Analysis of Over 140,000 European Descendants Identifies Genetically Predicted Blood Protein Biomarkers Associated with Prostate Cancer Risk

Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics

BACKGROUND

Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention.

METHODS

We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets.

FINDINGS

We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions.

INTERPRETATION

Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

FUNDING

This work was supported by Cancer Research UK (grant no. C8221/A29017).

Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC.

SIGNIFICANCE

PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.

Association between human blood metabolome and the risk of hypertension

Hypertension, commonly referred to as high blood pressure, is a chronic medical condition characterized by persistently elevated blood pressure levels. It is a prevalent global health issue, affecting a significant portion of the population worldwide. Hypertension is often asymptomatic, making it a silent but potentially dangerous condition if left untreated. Genetic instruments for 1,091 were from a recent comprehensive metabolome genome-wide association study (GWAS). Summary statistics of diastolic blood pressure (DBP) and systolic blood pressure (SBP) involving 757,601 sample size were analyzed. Two-sample Mendelian Randomization (MR) was conducted to assess causal effect of metabolites on DBP and SBP risk, and reverse MR analysis was performed to identify the DBP/SBP causal effect on blood metabolites. Twelve and twenty-two metabolites were identified to be associated with DBP and SBP, respectively. Sensitive analysis showed four metabolites had robustness association on BP. Reverse MR demonstrated DBP and SBP could decrease the tricosanoyl sphingomyelin (d18:1/23:0)* level and increase the 2-hydroxyhippurate (salicylurate) level in blood, respectively. Our findings reveal an association between blood metabolites and blood pressure (DBP and SBP), suggesting potential therapeutic targets for hypertension intervention.

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

A Splicing Transcriptome-Wide Association Study Identifies Candidate Altered Splicing for Prostate Cancer Risk

Prostate cancer (PCa) represents a huge public health burden among men. Many susceptibility genetic factors for PCa still remain unknown. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for PCa risk by assessing 79,194 cases and 61,112 controls of European ancestry in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia. We identified 120 splicing introns of 97 genes showing an association with PCa risk at false discovery rate (FDR)-corrected threshold (FDR <0.05). Of them, 33 genes were enriched in PCa-related diseases and function categories. Fine-mapping analysis suggested that 21 splicing introns of 19 genes were likely causally associated with PCa risk. Thirty-five splicing introns of 34 novel genes were identified to be related to PCa susceptibility for the first time, and 11 of the genes were enriched in a cancer-related network. Our study identified novel loci and splicing introns associated with PCa risk, which can improve our understanding of the etiology of this common malignancy.

Biochemical activity induced by a germline variation in KLK3 (PSA) associates with cellular function and clinical outcome in prostate cancer

Genetic variation at the 19q13.3 KLK locus is linked with prostate cancer susceptibility. The non-synonymous KLK3 SNP, rs17632542 (c.536T>C; Ile163Thr-substitution in PSA) is associated with reduced prostate cancer risk, however, the functional relevance is unknown. Here, we identify that the SNP variant-induced change in PSA biochemical activity as a previously undescribed function mediating prostate cancer pathogenesis. The 'Thr' PSA variant led to small subcutaneous tumours, supporting reduced prostate cancer risk. However, 'Thr' PSA also displayed higher metastatic potential with pronounced osteolytic activity in an experimental metastasis in-vivo model. Biochemical characterization of this PSA variant demonstrated markedly reduced proteolytic activity that correlated with differences in in-vivo tumour burden. The SNP is associated with increased risk for aggressive disease and prostate cancer-specific mortality in three independent cohorts, highlighting its critical function in mediating metastasis. Carriers of this SNP allele had reduced serum total PSA and a higher free/total PSA ratio that could contribute to late biopsy decisions and delay in diagnosis. Our results provide a molecular explanation for the prominent 19q13.3 KLK locus, rs17632542 SNP, association with a spectrum of prostate cancer clinical outcomes.

The Role of Fascin-1 in Human Urologic Cancers: A Promising Biomarker or Therapeutic Target?

Human cancer statistics show that an increased incidence of urologic cancers such as bladder cancer, prostate cancer, and renal cell carcinoma. Due to the lack of early markers and effective therapeutic targets, their prognosis is poor. Fascin-1 is an actin-binding protein, which functions in the formation of cell protrusions by cross-linking with actin filaments. Studies have found that fascin-1 expression is elevated in most human cancers and is related to outcomes such as neoplasm metastasis, reduced survival, and increased aggressiveness. Fascin-1 has been considered as a potential therapeutic target for urologic cancers, but there is no comprehensive review to evaluate these studies. This review aimed to provide an enhanced literature review, outline, and summarize the mechanism of fascin-1 in urologic cancers and discuss the therapeutic potential of fascin-1 and the possibility of its use as a potential marker. We also focused on the correlation between the overexpression of fascin-1 and clinicopathological parameters. Mechanistically, fascin-1 is regulated by several regulators and signaling pathways (such as long noncoding RNA, microRNA, c-Jun N-terminal kinase, and extracellular regulated protein kinases). The overexpression of fascin-1 is related to clinicopathologic parameters such as pathological stage, bone or lymph node metastasis, and reduced disease-free survival. Several fascin-1 inhibitors (G2, NP-G2-044) have been evaluated in vitro and in preclinical models. The study proved the promising potential of fascin-1 as a newly developing biomarker and a potential therapeutic target that needs further investigation. The data also highlight the inadequacy of fascin-1 to serve as a novel biomarker for prostate cancer.

