Epidemiology of Breast Cancer: Current Figures and Trends

Kumar, Ajay; Singla, Anshuja

doi:10.1007/978-981-13-3438-2_26

Cited by 11 publications

(10 citation statements)

References 15 publications

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“…Cancer is a major public health issue worldwide [1,2]. More specifically, breast cancer is the most common cause of cancer death in women in developing countries and the second most common cause of cancer death in developed countries [3]. Although death rates for breast cancer dropped by 40% from 1989 to 2016 [1], specific types of breast cancer are still incurable [4].…”

Section: Introductionmentioning

confidence: 99%

Rationally Designed Ruthenium Complexes for Breast Cancer Therapy

Golbaghi

Castonguay

2020

Molecules

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Since the discovery of the anticancer potential of ruthenium-based complexes, several species were reported as promising candidates for the treatment of breast cancer, which accounts for the greatest number of new cases in women every year worldwide. Among these ruthenium complexes, species containing bioactive ligand(s) have attracted increasing attention due to their potential multitargeting properties, leading to anticancer drug candidates with a broader range of cellular targets/modes of action. This review of the literature aims at providing an overview of the rationally designed ruthenium-based complexes that have been reported to date for which ligands were carefully selected for the treatment of hormone receptor positive breast cancers (estrogen receptor (ER+) or progesterone receptor (PR+)). In addition, this brief survey highlights some of the most successful examples of ruthenium complexes reported for the treatment of triple negative breast cancer (TNBC), a highly aggressive type of cancer, regardless of if their ligands are known to have the ability to achieve a specific biological function.

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Section: Introductionmentioning

confidence: 99%

Rationally Designed Ruthenium Complexes for Breast Cancer Therapy

Golbaghi

Castonguay

2020

Molecules

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“…Breast cancer is the leading cause of cancer-related deaths in women worldwide 1 . Poor survival rates and therapy failure in breast cancer are attributable to several factors most notably chemoresistance.…”

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confidence: 99%

ROR1 regulates chemoresistance in Breast Cancer via modulation of drug efflux pump ABCB1

Fultang

Illendula

Lin

et al. 2020

Sci Rep

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Chemoresistance is one of the leading causes of mortality in breast cancer (BC). Understanding the molecules regulating chemoresistance is critical in order to combat chemoresistant BC. Drug efflux pump ABCB1 is overexpressed in chemoresistant neoplasms where it effluxes various chemotherapeutic agents from cells. Because it is expressed in normal and cancerous cells alike, attempts at targeting ABCB1 directly have failed due to low specificity and disruption of normal tissue. A proposed method to inhibit ABCB1 is to target its cancer-specific, upstream regulators, mitigating damage to normal tissue. Few such cancer-specific upstream regulators have been described. Here we characterize ROR1 as an upstream regulator of ABCB1. ROR1 is highly expressed during development but not expressed in normal adult tissue. It is however highly expressed in several cancers. ROR1 is overexpressed in chemoresistant BC where it correlates with poor therapy response and tumor recurrence. Our data suggests, ROR1 inhibition sensitizes BC cells to chemo drugs. We also show ROR1 regulates ABCB1 stability and transcription via MAPK/ERK and p53. Validating our overall findings, inhibition of ROR1 directly correlated with decreased efflux of chemo-drugs from cells. Overall, our results highlight ROR1's potential as a therapeutic target for multidrug resistant malignancies. ROR1 knockdown potentiates DNA damage induced by chemo drugs.A mechanism of action common to both Pt-based and anthracycline chemotherapeutic agents is induction of DNA double stranded breaks leading to cell death 19 . We thus sought to investigate if ROR1 inhibition would promote chemo-induced DNA double strand breaks. We treated cells transfected with either ROR1 siRNA or control RNA, with Doxorubicin or Cisplatin, and monitored γH2a.x, a marker for DNA double strand breaks via immunofluorescence ( Fig. 3A, Supplementary Fig. 2). We observed potentiation of DNA double strand breaks induced by both drugs in cells where ROR1 was knocked down. γH2a.x foci counts were higher in the siROR1+ (Dox or Cis) groups compared to the siROR1-only and drug-only treatment groups (Fig. 3B). We similarly observed an increase in γH2a.x expression (mean fluorescence intensity) in Cis/Dox treated cells after ROR1 knockdown compared to the control group (Fig. 3C). Altogether, these data suggest ROR1 inhibition promotes chemo drug-induced DNA damage. Scientific RepoRtS |(2020) 10:1821 | https://doi.

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“…The authors like to thank Dr. Hossein Hajimirsadeghi [1] for his useful advice and invaluable help during this research.…”

Section: Acknowledgementsmentioning

confidence: 99%

“…Among cancers, primary breast cancer as a complex disease is the most commonly diagnosed carcinoma in women worldwide and will be fatal if it progresses towards the secondary-stage. It is the most common and the second most common cause of cancer death in women in developing regions and developed regions, respectively (1). Over the last 10 years, the incidence of breast cancer has increased almost 10 times (2).…”

Section: Introductionmentioning

confidence: 99%

EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

Mirsadeghi

Hosseini

Banaei-Moghaddam

et al. 2020

Preprint

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BackgroundToday, there are a lot of markers on the prognosis and diagnosis of complex diseases such as primary breast cancer. However, our understanding of the drivers that influence cancer aggression is limited.MethodsIn this work, we study somatic mutation data consists of 450 metastatic breast tumor samples from cBio Cancer Genomics Portal. We use four software tools to extract features from this data. Then, an ensemble classifier (EC) learning algorithm called EARN (Ensemble of Artificial Neural Network, Random Forest, and non-linear Support Vector Machine) is proposed to evaluate plausible driver genes for metastatic breast cancer (MBCA). ResultsThis study is an attempt to focus on the findings in several aspects of MBCA prognosis and diagnosis. First, drivers and passengers predicted by SVM, ANN, RF, and EARN are introduced. Second, biological inferences of predictions based on gene set enrichment analysis are discussed. Third, statistical validation and comparison of all learning methods based on evaluation metrics are done. Finally, the pathway enrichment analysis (PEA) using ReactomeFIVIz tool (FDR<0.03) for the top 100 genes predicted by EARN leads us to propose a new gene set panel for MBCA, including HDAC3, ABAT, GRIN1, PLCB1, and KPNA2 as well as NCOR1, TBL1XR1, SIRT4, KRAS, CACNA1E, PRKCG, GPS2, SIN3A, ACTB, KDM6B, and PRMT1. Furthermore, we compare results for MBCA to other outputs regarding 983 primary tumor samples of breast invasive carcinoma (BRCA) obtained from the Cancer Genome Atlas (TCGA). The comparison between outputs shows that ROC-AUC reached 99.24% using EARN for MBCA and 99.79% for BRCA. This statistical result is better than three individual classifiers in each case.ConclusionsThis research using an integrative approach assists precision oncologists to design compact targeted panels that eliminate the need for whole-genome/exome sequencing.

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Epidemiology of Breast Cancer: Current Figures and Trends

Cited by 11 publications

References 15 publications

Rationally Designed Ruthenium Complexes for Breast Cancer Therapy

Rationally Designed Ruthenium Complexes for Breast Cancer Therapy

ROR1 regulates chemoresistance in Breast Cancer via modulation of drug efflux pump ABCB1

EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

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