Breast Cancer Plasticity after Chemotherapy Highlights the Need for Re-Evaluation of Subtyping in Residual Cancer and Metastatic Tissues.

This research paper presents a novel approach to identifying biomarkers that can be used to prognosticate patients with triple-negative breast cancer (TNBC) eligible for neoadjuvant therapy. The study utilized survival and RNA sequencing data from a cohort of TNBC patients and identified 276 genes whose expression was related to survival in such patients. The gene expression data were then used to classify patients into two major groups based on the presence or absence of Wingless/Integrated-pathway (Wnt-pathway) and mesenchymal (Mes) markers (Wnt/Mes). Patients with a low expression of Wnt/Mes-related genes had a favorable outcome, with no deaths observed during follow-up, while patients with a high expression of Wnt/Mes genes had a higher mortality rate of 50% within 19 months. The identified gene list could be validated and potentially used to shape treatment options for TNBC patients eligible for neoadjuvant therapy providing valuable insights into the development of more effective treatments for TNBC. Our data also showed significant variation in gene expression profiles before and after chemotherapy, with most tumors switching to a more mesenchymal/stem cell-like profile. To verify this observation, we performed an in silico analysis to classify breast cancer tumors in Prediction Analysis of Microarray 50 (PAM50) molecular classes before treatment and after treatment using gene expression data. Our findings demonstrate that following drug intervention and metastasis, certain tumors undergo a transition to alternative subtypes, resulting in diminished therapeutic efficacy. This underscores the necessity for reevaluation of patients who have experienced relapse or metastasis post-chemotherapy, with a focus on molecular subtyping. Tailoring treatment strategies based on these refined subtypes is imperative to optimize therapeutic outcomes for affected individuals.

Caveolin-1 gene expression provides additional prognostic information combined with PAM50 risk of recurrence (ROR) score in breast cancer.

Combining information from the tumor microenvironment (TME) with PAM50 Risk of Recurrence (ROR) score could improve breast cancer prognostication. Caveolin-1 (CAV1) is a marker of an active TME. CAV1 is a membrane protein involved in cell signaling, extracellular matrix organization, and tumor-stroma interactions. We sought to investigate CAV1 gene expression in relation to PAM50 subtypes, ROR score, and their joint prognostic impact. CAV1 expression was compared between PAM50 subtypes and ROR categories in two cohorts (SCAN-B, n = 5326 and METABRIC, n = 1980). CAV1 expression was assessed in relation to clinical outcomes using Cox regression and adjusted for clinicopathological predictors. Effect modifications between CAV1 expression and ROR categories on clinical outcome were investigated using multiplicative and additive two-way interaction analyses. Differential gene expression and gene set enrichment analyses were applied to compare high and low expressing CAV1 tumors. All samples expressed CAV1 with the highest expression in the Normal-like subtype. Gene modules consistent with epithelial-mesenchymal transition (EMT), hypoxia, and stromal activation were associated with high CAV1 expression. CAV1 expression was inversely associated with ROR category. Interactions between CAV1 expression and ROR categories were observed in both cohorts. High expressing CAV1 tumors conferred worse prognosis only within the group classified as ROR high. ROR gave markedly different prognostic information depending on the underlying CAV1 expression. CAV1, a potential mediator between the malignant cells and TME, could be a useful biomarker that enhances and further refines PAM50 ROR risk stratification in patients with ROR high tumors and a potential therapeutic target.

Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification.

BACKGROUND: In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately. RESULTS: This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature. CONCLUSIONS: The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.

Molecular Characterization and Subtyping of Breast Cancer Cell Lines Provide Novel Insights into Cancer Relevant Genes.