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.

Systematic review of Mendelian randomization studies on risk of cancer

BACKGROUND

We aimed to map and describe the current state of Mendelian randomization (MR) literature on cancer risk and to identify associations supported by robust evidence.

METHODS

We searched PubMed and Scopus up to 06/10/2020 for MR studies investigating the association of any genetically predicted risk factor with cancer risk. We categorized the reported associations based on a priori designed levels of evidence supporting a causal association into four categories, namely robust, probable, suggestive, and insufficient, based on the significance and concordance of the main MR analysis results and at least one of the MR-Egger, weighed median, MRPRESSO, and multivariable MR analyses. Associations not presenting any of the aforementioned sensitivity analyses were not graded.

RESULTS

We included 190 publications reporting on 4667 MR analyses. Most analyses (3200; 68.6%) were not accompanied by any of the assessed sensitivity analyses. Of the 1467 evaluable analyses, 87 (5.9%) were supported by robust, 275 (18.7%) by probable, and 89 (6.1%) by suggestive evidence. The most prominent robust associations were observed for anthropometric indices with risk of breast, kidney, and endometrial cancers; circulating telomere length with risk of kidney, lung, osteosarcoma, skin, thyroid, and hematological cancers; sex steroid hormones and risk of breast and endometrial cancer; and lipids with risk of breast, endometrial, and ovarian cancer.

CONCLUSIONS

Despite the large amount of research on genetically predicted risk factors for cancer risk, limited associations are supported by robust evidence for causality. Most associations did not present a MR sensitivity analysis and were thus non-evaluable. Future research should focus on more thorough assessment of sensitivity MR analyses and on more transparent reporting.

Associations Between Genetically Predicted Plasma N-Glycans and Prostate Cancer Risk: Analysis of Over 140,000 European Descendants

Background

Previous studies suggest a potential link between glycosylation and prostate cancer. To better characterize the relationship between the two, we performed a study to comprehensively evaluate the associations between genetically predicted blood plasma N-glycan levels and prostate cancer risk.

Methods

Using genetic variants associated with N-glycan levels as instruments, we evaluated the associations between levels of 138 plasma N-glycans and prostate cancer risk. We analyzed data of 79,194 cases and 61,112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL.

Results

We identified three N-glycans with genetically predicted levels in plasma to be associated with prostate cancer risk after Bonferroni correction. The estimated odds ratios (95% confidence intervals) were 1.29 (1.20–1.40), 0.80 (0.74–0.88), and 0.79 (0.72–0.87) for PGP18, PGP33, and PGP109, respectively, per every one standard deviation increase in genetically predicted levels of N-glycan. However, the instruments for these N-glycans only involved one to two variants. The proportions of variations that can be explained by the instruments range from 1.58% to 2.95% for these three N-glycans.

Conclusion

We observed associations between genetically predicted levels of three N-glycans PGP18, PGP33, and PGP109 and prostate cancer risk. Given the correlated nature of the N-glycans and that many N-glycans share genetic loci, pleiotropy is a major concern. Future work is warranted to better characterize the relationship between N-glycans and prostate cancer.

Polygenic Risk Scores in Prostate Cancer Risk Assessment and Screening

More than 40% of the risk of developing prostate cancer (PCa) is from genetic factors. Genome-wide association studies have led to the discovery of more than 140 variants associated with PCa risk. Polygenic risk scores (PRS) generated using these variants show promise in identifying individuals at much higher (and lower) lifetime risk than the average man. PCa PRS also improve the predictive value of prostate-specific antigen screening, may inform the age for starting PCa screening, and are informative for development of more aggressive tumors. Despite the promise, few clinical trials have evaluated the benefit of PCa PRS for clinical care.

Associations between Genetically Predicted Circulating Protein Concentrations and Endometrial Cancer Risk

Simple Summary

Endometrial cancer is the leading female reproductive tract cancer in developed countries. Discovering new biomarkers is critical for understanding the etiology this cancer and identifying women with a higher risk of this cancer from the general population. Several blood protein biomarkers have been linked to endometrial cancer in previous studies, but these studies have assessed only a limited number of biomarkers usually among a small number of participants. The current study aimed at identifying novel circulating protein biomarkers of endometrial cancer by using the largest available dataset to date. Our finding suggested nine proteins to be associated with endometrial cancer risk, and five of the identified associations showed suggestive associations with risk of non-endometrioid EC, a much more lethal subtype. If validated by additional studies, our findings may contribute to understanding the pathogenesis of endometrial tumor development and facilitating the risk assessment of endometrial cancer.

