Multi-ethnic transcriptome-wide association study of prostate cancer

Germline Genetic Variants Associated with Somatic TMPRSS2:ERG Fusion Status in Prostate Cancer: A Genome-Wide Association Study

BACKGROUND

The prostate cancer subtype defined by the presence of TMPRSS2:ERG has been shown to be molecularly and epidemiologically distinct. However, few studies have investigated germline genetic variants associating with TMPRSS2:ERG fusion status.

METHODS

We performed a genome-wide association study with 396 TMPRSS2:ERG(+) cases, 390 TMPRSS2:ERG(-) cases, and 2,386 cancer-free controls from the Physicians' Health Study (PHS), the Health Professionals Follow-up Study (HPFS), and a Seattle-based Fred Hutchinson (FH) Cancer Center Prostate Cancer Study. We applied logistic regression models to test the associations between ∼5 million SNPs with TMPRSS2:ERG fusion status accounting for population stratification.

RESULTS

We did not identify genome-wide significant variants comparing the TMPRSS2:ERG(+) to the TMPRSS2:ERG(-) prostate cancer cases in the meta-analysis. When comparing TMPRSS2:ERG(+) prostate cancer cases with controls without prostate cancer, 10 genome-wide significant SNPs on chromosome 17q24.3 were observed in the meta-analysis. When comparing TMPRSS2:ERG(-) prostate cancer cases with controls without prostate cancer, two SNPs on chromosome 8q24.21 in the meta-analysis reached genome-wide significance.

CONCLUSIONS

We observed SNPs at several known prostate cancer risk loci (17q24.3, 1q32.1, and 8q24.21) that were differentially and exclusively associated with the risk of developing prostate tumors either with or without the gene fusion.

IMPACT

Our findings suggest that tumors with the TMPRSS2:ERG fusion exhibit a different germline genetic etiology compared with fusion negative cases.

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.

Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies

Transcriptome-wide association studies (TWASs) are a powerful approach to identify genes whose expression is associated with complex disease risk. However, non-causal genes can exhibit association signals due to confounding by linkage disequilibrium (LD) patterns and eQTL pleiotropy at genomic risk regions, which necessitates fine-mapping of TWAS signals. Here, we present MA-FOCUS, a multi-ancestry framework for the improved identification of genes underlying traits of interest. We demonstrate that by leveraging differences in ancestry-specific patterns of LD and eQTL signals, MA-FOCUS consistently outperforms single-ancestry fine-mapping approaches with equivalent total sample sizes across multiple metrics. We perform TWASs for 15 blood traits using genome-wide summary statistics (average n_EA = 511 k, n_AA = 13 k) and lymphoblastoid cell line eQTL data from cohorts of primarily European and African continental ancestries. We recapitulate evidence demonstrating shared genetic architectures for eQTL and blood traits between the two ancestry groups and observe that gene-level effects correlate 20% more strongly across ancestries than SNP-level effects. Lastly, we perform fine-mapping using MA-FOCUS and find evidence that genes at TWAS risk regions are more likely to be shared across ancestries than they are to be ancestry specific. Using multiple lines of evidence to validate our findings, we find that gene sets produced by MA-FOCUS are more enriched in hematopoietic categories than alternative approaches (p = 2.36 × 10^-15). Our work demonstrates that including and appropriately accounting for genetic diversity can drive more profound insights into the genetic architecture of complex traits.

Integrative assessment of CIP2A overexpression and mutational effects in human malignancies identifies possible deleterious variants

KIAA1524 is the gene encoding the human cancerous inhibitor of PP2A (CIP2A) protein which is regarded as a novel target for cancer therapy. It is overexpressed in 65%-90% of tissues in almost all studied human cancers. CIP2A expression correlates with cancer progression, disease aggressivity in lung cancer besides poor survival and resistance to chemotherapy in breast cancer. Herein, a pan-cancer analysis of public gene expression datasets was conducted showing significant upregulation of CIP2A in cancerous and metastatic tissues. CIP2A overexpression also correlated with poor survival of cancer patients. To determine the non-coding variants associated with CIP2A overexpression, 5'UTR and 3'UTR variants were annotated and scored using RegulomeDB and Enformer deep learning model. The 5'UTR variants rs1239349555, rs1576326380, and rs1231839144 were predicted to be potential regulators of CIP2A overexpression scoring best on RegulomeDB annotations with a high "2a" rank of supporting experimental data. These variants also scored the highest on Enformer predictions. Analysis of the 3'UTR variants of CIP2A predicted rs56255137 and rs58758610 to alter binding sites of hsa-miR-500a-5 and (hsa-miR-3671, hsa-miR-5692a) respectively. Both variants were also found in linkage disequilibrium with rs11709183 and rs147863209 respectively at r² ≥ 0.8. The aforementioned variants were found to be eQTL hits significantly associated with CIP2A overexpression. Further, analysis of rs11709183 and rs147863209 revealed a high "2b" rank on RegulomeDB annotations indicating a probable effect on DNAse transcription factors binding. The MuTarget analysis indicated that somatic mutations in TP53 are significantly associated with upregulated CIP2A in human cancers. Analysis of missense SNPs on CIP2A solved structure predicted seven deleterious effects. Four of these variants were also predicted as structurally and functionally destabilizing to CIP2A including; rs375108755, rs147942716, rs368722879, and rs367941403. Variant rs1193091427 was predicted as a potential intronic splicing mutation that might be responsible for the novel CIP2A variant (NOCIVA) in multiple myeloma. Finally, Enrichment of the Wnt/β-catenin pathway within the CIP2A regulatory gene network suggested potential of therapeutic combinations between FTY720 with Wnt/β-catenin, Plk1 and/or HDAC inhibitors to downregulate CIP2A which has been shown to be essential for the survival of different cancer cell lines.

