile datasets was screened to find differentially expressed genes (DEGs) across subtypes of BC and normal breast tissue samples. Protein-protein interaction network and gene set enrichment analysis were used to identify critical genes and pathways associated with BC subtypes. The differentially expressed genes from meta-analysis was validated using an independent comprehensive breast cancer RNA-sequencing dataset obtained from the Cancer Genome Atlas (TCGA). RESULTS: We identified 110 DEGs (13 DEGs in all and 97 DEGs in each subtype) across subtypes of BC. All subtypes had a small set of shared DEGs enriched in the Chemokine receptor bind chemokine pathway. Luminal A specific were enriched in the translational elongation process in mitochondria, and the enhanced process in luminal B subtypes was interferon-alpha/beta signaling. Cell cycle and mitotic DEGs were enriched in the basal-like group. All subtype-specific DEG genes (100%) were successfully validated for Luminal A, Luminal B, ERBB2, and Normal-like. However, the validation percentage for Basal-like group was 77.8%. CONCLUSION: Integrating researches such as a meta-analysis of gene expression might be more effective in uncovering subtype-specific DEGs and pathways than a single-study analysis. It would be more beneficial to increase the number of studies that use matched BC subtypes along with GEO profiling approaches to reach a better result regarding DEGs and reduce probable biases. However, achieving 77.8% overlap in basal-specific genes and complete concordance in specific genes related to other subtypes can implicate the strength of our analysis for discovering the subtype-specific genes.

Cystic hypersecretory carcinoma with invasion: Case series on a rare variant of carcinoma breast.

BACKGROUND: Cystic Hypersecretory Carcinoma (CHC) is a rare subset of breast carcinoma. It is part of a spectrum of cystic hypersecretory lesions which includes cystic hypersecretory hyperplasia (CHH), CHH with atypia, CHC in situ and CHC with invasion. Approximately 65 cases of cystic hypersecretory lesions have been reported; most of them were CHC in situ and only 19 cases of CHC with invasion have been reported so far. CASE PRESENTATION: We are reporting 2 cases of 47 and 62 year old women with a palpable breast mass for 6 and 1 month duration respectively. Trucut biopsy was carried out for both which showed high grade ductal carcinoma in situ with microinvasion in the first patient and the latter showed a tiny focus of invasive carcinoma. Simple mastectomy and modified radical mastectomy (MRM) were done for the respective cases; both showed dilated cystic spaces filled with eosinophilic secretions (thyroid colloid-like), lining neoplastic cells that showed variable degrees of proliferation, atypia and in situ carcinoma. There were foci of invasion in both cases and hence a morphological diagnosis of CHC with invasion was made. CONCLUSION: Owing to a smaller number of reported cases, little is known about the biological behavior, prognosis and molecular profile of cystic hypersecretory carcinoma.

Primary breast sarcoma: A case series.

BACKGROUND: Primary Breast Sarcomas (PBS) are rare malignancies and seen in less than <1 % of all breast malignancies. PBS are non epithelial, composed of mesenchymal mammary tissue and are difficult to diagnose from other sarcomas arising in breast. MATERIALS AND METHODS: A retrospective study was conducted in the Department of Pathology and slides of breast malignancies over a period of 5 years were reviewed. Out of total 1570 breast malignancies, 5 cases were reported as PBS. Diagnosis was made on the basis of Histopathology and IHC findings. RESULTS: Out of total 1570 cases, 5 cases were diagnosed as PBS (i.e. 0.32% of all cases). 3 out of 5 cases were males comprising of 60% of cases and 2 cases were females accounting for 40% of cases. The age group of presentation was 32-65 years with mean age being 48.5 years. A diagnosis of MPNST was rendered in two cases ( 1=M, 1=F), one each was diagnosed as DFSP ( with fibrosarcoma), Leiomyosarcoma and Fibrosarcoma. CONCLUSION: PBS is an extremely rare entity and locally aggressive. It requires diagnosis as its treatment protocol is different.