Shared heritability and functional enrichment across six solid cancers

Unraveling the genetic landscape of susceptibility to multiple primary cancers

With advances in cancer screening and treatment, there is a growing population of cancer survivors who may develop subsequent primary cancers. While hereditary cancer syndromes account for only a portion of multiple cancer cases, we sought to explore the role of common genetic variation in susceptibility to multiple primary tumors. We conducted a cross-ancestry genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) of 10,983 individuals with multiple primary cancers, 84,475 individuals with single cancer, and 420,944 cancer-free controls from two large-scale studies. Our GWAS identified six lead variants across five genomic regions that were significantly associated (p < 5 × 10-8) with the risk of developing multiple primary tumors (overall and invasive) relative to cancer-free controls (at 3q26, 8q24, 10q24, 11q13.3, and 17p13). We also found one variant significantly associated with multiple cancers when compared with single cancer cases (at 22q13.1). Multi-tissue TWAS detected associations with genes involved in telomere maintenance in two of these regions (ACTRT3 in 3q26 and SLK and STN1 in 10q24) and the development of multiple cancers. Additionally, the TWAS also identified several novel genes associated with multiple cancers, including two immune-related genes, IRF4 and TNFRSF6B. Telomere maintenance and immune dysregulation emerge as central, common pathways influencing susceptibility to multiple cancers. These findings underscore the importance of exploring shared mechanisms in carcinogenesis, offering insights for targeted prevention and intervention strategies.

The contribution of coding variants to the heritability of multiple cancer types using UK Biobank whole-exome sequencing data

Genome-wide association studies have been highly successful at identifying common variants associated with cancer; however, they do not explain all the inherited risks of cancer. Family-based studies, targeted sequencing, and, more recently, exome-wide association studies have identified rare coding variants in some genes associated with cancer risk, but the overall contribution of these variants to the heritability of cancer is less clear. Here, we describe a method to estimate the genome-wide contribution of rare coding variants to heritability that fits models to the burden effect sizes using an empirical Bayesian approach. We apply this method to the burden of protein-truncating variants in over 15,000 genes for 11 cancers in the UK Biobank using whole-exome sequencing data on over 400,000 individuals. We extend the method to consider the overlap of genes contributing to pairs of cancers. We found ovarian cancer to have the greatest proportion of heritability attributable to protein-truncating variants in genes (46%). The joint cancer models highlight significant clustering of cancer types, including a near-complete overlap in susceptibility genes for breast, ovarian, prostate, and pancreatic cancer. Our results provide insights into the contribution of rare coding variants to the heritability of cancer and identify additional genes with strong evidence of susceptibility to multiple cancer types.

A genome-wide cross-trait analysis characterizes the shared genetic architecture between lung and gastrointestinal diseases

Lung and gastrointestinal diseases often occur together, leading to more adverse health outcomes than when a disease of one of these systems occurs alone. However, the potential genetic mechanisms underlying lung-gastrointestinal comorbidities remain unclear. Here, we leverage lung and gastrointestinal trait data from individuals of European, East Asian and African ancestries, to perform a large-scale genetic cross trait analysis, followed by functional annotation and Mendelian randomization analysis to explore the genetic mechanisms involved in the development of lung-gastrointestinal comorbidities. Notably, we find significant genetic correlations between 27 trait pairs among the European population. The highest correlation is between chronic bronchitis and peptic ulcer disease. At the variant level, we identify 42 candidate pleiotropic genetic variants (3 of them previously uncharacterized) in 14 trait pairs by integrating cross-trait meta-analysis, fine-mapping and colocalization analyses. We also find 66 candidate pleiotropic genes, most of which were enriched in immune or inflammatory response-related activities. Causal inference approaches result in 4 potential lung-gastrointestinal associations. Introducing the gut microbiota as a variable establishes a relationship between the genus Parasutterella, gastro-oesophageal reflux disease and asthma. In summary, our findings highlight the genetic relationship between lung and gastrointestinal diseases, providing insights into the genetic mechanisms underlying the development of lung gastrointestinal comorbidities.

Bayesian estimation of shared polygenicity identifies drug targets and repurposable medicines for human complex diseases

Background

Complex diseases may share portions of their polygenic architectures which can be leveraged to identify drug targets with low off-target potential or repurposable candidates. However, the literature lacks methods which can make these inferences at scale using publicly available data.

Methods

We introduce a Bayesian model to estimate the polygenic structure of a trait using only gene-based association test statistics from GWAS summary data and returns gene-level posterior risk probabilities (PRPs). PRPs were used to infer shared polygenicity between 496 trait pairs and we introduce measures that can prioritize drug targets with low off-target effects or drug repurposing potential.

Results

Across 32 traits, we estimated that 69.5 to 97.5% of disease-associated genes are shared between multiple traits, and the estimated number of druggable genes that were only associated with a single disease ranged from 1 (multiple sclerosis) to 59 (schizophrenia). Estimating the shared genetic architecture of ALS with all other traits identified the KIT gene as a potentially harmful drug target because of its deleterious association with triglycerides, but also identified TBK1 and SCN11B as putatively safer because of their non-association with any of the other 31 traits. We additionally found 21 genes which are candidate repourposable targets for Alzheimer's disease (AD) (e.g., PLEKHA1, PPIB) and 5 for ALS (e.g., GAK, DGKQ).

Conclusions

The sets of candidate drug targets which have limited off-target potential are generally smaller compared to the sets of pleiotropic and putatively repurposable drug targets, but both represent promising directions for future experimental studies.

Rare germline structural variants increase risk for pediatric solid tumors

Pediatric solid tumors are a leading cause of childhood disease mortality. In this work, we examined germline structural variants (SVs) as risk factors for pediatric extracranial solid tumors using germline genome sequencing of 1765 affected children, their 943 unaffected parents, and 6665 adult controls. We discovered a sex-biased association between very large (>1 megabase) germline chromosomal abnormalities and increased risk of solid tumors in male children. The overall impact of germline SVs was greatest in neuroblastoma, where we uncovered burdens of ultrarare SVs that cause loss of function of highly expressed, mutationally constrained genes, as well as noncoding SVs predicted to disrupt chromatin domain boundaries. Collectively, we estimate that rare germline SVs explain 1.1 to 5.6% of pediatric cancer liability, establishing them as an important component of disease predisposition.

Multiomics of Aging and Aging-Related Diseases

Despite their astonishing biological diversity, surprisingly few shared traits connect all or nearly all living organisms. Aging, i.e., the progressive and irreversible decline in the function of multiple cells and tissues, is one of these fundamental features of all organisms, ranging from single-cell creatures to complex animals, alongside variability, adaptation, growth, healing, reproducibility, mobility, and, finally, death. Age is a key determinant for many pathologies, shaping the risks of incidence, severity, and treatment outcomes for cancer, neurodegeneration, heart failure, sarcopenia, atherosclerosis, osteoporosis, and many other diseases. In this review, we aim to systematically investigate the age-related features of the development of several diseases through the lens of multiomics: from genome instability and somatic mutations to pathway alterations and dysregulated metabolism.

