Genomics applied to the treatment of breast cancer

Circ_RPPH1 facilitates progression of breast cancer via miR-1296-5p/TRIM14 axis

Circular RNA Ribonuclease P RNA Component H1 (circ_RPPH1) and microRNA (miRNA) miR-1296-5p play a crucial role in breast cancer (BC), but the molecular mechanism is vague. Evidence showed that miR-1296-5p can activate tripartite motif-containing 14 (TRIM14). Clinical indications of eighty BC patients were collected and the circ_RPPH1 expression was detected using real-time quantitative PCR. MCF-7 and MDA-MB-231 cells were transfected with overexpression or knockdown of circ_RPPH1, miR-1296-5p, or TRIM14. Cell counting kit-8, cell cloning formation, wound healing, Transwell, and flow cytometry assays were performed to investigate the malignant phenotype of BC. The dual-luciferase reporter gene analyses were applied to reveal the interaction between these target genes. Subcutaneous tumorigenic model mice were established with circ_RPPH1 overexpression MDA-MB-231 cells in vivo; the tumor weight and volume, levels of miR-1296-5 and TRIM14 mRNA were measured. Western blot and immunohistochemistry were used to detect TRIM14 in cells and mice. Circ_RPPH1 levels were notably higher in BC patients and have been found to promote cell proliferation, invasion, and migration of BC cells. Circ_RPPH1 altered cell cycle and hindered apoptosis. Circ_RPPH1 knockdown or miR-1296-5p overexpression inhibited the malignant phenotype of BC. Furthermore, miR-1296-5p knockdown reversed circ_RPPH1's promotion effects on BC. Interestingly, TRIM14 overexpression counteracts the inhibitory effects of miR-1296-5p overexpression and circ_RPPH1 silencing on BC. Moreover, in BC tumor-bearing mice, circ_RPPH1 overexpression led to increased TRIM14 expression and facilitated tumor growth. Circ_RPPH1 enhanced BC progression through miR-1296-5p/TRIM14 axis, indicating its potential as a biomarker and therapeutic target in BC.

Safety of the Breast Cancer Adjuvant Radiotherapy in Ataxia-Telangiectasia Mutated Variant Carriers

The Ataxia-Telangiectasia Mutated (ATM) gene is implicated in DNA double-strand break repair. Controversies in clinical radiosensitivity remain known for monoallelic carriers of the ATM pathogenic variant (PV). An evaluation of the single-nucleotide polymorphism (SNP) rs1801516 (G-A) showed different results regarding late subcutaneous fibrosis after breast radiation therapy (RT). The main objective of this study was to evaluate acute and late toxicities in carriers of a rare ATM PV or predicted PV and in carriers of minor allele A of rs1801516 facing breast RT. Fifty women with localized breast cancer treated with adjuvant RT between 2000 and 2014 at Institut Curie were selected. Acute and late toxicities in carriers of a rare PV or predicted PV (n= 9), in noncarriers (n = 41) and in carriers of SNP rs1801516 (G-A) (n = 8), were examined. The median age at diagnosis was 53 years old and 82% of patients had an invasive ductal carcinoma and 84% were at clinical stage I-IIB. With a median follow-up of 13 years, no significant difference between carriers and noncarriers was found for acute toxicities (p > 0.05). The same results were observed for late toxicities without an effect from the rs1801516 genotype on toxicities. No significant difference in acute or late toxicities was observed between rare ATM variant carriers and noncarriers after breast RT for localized breast cancer.

Delineating intra-tumoral heterogeneity and tumor evolution in breast cancer using precision-based approaches

The burden of breast cancer continues to increase worldwide as it remains the most diagnosed tumor in females and the second leading cause of cancer-related deaths. Breast cancer is a heterogeneous disease characterized by different subtypes which are driven by aberrations in key genes such as BRCA1 and BRCA2, and hormone receptors. However, even within each subtype, heterogeneity that is driven by underlying evolutionary mechanisms is suggested to underlie poor response to therapy, variance in disease progression, recurrence, and relapse. Intratumoral heterogeneity highlights that the evolvability of tumor cells depends on interactions with cells of the tumor microenvironment. The complexity of the tumor microenvironment is being unraveled by recent advances in screening technologies such as high throughput sequencing; however, there remain challenges that impede the practical use of these approaches, considering the underlying biology of the tumor microenvironment and the impact of selective pressures on the evolvability of tumor cells. In this review, we will highlight the advances made thus far in defining the molecular heterogeneity in breast cancer and the implications thereof in diagnosis, the design and application of targeted therapies for improved clinical outcomes. We describe the different precision-based approaches to diagnosis and treatment and their prospects. We further propose that effective cancer diagnosis and treatment are dependent on unpacking the tumor microenvironment and its role in driving intratumoral heterogeneity. Underwriting such heterogeneity are Darwinian concepts of natural selection that we suggest need to be taken into account to ensure evolutionarily informed therapeutic decisions.

