Impact of TP53 mutations in breast cancer: Clinicopathological features and prognosisImpact of TP53 mutations in breast CA

Public neoantigens in breast cancer immunotherapy (Review)

Among women globally, breast cancer is the most prevalent cancer and the leading cause of cancer‑related death. Interestingly, though genetic mutations contribute to the disease, <15% of women diagnosed with breast cancer have a family history of the disease, suggesting a prevalence of sporadic genetic mutations in breast cancer development. In the rapidly rising field of cancer genomics, neoantigen‑based immunotherapy has come to the fore. The investigation of novel proteins arising from unique somatic mutations or neoantigens have opened a new pathway for both individualized and public cancer treatments. Because they are shared among individuals with similar genetic changes, public neoantigens provide an opportunity for 'off‑the‑shelf' anticancer therapies, potentially extending the benefits to a wider patient group. The present review aimed to highlight the role of shared or public neoantigens as therapeutic targets for patients with breast cancer, emphasizing common hotspot mutations of certain genes identified in breast cancer. The clinical utilization of public neoantigen‑based therapies for breast cancer treatment were also discussed.

The carcinogenic capacity of arsenic in normal epithelial breast cells and double-positive breast cancer cells

Background and aims

The carcinogenic effect of arsenic is a subject of controversy in relation to breast cancer. In our current research, we aimed to simulate the effects of chronic low-level arsenic exposure on breast cells by intoxicating MCF-10A and MCF-7 cells with 1 μM Arsenic trioxide (As2O3) for 3 weeks (3w) and 6 weeks (6w), respectively.

Methods

We assessed the cellular responses to As2O3 through various assays, including confocal fluorescence microscopy, flow cytometry for cell cycle analysis, Transwell invasion assay, scratch assay, and colony assay. Additionally, we analyzed the mutation burden in all the exposed cells by using the next generation sequencing technology.

Results

Our findings indicate that As2O3 has a minor carcinogenic effect in normal cells, with no definitive evidence of malignant transformation observed after 6 weeks of exposure. In the case of breast cancer cells, As2O3 exhibits a dual effect, both inhibitory and stimulatory. It leads to reduced colony formation ability at 6 weeks, while enhancing the cells' ability for invasion. The mutations triggered by As2O3 exposure are distributed across genes with both tumor-suppressive and oncogenic functions. Five mutations are common to both cell lines, involving the following genes: Kinase Insert Domain Receptor (KDR) (c.798+54G>A), Colony Stimulating Factor 1 Receptor (CSF1R) (c.*37AC>C, c.*35C>TC), SWI/SNF-Related Matrix-Associated Actin-Dependent Regulator of Chromatin Subfamily B Member 1 (SMARCB1) (c.1119-41C>T), and Fms-like Tyrosine Kinase 3 (FLT3) (c.1310-3T>C). Additionally, Human Epidermal Growth Factor Receptor 4 (ERBB4/HER4) (c.421+58A>G) and Human Epidermal Growth Factor Receptor 2 (HER2/ERBB2) (c.2307+46A>G) mutations were exclusively found in MCF-10A cells exposed to As2O3. Furthermore, MCF-7 cells exhibited unique mutations in the KIT Proto-Oncogene (KIT) (c.1594G>A) and TP53 (c.215C>G).

Conclusion

In summary, our study reveals that a 6-weeks exposure to arsenic has a limited carcinogenic effect in normal breast cells and a dual role in breast cancer cells.

The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers

BACKGROUND

Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers.

METHODS

We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers.

RESULTS

We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort.

CONCLUSIONS

This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies.

