Identifying Putative Susceptibility Genes and Evaluating Their Associations with Somatic Mutations in Human Cancers

A Cross-Tissue Transcriptome-Wide Association Study Reveals Novel Susceptibility Genes for Diabetic Kidney Disease in the FinnGen Cohort

Background/Objectives: Diabetic kidney disease (DKD) is a common diabetic complication, driven by a multifactorial pathogenesis that includes various genetic components. However, the precise causative genes and their underlying biological pathways remain poorly understood. Methods: We performed a cross-tissue transcriptome-wide association study (TWAS) of DKD using expression quantitative trait loci (eQTL) data from 49 tissues in the Genotype-Tissue Expression (GTEx) version 8 (v8) resource. Five complementary analytical frameworks-sparse canonical correlation analysis (sCCA), functional summary-based imputation (FUSION), fine-mapping of causal gene sets (FOCUS), summary-data-based Mendelian randomization (SMR), and multi-marker analysis of genomic annotation (MAGMA)-were integrated to nominate candidate genes. Causal inference was refined using Mendelian randomization (MR), and biological significance was evaluated through pathway enrichment, protein interaction networks, and druggability profiling. Results: We identified 23 candidate genes associated with DKD risk, of which 13 were supported by MR analysis. Among these, 10 represent previously unreported susceptibility genes. Notably, four genes-HLA-DRB1, HLA-DRB5, NOTCH4, and CYP21A2-encode potentially druggable proteins, with HLA-DRB5 and CYP21A2 both qualifying as novel susceptibility genes and therapeutic targets. These genes converge on immune modulation, steroid biosynthesis, DNA repair, and transcriptional regulation-processes central to DKD pathogenesis. Conclusions: Our study represents the first systematic cross-tissue TWAS of DKD, revealing a prioritized set of genetically and functionally supported susceptibility genes. The identification of druggable targets among these genes provides critical insight into the mechanistic underpinnings of DKD and highlights their potential for future therapeutic development. These findings enhance our understanding of DKD pathophysiology and offer a foundation for precision medicine strategies in nephrology.

Transcriptome-wide association study of alternative polyadenylation identifies susceptibility genes in non-small cell lung cancer

Alternative polyadenylation (APA) plays a crucial role in cancer development and prognosis. However, the molecular characteristics of APA related to non-small cell lung cancer (NSCLC) susceptibility remain understudied, especially in East Asian populations. In this study, we constructed an atlas of APA-regulated 3' untranslated region (3'UTR) and profiled its genetic regulation in 747 lung tissue samples (including tumors and paired normal tissues) from 417 NSCLC Chinese patients. We verified a significant global shortening of 3'UTRs in tumor samples compared to normal samples and underscored the value of APA-regulation as a prognostic marker. The 3'UTR APA quantitative trait loci (3'aQTL) was identified by regressing the percentage of distal poly(A) site usage index (PDUI) value on genetic variants. We found that a significant proportion 3'aQTLs are independent of genetic regulation of expression and are specific in Chinese. We also conducted a 3'UTR APA transcriptome-wide association study (3'aTWAS) by integrating the APA regulation atlas with a genome-wide association study (GWAS) for NSCLC involving 7035 cases and 185,413 cancer-free controls. We identified NSCLC-associated genes, highlighting TUBB, TEAD3, and PPP1R10. Combining the consistent results from colocalization analysis, differential APA analysis, and survival analysis, we provide novel evidence for the role TUBB APA regulation in NSCLC and identified potential upstream regulators. Overall, our study profiled the APA regulation and highlighted the substantial role of APA in NSCLC carcinogenesis and prognosis in East Asian populations.

Enhancing disease risk gene discovery by integrating transcription factor-linked trans-variants into transcriptome-wide association analyses

Transcriptome-wide association studies (TWAS) have been successful in identifying disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-variants to enhance model building for TF downstream target genes. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these prediction models to large GWAS datasets for breast, prostate, lung cancers and other diseases. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene expression prediction models and identifying disease-associated genes, as shown by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study shed new light on several genetically driven key TF regulators and their associated TF-gene regulatory networks underlying disease susceptibility.

Refining breast cancer genetic risk and biology through multi-ancestry fine-mapping analyses of 192 risk regions

Genome-wide association studies have identified approximately 200 genetic risk loci for breast cancer, but the causal variants and target genes are mostly unknown. We sought to fine-map all known breast cancer risk loci using genome-wide association study data from 172,737 female breast cancer cases and 242,009 controls of African, Asian and European ancestry. We identified 332 independent association signals for breast cancer risk, including 131 signals not reported previously, and for 50 of them, we narrowed the credible causal variants down to a single variant. Analyses integrating functional genomics data identified 195 putative susceptibility genes, enriched in PI3K/AKT, TNF/NF-κB, p53 and Wnt/β-catenin pathways. Single-cell RNA sequencing or in vitro experiment data provided additional functional evidence for 105 genes. Our study uncovered large numbers of association signals and candidate susceptibility genes for breast cancer, uncovered breast cancer genetics and biology, and supported the value of including multi-ancestry data in fine-mapping analyses.

Identifying somatic fingerprints of cancers defined by germline and environmental risk factors

Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.

Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes

Alternative polyadenylation (APA) modulates mRNA processing in the 3'-untranslated regions (3' UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. In this study, we conducted large APA-wide association studies to investigate associations between APA levels and cancer risk. Genetic models were built to predict APA levels in multiple tissues using genotype and RNA sequencing data from 1,337 samples from the Genotype-Tissue Expression project. Associations of genetically predicted APA levels with cancer risk were assessed by applying the prediction models to data from large genome-wide association studies of six common cancers among European ancestry populations: breast, ovarian, prostate, colorectal, lung, and pancreatic cancers. A total of 58 risk genes (corresponding to 76 APA sites) were associated with at least one type of cancer, including 25 genes previously not linked to cancer susceptibility. Of the identified risk APAs, 97.4% and 26.3% were supported by 3'-UTR APA quantitative trait loci and colocalization analyses, respectively. Luciferase reporter assays for four selected putative regulatory 3'-UTR variants demonstrated that the risk alleles of 3'-UTR variants, rs324015 (STAT6), rs2280503 (DIP2B), rs1128450 (FBXO38), and rs145220637 (LDHA), significantly increased the posttranscriptional activities of their target genes compared with reference alleles. Furthermore, knockdown of the target genes confirmed their ability to promote proliferation and migration. Overall, this study provides insights into the role of APA in the genetic susceptibility to common cancers. Significance: Systematic evaluation of associations of alternative polyadenylation with cancer risk reveals 58 putative susceptibility genes, highlighting the contribution of genetically regulated alternative polyadenylation of 3'UTRs to genetic susceptibility to cancer.

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 disease susceptibility genes by integrating cis-variants predicted gene expression with genome-wide association studies (GWAS) data. However, trans-located variants for predicting gene expression remain largely unexplored. Here, we introduce transTF-TWAS, which incorporates transcription factor (TF)-linked trans-located variants to enhance model building. Using data from the Genotype-Tissue Expression project, we predict gene expression and alternative splicing and applied these models to large GWAS datasets for breast, prostate, and lung cancers. We demonstrate that transTF-TWAS outperforms other existing TWAS approaches in both constructing gene prediction models and identifying disease-associated genes, as evidenced by simulations and real data analysis. Our transTF-TWAS approach significantly contributes to the discovery of disease risk genes. Findings from this study have shed new light on several genetically driven key regulators and their associated regulatory networks underlying disease susceptibility.

Integrative multi-omics analyses to identify the genetic and functional mechanisms underlying ovarian cancer risk regions

To identify credible causal risk variants (CCVs) associated with different histotypes of epithelial ovarian cancer (EOC), we performed genome-wide association analysis for 470,825 genotyped and 10,163,797 imputed SNPs in 25,981 EOC cases and 105,724 controls of European origin. We identified five histotype-specific EOC risk regions (p value <5 × 10-8) and confirmed previously reported associations for 27 risk regions. Conditional analyses identified an additional 11 signals independent of the primary signal at six risk regions (p value <10-5). Fine mapping identified 4,008 CCVs in these regions, of which 1,452 CCVs were located in ovarian cancer-related chromatin marks with significant enrichment in active enhancers, active promoters, and active regions for CCVs from each EOC histotype. Transcriptome-wide association and colocalization analyses across histotypes using tissue-specific and cross-tissue datasets identified 86 candidate susceptibility genes in known EOC risk regions and 32 genes in 23 additional genomic regions that may represent novel EOC risk loci (false discovery rate <0.05). Finally, by integrating genome-wide HiChIP interactome analysis with transcriptome-wide association study (TWAS), variant effect predictor, transcription factor ChIP-seq, and motifbreakR data, we identified candidate gene-CCV interactions at each locus. This included risk loci where TWAS identified one or more candidate susceptibility genes (e.g., HOXD-AS2, HOXD8, and HOXD3 at 2q31) and other loci where no candidate gene was identified (e.g., MYC and PVT1 at 8q24) by TWAS. In summary, this study describes a functional framework and provides a greater understanding of the biological significance of risk alleles and candidate gene targets at EOC susceptibility loci identified by a genome-wide association study.

Large-scale integration of omics and electronic health records to identify potential risk protein biomarkers and therapeutic drugs for cancer prevention and intervention

Identifying risk protein targets and their therapeutic drugs is crucial for effective cancer prevention. Here, we conduct integrative and fine-mapping analyses of large genome-wide association studies data for breast, colorectal, lung, ovarian, pancreatic, and prostate cancers, and characterize 710 lead variants independently associated with cancer risk. Through mapping protein quantitative trait loci (pQTL) for these variants using plasma proteomics data from over 75,000 participants, we identify 365 proteins associated with cancer risk. Subsequent colocalization analysis identifies 101 proteins, including 74 not reported in previous studies. We further characterize 36 potential druggable proteins for cancers or other disease indications. Analyzing >3.5 million electronic health records, we uncover five drugs (Haloperidol, Trazodone, Tranexamic Acid, Haloperidol, and Captopril) associated with increased cancer risk and two drugs (Caffeine and Acetazolamide) linked to reduced colorectal cancer risk. This study offers novel insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention.

Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes

Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.

A large-scale microRNA transcriptome-wide association study identifies two susceptibility microRNAs, miR-1307-5p and miR-192-3p, for colorectal cancer risk

Transcriptome-wide association studies (TWAS) have identified many putative susceptibility genes for colorectal cancer (CRC) risk. However, susceptibility miRNAs, critical dysregulators of gene expression, remain unexplored. We genotyped DNA samples from 313 CRC East Asian patients and performed small RNA sequencing in their normal colon tissues distant from tumors to build genetic models for predicting miRNA expression. We applied these models and data from genome-wide association studies (GWAS) including 23 942 cases and 217 267 controls of East Asian ancestry to investigate associations of predicted miRNA expression with CRC risk. Perturbation experiments separately by promoting and inhibiting miRNAs expressions and further in vitro assays in both SW480 and HCT116 cells were conducted. At a Bonferroni-corrected threshold of P < 4.5 × 10-4, we identified two putative susceptibility miRNAs, miR-1307-5p and miR-192-3p, located in regions more than 500 kb away from any GWAS-identified risk variants in CRC. We observed that a high predicted expression of miR-1307-5p was associated with increased CRC risk, while a low predicted expression of miR-192-3p was associated with increased CRC risk. Our experimental results further provide strong evidence of their susceptible roles by showing that miR-1307-5p and miR-192-3p play a regulatory role, respectively, in promoting and inhibiting CRC cell proliferation, migration, and invasion, which was consistently observed in both SW480 and HCT116 cells. Our study provides additional insights into the biological mechanisms underlying CRC development.

