A network analysis to identify mediators of germline-driven differences in breast cancer prognosis

Redox and metabolic reprogramming in breast cancer and cancer-associated adipose tissue

Redox and metabolic processes are tightly coupled in both physiological and pathological conditions. In cancer, their integration occurs at multiple levels and is characterized by synchronized reprogramming both in the tumor tissue and its specific but heterogeneous microenvironment. In breast cancer, the principal microenvironment is the cancer-associated adipose tissue (CAAT). Understanding how the redox-metabolic reprogramming becomes coordinated in human breast cancer is imperative both for cancer prevention and for the establishment of new therapeutic approaches. This review aims to provide an overview of the current knowledge of the redox profiles and regulation of intermediary metabolism in breast cancer while considering the tumor and CAAT of breast cancer as a unique Warburg's pseudo-organ. As cancer is now recognized as a systemic metabolic disease, we have paid particular attention to the cell-specific redox-metabolic reprogramming and the roles of estrogen receptors and circadian rhythms, as well as their crosstalk in the development, growth, progression, and prognosis of breast cancer.

Single nucleotide polymorphisms in the cannabinoid CB2 receptor: Molecular pharmacology and disease associations

Preclinical evidence implicating cannabinoid receptor 2 (CB2) in various diseases has led researchers to question whether CB2 genetics influence aetiology or progression. Associations between conditions and genetic loci are often studied via single nucleotide polymorphism (SNP) prevalence in case versus control populations. In the CNR2 coding exon, ~36 SNPs have high overall population prevalence (minor allele frequencies [MAF] ~37%), including non-synonymous SNP (ns-SNP) rs2501432 encoding CB2 63Q/R. Interspersed are ~27 lower frequency SNPs, four being ns-SNPs. CNR2 introns also harbour numerous SNPs. This review summarises CB2 ns-SNP molecular pharmacology and evaluates evidence from ~70 studies investigating CB2 genetic variants with proposed linkage to disease. Although CNR2 genetic variation has been associated with a wide variety of conditions, including osteoporosis, immune-related disorders, and mental illnesses, further work is required to robustly validate CNR2 disease links and clarify specific mechanisms linking CNR2 genetic variation to disease pathophysiology and potential drug responses.

Construction of a CCL20-centered circadian-signature based prognostic model in cervical cancer

BACKGROUND

Rather low vaccination rates for Human papillomavirus (HPV) and pre-existing cervical cancer patients with limited therapeutic strategies ask for more precise prognostic model development. On the other side, the clinical significance of circadian clock signatures in cervical cancer lacks investigation.

METHODS

Subtypes classification based upon eight circadian clock core genes were implemented in TCGA-CESC through k-means clustering methods. Afterwards, KEGG, GO and GSEA analysis were conducted upon differentially expressed genes (DEGs) between high and low-risk groups, and tumor microenvironment (TME) investigation by CIBERSORT and ESTIMATE. Furthermore, a prognostic model was developed by cox and lasso regression methods, and verified in GSE44001 by time-dependent receiver-operating characteristic curve (ROC) analysis. Lastly, FISH and IHC were used for validation of CCL20 expression in patients' specimens and U14 subcutaneous tumor models were built for TME composition.

RESULTS

We successfully classified cervical patients into high-risk and low-risk groups based upon circadian-oscillation-signatures. Afterwards, we built a prognostic risk model composed of GJB2, CCL20 and KRT24 with excellent predictive value on patients' overall survival (OS). We then proposed metabolism unbalance, especially for glycolysis, and immune related pathways to be major enriched signatures between the high-risk and low-risk groups. Then, we proposed an 'immune-desert'-like suppressive myeloid cells infiltration pattern in high-risk group TME and verified its resistance to immunotherapies. Finally, CCL20 was proved positively correlated with real-world patients' stages and induced significant less CD8⁺ T cells and more M2 macrophages infiltration in mouse model.

CONCLUSIONS

We unraveled a prognostic risk model based upon circadian oscillation and verified its solidity. Specifically, we unveiled distinct TME immune signatures in high-risk groups.

Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study

OBJECTIVE

To develop a clinically useful model that estimates the 10 year risk of breast cancer related mortality in women (self-reported female sex) with breast cancer of any stage, comparing results from regression and machine learning approaches.

DESIGN

Population based cohort study.

SETTING

QResearch primary care database in England, with individual level linkage to the national cancer registry, Hospital Episodes Statistics, and national mortality registers.

PARTICIPANTS

141 765 women aged 20 years and older with a diagnosis of invasive breast cancer between 1 January 2000 and 31 December 2020.

MAIN OUTCOME MEASURES

Four model building strategies comprising two regression (Cox proportional hazards and competing risks regression) and two machine learning (XGBoost and an artificial neural network) approaches. Internal-external cross validation was used for model evaluation. Random effects meta-analysis that pooled estimates of discrimination and calibration metrics, calibration plots, and decision curve analysis were used to assess model performance, transportability, and clinical utility.

RESULTS

During a median 4.16 years (interquartile range 1.76-8.26) of follow-up, 21 688 breast cancer related deaths and 11 454 deaths from other causes occurred. Restricting to 10 years maximum follow-up from breast cancer diagnosis, 20 367 breast cancer related deaths occurred during a total of 688 564.81 person years. The crude breast cancer mortality rate was 295.79 per 10 000 person years (95% confidence interval 291.75 to 299.88). Predictors varied for each regression model, but both Cox and competing risks models included age at diagnosis, body mass index, smoking status, route to diagnosis, hormone receptor status, cancer stage, and grade of breast cancer. The Cox model's random effects meta-analysis pooled estimate for Harrell's C index was the highest of any model at 0.858 (95% confidence interval 0.853 to 0.864, and 95% prediction interval 0.843 to 0.873). It appeared acceptably calibrated on calibration plots. The competing risks regression model had good discrimination: pooled Harrell's C index 0.849 (0.839 to 0.859, and 0.821 to 0.876, and evidence of systematic miscalibration on summary metrics was lacking. The machine learning models had acceptable discrimination overall (Harrell's C index: XGBoost 0.821 (0.813 to 0.828, and 0.805 to 0.837); neural network 0.847 (0.835 to 0.858, and 0.816 to 0.878)), but had more complex patterns of miscalibration and more variable regional and stage specific performance. Decision curve analysis suggested that the Cox and competing risks regression models tested may have higher clinical utility than the two machine learning approaches.

CONCLUSION

In women with breast cancer of any stage, using the predictors available in this dataset, regression based methods had better and more consistent performance compared with machine learning approaches and may be worthy of further evaluation for potential clinical use, such as for stratified follow-up.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Update Breast Cancer 2022 Part 3 - Early-Stage Breast Cancer

This review summarizes recent developments in the prevention and treatment of patients with early-stage breast cancer. The individual disease risk for different molecular subtypes was investigated in a large epidemiological study. With regard to treatment, new data are available from long-term follow-up of the Aphinity study, as well as new data on neoadjuvant therapy with atezolizumab in HER2-positive patients. Biomarkers, such as residual cancer burden, were investigated in the context of pembrolizumab therapy. A Genomic Grade Index study in elderly patients is one of a group of studies investigating the use of modern multigene tests to identify patients with an excellent prognosis in whom chemotherapy may be avoided. These and other aspects of the latest developments in the diagnosis and treatment of breast cancer are described in this review.

The impact of rare germline variants on human somatic mutation processes

Somatic mutations are an inevitable component of ageing and the most important cause of cancer. The rates and types of somatic mutation vary across individuals, but relatively few inherited influences on mutation processes are known. We perform a gene-based rare variant association study with diverse mutational processes, using human cancer genomes from over 11,000 individuals of European ancestry. By combining burden and variance tests, we identify 207 associations involving 15 somatic mutational phenotypes and 42 genes that replicated in an independent data set at a false discovery rate of 1%. We associate rare inherited deleterious variants in genes such as MSH3, EXO1, SETD2, and MTOR with two phenotypically different forms of DNA mismatch repair deficiency, and variants in genes such as EXO1, PAXIP1, RIF1, and WRN with deficiency in homologous recombination repair. In addition, we identify associations with other mutational processes, such as APEX1 with APOBEC-signature mutagenesis. Many of the genes interact with each other and with known mutator genes within cellular sub-networks. Considered collectively, damaging variants in the identified genes are prevalent in the population. We suggest that rare germline variation in diverse genes commonly impacts mutational processes in somatic cells.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