Continuous cell lines are important and commonly used in vitro models in breast cancer (BC) research. Selection of the appropriate model cell line is crucial and requires consideration of their molecular characteristics. To characterize BC cell line models in depth, we profiled a panel of 29 authenticated and publicly available BC cell lines by mRNA-sequencing, mutation analysis, and immunoblotting. Gene expression profiles separated BC cell lines in two major clusters that represent basal-like (mainly triple-negative BC) and luminal BC subtypes, respectively. HER2-positive cell lines were located within the luminal cluster. Mutation calling highlighted the frequent aberration of TP53 and BRCA2 in BC cell lines, which, therefore, share relevant characteristics with primary BC. Furthermore, we showed that the data can be used to find novel, potential oncogenic fusion transcripts, e.g., FGFR2::CRYBG1 and RTN4IP1::CRYBG1 in cell line MFM-223, and to elucidate the regulatory circuit of IRX genes and KLF15 as novel candidate tumor suppressor genes in BC. Our data indicated that KLF15 was activated by IRX1 and inhibited by IRX3. Moreover, KLF15 inhibited IRX1 in cell line HCC-1599. Each BC cell line carries unique molecular features. Therefore, the molecular characteristics of BC cell lines described here might serve as a valuable resource to improve the selection of appropriate models for BC research.

Advancing breast cancer subtyping: optimizing immunohistochemical staining classification with insights from real-world Taiwanese data.

Gene expression signatures provide valuable information to guide postoperative treatment in breast cancer (BC) patients. However, genetic tests are prohibitively expensive for the majority of BC patients. Immunohistochemical staining (IHC) subtype classification system has been widely used for treatment guideline and is affordable to most BC patients. We aimed to revise immunohistochemical staining (IHC) subtyping to better match gene expression-based Prediction Analysis of Microarray 50 (PAM50) subtyping. Real world data of 372 BC patients were recruited in the Tri-Service General Hospital between Jan 2019 and Dec 2021. Clinical pathological information, blood, twelve pathological tissue slide samples, and fresh surgical tumor specimens were collected to examine IHC and PAM50. Current IHC subtyping (cIHC) tends to misclassify PAM50-based luminal A (lum A) to luminal B (lum B) by 35.81%, PAM50-lum B to PAM50-lum A by 9.09%, PAM50-Her2-enriched to lum B by 61.11%, PAM50-based Her2-enriched to lum B by 61.11%, and PAM50-based basal-like to lum B by 33.33%. We used random forest to identify estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (Her2), and Ki-67 status as the best indicators for revised IHC subtyping (rIHC4) and revised the classification rules by stratified analysis and prediction efficacy. rIHC4 increased the concordance rate for PAM50 subtypes from 68.3% to 74.7%. Both sensitivity and precision increased in most rIHC4 subtypes. Sensitivity increased from 33.3% to 87.4% in the Her2-enriched subtype; precision increased more evidently in the basal-like and lum B subtypes, from 71.4% to 83.3% and 57% to 65.1%, respectively. Our rIHC4 subtyping improved consistency with the PAM50 subtype, which could improve clinical management of BC patients without increasing medical expense.

Application Of Multigene Panel Detection In Breast Cancer.

Precision medicine will be the direction of future medical development, especially in cancer diagnosis and treatment. With the deepening of breast cancer-related research, new factors related to diagnosis, treatment and prognosis are constantly being discovered. Researchers combine different factorsto form a multigene panel testing, guiding clinicians' decision-making. The application scope of multigene panel detection is constantly expanding. At present, it has been tried in the prognosis evaluation of lymph node-positive and human epidermal growth factor receptor 2-positive breast cancer patients and the early screening of breast cancer. With continuous technological advancement, there will be broader application prospects in the future. The current narrative review was planned to evaluate the recent advances in applying multigene panel testing in breast cancer cases.

Human Breast Tissue Microbiota Reveals Unique Microbial Signatures that Correlate with Prognostic Features in Adult Ethiopian Women with Breast Cancer.

Breast cancer (BC) is the leading cause of cancer mortality among women in Ethiopia. Overall, women of African ancestry have the highest death toll due to BC compared to other racial/ethnic groups. The cause of the disparity in mortality is unclear. Recently, studies conducted in the United States and other high-income countries highlighted the role of microbial dysbiosis in BC initiation, tumor growth, and treatment outcome. However, the extent to which inter-individual differences in the makeup of microbiota are associated with clinical and histopathological outcomes in Ethiopian women has not been studied. The goal of our study was to profile the microbiome in breast tumor and normal adjacent to tumor (NAT) tissues of the same donor and to identify associations between microbial composition and abundance and clinicopathological factors in Ethiopian women with BC. We identified 14 microbiota genera in breast tumor tissues that were distinct from NAT tissues, of which Sphingobium, Anaerococcus, Corynebacterium, Delftia, and Enhydrobacter were most significantly decreased in breast tumors compared to NAT tissues. Several microbial genera significantly differed by clinicopathological factors in Ethiopian women with BC. Specifically, the genus Burkholderia more strongly correlated with aggressive triple negative (TNBC) and basal-like breast tumors. The genera Alkanindiges, Anoxybacillus, Leifsonia, and Exiguobacterium most strongly correlated with HER2-E tumors. Luminal A and luminal B tumors also correlated with Anoxybacillus but not as strongly as HER2-E tumors. A relatively higher abundance of the genus Citrobacter most significantly correlated with advanced-stage breast tumors compared to early-stage tumors. This is the first study to report an association between breast microbial dysbiosis and clinicopathological factors in Ethiopian women.

