Pleiotropic associations of risk variants identified for other cancers with lung cancer risk: the PAGE and TRICL consortia

Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement

BACKGROUND

Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets.

METHODS

We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups.

RESULTS

Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations.

CONCLUSIONS

This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Systematic proteome-wide Mendelian randomization using the human plasma proteome to identify therapeutic targets for lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the predominant histological subtype of lung cancer and the leading cause of cancer-related mortality. Identifying effective drug targets is crucial for advancing LUAD treatment strategies.

METHODS

This study employed proteome-wide Mendelian randomization (MR) and colocalization analyses. We collected data on 1394 plasma proteins from a protein quantitative trait loci (pQTL) study involving 4907 individuals. Genetic associations with LUAD were derived from the Transdisciplinary Research in Cancer of the Lung (TRICL) study, including 11,245 cases and 54,619 controls. We integrated pQTL and LUAD genome-wide association studies (GWASs) data to identify candidate proteins. MR utilizes single nucleotide polymorphisms (SNPs) as genetic instruments to estimate the causal effect of exposure on outcome, while Bayesian colocalization analysis determines the probability of shared causal genetic variants between traits. Our study applied these methods to assess causality between plasma proteins and LUAD. Furthermore, we employed a two-step MR to quantify the proportion of risk factors mediated by proteins on LUAD. Finally, protein-protein interaction (PPI) analysis elucidated potential links between proteins and current LUAD medications.

RESULTS

We identified nine plasma proteins significantly associated with LUAD. Increased levels of ALAD, FLT1, ICAM5, and VWC2 exhibited protective effects, with odds ratios of 0.79 (95% CI 0.72-0.87), 0.39 (95% CI 0.28-0.55), 0.91 (95% CI 0.72-0.87), and 0.85 (95% CI 0.79-0.92), respectively. Conversely, MDGA2 (OR, 1.13; 95% CI 1.08-1.19), NTM (OR, 1.12; 95% CI 1.09-1.16), PMM2 (OR, 1.35; 95% CI 1.18-1.53), RNASET2 (OR, 1.15; 95% CI 1.08-1.21), and TFPI (OR, 4.58; 95% CI 3.02-6.94) increased LUAD risk. Notably, none of the nine proteins showed evidence of reverse causality. Bayesian colocalization indicated that RNASET2, TFPI, and VWC2 shared the same variant with LUAD. Furthermore, NTM and FLT1 demonstrated interactions with targets of current LUAD medications. Additionally, FLT1 and TFPI are currently under evaluation as therapeutic targets, while NTM, RNASET2, and VWC2 are potentially druggable. These findings shed light on LUAD pathogenesis, highlighting the tumor-promoting effects of RNASET2, TFPI, and NTM, along with the protective effects of VWC2 and FLT1, providing a significant biological foundation for future LUAD therapeutic targets.

CONCLUSIONS

Our proteome-wide MR analysis highlighted RNASET2, TFPI, VWC2, NTM, and FLT1 as potential drug targets for further clinical investigation in LUAD. However, the specific mechanisms by which these proteins influence LUAD remain elusive. Targeting these proteins in drug development holds the potential for successful clinical trials, providing a pathway to prioritize and reduce costs in LUAD therapeutics.

The Long Non-Coding RNA ANRIL in Cancers

ANRIL (Antisense Noncoding RNA in the INK4 Locus), a long non-coding RNA encoded in the human chromosome 9p21 region, is a critical factor for regulating gene expression by interacting with multiple proteins and miRNAs. It has been found to play important roles in various cellular processes, including cell cycle control and proliferation. Dysregulation of ANRIL has been associated with several diseases like cancers and cardiovascular diseases, for instance. Understanding the oncogenic role of ANRIL and its potential as a diagnostic and prognostic biomarker in cancer is crucial. This review provides insights into the regulatory mechanisms and oncogenic significance of the 9p21 locus and ANRIL in cancer.

Lymphocyte-Specific Protein-1 Suppresses Xenobiotic-Induced Constitutive Androstane Receptor and Subsequent Yes-Associated Protein-Activated Hepatocyte Proliferation

Activation of constitutive androstane receptor (CAR) transcription factor by xenobiotics promotes hepatocellular proliferation, promotes hypertrophy without liver injury, and induces drug metabolism genes. Previous work demonstrated that lymphocyte-specific protein-1 (LSP1), an F-actin binding protein and gene involved in human hepatocellular carcinoma, suppresses hepatocellular proliferation after partial hepatectomy. The current study investigated the role of LSP1 in liver enlargement induced by chemical mitogens, a regenerative process independent of tissue loss. 1,4-Bis [2-(3,5-dichloropyridyloxy)] benzene (TCPOBOP), a direct CAR ligand and strong chemical mitogen, was administered to global Lsp1 knockout and hepatocyte-specific Lsp1 transgenic (TG) mice and measured cell proliferation, hypertrophy, and expression of CAR-dependent drug metabolism genes. TG livers displayed a significant decrease in Ki-67 labeling and liver/body weight ratios compared with wild type on day 2. Surprisingly, this was reversed by day 5, due to hepatocyte hypertrophy. There was no difference in CAR-regulated drug metabolism genes between wild type and TG. TG livers displayed increased Yes-associated protein (YAP) phosphorylation, decreased nuclear YAP, and direct interaction between LSP1 and YAP, suggesting LSP1 suppresses TCPOBOP-driven hepatocellular proliferation, but not hepatocyte volume, through YAP. Conversely, loss of LSP1 led to increased hepatocellular proliferation on days 2, 5, and 7. LSP1 selectively suppresses CAR-induced hepatocellular proliferation, but not drug metabolism, through the interaction of LSP1 with YAP, supporting the role of LSP1 as a selective growth suppressor.