UACA locus is associated with breast cancer chemoresistance and survival

Few germline genetic variants have been robustly linked with breast cancer outcomes. We conducted trans-ethnic meta genome-wide association study (GWAS) of overall survival (OS) in 3973 breast cancer patients from the Pathways Study, one of the largest prospective breast cancer survivor cohorts. A locus spanning the UACA gene, a key regulator of tumor suppressor Par-4, was associated with OS in patients taking Par-4 dependent chemotherapies, including anthracyclines and anti-HER2 therapy, at a genome-wide significance level (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P = 1.27 \times 10^{ - 9}$$\end{document}P=1.27×10−9). This association was confirmed in meta-analysis across four independent prospective breast cancer cohorts (combined hazard ratio = 1.84, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P = 1.28 \times 10^{ - 11}$$\end{document}P=1.28×10−11). Transcriptome-wide association study revealed higher UACA gene expression was significantly associated with worse OS (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P = 4.68 \times 10^{ - 7}$$\end{document}P=4.68×10−7). Our study identified the UACA locus as a genetic predictor of patient outcome following treatment with anthracyclines and/or anti-HER2 therapy, which may have clinical utility in formulating appropriate treatment strategies for breast cancer patients based on their genetic makeup.

A single-nucleotide-polymorphism in the 5′-flanking region of MSX1 gene as a predictive marker candidate for platinum-based therapy of esophageal carcinoma

Background:

Platinum derivatives are important treatment options for patients with esophageal carcinoma (EC), and a predictive marker for platinum-based therapy is needed for precision medicine.

Patients and methods:

This study contained two cohorts consisting of EC patients treated using platinum-based chemoradiation therapy (CRT) as the first-line and another external cohort of nationwide clinicogenomic data from the BioBank Japan (BBJ).

Results:

Genome-wide association study (GWAS) of therapeutic outcomes, refractory disease or not, following platinum-based CRT as first-line in 94 patients in the first cohort suggested the association of 89 SNPs using p < 0.0001. The top 10 SNPs selected from each chromosomal region by odds ratio were evaluated for progression-free survival (PFS) and overall survival (OS) hazard ratios in the first cohort, resulting in four candidates (p < 0.0025). The four selected candidates were re-evaluated in another cohort of 24 EC patients, which included patients prospectively enrolled in this study to fulfill the sample size statistically suggested by the results of the first cohort, and of the four, only rs3815544 was replicated (p < 0.0125). Furthermore, this candidate genotype of rs3815544 proceeded to the re-evaluation study in an external cohort consisting of EC patients treated with platinum derivatives and/or by radiation therapy as the first-line treatment in BBJ, which confirmed that the alternative allele (G) of rs3815544 was statistically associated with non-response (SD or PD) to platinum-based therapy in EC patients (odds ratio = 1.801, p = 0.048). The methylation QTL database as well as online clinicogenomic databases suggested that the region including rs3815544 may regulate MSX1 expression through CpG methylation, and this down-regulation was statistically associated with poor prognosis after platinum-based therapies for EC.

Conclusion:

rs3815544 is a novel candidate predictive marker for platinum-based EC therapy.

