Genome-wide association study identifies novel breast cancer susceptibility loci

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer

Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events.

Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study

Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally accessible and cost-effective approach for risk evaluation. We analyzed complete blood count (CBC) tests from 396,848 women aged 40-70, who underwent breast imaging or biopsies within six months after their CBC test. Of these, 2861 (0.72%) were identified as cases: 1882 with BC confirmed by anatomopathological tests, and 979 with highly suspicious imaging (BI-RADS 5). The remaining 393,987 participants (99.28%), with BI-RADS 1 or 2 results, were classified as controls. The database was divided into modeling (including training and validation) and testing sets based on diagnostic certainty. The testing set comprised cases confirmed by anatomopathology and controls cancer-free for 4.5-6.5 years post-CBC. Our ridge regression model, incorporating neutrophil-lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64 (95% CI 0.64-0.65). We also demonstrate that these results are slightly better than those from a boosting machine learning model, LightGBM, plus having the benefit of being fully interpretable. Using the probabilistic output from this model, we divided the study population into four risk groups: high, moderate, average, and low risk, which obtained relative ratios of BC of 1.99, 1.32, 1.02, and 0.42, respectively. The aim of this stratification was to streamline prioritization, potentially improving the early detection of breast cancer, particularly in resource-limited environments. As a risk stratification tool, this model offers the potential for personalized breast cancer screening by prioritizing women based on their individual risk, thereby indicating a shift from a broad population strategy.

Expression- and splicing-based multi-tissue transcriptome-wide association studies identified multiple genes for breast cancer by estrogen-receptor status

BACKGROUND

Although several transcriptome-wide association studies (TWASs) have been performed to identify genes associated with overall breast cancer (BC) risk, only a few TWAS have explored the differences in estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancer. Additionally, these studies were based on gene expression prediction models trained primarily in breast tissue, and they did not account for alternative splicing of genes.

METHODS

In this study, we utilized two approaches to perform multi-tissue TWASs of breast cancer by ER subtype: (1) an expression-based TWAS that combined TWAS signals for each gene across multiple tissues and (2) a splicing-based TWAS that combined TWAS signals of all excised introns for each gene across tissues. To perform this TWAS, we utilized summary statistics for ER + BC from the Breast Cancer Association Consortium (BCAC) and for ER- BC from a meta-analysis of BCAC and the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA).

RESULTS

In total, we identified 230 genes in 86 loci that were associated with ER + BC and 66 genes in 29 loci that were associated with ER- BC at a Bonferroni threshold of significance. Of these genes, 2 genes associated with ER + BC at the 1q21.1 locus were located at least 1 Mb from published GWAS hits. For several well-studied tumor suppressor genes such as TP53 and CHEK2 which have historically been thought to impact BC risk through rare, penetrant mutations, we discovered that common variants, which modulate gene expression, may additionally contribute to ER + or ER- etiology.

CONCLUSIONS

Our study comprehensively examined how differences in common variation contribute to molecular differences between ER + and ER- BC and introduces a novel, splicing-based framework that can be used in future TWAS studies.

The FGFR2 Variant rs13387042 is Associated With Breast Cancer Risk: A Meta-Analysis and Systematic Review

OBJECTIVE

The association of FGFR2-rs13387042 polymorphism with breast cancer (BC) susceptibility in women remains inconclusive due to varying reports. In this study, we conducted a meta-analysis to explore the relationship between FGFR2-rs13387042 polymorphism and susceptibility to BC.

METHODS

Relevant literature were acquired through searches across multiple databases. Odds ratio (OR) values were pooled to assess the risk of BC for different alleles and genotypes. The heterogeneity among the included literature was evaluated. Sensitivity analysis was used to verify the stability of the results. Egger's linear regression test was used to assess the significance of publication bias of the included literature.

RESULTS

A total of 17 publications were included, encompassing 122,607 cases and 175,966 controls. There was significantly increased risk of BC for allele A compared with G (OR = 1.15, 95% CI = 1.14-1.67, P < .001), genotype AA compared with GG (OR = 1.34, 95% CI = 1.29-1.38, P < .001), and genotype GA compared with GG (OR = 1.19, 95% CI = 1.12-1.26, P < .001). Both Egger's test and funnel plot indicated the presence of publication bias. After adjusting potential publication bias by the trim-and-fill method, the comparison of allele A versus G (OR = 1.15, 95% CI = 1.13-1.17, P < .001), genotype AA versus GG (OR = 1.32, 95% CI = 1.28-1.37, P < .001), and genotype GA versus GG (OR = 1.15, 95% CI = 1.09-1.22, P < .001) remained statistically significant. In various subgroups, the allele A showed significantly higher risk of BC upon allele G in estrogen receptor (ER) positive BC, ER negative BC, progesterone receptor (PR) positive BC, PR negative BC, triple-negative BC, pathological grade I BC, grade II BC, and grade III breast cancer. The subsequent sensitivity analysis suggested the above findings stable and reliable.

CONCLUSION

In this study, we found that the allele A of the FGFR2-rs13387042 polymorphism is associated with increased risk of developing breast cancer. This study underscores its potential as a genetic marker for personalized risk assessment and targeted interventions.

Gender-Specific Genetic Predisposition to Breast Cancer: BRCA Genes and Beyond

Among neoplastic diseases, breast cancer (BC) is one of the most influenced by gender. Despite common misconceptions associating BC as a women-only disease, BC can also occur in men. Additionally, transgender individuals may also experience BC. Genetic risk factors play a relevant role in BC predisposition, with important implications in precision prevention and treatment. The genetic architecture of BC susceptibility is similar in women and men, with high-, moderate-, and low-penetrance risk variants; however, some sex-specific features have emerged. Inherited high-penetrance pathogenic variants (PVs) in BRCA1 and BRCA2 genes are the strongest BC genetic risk factor. BRCA1 and BRCA2 PVs are more commonly associated with increased risk of female and male BC, respectively. Notably, BRCA-associated BCs are characterized by sex-specific pathologic features. Recently, next-generation sequencing technologies have helped to provide more insights on the role of moderate-penetrance BC risk variants, particularly in PALB2, CHEK2, and ATM genes, while international collaborative genome-wide association studies have contributed evidence on common low-penetrance BC risk variants, on their combined effect in polygenic models, and on their role as risk modulators in BRCA1/2 PV carriers. Overall, all these studies suggested that the genetic basis of male BC, although similar, may differ from female BC. Evaluating the genetic component of male BC as a distinct entity from female BC is the first step to improve both personalized risk assessment and therapeutic choices of patients of both sexes in order to reach gender equality in BC care. In this review, we summarize the latest research in the field of BC genetic predisposition with a particular focus on similarities and differences in male and female BC, and we also discuss the implications, challenges, and open issues that surround the establishment of a gender-oriented clinical management for BC.

The largest genome-wide association study for breast cancer in Taiwanese Han population

PURPOSE

Breast cancer is a molecularly heterogeneous disease, and multiple genetic variants contribute to its development and prognosis. Most of previous genome-wide association studies (GWASs) and polygenic risk scores (PRSs) analyses focused on studying breast cancers of Caucasian populations, which may not be applicable to other population. Therefore, we conducted the largest breast cancer cohort of Taiwanese population to fill in the knowledge gap.

METHODS

A total of 152,534 Participants recruited by China Medical University Hospital between 2003 and 2019 were filtered by several patient selection criteria and GWAS quality control steps, resulting in the inclusion of 2496 cases and 9984 controls for this study. We then conducted GWAS for all breast cancers and PRS analyses for all breast cancers and the four breast cancer subtypes, including luminal A, luminal B, basal-like, and HER2-enriched.