Investigating the effect of sexual behaviour on oropharyngeal cancer risk: a methodological assessment of Mendelian randomization

BACKGROUND

Human papilloma virus infection is known to influence oropharyngeal cancer (OPC) risk, likely via sexual transmission. However, sexual behaviour has been correlated with other risk factors including smoking and alcohol, meaning independent effects are difficult to establish. We aimed to evaluate the causal effect of sexual behaviour on the risk of OPC using Mendelian randomization (MR).

METHODS

Genetic variants robustly associated with age at first sex (AFS) and the number of sexual partners (NSP) were used to perform both univariable and multivariable MR analyses with summary data on 2641 OPC cases and 6585 controls, obtained from the largest available genome-wide association studies (GWAS). Given the potential for genetic pleiotropy, we performed a number of sensitivity analyses: (i) MR methods to account for horizontal pleiotropy, (ii) MR of sexual behaviours on positive (cervical cancer and seropositivity for Chlamydia trachomatis) and negative control outcomes (lung and oral cancer), (iii) Causal Analysis Using Summary Effect estimates (CAUSE), to account for correlated and uncorrelated horizontal pleiotropic effects, (iv) multivariable MR analysis to account for the effects of smoking, alcohol, risk tolerance and educational attainment.

RESULTS

In univariable MR, we found evidence supportive of an effect of both later AFS (IVW OR = 0.4, 95%CI (0.3, 0.7), per standard deviation (SD), p = < 0.001) and increasing NSP (IVW OR = 2.2, 95%CI (1.3, 3.8) per SD, p = < 0.001) on OPC risk. These effects were largely robust to sensitivity analyses accounting for horizontal pleiotropy. However, negative control analysis suggested potential violation of the core MR assumptions and subsequent CAUSE analysis implicated pleiotropy of the genetic instruments used to proxy sexual behaviours. Finally, there was some attenuation of the univariable MR results in the multivariable models (AFS IVW OR = 0.7, 95%CI (0.4, 1.2), p = 0.21; NSP IVW OR = 0.9, 95%CI (0.5 1.7), p = 0.76).

CONCLUSIONS

Despite using genetic variants strongly related sexual behaviour traits in large-scale GWAS, we found evidence for correlated pleiotropy. This emphasizes a need for multivariable approaches and the triangulation of evidence when performing MR of complex behavioural traits.

NPC2 as a Prognostic Biomarker for Glioblastoma Based on Integrated Bioinformatics Analysis and Cytological Experiments

Glioblastoma (GBM) is one of the most common and fatal malignancies worldwide, while its prognostic biomarkers are still being explored. This study aims to identify potential genes with clinical and prognostic significance by integrating bioinformatics analysis and investigating their function in HNSCC. Based on the Single-cell RNA sequencing (scRNA-seq) results of H3K27M-glioma cells, computational bioinformatics methods were employed for selecting prognostic biomarker for GBM. The protein NPC2 (NPC Intracellular Cholesterol Transporter 2), which has been shown to be related to lipoprotein metabolism and innate immune system, was identified to be upregulated in GBM. NPC2 showed a relatively higher expression in GBM samples, and a negative correlation with tumor purity and tumor infiltrating immune cells. Additionally, NPC2 was knocked down in U87-MG and U251 cells line, and cell proliferation and migration capability were evaluated with CCK-8, scratch and transwell assay, respectively. Cytological experiments has shown that NPC2 overexpression inhibited GBM cells proliferation and migration, indicating its important role in GBM progression. This is the first investigation into the prognostic value of NPC2 interact with GBM. The potential molecular factor NPC2 have been identified as a prognostic biomarker for GBM.

Cross-cancer evaluation of polygenic risk scores for 16 cancer types in two large cohorts

Even distinct cancer types share biological hallmarks. Here, we investigate polygenic risk score (PRS)-specific pleiotropy across 16 cancers in European ancestry individuals from the Genetic Epidemiology Research on Adult Health and Aging cohort (16,012 cases, 50,552 controls) and UK Biobank (48,969 cases, 359,802 controls). Within cohorts, each PRS is evaluated in multivariable logistic regression models against all other cancer types. Results are then meta-analyzed across cohorts. Ten positive and one inverse cross-cancer associations are found after multiple testing correction. Two pairs show bidirectional associations; the melanoma PRS is positively associated with oral cavity/pharyngeal cancer and vice versa, whereas the lung cancer PRS is positively associated with oral cavity/pharyngeal cancer, and the oral cavity/pharyngeal cancer PRS is inversely associated with lung cancer. Overall, we validate known, and uncover previously unreported, patterns of pleiotropy that have the potential to inform investigations of risk prediction, shared etiology, and precision cancer prevention strategies.