Human basal-like breast cancer is represented by one of the two mammary tumor subtypes in dogs.

BACKGROUND: About 20% of breast cancers in humans are basal-like, a subtype that is often triple-negative and difficult to treat. An effective translational model for basal-like breast cancer is currently lacking and urgently needed. To determine whether spontaneous mammary tumors in pet dogs could meet this need, we subtyped canine mammary tumors and evaluated the dog-human molecular homology at the subtype level. METHODS: We subtyped 236 canine mammary tumors from 3 studies by applying various subtyping strategies on their RNA-seq data. We then performed PAM50 classification with canine tumors alone, as well as with canine tumors combined with human breast tumors. We identified feature genes for human BLBC and luminal A subtypes via machine learning and used these genes to repeat canine-alone and cross-species tumor classifications. We investigated differential gene expression, signature gene set enrichment, expression association, mutational landscape, and other features for dog-human subtype comparison. RESULTS: Our independent genome-wide subtyping consistently identified two molecularly distinct subtypes among the canine tumors. One subtype is mostly basal-like and clusters with human BLBC in cross-species PAM50 and feature gene classifications, while the other subtype does not cluster with any human breast cancer subtype. Furthermore, the canine basal-like subtype recaptures key molecular features (e.g., cell cycle gene upregulation, TP53 mutation) and gene expression patterns that characterize human BLBC. It is enriched in histological subtypes that match human breast cancer, unlike the other canine subtype. However, about 33% of canine basal-like tumors are estrogen receptor negative (ER-) and progesterone receptor positive (PR+), which is rare in human breast cancer. Further analysis reveals that these ER-PR+ canine tumors harbor additional basal-like features, including upregulation of genes of interferon-γ response and of the Wnt-pluripotency pathway. Interestingly, we observed an association of PGR expression with gene silencing in all canine tumors and with the expression of T cell exhaustion markers (e.g., PDCD1) in ER-PR+ canine tumors. CONCLUSIONS: We identify a canine mammary tumor subtype that molecularly resembles human BLBC overall and thus could serve as a vital translational model of this devastating breast cancer subtype. Our study also sheds light on the dog-human difference in the mammary tumor histology and the hormonal cycle.

The Prediction Analysis of Microarray 50 (PAM50) Gene Expression Classifier Utilized in Indeterminate-Risk Breast Cancer Patients in Hungary: A Consecutive 5-Year Experience.

BACKGROUND: Breast cancer has been categorized into molecular subtypes using immunohistochemical staining (IHC) and fluorescence in situ hybridization (FISH) since the early 2000s. However, recent research suggests that gene expression testing, specifically Prosigna® Prediction Analysis of Microarray 50 (PAM50), provides more accurate classification methods. In this retrospective study, we compared the results of IHC/FISH and PAM50 testing. We also examined the impact of various PAM50 parameters on overall survival (OS) and progression-free survival (PFS). RESULTS: We analyzed 42 unilateral breast cancer samples, with 18 classified as luminal A, 10 as luminal B, 8 as Human epidermal growth factor receptor 2 (HER2)-positive, and 6 as basal-like using PAM50. Interestingly, 17 out of the 42 samples (40.47%) showed discordant results between histopathological assessment and the PAM50 classifier. While routine IHC/FISH resulted in classification differences for a quarter to a third of samples within each subtype, all basal-like tumors were misclassified. Hormone receptor-positive tumors (hazard rate: 8.7803; p = 0.0085) and patients who had higher 10-year recurrence risk scores (hazard rate: 1.0539; p = 0.0201) had shorter OS and PFS. CONCLUSIONS: Our study supports the existing understanding of molecular subtypes in breast cancer and emphasizes the overlap between clinical characteristics and molecular subtyping. These findings underscore the value of gene expression profiling, such as PAM50, in improving treatment decisions for breast cancer patients.