Do sex hormones confound or mediate the effect of chronotype on breast and prostate cancer? A Mendelian randomization study

Morning-preference chronotype has been found to be protective against breast and prostate cancer. Sex hormones have been implicated in relation to chronotype and the development of both cancers. This study aimed to assess whether sex hormones confound or mediate the effect of chronotype on breast and prostate cancer using a Mendelian Randomization (MR) framework. Genetic variants associated with chronotype and sex hormones (total testosterone, bioavailable testosterone, sex hormone binding globulin, and oestradiol) (p<5×10-8) were obtained from published genome-wide association studies (n≤244,207 females and n≤205,527 males). These variants were used to investigate causal relationships with breast (nCases/nControls = 133,384/113,789) and prostate (nCases/nControls = 79,148/61,106) cancer using univariable, bidirectional and multivariable MR. In females, we found evidence for: I) Reduced risk of breast cancer per category increase in morning-preference (OR = 0.93, 95% CI:0. 88, 1.00); II) Increased risk of breast cancer per SD increase in bioavailable testosterone (OR = 1.10, 95% CI: 1.01, 1.19) and total testosterone (OR = 1.15, 95% CI:1.07, 1.23); III) Bidirectional effects between morning-preference and both bioavailable and total testosterone (e.g. mean SD difference in bioavailable testosterone = -0.08, 95% CI:-0.12, -0.05 per category increase in morning-preference vs difference in morning-preference category = -0.04, 95% CI: -0.08, 0.00 per SD increase in bioavailable testosterone). In males, we found evidence for: I) Reduced risk of prostate cancer per category increase in morning-preference (OR = 0.90, 95% CI: 0.83, 0.97) and II) Increased risk of prostate cancer per SD increase in bioavailable testosterone (OR = 1.22, 95% CI: 1.08, 1.37). No bidirectional effects were found between morning-preference and testosterone in males. While testosterone levels were causally implicated with both chronotype and cancer, there was inconsistent evidence for testosterone as a mediator of the relationship. The protective effect of morning-preference on both breast and prostate cancer is clinically interesting, although it may be difficult to effectively modify chronotype. Further studies are needed to investigate other potentially modifiable intermediates.

Exploring the Therapeutic Mechanisms of Huzhang-Shanzha Herb Pair against Coronary Heart Disease by Network Pharmacology and Molecular Docking

Background

Coronary heart disease (CHD) seriously affects human health, and its pathogenesis is closely related to atherosclerosis. The Huzhang (the root of Polygonum cuspidatum)–Shanzha (the fruit of Crataegus sp.), a classic herb pair, has been widely used for the treatment of CHD. In recent years, Huzhang–Shanzha herb pair (HSHP) was found to have a wide range of effects in CHD; however, its therapeutic specific mechanisms remain to be further explored. The aim of this study was to elucidate the molecular mechanism of HSHP in the treatment of CHD using a network pharmacology analysis approach.

Methods

The Batman-TCM database was used to explore bioactive compounds and corresponding targets of HSHP. CHD disease targets were extracted from Genecards, OMIM, PharmGkb, TTD, and DrugBank databases. Then, the protein-protein interaction (PPI) network was constructed using the STRING web platform and Cytoscape software. GO functional and KEGG pathway enrichment analyses were carried out on the Metascape web platform. Finally, molecular docking of the active components was assessed to verify the potential targets of HSHP to treat CHD by the AutoDock Vina and PyMOL software.

Results

Totally, 243 active components and 2459 corresponding targets of LDP were screened out. Eighty-five common targets of HSHP and CHD were identified. The results of the network analysis showed that resveratrol, anthranone, emodin, and ursolic acid could be defined as four therapeutic components. TNF, ESR1, NFКB1, PPARG, INS, TP53, NFКBIA, AR, PIK3R1, PIK3CA, PTGS2, and NR3C1 might be the 12 key targets. These targets were mainly involved in the regulation of biological processes, such as inflammatory responses and lipid metabolism. Enrichment analysis showed that the identified genes were mainly involved in fluid shear force, insulin resistance (IR), inflammation, and lipid metabolism pathways to contribute to CHD. This suggests that resveratrol, anthranone, emodin, and ursolic acid from HSHP can be the main therapeutic components of atherosclerosis.

Conclusion

Using network pharmacology, we provide new clues on the potential mechanism of action of HSHP in the treatment of CHD, which may be closely related to the fluid shear force, lipid metabolism, and inflammatory response.