RESULTS

The GWAS analyses identified 113 SNPs, 50 of which were novel. The PRS models for all breast cancers and the luminal A subtype showed positively correlated trends between the PRS and the risk of developing breast cancer. The odds ratios (95% confidence intervals) for the groups with the highest PRS in all breast cancers and the luminal A subtype were 5.33 (3.79-7.66) and 3.55 (2.13-6.14), respectively.

CONCLUSION

In summary, we explored the association of genetic variants with breast cancer in the largest Taiwanese cohort and developed two PRS models that can predict the risk of developing any breast cancer and the luminal A subtype in Taiwanese women.

Deciphering a proteomic signature for the early detection of breast cancer from breast milk: the role of quantitative proteomics

INTRODUCTION

Breast cancer is one of the most prevalent cancers among women in the United States. Current research regarding breast milk has been focused on the composition and its role in infant growth and development. There is little information about the proteins, immune cells, and epithelial cells present in breast milk which can be indicative of the emergence of BC cells and tumors.

AREAS COVERED

We summarize all breast milk studies previously done in our group using proteomics. These studies include 1D-PAGE and 2D-PAGE analysis of breast milk samples, which include within woman and across woman comparisons to identify dysregulated proteins in breast milk and the roles of these proteins in both the development of BC and its diagnosis. Our projected outlook for the use of milk for cancer detection is also discussed.

EXPERT OPINION

Analyzing the samples by multiple methods allows one to interrogate a set of samples with various biochemical methods that complement each other, thus providing a more comprehensive proteome. Complementing methods like 1D-PAGE, 2D-PAGE, in-solution digestion and proteomics analysis with PTM-omics, peptidomics, degradomics, or interactomics will provide a better understanding of the dysregulated proteins, but also the modifications or interactions between these proteins.

Genomic diversity in functionally relevant genes modifies neurodevelopmental versus neoplastic risks in individuals with germline PTEN variants

Individuals with germline PTEN variants (PHTS) have increased risks of the seemingly disparate phenotypes of cancer and neurodevelopmental disorders (NDD), including autism spectrum disorder (ASD). Etiology of the phenotypic variability remains elusive. Here, we hypothesized that decreased genomic diversity, manifested by increased homozygosity, may be one etiology. Comprehensive analyses of 376 PHTS patients of European ancestry revealed significant enrichment of homozygous common variants in genes involved in inflammatory processes in the PHTS-NDD group and in genes involved in differentiation and chromatin structure regulation in the PHTS-ASD group. Pathway analysis revealed pathways germane to NDD/ASD, including neuroinflammation and synaptogenesis. Collapsing analysis of the homozygous variants identified suggestive modifier NDD/ASD genes. In contrast, we found enrichment of homozygous ultra-rare variants in genes modulating cell death in the PHTS-cancer group. Finally, homozygosity burden as a predictor of ASD versus cancer outcomes in our validated prediction model for NDD/ASD performed favorably.

Investigation of common genetic risk factors between thyroid traits and breast cancer

Breast cancer (BC) risk is suspected to be linked to thyroid disorders, however observational studies exploring the association between BC and thyroid disorders gave conflicting results. We proposed an alternative approach by investigating the shared genetic risk factors between BC and several thyroid traits. We report a positive genetic correlation between BC and thyroxine (FT4) levels (corr = 0.13, p-value = 2.0 × 10-4) and a negative genetic correlation between BC and thyroid-stimulating hormone (TSH) levels (corr = -0.09, p-value = 0.03). These associations are more striking when restricting the analysis to estrogen receptor-positive BC. Moreover, the polygenic risk scores (PRS) for FT4 and hyperthyroidism are positively associated to BC risk (OR = 1.07, 95%CI: 1.00-1.13, p-value = 2.8 × 10-2 and OR = 1.04, 95%CI: 1.00-1.08, p-value = 3.8 × 10-2, respectively), while the PRS for TSH is inversely associated to BC risk (OR = 0.93, 95%CI: 0.89-0.97, p-value = 2.0 × 10-3). Using the PLACO method, we detected 49 loci associated to both BC and thyroid traits (p-value < 5 × 10-8), in the vicinity of 130 genes. An additional colocalization and gene-set enrichment analyses showed a convincing causal role for a known pleiotropic locus at 2q35 and revealed an additional one at 8q22.1 associated to both BC and thyroid cancer. We also found two new pleiotropic loci at 14q32.33 and 17q21.31 that were associated to both TSH levels and BC risk. Enrichment analyses and evidence of regulatory signals also highlighted brain tissues and immune system as candidates for obtaining associations between BC and TSH levels. Overall, our study sheds light on the complex interplay between BC and thyroid traits and provides evidence of shared genetic risk between those conditions.

FGFR2 genetic variants in women with breast cancer

Black African populations are more genetically diverse than others, but genetic variants have been studied primarily in European populations. The present study examined the association of four single nucleotide polymorphisms (SNPs) of the fibroblast growth factor receptor 2, associated with breast cancer in non‑African populations, with breast cancer in Black, southern African women. Genomic DNA was extracted from whole blood samples of 1,001 patients with breast cancer and 1,006 controls (without breast cancer), and the rs2981582, rs35054928, rs2981578, and rs11200014 polymorphisms were analyzed using allele‑specific Kompetitive allele‑specific PCR™, and the χ2 or Fisher's exact tests were used to compare the genotype frequencies. There was no association between those SNPs and breast cancer in the studied cohort, although an association was identified between the C/C homozygote genotype for rs2981578 and invasive lobular carcinoma. These results show that genetic biomarkers of breast cancer risk in European populations are not necessarily associated with risk in sub‑Saharan African populations. African populations are more heterogenous than other populations, and the information from this population can help focus genetic risks of cancer in this understudied population.

Single nucleotide polymorphisms associated with female breast cancer susceptibility in Chinese population

Breast cancer is a complex disease influenced by both external and internal factors, among which genetic factors play a critical role. Single-nucleotide polymorphisms (SNPs) are major contributors to the heritability of breast cancer, and their frequencies vary across ethnic groups. In this study, we aimed to investigate the association between 34 SNPs identified in previous genome-wide association studies (GWAS) and overall breast cancer risk, as well as breast cancer subtypes, in the Chinese female population. To accomplish this, we conducted an extensive association analysis using the high-throughput Sequenom MassARRAY® platform in a case-control study comprising 1848 breast cancer patients and 709 healthy controls. Our analysis, which utilized the SNPassoc package in R based on chi-squared (χ2) test and genetic model analysis, identified significant associations between breast cancer risk and SNP rs12493607 (TGFBR2, risk allele C, OR = 1.28 [1.11-1.47], P = 0.0005), as well as a less conservatively significant association with rs4784227 (CASC16, risk allele T, OR = 1.24 [1.08-1.42], P = 0.0017) and rs2046210 (ESR1, risk allele A, OR = 1.50 [1.16-1.95], P = 0.0016). Furthermore, our stratified analyses revealed that rs12493607 was significantly associated with invasive carcinoma, estrogen receptor (ER)-positive, progesterone receptor (PR)-positive, HER2-negative, and young (aged younger than 45) breast cancer. SNP rs4784227 and rs3803662 (CASC16) were associated with invasive carcinoma and ER-positive breast cancer, while rs2046210 was linked to ductal carcinoma in situ, ER-negative, PR-negative, HER2-positive, and elder (aged more than 45) breast cancers. SNPs rs10484919 (ESR1) and rs1038304 (CCDC170) showed links to HER2-positive breast cancer, and rs616488 (PEX14) with premenopausal breast cancer. In summary, our study shed light on the relationship between SNPs and breast cancer susceptibility within a vast Chinese cohort, supporting the development of polygenetic risk scores for the Chinese population. These findings provide valuable insights into the genetic basis of breast cancer and have important implications for risk prediction, early detection, and personalized treatment of this disease.