Analysis of the association of HER-2 low carcinomas and PAM50 assay in hormone receptor positive early-stage breast cancer.

BACKGROUND: HER2-low has emerged as a new predictive biomarker in metastatic breast cancer. However, its prognostic value in early-stage carcinomas needs to be revisited. We aimed to evaluate the association of HER2-low carcinomas with PAM50 risk groups combined with clinicopathological variables in early breast cancer. METHODS: We conducted a retrospective analysis of 332 patients with early-stage breast cancer that underwent PAM50 signature analysis between 2015 and 2021at Hospital Universitario 12 de Octubre (Madrid, Spain). Clinical and pathological variables were collected from medical records. After adjusting for potential confounders, we estimated Odds Ratio (OR) and 95% confidence interval for high-risk PAM50 subgroup, comparing HER2-low versus HER2-zero carcinomas by multivariable logistic regression. P values below 0.05 were deemed statistically significant. RESULTS: 192 (57%) patients were classified as HER2-low carcinomas. Median follow-up was 34 months. Adjusted OR for high-risk PAM50 when comparing HER2-low versus HER2-zero carcinomas was 1.31 (95% CI: 0.75-2.30, p = 0.33). The multivariable model detected significant associations for Ki-67% (≥20% vs. <20%: OR = 4.03, 95% CI: 2.15-7.56, p < 0.001), T staging category (T2/T3 vs. T1: OR = 3.44, 95% CI: 1.96-6.04, p < 0.001), progesterone receptor (PR ≥ 20% vs. <20%: OR = 0.44, 95% CI: 0.23-0.83, p = 0.01), nodal staging category (N+ vs. N0: OR = 3.8, 95% CI: 1.89-7.62, p < 0.001) and histological grade (grade 2 vs. 1: OR = 2.41, 95% CI: 1.01-5.73, p = 0.04; grade 3 vs 1: OR = 5.40, 95%CI: 1.98-14.60, p = 0.001). CONCLUSIONS: In this early-stage breast cancer cohort, HER2-low was not associated with a high-risk PAM50 compared to HER2-zero carcinomas. Ki-67 ≥ 20%, T2/T3, histological grade 2/3, N+ and PR<20% were significantly associated to a high-risk PAM50.

LINC00426 is a potential immune phenotype-related biomarker and an overall survival predictor in PAM50 luminal B breast cancer.

Background: Breast cancer (BRCA) represents the most frequent diagnosed malignancy in women worldwide. Despite treatment advances, BRCAs eventually develop resistance to targeted therapies, resulting in poor prognosis. The identification of new biomarkers, like immune-related long non-coding RNAs (lncRNAs), could contribute to the clinical management of BRCA patients. In this report, we evaluated the LINC00426 expression in PAM50 BRCA subtypes from two clinical independent cohorts (BRCA-TCGA and GEO-GSE96058 datasets). Methods and results: Using Cox regression models and Kaplan-Meier survival analyses, we identified that LINC00426 expression was a consistent overall survival (OS) predictor in luminal B (LB) BRCA patients. Subsequently, differential gene expression and gene set enrichment analyses identified that LINC00426 expression was associated with different immune-related and cancer-related pathways and processes in LB BRCA. Additionally, the LINC00426 expression was correlated with the infiltration level of diverse immune cell populations, alongside immune checkpoint and cytolytic activity-related gene expression. Conclusion: This evidence suggests that LINC00426 is a potential biomarker of immune phenotype and an OS predictor in PAM50 LB BRCA.

Discordance between PAM50 intrinsic subtyping and immunohistochemistry in South African women with breast cancer.