Cross-Cancer Pleiotropic Analysis Reveals Novel Susceptibility Loci for Lung Cancer

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with cancer risk, several of which have shown pleiotropic effects across cancers. Therefore, we performed a systematic cross-cancer pleiotropic analysis to detect the effects of GWAS-identified variants from non-lung cancers on lung cancer risk in 12,843 cases and 12,639 controls from four lung cancer GWASs. The overall association between variants in each cancer and risk of lung cancer was explored using sequential kernel association test (SKAT) analysis. For single variant analysis, we combined the result of specific study using fixed-effect meta-analysis. We performed functional exploration of significant associations based on features from public databases. To further detect the biological mechanism underlying identified observations, pathway enrichment analysis were conducted with R package “clusterProfiler.” SNP-set analysis revealed the overall associations between variants of 8 cancer types and lung cancer risk. Single variant analysis identified 6 novel SNPs related to lung cancer risk after multiple correction (P_fdr < 0.10), including rs1707302 (1p34.1, OR = 0.93, 95% CI: 0.90–0.97, P = 7.60 × 10⁻⁴), rs2516448 (6p21.33, OR = 1.07, 95% CI: 1.03–1.11, P = 1.00 × 10⁻³), rs3869062 (6p22.1, OR = 0.91, 95% CI: 0.86–0.96, P = 7.10 × 10⁻⁴), rs174549 (11q12.2, OR = 0.90, 95% CI: 0.87–0.94, P = 1.00 × 10⁻⁷), rs7193541 (16q23.1, OR = 0.93, 95% CI: 0.90–0.96, P = 1.20 × 10⁻⁴), and rs8064454 (17q12, OR = 1.07, 95% CI: 1.03–1.11, P = 4.30 × 10⁻⁴). The eQTL analysis and functional annotation suggested that these variants might modify lung cancer susceptibility through regulating the expression of related genes. Pathway enrichment analysis showed that genes modulated by these variants play important roles in cancer carcinogenesis. Our findings demonstrate the pleiotropic associations between non-lung cancer susceptibility loci and lung cancer risk, providing important insights into the shared mechanisms of carcinogenesis across cancers.

The factors associated with maternal consent to human papillomavirus vaccination among adolescents in Israel

Purpose: To evaluate the knowledge and attitudes toward the human papillomavirus (HPV) vaccine among mothers of 8th graders in Israel, and to determine the factors associated with maternal consent to the HPV vaccine.Methods: We conducted a cross-sectional study among mothers of 8th grade students in 27 schools in Haifa and Northern districts of Israel during the 2016-17 school year. Data were collected using a structured telephone questionnaire.Results: 313 mothers answered the questionnaire (response rate = 91.8%). The mean knowledge level score was low (3.96 points [out of 10] ±2.68). Knowledge level was positively associated with Jewish nationality, being secular in religious practice and higher education. The attitude mean score was low-moderate (11.22 points [out of 18] ± 5.01). Attitude score was positively associated with Arab nationality. No significant association was found between knowledge level and attitudes. According to multivariate analysis, mothers' consent to the HPV vaccine was associated with the knowledge level score (OR = 0.82; 95%CI 0.68-0.98), the attitude score (OR = 1.76; 95%CI 1.53-2.02) and nationality (OR = 27.86, 95%CI 3.41-227.56).Conclusions: The knowledge level and attitudes toward the HPV vaccine were found to be unsatisfactory with racial disparities between Arabs and Jews. Jewish mothers compared with Arab mothers, mothers with a higher knowledge level or less positive attitudes were less likely to consent to the vaccine. These findings could contribute toward adapting programs to the different Israeli sectors in order to improve the rates of HPV vaccine receipt among adolescents.

The Future of Genomic Studies Must Be Globally Representative: Perspectives from PAGE

The past decade has seen a technological revolution in human genetics that has empowered population-level investigations into genetic associations with phenotypes. Although these discoveries rely on genetic variation across individuals, association studies have overwhelmingly been performed in populations of European descent. In this review, we describe limitations faced by single-population studies and provide an overview of strategies to improve global representation in existing data sets and future human genomics research via diversity-focused, multiethnic studies. We highlight the successes of individual studies and meta-analysis consortia that have provided unique knowledge. Additionally, we outline the approach taken by the Population Architecture Using Genomics and Epidemiology (PAGE) study to develop best practices for performing genetic epidemiology in multiethnic contexts. Finally, we discuss how limiting investigations to single populations impairs findings in the clinical domain for both rare-variant identification and genetic risk prediction.

Cross-Cancer Pleiotropic Associations with Lung Cancer Risk in African Americans

BACKGROUND

Identifying genetic variants with pleiotropic associations across multiple cancers can reveal shared biologic pathways. Prior pleiotropic studies have primarily focused on European-descent individuals. Yet population-specific genetic variation can occur, and potential pleiotropic associations among diverse racial/ethnic populations could be missed. We examined cross-cancer pleiotropic associations with lung cancer risk in African Americans.

METHODS

We conducted a pleiotropic analysis among 1,410 African American lung cancer cases and 2,843 controls. We examined 36,958 variants previously associated (or in linkage disequilibrium) with cancer in prior genome-wide association studies. Logistic regression analyses were conducted, adjusting for age, sex, global ancestry, study site, and smoking status.

