Different Types Of Breast Cancer Research Articles

Impact of residence on the association between benzo[a]pyrene-DNA adduct levels and CYP1B1 gene polymorphisms in breast cancer patients

Globally, breast cancer is the primary cause of cancer-related death, and rising incidence rates are anticipated. Im¬proving illness prevention and treatment strategies requires a better understanding of the interactions occurring be¬tween genetic variables, environmental exposures, and disease pathogenesis. This study investigated the impact of residence on the association between benzo[a]pyrene-DNA adduct levels and CYP1B1 gene polymorphisms in breast cancer patients. In brief, 58 female breast cancer patients in Babylon, Iraq were recruited as subjects of this cross-sectional study. We gathered clinical information (including residency, age, age at diagnosis, and haematological markers), and by using molecular and biochemical methods, the CYP1B1 polymorphisms and the benzo[a]pyrene-DNA adduct levels were assessed. Among the different types of breast cancer, there was no apparent association between the residence and CYP1B1 polymorphisms. However, the amounts of benzo[a]pyrene-DNA adduct varied according to where a patient lived, with urban residents showing higher concentrations than rural residents. Benzo[a]pyrene-DNA adduct levels were shown to be correlated with specific polymorphisms in the CYP1B1 gene. Our study highlights the intricate connections between environmental exposures, genetic variables, and place of res¬idency in the aetiology of breast cancer. Variations in quantities of benzo[a]pyrene-DNA adducts imply possible func¬tions for environmental carcinogens, although no substantial correlation was found between genetic polymorphisms and the place of residence.

Abstract PO2-24-07: Investigation into potential Estrogen Receptor (ER)-mediated regulation of Phosphoserine Aminotransferase (PSAT1)

Abstract Breast cancer is one of the leading causes of death among women. Expression of different proteins, such as hormone receptors (HR), are used to classify different types of breast cancer (i.e., HR+BC or triple negative breast cancer (TNBC)). While activation of the estrogen receptor (ER) in HR+BC is known to regulate a vast number of downstream targets in promoting breast cancer, there may be additional ER-regulated processes that are still unknown. The serine synthetic pathway has been demonstrated to be crucial in the proliferation and survival of select subtypes of breast cancer and phosphoserine aminotransferase 1 (PSAT1) is a key regulatory step. Previous studies have demonstrated higher PSAT1 levels in cells lacking ER and we now show a negative correlation between ERα protein levels and PSAT1 transcripts in human patient samples. This indicates that ER may potentially regulate PSAT1 expression. To initially investigate this potential relationship, either WT or constitutively active mutant ERα was expressed in TNBC cells (MDA-MB-231), which have high endogenous PSAT1 expression and no ER. Immunoblot was used to verify ERα levels and to assess effects on PSAT1. We observed a qualitative trend in decreasing PSAT1 levels with re-expression of ERα in the TNBC cells. While the variabilities in these results preclude any conclusion of a ERα:PSAT1 regulatory axis, the lack of a quantifiable effect on PSAT1 levels is postulated to be due to the transient nature of ERα expression/suppression in these systems. A more profound effect would be expected under durable changes in ERα levels and is a focus on-going studies. Citation Format: Mary Sumlut, James Wittliff, Brian Clem. Investigation into potential Estrogen Receptor (ER)-mediated regulation of Phosphoserine Aminotransferase (PSAT1) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-24-07.

BC-QNet: A quantum-infused ELM model for breast cancer diagnosis

The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes.