PURPOSE: Breast cancer is a heterogeneous disease with different gene expression profiles, treatment options and outcomes. In South Africa, tumors are classified using immunohistochemistry. In high-income countries multiparameter genomic assays are being utilized with implications for tumor classification and treatment. METHODS: In a cohort of 378 breast cancer patients from the SABCHO study, we investigated the concordance between tumor samples classified by IHC and the PAM50 gene assay. RESULTS: IHC classified patients as ER-positive (77.5%), PR-positive (70.6%), and HER2-positive (32.3%). These results, together with Ki67, were used as surrogates for intrinsic subtyping, and showed 6.9% IHC-A-clinical, 72.7% IHC-B-clinical, 5.3% IHC-HER2-clinical and 15.1% triple negative cancer (TNC). Typing using the PAM50 gave 19.3% luminal-A, 32.5% luminal-B, 23.5% HER2-enriched and 24.6% basal-like. The basal-like and TNC had the highest concordance, while the luminal-A and IHC-A group had the lowest concordance. By altering the cutoff for Ki67, and realigning the HER2/ER/PR-positive patients to IHC-HER2, we improved concordance with the intrinsic subtypes. CONCLUSION: We suggest that the Ki67 be changed to a cutoff of 20-25% in our population to better reflect the luminal subtype classifications. This change would inform treatment options for breast cancer patients in settings where genomic assays are unaffordable.

Reproducibility and intratumoral heterogeneity of the PAM50 breast cancer assay.

BACKGROUND: The PAM50 assay is used routinely in clinical practice to determine breast cancer prognosis and management; however, research assessing how technical variation and intratumoral heterogeneity contribute to misclassification and reproducibility of these tests is limited. METHODS: We evaluated the impact of intratumoral heterogeneity on the reproducibility of results for the PAM50 assay by testing RNA extracted from formalin-fixed paraffin embedded breast cancer blocks sampled at distinct spatial locations. Samples were classified according to intrinsic subtype (Luminal A, Luminal B, HER2-enriched, Basal-like, or Normal-like) and risk of recurrence with proliferation score (ROR-P, high, medium, or low). Intratumoral heterogeneity and technical reproducibility (replicate assays on the same RNA) were assessed as percent categorical agreement between paired intratumoral and replicate samples. Euclidean distances between samples, calculated across the PAM50 genes and the ROR-P score, were compared for concordant vs. discordant samples. RESULTS: Technical replicates (N = 144) achieved 93% agreement for ROR-P group and 90% agreement on PAM50 subtype. For spatially distinct biological replicates (N = 40 intratumoral replicates), agreement was lower (81% for ROR-P and 76% for PAM50 subtype). The Euclidean distances between discordant technical replicates were bimodal, with discordant samples showing higher Euclidian distance and biologic heterogeneity. CONCLUSION: The PAM50 assay achieved very high technical reproducibility for breast cancer subtyping and ROR-P, but intratumoral heterogeneity is revealed by the assay in a small proportion of cases.

Clinical implications of the intrinsic molecular subtypes in hormone receptor-positive and HER2-negative metastatic breast cancer.

Traditionally, the classification of breast cancer relies on the expression of immunohistochemical (IHC) biomarkers readily available in clinical practice. Using highly standardized and reproducible assays across patient cohorts, intrinsic molecular subtypes of breast cancer - also called "intrinsic subtypes" (IS) - have been identified based on the expression of 50 genes. Although IHC-based subgroups and IS moderately correlate to each other, they are not superimposable. In fact, non-luminal biology has been detected in a substantial proportion (5-20%) of hormone receptor-positive (HoR+) tumors, has prognostic value, and identifies reduced and increased sensitivity to endocrine therapy and chemotherapy, respectively. During tumor progression, a shift toward a non-luminal estrogen-independent and more aggressive phenotype has been demonstrated. Intrinsic genomic instability and cell plasticity, alone or combined with external constraints deriving from treatment selective pressure or interplay with the tumor microenvironment, may represent the determinants of such biological diversity between primary and metastatic disease, and during metastatic tumor evolution. In this review, we describe the distribution and the clinical behavior of IS as the disease progresses, focusing on HoR+/HER2-negative advanced breast cancer. In addition, we provide an overview of the ongoing clinical trials aiming to validate the predictive and prognostic value of IS towards their incorporation into routine care.

Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique.