RESULTS

We identified three novel genomic regions significantly associated (FDR-corrected P <0.10) with lung cancer risk (rs336958 on 5q14.3, rs7186207 on 16q22.2, and rs11658063 on 17q12). On chromosome16q22.2, rs7186207 was significantly associated with reduced risk [OR = 0.43; 95% confidence interval (CI), 0.73-0.89], and functional annotation using GTEx showed rs7186207 modifies DHODH gene expression. The minor allele at rs336958 on 5q14.3 was associated with increased lung cancer risk (OR = 1.47; 95% CI, 1.22-1.78), whereas the minor allele at rs11658063 on 17q12 was associated with reduced risk (OR = 0.80; 95% CI, 0.72-0.90).

CONCLUSIONS

We identified novel associations on chromosomes 5q14.3, 16q22.2, and 17q12, which contain HNF1B, DHODH, and HAPLN1 genes, respectively. SNPs within these regions have been previously associated with multiple cancers. This is the first study to examine cross-cancer pleiotropic associations for lung cancer in African Americans.

IMPACT

Our findings demonstrate novel cross-cancer pleiotropic associations with lung cancer risk in African Americans.

Drosophila strain specific response to cisplatin neurotoxicity

Drosophila melanogaster has recently been developed as a simple, in vivo, genetic model of chemotherapy-induced peripheral neuropathy. Flies treated with the chemotherapy agent cisplatin display both a neurodegenerative phenotype and cell death in rapidly dividing follicles, mimicking the cell specific responses seen in humans. Cisplatin induces climbing deficiencies and loss of fertility in a dose dependent manner. Drosophila sensitivity to cisplatin in both cell types is affected by genetic background. We show that mutation or RNAi-based knockdown of genes known to be associated with CIPN incidence in humans affect sensitivity of flies to CIPN. Drosophila is a promising model with which to study the effect of genetics on sensitivity to CIPN.

Ubiquitination and adaptive responses to BRAF inhibitors in Melanoma

Response to targeted therapies is limited by the activation or inhibition of feedback loops. Here we report the ubiquitin specific peptidase 28/F-box WD repeat-containing protein 7 (USP28/FBW7) complex functions as a negative regulator of mitogen-activated protein kinase (MAPK) pathway by targeting v-raf murine sarcoma viral oncogene homolog B (BRAF) for degradation, a process which is lost in a large proportion of BRAF mutant melanoma patients, resulting in resistance to BRAF inhibitor therapies.

An efficient concordant integrative analysis of multiple large-scale two-sample expression data sets

Motivation

We have proposed a mixture model based approach to the concordant integrative analysis of multiple large-scale two-sample expression datasets. Since the mixture model is based on the transformed differential expression test P-values (z-scores), it is generally applicable to the expression data generated by either microarray or RNA-seq platforms. The mixture model is simple with three normal distribution components for each dataset to represent down-regulation, up-regulation and no differential expression. However, when the number of datasets increases, the model parameter space increases exponentially due to the component combination from different datasets.

Results

In this study, motivated by the well-known generalized estimating equations (GEEs) for longitudinal data analysis, we focus on the concordant components and assume that the proportions of non-concordant components follow a special structure. We discuss the exchangeable, multiset coefficient and autoregressive structures for model reduction, and their related expectation-maximization (EM) algorithms. Then, the parameter space is linear with the number of datasets. In our previous study, we have applied the general mixture model to three microarray datasets for lung cancer studies. We show that more gene sets (or pathways) can be detected by the reduced mixture model with the exchangeable structure. Furthermore, we show that more genes can also be detected by the reduced model. The Cancer Genome Atlas (TCGA) data have been increasingly collected. The advantage of incorporating the concordance feature has also been clearly demonstrated based on TCGA RNA sequencing data for studying two closely related types of cancer.

Availability and Implementation

Additional results are included in a supplemental file. Computer program R-functions are freely available at http://home.gwu.edu/∼ylai/research/Concordance.

Contact

ylai@gwu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Clinical and molecular predictors of long-term response in HER2 positive metastatic breast cancer patients

BACKGROUND

HER2+ metastatic breast cancer (MBC) is a poor prognosis disease, unusually curable. To date, no predictive factors have been clearly correlated with long-term response to anti-HER2 agents.

METHODS

54 HER2+ MBC patients treated with HER2 targeted therapy as first line treatment were analysed: 40 with a time to progression longer than 3 years in Long Responders (LR) group and 14 with a progression disease within one year of anti-HER2 therapy in a control group named Early Progressors (EP). The expression of 770 genes and 13 molecular pathways were evaluated using Nanostring PanCancer pathway panel performed on FFPE BC tissues.

RESULTS

Considering baseline patients and tumor characteristics, EP women had more CNS spread and more metastatic burden of disease compared to LR (p > 0.05). Gene expression analysis identified 30 genes with significantly different expression in the two cohorts; five were driver genes (BRCA1, PDGFRA, AR, PHF6 and MSH2). The majority of these genes were over-expressed, mainly in LR patients, and encoded growth factors, pro- or anti-inflammatory interleukins and DNA repair factors. Only four genes were down regulated, all in EP group (TNFSF10, CACNG1, IL20RB and BRCA1). Most of these genes were involved in MAPK and PI3K pathways. MAPK pathway was differently expressed between LR and EP (p = 0.05). PI3K was the only pathway overexpressed in EP patients.

CONCLUSIONS

Whole genome expression analysis comparing LR vs. EP identified a group of genes that may predict more favourable long-term outcomes. Up-regulation of MAPK and down-regulation of PI3K pathway could be a positive predictive factors. Further clinical implications are warranted.