Omics-Based Investigations of Breast Cancer

Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

Multiomics technologies: role in disease biomarker discoveries and therapeutics

 

Medical research has been revolutionized after the publication of the full human genome. This was the major landmark that paved the way for understanding the biological functions of different macro and micro molecules. With the advent of different high-throughput technologies, biomedical research was further revolutionized. These technologies constitute genomics, transcriptomics, proteomics, metabolomics, etc. Collectively, these high-throughputs are referred to as multi-omics technologies. In the biomedical field, these omics technologies act as efficient and effective tools for disease diagnosis, management, monitoring, treatment and discovery of certain novel disease biomarkers. Genotyping arrays and other transcriptomic studies have helped us to elucidate the gene expression patterns in different biological states, i.e. healthy and diseased states. Further omics technologies such as proteomics and metabolomics have an important role in predicting the role of different biological molecules in an organism. It is because of these high throughput omics technologies that we have been able to fully understand the role of different genes, proteins, metabolites and biological pathways in a diseased condition. To understand a complex biological process, it is important to apply an integrative approach that analyses the multi-omics data in order to highlight the possible interrelationships of the involved biomolecules and their functions. Furthermore, these omics technologies offer an important opportunity to understand the information that underlies disease. In the current review, we will discuss the importance of omics technologies as promising tools to understand the role of different biomolecules in diseases such as cancer, cardiovascular diseases, neurodegenerative diseases and diabetes.

SUMMARY POINTS

Discovery of genes positively modulating treatment effect using potential outcome framework and Bayesian update

Background

The recent explosion of cancer genomics provides extensive information about mutations and gene expression changes in cancer. However, most of the identified gene mutations are not clinically utilized. It remains uncertain whether the presence of a certain genetic alteration will affect treatment response. Conventional statistics have limitations for causal inferences and are hard to gain sufficient power in genomic datasets. Here, we developed and evaluated a C-search algorithm for searching the causal genes that maximize the effect of the treatment.

Methods

The algorithm was developed based on the potential outcome framework and Bayesian posterior update. The precision of the algorithm was validated using a simulation dataset. The algorithm was implemented to a cBioPortal dataset. The genes discovered by the algorithm were externally validated within CancerSCAN screening data from Samsung Medical Center.

Results

Simulation data analysis showed that the C-search algorithm was able to identify nine causal genes out of ten. The C-search algorithm shows the discovery rate rapidly increasing until the 1500 data instances. Meanwhile, the log-rank test shows a slower increase in performance. The C-search algorithm was able to suggest nine causal genes from the cBioPortal Metabric dataset. Treating the patients with the causal genes is associated with better survival outcome in both the cBioPortal dataset and the CancerSCAN dataset which is used for external validation.

Conclusions

Our C-search algorithm demonstrated better performance to identify causal effects of the genes than multiple log-rank test analysis especially within a limited number of data. The result suggests that the C-search can discover the causal genes from various genetic datasets, where the number of samples is limited compared to the number of variables.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01852-3.

‘Breast Cancer Resistance Likelihood and Personalized Treatment Through Integrated Multiomics’

In recent times, enormous progress has been made in improving the diagnosis and therapeutic strategies for breast carcinoma, yet it remains the most prevalent cancer and second highest contributor to cancer-related deaths in women. Breast cancer (BC) affects one in eight females globally. In 2018 alone, 1.4 million cases were identified worldwide in postmenopausal women and 645,000 cases in premenopausal females, and this burden is constantly increasing. This shows that still a lot of efforts are required to discover therapeutic remedies for this disease. One of the major clinical complications associated with the treatment of breast carcinoma is the development of therapeutic resistance. Multidrug resistance (MDR) and consequent relapse on therapy are prevalent issues related to breast carcinoma; it is due to our incomplete understanding of the molecular mechanisms of breast carcinoma disease. Therefore, elucidating the molecular mechanisms involved in drug resistance is critical. For management of breast carcinoma, the treatment decision not only depends on the assessment of prognosis factors but also on the evaluation of pathological and clinical factors. Integrated data assessments of these multiple factors of breast carcinoma through multiomics can provide significant insight and hope for making therapeutic decisions. This omics approach is particularly helpful since it identifies the biomarkers of disease progression and treatment progress by collective characterization and quantification of pools of biological molecules within and among the cancerous cells. The scrupulous understanding of cancer and its treatment at the molecular level led to the concept of a personalized approach, which is one of the most significant advancements in modern oncology. Likewise, there are certain genetic and non-genetic tests available for BC which can help in personalized therapy. Genetically inherited risks can be screened for personal predisposition to BC, and genetic changes or variations (mutations) can also be identified to decide on the best treatment. Ultimately, further understanding of BC at the molecular level (multiomics) will define more precise choices in personalized medicine. In this review, we have summarized therapeutic resistance associated with BC and the techniques used for its management.