Germline variants and breast cancer survival in patients with distant metastases at primary breast cancer diagnosis

Breast cancer metastasis accounts for most of the deaths from breast cancer. Identification of germline variants associated with survival in aggressive types of breast cancer may inform understanding of breast cancer progression and assist treatment. In this analysis, we studied the associations between germline variants and breast cancer survival for patients with distant metastases at primary breast cancer diagnosis. We used data from the Breast Cancer Association Consortium (BCAC) including 1062 women of European ancestry with metastatic breast cancer, 606 of whom died of breast cancer. We identified two germline variants on chromosome 1, rs138569520 and rs146023652, significantly associated with breast cancer-specific survival (P = 3.19 × 10-8 and 4.42 × 10-8). In silico analysis suggested a potential regulatory effect of the variants on the nearby target genes SDE2 and H3F3A. However, the variants showed no evidence of association in a smaller replication dataset. The validation dataset was obtained from the SNPs to Risk of Metastasis (StoRM) study and included 293 patients with metastatic primary breast cancer at diagnosis. Ultimately, larger replication studies are needed to confirm the identified associations.

The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence

Comprehensive analysis of omics data, such as genome, transcriptome, proteome, metabolome, and interactome, is a crucial technique for elucidating the complex mechanism of cancer onset and progression. Recently, a variety of new findings have been reported based on multi-omics analysis in combination with various clinical information. However, integrated analysis of multi-omics data is extremely labor intensive, making the development of new analysis technology indispensable. Artificial intelligence (AI), which has been under development in recent years, is quickly becoming an effective approach to reduce the labor involved in analyzing large amounts of complex data and to obtain valuable information that is often overlooked in manual analysis and experiments. The use of AI, such as machine learning approaches and deep learning systems, allows for the efficient analysis of massive omics data combined with accurate clinical information and can lead to comprehensive predictive models that will be desirable for further developing individual treatment strategies of immunotherapy and molecular target therapy. Here, we aim to review the potential of AI in the integrated analysis of omics data and clinical information with a special focus on recent advances in the discovery of new biomarkers and the future direction of personalized medicine in non-small lung cancer.

Association of germline genetic variants with breast cancer-specific survival in patient subgroups defined by clinic-pathological variables related to tumor biology and type of systemic treatment

BACKGROUND

Given the high heterogeneity among breast tumors, associations between common germline genetic variants and survival that may exist within specific subgroups could go undetected in an unstratified set of breast cancer patients.

METHODS

We performed genome-wide association analyses within 15 subgroups of breast cancer patients based on prognostic factors, including hormone receptors, tumor grade, age, and type of systemic treatment. Analyses were based on 91,686 female patients of European ancestry from the Breast Cancer Association Consortium, including 7531 breast cancer-specific deaths over a median follow-up of 8.1 years. Cox regression was used to assess associations of common germline variants with 15-year and 5-year breast cancer-specific survival. We assessed the probability of these associations being true positives via the Bayesian false discovery probability (BFDP < 0.15).

RESULTS

Evidence of associations with breast cancer-specific survival was observed in three patient subgroups, with variant rs5934618 in patients with grade 3 tumors (15-year-hazard ratio (HR) [95% confidence interval (CI)] 1.32 [1.20, 1.45], P = 1.4E-08, BFDP = 0.01, per G allele); variant rs4679741 in patients with ER-positive tumors treated with endocrine therapy (15-year-HR [95% CI] 1.18 [1.11, 1.26], P = 1.6E-07, BFDP = 0.09, per G allele); variants rs1106333 (15-year-HR [95% CI] 1.68 [1.39,2.03], P = 5.6E-08, BFDP = 0.12, per A allele) and rs78754389 (5-year-HR [95% CI] 1.79 [1.46,2.20], P = 1.7E-08, BFDP = 0.07, per A allele), in patients with ER-negative tumors treated with chemotherapy.

CONCLUSIONS

We found evidence of four loci associated with breast cancer-specific survival within three patient subgroups. There was limited evidence for the existence of associations in other patient subgroups. However, the power for many subgroups is limited due to the low number of events. Even so, our results suggest that the impact of common germline genetic variants on breast cancer-specific survival might be limited.