A Multiethnic Germline-Somatic Association Database Deciphers Multilayered and Interconnected Genetic Mutations in Cancer

Inherited germline and acquired somatic alterations can both promote human tumor development. Elucidating the cooperation between somatic and germline genetic alterations that drive tumorigenesis could help inform precision cancer prevention and treatment strategies. Here, leveraging genomic genotyping and sequencing data from 9,029 patients with cancer with European, East Asian, and African ancestry, we performed a pan-cancer analysis to evaluate the associations between germline SNPs and somatic alterations, including single-nucleotide variant and small insertion/deletion mutations, copy-number variation, tumor mutational burden, and mutational signatures. Genome-wide significant germline-somatic pairs were abundant, and most of the associations were observed in one cancer type and one ancestry group. A user-friendly interactive Multiethnic Germline-Somatic Association (MGSA) database (http://wanglab-hkust.cn:3838/MGSA/) was developed, which can be used to query, browse, and download the results of the association analyses. Moreover, the MGSA database offers additional survival analysis and functional annotation. Together, this work provides a resource for uncovering the clinical and biological roles of associations between germline variants and somatic alterations in human cancer.

SIGNIFICANCE

Comprehensive analysis of connections between germline variants and somatic events in cancer offers a resource for investigating the functional significance of genetic mutations and exploring genetic factors contributing to racial disparities.

Genetic insights into carbohydrate sulfotransferase 8 and its impact on the immunotherapy efficacy of cancer

Immune checkpoint blockade (ICB) is a promising therapy for solid tumors, but its effectiveness depends on biomarkers that are not precise. Here, we utilized genome-wide association study to investigate the association between genetic variants and tumor mutation burden to interpret ICB response. We identified 16 variants (p < 5 × 10-8) probed to 17 genes on 9 chromosomes. Subsequent analysis of one of the most significant loci in 19q13.11 suggested that the rs111308825 locus at the enhancer is causal, as its A allele impairs KLF2 binding, leading to lower carbohydrate sulfotransferase 8 (CHST8) expression. Breast cancer cells expressing CHST8 suppress T cell activation, and Chst8 loss attenuates tumor growth in a syngeneic mouse model. Further investigation revealed that programmed death-ligand 1 (PD-L1) and its homologs could be sulfated by CHST8, resulting in M2-like macrophage enrichment in the tumor microenvironment. Finally, we confirmed that low-CHST8 tumors have better ICB response, supporting the genetic effect and clinical value of rs111308825 for ICB efficacy prediction.

Genomic and transcriptomic analysis of breast cancer identifies novel signatures associated with response to neoadjuvant chemotherapy

BACKGROUND

Neoadjuvant chemotherapy (NAC) has become a standard treatment strategy for breast cancer (BC). However, owing to the high heterogeneity of these tumors, it is unclear which patient population most likely benefit from NAC. Multi-omics offer an improved approach to uncovering genomic and transcriptomic changes before and after NAC in BC and to identifying molecular features associated with NAC sensitivity.

METHODS

We performed whole-exome and RNA sequencing on 233 samples (including matched pre- and post-treatment tumors) from 50 BC patients with rigorously defined responses to NAC and analyzed changes in the multi-omics landscape. Molecular features associated with NAC response were identified and validated in a larger internal, and two external validation cohorts, as well as in vitro experiments.

RESULTS

The most frequently altered genes were TP53, TTN, and MUC16 in both pre- and post-treatment tumors. In comparison with pre-treatment tumors, there was a significant decrease in C > A transversion mutations in post-treatment tumors (P = 0.020). NAC significantly decreased the mutation rate (P = 0.006) of the DNA repair pathway and gene expression levels (FDR = 0.007) in this pathway. NAC also significantly changed the expression level of immune checkpoint genes and the abundance of tumor-infiltrating immune and stroma cells, including B cells, activated dendritic cells, γδT cells, M2 macrophages and endothelial cells. Furthermore, there was a higher rate of C > T substitutions in NAC nonresponsive tumors than responsive ones, especially when the substitution site was flanked by C and G. Importantly, there was a unique amplified region at 8p11.23 (containing ADGRA2 and ADRB3) and a deleted region at 3p13 (harboring FOXP1) in NAC nonresponsive and responsive tumors, respectively. Particularly, the CDKAL1 missense variant P409L (p.Pro409Leu, c.1226C > T) decreased BC cell sensitivity to docetaxel, and ADGRA2 or ADRB3 gene amplifications were associated with worse NAC response and poor prognosis in BC patients.

CONCLUSIONS

Our study has revealed genomic and transcriptomic landscape changes following NAC in BC, and identified novel biomarkers (CDKAL1P409L, ADGRA2 and ADRB3) underlying chemotherapy resistance and poor prognosis, which could guide the development of personalized treatments for BC.