ABBREVIATIONS

BC: breast cancer; MBC: metastatic breast cancer; LR: long responder; EP: early progressor; FFPE: formalin-fixed paraffin-embedded; CNS: central nervous system; PFS: progression free survival; OS: overall survival.

Telomere Maintenance Mechanisms in Cancer

Tumour cells can adopt telomere maintenance mechanisms (TMMs) to avoid telomere shortening, an inevitable process due to successive cell divisions. In most tumour cells, telomere length (TL) is maintained by reactivation of telomerase, while a small part acquires immortality through the telomerase-independent alternative lengthening of telomeres (ALT) mechanism. In the last years, a great amount of data was generated, and different TMMs were reported and explained in detail, benefiting from genome-scale studies of major importance. In this review, we address seven different TMMs in tumour cells: mutations of the TERT promoter (TERTp), amplification of the genes TERT and TERC, polymorphic variants of the TERT gene and of its promoter, rearrangements of the TERT gene, epigenetic changes, ALT, and non-defined TMM (NDTMM). We gathered information from over fifty thousand patients reported in 288 papers in the last years. This wide data collection enabled us to portray, by organ/system and histotypes, the prevalence of TERTp mutations, TERT and TERC amplifications, and ALT in human tumours. Based on this information, we discuss the putative future clinical impact of the aforementioned mechanisms on the malignant transformation process in different setups, and provide insights for screening, prognosis, and patient management stratification.

ncRNA-disease association prediction based on sequence information and tripartite network

Background

Current technology has demonstrated that mutation and deregulation of non-coding RNAs (ncRNAs) are associated with diverse human diseases and important biological processes. Therefore, developing a novel computational method for predicting potential ncRNA-disease associations could benefit pathologists in understanding the correlation between ncRNAs and disease diagnosis, treatment, and prevention. However, only a few studies have investigated these associations in pathogenesis.

Results

This study utilizes a disease-target-ncRNA tripartite network, and computes prediction scores between each disease-ncRNA pair by integrating biological information derived from pairwise similarity based upon sequence expressions with weights obtained from a multi-layer resource allocation technique. Our proposed algorithm was evaluated based on a 5-fold-cross-validation with optimal kernel parameter tuning. In addition, we achieved an average AUC that varies from 0.75 without link cut to 0.57 with link cut methods, which outperforms a previous method using the same evaluation methodology. Furthermore, the algorithm predicted 23 ncRNA-disease associations supported by other independent biological experimental studies.

Conclusions

Taken together, these results demonstrate the capability and accuracy of predicting further biological significant associations between ncRNAs and diseases and highlight the importance of adding biological sequence information to enhance predictions.

Genetic variants in TERT are associated with risk of gastric cancer in a Chinese Han population

Telomerase reverse transcriptase (TERT) is a gene within the cancer susceptibility region located at Chr5p15.33, which is associated with multiple cancer types. In this study, we validated the association between TERT polymorphisms and gastric cancer (GC) risk with a case-control study in a Chinese Han population. A total of 302 GC patients and 300 control individuals were recruited. We identified three single nucleotide polymorphisms (SNPs) in TERT that were associated with GC. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated in logistic regression models after adjusting for age and gender to assess the association. The minor alleles of three SNPs were associated with increased GC risk inallelic model analysis. For two of the SNPs, rs10069690 and rs2853676,, the dominant and additive model frequencies were higher in GC cases compared to controls. Further haplotype analysis revealed a protective effect of haplotype “CG” of the TERT gene, while the haplotype “TA” increased GC risk.Our resultsprovide new evidence for the association between TERT and GC susceptibility in the Chinese Han population.

Identification of Pleiotropic Cancer Susceptibility Variants from Genome-Wide Association Studies Reveals Functional Characteristics

Background: There exists compelling evidence that some genetic variants are associated with the risk of multiple cancer sites (i.e., pleiotropy). However, the biological mechanisms through which the pleiotropic variants operate are unclear.Methods: We obtained all cancer risk associations from the National Human Genome Research Institute-European Bioinformatics Institute GWAS Catalog, and correlated cancer risk variants were clustered into groups. Pleiotropic variant groups and genes were functionally annotated. Associations of pleiotropic cancer risk variants with noncancer traits were also obtained.Results: We identified 1,431 associations between variants and cancer risk, comprised of 989 unique variants associated with 27 unique cancer sites. We found 20 pleiotropic variant groups (2.1%) composed of 33 variants (3.3%), including novel pleiotropic variants rs3777204 and rs56219066 located in the ELL2 gene. Relative to single-cancer risk variants, pleiotropic variants were more likely to be in genes (89.0% vs. 65.3%, P = 2.2 × 10-16), and to have somewhat larger risk allele frequencies (median RAF = 0.49 versus 0.39, P = 0.046). The 27 genes to which the pleiotropic variants mapped were suggestive for enrichment in response to radiation and hypoxia, alpha-linolenic acid metabolism, cell cycle, and extension of telomeres. In addition, we observed that 8 of 33 pleiotropic cancer risk variants were associated with 16 traits other than cancer.Conclusions: This study identified and functionally characterized genetic variants showing pleiotropy for cancer risk.Impact: Our findings suggest biological pathways common to different cancers and other diseases, and provide a basis for the study of genetic testing for multiple cancers and repurposing cancer treatments. Cancer Epidemiol Biomarkers Prev; 27(1); 75-85. ©2017 AACR.