BACKGROUND: The recent development of artificial intelligence (AI) technologies coupled with medical imaging data has gained considerable attention, and offers a non-invasive approach for cancer diagnosis and prognosis. In this context, improved breast cancer (BC) molecular characteristics assessment models are foreseen to enable personalized strategies with better clinical outcomes compared to existing screening strategies. And it is a promising approach to developing models for hormone receptors (HR) and subtypes of BC patients from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. METHODS: In this institutional review board-approved study, 174 BC patients with both DCE-MRI and RNA-seq data in the local database were analyzed. Slice images from tumor lesions and multi-scale peri-tumor regions were used as model inputs, and five representative pre-trained transfer learning (TF) networks, such as Inception-v3 and Xception, were employed to establish prediction models. A comprehensive analysis was performed using five-fold cross-validation to avoid overfitting, and accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) to evaluate model performance. RESULTS: Xception achieved the superior results when using solely tumor regions, with highest AUROCs of 0.844 (95% CI: [0.841, 0.847]) and 0.784 (95% CI: [0.781, 0.788]) for estrogen receptor (ER) and progesterone receptor (PR), respectively, and best ACC of 0.467 (95% CI: [0.462, 0.470]) for PAM50 subtypes. A significant improvement in the model performance was observed when images of the peri-tumor region were included, with optimal results achieved using images of the tumor and the 10 mm peri-tumor regions. Xception-based TF models performed most effectively in predicting ER and PR statuses, with the AUROCs were 0.942 (95% CI: [0.940, 0.944]) and 0.920 (95% CI: [0.917, 0.922]), respectively, whereas for PAM50 subtypes, the Inception-v3-based network yielded the highest ACC as 0.742 (95% CI: [0.738, 0.746]). CONCLUSIONS: Transfer learning analysis based on DCE-MRI data of tumor and peri-tumor regions was helpful to the non-invasive assessment of molecular characteristics of BC.

Intrinsic subtypes in Ethiopian breast cancer patient.

PURPOSE: The recent development of multi-gene assays for gene expression profiling has contributed significantly to the understanding of the clinically and biologically heterogeneous breast cancer (BC) disease. PAM50 is one of these assays used to stratify BC patients and individualize treatment. The present study was conducted to characterize PAM50-based intrinsic subtypes among Ethiopian BC patients. PATIENTS AND METHODS: Formalin-fixed paraffin-embedded tissues were collected from 334 BC patients who attended five different Ethiopian health facilities. All samples were assessed using the PAM50 algorithm for intrinsic subtyping. RESULTS: The tumor samples were classified into PAM50 intrinsic subtypes as follows: 104 samples (31.1%) were luminal A, 91 samples (27.2%) were luminal B, 62 samples (18.6%) were HER2-enriched and 77 samples (23.1%) were basal-like. The intrinsic subtypes were found to be associated with clinical and histopathological parameters such as steroid hormone receptor status, HER2 status, Ki-67 proliferation index and tumor differentiation, but not with age, tumor size or histological type. An immunohistochemistry-based classification of tumors (IHC groups) was found to correlate with intrinsic subtypes. CONCLUSION: The distribution of the intrinsic subtypes confirms previous immunohistochemistry-based studies from Ethiopia showing potentially endocrine-sensitive tumors in more than half of the patients. Health workers in primary or secondary level health care facilities can be trained to offer endocrine therapy to improve breast cancer care. Additionally, the findings indicate that PAM50-based classification offers a robust method for the molecular classification of tumors in the Ethiopian context.

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced-MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer.

Background: To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively. Methods: Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial-temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis. Results: Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001). Conclusions: Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.

Molecular Subtyping of Invasive Breast Cancer Using a PAM50-Based Multigene Expression Test-Comparison with Molecular-Like Subtyping by Tumor Grade/Immunohistochemistry and Influence on Oncologist's Decision on Systemic Therapy in a Real-World Setting.