Data-adaptive and pathway-based tests for association studies between somatic mutations and germline variations in human cancers

Cancer is a disease driven by a combination of inherited genetic variants and somatic mutations. Recently available large-scale sequencing data of cancer genomes have provided an unprecedented opportunity to study the interactions between them. However, previous studies on this topic have been limited by simple, low statistical power tests such as Fisher's exact test. In this paper, we design data-adaptive and pathway-based tests based on the score statistic for association studies between somatic mutations and germline variations. Previous research has shown that two single-nucleotide polymorphism (SNP)-set-based association tests, adaptive sum of powered score (aSPU) and data-adaptive pathway-based (aSPUpath) tests, increase the power in genome-wide association studies (GWASs) with a single disease trait in a case-control study. We extend aSPU and aSPUpath to multi-traits, that is, somatic mutations of multiple genes in a cohort study, allowing extensive information aggregation at both SNP and gene levels.p $p$ -values from different parameters assuming varying genetic architecture are combined to yield data-adaptive tests for somatic mutations and germline variations. Extensive simulations show that, in comparison with some commonly used methods, our data-adaptive somatic mutations/germline variations tests can be applied to multiple germline SNPs/genes/pathways, and generally have much higher statistical powers while maintaining the appropriate type I error. The proposed tests are applied to a large-scale real-world International Cancer Genome Consortium whole genome sequencing data set of 2583 subjects, detecting more significant and biologically relevant associations compared with the other existing methods on both gene and pathway levels. Our study has systematically identified the associations between various germline variations and somatic mutations across different cancer types, which potentially provides valuable utility for cancer risk prediction, prognosis, and therapeutics.

Large-scale alternative polyadenylation (APA)-wide association studies to identify putative susceptibility genes in human common cancers

Alternative polyadenylation (APA) modulates mRNA processing in the 3' untranslated regions (3'UTR), which affect mRNA stability and translation efficiency. Here, we build genetic models to predict APA levels in multiple tissues using sequencing data of 1,337 samples from the Genotype-Tissue Expression, and apply these models to assess associations between genetically predicted APA levels and cancer risk with data from large genome-wide association studies of six common cancers, including breast, ovary, prostate, colorectum, lung, and pancreas among European-ancestry populations. At a Bonferroni-corrected P □<□0.05, we identify 58 risk genes, including seven in newly identified loci. Using luciferase reporter assays, we demonstrate that risk alleles of 3'UTR variants, rs324015 ( STAT6 ), rs2280503 ( DIP2B ), rs1128450 ( FBXO38 ) and rs145220637 ( LDAH ), could significantly increase post-transcriptional activities of their target genes compared to reference alleles. Further gene knockdown experiments confirm their oncogenic roles. Our study provides additional insight into the genetic susceptibility of these common cancers.

Germline Cancer Gene Expression Quantitative Trait Loci Are Associated with Local and Global Tumor Mutations

Somatic mutations drive cancer development and are relevant to patient responses to treatment. Emerging evidence shows that variations in the somatic genome can be influenced by the germline genetic background. However, the mechanisms underlying these germline-somatic associations remain largely obscure. We hypothesized that germline variants can influence somatic mutations in a nearby cancer gene ("local impact") or a set of recurrently mutated cancer genes across the genome ("global impact") through their regulatory effect on gene expression. To test this hypothesis, tumor targeted sequencing data from 12,413 patients across 11 cancer types in the Dana-Farber Profile cohort were integrated with germline cancer gene expression quantitative trait loci (eQTL) from the Genotype-Tissue Expression Project. Variants that upregulate ATM expression were associated with a decreased risk of somatic ATM mutations across 8 cancer types. GLI2, WRN, and CBFB eQTL were associated with global tumor mutational burden of cancer genes in ovarian cancer, glioma, and esophagogastric carcinoma, respectively. An EPHA5 eQTL was associated with mutations in cancer genes specific to colorectal cancer, and eQTL related to expression of APC, WRN, GLI1, FANCA, and TP53 were associated with mutations in genes specific to endometrial cancer. These findings provide evidence that germline-somatic associations are mediated through expression of specific cancer genes, opening new avenues for research on the underlying biological processes.

SIGNIFICANCE

Analysis of associations between the germline genetic background and somatic mutations in patients with cancer suggests that germline variants can influence local and global tumor mutations by altering expression of cancer-related genes. See related commentary by Kar, p. 1165.

Racial/Ethnic and Sex Differences in Somatic Cancer Gene Mutations among Patients with Early-Onset Colorectal Cancer

Molecular features underlying colorectal cancer disparities remain uncharacterized. Here, we investigated somatic mutation patterns by race/ethnicity and sex among 5,856 non-Hispanic white (NHW), 535 non-Hispanic Black (NHB), and 512 Asian/Pacific Islander (API) patients with colorectal cancer (2,016 early-onset colorectal cancer patients: sequencing age <50 years). NHB patients with early-onset nonhypermutated colorectal cancer, but not API patients, had higher adjusted tumor mutation rates than NHW patients. There were significant differences for LRP1B, FLT4, FBXW7, RNF43, ATRX, APC, and PIK3CA mutation frequencies in early-onset nonhypermutated colorectal cancers between racial/ethnic groups. Heterogeneities by race/ethnicity were observed for the effect of APC, FLT4, and FAT1 between early-onset and late-onset nonhypermutated colorectal cancer. By sex, heterogeneity was observed for the effect of EP300, BRAF, WRN, KRAS, AXIN2, and SMAD2. Males and females with nonhypermutated colorectal cancer had different trends in EP300 mutations by age group. These findings define genomic patterns of early-onset nonhypermutated colorectal cancer by race/ethnicity and sex, which yields novel biological clues into early-onset colorectal cancer disparities.