Molecular Classification, Treatment, and Genetic Biomarkers in Triple-Negative Breast Cancer: A Review

Breast cancer is the most common malignancy and the second most common cause of cancer-related mortality in women. Triple-negative breast cancers do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptor 2 and have a higher recurrence rate, greater metastatic potential, and lower overall survival rate than those of other breast cancers. Treatment of triple-negative breast cancer is challenging; molecular-targeted therapies are largely ineffective and there is no standard treatment. In this review, we evaluate current attempts to classify triple-negative breast cancers based on their molecular features. We also describe promising treatment methods with different advantages and discuss genetic biomarkers and other prediction tools. Accurate molecular classification of triple-negative breast cancers is critical for patient risk categorization, treatment decisions, and surveillance. This review offers new ideas for more effective treatment of triple-negative breast cancer and identifies novel targets for drug development.

BRPCA: Bounded Robust Principal Component Analysis to Incorporate Similarity Network for N7-Methylguanosine(m7G) Site-Disease Association Prediction

Recent studies have revealed that N7-methylguanosine(m⁷G) plays a pivotal role in various biological processes and disease pathogenesis. To date, transcriptome-wide m⁷G modification sites have been identified by high-throughput sequencing approaches, and some related information has been recorded in a few biological databases. However, the mechanism of site action in disease remains uncharted. Wet experiments can help identify true m⁷G sites with high confidence, but it is time-consuming to find the true ones in such a large number of sites, which will also cost too much. Thus, computational methods are emergently needed to predict the associations between m⁷G sites and various diseases, thus help to uncover potential active sites for specific diseases. In this article, we proposed a bounded robust principal component analysis (BRPCA) method to predict unknown m⁷G-disease association based on similarity information. Importantly, BRPCA tolerates the noise and redundancy existing in association and similarity information. Moreover, a suitable bounded constraint is incorporated into BRPCA to ensure that the predicted association scores locate in a meaningful interval. The extensive experiments demonstrate the superiority and robustness of the BRPCA.

Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer

Approximately 30% of breast cancer (BC) patients suffer from disease relapse after definitive treatment. Monitoring BC at baseline and disease progression using comprehensive genomic profiling would facilitate the prediction of prognosis. We retrospectively studied 101 BC patients ultimately experiencing relapse and/or metastases. The baseline and circulating tumor DNA‐monitoring cohorts included patients with baseline tumor tissue and serial plasma samples, respectively. Samples were analyzed with targeted next‐generation sequencing of 425 cancer‐relevant genes. Of 35 patients in the baseline cohort, patients with TP53 mutations (P < 0.01), or CTCF/GNAS mutations (P < 0.01) displayed inferior disease‐free survival, and patients harboring TP53 (P = 0.06) or NOTCH1 (P = 0.06) mutations showed relatively poor overall survival (OS), compared to patients with wild‐type counterparts. Of the 59 patients with serial plasma samples, 11 patients who were newly detected with TP53 mutations had worse OS than patients whose TP53 mutational status remained negative (P < 0.01). These results indicate that an inferior prognosis of advanced breast cancer was potentially associated with baseline TP53, CTCF, and NOTCH1 alterations. Newly identified TP53 mutations after relapse and/or metastasis was another potential prognostic biomarker of poor prognosis.

Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach

Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.987), (2) three equivalent OA cartilage-specific biosignatures containing four genes each (AUC 0.978 and precision 0.986) and (3) a four-gene pancreatic β-cell-specific biosignature (AUC 0.984 and precision 0.995). Next, the BrCa biosignature was validated using an independent ccfDNA dataset showing an AUC and precision of 1.000, verifying the biosignature’s applicability in liquid biopsy. Functional and protein interaction prediction analysis revealed that most DMGs identified are involved in pathways known to be related to the studied diseases or pointed to new ones. Overall, our data-driven approach contributes to the maximum exploitation of high-throughput methylome readings, helping to establish specific disease profiles to be applied in clinical practice and to understand human pathology.

miR-875 Serves as A Candidate Biomarker for Detection and Prognosis and Is Correlated with PHH3 Index Levels in Breast Cancer Patients

BACKGROUND

miRNAs play crucial roles in cancers. This study investigated the potential value of miR-875 to be a detective or/and prognostic marker and evaluate the correlation between its expression and PHH3 index levels in breast cancer.