Enhancing Disease Risk Gene Discovery by Integrating Transcription Factor-Linked Trans-located Variants into Transcriptome-Wide Association Analyses

Transcriptome-wide association studies (TWAS) have been successful in identifying putative disease susceptibility genes by integrating gene expression predictions with genome-wide association studies (GWAS) data. However, current TWAS models only consider cis-located variants to predict gene expression. Here, we introduce transTF-TWAS, which includes transcription factor (TF)-linked trans-located variants for model building. Using data from the Genotype-Tissue Expression project, we predict alternative splicing and gene expression and applied these models to large GWAS datasets for breast, prostate, and lung cancers. Our analysis revealed 887 putative cancer susceptibility genes, including 465 in regions not yet reported by previous GWAS and 137 in known GWAS loci but not yet reported previously, at Bonferroni-corrected P < 0.05. We demonstrate that transTF-TWAS surpasses other approaches in both building gene prediction models and identifying disease-associated genes. These results have shed new light on several genetically driven key regulators and their associated regulatory networks underlying disease susceptibility.

MAP3K1 expression is associated with progression and poor prognosis of hormone receptor-positive, HER2-negative early-stage breast cancer

PURPOSE

In this study, we assessed whether the overexpression of MAP3K1 promotes the proliferation, migration, and invasion of breast cancer cells, which affect the prognosis of hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative early stage breast cancer.

METHODS

Two HR-positive, HER2-negative breast cancer cell lines (MCF7 and T-47D) overexpressing MAP3K1 were transfected with two MAP3K1 short hairpin RNA plasmids (shMAP3K1 [#3] and shMAP3K1 [#5]). The proliferation, migration, and invasion of these cells were then examined. We assessed whether shMAP3K1 affects the cell cycle, levels of downstream signaling molecules (ERK, JNK, p38 MAPK, and NF-κB), and sensitivity to chemotherapeutic and hormonal agents. To assess the anti-tumor effect of MAP3K1 knockdown in the breast cancer orthotopic model, MCF7 and T-47D cells treated with or without shMAP3K1 (#3) and shMAP3K1 (#5) were inoculated into the mammary fat pads of mice. In total, 182 patients with HR-positive, HER2-negative T1 and T2 breast cancer and 0-3 nodal metastases were included. Additionally, 73 patients with T1 and T2 breast cancer and negative nodes who received adjuvant endocrine therapy alone were selected as an independent validation cohort.

RESULTS

In both cell lines, shMAP3K1 (#3) and shMAP3K1 (#5) significantly reduced cell growth, migration, and invasion by downregulating MMP-9 and by blocking the G2/M phase of the cell cycle and its regulatory molecule cyclin B1. Moreover, both shMAP3K1 (#3) and shMAP3K1 (#5) downregulated ERK-, JNK-, p38 MAPK-, and NF-κB-dependent gene transcription and enhanced the sensitivity of both cell lines to doxorubicin, docetaxel, and tamoxifen. We observed that both shMAP3K1 (#3) and shMAP3K1 (#5) inhibited tumor growth compared with that in the scrambled group of MCF7 and T-47D cell orthotopic tumors. Patients with MAP3K1 overexpression exhibited significantly poorer 10-year disease-free survival (DFS) (70.4% vs. 88.6%, p = 0.003) and overall survival (OS) (81.9% vs. 96.3%, p = 0.001) than those without MAP3K1 overexpression. Furthermore, phospho-ERK (p < 0.001) and phospho-JNK (p < 0.001) expressions were significantly associated with MAP3K1 expression, and both phospho-ERK and phospho-JNK expressions were significantly correlated with poor 10-year DFS and OS. These biological findings, including a significant association between DFS and OS, and the expressions of MAP3K1, phospho-ERK, and phospho-JNK were further validated in an independent cohort. Multivariate analysis identified MAP3K1 expression as an independent poor prognostic factor for DFS and OS.

CONCLUSION

Our results indicate that the overexpression of MAP3K1 plays a major role in the poor prognosis of HR-positive, HER2-negative early stage breast cancer.

Targeting FGFR Pathways in Gastrointestinal Cancers: New Frontiers of Treatment

In carcinogenesis of the gastrointestinal (GI) tract, the deregulation of fibroblast growth factor receptor (FGFR) signaling plays a critical role. The aberrant activity of this pathway is described in approximately 10% of gastric cancers and its frequency increases in intrahepatic cholangiocarcinomas (iCCAs), with an estimated frequency of 10-16%. Several selective FGFR inhibitors have been developed in the last few years with promising results. For example, targeting the FGFR pathway is now a fundamental part of clinical practice when treating iCCA and many clinical trials are ongoing to test the safety and efficacy of anti-FGFR agents in gastric, colon and pancreatic cancer, with variable results. However, the response rates of anti-FGFR drugs are modest and resistances emerge rapidly, limiting their efficacy and causing disease progression. In this review, we aim to explore the landscape of anti-FGFR inhibitors in relation to GI cancer, with particular focus on selective FGFR inhibitors and drug combinations that may lead to overcoming resistance mechanisms and drug-induced toxicities.

DNA methylation at quantitative trait loci (mQTLs) varies with cell type and nonheritable factors and may improve breast cancer risk assessment

To individualise breast cancer (BC) prevention, markers to follow a person's changing environment and health extending beyond static genetic risk scores are required. Here, we analysed cervical and breast DNA methylation (n = 1848) and single nucleotide polymorphisms (n = 1442) and demonstrate that a linear combination of methylation levels at 104 BC-associated methylation quantitative trait loci (mQTL) CpGs, termed the WID™-qtBC index, can identify women with breast cancer in hormone-sensitive tissues (AUC = 0.71 [95% CI: 0.65-0.77] in cervical samples). Women in the highest combined risk group (high polygenic risk score and WID™-qtBC) had a 9.6-fold increased risk for BC [95% CI: 4.7-21] compared to the low-risk group and tended to present at more advanced stages. Importantly, the WID™-qtBC is influenced by non-genetic BC risk factors, including age and body mass index, and can be modified by a preventive pharmacological intervention, indicating an interaction between genome and environment recorded at the level of the epigenome. Our findings indicate that methylation levels at mQTLs in relevant surrogate tissues could enable integration of heritable and non-heritable factors for improved disease risk stratification.

Machine learning reveals genetic modifiers of the immune microenvironment of cancer

Heritability in the immune tumor microenvironment (iTME) has been widely observed yet remains largely uncharacterized. Here, we developed a machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWASs) for breast cancer (BrCa) incidence. A random forest model was trained on a positive set of immune-oncology (I-O) targets, and then used to assign I-O target probability scores to 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Cluster analysis of the most probable candidates revealed two subfamilies of genes related to effector functions and adaptive immune responses, suggesting that iTME modifiers impact multiple aspects of anticancer immunity. Two of the top ranking BrCa candidates, LSP1 and TLR1, were orthogonally validated as iTME modifiers using BrCa patient biopsies and comparative mapping studies, respectively. Collectively, these data demonstrate a robust and flexible framework for functionally fine-mapping GWAS risk loci to identify translatable therapeutic targets.

DCIS and LCIS: Are the Risk Factors for Developing In Situ Breast Cancer Different?

Ductal carcinoma in situ (DCIS) is widely accepted as a precursor of invasive ductal carcinoma (IDC). Lobular carcinoma in situ (LCIS) is considered a risk factor for invasive lobular carcinoma (ILC), and it is unclear whether LCIS is also a precursor. Therefore, it would be expected that similar risk factors predispose to both DCIS and IDC, but not necessarily LCIS and ILC. This study examined associations with risk factors using data from 3075 DCIS cases, 338 LCIS cases, and 1584 controls aged 35-60, recruited from the UK-based GLACIER and ICICLE case-control studies between 2007 and 2012. Analysis showed that breastfeeding in parous women was protective against DCIS and LCIS, which is consistent with research on invasive breast cancer (IBC). Additionally, long-term use of HRT in post-menopausal women increased the risk of DCIS and LCIS, with a stronger association in LCIS, similar to the association with ILC. Contrary to findings with IBC, parity and the number of births were not protective against DCIS or LCIS, while oral contraceptives showed an unexpected protective effect. These findings suggest both similarities and differences in risk factors for DCIS and LCIS compared to IBC and that there may be justification for increased breast surveillance in post-menopausal women taking long-term HRT.

