Metastatic Pattern of Breast Cancer by Histologic Grade: A SEER Population-based Study.

Population-based estimates of the differences -in metastatic pattern, incidence, and prognosis of breast cancer patients by histologic grade at breast cancer diagnosis are lacking. Patients with breast cancer and metastases at the time of diagnosis were identified using the Surveillance, Epidemiology, and End Results (SEER) database. Multivariable logistic and Cox regression were performed to determine the effect of histologic grade on the presence of metastases at diagnosis and all-cause mortality. We identified a population-based sample of adult patients diagnosed with invasive breast cancer between 2010 and 2015 for whom the presence or absence of metastases was known. We depicted the landscape of metastatic pattern of breast cancer histologic grade that the percentage of bone metastasis was decreasing with higher histologic grade, while the percentages of lung and brain metastasis were increasing. Higher histologic grade was associated with a greater incidence of all metastatic lesions. Median durations of survival with distant metastasis were 41 months (Grade I), 34 months (Grade II), 21 months (Grade III), 13 months (Grade IV), and 16 months (unknown histologic grade). Grade III and unknown histologic grade represent the most common part of patients with metastatic disease, but not for breast cancer patients without metastasis. In multivariate analysis, Grade II, III, IV, and unknown histologic grade were associated with significantly greater odds of patients with metastatic disease to any distant site, compared with Grade I, but not to bone. Grade III was associated with increased all-cause mortality among patients having metastases to any sites, bone, brain, liver, and lung compared with Grade I, but not Grade II and Grade IV. Breast cancer histologic grades are associated with distinct patterns of metastatic spread and notable differences in survival.

Molecular and Clinical Characterization of CD80 Expression via Large-Scale Analysis in Breast Cancer.

Cancer immunotherapy is emerging as a novel promising therapy option for cancer patients. Despite the critical role of CD80 in the regulation of immune responses, the expression and biological functions of CD80 in breast cancer remain unknown. In this study, we aimed to investigate the role of CD80 both clinically and molecularly in breast cancer at a transcriptome level. Herein, we first analyzed the transcriptome profile and relevant clinical information derived from a total of 1090 breast cancer patients recorded in The Cancer Genome Atlas database and then validated this in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database (n = 1904). We revealed the associations of CD80 and the main molecular and clinical characteristics of breast cancer. The gene ontology analysis and Gene Set Variation Analysis of the CD80-related genes revealed that CD80 was closely correlated with immune responses and inflammatory activities in breast cancer. Moreover, the CD80 expression showed a remarkable positive correlation with several infiltrated immune cell populations. In summary, the CD80 expression was closely correlated with the malignancy of breast cancer, and our findings suggest that CD80 might be a promising target for immunotherapeutic strategies. To the best of our knowledge, this is the first integrative study characterizing the role of the CD80 expression in breast cancer via large-scale analyses.

TIGIT-related transcriptome profile and its association with tumor immune microenvironment in breast cancer.

Immune checkpoints are intensively investigated as targets in cancer immunotherapy. T-cell immunoreceptor with immunoglobulin (Ig) and ITIM domains (TIGIT) are recently emerging as a novel promising target in cancer immunotherapy. Herein, we systematically investigated TIGIT-related transcriptome profile and relevant clinical information derived from a total of 2994 breast cancer patients recorded in The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). We uncovered the relationship between TIGIT and major molecular and clinical characteristics in breast cancer. More importantly, we depicted the landscape of associations between TIGIT and other immune cell populations. Gene ontology analyses and Gene Set Variation Analysis (GSVA) of genes correlated with TIGIT revealed that TIGIT were mainly involved in immune responses and inflammatory activities. In summary, TIGIT expression was tightly related to the aggressiveness of breast cancer; TIGIT might manipulate anti-tumor immune responses by impacting not only T cells but also other immune cells. To the best of our knowledge, this is by far the most comprehensive and largest study characterizing the molecular and clinical features of TIGIT in breast cancer through large-scale transcriptome data.

Transcriptome Analysis Reveals MFGE8-HAPLN3 Fusion as a Novel Biomarker in Triple-Negative Breast Cancer.

BACKGROUND: Triple-negative breast cancer (TNBC) is a highly aggressive cancer with poor prognosis. The lack of effective targeted therapies for TNBC remains a profound clinical challenge. Fusion transcripts play critical roles in carcinogenesis and serve as valuable diagnostic and therapeutic targets in cancer. The present study aimed to identify novel fusion transcripts in TNBC. METHODS: We analyzed the RNA sequencing data of 360 TNBC samples to identify and filter fusion candidates through SOAPfuse and ChimeraScan analysis. The characteristics, including recurrence, fusion type, chromosomal localization, TNBC subgroup distribution, and clinicopathological correlations, were analyzed in all candidates. Furthermore, we selected the promising fusion transcript and predicted its fusion type and protein coding capacity. RESULTS: Using the RNA sequencing data, we identified 189 fusion transcripts in TNBC, among which 22 were recurrent fusions. Compared to para-tumor tissues, TNBC tumor tissues accumulated more fusion events, especially in high-grade tumors. Interestingly, these events were enriched at specific chromosomal loci, and the distribution pattern varied in different TNBC subtypes. The vast majority of fusion partners were discovered on chromosomes 1p, 11q, 19p, and 19q. Besides, fusion events mainly clustered on chromosome 11 in the immunomodulatory subtype and chromosome 19 in the luminal androgen receptor subtype of TNBC. Considering the tumor specificity and frameshift mutation, we selected MFGE8-HAPLN3 as a novel biomarker and further validated it in TNBC samples using PCR and Sanger sequencing. Further, we successfully identified three types of MFGE8-HAPLN3 (E6-E2, E5-E3, and E6-E3) and predicted the ORF of E6-E2, which could encode a protein of 712 amino acids, suggesting its critical role in TNBC. CONCLUSIONS: Improved bioinformatic stratification and comprehensive analysis identified the fusion transcript MFGE8-HAPLN3 as a novel biomarker with promising clinical application in the future.