METHODS

miR-875 expression was determined in breast cancer serum and tissues by a quantitative real-time reverse transcription-polymerase chain reaction. The detective value of serum miR-875 expression for breast cancer was assessed by receiver operating characteristic analysis. Then the associations of miR-875 expression with clinical characteristics of patients and overall survival were evaluated by χ² test and Kaplan-Meier curve methods, respectively.

RESULTS

The expression of miR-875 was increased in both breast cancer serum and tissues compared with respective controls. The high miR-875 expression in serum and tissues was associated with positive lymph node metastasis and advanced TNM stages. Besides, miR-875 expression in tissues was positively associated with the PHH3 index. And serum miR-875 could screen breast cancer patients from healthy individuals. Moreover, breast cancer patients with both high expression of miR-875 and PHH3 index had shorter overall survival.

CONCLUSION

This study suggested that miR-875 expression may be suitable as a potential breast cancer detection and prognosis biomarker. And the miR-875 expression in tissues was positively associated with the PHH3 index, suggesting that miR-875 expression may be involved in tumor proliferation of breast cancer.

Association of Body Mass Index With Somatic Mutations in Breast Cancer

Background

The relationship between body mass index (BMI) and the prognosis or treatment response in patients with breast cancer has been demonstrated in previous studies, but the somatic mutation profiles in breast cancer patients with different BMIs have not been explored.

Methods

In the present study, the somatic mutation profiles in 421 female breast cancer patients who were stratified into three subgroups based on BMI (normal weight, overweight/obese, and underweight) were investigated. Capture-based targeted sequencing was performed using a panel comprising 520 cancer-related genes.

Results

A total of 3547 mutations were detected in 390 genes. In breast cancer patients with different BMI statuses, the tumors exhibited high mutation frequency and burden. TP53 was the most common gene in the three groups, followed by PIK3CA, ERBB2, and CDK12. Meanwhile, the mutation hotspots in TP53 and PIK3CA were the same in the three BMI groups. More JAK1 mutations were identified in underweight patients than those in normal patients. Except for JAK1, differentially mutated genes in postmenopausal patients were completely different from those in premenopausal patients. The distribution of mutation types was significantly different among BMI groups in the postmenopausal group. Underweight patients in the postmenopausal group harbored more TP53 mutations, more amplifications, and more mutations in genes involved in the WNT signaling pathway.

Conclusions

Our next-generation sequencing (NGS)-based gene panel analysis revealed the gene expression profiles of breast cancer patients with different BMI statuses. Although genes with high mutation frequency and burden were found in different BMI groups, some subtle differences could not be ignored. JAK1 mutations might play a vital role in the progression of breast cancer in underweight patients, and this needs further analysis. Postmenopausal underweight patients with breast cancer have more aggressive characteristics, such as TP53 mutations, more amplifications, and more mutations in genes involved in the WNT signaling pathway. This study provides new evidence for understanding the characteristics of breast cancer patients with different BMIs.

An In Vitro Evaluation of the Molecular Mechanisms of Action of Medical Plants from the Lamiaceae Family as Effective Sources of Active Compounds against Human Cancer Cell Lines

It is predicted that 1.8 million new cancer cases will be diagnosed worldwide in 2020; of these, the incidence of lung, colon, breast, and prostate cancers will be 22%, 9%, 7%, and 5%, respectively according to the National Cancer Institute. As the global medical cost of cancer in 2020 will exceed about $150 billion, new approaches and novel alternative chemoprevention molecules are needed. Research indicates that the plants of the Lamiaceae family may offer such potential. The present study reviews selected species from the Lamiaceae and their active compounds that may have the potential to inhibit the growth of lung, breast, prostate, and colon cancer cells; it examines the effects of whole extracts, individual compounds, and essential oils, and it discusses their underlying molecular mechanisms of action. The studied members of the Lamiaceae are sources of crucial phytochemicals that may be important modulators of cancer-related molecular targets and can be used as effective factors to support anti-tumor treatment.