Unraveling the genetic landscape of susceptibility to multiple primary cancers

With advances in cancer screening and treatment, there is a growing population of cancer survivors who may develop subsequent primary cancers. While hereditary cancer syndromes account for only a portion of multiple cancer cases, we sought to explore the role of common genetic variation in susceptibility to multiple primary tumors. We conducted a cross-ancestry genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) of 10,983 individuals with multiple primary cancers, 84,475 individuals with single cancer, and 420,944 cancer-free controls from two large-scale studies. Our GWAS identified six lead variants across five genomic regions that were significantly associated (P<5×10-8) with the risk of developing multiple primary tumors (overall and invasive) relative to cancer-free controls (at 3q26, 8q24, 10q24, 11q13.3, and 17p13). We also found one variant significantly associated with multiple cancers when comparing to single cancer cases (at 22q13.1). Multi-tissue TWAS detected associations with genes involved in telomere maintenance in two of these regions (ACTRT3 in 3q26 and SLK and STN1 in 10q24) and the development of multiple cancers. Additionally, the TWAS also identified several novel genes associated with multiple cancers, including two immune-related genes, IRF4 and TNFRSF6B. Telomere maintenance and immune dysregulation emerge as central, common pathways influencing susceptibility to multiple cancers. These findings underscore the importance of exploring shared mechanisms in carcinogenesis, offering insights for targeted prevention and intervention strategies.

Nonadditive Effects of Common Genetic Variants Have a Negligent Contribution to Cancer Heritability

BACKGROUND

Contribution of dominance effects to cancer heritability is unknown. We leveraged existing genome-wide association data for seven cancers to estimate the contribution of dominance effects to the heritability of individual cancer types.

METHODS

We estimated the proportion of phenotypic variation caused by dominance genetic effects using genome-wide association data for seven cancers (breast, colorectal, lung, melanoma, nonmelanoma skin, ovarian, and prostate) in a total of 166,772 cases and 284,824 controls.

RESULTS

We observed no evidence of a meaningful contribution of dominance effects to cancer heritability. By contrast, additive effects ranged between 0.11 and 0.34.

CONCLUSIONS

In line with studies of other human traits, the dominance effects of common genetic variants play a minimal role in cancer etiology.

IMPACT

These results support the assumption of an additive inheritance model when conducting cancer association studies with common genetic variants.

Context-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genes

Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, underlying mechanisms and target genes are largely unknown, as most risk-associated variants might regulate gene expression in a context-specific manner. Here, we generate a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Identified candidate cis-regulatory elements (cCREs) are largely cell type-specific, with 37% detected in one cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs combined with transcription factor footprinting prioritize the variants for 68% of the GWAS loci. CCV-colocalization and trait relevance score indicate that epithelial and immune cell categories, including rare cell types, contribute to lung cancer susceptibility the most. A multi-level cCRE-gene linking system identifies candidate susceptibility genes from 57% of the loci, where most loci display cell-category-specific target genes, suggesting context-specific susceptibility gene function.

Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility genes in the BCAC and OCAC consortia

Transcriptome-wide association studies (TWASs) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have focused on the regulatory effects of risk-associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWASs of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole-genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents an in-depth look into the role of trans eQTLs in the complex molecular mechanisms underlying these diseases.

Galloyl-oligochitosan nano-vehicles for effective and controlled propolis delivery targeting upgrading its antioxidant and antiproliferative potential

A new conjugate, galloyl-oligochitosan nanoparticles (GOCNPs), was fabricated and used as nano-vehicle for effective and controlled delivery of propolis extract (PE) in the form of PE#GOCNPs, targeting improving its pharmaceutical potential. H-bonding interactions between the carboxyl, amino, and hydroxyl groups of the GOCNPs and PE resulted in successful encapsulation, with an entrapment efficacy of 97.3 %. The PE#GOCNPs formulation also exhibited excellent physicochemical stability and time-triggered drug release characteristics under physiological conditions. Furthermore, PE#GOCNPs showed significant activity against MCF-7 and HEPG2 carcinoma cells by scavenging free oxygen radicals and upregulating antioxidant enzymes. Additionally, PE#GOCNPs displayed anti-inflammatory properties by increasing IL10 and reducing pro-inflammatory cytokines more effectively than celecoxib. Furthermore, PE#GOCNPs reduced the expression of epidermal growth factor receptor (EGFR) and survivin genes. Furthermore, the encapsulated PE demonstrated significant activity in suppressing sonic hedgehog protein (SHH). The use of GOCNPs in combination with propolis presents a promising new strategy for chemotherapy with reduced toxicity and enhanced biocompatibility. This novel approach has the potential to revolutionize the field of chemotherapy. Future studies should focus on the application of the encapsulated PE in various cancer cell lines, distinct gene expression factors, and cell cycles.

Antihypertensive drug targets and breast cancer risk: a two-sample Mendelian randomization study

Findings on the correlation between the use of antihypertensive medication and the risk of breast cancer (BC) have been inconsistent. We performed a two-sample Mendelian randomization (MR) using instrumental variables to proxy changes in gene expressions of antihypertensive medication targets to interrogate this. Genetic instruments for expression of antihypertensive drug target genes were identified with expression quantitative trait loci in blood, which should be associated with systolic blood pressure to proxy for the effect of antihypertensive drug. The association between genetic variants and BC risk were obtained from genome-wide association study summary statistics. The summary-based MR was employed to estimate the drug effects on BC risk. We further performed sensitivity analyses to confirm the discovered MR associations such as assessment of horizontal pleiotropy, colocalization, and multiple tissue enrichment analyses. The overall BC risk was only associated with SLC12A2 gene expression at a Bonferroni-corrected threshold. One standard deviation (SD) decrease of SLC12A2 gene expression in blood was associated with a decrease of 1.12 (95%CI, 0.80-1.58) mmHg of systolic blood pressure, but a 16% increased BC risk (odds ratio, 1.16, 95% confidential interval, 1.06-1.28). This signal was further observed for estrogen receptor positive (ER +) BC (1.17, 1.06-1.28). In addition, one SD decrease in expression of PDE1B in blood was associated with 7% decreased risk of ER + BC (0.93, 0.90-0.97). We detected no evidence of horizontal pleiotropy for these associations and the probability of the causal variants being shared between the gene expression and BC risk was 81.5, 40.5 and 66.8%, respectively. No significant association was observed between other target gene expressions and BC risk. Changes in expression of SLC12A2 and PDE1B mediated possibly via antihypertensive drugs may result in increased and decreased BC risk, respectively.