Gene expression analysis of invasive breast carcinoma yields differential patterns in luminal subtypes of breast cancer

Breast cancer is a heterogeneous disease, and new biomarkers are needed for more accurate classification and prediction of prognosis. The goal of this study is to assess the expression of breast cancer classification genes, to identify new molecular signatures in different intrinsic subtypes of breast cancer and to correlate their expression with different clinical variables. The study included 84 female patients newly diagnosed with non-metastatic breast cancer at the outpatient clinic at the National Cancer Institute, Cairo University, Egypt. Detection of 17 breast cancer classification genes was done using RT-PCR in tumor and normal tissues. Estrogen receptor (ER), progesterone receptor (PR), HER2, and Ki67 expression were assessed using IHC assay for intrinsic subtyping. Combined expression of FOXA1 and GATA3 was statistically higher in luminal subtypes in comparison to non-luminal subtypes. In Luminal A subtype; GRB7, EGFR, PTGS2, ID1, and KRT5 were significantly downregulated. FOXA1 and GATA3 were significantly upregulated in luminal B subtype, where EGFR and PTGS2 were significantly downregulated. While ESR1, EGFR, KRT5 and PTGS2 showed significantly low expression in tumor tissue in Her2 enriched subtype, TFF3 was significantly downregulated in triple negative subtype. GATA3 and FOXA1 expression exhibited significant correlation with tumor grade. Furthermore, GATA3, FOXA1, ESR1, and ID1 were also correlated significantly with staging of the tumor. Combined expression of ESR1, FOXA1 and GATA3 represents a molecular signature of luminal subtypes. Long term follow-up is needed to investigate the prognostic effect of breast cancer classification genes found in this study.

Axillary Clearance Following Positive Sentinel Lymph Node Biopsy in Symptomatic Breast Cancer

BACKGROUND/AIM

Symptomatic cancers display a different biological behaviour from screen-detected cancers, which may impact the management of axillary metastases. We aimed to determine the role of unselected axillary nodal clearance (ANC) in symptomatic patients with positive sentinel node biopsies (SNBs).

PATIENTS AND METHODS

A case-note review was performed on 95 symptomatic breast cancer patients who underwent ANC following positive SNB.

RESULTS

Thirty-eight (40%) patients were treated with a mastectomy and 57 (60%) with breast-conserving surgery. At ANC, 25 patients (26.3%) showed evidence of further lymph node metastases, with 15 (60%) having two or fewer macrometastases. The presence of more than 2 SNB macrometastases was associated with further ANC metastases (p<0.001). The presence of further metastases at ANC was not associated with either reduced overall survival or disease-free survival.

CONCLUSION

A number of symptomatic breast cancer patients with positive SNBs may be overtreated. Ongoing trials examining the management of low volume SNB macrometastases need to consider the symptomatic subgroup in their conclusions.

Optimally designed theranostic system based folic acids and chitosan as a promising mucoadhesive delivery system for encapsulating curcumin LbL nano-template against invasiveness of breast cancer

Curcumin is a potential candidate in cancer therapy due to its ability to inhibit many signalling pathways at the same time of exposure because of its unique content of aromatic ring, B diketone, olefinic linker, and O methoxy phenolic groups. Its applications in biomedical therapy is limited because of its sensitivity, and its rapid degradation. In the current study, curcumin inserted into polyelectrolyte pairs (protamine and dextran) and then was functionalized by folic acid conjugated chitosan used for the first time, as theranostic system. Such this strategy allows to improve its mucoadhesion and penetration that increases their accumulation inside cancer cells. CUR-LbL NPs were then used to investigate drug release inside Human Mammary Carcinoma (MCF-7 cell lines) after their incubations for 3 h, 6 h and 24 h. Flow cytometry indicated that the percentages of apoptosis, necrosis and cell cycle arrest were increased significantly in MCF-7 cell lines treated by CUR-LbL NPs. Furthermore, SEM image showed many debris in the section of MCF-7 treated by CUR-LbL NPs. Here, it can be summarized that curcumin functionalized by multi-layered polyelectrolyte capsules can be used as a model to study the fate of the adsorbed nanocarriers and to investigate the drug release inside cells.

