Beyond Chemotherapies: Recent Strategies in Breast Cancer Treatment

Breast cancer treatment and its impact on survival in Morocco: a study over a decade

BACKGROUND

In Morocco, much progress has been made in breast cancer treatment. However, there is limited information on survival outcomes of breast cancer patients according to their therapeutic management.

METHODS

A pattern-of-care study was conducted in Morocco's two main oncology centres: Rabat and Casablanca and has shown that major progress has been made in the quality of care with survival rates comparable to those in developed countries. The present study focuses on the different therapeutic strategies used in breast cancer and their impact on prognosis. Patients were classified into two categories: those considered as appropriately managed and those who were not.

RESULTS

A total of 1901 women with stage I to III breast cancer were included in this study, the majority (53%) were adequately managed and had better disease-free survival (DFS) rates than those who were not: DFS at 3 years (88% versus 62%) and at 5 years (80% versus 50%). Potential significant determinants of better management were: treatment in Rabat's oncology centre, treatment between 2008 and 2012, being aged younger than 60 years, and early TN stage.

CONCLUSION

This study demonstrated the value of proper integrated and coordinated management in a comprehensive cancer centre, to improve breast cancer survival.

DDIT4 Downregulation by siRNA Approach Increases the Activity of Proteins Regulating Fatty Acid Metabolism upon Aspirin Treatment in Human Breast Cancer Cells

Repositioning of aspirin for a more effective breast cancer (BC) treatment requires identification of predictive biomarkers. However, the molecular mechanism underlying the anticancer activity of aspirin remains fully undefined. Cancer cells enhance de novo fatty acid (FA) synthesis and FA oxidation to maintain a malignant phenotype, and the mechanistic target of rapamycin (mTORC1) is required for lipogenesis. We, therefore, aimed to test if the expression of mTORC1 suppressor DNA damage-inducible transcript (DDIT4) affects the activity of main enzymes in FA metabolism after aspirin treatment. MCF-7 and MDA-MB-468 human BC cell lines were transfected with siRNA to downregulate DDIT4. The expression of carnitine palmitoyltransferase 1 A (CPT1A) and serine 79-phosphorylated acetyl-CoA carboxylase 1 (ACC1) were analyzed by Western Blotting. Aspirin enhanced ACC1 phosphorylation by two-fold in MCF-7 cells and had no effect in MDA-MB-468 cells. Aspirin did not change the expression of CPT1A in either cell line. We have recently reported DDIT4 itself to be upregulated by aspirin. DDIT4 knockdown resulted in 1.5-fold decreased ACC1 phosphorylation (dephosphorylation activates the enzyme), 2-fold increased CPT1A expression in MCF-7 cells, and 2.8-fold reduced phosphorylation of ACC1 following aspirin exposure in MDA-MB-468 cells. Thus, DDIT4 downregulation raised the activity of main lipid metabolism enzymes upon aspirin exposure which is an undesired effect as FA synthesis and oxidation are linked to malignant phenotype. This finding may be clinically relevant as DDIT4 expression has been shown to vary in breast tumors. Our findings justify further, more extensive investigation of the role of DDIT4 in aspirin's effect on fatty acid metabolism in BC cells.

Calcium signal modulation in breast cancer aggressiveness

Breast cancer (BC) is the second most common cancer and cause of death in women. The aggressive subtypes including triple negative types (TNBCs) show a resistance to chemotherapy, impaired immune system, and a worse prognosis. From a histological point of view, TNBCs are deficient in oestrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (HER2⁺) expression. Many studies reported an alteration in the expression of calcium channels, calcium binding proteins and pumps in BC that promote proliferation, survival, resistance to chemotherapy, and metastasis. Moreover, Ca²⁺ signal remodeling and calcium transporters expression have been associated to TNBCs and HER2⁺ BC subtypes. This review provides insight into the underlying alteration of the expression of calcium-permeable channels, pumps, and calcium dependent proteins and how this alteration plays an important role in promoting metastasis, metabolic switching, inflammation, and escape to chemotherapy treatment and immune surveillance in aggressive BC including TNBCs models and highly metastatic BC tumors.

Inositol (1,4,5)-Trisphosphate Receptors in Invasive Breast Cancer: A New Prognostic Tool?

Simple Summary

The inositol-trisphosphate receptor (IP₃R) is a key player in physiological and pathological intracellular calcium signaling. The objective of the present study was to assess the putative value of the three IP₃R subtypes as prognostic biomarkers in breast cancer. We found that IP₃R3 is the most strongly expressed subtype in breast cancer tissue. Furthermore, IP₃R3 and IP₃R1 are significantly more expressed in invasive breast cancer tissue than in non-tumor tissue. In contrast to IP₃R1 and IP₃R2, the expression of IP₃R3 was positively correlated with prognostic factors including tumor size, regional node invasion, histologic grade, proliferation index, and hormonal status. By analyzing public databases, we found that the expression of all IP₃R subtypes is significantly correlated with the overall survival and disease-free survival of patients with breast cancer. We conclude that relative to the other two IP₃R subtypes, IP₃R3 expression is upregulated in breast cancer and is correlated with prognostic factors. We strongly believe that our results will open up new perspectives with regard to the link between IP₃Rs and breast cancer aggressiveness.