Exome sequencing identifies breast cancer susceptibility genes and defines the contribution of coding variants to breast cancer risk

Linkage and candidate gene studies have identified several breast cancer susceptibility genes, but the overall contribution of coding variation to breast cancer is unclear. To evaluate the role of rare coding variants more comprehensively, we performed a meta-analysis across three large whole-exome sequencing datasets, containing 26,368 female cases and 217,673 female controls. Burden tests were performed for protein-truncating and rare missense variants in 15,616 and 18,601 genes, respectively. Associations between protein-truncating variants and breast cancer were identified for the following six genes at exome-wide significance (P < 2.5 × 10-6): the five known susceptibility genes ATM, BRCA1, BRCA2, CHEK2 and PALB2, together with MAP3K1. Associations were also observed for LZTR1, ATR and BARD1 with P < 1 × 10-4. Associations between predicted deleterious rare missense or protein-truncating variants and breast cancer were additionally identified for CDKN2A at exome-wide significance. The overall contribution of coding variants in genes beyond the previously known genes is estimated to be small.

FGF/FGFR1 system in paired breast tumor-adjacent and tumor tissues, associations with mammographic breast density and tumor characteristics

Introduction

Mammographic breast density (MBD) is an established breast cancer risk factor, yet the underlying molecular mechanisms remain to be deciphered. Fibroblast growth factor receptor 1 (FGFR1) amplification is associated with breast cancer development and aberrant FGF signaling found in the biological processes related to both high mammographic density and breast cancer microenvironment. The aim of this study was to investigate the FGF/FGFR1 expression in-between paired tumor-adjacent and tumor tissues from the same patient, and its associations with MBD and tumor characteristics.

Methods

FGFR1 expression in paired tissues from 426 breast cancer patients participating in the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) cohort study was analyzed by immunohistochemistry. FGF ligand expression was obtained from RNA-sequencing data for 327 of the included patients.

Results

FGFR1 levels were differently expressed in tumor-adjacent and tumor tissues, with increased FGFR1 levels detected in 58% of the tumors. High FGFR1 expression in tumor tissues was associated with less favorable tumor characteristics; high histological grade (OR=1.86, 95% CI 1.00-3.44), high Ki67 proliferative index (OR=2.18, 95% CI 1.18-4.02) as well as tumors of Luminal B-like subtype (OR=2.56, 95%CI 1.29-5.06). While no clear association between FGFR1 expression and MBD was found, FGF ligand (FGF1, FGF11, FGF18) expression was positively correlated with MBD.

Discussion

Taken together, these findings support a role of the FGF/FGFR1 system in early breast cancer which warrants further investigation in the MBD-breast cancer context.

Genetic and lifestyle factors for breast cancer risk assessment in Southeast China

BACKGROUND

Despite the rising incidence and mortality of breast cancer among women in China, there are currently few predictive models for breast cancer in the Chinese population and with low accuracy. This study aimed to identify major genetic and life-style risk factors in a Chinese population for potential application in risk assessment models.

METHODS

A case-control study in southeast China was conducted including 1321 breast cancer patients and 2045 controls during 2013-2016, in which the data were randomly divided into a training set and a test set on a 7:3 scale. The association between genetic and life-style factors and breast cancer was examined using logistic regression models. Using AUC curves, we also compared the performance of the logistic model to machine learning models, namely LASSO regression model and support vector machine (SVM), and the scores calculated from CKB, Gail and Tyrer-Cuzick models in the test set.

RESULTS

Among all factors considered, the best model was achieved when polygenetic risk score, lifestyle, and reproductive factors were considered jointly in the logistic regression model (AUC = 0.73; 95% CI: 0.70-0.77). The models created in this study performed better than those using scores calculated from the CKB, Gail, and Tyrer-Cuzick models. However, the logistic model and machine learning models did not significantly differ from one another.

CONCLUSION

In summary, we have found genetic and lifestyle risk predictors for breast cancer with moderate discrimination, which might provide reference for breast cancer screening in southeast China. Further population-based studies are needed to validate the model for future applications in personalized breast cancer screening programs.

Rewired Metabolism Caused by the Oncogenic Deregulation of MYC as an Attractive Therapeutic Target in Cancers

MYC is one of the most deregulated oncogenes on multiple levels in cancer. As a node transcription factor, MYC plays a diverse regulatory role in many cellular processes, including cell cycle and metabolism, both in physiological and pathological conditions. The relentless growth and proliferation of tumor cells lead to an insatiable demand for energy and nutrients, which requires the rewiring of cellular metabolism. As MYC can orchestrate all aspects of cellular metabolism, its altered regulation plays a central role in these processes, such as the Warburg effect, and is a well-established hallmark of cancer development. However, our current knowledge of MYC suggests that its spatial- and concentration-dependent contribution to tumorigenesis depends more on changes in the global or relative expression of target genes. As the direct targeting of MYC is proven to be challenging due to its relatively high toxicity, understanding its underlying regulatory mechanisms is essential for the development of tumor-selective targeted therapies. The aim of this review is to comprehensively summarize the diverse forms of MYC oncogenic deregulation, including DNA-, transcriptional- and post-translational level alterations, and their consequences for cellular metabolism. Furthermore, we also review the currently available and potentially attractive therapeutic options that exploit the vulnerability arising from the metabolic rearrangement of MYC-driven tumors.

Screening properties of trend tests in genetic association studies

In genome-wide association study, extracting disease-associated genetic variants among millions of single nucleotide polymorphisms is of great importance. When the response is a binary variable, the Cochran-Armitage trend tests and associated MAX test are among the most widely used methods for association analysis. However, the theoretical guarantees for applying these methods to variable screening have not been built. To fill this gap, we propose screening procedures based on adjusted versions of these methods and prove their sure screening properties and ranking consistency properties. Extensive simulations are conducted to compare the performances of different screening procedures and demonstrate the robustness and efficiency of MAX test-based screening procedure. A case study on a dataset of type 1 diabetes further verifies their effectiveness.

Evaluation of the Association between FGFR2 Gene Polymorphisms and Breast Cancer Risk in the Bangladeshi Population

Breast cancer is considered the most frequent cause of mortality from malignancy among females. Fibroblast growth factor receptor 2 (FGFR2) gene polymorphisms are highly related to the risk of breast cancer. However, no investigation has been carried out to determine the association of FGFR2 gene polymorphisms in the Bangladeshi population. Based on polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP), this study was performed to evaluate the association of FGFR2 (rs1219648, rs2420946, and rs2981582) variants in 446 Bangladeshi women (226 cases and 220 controls). A significant association of the FGFR2 rs1219648 variant with breast malignancy was reported in additive model 1 (aOR = 2.87, p < 0.0001), additive model 2 (aOR = 5.62, p < 0.0001), the dominant model (aOR = 2.87, p < 0.0001), the recessive model (aOR = 4.04, p < 0.0001), and the allelic model (OR = 2.16, p < 0.0001). This investigation also explored the significant association of the rs2981582 variant with the risk of breast cancer in additive model 2 (aOR = 2. 60, p = 0.010), the recessive model (aOR = 2.47, p = 0.006), and the allelic model (OR = 1.39, p = 0.016). However, the FGFR2 rs2420946 polymorphism showed no association with breast cancer except in the overdominant model (aOR = 0.62, p = 0.048). Furthermore, GTT (p < 0.0001) haplotypes showed a correlation with breast cancer risk, and all variants showed strong linkage disequilibrium. Moreover, in silico gene expression analysis showed that the FGFR2 level was upregulated in BC tissues compared to healthy tissues. This study confirms the association of FGFR2 polymorphisms with breast cancer risk.