Genome-wide association analyses of breast cancer in women of African ancestry identify new susceptibility loci and improve risk prediction

We performed genome-wide association studies of breast cancer including 18,034 cases and 22,104 controls of African ancestry. Genetic variants at 12 loci were associated with breast cancer risk (P < 5 × 10-8), including associations of a low-frequency missense variant rs61751053 in ARHGEF38 with overall breast cancer (odds ratio (OR) = 1.48) and a common variant rs76664032 at chromosome 2q14.2 with triple-negative breast cancer (TNBC) (OR = 1.30). Approximately 15.4% of cases with TNBC carried six risk alleles in three genome-wide association study-identified TNBC risk variants, with an OR of 4.21 (95% confidence interval = 2.66-7.03) compared with those carrying fewer than two risk alleles. A polygenic risk score (PRS) showed an area under the receiver operating characteristic curve of 0.60 for the prediction of breast cancer risk, which outperformed PRS derived using data from females of European ancestry. Our study markedly increases the population diversity in genetic studies for breast cancer and demonstrates the utility of PRS for risk prediction in females of African ancestry.

Breast Cancer Polygenic Risk Score Validation and Effects of Variable Imputation

Breast cancer (BC) is a complex disease affecting one in eight women in the USA. Advances in population genomics have led to the development of polygenic risk scores (PRSs) with the potential to augment current risk models, but replication is often limited. We evaluated 2 robust PRSs with 313 and 3820 SNPs and the effects of multiple genotype imputation replications in BC cases and control populations. Biological samples from BC cases and cancer-free controls were drawn from three European ancestry cohorts. Genotyping on the Illumina Global Screening Array was followed by stringent quality control measures and 20 genotype imputation replications. A total of 468 unrelated cases and 4337 controls were scored, revealing significant differences in mean PRS percentiles between cases and controls (p < 0.001) for both SNP sets (313-SNP PRS: 52.81 and 48.07; 3820-SNP PRS: 55.45 and 49.81), with receiver operating characteristic curve analysis showing area under the curve values of 0.596 and 0.603 for the 313-SNP and 3820-SNP PRS, respectively. PRS fluctuations (from ~2-3% up to 9%) emerged across imputation iterations. Our study robustly reaffirms the predictive capacity of PRSs for BC by replicating their performance in an independent BC population and showcases the need to average imputed scores for reliable outcomes.

Exploring the cross-cancer effect of circulating proteins and discovering potential intervention targets for 13 site-specific cancers

BACKGROUND

The proteome is an important reservoir of potential therapeutic targets for cancer. This study aimed to examine the causal associations between plasma proteins and cancer risk and to identify proteins with cross-cancer effects.

METHODS

Genetic instruments for 3991 plasma proteins were extracted from a large-scale proteomic study. Summary-level data of 13 site-specific cancers were derived from publicly available datasets. Proteome-wide Mendelian randomization and colocalization analyses were used to investigate the causal effect of circulating proteins on cancers. Protein-protein interactions and druggability assessment were conducted to prioritize potential therapeutic targets. Finally, systematical Mendelian randomization analysis between healthy lifestyle factors and cancer-related proteins was conducted to identify which proteins could act as interventional targets by lifestyle changes.

RESULTS

Genetically determined circulating levels of 58 proteins were statistically significantly associated with 7 site-specific cancers. A total of 39 proteins were prioritized by colocalization, of them, 11 proteins (ADPGK, CD86, CLSTN3, CSF2RA, CXCL10, GZMM, IL6R, NCR3, SIGLEC5, SIGLEC14, and TAPBP) were observed to have cross-cancer effects. Notably, 5 of these identified proteins (CD86, CSF2RA, CXCL10, IL6R, and TAPBP) have been targeted for drug development in cancer therapy; 8 proteins (ADPGK, CD86, CXCL10, GZMM, IL6R, SIGLEC5, SIGLEC14, TAPBP) could be modulated by healthy lifestyles.

CONCLUSION

Our study identified 39 circulating protein biomarkers with convincing causal evidence for 7 site-specific cancers, with 11 proteins demonstrating cross-cancer effects, and prioritized the proteins as potential intervention targets by either drugs or lifestyle changes, which provided new insights into the etiology, prevention, and treatment of cancers.

Epidemiological and Genetic Analyses of Schizophrenia and Breast Cancer

BACKGROUND AND HYPOTHESIS

While the phenotypic association between schizophrenia and breast cancer has been observed, the underlying intrinsic link is not adequately understood. We aim to conduct a comprehensive interrogation on both phenotypic and genetic relationships between schizophrenia and breast cancer.

STUDY DESIGN

We first used data from UK Biobank to evaluate a phenotypic association and performed an updated meta-analysis incorporating existing cohort studies. We then leveraged genomic data to explore the shared genetic architecture through a genome-wide cross-trait design.

STUDY RESULTS

Incorporating results of our observational analysis, meta-analysis of cohort studies suggested a significantly increased incidence of breast cancer among women with schizophrenia (RR = 1.30, 95% CIs = 1.14-1.48). A positive genomic correlation between schizophrenia and overall breast cancer was observed (rg = 0.12, P = 1.80 × 10-10), consistent across ER+ (rg  = 0.10, P = 5.74 × 10-7) and ER- subtypes (rg = 0.09, P = .003). This was further corroborated by four local signals. Cross-trait meta-analysis identified 23 pleiotropic loci between schizophrenia and breast cancer, including five novel loci. Gene-based analysis revealed 27 shared genes. Mendelian randomization demonstrated a significantly increased risk of overall breast cancer (OR = 1.07, P = 4.81 × 10-10) for genetically predisposed schizophrenia, which remained robust in subgroup analysis (ER+: OR = 1.10, P = 7.26 × 10-12; ER-: OR = 1.08, P = 3.50 × 10-6). No mediation effect and reverse causality was found.

CONCLUSIONS

Our study demonstrates an intrinsic link underlying schizophrenia and breast cancer, which may inform tailored screening and management of breast cancer in schizophrenia.