SIGNIFICANCE

NHBs, but not APIs, with early-onset nonhypermutated colorectal cancer had higher adjusted tumor mutation rates versus NHWs. Differences for FLT4, FBXW7, RNF43, LRP1B, APC, PIK3CA, and ATRX mutation rates between racial/ethnic groups and EP300, KRAS, AXIN2, WRN, BRAF, and LRP1B mutation rates by sex were observed in tumors of young patients. See related commentary by Shen et al., p. 530 . This article is highlighted in the In This Issue feature, p. 517.

Integrating transcription factor occupancy with transcriptome-wide association analysis identifies susceptibility genes in human cancers

Transcriptome-wide association studies (TWAS) have successfully discovered many putative disease susceptibility genes. However, TWAS may suffer from inaccuracy of gene expression predictions due to inclusion of non-regulatory variants. By integrating prior knowledge of susceptible transcription factor occupied elements, we develop sTF-TWAS and demonstrate that it outperforms existing TWAS approaches in both simulation and real data analyses. Under the sTF-TWAS framework, we build genetic models to predict alternative splicing and gene expression in normal breast, prostate and lung tissues from the Genotype-Tissue Expression project and apply these models to data from large genome-wide association studies (GWAS) conducted among European-ancestry populations. At Bonferroni-corrected P < 0.05, we identify 354 putative susceptibility genes for these cancers, including 189 previously unreported in GWAS loci and 45 in loci unreported by GWAS. These findings provide additional insight into the genetic susceptibility of human cancers. Additionally, we show the generalizability of the sTF-TWAS on non-cancer diseases.

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.

Association between germline variants and somatic mutations in colorectal cancer

Colorectal cancer (CRC) is a heterogeneous disease with evidence of distinct tumor types that develop through different somatically altered pathways. To better understand the impact of the host genome on somatically mutated genes and pathways, we assessed associations of germline variations with somatic events via two complementary approaches. We first analyzed the association between individual germline genetic variants and the presence of non-silent somatic mutations in genes in 1375 CRC cases with genome-wide SNPs data and a tumor sequencing panel targeting 205 genes. In the second analysis, we tested if germline variants located within previously identified regions of somatic allelic imbalance were associated with overall CRC risk using summary statistics from a recent large scale GWAS (n≃125 k CRC cases and controls). The first analysis revealed that a variant (rs78963230) located within a CNA region associated with TLR3 was also associated with a non-silent mutation within gene FBXW7. In the secondary analysis, the variant rs2302274 located in CDX1/PDGFRB frequently gained/lost in colorectal tumors was associated with overall CRC risk (OR = 0.96, p = 7.50e-7). In summary, we demonstrate that an integrative analysis of somatic and germline variation can lead to new insights about CRC.

3D chromatin architecture and transcription regulation in cancer

Chromatin has distinct three-dimensional (3D) architectures important in key biological processes, such as cell cycle, replication, differentiation, and transcription regulation. In turn, aberrant 3D structures play a vital role in developing abnormalities and diseases such as cancer. This review discusses key 3D chromatin structures (topologically associating domain, lamina-associated domain, and enhancer-promoter interactions) and corresponding structural protein elements mediating 3D chromatin interactions [CCCTC-binding factor, polycomb group protein, cohesin, and Brother of the Regulator of Imprinted Sites (BORIS) protein] with a highlight of their associations with cancer. We also summarise the recent development of technologies and bioinformatics approaches to study the 3D chromatin interactions in gene expression regulation, including crosslinking and proximity ligation methods in the bulk cell population (ChIA-PET and HiChIP) or single-molecule resolution (ChIA-drop), and methods other than proximity ligation, such as GAM, SPRITE, and super-resolution microscopy techniques.

The Many Faces of G Protein-Coupled Receptor 143, an Atypical Intracellular Receptor

GPCRs transform extracellular stimuli into a physiological response by activating an intracellular signaling cascade initiated via binding to G proteins. Orphan G protein-coupled receptors (GPCRs) hold the potential to pave the way for development of new, innovative therapeutic strategies. In this review we will introduce G protein-coupled receptor 143 (GPR143), an enigmatic receptor in terms of classification within the GPCR superfamily and localization. GPR143 has not been assigned to any of the GPCR families due to the lack of common structural motifs. Hence we will describe the most important motifs of classes A and B and compare them to the protein sequence of GPR143. While a precise function for the receptor has yet to be determined, the protein is expressed abundantly in pigment producing cells. Many GPR143 mutations cause X-linked Ocular Albinism Type 1 (OA1, Nettleship-Falls OA), which results in hypopigmentation of the eyes and loss of visual acuity due to disrupted visual system development and function. In pigment cells of the skin, loss of functional GPR143 results in abnormally large melanosomes (organelles in which pigment is produced). Studies have shown that the receptor is localized internally, including at the melanosomal membrane, where it may function to regulate melanosome size and/or facilitate protein trafficking to the melanosome through the endolysosomal system. Numerous additional roles have been proposed for GPR143 in determining cancer predisposition, regulation of blood pressure, development of macular degeneration and signaling in the brain, which we will briefly describe as well as potential ligands that have been identified. Furthermore, GPR143 is a promiscuous receptor that has been shown to interact with multiple other melanosomal proteins and GPCRs, which strongly suggests that this orphan receptor is likely involved in many different physiological actions.

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.