Abstract

Breast cancer is the leading cause of cancer death among women in worldwide and France. The disease prognosis and treatment differ from one breast cancer subtype to another, and the disease outcome depends on many prognostic factors. Deregulation of ion flux (especially Ca²⁺ flux) is involved in many pathophysiology processes, including carcinogenesis. Inside the cell, the inositol-trisphosphate receptor (IP₃R) is a major player in the regulation of the Ca²⁺ flux from the endoplasmic reticulum to the cytoplasm. The IP₃Rs (and particularly the IP₃R3 subtype) are known to be involved in proliferation, migration, and invasion processes in breast cancer cell lines. The objective of the present study was to evaluate the potential value of IP₃Rs as prognostic biomarkers in breast cancer. We found that expression levels of IP₃R3 and IP₃R1 (but not IP₃R2) were significantly higher in invasive breast cancer of no special type than in non-tumor tissue from the same patient. However, the IP₃R3 subtype was expressed more strongly than the IP₃R1 and IP₃R2 subtypes. Furthermore, the expression of IP₃R3 (but not of IP₃R1 or IP₃R2) was positively correlated with prognostic factors such as tumor size, regional node invasion, histologic grade, proliferation index, and hormone receptor status. In an analysis of public databases, we found that all IP₃Rs types are significantly associated with overall survival and progression-free survival in patients with breast cancer. We conclude that relative to the other two IP₃R subtypes, IP₃R3 expression is upregulated in breast cancer and is correlated with prognostic factors.

Transcriptomic Analysis of Breast Cancer Patients Sensitive and Resistant to Chemotherapy: Looking for Overall Survival and Drug Resistance Biomarkers

Worldwide breast cancer ranks first in mortality and incidence rates in women over 20 years old. Rather than one disease, breast cancer is a heterogeneous group of diseases that express distinct molecular profiles. Neoadjuvant chemotherapy is an important therapeutic strategy for breast cancer patients independently of their molecular subtype, with the drawback of resistance development. In addition, chemotherapy has adverse effects that combined with resistance could contribute to lower overall survival. Although great efforts have been made to find diagnostic and prognostic biomarkers for breast cancer and for response to targeted and immune therapy for this pathology, little has been explored regarding biomarkers of response to anthracyclines and taxanes based neoadjuvant chemotherapy. This work aimed to evaluate the molecular profile of patients who received neoadjuvant chemotherapy to identify differentially expressed genes (DEGs) that could be used as biomarkers of chemotherapy response and overall survival. Breast cancer patients who were candidates for neoadjuvant chemotherapy were enrolled in this study. After treatment and according to their pathological response, they were assigned as sensitive or resistant. To evaluate DEGs, Gene Ontology, Kyoto Encyclopedia Gene and Genome (KEGG), and protein–protein interactions, RNA-seq information from all patients was obtained by next-generation sequencing. A total of 1985 DEGs were found, and KEGG analysis indicated a great number of DEGs in metabolic pathways, pathways in cancer, cytokine–cytokine receptor interactions, and neuroactive ligand-receptor interactions. A selection of 73 DEGs was used further for an analysis of overall survival using the METABRIC study and the ductal carcinoma dataset of The Cancer Genome Atlas (TCGA) database. Nine DEGs correlated with overall survival, of which the subexpression of C1QTNF3, CTF1, OLFML3, PLA2R1, PODN, KRT15, HLA-A, and the overexpression of TUBB and TCP1 were found in resistant patients and related to patients with lower overall survival.

Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

BACKGROUND

Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type.

MATERIALS AND METHODS

Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification.

RESULTS

A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk.

CONCLUSIONS

CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

Collagen Type X Alpha 1 (COL10A1) Contributes to Cell Proliferation, Migration, and Invasion by Targeting Prolyl 4-Hydroxylase Beta Polypeptide (P4HB) in Breast Cancer

Background

Breast cancer, a common malignant tumor, has been considered as the leading cause of cancer-related death in women. Collagen type X alpha 1 (COL10A1) is overexpressed in breast cancer. The current study was designed to determine the functional involvement and regulatory mechanism of COL10A1 on the growth and metastasis of breast cancer.

Material/Methods

COL10A1 and Prolyl 4-hydroxylase beta polypeptide (P4HB) expressions in normal tissues and tumor tissues of breast cancer patients were obtained from the GEPIA dataset. COL10A1 and P4HB levels in breast cancer cell lines were detected by real-time quantitative polymerase chain reaction (RT-qPCR) and western blot analysis. Furthermore, the interaction between COL10A1 and P4HB was confirmed by co-immunoprecipitation (Co-IP) assay. Cell Counting Kit-8 (CCK-8) and colony formation assay were applied to evaluate cell proliferation and clone-forming abilities of breast cancer cells. In addition, wound healing assay and transwell assay were performed to measure cell migration and invasion capabilities, respectively, in breast cancer.

Results

The GEPIA dataset presented overexpressed COL10A1 and P4HB in tumor tissues of breast cancer patients. COL10A1 and P4HB expression levels were greatly upregulated in breast cancer cell lines. In addition, COL10A1 could directly interact with P4HB. Functionally, overexpressed COL10A1 boosted the proliferation and metastasis of breast cancer cells and silenced COL10A1 impeded the progression of breast cancer. More importantly, knockdown of P4HB weakened the promoting effects of overexpressed COL10A1 on cell proliferation, migration, and invasion in breast cancer.

Conclusions

COL10A1 promotes the malignant progression of breast cancer by upregulating P4HB expression, indicating that COL10A1 functions as an oncogene in breast cancer.