A distinct class of pan-cancer susceptibility genes revealed by an alternative polyadenylation transcriptome-wide association study

Alternative polyadenylation plays an important role in cancer initiation and progression; however, current transcriptome-wide association studies mostly ignore alternative polyadenylation when identifying putative cancer susceptibility genes. Here, we perform a pan-cancer 3' untranslated region alternative polyadenylation transcriptome-wide association analysis by integrating 55 well-powered (n > 50,000) genome-wide association studies datasets across 22 major cancer types with alternative polyadenylation quantification from 23,955 RNA sequencing samples across 7,574 individuals. We find that genetic variants associated with alternative polyadenylation are co-localized with 28.57% of cancer loci and contribute a significant portion of cancer heritability. We further identify 642 significant cancer susceptibility genes predicted to modulate cancer risk via alternative polyadenylation, 62.46% of which have been overlooked by traditional expression- and splicing- studies. As proof of principle validation, we show that alternative alleles facilitate 3' untranslated region lengthening of CRLS1 gene leading to increased protein abundance and promoted proliferation of breast cancer cells. Together, our study highlights the significant role of alternative polyadenylation in discovering new cancer susceptibility genes and provides a strong foundational framework for enhancing our understanding of the etiology underlying human cancers.

Cis- and trans-eQTL TWAS of breast and ovarian cancer identify more than 100 risk associated genes in the BCAC and OCAC consortia

Transcriptome-wide association studies (TWAS) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have only considered regulatory effects of risk associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWAS of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents a first look into the role of trans-eQTLs in the complex molecular mechanisms underlying these diseases.

Clustering of colon, lung, and other cancer susceptibility genes with protein tyrosine phosphatases and protein kinases in multiple short genomic regions

Interactions of large gene families are poorly understood. We found that human, mouse, and rat colon and lung cancer susceptibility genes, presently considered as separate gene families, were frequently pairwise linked. The orthologous mouse map positions of 142 of 159 early discovered colon and lung cancer susceptibility genes formed 41 genomic clusters conserved >70 million years. These linked gene pairs concordantly affected both tumors and their majority was linked with two other gene families - protein tyrosine phosphatases and cancer driver protein kinases. 25% of both protein tyrosine phosphatases and protein kinases mapped <1 cM from a colon or lung cancer susceptibility gene, and 50% in <3 cM. Similar linkage was detected with most other human susceptibility genes that controlled 29 different cancer types. This concentration of tumor susceptibility genes with protein tyrosine phosphatases and driver protein kinases in multiple relatively short genomic regions suggests their possible functional diversity.

Context-aware single-cell multiome approach identified cell-type specific lung cancer susceptibility genes

Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, the genetic mechanisms and target genes underlying these loci are largely unknown, as most risk-associated-variants might regulate gene expression in a context-specific manner. Here, we generated a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Accessible chromatin peak detection identified cell-type-specific candidate cis-regulatory elements (cCREs) from each lung cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs prioritized the variants for 68% of the GWAS loci, a subset of which was also supported by transcription factor abundance and footprinting. cCRE colocalization and single-cell based trait relevance score nominated epithelial and immune cells as the main cell groups contributing to lung cancer susceptibility. Notably, cCREs of rare proliferating epithelial cell types, such as AT2-proliferating (0.13%) and basal cells (1.8%), overlapped with CCVs, including those in TERT. A multi-level cCRE-gene linking system identified candidate susceptibility genes from 57% of lung cancer loci, including those not detected in tissue- or cell-line-based approaches. cCRE-gene linkage uncovered that adjacent genes expressed in different cell types are correlated with distinct subsets of coinherited CCVs, including JAML and MPZL3 at the 11q23.3 locus. Our data revealed the cell types and contexts where the lung cancer susceptibility genes are functional.

Investigating the tissue specificity and prognostic impact of cis-regulatory cancer risk variants

The tissue-specific incidence of cancers and their genetic basis are poorly understood. Although prior studies have shown global correlation across tissues for cancer risk single-nucleotide polymorphisms (SNPs) identified through genome-wide association studies (GWAS), any shared functional regulation of gene expression on a per SNP basis has not been well characterized. We set to quantify cis-mediated gene regulation and tissue sharing for SNPs associated with eight common cancers. We identify significant tissue sharing for individual SNPs and global enrichment for breast, colorectal, and Hodgkin lymphoma cancer risk SNPs in multiple tissues. In addition, we observe increasing tissue sharing for cancer risk SNPs overlapping with super-enhancers for breast cancer and Hodgkin lymphoma providing further evidence of tissue specificity. Finally, for genes under cis-regulation by breast cancer SNPs, we identify a phenotype characterized by low expression of tumor suppressors and negative regulators of the WNT pathway associated with worse freedom from progression and overall survival in patients who eventually develop breast cancer. Our results introduce a paradigm for functionally annotating individual cancer risk SNPs and will inform the design of future translational studies aimed to personalize assessment of inherited cancer risk across tissues.

Pan-cancer and cross-population genome-wide association studies dissect shared genetic backgrounds underlying carcinogenesis

Integrating genomic data of multiple cancers allows de novo cancer grouping and elucidating the shared genetic basis across cancers. Here, we conduct the pan-cancer and cross-population genome-wide association study (GWAS) meta-analysis and replication studies on 13 cancers including 250,015 East Asians (Biobank Japan) and 377,441 Europeans (UK Biobank). We identify ten cancer risk variants including five pleiotropic associations (e.g., rs2076295 at DSP on 6p24 associated with lung cancer and rs2525548 at TRIM4 on 7q22 nominally associated with six cancers). Quantifying shared heritability among the cancers detects positive genetic correlations between breast and prostate cancer across populations. Common genetic components increase the statistical power, and the large-scale meta-analysis of 277,896 breast/prostate cancer cases and 901,858 controls identifies 91 newly genome-wide significant loci. Enrichment analysis of pathways and cell types reveals shared genetic backgrounds across said cancers. Focusing on genetically correlated cancers can contribute to enhancing our insights into carcinogenesis.

Genome-wide analyses characterize shared heritability among cancers and identify novel cancer susceptibility regions

BACKGROUND

The shared inherited genetic contribution to risk of different cancers is not fully known. In this study, we leverage results from 12 cancer genome-wide association studies (GWAS) to quantify pairwise genome-wide genetic correlations across cancers and identify novel cancer susceptibility loci.

METHODS

We collected GWAS summary statistics for 12 solid cancers based on 376 759 participants with cancer and 532 864 participants without cancer of European ancestry. The included cancer types were breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancers. We conducted cross-cancer GWAS and transcriptome-wide association studies to discover novel cancer susceptibility loci. Finally, we assessed the extent of variant-specific pleiotropy among cancers at known and newly identified cancer susceptibility loci.

RESULTS

We observed widespread but modest genome-wide genetic correlations across cancers. In cross-cancer GWAS and transcriptome-wide association studies, we identified 15 novel cancer susceptibility loci. Additionally, we identified multiple variants at 77 distinct loci with strong evidence of being associated with at least 2 cancer types by testing for pleiotropy at known cancer susceptibility loci.

CONCLUSIONS

Overall, these results suggest that some genetic risk variants are shared among cancers, though much of cancer heritability is cancer-specific and thus tissue-specific. The increase in statistical power associated with larger sample sizes in cross-disease analysis allows for the identification of novel susceptibility regions. Future studies incorporating data on multiple cancer types are likely to identify additional regions associated with the risk of multiple cancer types.