The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection

Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.

Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry

BACKGROUND

Low-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes.

METHODS

We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes' coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry.

RESULTS

In European ancestry samples, 14 genes were significantly associated (q < 0.05) with BC. Of those, two genes, FMNL3 (P = 6.11 × 10-6) and AC058822.1 (P = 1.47 × 10-4), represent new associations. High FMNL3 expression has previously been linked to poor prognosis in several other cancers. Meta-analysis of samples with diverse ancestry discovered further associations including established candidate genes ESR1 and CBLB. Furthermore, literature review and database query found further support for a biologically plausible link with cancer for genes CBLB, FMNL3, FGFR2, LSP1, MAP3K1, and SRGAP2C.

CONCLUSIONS

Using extended gene-based aggregation tests including coding and regulatory variation, we report identification of plausible target genes for previously identified single-marker associations with BC as well as the discovery of novel genes implicated in BC development. Including multi ancestral cohorts in this study enabled the identification of otherwise missed disease associations as ESR1 (P = 1.31 × 10-5), demonstrating the importance of diversifying study cohorts.

Synthesis of Biocompatible Nanoporous ZIF-8-Gum Arabic as a New Carrier for the Targeted Delivery of Curcumin

The synthesis of biocompatible nanoporous zeolitic imidazolate framework-8 (ZIF-8) was performed in the presence of gum arabic (GA), curcumin (CCM), and folic acid (FA) as a template for the biomineralization process, a natural anticancer component, and a targeting agent, respectively. The synthesis of ZIF-8-GA-CCM-FA was completed in a single step at room temperature in aqueous media with a minimum amount of ethanol at a linker/metal molar ratio of 10. FA was dissolved by the alkaline medium produced by a 2-methyl imidazolium (HmIm) linker without using any toxic organic solvent or additional conjugation agents. The FA-modified carrier can target the folate receptors on Hela cells. To the best of our knowledge, this is the first report about the one-pot encapsulation of CCM and FA in a biocompatible ZIF-8-GA framework in a green solvent. This method enables high CCM loading in the ZIF-8-GA framework structure (ca. 90%) at a short time of 15 min. The effect of CCM concentration was investigated on the size, morphology, and crystallinity of the synthesized structures. The products were characterized with field emission scanning electron microscopy, Brunauer-Emmett-Teller surface area analysis, X-ray diffraction, Fourier transform infrared, and UV-vis spectroscopy techniques. The release rate of CCM from ZIF-8-GA-CCM-FA was studied at different pH values. In vitro drug release of CCM was higher in the acidic medium (pH 5.5, 6.5) compared to physiological pH (7.4). The cytotoxicity of ZIF-8-GA, ZIF-8-GA-CCM, and ZIF-8-GA-CCM-FA structures was evaluated by the standard 3-(4,5 dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay on the three cell lines (fibroblast (normal cell), Hela (FR-positive), and A549 (FR-negative). These results suggested that the ZIF-8-GA-CCM-FA framework can have a promising effect on the targeted treatment of cancer cells.

To Investigate Growth Factor Receptor Targets and Generate Cancer Targeting Inhibitors

Receptor tyrosine kinase (RTK) regulates multiple pathways, including Mitogenactivated protein kinases (MAPKs), PI3/AKT, JAK/STAT pathway, etc. which has a significant role in the progression and metastasis of tumor. As RTK activation regulates numerous essential bodily processes, including cell proliferation and division, RTK dysregulation has been identified in many types of cancers. Targeting RTK is a significant challenge in cancer due to the abnormal upregulation and downregulation of RTK receptors subfamily EGFR, FGFR, PDGFR, VEGFR, and HGFR in the progression of cancer, which is governed by multiple RTK receptor signalling pathways and impacts treatment response and disease progression. In this review, an extensive focus has been carried out on the normal and abnormal signalling pathways of EGFR, FGFR, PDGFR, VEGFR, and HGFR and their association with cancer initiation and progression. These are explored as potential therapeutic cancer targets and therefore, the inhibitors were evaluated alone and merged with additional therapies in clinical trials aimed at combating global cancer.

SB Digestor: a tailored driver gene identification tool for dissecting heterogeneous Sleeping Beauty transposon-induced tumors

Sleeping Beauty (SB) insertional mutagenesis has been widely used for genome-wide functional screening in mouse models of human cancers, however, intertumor heterogeneity can be a major obstacle in identifying common insertion sites (CISs). Although previous algorithms have been successful in defining some CISs, they also miss CISs in certain situations. A major common characteristic of these previous methods is that they do not take tumor heterogeneity into account. However, intertumoral heterogeneity directly influences the sequence read number for different tumor samples and then affects CIS identification. To precisely detect and define cancer driver genes, we developed SB Digestor, a computational algorithm that overcomes biological heterogeneity to identify more potential driver genes. Specifically, we define the relationship between the sequenced read number and putative gene number to deduce the depth cutoff for each tumor, which can reduce tumor complexity and precisely reflect intertumoral heterogeneity. Using this new tool, we re-analyzed our previously published SB-based screening dataset and identified many additional potent drivers involved in Brca1-related tumorigenesis, including Arhgap42, Tcf12, and Fgfr2. SB Digestor not only greatly enhances our ability to identify and prioritize cancer drivers from SB tumors but also substantially deepens our understanding of the intrinsic genetic basis of cancer.

Dynamic metabolome profiling uncovers potential TOR signaling genes

Although the genetic code of the yeast Saccharomyces cerevisiae was sequenced 25 years ago, the characterization of the roles of genes within it is far from complete. The lack of a complete mapping of functions to genes hampers systematic understanding of the biology of the cell. The advent of high-throughput metabolomics offers a unique approach to uncovering gene function with an attractive combination of cost, robustness, and breadth of applicability. Here, we used flow-injection time-of-flight mass spectrometry to dynamically profile the metabolome of 164 loss-of-function mutants in TOR and receptor or receptor-like genes under a time course of rapamycin treatment, generating a dataset with >7000 metabolomics measurements. In order to provide a resource to the broader community, those data are made available for browsing through an interactive data visualization app hosted at https://rapamycin-yeast.ethz.ch. We demonstrate that dynamic metabolite responses to rapamycin are more informative than steady-state responses when recovering known regulators of TOR signaling, as well as identifying new ones. Deletion of a subset of the novel genes causes phenotypes and proteome responses to rapamycin that further implicate them in TOR signaling. We found that one of these genes, CFF1, was connected to the regulation of pyrimidine biosynthesis through URA10. These results demonstrate the efficacy of the approach for flagging novel potential TOR signaling-related genes and highlight the utility of dynamic perturbations when using functional metabolomics to deliver biological insight.

Hereditary breast cancer: syndromes, tumour pathology and molecular testing

Hereditary factors account for a significant proportion of breast cancer risk. Approximately 20% of hereditary breast cancers are attributable to pathogenic variants in the highly penetrant BRCA1 and BRCA2 genes. A proportion of the genetic risk is also explained by pathogenic variants in other breast cancer susceptibility genes, including ATM, CHEK2, PALB2, RAD51C, RAD51D and BARD1, as well as genes associated with breast cancer predisposition syndromes - TP53 (Li-Fraumeni syndrome), PTEN (Cowden syndrome), CDH1 (hereditary diffuse gastric cancer), STK11 (Peutz-Jeghers syndrome) and NF1 (neurofibromatosis type 1). Polygenic risk, the cumulative risk from carrying multiple low-penetrance breast cancer susceptibility alleles, is also a well-recognised contributor to risk. This review provides an overview of the established breast cancer susceptibility genes as well as breast cancer predisposition syndromes, highlights distinct genotype-phenotype correlations associated with germline mutation status and discusses molecular testing and therapeutic implications in the context of hereditary breast cancer.