"Stealth dissemination" of macrophage-tumor cell fusions cultured from blood of patients with pancreatic ductal adenocarcinoma

Here we describe isolation and characterization of macrophage-tumor cell fusions (MTFs) from the blood of pancreatic ductal adenocarcinoma (PDAC) patients. The MTFs were generally aneuploidy, and immunophenotypic characterizations showed that the MTFs express markers characteristic of PDAC and stem cells, as well as M2-polarized macrophages. Single cell RNASeq analyses showed that the MTFs express many transcripts implicated in cancer progression, LINE1 retrotransposons, and very high levels of several long non-coding transcripts involved in metastasis (such as MALAT1). When cultured MTFs were transplanted orthotopically into mouse pancreas, they grew as obvious well-differentiated islands of cells, but they also disseminated widely throughout multiple tissues in "stealth" fashion. They were found distributed throughout multiple organs at 4, 8, or 12 weeks after transplantation (including liver, spleen, lung), occurring as single cells or small groups of cells, without formation of obvious tumors or any apparent progression over the 4 to 12 week period. We suggest that MTFs form continually during PDAC development, and that they disseminate early in cancer progression, forming "niches" at distant sites for subsequent colonization by metastasis-initiating cells.

Quantifying the Genetic Correlation between Multiple Cancer Types

Background: Many cancers share specific genetic risk factors, including both rare high-penetrance mutations and common SNPs identified through genome-wide association studies (GWAS). However, little is known about the overall shared heritability across cancers. Quantifying the extent to which two distinct cancers share genetic origin will give insights to shared biological mechanisms underlying cancer and inform design for future genetic association studies.Methods: In this study, we estimated the pair-wise genetic correlation between six cancer types (breast, colorectal, lung, ovarian, pancreatic, and prostate) using cancer-specific GWAS summary statistics data based on 66,958 case and 70,665 control subjects of European ancestry. We also estimated genetic correlations between cancers and 14 noncancer diseases and traits.Results: After adjusting for 15 pair-wise genetic correlation tests between cancers, we found significant (P < 0.003) genetic correlations between pancreatic and colorectal cancer (rg = 0.55, P = 0.003), lung and colorectal cancer (rg = 0.31, P = 0.001). We also found suggestive genetic correlations between lung and breast cancer (rg = 0.27, P = 0.009), and colorectal and breast cancer (rg = 0.22, P = 0.01). In contrast, we found no evidence that prostate cancer shared an appreciable proportion of heritability with other cancers. After adjusting for 84 tests studying genetic correlations between cancer types and other traits (Bonferroni-corrected P value: 0.0006), only the genetic correlation between lung cancer and smoking remained significant (rg = 0.41, P = 1.03 × 10-6). We also observed nominally significant genetic correlations between body mass index and all cancers except ovarian cancer.Conclusions: Our results highlight novel genetic correlations and lend support to previous observational studies that have observed links between cancers and risk factors.Impact: This study demonstrates modest genetic correlations between cancers; in particular, breast, colorectal, and lung cancer share some degree of genetic basis. Cancer Epidemiol Biomarkers Prev; 26(9); 1427-35. ©2017 AACR.

Pathway-analysis of published genome-wide association studies of lung cancer: A potential role for the CYP4F3 locus

The fatty acids (FAs) metabolism is suggested to play a pivotal role in the development of lung cancer, and we explored that by conducting a pathway-based analysis. We performed a meta-analysis of published datasets of six genome wide association studies (GWASs) from the Transdisciplinary Research in Cancer of the Lung (TRICL) consortium, which included 12 160 cases with lung cancer and 16 838 cancer-free controls. A total of 30 722 single-nucleotide polymorphisms (SNPs) from 317 genes relevant to FA metabolic pathways were identified. An additional dataset from the Harvard Lung Cancer Study with 984 cases and 970 healthy controls was also added to the final meta-analysis. In the initial meta-analysis, 26 of 28 SNPs that passed false discovery rate multiple tests were mapped to the CYP4F3 gene. Among the 26 top ranked hits was a proxy SNP, CYP4F3 rs4646904 (P = 8.65 × 10-6 , FDR = 0.018), which is suggested to change splicing pattern/efficiency and to be associated with gene expression levels. However, after adding data of rs4646904 from the Harvard GWAS, the significance in the combined analysis was reduced to P = 3.52 × 10-3 [odds ratio (OR) = 1.07, 95% confidence interval (95%CI) = 1.03-1.12]. Interestingly, the small Harvard dataset also pointed to the same direction of the association in subgroups of smokers (OR = 1.07) and contributed to a combined OR of 1.13 (95% CI = 1.06-1.20, P = 6.70 × 10-5 ). The results suggest that a potentially functional SNP in CYP4F3 (rs4646904) may contribute to the etiology of lung cancer, especially in smokers. Additional mechanistic studies are warranted to unravel the potential biological significance of the finding.

Genetic polymorphisms in TERT are associated with increased risk of esophageal cancer

Single nucleotide polymorphisms (SNPs) in TERT may be associated with susceptibility to esophageal cancer. In this study, we analyzed the association between TERT SNPs and risk of esophageal cancer in 386 esophageal cancer patients and 495 healthy subjects from the Xi'an area of China. Of the four SNPs examined, rs10069690 and rs2242652 were correlated with esophageal cancer risk. Additionally, after adjusting for age and gender, the "Trs10069690Ars2242652", "Trs10069690Grs2242652" haplotypes were associated with an increased risk of esophageal cancer, while the and "Crs10069690Grs2242652" haplotype was associated with a decreased risk of esophageal cancer. These findings suggest that TERT polymorphisms may contribute to the development of esophageal cancer.