Abstract 4723: Target engagement sheds light on difference in drug efficacy in breast cancer cell lines

Abstract Breast cancer is a serious disease that affects millions of women worldwide, and 700,000 people succumb to the disease annually. Breast cancers are divided into several functional subgroups based on molecular characteristics and growth pattern: Luminal A and B, HER2 positive (HER2+) and triple negative. The subgroups are important from a clinical perspective as they partially guide treatment strategies. In this study we wanted to investigate the difference in target engagement with four drugs indicated for treatment of different types of breast cancer: Alpelisib, Neratinib, Vinorelbine, and Docetaxel. Five different human breast cancer cell lines were included, representing Luminal A (MCF-7, T-47D), HER2+ (SK-BR-3, BT-474), and triple negative (BT-20). We used CETSA® (CEllular Thermal Shift Assay) coupled to mass spectrometry detection to assess proteome wide changes in protein thermal stability in intact breast cancer cell lines. CETSA is unique as a target engagement detection modality as it allows for target engagement assessment in intact cells with unaltered drug molecules. Also, as biology is intact in live cells it is possible to follow compound induced downstream signaling or phenotypic events that result in changed thermal stability of proteins. In addition to assessing protein thermal stability with CETSA, which is a short treatment of 1 h, we also subjected cells in culture to prolonged drug exposures, ≤72 h, in order to measure viability. This was done to correlate early changes in protein thermal stability to the viability phenotype that manifested later. In general, responses both in protein thermal stability and cell viability was heterogenous among the cell lines. The most homogenous response in viability was observed for the Phosphatidyl Inositol 3’ kinase inhibitor Alpelisib, which reduced viability to 20 - 40% in all cell lines. In contrast, partial resistance was shown to the other drugs by at least one of the cell lines. For Alpelisib, the amount and type of proteins that were thermally shifted correlated with the effect on viability. The covalent HER2 inhibitor Neratinib was most effective in inhibiting viability in HER2+ cells and proteins of the HER2 pathway were thermally shifted in these cells, which was not observed for the other cell lines. Viability of the other cell lines were also affected by Neratinib, but only at higher concentrations. Both microtubule inhibitors induced thermal stabilization of tubulins, but that did not always translate into efficacy in the viability assay. However, sensitive cell lines showed thermal shifts of tubulins at lower concentrations of compound. In oncology drug development it is important that effects on viability are driven by on-target efficacy and not by off-target effects. Here we show the benefit of connecting knowledge of target engagement and phenotypic readouts; what targets are engaged, at which concentrations, and what is the corresponding phenotype. Citation Format: Tomas Friman, Tuomas Tolvanen, Merve Kacal, Victoria Brehmer, Stina Lundgren, Laurence Arnold, Daniel Martinez Molina. Target engagement sheds light on difference in drug efficacy in breast cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4723.

CDH1 gene as biomarker towards breast cancer prediction*

Breast cancer is considered to be happened due to genetic aberration. Out of several genes expressed, it is found that cadherin 1, type 1 (CDH1) is responsible in several ways to control the metabolic order in human. Deregulation of the function of protein E-cadherin, expressed from CDH1 plays an important role in lobular breast cancer. In order to understand the root cause of this recent claim, we focus on CDH1 gene: whether the genetic information translated due to any deviation/alteration/modification in its sequence is related to the occurrence of the different types breast cancer. Towards this end, quantitative analysis of different biophysical and bio-chemical properties of CDH1 gene in genomic and proteomic levels from the available genomic (cDNA) sequences of CDH1 gene (obtained from the COSMIC Database for 78 patients, suffering from various types of breast cancer) clearly emphasizes that alternation/modification in the sequence of the CDH1 gene can be detrimental. Furthermore, Random forest, K-nearest neighbour and stochastic gradient descent (SGD) algorithms are applied on the derived dataset to classify the types of breast cancer, and to validate our hypothesis regarding the acute role of CDH1 as potential bio marker for breast cancer. Analysis of the mutated CDH1 gene sequences, and their related parameters using aforesaid machine learning techniques clearly establish that CDH1 gene can take the deterministic role in predicting the chances of occurrences of different types of breast cancer with an accuracy of > 90 % . Such an observation opens a new paradigm in diagnostic approach of breast cancer. Communicated by Ramaswamy H. Sarma

Genetic Loss of miR-205 Causes Increased Mammary Gland Development.