Survival Differences in Chinese Versus White Women With Breast Cancer in the United States: A SEER-Based Analysis

PURPOSE

The affect of race on breast cancer prognosis is not well understood. We compared crude and adjusted breast cancer survival rates of Chinese women versus White women in the United States.

METHODS

We conducted a cohort study of Chinese and White women with breast cancer diagnosed between 2004 to 2015 in the SEER 18 registries database. We abstracted information on age at diagnosis, tumor size, grade, lymph node status, receptor status, surgical treatment, receipt of radiotherapy and chemotherapy, and death. We compared crude breast cancer–specific mortality between the two ethnic groups. We calculated adjusted hazard ratios (HRs) in a propensity-matched design using the Cox proportional hazards model. P < .05 was considered statistically significant.

RESULTS

There were 7,553 Chinese women (1.8%) and 414,618 White women (98.2%) with stage I-IV breast cancer in the SEER database. There were small differences in demographics, nodal burden, and clinical stage between Chinese and White women. Ten-year breast cancer–specific survival was 88.8% for Chinese women and 85.6% for White women (HR, 0.73; 95% CI, 0.67 to 0.80; P < .0001). In a propensity-matched analysis among women with stage I–IIIC breast cancer, the HR was 0.71 (95% CI, 0.62 to 0.81; P < .0001). Annual mortality rates in White women exceeded those in Chinese women for the first 9 years after diagnosis.

CONCLUSION

Chinese women in the United States have superior breast cancer–specific survival compared with White women. The reason for the observed difference is not clear. Differences in demographic and tumor features between Chinese and White women with breast cancer may contribute to the disparity, as may the possibility of intrinsic biologic differences.

Nanobodies as efficient drug-carriers: Progress and trends in chemotherapy

Nanobodies (Nb) have a promising future as a part of next generation chemodrug delivery systems. Nb, or VHH, are small (15 kDa) monomeric antibody fragments consisting of the antigen binding region of heavy chain antibodies. Heavy chain antibodies are naturally produced by camelids, however the structure of their VHH regions can be readily reproduced in industrial expression systems, such as bacteria or yeast. Due to their small size, high solubility, remarkable stability, manipulatable characteristics, excellent in vivo tissue penetration, conjugation advantages, and ease of production, Nb have many advantages when compared against their antibody precursors. In this review, we discuss the generation and selection of Nbs via phage display libraries for easy screening, and the conjugation techniques involved in creating target-specific nanocarriers. Furthermore, we provide a comprehensive overview of recent developments and perspectives in the field of Nb drug conjugates (NDCs) and Nb-based drug vehicles (NDv) with respect to antitumor therapeutics.

Cross-species physiological interactions of endocrine disrupting chemicals with the circadian clock

Endocrine disrupting chemicals (EDCs) are endocrine-active chemical pollutants that disrupt reproductive, neuroendocrine, cardiovascular and metabolic health across species. The circadian clock is a transcriptional oscillator responsible for entraining 24-hour rhythms of physiology, behavior and metabolism. Extensive bidirectional cross talk exists between circadian and endocrine systems and circadian rhythmicity is present at all levels of endocrine control, from synthesis and release of hormones, to sensitivity of target tissues to hormone action. In mammals, a range of hormones directly alter clock gene expression and circadian physiology via nuclear receptor (NR) binding and subsequent genomic action, modulating physiological processes such as nutrient and energy metabolism, stress response, reproductive physiology and circadian behavioral rhythms. The potential for EDCs to perturb circadian clocks or circadian-driven physiology is not well characterized. For this reason, we explore evidence for parallel endocrine and circadian disruption following EDC exposure across species. In the reviewed studies, EDCs dysregulated core clock and circadian rhythm network gene expression in brain and peripheral organs, and altered circadian reproductive, behavioral and metabolic rhythms. Circadian impacts occurred in parallel to endocrine and metabolic alterations such as impaired fertility and dysregulated metabolic and energetic homeostasis. Further research is warranted to understand the nature of interaction between circadian and endocrine systems in mediating physiological effects of EDC exposure at environmental levels.