Association between GWAS-identified lung adenocarcinoma susceptibility loci and EGFR mutations in never-smoking Asian women, and comparison with findings from Western populations

To evaluate associations by EGFR mutation status for lung adenocarcinoma risk among never-smoking Asian women, we conducted a meta-analysis of 11 loci previously identified in genome-wide association studies (GWAS). Genotyping in an additional 10,780 never-smoking cases and 10,938 never-smoking controls from Asia confirmed associations with eight known single nucleotide polymorphisms (SNPs). Two new signals were observed at genome-wide significance (P < 5 × 10-8), namely, rs7216064 (17q24.3, BPTF), for overall lung adenocarcinoma risk, and rs3817963 (6p21.3, BTNL2) which is specific to cases with EGFR mutations. In further sub-analyses by EGFR status, rs9387478 (ROS1/DCBLD1) and rs2179920 (HLA-DPB1) showed stronger estimated associations in EGFR-positive compared to EGFR-negative cases. Comparison of the overall associations with published results in Western populations revealed that the majority of these findings were distinct, underscoring the importance of distinct contributing factors for smoking and non-smoking lung cancer. Our results extend the catalogue of regions associated with lung adenocarcinoma in non-smoking Asian women and highlight the importance of how the germline could inform risk for specific tumour mutation patterns, which could have important translational implications.

Landscape of Pleiotropic Proteins Causing Human Disease: Structural and System Biology Insights

Pleiotropy is the phenomenon by which the same gene can result in multiple phenotypes. Pleiotropic proteins are emerging as important contributors to rare and common disorders. Nevertheless, little is known on the mechanisms underlying pleiotropy and the characteristic of pleiotropic proteins. We analyzed disease-causing proteins reported in UniProt and observed that 12% are pleiotropic (variants in the same protein cause more than one disease). Pleiotropic proteins were enriched in deleterious and rare variants, but not in common variants. Pleiotropic proteins were more likely to be involved in the pathogenesis of neoplasms, neurological, and circulatory diseases and congenital malformations, whereas non-pleiotropic proteins in endocrine and metabolic disorders. Pleiotropic proteins were more essential and had a higher number of interacting partners compared with non-pleiotropic proteins. Significantly more pleiotropic than non-pleiotropic proteins contained at least one intrinsically long disordered region (P < 0.001). Deleterious variants occurring in structurally disordered regions were more commonly found in pleiotropic, rather than non-pleiotropic proteins. In conclusion, pleiotropic proteins are an important contributor to human disease. They represent a biologically different class of proteins compared with non-pleiotropic proteins and a better understanding of their characteristics and genetic variants can greatly aid in the interpretation of genetic studies and drug design.

Considerations for the combination of anticancer vaccines and immune checkpoint inhibitors

INTRODUCTION

Over the past few years, trials evaluating immunotherapies, particularly immune checkpoint inhibitors, have revolutionized the standard model of cancer treatment, demonstrating significant antitumor responses and improved clinical outcomes across a wide array of tumors types. Yet, despite these compelling data, a major limitation has been that only a fraction of patients mount a response to single-agent immune checkpoint inhibition. However, a growing amount of preclinical and clinical data suggests that combining immune checkpoint inhibition, either with other immune checkpoint inhibitors or with therapeutic cancer vaccines, has the potential to improve the proportion of patients seeing long-term durable responses with these therapies.

AREAS COVERED

We have reviewed the reported data on immune checkpoint inhibition as monotherapy and as combination therapy with other immune checkpoint inhibitors or therapeutic cancer vaccines. Data is reviewed on agents with FDA approval or breakthrough designation as of the writing of this manuscript.

EXPERT OPINION

Particular focus is given to the combination of immune checkpoint inhibitors and therapeutic cancer vaccines which has the potential to increase efficacy compared to single agent immune checkpoint inhibition with minimal added toxicity.

Unravelling the human genome-phenome relationship using phenome-wide association studies

Advances in genotyping technology have, over the past decade, enabled the focused search for common genetic variation associated with human diseases and traits. With the recently increased availability of detailed phenotypic data from electronic health records and epidemiological studies, the impact of one or more genetic variants on the phenome is starting to be characterized both in clinical and population-based settings using phenome-wide association studies (PheWAS). These studies reveal a number of challenges that will need to be overcome to unlock the full potential of PheWAS for the characterization of the complex human genome-phenome relationship.

Tankyrase 1 polymorphism associated with an increased risk in developing non-small cell lung cancer in a Chinese population: a proof-of-principle study

OBJECTIVES

Tankyrase 1 (TNKS1), a poly (ADP-ribose) polymerase, regulates telomere length and apoptosis in cells, overexpression of which occurred in non-small cell lung cancer (NSCLC). This study investigated TNKS1 single-nucleotide polymorphisms (SNPs) for association with a risk in NSCLC development in a Chinese population.

METHODS

NSCLC cases and healthy controls of 500 each were recruited for genotyping of 24 TNKS1 SNPs. The association between genotype and NSCLC risk was evaluated by computing the odds ratio (OR) and 95% confidence interval (CI) with multivariate unconditional logistic regression analyses. Haploview software was to analyze association between haplotypes and NSCLC risk.