Clinicopathological and prognostic significance of programmed cell death ligand 1 expression in patients diagnosed with breast cancer: meta-analysis

BACKGROUND

Uncertainty exists regarding the clinical relevance of programmed cell death ligand 1 (PD-L1) expression in breast cancer.

METHODS

A systematic review was performed in accordance with PRISMA guidelines. Observational studies that compared high versus low expression of PD-L1 on breast cancer cells were identified. Log hazard ratios (HRs) for disease-free and overall survival and their standard errors were calculated from Kaplan-Meier curves or Cox regression analyses, and pooled using the inverse-variance method. Dichotomous variables were pooled as odds ratios (ORs) using the Mantel-Haenszel method.

RESULTS

Sixty-five studies with 19 870 patients were included; 14 404 patients were classified as having low and 4975 high PD-L1 expression. High PD-L1 was associated with achieving a pathological complete response following neoadjuvant chemotherapy (OR 3.30, 95 per cent confidence interval 1.19 to 9.16; P < 0.01; I2 = 85 per cent). Low PD-L1 expression was associated with human epidermal growth factor receptor 2 (OR 3.98, 1.81 to 8.75; P < 0.001; I2 = 96 per cent) and luminal (OR 14.93, 6.46 to 34.51; P < 0.001; I2 = 99 per cent) breast cancer subtypes. Those with low PD-L1 had favourable overall survival rates (HR 1.30, 1.05 to 1.61; P = 0.02; I2 = 85 per cent).

CONCLUSION

Breast cancers with high PD-L1 expression are associated with aggressive clinicopathological and immunohistochemical characteristics and are more likely to achieve a pathological complete response following neoadjuvant chemotherapy. These breast cancers are, however, associated with worse overall survival outcomes.

Incorporating Genomic and Genetic Testing into the Treatment of Metastatic Luminal Breast Cancer

BACKGROUND

Treatment of patients with luminal metastatic breast cancer (MBC) has become even more complex over the last few years as molecular profiling has begun to alter disease management. It is well accepted that MBC is not curable but is treatable. Today we are able to prolong progression-free survival and partly overall survival with targeted and more individual treatment strategies adjusted according to the molecular subtype.

SUMMARY

Genetic and genomic testing has become therapeutically relevant in luminal MBC and is therefore an integral component within the treatment spectrum. By now, germline testing of BRCA1 and BRCA2 and somatic testing for PIK3CA mutations are inevitable elements in disease management and the current state of the art in luminal MBC patients. Furthermore, testing of ESR1 resistance mutation, ERBB2 mutation, microsatellite instability, and neurotrophic tyrosine receptor kinase (NTRK) gene fusion (mainly in secretory breast cancer) has recently gained increasing attention. However, based on the expanding role of personalized medicine, clinicians are now faced with substantial new challenges and possibly unsuspected possibilities. The following review summarizes current developments in genetic and genomic testing in luminal MBC.

KEY MESSAGES

In luminal MBC genomics have become an integral component within the spectrum of oncological treatment establishing novel therapeutic facilities. Further developments in treatment personalization adjusted according to the molecular subtype should become increasingly important in order to enhance the progress of de-escalation of chemotherapy in luminal MBC. However, based on the expanding role of personalized medicine, clinicians are now faced with substantial new challenges and possibly unsuspected possibilities.

In Silico Design and Evaluation of PRAME+FliCΔD2D3 as a New Breast Cancer Vaccine Candidate

Background

The most prevalent cancer in women over the world is breast cancer. Immunotherapy is a promising method to effectively treat cancer patients. Among various immunotherapy methods, tumor antigens stimulate the immune system to eradicate cancer cells. Preferentially expressed antigen in melanoma (PRAME) is mainly overexpressed in breast cancer cells, and has no expression in normal tissues. FliCΔD2D3, as truncated flagellin (FliC), is an effective toll-like receptor 5 (TLR5) agonist with lower inflammatory responses. The objective of the present study was to utilize bioinformatics methods to design a chimeric protein against breast cancer.