Role of DEK in carcinogenesis, diagnosis, prognosis, and therapeutic outcome of breast cancer: An evidence-based clinical review

Breast cancer is a significantly burdening women's cancer with limited diagnostic modalities. DEK is a novel biomarker overexpressed in breast cancers, currently exhaustively researched for its diagnosis and prognosis. Search for relevant meta-analyses, cohorts, and experimental studies in the last fifteen years was done in five large scientific databases. Non-English, non-full text articles or unrelated studies were excluded. Thirteen articles discussed the potential of DEK to estimate breast cancer characteristics, treatment outcomes, and prognosis. This proto-oncogene plays a role in breast carcinogenesis, increasing tumour proliferation and invasion, preventing apoptosis, and creating an immunodeficient tumour milieu with M2 tumour-associated macrophages. DEK is also associated with worse clinicopathological features and survival in breast cancer patients. Using a Kaplan-Meier plotter data analysis, DEK expression predicts worse overall survival (HR 1.24, 95%CI: 1.01-1.52, p = 0.039), comparable to other biomarkers. DEK is a promising novel biomarker requiring further research to determine its bedside applications.

MAP3K1 rs889312 polymorphism and cancer prognosis: A systematic review and meta-analysis

BACKGROUND

Accumulating studies have evaluated the association between MAP3K1 polymorphisms and cancer prognosis. However, the results of these studies are conflicting. Given the potential impact of MAP3K1 rs889312 SNP on the prognosis of various cancers, this meta-analysis was performed to obtain solid and credible evidence.

METHODS AND MATERIALS

This study was performed according to the PRISMA 2020 statement. A comprehensive article search was conducted to find and select articles from multiple databases, including PubMed, Google Scholar, Web of Science, EMBASE and the Cochrane Library, published up to 15th September 2022. The data analysis was performed with Review Manager v5.2. Pooled HR with its 95% CI and p-value was calculated where HR >1 suggests worse/poor survival and HR <1 suggests better survival of cancer patients.

RESULTS

A total of five articles comprising 24 439 patients were included for both qualitative and quantitative data synthesis. It was found that only the dominant genetic model (AC + CC vs. AA) showed a statistically significant poor overall survival for MAP3K1 rs889312 polymorphism (HR = 1.25, 95% CI = 1.06-1.47, p = .01). In addition, publication bias analysis by the Egger's test and the Begg-Mazumdar test reported no significant bias in the analysis of overall survival (p > .05).

CONCLUSIONS

The present study concludes that MAP3K1 gene rs889312 polymorphism plays a prognostic role in the survival of cancer patients. However, future research is recommended that will analyze more MAP3K SNPs along with rs889312, which may reveal more credible outcomes in terms of cancer prognosis.

The Revelation of Continuously Organized, Co-Overexpressed Protein-Coding Genes with Roles in Cellular Communications in Breast Cancer

Many human cancers, including breast cancer, are polygenic and involve the co-dysregulation of multiple regulatory molecules and pathways. Though the overexpression of genes and amplified chromosomal regions have been closely linked in breast cancer, the notion of the co-upregulation of genes at a single locus remains poorly described. Here, we describe the co-overexpression of 34 continuously organized protein-coding genes with diverse functions at 8q.24.3(143437655-144326919) in breast and other cancer types, the CanCord34 genes. In total, 10 out of 34 genes have not been reported to be overexpressed in breast cancer. Interestingly, the overexpression of CanCord34 genes is not necessarily associated with genomic amplification and is independent of hormonal or HER2 status in breast cancer. CanCord34 genes exhibit diverse known and predicted functions, including enzymatic activities, cell viability, multipotency, cancer stem cells, and secretory activities, including extracellular vesicles. The co-overexpression of 33 of the CanCord34 genes in a multivariant analysis was correlated with poor survival among patients with breast cancer. The analysis of the genome-wide RNAi functional screening, cell dependency fitness, and breast cancer stem cell databases indicated that three diverse overexpressed CanCord34 genes, including a component of spliceosome PUF60, a component of exosome complex EXOSC4, and a ribosomal biogenesis factor BOP1, shared roles in cell viability, cell fitness, and stem cell phenotypes. In addition, 17 of the CanCord34 genes were found in the microvesicles (MVs) secreted from the mesenchymal stem cells that were primed with MDA-MB-231 breast cancer cells. Since these MVs were important in the chemoresistance and dedifferentiation of breast cancer cells into cancer stem cells, these findings highlight the significance of the CanCord34 genes in cellular communications. In brief, the persistent co-overexpression of CanCord34 genes with diverse functions can lead to the dysregulation of complementary functions in breast cancer. In brief, the present study provides new insights into the polygenic nature of breast cancer and opens new research avenues for basic, preclinical, and therapeutic studies in human cancer.

The correlation of leukocyte-specific protein 1 (LSP1) rs3817198(T>C) polymorphism with breast cancer: A meta-analysis

BACKGROUND

Multiple studies have investigated the correlation of single nucleotide polymorphisms (SNPs) in leukocyte-specific protein 1 (LSP1) with susceptibility to breast cancer (BC) and have yielded inconsistent conclusions, particularly rs3817198(T > C). Consequently, we performed a meta-analysis to estimate this relationship more comprehensively.

METHODS

Four databases were utilized to locate eligible publications: PubMed, Embase, Web of Science, and China National Knowledge Infrastructure. This meta-analysis included 14 studies, including 22 reports of 33194 cases and 36661 controls. The relationship of rs3817198 polymorphism with breast cancer was estimated using odds ratios (ORs) with 95% confidence intervals (CIs). The LSP1 co-expression network was constructed by STRING, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using DAVIDE. Download TCGA breast cancer mRNA-seq data and analyze the relationship between LSP1 expression and breast cancer chemotherapy sensitivity.

RESULTS

The results indicated that rs3817198(T > C) was positively correlated to with breast malignancy (dominant model: OR = 1.11, 95%CI = 1.06-1.17; recessive model: OR = 1.10, 95%CI = 1.04-1.15; heterozygous model: OR = 1.09, 95%CI = 1.04-1.15; homozygous model: OR = 1.18, 95%CI = 1.09-1.28; additive model: OR = 1.09, 95%CI = 1.05-1.13), among Caucasians and Asians. However, rs3817198(T > C) may reduce the risk of breast carcinoma in Africans. Rs3817198(T > C) might result in breast carcinoma in individuals with BRCA1 and BRCA2 variants and can contribute to estrogen receptor (ER)-positive breast carcinoma. The expression of LSP1 was inversely correlated with the IC50 of doxorubicin (P = 8.91e-15, Cor = -0.23), 5-fluorouracil (P = 1.18e-22, Cor = -0.29), and cisplatin (P = 1.35e-42, Cor = -0.40).

CONCLUSION

Our study identified that LSP1 rs3817198 polymorphism might result in breast malignancy, particularly among Caucasians and Asians, but lower breast cancer susceptibility in African populations. The expression of LSP1 was negatively correlated with the IC50 of doxorubicin, 5-fluorouracil, and cisplatin.

