Molecular Subtypes, Metastatic Pattern and Patient Age in Breast Cancer: An Analysis of Italian Network of Cancer Registries (AIRTUM) Data.
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SUBMITTER: Tagliabue G
PROVIDER: S-EPMC8706111 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeThe 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 methodsFormalin-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.ResultsThe 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.ConclusionThe 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.
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Project description:Breast cancer is a heterogeneous and complex disease, a clear manifestation of this is its classification into different molecular subtypes. On the other hand, gene transcriptional networks may exhibit different modular structures that can be related to known biological processes. Thus, modular structures in transcriptional networks may be seen as manifestations of regulatory structures that tightly controls biological processes. In this work, we identify modular structures on gene transcriptional networks previously inferred from microarray data of molecular subtypes of breast cancer: luminal A, luminal B, basal, and HER2-enriched. We analyzed the modules (communities) found in each network to identify particular biological functions (described in the Gene Ontology database) associated to them. We further explored these modules and their associated functions to identify common and unique features that could allow a better level of description of breast cancer, particularly in the basal-like subtype, the most aggressive and poor prognosis manifestation. Our findings related to the immune system and a decrease in cell death-related processes in basal subtype could help to understand it and design strategies for its treatment.
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Project description:Small primary breast cancers can show surprisingly high potential for metastasis. Clinical decision-making for tumor aggressiveness, including molecular profiling, relies primarily on analysis of the cancer cells. Here we show that this analysis is insufficient - that the stromal microenvironment of the primary tumor plays a key role in tumor cell dissemination and implantation at distant sites. We previously described 2 cancer-associated fibroblasts (CAFs) that either express (CD146+) or lack (CD146-) CD146 (official symbol MCAM, alias MUC18). We now find that when mixed with human breast cancer cells, each fibroblast subtype determines the fate of cancer cells: CD146- fibroblasts promoted increased metastasis compared with CD146+ fibroblasts. Potentially novel quantitative and qualitative proteomic analyses showed that CD146+ CAFs produced an environment rich in basement membrane proteins, while CD146- CAFs exhibited increases in fibronectin 1, lysyl oxidase, and tenascin C, all overexpressed in aggressive disease. We also show clinically that CD146- CAFs predicted for likelihood of lymph node involvement even in small primary tumors (<5 cm). Clearly small tumors enriched for CD146- CAFs require aggressive treatments.
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Project description:Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression-methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes.
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Project description:Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.
| S-EPMC5118907 | biostudies-literature
Project description:PurposeThe aim of this study was to compare the functional characteristics of two computer-based systems for quality control of cancer registry data through analysis of their output differences.MethodsThe study used cancer incidence data from 22 of the 49 registries of the Italian Network of Cancer Registries registered between 1986 and 2017. Two different data checking systems developed by the WHO International Agency for Research on Cancer (IARC) and the Joint Research Center (JRC) with the European Network of Cancer Registries (ENCR) and routinely used by registrars were used to check the quality of the data. The outputs generated by the two systems on the same dataset of each registry were analyzed and compared.ResultsThe study included a total of 1,305,689 cancer cases. The overall quality of the dataset was high, with 86% (81.7-94.1) microscopically verified cases and only 1.3% (0.03-3.06) cases with a diagnosis by death certificate only. The two check systems identified a low percentage of errors (JRC-ENCR 0.17% and IARC 0.003%) and about the same proportion of warnings (JRC-ENCR 2.79% and IARC 2.42%) in the dataset. Forty-two cases (2% of errors) and 7067 cases (11.5% of warnings) were identified by both systems in equivalent categories. 11.7% of warnings related to TNM staging were identified by the JRC-ENCR system only. The IARC system identified mainly incorrect combination of tumor grade and morphology (72.5% of warnings).ConclusionBoth systems apply checks on a common set of variables, but some variables are checked by only one of the systems (for example, checks on patient follow-up and tumor stage at diagnosis are included by the JRC-ENCR system only). Most errors and warnings were categorized differently by the two systems, but usually described the same issues, with warnings related to "morphology" (JRC-ENCR) and "histology" (IARC) being the most frequent. It is important to find the right balance between the need to maintain high standards of data quality and the workability of such systems in the daily routine of the cancer registry.
| S-EPMC10250004 | biostudies-literature
Project description:The purpose of the present study was to analyze the clinical and pathological characteristics, treatment, and prognosis of de novo metastatic breast cancer (DnMBC). Information regarding 1,890 patients treated for advanced breast cancer at the Tianjin Medical University Cancer Hospital between January 2008 to December 2017 was collected. Clinicopathological characteristics, treatments and outcomes of these patients were compared using the chi-square test, log-rank test, and Cox regression analysis. A total of 171 patients were diagnosed with DnMBC. The median age at diagnosis was 53 years (range, 23-77). The percentage of T4 staging was higher (37.4%), 69.6% of patients were estrogen receptor (ER) positive, 59.1% were progesterone receptor positive, 29.8% had positive human epidermal growth factor receptor 2 (HER2) status, 68.4% had Ki-67 ?20%, 55% had oligometastasis at the initial diagnosis, ~87.7% were treated with chemotherapy initially and 24% received palliative surgery for the primary tumor. After a median follow-up time of 26 months, the median progression-free survival (PFS) and overall survival (OS) among patients with DnMBC were 11 (8.7-13.3) months and 34 (27.9-40.1) months, respectively. In the multivariable model, ER status and sites of first metastasis (oligometastasis or polymetastasis) were identified to be independent predictors of PFS (P<0.05); ER status, primary tumor stage, and surgical treatment of primary tumors were identified to be independent predictors of OS (P<0.05). In conclusion, the clinicopathological characteristics of DnMBC are greater invasiveness and a higher risk of progression. Palliative surgical treatment may improve the prognosis of HR+/HER2-patients with oligometastasis. Therefore, individualized treatment as required is particularly important.