10 Years of GWAS discovery in endometrial cancer: Aetiology, function and translation

Endometrial cancer is a common gynaecological cancer with increasing incidence and mortality. In the last decade, endometrial cancer genome-wide association studies (GWAS) have provided a resource to explore aetiology and for functional interpretation of heritable risk variation, informing endometrial cancer biology. Indeed, GWAS data have been used to assess relationships with other traits through correlation and Mendelian randomisation analyses, establishing genetic relationships and potential risk factors. Cross-trait GWAS analyses have increased statistical power and identified novel endometrial cancer risk variation related to other traits. Functional analysis of risk loci has helped prioritise candidate susceptibility genes, revealing molecular mechanisms and networks. Lastly, risk scores generated using endometrial cancer GWAS data may allow for clinical translation through identification of patients at high risk of disease. In the next decade, this knowledge base should enable substantial progress in our understanding of endometrial cancer and, potentially, new approaches for its screening and treatment.

Genetic variations of DNA bindings of FOXA1 and co-factors in breast cancer susceptibility

Identifying transcription factors (TFs) whose DNA bindings are altered by genetic variants that regulate susceptibility genes is imperative to understand transcriptional dysregulation in disease etiology. Here, we develop a statistical framework to analyze extensive ChIP-seq and GWAS data and identify 22 breast cancer risk-associated TFs. We find that, by analyzing genetic variations of TF-DNA bindings, the interaction of FOXA1 with co-factors such as ESR1 and E2F1, and the interaction of TFs with chromatin features (i.e., enhancers) play a key role in breast cancer susceptibility. Using genetic variants occupied by the 22 TFs, transcriptome-wide association analyses identify 52 previously unreported breast cancer susceptibility genes, including seven with evidence of essentiality from functional screens in breast relevant cell lines. We show that FOXA1 and co-factors form a core TF-transcriptional network regulating the susceptibility genes. Our findings provide additional insights into genetic variations of TF-DNA bindings (particularly for FOXA1) underlying breast cancer susceptibility.

The identification of transcription factors (TFs) whose binding sites are affected by risk genetic variants remains crucial. Here, the authors develop a statistical framework to analyse ChIP-seq and GWAS data, identify 22 breast cancer risk-associated TFs and a core TF-transcriptional network for FOXA1 and co-factors.

Low Grade Papillary Sinonasal (Schneiderian) Carcinoma: A Series of Five Cases of a Unique Malignant Neoplasm with Comparison to Inverted Papilloma and Conventional Nonkeratinizing Squamous Cell Carcinoma

There have been a few case reports and one small series of low grade papillary sinonasal (Schneiderian) carcinomas (LGPSC) which mimic papillomas but have overtly invasive growth and which occasionally metastasize. We describe the morphologic, clinical, immunohistochemical, and molecular features of five patients with LGPSC compared with eight cases each of inverted papilloma (IP) and conventional nonkeratinizing squamous cell carcinoma (SCC) with papillary growth. All LGPSC were nested with predominantly pushing invasion, no stromal reaction, and frequent surface papillary growth. All consisted of one cell type only, with polygonal cells with round nuclei, no (or limited) cytologic atypia, low mitotic activity, and prominent neutrophilic infiltrate. One patient had slightly more infiltrative bone invasion, another lymphovascular, perineural, and skeletal muscle invasion, and a third nodal metastasis after 17 years. By comparison, IPs had bland cytology, neutrophilic microabscesses, mixed immature squamous, goblet cell, and respiratory epithelium, and extremely low mitotic activity. Nonkeratinizing SCCs had basaloid-appearing cells with nuclear pleomorphism, brisk mitotic activity, and apoptosis. All LGPSC were p63 positive. Mitotic activity and Ki67 indices were significantly higher for LGPSCs than IPs and significantly lower than NKSCCs, while p53 immunohistochemistry in LGPSC was identical to nonkeratinizing SCC and higher than for IP. Sequencing showed all five tumors to harbor a MUC6 mutation, one tumor to harbor CDKN2A and PIK3R1 mutations, and one tumor to harbor a NOTCH1 mutation. All LGPSC lacked EGFR and KRAS mutations and lacked copy number variations of any main cancer genes. At a median follow up of 12 months, two LGPSC recurred locally, and one patient died after massive local recurrences and nodal metastases. LGPSC is a distinct, de novo sinonasal carcinoma that can be differentiated from papillomas by morphology and selected immunohistochemistry.

Identifying Novel Susceptibility Genes for Colorectal Cancer Risk From a Transcriptome-Wide Association Study of 125,478 Subjects

BACKGROUND AND AIMS

Susceptibility genes and the underlying mechanisms for the majority of risk loci identified by genome-wide association studies (GWAS) for colorectal cancer (CRC) risk remain largely unknown. We conducted a transcriptome-wide association study (TWAS) to identify putative susceptibility genes.

METHODS

Gene-expression prediction models were built using transcriptome and genetic data from the 284 normal transverse colon tissues of European descendants from the Genotype-Tissue Expression (GTEx), and model performance was evaluated using data from The Cancer Genome Atlas (n = 355). We applied the gene-expression prediction models and GWAS data to evaluate associations of genetically predicted gene-expression with CRC risk in 58,131 CRC cases and 67,347 controls of European ancestry. Dual-luciferase reporter assays and knockdown experiments in CRC cells and tumor xenografts were conducted.

RESULTS

We identified 25 genes associated with CRC risk at a Bonferroni-corrected threshold of P < 9.1 × 10-6, including genes in 4 novel loci, PYGL (14q22.1), RPL28 (19q13.42), CAPN12 (19q13.2), MYH7B (20q11.22), and MAP1L3CA (20q11.22). In 9 known GWAS-identified loci, we uncovered 9 genes that have not been reported previously, whereas 4 genes remained statistically significant after adjusting for the lead risk variant of the locus. Through colocalization analysis in GWAS loci, we additionally identified 12 putative susceptibility genes that were supported by TWAS analysis at P < .01. We showed that risk allele of the lead risk variant rs1741640 affected the promoter activity of CABLES2. Knockdown experiments confirmed that CABLES2 plays a vital role in colorectal carcinogenesis.