MiRNAs play crucial roles in a broad spectrum of biological processes, both physiological and pathological. Different reports implicate miR-205 in the control of breast stem cell properties. Differential miR-205 expression has been observed in different stages of mammary gland development and maturation. However, a functional role in this process has not been clearly demonstrated. We generated an miR-205 knockout in the FVB/N mouse strain, which is viable and characterized by enhanced mammary gland development. Indeed, mammary glands of miR-205-/- female mice at different ages (1.5 and 5.5 months) show increased outgrowth and branching. This evidence is consistent with our previously reported data demonstrating the direct miR-205-mediated targeting of HER3, a master regulator of mammary gland development, and the oncosuppressive activity of this microRNA in different types of breast cancer.

Investigating the trends of incidence rates of breast cancer in Southern Iran: a population based survey

BackgroundThe overall incidence of breast cancer is different all over the world and even within a nation. The present study aims to investigate the stratum-specific incidence trends of breast cancer in southern Iran.MethodsIn this retrospective cohort study, the data of Fars Population-Based Cancer Registry was used during 2001–2018. New cancer cases with ICD-O-3 codes C50.0 to C50.9 were categorized based on age group, morphology, and topography. Age-specific incidence rates of breast cancer were calculated during 2001–2018. Annual overall and truncated age-standardized incidence rates and their 95% Confidence Intervals (CIs) were also calculated. Afterward, the Annual Percentage Changes (APCs) of the age-specific and age-standardized incidence rates of breast cancer during 2001–2018 were calculated using Joinpoint regression software.ResultsAn increasing trend was observed in the incidence of breast cancer among women during 2001–2018 (APC of age-standardized incidence rates: 9.5 (95% CI: 7.5, 11.5)).However, the trend was increasing less during the recent years. The APC of age-standardized rates decreased from 15.03 (95% CI: 10.4, 19.8) in 2007 to 6.15(95% CI: 4.0, 8.4) in 2018. The most common morphology of breast cancer was invasive ductal carcinoma (77.3% in females and 75.1% in males) and its trend was similar to the general trend of different types of breast cancer. The most common site of breast cancer was the upper outer quadrant. Most breast cancer cases were female and males accounted for 2.45% of the cases. Among females, 40–55 was the most prevalent age group.ConclusionThe incidence of breast cancer among women living in southern Iran showed an increasing trend from 2001 to 2018. However, the rate of increase exhibited a milder slope during the more recent years. Based on the higher prevalence of breast cancer in the 40–55 age group observed in the present study, it offers valuable insight into the potential reduction of the breast cancer screening age from 50 to 40 years for healthy Iranian women. However, before implementing such a policy change, it is crucial to conduct additional studies that specifically examine the cost-effectiveness, as well as the potential benefits and risks associated with this alteration.

A Novel Profound Learning-Based System for Breast Cancer Location and Classification Utilizing Exchange Learning

Breast cancer is the leading cause of death in Asian women. Breast cancer can be detected early and treated aggressively. In this paper, we propose a new deep learning framework utilizing the index learning concept for breast cancer diagnosis and imaging. Typically, in-depth questions from experts are designed and presented in a specific format. Unlike classical learning paradigms, which are based on parallel development and emergence, the goal is to use the knowledge gained from solving one problem to solve another problem. In this study, we used CNN pretrainers to extract features from images, such as Google Net, VGGNet (Visual Geometry Group Network), and ResNet (Unsupervised Network), which combine the principles of neural networks and social networks. This has already happened. Contains partial information. Use the blunt treasure in the veil. A standard data model was used to evaluate the proposed optical products. Chemical images of different types of breast cancer have been discovered in archaeological finds and have proven useful in educational exploration into the unknown.