Prune-1 drives polarization of tumor-associated macrophages (TAMs) within the lung metastatic niche in triple-negative breast cancer

M2-tumor-associated macrophages (M2-TAMs) in the tumor microenvironment represent a prognostic indicator for poor outcome in triple-negative breast cancer (TNBC).

Here we show that Prune-1 overexpression in human TNBC patients has positive correlation to lung metastasis and infiltrating M2-TAMs. Thus, we demonstrate that Prune-1 promotes lung metastasis in a genetically engineered mouse model of metastatic TNBC augmenting M2-polarization of TAMs within the tumor microenvironment. Thus, this occurs through TGF-β enhancement, IL-17F secretion, and extracellular vesicle protein content modulation.

We also find murine inactivating gene variants in human TNBC patient cohorts that are involved in activation of the innate immune response, cell adhesion, apoptotic pathways, and DNA repair. Altogether, we indicate that the overexpression of Prune-1, IL-10, COL4A1, ILR1, and PDGFB, together with inactivating mutations of PDE9A, CD244, Sirpb1b, SV140, Iqca1, and PIP5K1B genes, might represent a route of metastatic lung dissemination that need future prognostic validations.

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Prune-1 correlates to M2-TAMs confirming lung metastatic dissemination in GEMM

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Cytokines and EV proteins are responsible of M2-TAMs polarization processes

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A small molecule with immunomodulatory properties ameliorates metastatic dissemination

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Identification of gene variants within immune response and cell adhesion in TNBC

Breast cancer risk factors and their effects on survival: a Mendelian randomisation study

BACKGROUND

Observational studies have investigated the association of risk factors with breast cancer prognosis. However, the results have been conflicting and it has been challenging to establish causality due to potential residual confounding. Using a Mendelian randomisation (MR) approach, we aimed to examine the potential causal association between breast cancer-specific survival and nine established risk factors for breast cancer: alcohol consumption, body mass index, height, physical activity, mammographic density, age at menarche or menopause, smoking, and type 2 diabetes mellitus (T2DM).

METHODS

We conducted a two-sample MR analysis on data from the Breast Cancer Association Consortium (BCAC) and risk factor summary estimates from the GWAS Catalog. The BCAC data included 86,627 female patients of European ancestry with 7054 breast cancer-specific deaths during 15 years of follow-up. Of these, 59,378 were estrogen receptor (ER)-positive and 13,692 were ER-negative breast cancer patients. For the significant association, we used sensitivity analyses and a multivariable MR model. All risk factor associations were also examined in a model adjusted by other prognostic factors.

RESULTS

Increased genetic liability to T2DM was significantly associated with worse breast cancer-specific survival (hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.03-1.17, P value [P] = 0.003). There were no significant associations after multiple testing correction for any of the risk factors in the ER-status subtypes. For the reported significant association with T2DM, the sensitivity analyses did not show evidence for violation of the MR assumptions nor that the association was due to increased BMI. The association remained significant when adjusting by other prognostic factors.

CONCLUSIONS

This extensive MR analysis suggests that T2DM may be causally associated with worse breast cancer-specific survival and therefore that treating T2DM may improve prognosis.

Population Graph-Based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder

With the advancement of brain imaging techniques and a variety of machine learning methods, significant progress has been made in brain disorder diagnosis, in particular Autism Spectrum Disorder. The development of machine learning models that can differentiate between healthy subjects and patients is of great importance. Recently, graph neural networks have found increasing application in domains where the population's structure is modeled as a graph. The application of graphs for analyzing brain imaging datasets helps to discover clusters of individuals with a specific diagnosis. However, the choice of the appropriate population graph becomes a challenge in practice, as no systematic way exists for defining it. To solve this problem, we propose a population graph-based multi-model ensemble, which improves the prediction, regardless of the choice of the underlying graph. First, we construct a set of population graphs using different combinations of imaging and phenotypic features and evaluate them using Graph Signal Processing tools. Subsequently, we utilize a neural network architecture to combine multiple graph-based models. The results demonstrate that the proposed model outperforms the state-of-the-art methods on Autism Brain Imaging Data Exchange (ABIDE) dataset.