Abstract

Endometrial cancer (EC) is the leading female reproductive tract malignancy in developed countries. Currently, genome-wide association studies (GWAS) have identified 17 risk loci for EC. To identify novel EC-associated proteins, we used previously reported protein quantitative trait loci for 1434 plasma proteins as instruments to evaluate associations between genetically predicted circulating protein concentrations and EC risk. We studied 12,906 cases and 108,979 controls of European descent included in the Endometrial Cancer Association Consortium, the Epidemiology of Endometrial Cancer Consortium, and the UK Biobank. We observed associations between genetically predicted concentrations of nine proteins and EC risk at a false discovery rate of <0.05 (p-values range from 1.14 × 10⁻¹⁰ to 3.04 × 10⁻⁴). Except for vascular cell adhesion protein 1, all other identified proteins were independent from known EC risk variants identified in EC GWAS. The respective odds ratios (95% confidence intervals) per one standard deviation increase in genetically predicted circulating protein concentrations were 1.21 (1.13, 1.30) for DNA repair protein RAD51 homolog 4, 1.27 (1.14, 1.42) for desmoglein-2, 1.14 (1.07, 1.22) for MHC class I polypeptide-related sequence B, 1.05 (1.02, 1.08) for histo-blood group ABO system transferase, 0.77 (0.68, 0.89) for intestinal-type alkaline phosphatase, 0.82 (0.74, 0.91) for carbohydrate sulfotransferase 15, 1.07 (1.03, 1.11) for D-glucuronyl C5-epimerase, and 1.07 (1.03, 1.10) for CD209 antigen. In conclusion, we identified nine potential EC-associated proteins. If validated by additional studies, our findings may contribute to understanding the pathogenesis of endometrial tumor development and identifying women at high risk of EC along with other EC risk factors and biomarkers.

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.

Associations Between Genetically Predicted Protein Levels and COVID-19 Severity

It is critical to identify potential causal targets for SARS-CoV-2, which may guide drug repurposing options. We assessed the associations between genetically predicted protein levels and COVID-19 severity. Leveraging data from the COVID-19 Host Genetics Initiative comparing 6492 hospitalized COVID-19 patients and 1 012 809 controls, we identified 18 proteins with genetically predicted levels to be associated with COVID-19 severity at a false discovery rate of <0.05, including 12 that showed an association even after Bonferroni correction. Of the 18 proteins, 6 showed positive associations and 12 showed inverse associations. In conclusion, we identified 18 candidate proteins for COVID-19 severity.

Biomarkers, Master Regulators and Genomic Fabric Remodeling in a Case of Papillary Thyroid Carcinoma

Publicly available (own) transcriptomic data have been analyzed to quantify the alteration in functional pathways in thyroid cancer, establish the gene hierarchy, identify potential gene targets and predict the effects of their manipulation. The expression data have been generated by profiling one case of papillary thyroid carcinoma (PTC) and genetically manipulated BCPAP (papillary) and 8505C (anaplastic) human thyroid cancer cell lines. The study used the genomic fabric paradigm that considers the transcriptome as a multi-dimensional mathematical object based on the three independent characteristics that can be derived for each gene from the expression data. We found remarkable remodeling of the thyroid hormone synthesis, cell cycle, oxidative phosphorylation and apoptosis pathways. Serine peptidase inhibitor, Kunitz type, 2 (SPINT2) was identified as the Gene Master Regulator of the investigated PTC. The substantial increase in the expression synergism of SPINT2 with apoptosis genes in the cancer nodule with respect to the surrounding normal tissue (NOR) suggests that SPINT2 experimental overexpression may force the PTC cells into apoptosis with a negligible effect on the NOR cells. The predictive value of the expression coordination for the expression regulation was validated with data from 8505C and BCPAP cell lines before and after lentiviral transfection with DDX19B.

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.

Associations between Genetically Predicted Blood Protein Biomarkers and Pancreatic Cancer Risk

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies, with few known risk factors and biomarkers. Several blood protein biomarkers have been linked to PDAC in previous studies, but these studies have assessed only a limited number of biomarkers, usually in small samples. In this study, we evaluated associations of circulating protein levels and PDAC risk using genetic instruments.

METHODS

To identify novel circulating protein biomarkers of PDAC, we studied 8,280 cases and 6,728 controls of European descent from the Pancreatic Cancer Cohort Consortium and the Pancreatic Cancer Case-Control Consortium, using genetic instruments of protein quantitative trait loci.

RESULTS

We observed associations between predicted concentrations of 38 proteins and PDAC risk at an FDR of < 0.05, including 23 of those proteins that showed an association even after Bonferroni correction. These include the protein encoded by ABO, which has been implicated as a potential target gene of PDAC risk variant. Eight of the identified proteins (LMA2L, TM11D, IP-10, ADH1B, STOM, TENC1, DOCK9, and CRBB2) were associated with PDAC risk after adjusting for previously reported PDAC risk variants (OR ranged from 0.79 to 1.52). Pathway enrichment analysis showed that the encoding genes for implicated proteins were significantly enriched in cancer-related pathways, such as STAT3 and IL15 production.

CONCLUSIONS

We identified 38 candidates of protein biomarkers for PDAC risk.

IMPACT

This study identifies novel protein biomarker candidates for PDAC, which if validated by additional studies, may contribute to the etiologic understanding of PDAC development.