Circulating miRNAs signature on breast cancer: the MCC-Spain project

PurposeTo build models combining circulating microRNAs (miRNAs) able to identify women with breast cancer as well as different types of breast cancer, when comparing with controls without breast cancer.MethodmiRNAs analysis was performed in two phases: screening phase, with a total n = 40 (10 controls and 30 BC cases) analyzed by Next Generation Sequencing, and validation phase, which included 131 controls and 269 cases. For this second phase, the miRNAs were selected combining the screening phase results and a revision of the literature. They were quantified using RT-PCR. Models were built using logistic regression with LASSO penalization.ResultsThe model for all cases included seven miRNAs (miR-423-3p, miR-139-5p, miR-324-5p, miR-1299, miR-101-3p, miR-186-5p and miR-29a-3p); which had an area under the ROC curve of 0.73. The model for cases diagnosed via screening only took in one miRNA (miR-101-3p); the area under the ROC curve was 0.63. The model for disease-free cases in the follow-up had five miRNAs (miR-101-3p, miR-186-5p, miR-423-3p, miR-142-3p and miR-1299) and the area under the ROC curve was 0.73. Finally, the model for cases with active disease in the follow-up contained six miRNAs (miR-101-3p, miR-423-3p, miR-139-5p, miR-1307-3p, miR-331-3p and miR-21-3p) and its area under the ROC curve was 0.82.ConclusionWe present four models involving eleven miRNAs to differentiate healthy controls from different types of BC cases. Our models scarcely overlap with those previously reported.

Current and future research on the association between gut microbiota and breast cancer.

Breast cancer (BC) is a prevalent malignancy. There exists a strong association between gut microbiota (GM) and the development of BC. The GM composition in individuals with BC significantly differs from that in their healthy counterparts. Furthermore, the distribution of GM varies significantly among individuals with different types of BC. The GM can impact BC through metabolite secretion, the gut-mammary axis, and other pathways. Modulating the GM can serve as a very promising potential therapeutic strategy in the treatment of BC. This article will summarize existing research, focusing on the relationship between intestinal microbiota and BC. At the same time, the project will also analyze the application value of intestinal microorganisms in BC intervention work, so as to provide a reference for the further development of BC prevention and treatment work.

Naturally Occurring Phytochemicals to Target Breast Cancer Cell Signaling.

Almost 70% of clinically used antineoplastic drugs are originated from natural products such as plants, marine organism, and microorganisms and some of them are also structurally modified natural products. The naturally occurring drugs may specifically act as inducers of selective cytotoxicity, anti-metastatic, anti-mutagenic, anti-angiogenesis, antioxidant accelerators, apoptosis inducers, autophagy inducers, and cell cycle inhibitors in cancer therapy. Precisely, several reports have demonstrated the involvement of naturally occurring anti-breast cancer drugs in regulating the expression of oncogenic and tumor suppressors associated with carcinogen metabolism and signaling pathways. Anticancer therapies based on nanotechnology have the potential to improve patient outcomes through targeted therapy, improved drug delivery, and combination therapies. This paper has reviewed the current treatment for breast cancer and the potential disadvantages of those therapies, besides the various mechanism used by naturally occurring phytochemicals to induce apoptosis in different types of breast cancer. Along with this, the contribution of nanotechnology in improving the effectiveness of anticancer drugs was also reviewed. With the development of sciences and technologies, phytochemicals derived from natural products are continuously discovered; however, the search for novel natural products as chemoprevention drugs is still ongoing, especially for the advanced stage of breast cancer. Continued research and development in this field hold great promise for advancing cancer care and improving patient outcomes.

An in-silico investigation and network pharmacology based approach to explore the anti-breast-cancer potential of Tecteria coadunata (Wall.) C. Chr.

Uncontrolled cell proliferation is a common definition of cancer. After lung carcinoma, breast neoplasm is the second-most prevalent kind of cancer. The majority of breast cancer cells and healthy breast cells both have receptors for circulating oestrogen and progesterone. In order to promote the development and division of cancer cells, oestrogen and progesterone bind to the receptors and may collaborate with growth factors (such as oncogenes and mutant tumour suppressor genes). As per the literature, Tecteria coadunata (Wall.) C. Chr. has anticancer, antioxidant and anti-inflammatory potential. After the hydroalcoholic extraction of this rhizome, total of 200 phytochemicals were retrieved from HR-LCMS analysis. In this current study, Network pharmacology was carried out to explore the rationale of Tecteria coadunata (Wall.) C. Chr. by using different database using Cytoscape software. The network depicted the interaction of Bioactives with their targets and their association with several disease, especially breast cancer. Tecteria coadunata (Wall.) C. Chr. has offered new relationship with variety of genes and its applications in different types of breast cancers. Further Gene Ontology was carried out and it showed key targets were TP53, BRCA2, PGR and CHEK 2. Further Signalling pathways were also enriched. Flex-X software was used for molecular docking studies, and it verified that Dopaxanthin, Dantrolene and Orotidin shows the highest binding affinities with key targets. Additionally, Pharmacokinetic analysis revealed that all top three lead compounds which follows the Lipinski Rule (Rule of three) without interrupting the conditions of bioavailability with minimal toxicity. Communicated by Ramaswamy H. Sarma

Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment.

Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%-15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors (ERs, PRs, and HERs). Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker (SETD7) was sensitive to all six drugs on the shortlist, while two others (SRARP and YIPF5) were sensitive to both radiation and drugs. Furthermore, we did not find any radioresistance markers for the TNBC. The proposed biomarkers and drug sensitivity analysis will provide potential candidates for future clinical investigation.

Metabolic heterogeneity in TNBCs: A potential determinant of therapeutic efficacy of 2-deoxyglucose and metformin combinatory therapy

Breast cancers (BCs) remain the leading cause of cancer-related deaths among women worldwide. Among the different types of BCs, treating the highly aggressive, invasive, and metastatic triple-negative BCs (TNBCs) that do not respond to hormonal/human epidermal growth factor receptor 2 (HER2) targeted interventions since they lack ER/PR/HER2 receptors remains challenging. While almost all BCs depend on glucose metabolism for their proliferation and survival, studies indicate that TNBCs are highly dependent on glucose metabolism compared to non-TNBC malignancies. Hence, limiting/inhibiting glucose metabolism in TNBCs should curb cell proliferation and tumor growth. Previous reports, including ours, have shown the efficacy of metformin, the most widely prescribed antidiabetic drug, in reducing cell proliferation and growth in MDA-MB-231 and MDA-MB-468 TNBC cells. In the current study, we investigated and compared the anticancer effects of either metformin (2 mM) in glucose-starved or 2-deoxyglucose (10 mM; glycolytic inhibitor; 2DG) exposed MDA-MB-231 and MDA-MB-468 TNBC cells. Assays for cell proliferation, rate of glycolysis, cell viability, and cell-cycle analysis were performed. The status of proteins of the mTOR pathway was assessed by Western blot analysis. Metformin treatment in glucose-starved and 2DG (10 mM) exposed TNBC cells inhibited the mTOR pathway compared to non-treated glucose-starved cells or 2DG/metformin alone treated controls. Cell proliferation is also significantly reduced under these combination treatment conditions. The results indicate that combining a glycolytic inhibitor and metformin could prove an efficient therapeutic approach for treating TNBCs, albeit the efficacy of the combination treatment may depend on metabolic heterogeneity across various subtypes of TNBCs.

Abstract 6526: Investigation levels of trace elements in breast cancer types using laser spectroscopic techniques

Abstract Breast cancer (BC) is the 2nd leading cause of death from cancer in women. Women have a lifetime risk of breast cancer of around 31%, which makes it the first most common malignancy in women. Diagnosing cancer and classifying its types is a complex process that relies heavily on sample analysis. Early detection of cancer diseases is one of the important and main factors in determining treatment and saving many patients before the disease worsens and reaches dangerous stages. Alterations in the levels of trace elements have been observed in breast cancer (BC) patients. However, the relationships between these alterations and the metabolic and clinical consequences of BC are unknown. The aim of the present work is to study in vitro possibilities and the effectiveness of introducing an efficient optical spectroscopic technique such as LIBS for the identification types of breast cancer, as well as determining the disease grade and severity. The ultimate goal of this work is to investigate the concentrations of several trace elements in BC patients, and their relationships with the molecular subtypes of tumors. The atomic transition of the neutral element lines of Fe, Al, Na, Ca, K, V, Co and Pb was chosen to determine the trace element levels of four breast cancer types. We found that the measured average level of trace elements can differentiate between different types of breast cancer. Citation Format: Ola Sayed Ahmed, hisham imam. Investigation levels of trace elements in breast cancer types using laser spectroscopic techniques [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6526.