Cross-cancer pleiotropic analysis identifies three novel genetic risk loci for colorectal cancer

BACKGROUND

To understand the shared genetic basis between colorectal cancer (CRC) and other cancers and identify potential pleiotropic loci for compensating the missing genetic heritability of CRC.

METHODS

We conducted a systematic genome-wide pleiotropy scan to appraise associations between cancer-related genetic variants and CRC risk among European populations. Single nucleotide polymorphism (SNP)-set analysis was performed using data from the UK Biobank and the Study of Colorectal Cancer in Scotland (10 039 CRC cases and 30 277 controls) to evaluate the overlapped genetic regions for susceptibility of CRC and other cancers. The variant-level pleiotropic associations between CRC and other cancers were examined by CRC genome-wide association study meta-analysis and the pleiotropic analysis under composite null hypothesis (PLACO) pleiotropy test. Gene-based, co-expression and pathway enrichment analyses were performed to explore potential shared biological pathways. The interaction between novel genetic variants and common environmental factors was further examined for their effects on CRC.

RESULTS

Genome-wide pleiotropic analysis identified three novel SNPs (rs2230469, rs9277378 and rs143190905) and three mapped genes (PIP4K2A, HLA-DPB1 and RTEL1) to be associated with CRC. These genetic variants were significant expressions quantitative trait loci in colon tissue, influencing the expression of their mapped genes. Significant interactions of PIP4K2A and HLA-DPB1 with environmental factors, including smoking and alcohol drinking, were observed. All mapped genes and their co-expressed genes were significantly enriched in pathways involved in carcinogenesis.

CONCLUSION

Our findings provide an important insight into the shared genetic basis between CRC and other cancers. We revealed several novel CRC susceptibility loci to help understand the genetic architecture of CRC.

Appraising the associations between systemic iron status and epigenetic clocks: A genetic correlation and bidirectional Mendelian Randomization study

BACKGROUND

Genetic correlations and bidirectional causal effects between systemic iron status and epigenetic clocks have not been fully investigated, although observational studies have suggested systemic iron status is associated with human aging.

OBJECTIVES

We explored the genetic correlations and bidirectional causal effects between systemic iron status and epigenetic clocks.

METHODS

Leveraging large-scale genome-wide association study summary-level statistics for four systemic iron status biomarkers (ferritin, serum iron, transferrin, transferrin saturation) (N = 48,972) and four measures for epigenetic age (GrimAge, PhenoAge, IEAA, HannumAge) (N = 34,710), genetic correlations and bidirectional causal effects were estimated between them mainly by applying linkage disequilibrium score (LDSC) regression, Mendelian randomization (MR), and MR based on Bayesian model averaging (MR-BMA). The main analyses were conducted employing multiplicative random effects inverse variance weighted MR. MR-Egger, weighted median, weighted mode, and MR-PRESSO were performed as sensitivity analyses to support the robustness of causal effects.

RESULTS

The LDSC results illustrated genetic correlations (Rg) between serum iron and PhenoAge (Rg = 0.1971, p = 0.048) and between transferrin saturation and PhenoAge (Rg = 0.196, p = 0.0469). We found that increased ferritin and transferrin saturation significantly increased all four measures of epigenetic age acceleration (all p < 0.0125, beta > 0). Each standard deviation genetically increases in serum iron only significantly associated with increased IEAA acceleration (beta = 0.36, 95% CI 0.16-0.57, p = 6.01E-04) and increased HannumAge acceleration (beta = 0.32, 95% CI 0.11-0.52, p = 2.69E-03). Evidence showed a suggestively significant causal effect of transferrin on epigenetic age acceleration (all 0.0125 < p <0.05). Additionally, reverse MR study indicated no significant causal effect of epigenetic clocks on systemic iron status.

CONCLUSIONS

All four iron status biomarkers had a significant or suggestively significant causal effect on epigenetic clocks, whereas reverse MR studies did not.

Family Matters: Germline Testing in Thoracic Cancers

Most thoracic cancers arise via a series of stepwise somatic alterations driven by a well-defined carcinogen (ie, tobacco or asbestos for lung cancer and mesothelioma, respectively). A small proportion can emerge on a background of pathogenic germline variants (PGVs), which have the property of heritability. In general, PGVs may be initially suspected on the basis of the presence of specific clinical features. Such gene × environment interactions significantly increase the risk of developing lung cancer (1.5- to 3.2-fold). PGVs have been discovered involving the actionable driver oncogene, epidermal growth factor receptor (EGFR), with an EGFR T790M PGV rate of 0.3%-0.9% in the nonsquamous non-small-cell lung cancer subtype. Its appearance during routine somatic DNA sequencing in those patients who have not had a previous tyrosine kinase inhibitor should raise suspicion. In patients with sporadic mesothelioma, BAP1 is the most frequently mutated tumor driver, with a PGV rate between 2.8% and 8%, associated with a favorable prognosis. BAP1 PGVs accelerate mesothelioma tumorigenesis after asbestos exposure in preclinical models and may be partly predicted by clinical criteria. At present, routine germline genetic testing for thoracic cancers is not a standard practice. Expert genetic counseling is, therefore, required for patients who carry a PGV. Ongoing studies aim to better understand the natural history of patients harboring PGVs to underpin future cancer prevention, precise counseling, and cancer management with the goal of improving the quality and length of life.

Management of pulmonary nodules in women with pregnant intention: A review with perspective

The process for the management of pulmonary nodules in women with pregnant intention remains a challenge. There was a certain proportion of targeted female patients with high-risk lung cancer, and anxiety for suspicious lung cancer in early stage also exists. A comprehensive review of hereditary of lung cancer, effects of sexual hormone on lung cancer, natural history of pulmonary nodules, and computed tomography imaging with radiation exposure based on PubMed search was completed. The heredity of lung cancer and effects of sexual hormone on lung cancer are not the decisive factors, and the natural history of pulmonary nodules and the radiation exposure of imaging should be the main concerns. The management of incidental pulmonary nodules in young women with pregnant intention is an intricate and indecisive problem we have to encounter. The balance between the natural history of pulmonary nodules and the radiation exposure of imaging should be weighed.

