Triple-Negative Breast Cancer: Histopathologic Features, Genomics, and Treatment

Feasibility of risk assessment for breast cancer molecular subtypes

PURPOSE

Few breast cancer risk assessment models account for the risk profiles of different tumor subtypes. This study evaluated whether a subtype-specific approach improves discrimination.

METHODS

Among 3389 women who had a screening mammogram and were later diagnosed with invasive breast cancer we performed multinomial logistic regression with tumor subtype as the outcome and known breast cancer risk factors as predictors. Tumor subtypes were defined by expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) based on immunohistochemistry. Discrimination was assessed with the area under the receiver operating curve (AUC). Absolute risk of each subtype was estimated by proportioning Gail absolute risk estimates by the predicted probabilities for each subtype. We then compared risk factor distributions for women in the highest deciles of risk for each subtype.

RESULTS

There were 3,073 ER/PR+ HER2 - , 340 ER/PR +HER2 + , 126 ER/PR-ER2+, and 300 triple-negative breast cancers (TNBC). Discrimination differed by subtype; ER/PR-HER2+ (AUC: 0.64, 95% CI 0.59, 0.69) and TNBC (AUC: 0.64, 95% CI 0.61, 0.68) had better discrimination than ER/PR+HER2+ (AUC: 0.61, 95% CI 0.58, 0.64). Compared to other subtypes, patients at high absolute risk of TNBC were younger, mostly Black, had no family history of breast cancer, and higher BMI. Those at high absolute risk of HER2+ cancers were younger and had lower BMI.

CONCLUSION

Our study provides proof of concept that stratifying risk prediction for breast cancer subtypes may enable identification of patients with unique profiles conferring increased risk for tumor subtypes.

FBXO8 is a novel prognostic biomarker in different molecular subtypes of breast cancer and suppresses breast cancer progression by targeting c-MYC

F-box only protein 8 (FBXO8) is a recently identified member of the F-box proteins, showcasing its novelty in this protein family. Extensive research has established FBXO8's role as a tumor suppressor in various cancers, including hepatocellular carcinoma, and colorectal cancer, Nevertheless, its functional, mechanistic, and prognostic roles in primary and metastatic breast cancer, particularly in different molecular subtypes of breast cancer, various stages, as well as its potential implications in immunotherapy, tumor microenvironment, and prognostic survival among breast cancer patients, remain unexplored. In this article, we employed a multi-dimensional investigation leveraging TCGA, TIMER, TISIDB, STRING, MEXPRESS, UALCAN, and cBioPortal databases to explore the underlying suppression mechanism of FBXO8 in breast cancer. FBXO8 negatively correlates with MYC, NOTCH, WNT and inflammatory signaling pathways in breast tumor microenvironment. Furthermore we conducted RT-PCR, western blot, cell proliferation, cell migration, and mRNA target gene RT-PCR analyses to elucidate the role of FBXO8 in breast cancer progression. Mechanistically, PTEN and FBXW7 expression were down-regulated and MYC, IL10, IL6, NOTCH1, WNT6 mRNA expressions were up-regulated in FBXO8 knockdown cell lines. c-MYC silenced cells showed an increase in FBXO8 protein level, which suggests a negative feedback loop between FBXO8 and c-MYC to control breast cancer metastasis. These findings illuminate the novel role of FBXO8 as a prognostic and therapeutic target across different molecular subtypes of breast cancer. Finally, through the utilization of virtual screening and Molecular Dynamics simulations, we successfully identified two FDA-approved medications, Ledipasvir and Paritaprevir, that demonstrated robust binding capabilities and interactions with FBXO8.

Prognostic Molecular Biomarkers in Breast Cancer Lesions with Non-Mass Enhancement on MR

Clustered ring enhancement (CRE) is a new lexicon for non-mass enhancement (NME) of breast MR in the 5th BIRADS, indicating a high suspicion of malignancy. We wonder if the presence of CRE correlates with expression of prognostic molecular biomarkers of breast cancer. A total of 58 breast lesions, which MRI reported with NME, were collected between July 2013 and December 2018. The patterns of enhancement including CRE were reviewed and the pathological results with expression of molecular biomarkers were collected. The association between MRI NME, pathological, and IHC stain findings were investigated under univariate analysis. A total of 58 breast lesions were pathologically proven to have breast cancer, comprising 31 lesions with CRE and 27 lesions without CRE on breast MRI. The expression of the estrogen receptor (ER) (p = 0.017) and the progesterone receptor (PR) (p = 0.017) was significantly lower in lesions with CRE as compared with those without CRE. The expression of Ki-67 (≥25%) was significantly higher in lesions with CRE (p = 0.046). The lesions with CRE had a lower expression ratio of ER (50.71 ± 45.39% vs. 74.26 ± 33.59%, p = 0.028). Our study indicated that lesions with CRE may possess different features from those without CRE in molecular expression, bearing a more aggressive behavior.

Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis

Breast cancer (BC) has emerged as an extremely destructive malignancy, causing significant harm to female patients and society at large. Proteomic research holds great promise for early diagnosis and treatment of diseases, and the integration of proteomics with genomics can offer valuable assistance in the early diagnosis, treatment, and improved prognosis of BC patients. In this study, we downloaded breast cancer protein expression data from The Cancer Genome Atlas (TCGA) and combined proteomics with genomics to construct a proteomic-based prognostic model for BC. This model consists of nine proteins (HEREGULIN, IDO, PEA15, MERIT40_pS29, CIITA, AKT2, CD171 DVL3, and CABL9). The accuracy of the model in predicting the survival prognosis of BC patients was further validated through risk curve analysis, survival curve analysis, and independent prognostic analysis. We further confirmed the impact of differential expression of these nine key proteins on overall survival in BC patients, and the differential expression of the key proteins and their encoding genes was validated using immunohistochemical staining. Enrichment analysis revealed functional associations primarily related to PPAR signaling pathway, steroid hormone metabolism, chemokine signaling pathway, DNA conformation changes, immunoglobulin production, and immunoglobulin complex in the high- and low-risk groups. Immune infiltration analysis revealed differential expression of immune cells between the high- and low-risk groups, providing a theoretical basis for subsequent immunotherapy. The model constructed in this study can predict the survival of BC patients, and the identified key proteins may serve as biomarkers to aid in the early diagnosis of BC. Enrichment analysis and immune infiltration analysis provide a necessary theoretical basis for further exploration of the molecular mechanisms and subsequent immunotherapy.