Identification of Bone Metastatic and Prognostic Alternative Splicing Signatures in Prostate Adenocarcinoma

As the most common nonepithelial malignancy, prostate adenocarcinoma (PRAD) is the fifth chief cause of cancer mortality in men. Distant metastasis often occurs in advanced PRAD and most patients are dying from it. However, the mechanism of PRAD progression and metastasis is still unclear. It’s widely reported that more than 94% of genes are selectively splicing in humans and many isoforms are particularly related with cancer progression and metastasis. Spliceosome mutations occur in a mutually exclusive manner in breast cancer, and different components of spliceosomes are targets of somatic mutations in different types of breast cancer. Existing evidence strongly supports the key role of alternative splicing in breast cancer biology, and innovative tools are being developed to use splicing events for diagnostic and therapeutic purposes. In order to identify if the PRAD metastasis is associated with alternative splicing events (ASEs), the RNA sequencing data and ASEs data of 500 PRAD patients were retrieved from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases. By Lasso regression, five genes were screened to construct the prediction model, with a good reliability by ROC curve. Additionally, results in both univariate and multivariate Cox regression analysis confirmed the well prognosis efficacy of the prediction model (both P < 0.001). Moreover, a potential splicing regulatory network was established and after multiple-database validation, we supposed that the signaling axis of HSPB1 up-regulating the PIP5K1C − 46,721 − AT (P < 0.001) might mediate the tumorigenesis, progression and metastasis of PRAD via the key members of Alzheimer’s disease pathway (SRC, EGFR, MAPT, APP and PRKCA) (P < 0.001).

Network biology of lapatinib resistance in different types of HER2-positive breast cancers informs the prioritization of the combination anticancer treatments as anti-resistance interventions

Background: With widespread therapeutic advancement, targeted anticancer therapeutics are taking over traditional treatment protocols. Nevertheless, the resistance to targeted therapeutics has halted the enthusiastic treatment response. An example of targeted therapy is Lapatinib. Lapatinib is a tyrosine kinase inhibitor used in HER2-positive breast cancer. It is widely used in HER2-positive metastatic breast cancer in combination with other drugs. Methods: This study's main objective was to provide a plausible mechanistic insight into lapatinib’s resistance in two HER2 positive breast cancer cell lines, SKBR3 and BT-474. We performed gene set analysis to identify the differentially expressed genes (DEG) in response to treatment with lapatinib from the gene expression profiles obtained from GSE38376 and GSE16179. The DEG was then analyzed by Ingenuity Pathway Analysis (IPA). Results: The IPA analysis showed that the increased expression of Hypoxia-inducible factor-1 alpha (HIF-1α) and Wnt/β-catenin and their related networks were associated with resistance and poor prognosis in SKBR3 and BT-474 cell lines, respectively. Although both cell lines are categorized as HER2 positive cell lines and in some reports were used interchangeably, in our hands, the two cell lines exhibited different biological pathways underlying resistance to lapatinib. In addition, among the other top canonical pathways, TNF was identified as the top upstream regulator in SKBR3 cell lines, whereas the Microphthalmia-associated transcription factor (MITF) was predicted as a top regulator in BT-474 cell lines. Conclusions: This study highlights the relevance of HIF-1α and Wnt/β-catenin compensatory networks in resistance toward lapatinib. Our findings outline the activation of angiogenesis and invasion processes in resistant cells with differential underlining gene networks in two different HER2 positive cell lines. The two cell lines reflect two different types of breast cancer, and hence the treatment strategy to avoid resistance should be planned differently.