| S-EPMC7068499 | biostudies-literature
Project description:Biological changes that occur during metastatic progression of breast cancer are still incompletely characterized. In this study, we compared intrinsic molecular subtypes and gene expression in 123 paired primary and metastatic tissues from breast cancer patients. Intrinsic subtype was identified using a PAM50 classifier and χ2 tests determined the differences in variable distribution. The rate of subtype conversion was 0% in basal-like tumors, 23.1% in HER2-enriched (HER2-E) tumors, 30.0% in luminal B tumors, and 55.3% in luminal A tumors. In 40.2% of cases, luminal A tumors converted to luminal B tumors, whereas in 14.3% of cases luminal A and B tumors converted to HER2-E tumors. We identified 47 genes that were expressed differentially in metastatic versus primary disease. Metastatic tumors were enriched for proliferation-related and migration-related genes and diminished for luminal-related genes. Expression of proliferation-related genes were better at predicting overall survival in metastatic disease (OSmet) when analyzed in metastatic tissue rather than primary tissue. In contrast, a basal-like gene expression signature was better at predicting OSmet in primary disease compared with metastatic tissue. We observed correlations between time to tumor relapse and the magnitude of changes of proliferation, luminal B, or HER2-E signatures in metastatic versus primary disease. Although the intrinsic subtype was largely maintained during metastatic progression, luminal/HER2-negative tumors acquired a luminal B or HER2-E profile during metastatic progression, likely reflecting tumor evolution or acquisition of estrogen independence. Overall, our analysis revealed the value of stratifying gene expression by both cancer subtype and tissue type, providing clinicians more refined tools to evaluate prognosis and treatment. Cancer Res; 77(9); 2213-21. ©2017 AACR.
| S-EPMC5822682 | biostudies-literature
Project description:BackgroundBreast cancer incidence is higher among black women than white women before age 40 years, but higher among white women than black women after age 40 years (black-white crossover). We used newly available population-based data to examine whether the age-specific incidences of breast cancer subtypes vary by race and ethnicity.MethodsWe classified 91908 invasive breast cancers diagnosed in California between January 1, 2006, and December 31, 2009, by subtype based on tumor expression of estrogen receptor (ER) and progesterone receptor (PR)-together referred to as hormone receptor (HR)-and human epidermal growth factor receptor 2 (HER2). Breast cancer subtypes were classified as ER or PR positive and HER2 negative (HR(+)/HER2(-)), ER or PR positive and HER2 positive (HR(+)/HER2(+)), ER and PR negative and HER2 positive (HR(-)/HER2(+)), and ER, PR, and HER2 negative (triple-negative). We calculated and compared age-specific incidence rates, incidence rate ratios, and 95% confidence intervals by subtype and race (black, white, Hispanic, and Asian). All P values are two-sided.ResultsWe did not observe an age-related black-white crossover in incidence for any molecular subtype of breast cancer. Compared with white women, black women had statistically significantly higher rates of triple-negative breast cancer at all ages but statistically significantly lower rates of HR(+)/HER2(-) breast cancers after age 35 years (all P < .05). The age-specific incidence of HR(+)/HER2(+) and HR(-)/HER2(+) subtypes did not vary markedly between white and black women.ConclusionsThe black-white crossover in breast cancer incidence occurs only when all breast cancer subtypes are combined and relates largely to higher rates of triple-negative breast cancers and lower rates of HR(+)/HER2(-) breast cancers in black vs white women.
| S-EPMC3640371 | biostudies-literature
Project description:BackgroundTo assess metastatic breast cancer (MBC) patient psychological factors, perceptions, and comprehension of tumor genomic testing.MethodsIn a prospective, single institution, single-arm trial, patients with MBC underwent next-generation sequencing at study entry with sequencing results released at progression. Patients who completed surveys before undergoing sequencing were included in the present secondary analysis (n = 58). We administered four validated psychosocial measures: Center for Epidemiologic Studies Depression Scale, Beck Anxiety Inventory, Trust in Physician Scale, and Communication and Attitudinal Self-Efficacy scale for Cancer. Genetic comprehension was assessed using 7-question objective and 6-question subjective measures. Longitudinal data were assessed (n = 40) using paired Wilcoxon signed rank and McNemar's test of agreement.ResultsThere were no significant differences between the beginning and end of study in depression, anxiety, physician trust, or self-efficacy (median time on study: 7.6 months). Depression and anxiety were positively associated with each other and both negatively associated with self-efficacy. Self-efficacy decreased from pre- to post-genomic testing (p = 0.05). Objective genetics comprehension did not significantly change from pre- to post-genomic testing, but patients expressed increased confidence in their ability to teach others about genetics (p = 0.04). Objective comprehension was significantly lower in non-white patients (p = 0.02) and patients with lower income (p = 0.04).ConclusionsThis is the only study, to our knowledge, to longitudinally evaluate multiple psychological metrics in MBC as patients undergo tumor genomic testing. Overall, psychological dimensions remained stable over the duration of tumor genomic testing. Among patients with MBC, depression and anxiety metrics were negatively correlated with patient self-efficacy. Patients undergoing somatic genomic testing had limited genomic knowledge, which varied by demographic groups and may warrant additional educational intervention.Clinical trial informationNCT01987726, registered November 13, 2013.
| S-EPMC7201768 | biostudies-literature