Predicting Cancer Risk from Germline Whole-exome Sequencing Data Using a Novel Context-based Variant Aggregation Approach

Many studies have shown that the distributions of the genomic, nucleotide, and epigenetic contexts of somatic variants in tumors are informative of cancer etiology. Recently, a new direction of research has focused on extracting signals from the contexts of germline variants and evidence has emerged that patterns defined by these factors are associated with oncogenic pathways, histologic subtypes, and prognosis. It remains an open question whether aggregating germline variants using meta-features capturing their genomic, nucleotide, and epigenetic contexts can improve cancer risk prediction. This aggregation approach can potentially increase statistical power for detecting signals from rare variants, which have been hypothesized to be a major source of the missing heritability of cancer. Using germline whole-exome sequencing data from the UK Biobank, we developed risk models for 10 cancer types using known risk variants (cancer-associated SNPs and pathogenic variants in known cancer predisposition genes) as well as models that additionally include the meta-features. The meta-features did not improve the prediction accuracy of models based on known risk variants. It is possible that expanding the approach to whole-genome sequencing can lead to gains in prediction accuracy.

Significance

There is evidence that cancer is partly caused by rare genetic variants that have not yet been identified. We investigate this issue using novel statistical methods and data from the UK Biobank.

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.

Immunogenetic Profiles and Associations of Breast, Cervical, Ovarian, and Uterine Cancers

It is increasingly recognized that the human immune response influences cancer risk, progression, and survival; consequently, there is growing interest in the role of human leukocyte antigen (HLA), genes that play a critical role in initiating the immune response, on cancer. Recent evidence documented clustering of cancers based on immunogenetic profiles such that breast and ovarian cancers clustered together as did uterine and cervical cancers. Here we extend that line of research to evaluate the HLA profile of those 4 cancers and their associations. Specifically, we evaluated the associations between the frequencies of 127 HLA alleles and the population prevalences of breast, ovarian, cervical, and uterine cancer in 14 countries in Continental Western Europe. Factor analysis and hierarchical clustering were used to evaluate groupings of cancers based on their immunogenetic profiles. The results documented highly similar immunogenetic profiles for breast and ovarian cancers that were characterized predominantly by protective HLA effects. In addition, highly similar immunogenetic profiles for cervical and uterine cancers were observed that were, conversely, characterized by susceptibility effects. In light of the role of HLA in host immune system protection against non-self antigens, these findings suggest that certain cancers may be associated with similar contributory factors such as viral oncoproteins or neoantigens.

[Genome-wide studies of comorbidity of somatic and mental diseases]

Studies of the genomic architecture of complex phenotypes, which include common somatic and mental diseases, have shown that they are characterized by a high degree of polygenicity, i.e. participation of a large number of genes associated with the risk of developing these diseases. In this regard, it is of interest to establish the genetic overlapping between these two groups of diseases. The aim of the review is to analyze genetic studies of the comorbidity of somatic and mental diseases in terms of the universality and specificity of mental disorders in somatic diseases, the reciprocal relationships of these types of pathologies, and the modulating influence of environmental factors on comorbidity. The results of the analysis indicate the existence of a common genetic predisposition to mental and somatic diseases. At the same time, the presence of common genes does not exclude the specificity of the development of mental disorders depending on a specific somatic pathology. It can be assumed that there are genes that are both unique to a particular somatic and comorbid mental illness, and genes that are common to these diseases. Common genes may have varying degrees of specificity, that is, they may be of a universal nature, which, for example, manifests itself in the development of MDD in various somatic diseases, or be specific only for a couple of individual diseases (schizophrenia - breast cancer). At the same time, common genes can have a multidirectional effect, which also contributes to the specificity of comorbidity. In addition, when searching for common genes for somatic and mental diseases, it is necessary to take into account the modulating influence of such confounders as treatment, unhealthy life style, behavioral characteristics, which can also differ in specificity depending on the diseases under consideration.

Rat Mammary carcinoma susceptibility 3 (Mcs3) pleiotropy, socioenvironmental interaction, and comparative genomics with orthologous human 15q25.1-25.2

Genome-wide association studies of breast cancer susceptibility have revealed risk-associated genetic variants and nominated candidate genes; however, the identification of causal variants and genes is often undetermined by genome-wide association studies. Comparative genomics, utilizing Rattus norvegicus strains differing in susceptibility to mammary tumor development, is a complimentary approach to identify breast cancer susceptibility genes. Mammary carcinoma susceptibility 3 (Mcs3) is a Copenhagen (COP/NHsd) allele that confers resistance to mammary carcinomas when introgressed into a mammary carcinoma susceptible Wistar Furth (WF/NHsd) genome. Here, Mcs3 was positionally mapped to a 7.2-Mb region of RNO1 spanning rs8149408 to rs107402736 (chr1:143700228-150929594, build 6.0/rn6) using WF.COP congenic strains and 7,12-dimethylbenz(a)anthracene-induced mammary carcinogenesis. Male and female WF.COP-Mcs3 rats had significantly lower body mass compared to the Wistar Furth strain. The effect on female body mass was observed only when females were raised in the absence of males indicating a socioenvironmental interaction. Furthermore, female WF.COP-Mcs3 rats, raised in the absence of males, did not develop enhanced lobuloalveolar morphologies compared to those observed in the Wistar Furth strain. Human 15q25.1-25.2 was determined to be orthologous to rat Mcs3 (chr15:80005820-82285404 and chr15:83134545-84130720, build GRCh38/hg38). A public database search of 15q25.1-25.2 revealed genome-wide significant and nominally significant associations for body mass traits and breast cancer risk. These results support the existence of a breast cancer risk-associated allele at human 15q25.1-25.2 and warrant ultrafine mapping of rat Mcs3 and human 15q25.1-25.2 to discover novel causal genes and variants.

Functional studies of lung cancer GWAS beyond association

Fourteen years after the first genome-wide association study (GWAS) of lung cancer was published, approximately 45 genomic loci have now been significantly associated with lung cancer risk. While functional characterization was performed for several of these loci, a comprehensive summary of the current molecular understanding of lung cancer risk has been lacking. Further, many novel computational and experimental tools now became available to accelerate the functional assessment of disease-associated variants, moving beyond locus-by-locus approaches. In this review, we first highlight the heterogeneity of lung cancer GWAS findings across histological subtypes, ancestries and smoking status, which poses unique challenges to follow-up studies. We then summarize the published lung cancer post-GWAS studies for each risk-associated locus to assess the current understanding of biological mechanisms beyond the initial statistical association. We further summarize strategies for GWAS functional follow-up studies considering cutting-edge functional genomics tools and providing a catalog of available resources relevant to lung cancer. Overall, we aim to highlight the importance of integrating computational and experimental approaches to draw biological insights from the lung cancer GWAS results beyond association.

A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast.

Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

Many genetic variants affect disease risk by altering context-dependent gene regulation. Such variants are difficult to study mechanistically using current methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs). To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for identifying genotypic and allele-specific effects on chromatin that are also associated with disease. In prostate cancer, CWAS identified regulatory elements and androgen receptor-binding sites that explained the association at 52 of 98 known prostate cancer risk loci and discovered 17 additional risk loci. CWAS implicated key developmental transcription factors in prostate cancer risk that are overlooked by eQTL-based approaches due to context-dependent gene regulation. We experimentally validated associations and demonstrated the extensibility of CWAS to additional epigenomic datasets and phenotypes, including response to prostate cancer treatment. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting transcriptional regulation.