Prostate Cancer Risk Calculators for Healthy Populations: Systematic Review

Background

Screening for prostate cancer has long been a debated, complex topic. The use of risk calculators for prostate cancer is recommended for determining patients’ individual risk of cancer and the subsequent need for a prostate biopsy. These tools could lead to better discrimination of patients in need of invasive diagnostic procedures and optimized allocation of health care resources

Objective

The goal of the research was to systematically review available literature on the performance of current prostate cancer risk calculators in healthy populations by comparing the relative impact of individual items on different cohorts and on the models’ overall performance.

Methods

We performed a systematic review of available prostate cancer risk calculators targeted at healthy populations. We included studies published from January 2000 to March 2021 in English, Spanish, French, Portuguese, or German. Two reviewers independently decided for or against inclusion based on abstracts. A third reviewer intervened in case of disagreements. From the selected titles, we extracted information regarding the purpose of the manuscript, analyzed calculators, population for which it was calibrated, included risk factors, and the model’s overall accuracy.

Results

We included a total of 18 calculators from 53 different manuscripts. The most commonly analyzed ones were the Prostate Cancer Prevention Trial (PCPT) and European Randomized Study on Prostate Cancer (ERSPC) risk calculators developed from North American and European cohorts, respectively. Both calculators provided high diagnostic ability of aggressive prostate cancer (AUC as high as 0.798 for PCPT and 0.91 for ERSPC). We found 9 calculators developed from scratch for specific populations that reached a diagnostic ability as high as 0.938. The most commonly included risk factors in the calculators were age, prostate specific antigen levels, and digital rectal examination findings. Additional calculators included race and detailed personal and family history.

Conclusions

Both the PCPR and ERSPC risk calculators have been successfully adapted for cohorts other than the ones they were originally created for with no loss of diagnostic ability. Furthermore, designing calculators from scratch considering each population’s sociocultural differences has resulted in risk tools that can be well adapted to be valid in more patients. The best risk calculator for prostate cancer will be that which has been calibrated for its intended population and can be easily reproduced and implemented.

Trial Registration

PROSPERO CRD42021242110; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=242110

Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations

BACKGROUND

Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed.

METHODS

To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including n = 229 African American and n = 381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, n = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (n = 27,955) and Hispanic/Latino (n = 28,324) ancestry participants.

RESULTS

Our results revealed 24 suggestive signals (p < 1 × 10-4) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (n = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN.

CONCLUSIONS

These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.

snpXplorer: a web application to explore human SNP-associations and annotate SNP-sets

Genetic association studies are frequently used to study the genetic basis of numerous human phenotypes. However, the rapid interrogation of how well a certain genomic region associates across traits as well as the interpretation of genetic associations is often complex and requires the integration of multiple sources of annotation, which involves advanced bioinformatic skills. We developed snpXplorer, an easy-to-use web-server application for exploring Single Nucleotide Polymorphisms (SNP) association statistics and to functionally annotate sets of SNPs. snpXplorer can superimpose association statistics from multiple studies, and displays regional information including SNP associations, structural variations, recombination rates, eQTL, linkage disequilibrium patterns, genes and gene-expressions per tissue. By overlaying multiple GWAS studies, snpXplorer can be used to compare levels of association across different traits, which may help the interpretation of variant consequences. Given a list of SNPs, snpXplorer can also be used to perform variant-to-gene mapping and gene-set enrichment analysis to identify molecular pathways that are overrepresented in the list of input SNPs. snpXplorer is freely available at https://snpxplorer.net. Source code, documentation, example files and tutorial videos are available within the Help section of snpXplorer and at https://github.com/TesiNicco/snpXplorer.

Narrative review: predicting future molecular and clinical profiles of prostate cancer in the United States

Prostate cancer represents the most frequent tumor in men, accounting for the 21% of all diagnosed tumors, with 191,930 new cases and 33,330 deaths estimated in 2020. Advanced prostate cancer represents a heterogeneous disease, ranging from hormone naive or hormone sensitive to castration resistant. The therapeutic armamentarium for this disease has been implemented in the last years by novel hormonal therapies and chemotherapies. However, the percentage of patients who achieve complete responses still results negligible. On this scenario, the design of clinical trials investigating new therapeutic approaches represent a dramatic medical need. Predicting cancer incidence may be fundamental to design specific clinical trials, to optimize the allocation of economic resources, and to plan future cancer control programs. ERG, SPOP and DDR genes alterations can act as therapeutic targets in prostate cancer patients and can be tested to identify a gene-selected patient population to enrol in specific trials. According to our predictions, ERG gene fusions will be the most predominant molecular subtype, accounting for 69,050 new cases in 2030. Mutation in SPOP gene will be diagnosed in 16,512 tumors, corresponding to the number of cases associated with alterations in DDR genes (including 7,956 BRCA2 mutated tumors). In this article, we analyzed and discussed the future molecular and clinical profiles of prostate cancer in the United States, aimed to describe a series of distinct subpopulations and to quantify potential clinical trial candidates in the next years.