Methods

The physicochemical properties, solubility, and secondary structures of PRAME+FliCΔD2D3 were predicted using the tools ProtParam, Protein-sol, and GOR IV, respectively. The 3D structure of the chimeric protein was built using I-TASSER and refined with GalaxyRefine, RAMPAGE, and PROCHECK. ANTIGENpro and VaxiJen were used to evaluate protein antigenicity, and allergenicity was checked using AlgPred and Allergen FP. Major histocompatibility complex )MHC( and cytotoxic T-lymphocytes )CTL( binding peptides were predicted using HLApred and CTLpred. Finally, B-cell continuous and discontinuous epitopes were predicted using ABCpred and ElliPro, respectively.

Results

The stability and solubility of PRAME+FliCΔD2D3 were analyzed, and its secondary and tertiary structures were predicted. The results showed that the derived peptides could bind to MHCs and CTLs. The designed chimeric protein possessed both linear and conformational epitopes with a high binding affinity to B-cell epitopes.

Conclusion

PRAME+FliCΔD2D3 is a stable and soluble chimeric protein that can stimulate humoral and cellular immunity. The obtained results can be utilized for the development of an experimental vaccine against breast cancer.

Application of "omics" sciences to the prediction of bone metastases from breast cancer: State of the art

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Breast cancer (BC) is the first cause of cancer-related death in women.

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Most patients with advanced BC develop bone metastases (BM).

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Omics technologies have been applied to identify putative BM “predicting” biomarkers.

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Prospective studies are needed before any clinical application of such biomarkers.

Breast cancer (BC) is the most frequent malignancy and the first cause of cancer-related death in women.

The majority of patients with advanced BC develop skeletal metastases which may ultimately lead to serious complications, termed skeletal-related events, that often dramatically impact on quality of life and survival.

Therefore, the identification of biomarkers able to stratify BC patient risk to develop bone metastases (BM) is fundamental to define personalized diagnostic and therapeutic strategies, possibly at the earliest stages of the disease.

In this regard, the advent of “omics” sciences boosted the investigation of several putative biomarkers of BC osteotropism, including deregulated genes, proteins and microRNAs.

The present review revisits the current knowledge on BM development in BC and the most recent studies exploring potential BM-predicting biomarkers, based on the application of omics sciences to the study of primary breast malignancies.

'Omics Approaches to Explore the Breast Cancer Landscape

Breast cancer incidence is increasing worldwide with more than 600,000 deaths reported in 2018 alone. In current practice treatment options for breast cancer patients consists of surgery, chemotherapy, radiotherapy or targeting of classical markers of breast cancer subtype: estrogen receptor (ER) and HER2. However, these treatments fail to prevent recurrence and metastasis. Improved understanding of breast cancer and metastasis biology will help uncover novel biomarkers and therapeutic opportunities to improve patient stratification and treatment. We will first provide an overview of current methods and models used to study breast cancer biology, focusing on 2D and 3D cell culture, including organoids, and on in vivo models such as the MMTV mouse model and patient-derived xenografts (PDX). Next, genomic, transcriptomic, and proteomic approaches and their integration will be considered in the context of breast cancer susceptibility, breast cancer drivers, and therapeutic response and resistance to treatment. Finally, we will discuss how 'Omics datasets in combination with traditional breast cancer models are useful for generating insights into breast cancer biology, for suggesting individual treatments in precision oncology, and for creating data repositories to undergo further meta-analysis. System biology has the potential to catalyze the next great leap forward in treatment options for breast cancer patients.

Precision Nutrition and Cancer Relapse Prevention: A Systematic Literature Review

Cancer mortality rates are undergoing a global downward trend; however, metastasis and relapse after surgery and adjuvant treatments still correlate with poor prognosis and represent the most significant challenges in the treatment of this disease. Advances in genomics, metabolomics, and proteomics are improving our understanding regarding cancer metabolic diversity, resulting in detailed classifications of tumors and raising the effectiveness of precision medicine. Likewise, the growing knowledge of interactions between nutrients and the expression of certain genes could lead to cancer therapies based on precision nutrition strategies. This review aims to identify the recent advances in the knowledge of the mechanistic role of bioactive phytochemicals in foodstuffs in tumor progression, metastasis, and chemo-resistance in order to assess their potential use in precision nutrition therapies targeting relapse in lung, breast, colon, and prostate cancer, and leukemia. A considerable number of bioactive phytochemicals in foodstuffs were identified in the literature with proven effects modulating tumor growth, progression, and metastasis. In addition, the use of foodstuffs in cancer, and specifically in relapse therapies, is being reinforced by the development of different formulations that significantly increase the therapeutic efficiency of these products. This can open the possibility for testing combinations of bioactive phytochemicals with cancer relapse treatments as a potential prevention strategy.