HIFs, angiogenesis, and metabolism: elusive enemies in breast cancer

Hypoxia-inducible factors (HIFs) and the HIF-dependent cancer hallmarks angiogenesis and metabolic rewiring are well-established drivers of breast cancer aggressiveness, therapy resistance, and poor prognosis. Targeting of HIF and its downstream targets in angiogenesis and metabolism has been unsuccessful so far in the breast cancer clinical setting, with major unresolved challenges residing in target selection, development of robust biomarkers for response prediction, and understanding and harnessing of escape mechanisms. This Review discusses the pathophysiological role of HIFs, angiogenesis, and metabolism in breast cancer and the challenges of targeting these features in patients with breast cancer. Rational therapeutic combinations, especially with immunotherapy and endocrine therapy, seem most promising in the clinical exploitation of the intricate interplay of HIFs, angiogenesis, and metabolism in breast cancer cells and the tumor microenvironment.

Predicting breast cancer risk using interacting genetic and demographic factors and machine learning

Breast cancer (BC) is a multifactorial disease and the most common cancer in women worldwide. We describe a machine learning approach to identify a combination of interacting genetic variants (SNPs) and demographic risk factors for BC, especially factors related to both familial history (Group 1) and oestrogen metabolism (Group 2), for predicting BC risk. This approach identifies the best combinations of interacting genetic and demographic risk factors that yield the highest BC risk prediction accuracy. In tests on the Kuopio Breast Cancer Project (KBCP) dataset, our approach achieves a mean average precision (mAP) of 77.78 in predicting BC risk by using interacting genetic and Group 1 features, which is better than the mAPs of 74.19 and 73.65 achieved using only Group 1 features and interacting SNPs, respectively. Similarly, using interacting genetic and Group 2 features yields a mAP of 78.00, which outperforms the system based on only Group 2 features, which has a mAP of 72.57. Furthermore, the gene interaction maps built from genes associated with SNPs that interact with demographic risk factors indicate important BC-related biological entities, such as angiogenesis, apoptosis and oestrogen-related networks. The results also show that demographic risk factors are individually more important than genetic variants in predicting BC risk.

Exosomal microRNA-26b-5p down-regulates ATF2 to enhance radiosensitivity of lung adenocarcinoma cells

Lung adenocarcinoma (LUAD), as the most common subtype of non‐small cell lung cancer, is responsible for more than 500 000 deaths worldwide annually. In this study, we identify a novel microRNA‐26b‐5p (miR‐26b‐5p) and elucidated its function on LUAD. The survival rate of parent LUAD cells and radiation‐resistant LUAD cells were determined using clonogenic survival assay. We overexpressed or inhibited miR‐26b‐5p in LUAD, and the correlation between activating transcription factor 2 (ATF2) and miR‐26b‐5p was determined using integrated bioinformatics analysis and dual‐luciferase reporter gene assay. Exosomes derived from A549 cell lines were then detected using Western blot assay, followed by co‐transfection with radiation‐resistant A549R cells. LUAD tissues and serum were collected, followed by miR‐26b‐5p relative expression quantification using RT‐qPCR. miR‐26b‐5p was identified as the most differentially expressed miRNA and was down‐regulated in LUAD. Radiation‐resistant cells were more resistant to X‐radiation compared with parent cells. miR‐26b‐5p overexpression and X‐irradiation led to enhanced radiosensitivity of LUAD cells. ATF2 was negatively targeted by miR‐26b‐5p. Exosomal miR‐26b‐5p derived from A549 cells could be transported to irradiation‐resistant LUAD cells and inhibit ATF2 expression to promote DNA damage, apoptosis and radiosensitivity of LUAD cells, which was verified using serum‐based miR‐26b‐5p. Our results show a regulatory network of miR‐26b‐5p on radiosensitivity of LUAD cells, which may serve as a non‐invasive biomarker for LUAD.