RESULTS

TNKS1 rs6601328 A allele was associated with a lower risk in developing NSCLC and adenocarcinoma (ADC) (OR=0.71; 95% CI, 0.51-0.99 and OR=0.70; 95% CI, 0.50-0.99), whereas TNKS1 rs11991621 C allele (OR=1.44; 95% CI, 1.03-2.03), rs11991621 C/C (OR=1.44, 95% CI, 1.03-2.35; P=0.03), and rs10503380 G/G (OR= 1.56, 95% CI, 1.09-2.50, P=0.02) were associated with a higher risk in developing NSCLC or ADC in females and rs6601328 A/A major allele (OR=1.39; 95% CI, 1.00-1.92; P=0.047) and rs7015700 G/G (OR= 1.51, 95% CI, 1.04-2.21) was associated with an increased NSCLC or ADC risk in males but a reduced NSCLC risk (OR=0.63; 95% CI, 0.42-0.96) and ADC risk (OR=0.64; 95% CI, 0.42-0.97) in females. Haploview showed that there were three Haplotype Blocks associated with NSCLC risk. However, TNKS1 rs12541709 C/C was associated with protective effect against ADC (OR=0.75; 95% CI, 0.56-0.99; P=0.04) in this Chinese population.

CONCLUSION

TNKS1 SNPs (rs11991621 rs10503380, and rs7015700) were associated with NSCLC risk, whereas rs6601328 and rs12541709 inversely associated with NSCLC or ADC risk in this Chinese population.

The detection and characterization of pleiotropy: discovery, progress, and promise

The impact of a single genetic locus on multiple phenotypes, or pleiotropy, is an important area of research. Biological systems are dynamic complex networks, and these networks exist within and between cells. In humans, the consideration of multiple phenotypes such as physiological traits, clinical outcomes and drug response, in the context of genetic variation, can provide ways of developing a more complete understanding of the complex relationships between genetic architecture and how biological systems function in health and disease. In this article, we describe recent studies exploring the relationships between genetic loci and more than one phenotype. We also cover methodological developments incorporating pleiotropy applied to model organisms as well as humans, and discuss how stepping beyond the analysis of a single phenotype leads to a deeper understanding of complex genetic architecture.

Leveraging Epidemiologic and Clinical Collections for Genomic Studies of Complex Traits

BACKGROUND/AIMS

Present-day limited resources demand DNA and phenotyping alternatives to the traditional prospective population-based epidemiologic collections.

METHODS

To accelerate genomic discovery with an emphasis on diverse populations, we--as part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study--accessed all non-European American samples (n = 15,863) available in BioVU, the Vanderbilt University biorepository linked to de-identified electronic medical records, for genomic studies as part of the larger Population Architecture using Genomics and Epidemiology (PAGE) I study. Given previous studies have cautioned against the secondary use of clinically collected data compared with epidemiologically collected data, we present here a characterization of EAGLE BioVU, including the billing and diagnostic (ICD-9) code distributions for adult and pediatric patients as well as comparisons made for select health metrics (body mass index, glucose, HbA1c, HDL-C, LDL-C, and triglycerides) with the population-based National Health and Nutrition Examination Surveys (NHANES) linked to DNA samples (NHANES III, n = 7,159; NHANES 1999-2002, n = 7,839).

RESULTS

Overall, the distributions of billing and diagnostic codes suggest this clinical sample is a mixture of healthy and sick patients like that expected for a contemporary American population.

CONCLUSION

Little bias is observed among health metrics, suggesting this clinical collection is suitable for genomic studies along with traditional epidemiologic cohorts.

Association between the TERT Genetic Polymorphism rs2853676 and Cancer Risk: Meta-Analysis of 76,108 Cases and 134,215 Controls

Background

Several recent studies have identified that the TERT genetic polymorphism rs2853676 is associated with cancer risk, but presented inconsistent results. We investigated these inconclusive results by performing a meta-analysis to systematically evaluate the association.

Methods

We conducted a search in PubMed, Google Scholar and ISI Web of Science to select studies on the association between TERT rs2853676 and cancer risk. We conducted a stratified analysis using cancer type, ethnicity and source of controls. We calculated the odds ratios (OR) and 95% confidence intervals (CI). Article quality, heterogeneity, sensitivity, publication bias and statistical power were also assessed.

Results

26 articles covering 76 108 cases and 134 215 controls met our inclusion criteria. A significant association between TERT rs2853676 allele A and cancer susceptibility was demonstrated under a per-allele risk analysis (OR = 1.08, 95% CI = 1.04-1.13). Stratification analysis revealed an increased cancer risk in subgroups of glioma, lung cancer and ovarian cancer. No significant increase was found in melanoma, breast cancer, pancreatic cancer and colorectal cancer. In a subgroup analysis of lung cancer, a statistically significant increase was only observed in adenocarcinoma. Moreover, a stratified analysis performed for ethnic groups revealed that the significant increase was only observed in Caucasians, whereas a non-significant increase was found in Asians.

Conclusions

This meta-analysis suggests that the TERT genetic polymorphism rs2853676 is associated with increased risk of glioma, lung adenocarcinoma and ovarian cancer among Caucasians. Further functional studies are warranted to validate this association and investigate further.