Prognostic Markers Related to Triple-Negative Breast Cancer

Background: The triple-negative tumor is a high-risk tumor as a targeted therapy to these proteins is not possible with this type of cancer. Objective: To investigate the prognostic factors that can help in treatment selection in the triple-negative phenotype breast tumors Study design: A cross-sectional study Place and duration: This study was conducted at People's University of Medical and Health Sciences Nawabshah from Nov 2021 to Nov 2022 Methodology: The present study examined numerous cases of invasive carcinoma of the breast. The patients visited for follow-up for a long time and they were examined by tissue microarray. The series was stained through concurrent immune-histochemical prognostic panels. This was done to specify the subgroups of different types of breast cancer and for the identification of prognostic markers as well as the aggressive behavior of the tumors. Results: In the beginning, a total of 280 cases were included in the study, out of which 45 (16.07%) patients had triple-negative breast cancer. Most of these cancers were grade 3 carcinomas. A strong association was seen with pushing margins, development of recurrence, large size, poorer Nottingham Prognostic Index, and distant metastasis. Moreover, the association was also seen with loss of expression of E-cadherin and androgen receptors, basal phenotype, p53, EGFR, and P-cadherin. The size of the tumor, androgen receptors, and lymph node staging were the most valuable prognostic markers. Androgen receptor and size had prognostic significance in the tumor subgroup with lymph node-positive tumors. On the other hand, the basal phenotype was the only prognostic marker in the subgroup with lymph node-negative tumors. Some other parameters considered in the present study are histological grade, size of the tumor, age of the patient, and vascular invasion. Conclusion: The most significant markers identified were basal phenotype and androgen receptor. Also, tumor size and the status of the lymph node are quite significant in low-risk and high-risk patients while selecting surgical or non-surgical treatment for the triple-negative tumors Keywords: Triple-negative, breast tumor, prognostic markers, androgen receptors

Implications of Aromatase Inhibitor Therapy in Postmenopausal Breast Cancer

Aromatase Inhibitor(AI) therapy is a crucial treatment for regulating estrogen production that combats breast cancer in postmenopausal patients. Clinical trials have emphasized its importance, as it has surpassed its prior efficacy through each succeeding generation of AI therapy and surpassed other treatment options like estrogen modulators(Tamoxifen) and Progestin therapy(Megace/Megestrol Acetate). There is a big difference between AI therapy and other therapies, though they all ultimately attempt to stop the proliferation of cancerous cells. AI therapy has been shown to be effective; however, some side effects of its use have been challenging its place in recent studies. Many physicians have noticed and questioned its use, and future research addressing these concerns needs to be executed in order to enhance the quality of life for our breast cancer patients. In this paper, an overview of the current status of breast cancer, the details of breast cancer and its development, the different types of breast cancer, the workings of AI therapy, clinical trials of Aromatase Inhibitor therapy, a comparison of different therapies, Physician input, and future implications of its effectiveness will be discussed in detail.

Omics sciences and precision medicine in breast and ovarian cancer.

Human breast carcinoma is a complex disease, affecting 1 in 8 women worldwide. The seriousness of the disease increases when the definite cause of the disease remains obscure, thus making prognosis challenging. Researchers are emphasizing on adapting more advanced and targeted therapeutic approaches to address the multifaceted impacts of the disease. Hence, modern multi-omics systems have gained popularity among clinicians, as they offer insights into the genomic, pharmacogenomic, metabolomic, and microbiomic factors, thus allowing researchers to develop targeted and personalized approaches for breast cancer prevention and early detection, and eventually improving patient outcomes. The primary focus of this study is to elucidate, through the integration of multi-omics research findings, the inherent molecular origins of diverse subtypes of breast cancer and to evaluate the effectiveness of these findings in reducing breast cancer-related mortalities. Thorough investigation was conducted by reviewing reputable and authoritative medical journals, e-books, and online databases dedicated to cancer research. The Mendelian inheritance in man database (OMIM) was used to scrutinize specific genes and their respective loci associated with the development of different types of breast cancer. Our present research revealed the holistic picture of sundry molecular, genomic, pharmacogenomic, metabolomic, and microbiomic features of breast cancer. Such findings, like genetic alterations in highly penetrant genes, plus metabolomic and microbiomic signatures of breast cancer, unveil valuable insights and show great potential for multi-omics research in breast oncology. Further research in omics sciences pertaining to breast cancer are at the forefront of shaping precise treatment and bolstering patient survival.