CONCLUSIONS

Our study reveals new putative susceptibility genes and provides new insight into the biological mechanisms underlying CRC development.

Multi-omics analysis to identify susceptibility genes for colorectal cancer

Most genetic variants for colorectal cancer (CRC) identified in genome-wide association studies (GWAS) are located in intergenic regions, implying pathogenic dysregulations of gene expression. However, comprehensive assessments of target genes in CRC remain to be explored. We conducted a multi-omics analysis using transcriptome and/or DNA methylation data from the Genotype-Tissue Expression, The Cancer Genome Atlas and the Colonomics projects. We identified 116 putative target genes for 45 GWAS-identified variants. Using summary-data-based Mendelian randomization approach (SMR), we demonstrated that the CRC susceptibility for 29 out of the 45 CRC variants may be mediated by cis-effects on gene regulation. At a cutoff of the Bonferroni-corrected PSMR < 0.05, we determined 66 putative susceptibility genes, including 39 genes that have not been previously reported. We further performed in vitro assays for two selected genes, DIP2B and SFMBT1, and provide functional evidence that they play a vital role in colorectal carcinogenesis via disrupting cell behavior, including migration, invasion and epithelial-mesenchymal transition. Our study reveals a large number of putative novel susceptibility genes and provides additional insight into the underlying mechanisms for CRC genetic risk loci.

Tumor Mutational Burden Is Polygenic and Genetically Associated with Complex Traits and Diseases

Tumor mutational burden (TMB) is an emerging biomarker of response to immunotherapy in solid tumors. However, the extent to which variation in TMB between patients is attributable to germline genetic variation remains elusive. Here, using 7,004 unrelated patients of European descent across 33 cancer types from The Cancer Genome Atlas, we show that pan-cancer TMB is polygenic with approximately 13% of its variation explained by approximately 1.1 million common variants altogether. We identify germline variants that affect TMB in stomach adenocarcinoma through altering the expression levels of BAG5 and KLC1. Further analyses provide evidence that TMB is genetically associated with complex traits and diseases, such as smoking, rheumatoid arthritis, height, and cancers, and some of the associations are likely causal. Overall, these results provide new insights into the genetic basis of somatic mutations in tumors and may inform future efforts to use genetic variants to stratify patients for immunotherapy. SIGNIFICANCE: This study provides evidence for a polygenic architecture of tumor mutational burden and opens an avenue for the use of whole-genome germline genetic variations to stratify patients with cancer for immunotherapy.

PD-L1 versus tumor mutation burden: Which is the better immunotherapy biomarker in advanced non-small cell lung cancer?

PD-L1 and tumor mutation burden (TMB) are the most widely used immunotherapy biomarkers to identify populations who would attain clinical benefit, with the higher values predicting better therapeutic efficacy. This review addresses the predictive values and unresolved challenges of these two biomarkers. PD-1 and PD-L1 inhibitors have induced durable and effective responses in patients with advanced non-small cell lung cancer, confirmed by multiple clinical trials and real-world studies. Different clinical trials, involving both PD-1/PD-L1 inhibitors alone and combination regimens, adopted either PD-L1 or TMB to stratify the patients, although the predictive capabilities of these two biomarkers are different. In the first-line setting, PD-L1 of 50% or more as a cut-off value can help select candidates for pembrolizumab or atezolizumab monotherapy; however, these two biomarkers poorly predict the efficacy of immunotherapy combination regimens as first-line treatments. In the second-line setting, although patients can benefit from nivolumab regardless of PD-L1 expression, both PD-L1 and blood TMB can be used as biomarkers to find patients suitable for atezolizumab. Except for inaccurate predictiveness, there are many unresolved problems with regard to the two biomarkers, such as the lack of standard detection methods, and their susceptibilities to other dynamic changes. The predictive values of TMB and PD-L1 were low in most circumstances; however, PD-L1 expression greater than ≥ 50% can help select appropriate patients for pembrolizumab and atezolizumab, respectively, as first-line monotherapies. Higher PD-L1 or TMB was associated with greater efficacy for atezolizumab as a second-line monotherapy.

Spectrum of Somatic Cancer Gene Variations Among Adults With Appendiceal Cancer by Age at Disease Onset

Importance

The incidence of appendiceal cancer (AC) is rising, particularly among individuals younger than 50 years (early-onset AC), with unexplained etiologies. The unique spectrum of somatic cancer gene variations among patients with early-onset AC is largely undetermined.

Objective

To characterize the frequency of somatic variations and genomic patterns among patients with early-onset (age <50 years) vs late-onset (age ≥50 years) AC.

Design, Setting, and Participants

This cohort study included individuals aged 18 years and older diagnosed with pathologically verified AC. Cases with clinical-grade targeted sequencing data from January 1, 2011, to December 31, 2019, were identified from the international clinicogenomic data-sharing consortium American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE). Data analysis was conducted from May to September 2020.

Exposures

Age at disease onset.

Main Outcomes and Measures

Somatic variation prevalence and spectrum in AC patients was determined. Variation comparisons between early-onset and late-onset AC were evaluated using multivariable logistic regression with adjustment for sex, race/ethnicity, histological subtype, sequencing center, and sample type.