Resequencing of sweetpotato germplasm resources reveals key loci associated with multiple agronomic traits

Sweetpotato is an important crop that exhibits hexaploidy and high heterozygosity, which limits gene mining for important agronomic traits. Here, 314 sweetpotato germplasm resources were deeply resequenced, and 4 599 509 SNPs and 846 654 InDels were generated, among which 196 124 SNPs were nonsynonymous and 9690 InDels were frameshifted. Based on the Indels, genome-wide marker primers were designed, and 3219 of 40 366 primer pairs were selected to construct the core InDel marker set. The molecular ID of 104 sweetpotato samples verified the availability of these primers. The sweetpotato population structures were then assessed through multiple approaches using SNPs, and diverse approaches demonstrated that population stratification was not obvious for most Chinese germplasm resources. As many as 20 important agronomic traits were evaluated, and a genome-wide association study was conducted on these traits. A total of 19 high-confidence loci were detected in both models. These loci included several candidate genes, such as IbMYB1, IbZEP1, and IbYABBY1, which might be involved in anthocyanin metabolism, carotenoid metabolism, and leaf morphogenesis, respectively. Among them, IbZEP1 and IbYABBY1 were first reported in sweetpotato. The variants in the promoter and the expression levels of IbZEP1 were significantly correlated with flesh color (orange or not orange) in sweetpotato. The expression levels of IbYABBY1 were also correlated with leaf shape. These results will assist in genetic and breeding studies in sweetpotato.

Association between the rs4784227-CASC16 polymorphism and the risk of breast cancer: A meta-analysis

OBJECTIVE

Although several studies have identified an association between the rs4784227-cancer susceptibility candidate gene 16 (CASC16) polymorphism and breast cancer, the results remain inconclusive. Therefore, we conducted a meta-analysis to assess the relationship between the rs4784227-CASC16 polymorphism and breast cancer risk.

METHODS

Studies were searched in the PubMed, Web of Science, Embase, Google Scholar, and Cochran Library databases until June 10, 2021, to identify all potential literature on rs4784227-CASC16 polymorphism and breast cancer risk association. Fixed-effect or random-effect models were used to calculate odds ratios (ORs) and their corresponding 95% confidence intervals (95% CIs). Subgroup analyses, publication bias, and sensitivity analyses were also conducted.

RESULTS

Seventeen eligible studies involving 34,719 subjects (18,445 cases and 16,274 healthy controls) from 7 articles were included in the current meta-analysis. The pooled ORs regarding the association between the rs4784227-CASC16 polymorphism and breast cancer risk were statistically significant [T vs C: OR = 1.244, 95% CI = 1.202-1.287; TT vs CT + CC: OR = 1.407, 95% CI = 1.296-1.528; CC vs CT + TT: OR = 0.777, 95% CI = 0.745-0.811; TT vs CC: OR = 1.544, 95% CI = 1.419-1.681; CT vs CC: OR = 1.244, 95% CI = 1.189-1.301]. On subgroup analysis, the rs4784227-CASC16 T/C gene has a certain correlation with breast cancer susceptibility in Asian and North American populations, but no significant risk in the Australian population.

CONCLUSION

Our pooled analysis showed a significant association between the rs4784227- (T) allele and breast cancer susceptibility in Asian and North American populations, and intervention with this mutation might be a new therapeutic strategy for breast cancer. However, large-scale and well-designed studies are needed in different populations to further evaluate the role of the rs4784227-CASC16 polymorphism in breast cancer.

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

Genome-wide association studies have succeeded in identifying genetic variants associated with complex diseases, but the findings have not been well interpreted biologically. Although it is widely accepted that epistatic interactions of high-order single nucleotide polymorphisms (SNPs) [(1) Single nucleotide polymorphisms (SNP) are mainly deoxyribonucleic acid (DNA) sequence polymorphisms caused by variants at a single nucleotide at the genome level. They are the most common type of heritable variation in humans.] are important causes of complex diseases, the combinatorial explosion of millions of SNPs and multiple tests impose a large computational burden. Moreover, it is extremely challenging to correctly distinguish high-order SNP epistatic interactions from other high-order SNP combinations due to small sample sizes. In this study, a multitasking harmony search algorithm (MTHSA-DHEI) is proposed for detecting high-order epistatic interactions [(2) In classical genetics, if genes X1 and X2 are mutated and each mutation by itself produces a unique disease status (phenotype) but the mutations together cause the same disease status as the gene X1 mutation, gene X1 is epistatic and gene X2 is hypostatic, and gene X1 has an epistatic effect (main effect) on disease status. In this work, a high-order epistatic interaction occurs when two or more SNP loci have a joint influence on disease status.], with the goal of simultaneously detecting multiple types of high-order (k1-order, k2-order, …, kn-order) SNP epistatic interactions. Unified coding is adopted for multiple tasks, and four complementary association evaluation functions are employed to improve the capability of discriminating the high-order SNP epistatic interactions. We compare the proposed MTHSA-DHEI method with four excellent methods for detecting high-order SNP interactions for 8 high-orderepistatic interaction models with no marginal effect (EINMEs) and 12 epistatic interaction models with marginal effects (EIMEs) (*) and implement the MTHSA-DHEI algorithm with a real dataset: age-related macular degeneration (AMD). The experimental results indicate that MTHSA-DHEI has power and an F1-score exceeding 90% for all EIMEs and five EINMEs and reduces the computational time by more than 90%. It can efficiently perform multiple high-order detection tasks for high-order epistatic interactions and improve the discrimination ability for diverse epistasis models.

The TOX subfamily: all-round players in the immune system

The thymocyte selection-related HMG box protein (TOX) subfamily comprises evolutionarily conserved DNA-binding proteins, and is expressed in certain immune cell subsets and plays key roles in the development of CD4+ T cells, innate lymphoid cells (ILCs), T follicular helper (Tfh) cells, and in CD8+ T-cell exhaustion. Although its roles in CD4+ T and natural killer (NK) cells have been extensively studied, recent findings have demonstrated previously unknown roles for TOX in the development of ILCs, Tfh cells, as well as CD8+ T-cell exhaustion; however, the molecular mechanism underlying TOX regulation of these immune cells remains to be elucidated. In this review, we discuss recent studies on the influence of TOX on the development of various immune cells and CD8+ T-cell exhaustion and the roles of specific TOX family members in the immune system. Moreover, this review suggests candidate regulatory targets for cell therapy and immunotherapies.

Poly (ADP-ribose) polymerase inhibitors (PARPi) for advanced malignancies with multiple DNA-repair genetic aberrations

INTRODUCTION

Poly (ADP-ribose) polymerase inhibitors (PARPi) have been approved for the treatment of advanced tumors with defects in genes involved in homologous recombination repair (HRR), including cancers of the prostate, pancreas, breast, and ovary. In these advanced tumors, PARPi afford 'synthetic lethality' by blocking the PARP-associated repair pathway in cancer cells with HRR genetic mutations, resulting in chromosome instability and cellular apoptosis. According to the synthetic lethality theory, patients with a greater burden of genetic alterations, in proportion (relative quantity) or category, would have more satisfactory outcomes after PARPi administration. However, this issue remains obscure based on the existing sporadic evidence.

AREAS COVERED

We summarize the therapeutic effects of PARPi in advanced tumors with multiple HRR genetic mutations, and attempted to compare these results with those obtained for cancers with a single mutation.

EXPERT OPINION

Limited evidence has provided a possibly encouraging response to PARPi among patients carrying multiple HRR genetic mutations compared with those with a single mutation (although the treatment effect was negative in some patients). Further research is needed to understand the role of PARPi in tumor cells with multiple HRR genetic mutations.