In intermediate risk hormone receptor (HR) positive, HER2 negative breast cancer (BC), the decision regarding adjuvant chemotherapy might be facilitated by multigene expression tests. In all, 142 intermediate risk BCs were investigated using the PAM50-based multigene expression test Prosigna® in a prospective multicentric study. In 119/142 cases, Prosigna® molecular subtyping was compared with local and two central (C1 and C6) molecular-like subtypes relying on both immunohistochemistry (IHC; HRs, HER2, Ki-67) and IHC + tumor grade (IHC+G) subtyping. According to local IHC, 35.4% were Luminal A-like and 64.6% Luminal B-like subtypes (local IHC+G subtype: 31.9% Luminal A-like; 68.1% Luminal B-like). In contrast to local and C1 subtyping, C6 classified >2/3 of cases as Luminal A-like. Pairwise agreement between Prosigna® subtyping and molecular-like subtypes was fair to moderate depending on molecular-like subtyping method and center. The best agreement was observed between Prosigna® (53.8% Luminal A; 44.5% Luminal B) and C1 surrogate subtyping (Cohen's kappa = 0.455). Adjuvant chemotherapy was suggested to 44.2% and 88.6% of Prosigna® Luminal A and Luminal B cases, respectively. Out of all Luminal A-like cases (locally IHC/IHC+G subtyping), adjuvant chemotherapy was recommended if Prosigna® testing classified as Prosigna® Luminal A at high / intermediate risk or upgraded to Prosigna® Luminal B.

Role of Intrinsic Subtype Analysis with PAM50 in Hormone Receptors Positive HER2 Negative Metastatic Breast Cancer: A Systematic Review.

Endocrine therapy (ET), associated with CDK 4/6 inhibitors, represents the first choice of treatment for HR+/HER2- metastatic breast cancer (mBC). Primary or secondary endocrine resistance could develop; however validated biomarkers capable of predicting such a conditions are not available. Several studies have shown that HR+/HER2- mBC comprises five intrinsic subtypes. The purpose of this systematic review was to analyze the potential correlations between intrinsic subtype, efficacy of treatment, and patient outcome. Five papers that analyzed the intrinsic subtype with PAM50 assay in patients (pts) with HR+/HER2- mBC treated with ET (alone or in combination) within seven phase III clinical trials (EGF30008, BOLERO-2, PALOMA-2,3, MONALEESA-2,3,7) were identified. Non-luminal subtypes are more frequent in endocrine-resistant pts and in metastatic sites (vs. primary tumors), have less benefit from ET, and worse prognosis. Among these, HER2-enriched subtypes are similar to HER2+ tumors and benefit from the addition of anti-HER2 agents (lapatinib) and, for less clear reasons, of ribociclib (unconfirmed data for palbociclib and everolimus). Basal-like subtypes are similar to triple-negative tumors, making them more sensitive to chemotherapy. The intrinsic subtype is also not static but can vary over time with the evolution of the disease. Currently, the intrinsic subtype does not play a decisive role in the choice of treatment in clinical practice, but has potential prognostic and predictive value that should be further investigated.

EARS2 significantly coexpresses with PALB2 in breast and pancreatic cancer.

BACKGROUND: PALB2 (BRCA2 partner and localizer) is a BRCA2-interacting protein that is required for BRCA2 genome caretaker tasks and interacts with BRCA1. Women with PALB2 mutation have a 40% to 60% higher risk of breast cancer, almost equivalent to women who have BRCA mutations. PALB2 mutation may also increase the risk of pancreatic cancer. New guidelines for PALB2 mutation in breast cancer advise pancreatic cancer screening, which includes M.R.I.s of the pancreas as well as endoscopic ultrasonography, for women who have a family history of pancreatic cancer. Using the Cancer Genome Atlas (TCGA) and The Human Protein Atlas we examined genes that co-express with PALB2 in breast and pancreatic cancer. METHODS: We used cBioPortal for Cancer Genomics to analyze data in TCGA. cBioPortal provides visualization, analysis and download of large-scale cancer genomics data sets. We used the UCSC Xena Browser to additionally analyze gene expression in TCGA. RESULTS: Six genes, EARS2, ARL6IP1, DNAJA3, KNOP1, RPUSD1, and TMEM186, significantly coexpressed with PALB2 in both breast and pancreatic cancer. Glutamyl-tRNA synthetase 2 (EARS2) was the only gene coexpressing with PALB2 in the breast and pancreatic cancer subjects that was significantly related to pancreatic cancer survival. Elevated PALB2 and EARS2 gene expression are both significantly associated with the PAM50 Luminal B subtype and high risk of recurrence, suggesting why these women may need active intervention, such as prophylactic mastectomy. CONCLUSIONS: EARS2 expression might be a risk factor for pancreatic cancer in breast cancer patients with PALB2 mutations. By assessing EARS2 expression in breast tumors, the clinician might obtain a second piece of information that, with family history of pancreatic cancer, could inform the decision to perform pancreatic cancer screening.