Cross-ancestry genome-wide meta-analysis of 61,047 cases and 947,237 controls identifies new susceptibility loci contributing to lung cancer

To identify new susceptibility loci to lung cancer among diverse populations, we performed cross-ancestry genome-wide association studies in European, East Asian and African populations and discovered five loci that have not been previously reported. We replicated 26 signals and identified 10 new lead associations from previously reported loci. Rare-variant associations tended to be specific to populations, but even common-variant associations influencing smoking behavior, such as those with CHRNA5 and CYP2A6, showed population specificity. Fine-mapping and expression quantitative trait locus colocalization nominated several candidate variants and susceptibility genes such as IRF4 and FUBP1. DNA damage assays of prioritized genes in lung fibroblasts indicated that a subset of these genes, including the pleiotropic gene IRF4, potentially exert effects by promoting endogenous DNA damage.

Genome-wide cross-cancer analysis illustrates the critical role of bimodal miRNA in patient survival and drug responses to PI3K inhibitors

Heterogeneity of cancer means many tumorigenic genes are only aberrantly expressed in a subset of patients and thus follow a bimodal distribution, having two modes of expression within a single population. Traditional statistical techniques that compare sample means between cancer patients and healthy controls fail to detect bimodally expressed genes. We utilize a mixture modeling approach to identify bimodal microRNA (miRNA) across cancers, find consistent sources of heterogeneity, and identify potential oncogenic miRNA that may be used to guide personalized therapies. Pathway analysis was conducted using target genes of the bimodal miRNA to identify potential functional implications in cancer. In vivo overexpression experiments were conducted to elucidate the clinical importance of bimodal miRNA in chemotherapy treatments. In nine types of cancer, tumors consistently displayed greater bimodality than normal tissue. Specifically, in liver and lung cancers, high expression of miR-105 and miR-767 was indicative of poor prognosis. Functional pathway analysis identified target genes of miR-105 and miR-767 enriched in the phosphoinositide-3-kinase (PI3K) pathway, and analysis of over 200 cancer drugs in vitro showed that drugs targeting the same pathway had greater efficacy in cell lines with high miR-105 and miR-767 levels. Overexpression of the two miRNA facilitated response to PI3K inhibitor treatment. We demonstrate that while cancer is marked by considerable genetic heterogeneity, there is between-cancer concordance regarding the particular miRNA that are more variable. Bimodal miRNA are ideal biomarkers that can be used to stratify patients for prognosis and drug response in certain types of cancer.

Bimodal genes can be defined as those having two modes of expression within the same population. A variety of statistical methodologies have been employed to assess bimodal gene expression, but current methods and their applications have been limited. Given the advances in next-generation sequencing as well as the extensive regulatory role of miRNA, assessing bimodality in miRNA-seq data can greatly broaden our understanding of factors underlying tumor progression. The goal of the current study was to utilize a novel mixture modeling approach to identify bimodal miRNA and then demonstrate their importance in cancer by evaluating their ability to predict overall survival and drug response. Our results showed that high levels of bimodal miRNA expression was characteristic of cancer. Additionally, several bimodal miRNA were common to multiple cancer types, suggesting that certain miRNA consistently account for tumor heterogeneity and may be involved in general oncogenic processes. Our study points to the potential of bimodal miRNA to facilitate precise prognostic evaluation and effective treatment strategies.

Immunogenetic clustering of 30 cancers

Human leukocyte antigen (HLA) genes have been implicated in cancer risk and shared heritability of different types of cancer. In this immunogenetic epidemiological study we first computed a Cancer-HLA profile for 30 cancer types characterized by the correlation between the prevalence of each cancer and the population frequency of 127 HLA alleles, and then used multidimensional scaling to evaluate the possible clustering of those Cancer-HLA associations. The results indicated the presence of three clusters, broadly reflecting digestive-skin-cervical cancers, reproductive and endocrine systems cancers, and brain and androgen-associated cancers. The clustering of cancer types documented here is discussed in terms of mechanisms underlying shared Cancer-HLA associations.

Inferring Potential Cancer Driving Synonymous Variants

Synonymous single nucleotide variants (sSNVs) are often considered functionally silent, but a few cases of cancer-causing sSNVs have been reported. From available databases, we collected four categories of sSNVs: germline, somatic in normal tissues, somatic in cancerous tissues, and putative cancer drivers. We found that screening sSNVs for recurrence among patients, conservation of the affected genomic position, and synVep prediction (synVep is a machine learning-based sSNV effect predictor) recovers cancer driver variants (termed proposed drivers) and previously unknown putative cancer genes. Of the 2.9 million somatic sSNVs found in the COSMIC database, we identified 2111 proposed cancer driver sSNVs. Of these, 326 sSNVs could be further tagged for possible RNA splicing effects, RNA structural changes, and affected RBP motifs. This list of proposed cancer driver sSNVs provides computational guidance in prioritizing the experimental evaluation of synonymous mutations found in cancers. Furthermore, our list of novel potential cancer genes, galvanized by synonymous mutations, may highlight yet unexplored cancer mechanisms.

Race-associated molecular changes in gynecologic malignancies

The difference in cancer morbidity and mortality between individuals of different racial groups is complex. Health disparities provide a framework to explore potential connections between poor outcomes and individuals of different racial backgrounds. This study identifies genomic changes in African-American patients with gynecologic malignancies, a population with well-established disparities in outcomes. Our data explore whether social health disparities might mediate interactions between the environment and tumor epigenomes and genomes that can be identified. Using The Cancer Genetic Ancestry Atlas, which encodes data from The Cancer Genome Atlas by ancestry and allows for systematic analyses of sequencing data by racial group, we performed large-scale, comparative analyses to identify novel targets with alterations in methylation, transcript, and microRNA expression between tumors from women of European American or African American racial groups across all gynecologic malignancies. We identify novel discrete genomic changes in these complex malignancies and suggest a framework for identifying novel therapeutic targets for future investigation.

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.

Changes in gene-gene interactions associated with cancer onset and progression are largely independent of changes in gene expression

Recent findings indicate that changes underlying cancer onset and progression are not only attributable to changes in DNA structure and expression of individual genes but to changes in interactions among these genes as well. We examined co-expression changes in gene-network structure occurring during the onset and progression of nine different cancer types. Network complexity is generally reduced in the transition from normal precursor tissues to corresponding primary tumors. Cross-tissue cancer network similarity generally increases in early-stage cancers followed by a subsequent loss in cross-tissue cancer similarity as tumors reacquire cancer-specific network complexity. Gene-gene connections remaining stable through cancer development are enriched for “housekeeping” gene functions, whereas newly acquired interactions are associated with established cancer-promoting functions. Surprisingly, >90% of changes in gene-gene network interactions in cancers are not associated with changes in the expression of network genes relative to normal precursor tissues.