Results

In total 385 individuals (mean [SD] age at diagnosis, 56.0 [12.4] years; 187 [48.6%] men; 306 [79.5%] non-Hispanic White individuals) with AC were included in this study, and 109 patients (28.3%) were diagnosed with early-onset AC. Race/ethnicity differed by age at disease onset; non-Hispanic Black individuals accounted for a larger proportion of early-onset vs late-onset cases (9 of 109 [8.3%] vs 11 of 276 [4.0%]; P = 0.04). Compared with patients aged 50 years or older at diagnosis, patients with early-onset AC had significantly higher odds of presenting with nonsilent variations in PIK3CA, SMAD3, and TSC2 (PIK3CA: odds ratio [OR], 4.58; 95% CI, 1.72-12.21; P = .002; SMAD3: OR, 7.37; 95% CI, 1.24-43.87; P = .03; TSC2: OR, 12.43; 95% CI, 1.03-149.59; P = .047). In contrast, patients with early-onset AC had a 60% decreased odds of presenting with GNAS nonsilent variations compared with patients with late-onset AC (OR, 0.40; 95% CI, 0.21-0.76, P = .006). By histological subtype, young patients with mucinous adenocarcinomas of the appendix had 65% decreased odds of variations in GNAS compared with late-onset cases in adjusted models (OR, 0.35; 95% CI, 0.15-0.79; P = .01). Similarly, patients with early-onset nonmucinous appendiceal adenocarcinomas had 72% decreased odds of presenting with GNAS variations vs late-onset cases, although these findings did not reach significance (OR, 0.28; 95% CI, 0.07-1.14; P = .08). GNAS and TP53 variations were mutually exclusive in ACs among early-onset and late-onset cases (P < .05).

Conclusions and Relevance

In the study, AC diagnosed among younger individuals harbored a distinct genomic landscape compared with AC diagnosed among older individuals. Development of therapeutic modalities that target these unique molecular features may yield clinical implications specifically for younger patients.

The mutational landscape of early- and typical-onset oral tongue squamous cell carcinoma

BACKGROUND

The incidence of oral tongue squamous cell carcinoma (OTSCC) is increasing among younger birth cohorts. The etiology of early-onset OTSCC (diagnosed before the age of 50 years) and cancer driver genes remain largely unknown.

METHODS

The Sequencing Consortium of Oral Tongue Cancer was established through the pooling of somatic mutation data of oral tongue cancer specimens (n = 227 [107 early-onset cases]) from 7 studies and The Cancer Genome Atlas. Somatic mutations at microsatellite loci and Catalog of Somatic Mutations in Cancer mutation signatures were identified. Cancer driver genes were identified with the MutSigCV and WITER algorithms. Mutation comparisons between early- and typical-onset OTSCC were evaluated via linear regression with adjustments for patient-related factors.

RESULTS

Two novel driver genes (ATXN1 and CDC42EP1) and 5 previously reported driver genes (TP53, CDKN2A, CASP8, NOTCH1, and FAT1) were identified. Six recurrent mutations were identified, with 4 occurring in TP53. Early-onset OTSCC had significantly fewer nonsilent mutations even after adjustments for tobacco use. No associations of microsatellite locus mutations and mutation signatures with the age of OTSCC onset were observed.

CONCLUSIONS

This international, multicenter consortium is the largest study to characterize the somatic mutational landscape of OTSCC and the first to suggest differences by age of onset. This study validates multiple previously identified OTSCC driver genes and proposes 2 novel cancer driver genes. In analyses by age, early-onset OTSCC had a significantly smaller somatic mutational burden that was not explained by differences in tobacco use.

LAY SUMMARY

This study identifies 7 specific areas in the human genetic code that could be responsible for promoting the development of tongue cancer. Tongue cancer in young patients (under the age of 50 years) has fewer overall changes to the genetic code in comparison with tongue cancer in older patients, but the authors do not think that this is due to differences in smoking rates between the 2 groups. The cause of increasing cases of tongue cancer in young patients remains unclear.

From tobacco smoking to cancer mutational signature: a mediation analysis strategy to explore the role of epigenetic changes

Background

Tobacco smoking is associated with a unique mutational signature in the human cancer genome. It is unclear whether tobacco smoking-altered DNA methylations and gene expressions affect smoking-related mutational signature.

Methods

We systematically analyzed the smoking-related DNA methylation sites reported from five previous casecontrol studies in peripheral blood cells to identify possible target genes. Using the mediation analysis approach, we evaluated whether the association of tobacco smoking with mutational signature is mediated through altered DNA methylation and expression of these target genes in lung adenocarcinoma tumor tissues.

Results

Based on data obtained from 21,108 blood samples, we identified 374 smoking-related DNA methylation sites, annotated to 248 target genes. Using data from DNA methylations, gene expressions and smoking-related mutational signature generated from ~ 7700 tumor tissue samples across 26 cancer types from The Cancer Genome Atlas (TCGA), we found 11 of the 248 target genes whose expressions were associated with smoking-related mutational signature at a Bonferroni-correction P < 0.001. This included four for head and neck cancer, and seven for lung adenocarcinoma. In lung adenocarcinoma, our results showed that smoking increased the expression of three genes, AHRR, GPR15, and HDGF, and decreased the expression of two genes, CAPN8, and RPS6KA1, which were consequently associated with increased smoking-related mutational signature. Additional evidence showed that the elevated expression of AHRR and GPR15 were associated with smoking-altered hypomethylations at cg14817490 and cg19859270, respectively, in lung adenocarcinoma tumor tissues. Lastly, we showed that decreased expression of RPS6KA1, were associated with poor survival of lung cancer patients.

Conclusions

Our findings provide novel insight into the contributions of tobacco smoking to carcinogenesis through the underlying mechanisms of the elevated mutational signature by altered DNA methylations and gene expressions.