Inhibition of Fibroblast Growth Factor Receptor Attenuates Ultraviolet B-Induced Skin Carcinogenesis

Altered FGFR signaling has been shown to play a role in a number of cancers. However, the role of FGFR signaling in the development and progression of ultraviolet B-induced (UVB) induced cutaneous squamous cell carcinoma (cSCC) remains unclear. In the current study, the effect of UVB radiation on FGFR activation and its downstream signaling in mouse skin epidermis was examined. In addition, the impact of FGFR inhibition on UVB-induced signaling and skin carcinogenesis was also investigated. Exposure of mouse dorsal skin to UVB significantly increased phosphorylation of FGFRs in the epidermis as well as activation of downstream signaling pathways, including AKT/mTOR, STATs and MAPK. Topical application of the pan-FGFR inhibitor AZD4547 to mouse skin prior to exposure to UVB significantly inhibited FGFR phosphorylation as well as mTORC1, STAT3 and MAPK activation (i.e., phosphorylation). Moreover, AZD4547 pretreatment significantly inhibited UVB-induced epidermal hyperplasia and hyperproliferation and reduced infiltration of mast cells and macrophages into the dermis. AZD4547 treatment also significantly inhibited mRNA expression of inflammatory genes in the epidermis. Finally, mice treated topically with AZD4547 prior to UVB exposure showed decreased cSCC incidence and increased survival rate. Collectively, the current data supports the hypothesis that inhibition of FGFR in epidermis may provide a new strategy to prevent and/or treat UVB-induced cSCC.

Genome-wide interaction analysis of menopausal hormone therapy use and breast cancer risk among 62,370 women

Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values < 5 × 10-8 as genome-wide significant, and p-values < 1 × 10-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants. None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values < 1 × 105. The strongest evidence was found for rs4674019 (p-value = 2.27 × 10-7), which showed genome-wide significant interaction (p-value = 3.8 × 10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT-breast cancer risk association.

Functional annotation of breast cancer risk loci: current progress and future directions

Genome-wide association studies coupled with large-scale replication and fine-scale mapping studies have identified more than 150 genomic regions that are associated with breast cancer risk. Here, we review efforts to translate these findings into a greater understanding of disease mechanism. Our review comes in the context of a recently published fine-scale mapping analysis of these regions, which reported 352 independent signals and a total of 13,367 credible causal variants. The vast majority of credible causal variants map to noncoding DNA, implicating regulation of gene expression as the mechanism by which functional variants influence risk. Accordingly, we review methods for defining candidate-regulatory sequences, methods for identifying putative target genes and methods for linking candidate-regulatory sequences to putative target genes. We provide a summary of available data resources and identify gaps in these resources. We conclude that while much work has been done, there is still much to do. There are, however, grounds for optimism; combining statistical data from fine-scale mapping with functional data that are more representative of the normal "at risk" breast, generated using new technologies, should lead to a greater understanding of the mechanisms that influence an individual woman's risk of breast cancer.

Risk Association of TOX3 and MMP7 Gene Polymorphisms with Sporadic Breast Cancer in Mexican Women

Breast cancer (BC) has one of the highest incidences and mortality worldwide. Single nucleotide polymorphisms (SNPs) in TOX3 rs3803662 and MMP7 rs1943779 have been associated with susceptibility to BC. In this case-control study, we evaluated the association of rs3803662 (TOX3)/rs1943779 (MMP7) SNPs with clinical features, immunohistochemical reactivity, and risk association with BC in women from northeastern Mexico. We compared 212 BC cases and 212 controls. DNA was isolated from peripheral blood to perform the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay. We calculated genotype frequencies, odds ratios, and 95% confidence intervals. We found that CT (Cytocine-Thymine) and TT (Thymine -Thymine) genotypes, and T alleles of TOX3 rs3803662, were associated with BC risk (p = 0.034, p = 0.011, respectively). SNP TOX3 rs3803662 was associated with positive progesterone receptors (PR) and triple-negative BC (TNBC) but not with estrogen receptor (ER) or HER2 reactivity. CT and TT genotypes (p = 0.006) and T alleles (p = 0.002) of SNP MMP7 rs1943779 were associated with risk of BC. We found that T alleles of TOX3 rs3803662 and MMP7 rs1943779 SNPs are associated with BC risk. These findings contribute to personalized medicine in Mexican women.

Fibroblast growth factor receptor signalling dysregulation and targeting in breast cancer

Fibroblast Growth Factor Receptor (FGFR) signalling plays a critical role in breast embryonal development, tissue homeostasis, tumorigenesis and metastasis. FGFR, its numerous FGF ligands and signalling partners are often dysregulated in breast cancer progression and are one of the causes of resistance to treatment in breast cancer. Furthermore, FGFR signalling on epithelial cells is affected by signals from the breast microenvironment, therefore increasing the possibility of breast developmental abnormalities or cancer progression. Increasing our understanding of the multi-layered roles of the complex family of FGFRs, their ligands FGFs and their regulatory partners may offer novel treatment strategies for breast cancer patients, as a single agent or rational co-target, which will be explored in depth in this review.

Associations of genetic susceptibility to 16 cancers with risk of breast cancer overall and by intrinsic subtypes

Certain genetic variants are associated with risks of multiple cancers. We investigated breast cancer risk with overall genetic susceptibility to each of 16 other cancers. We constructed polygenic risk scores (PRS) for 16 cancers using risk variants identified by genome-wide association studies. We evaluated the associations of these PRSs with breast cancer risk (overall and by subtypes) using Breast Cancer Association Consortium data, including 106,278 cases and 91,477 controls of European ancestry. Odds ratios (OR) and 95% confidence intervals (CIs) were estimated to measure the association of each PRS with breast cancer risk. Data from the UK Biobank, including 4,337 cases and 209,983 non-cases, were used to replicate the findings. A 5%–8% significantly elevated risk of overall breast cancer was associated with per unit increase of the PRS for glioma and cancers of the corpus uteri, stomach, or colorectum. Analyses by subtype revealed that the PRS for corpus uteri cancer (OR = 1.09; 95% CI, 1.03–1.15) and stomach cancer (OR = 1.07; 95% CI, 1.03–1.12) were associated with estrogen receptor-positive breast cancer, while ovarian cancer PRS was associated with triple-negative breast cancer (OR = 1.25; 95% CI, 1.01–1.55). UK Biobank data supported the positive associations of overall breast cancer risk with PRS for melanoma and cancers of the stomach, colorectum, and ovary. Our study provides strong evidence for shared genetic susceptibility of breast cancer with several other cancers. Results from our study help uncover the genetic basis for breast and other cancers and identify individuals at high risk for multiple cancers.

Feasibility of personalized screening and prevention recommendations in the general population through breast cancer risk assessment: results from a dedicated risk clinic

PURPOSE

A personalized approach to prevention and early detection based on known risk factors should contribute to early diagnosis and treatment of breast cancer. We initiated a risk assessment clinic for all women wishing to undergo an individual breast cancer risk assessment.

METHODS

Women underwent a complete breast cancer assessment including a questionnaire, mammogram with evaluation of breast density, collection of saliva sample, consultation with a radiologist, and a breast cancer specialist. Women aged 40 or older, with 0 or 1 first-degree relative with breast cancer diagnosed after the age of 40 were eligible for risk assessment using MammoRisk, a machine learning-based tool that provides an individual 5-year estimated risk of developing breast cancer based on the patient's clinical data and breast density, with or without polygenic risk scores (PRSs). DNA was extracted from saliva samples for genotyping of 76 single-nucleotide polymorphisms. The individual risk was communicated to the patient, with individualized screening and prevention recommendations.

RESULTS

A total of 290 women underwent breast cancer assessment, among which 196 women (68%) were eligible for risk assessment using MammoRisk (median age 52, range 40-72). When PRS was added to MammoRisk, 40% (n = 78) of patients were assigned a different risk category, with 28% (n = 55) of patients changing from intermediate to moderate or high risk.

CONCLUSION

Individual risk assessment is feasible in the general population. Screening recommendations could be given based on individual risk. The use of PRS changed the risk score and screening recommendations in 40% of women.

Common variants in breast cancer risk loci predispose to distinct tumor subtypes

BACKGROUND

Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear.

METHODS

Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes.

RESULTS

Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rate < 5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at p < 0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions.

CONCLUSION

This report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.

From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits

Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.