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Gene-gene network complexity is reduced in the transition from normal to cancer

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Network similarity across cancer types is higher in early-stage versus late-stage cancers

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Network interactions among housekeeping genes are stable through cancer development

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<10% of changes in network interactions in cancer involve changes in gene expression

Dinucleotide tag-based parallel reporter gene assay method enables efficient identification of regulatory mutations

BACKGROUND

The causal single nucleotide polymorphisms (SNPs) leading to increased cancer predisposition mainly function as gene regulatory elements, the evaluation of which largely relies on the parallel reporter gene assay system. However, the common DNA barcodes used in parallel reporter gene assay systems typically because nucleotide composition bias, and many barcodes must be allocated for each sequence to reduce the bias effect.

MAIN METHODS AND MAJOR RESULTS

Here, a versatile dinucleotide-tag reporter system (DiR) that enables parallel analysis of regulatory elements with minimized bias based on next-generation sequencing is described. The DiR system is more robust than the classical luciferase assay method, particularly for the investigation of moderate-level regulatory elements. The authors applied the DiR-seq assay in the functional evaluation of SNPs with prostate cancer risk and nominated two and six regulatory SNPs in PC-3 and LNCaP cells, respectively.

CONCLUSIONS AND IMPLICATIONS

The DiR system has great potential to advance the functional study of SNPs associated with polygenic disease risks.

Predicting master transcription factors from pan-cancer expression data

Critical developmental “master transcription factors” (MTFs) can be subverted during tumorigenesis to control oncogenic transcriptional programs. Current approaches to identifying MTFs rely on ChIP-seq data, which is unavailable for many cancers. We developed the CaCTS (Cancer Core Transcription factor Specificity) algorithm to prioritize candidate MTFs using pan-cancer RNA sequencing data. CaCTS identified candidate MTFs across 34 tumor types and 140 subtypes including predictions for cancer types/subtypes for which MTFs are unknown, including e.g. PAX8, SOX17, and MECOM as candidates in ovarian cancer (OvCa). In OvCa cells, consistent with known MTF properties, these factors are required for viability, lie proximal to superenhancers, co-occupy regulatory elements globally, co-bind loci encoding OvCa biomarkers, and are sensitive to pharmacologic inhibition of transcription. Our predictions of MTFs, especially for tumor types with limited understanding of transcriptional drivers, pave the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction

Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome-exposome (gxe) and exposome-exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson's correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.

Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null hypothesis testing

Pleiotropy has important implication on genetic connection among complex phenotypes and facilitates our understanding of disease etiology. Genome-wide association studies provide an unprecedented opportunity to detect pleiotropic associations; however, efficient pleiotropy test methods are still lacking. We here consider pleiotropy identification from a methodological perspective of high-dimensional composite null hypothesis and propose a powerful gene-based method called MAIUP. MAIUP is constructed based on the traditional intersection-union test with two sets of independent P-values as input and follows a novel idea that was originally proposed under the high-dimensional mediation analysis framework. The key improvement of MAIUP is that it takes the composite null nature of pleiotropy test into account by fitting a three-component mixture null distribution, which can ultimately generate well-calibrated P-values for effective control of family-wise error rate and false discover rate. Another attractive advantage of MAIUP is its ability to effectively address the issue of overlapping subjects commonly encountered in association studies. Simulation studies demonstrate that compared with other methods, only MAIUP can maintain correct type I error control and has higher power across a wide range of scenarios. We apply MAIUP to detect shared associated genes among 14 psychiatric disorders with summary statistics and discover many new pleiotropic genes that are otherwise not identified if failing to account for the issue of composite null hypothesis testing. Functional and enrichment analyses offer additional evidence supporting the validity of these identified pleiotropic genes associated with psychiatric disorders. Overall, MAIUP represents an efficient method for pleiotropy identification.

Molecular pathogenesis of hereditary lung cancer: a literature review

Among all cancer types, pulmonary cancer has the highest mortality rate. Tobacco consumption remains the major risk factor for the development of lung cancer. However, many studies revealed a correlation between inherited genetic variants and predisposition to lung cancer, especially in nonsmokers. To date, genetic testing for the detection of germline mutations is not yet recommended in patients with lung cancer and testing is focused on somatic alterations given their implication in the treatment choice. Understanding the impact of genetic predisposition on the occurrence of lung cancer is essential to enable the introduction of accurate guidelines and recommendations that might reduce mortality. In this review paper, we describe familial lung cancer, and expose germline mutations that are linked to this type of cancer. We also report pathogenic genetic variants linked to syndromes associated with lung cancer.

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies candidate susceptibility genes for breast and ovarian cancer

Familial, sequencing, and genome-wide association studies (GWASs) and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWASs) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through application of a pleiotropy-guided conditional/conjunction false discovery rate (FDR) approach in the setting of a TWASs. This identified 14 candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were >1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. The 22 genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.

Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the TERT and CLPTM1L genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis.

Association of genetic variants of FBXO32 and FOXO6 in the FOXO pathway with breast cancer risk

Forkhead box class O (FOXO) transcription factors play a pivotal role in regulating a variety of biological processes, including organismal development, cell signaling, cell metabolism, and tumorigenesis. Therefore, we hypothesize that genetic variants in FOXO pathway genes are associated with breast cancer (BC) risk. To test this hypothesis, we conducted a large meta-analysis using 14 published genome-wide association study (GWAS) data sets in the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study. We assessed associations between 5214 (365 genotyped in DRIVE and 4849 imputed) common single-nucleotide polymorphisms (SNPs) in 55 FOXO pathway genes and BC risk. After multiple comparison corrections by the Bayesian false-discovery probability method, we found five SNPs to be significantly associated with BC risk. In stepwise multivariate logistic regression analysis with adjustment for age, principal components, and previously published SNPs in the same data set, three independent SNPs (i.e., FBXO32 rs10093411 A>G, FOXO6 rs61229336 C>T, and FBXO32 rs62521280 C>T) remained to be significantly associated with BC risk (p = 0.0008, 0.0011, and 0.0017, respectively). Additional expression quantitative trait loci analysis revealed that the FBXO32 rs62521280 T allele was associated with decreased messenger RNA (mRNA) expression levels in breast tissue, while the FOXO6 rs61229336 T allele was found to be associated with decreased mRNA expression levels in the whole blood cells. Once replicated by other investigators, these genetic variants may serve as new biomarkers for BC risk.