Genetically regulated expression underlies cellular sensitivity to chemotherapy in diverse populations

Genetic influence of meningioma on cisplatin resistance: a Mendelian randomization analysis

BACKGROUND

Although various aspects of cisplatin resistance have been studied, the impact of genetic variations still needs to be explored.

AIM

This study aimed to investigate the impact of cisplatin on meningiomas using a two-sample Mendelian randomization (MR) approach, employing genetic variants associated with cisplatin use as instrumental variables.

METHOD

We conducted a two-sample MR analysis using genome-wide association study (GWAS) data. Instrumental variables were derived from single-nucleotide polymorphisms (SNPs) associated with meningioma to estimate the causal relationship with cisplatin resistance. Sensitivity analyses were performed to confirm the findings.

RESULTS

Genetic predisposition to meningioma significantly increased the risk of cisplatin resistance (odds ratio (OR): 1.63; 95% confidence interval (CI) 1.44-1.85, P < 0.05). Sensitivity analyses supported the causal link.

CONCLUSION

This MR study suggests that genetic predisposition to meningioma increases susceptibility to cisplatin resistance. Further research is needed to uncover the mechanisms behind these causal effects.

AteMeVs: An R package for the estimation of the average treatment effect with measurement error and variable selection for confounders

In causal inference, the estimation of the average treatment effect is often of interest. For example, in cancer research, an interesting question is to assess the effects of the chemotherapy treatment on cancer, with the information of gene expressions taken into account. Two crucial challenges in this analysis involve addressing measurement error in gene expressions and handling noninformative gene expressions. While analytical methods have been developed to address those challenges, no user-friendly computational software packages seem to be available to implement those methods. To close this gap, we develop an R package, called AteMeVs, to estimate the average treatment effect using the inverse-probability-weighting estimation method to handle data with both measurement error and spurious variables. This developed package accommodates the method proposed by Yi and Chen (2023) as a special case, and further extends its application to a broader scope. The usage of the developed R package is illustrated by applying it to analyze a cancer dataset with information of gene expressions.

Enhancer mutations modulate the severity of chemotherapy-induced myelosuppression

Non-small cell lung cancer is often diagnosed at advanced stages, and many patients are still treated with classical chemotherapy. The unselective nature of chemotherapy often results in severe myelosuppression. Previous studies showed that protein-coding mutations could not fully explain the predisposition to myelosuppression. Here, we investigate the possible role of enhancer mutations in myelosuppression susceptibility. We produced transcriptome and promoter-interaction maps (using HiCap) of three blood stem-like cell lines treated with carboplatin or gemcitabine. Taking advantage of publicly available enhancer datasets, we validated HiCap results in silico and in living cells using epigenetic CRISPR technology. We also developed a network approach for interactome analysis and detection of differentially interacting genes. Differential interaction analysis provided additional information on relevant genes and pathways for myelosuppression compared with differential gene expression analysis at the bulk level. Moreover, we showed that enhancers of differentially interacting genes are highly enriched for variants associated with differing levels of myelosuppression. Altogether, our work represents a prominent example of integrative transcriptome and gene regulatory datasets analysis for the functional annotation of noncoding mutations.

Eleven metabolism‑related genes composed of Stard5 predict prognosis and contribute to EMT phenotype in HCC

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, with a high mortality and poor survival rate. Abnormal tumor metabolism is considered a hallmark of HCC and is a potential therapeutic target. This study aimed to identify metabolism-related biomarkers to evaluate the prognosis of patients with HCC.

METHOD

The Cancer Genome Atlas (TCGA) database was used to explore differential metabolic pathways based on high and low epithelial-mesenchymal transition (EMT) groupings. Genes in differential metabolic pathways were obtained for HCC metabolism-related molecular subtype analysis. Differentially expressed genes (DEGs) from the three subtypes were subjected to Lasso Cox regression analysis to construct prognostic risk models. Stard5 expression in HCC patients was detected by western blot and immunohistochemistry (IHC), and the role of Stard5 in the metastasis of HCC was investigated by cytological experiments.

RESULTS

Unsupervised clustering analysis based on metabolism-related genes revealed three subtypes in HCC with differential prognosis. A risk prognostic model was constructed based on 11 genes (STARD5, FTCD, SCN4A, ADH4, CFHR3, CYP2C9, CCL14, GADD45G, SOX11, SCIN, and SLC2A1) obtained by LASSO Cox regression analysis of the three subtypes of DEGs. We validated that the model had a good predictive power. In addition, we found that the high-risk group had a poor prognosis, higher proportion of Tregs, and responded poorly to chemotherapy. We also found that Stard5 expression was markedly decreased in HCC tissues, which was associated with poor prognosis and EMT. Knockdown of Stard5 contributed to the invasion and migration of HCC cells. Overexpression of Stard5 inhibited EMT in HCC cells.

CONCLUSION

We developed a new model based on 11 metabolism-related genes, which predicted the prognosis and response to chemotherapy or immunotherapy for HCC. Notably, we demonstrated for the first time that Stard5 acted as a tumor suppressor by inhibiting metastasis in HCC.

Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective

Artificial intelligence (AI) and other data-driven technologies hold great promise to transform healthcare and confer the predictive power essential to precision medicine. However, the existing biomedical data, which are a vital resource and foundation for developing medical AI models, do not reflect the diversity of the human population. The low representation in biomedical data has become a significant health risk for non-European populations, and the growing application of AI opens a new pathway for this health risk to manifest and amplify. Here we review the current status of biomedical data inequality and present a conceptual framework for understanding its impacts on machine learning. We also discuss the recent advances in algorithmic interventions for mitigating health disparities arising from biomedical data inequality. Finally, we briefly discuss the newly identified disparity in data quality among ethnic groups and its potential impacts on machine learning.

FinnGen provides genetic insights from a well-phenotyped isolated population

Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < 5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored1,2. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10-11) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.

Pharmacogenomic and Statistical Analysis

Genetic variants can alter response to drugs and other therapeutic interventions. The study of this phenomenon, called pharmacogenomics, is similar in many ways to other types of genetic studies but has distinct methodological and statistical considerations. Genetic variants involved in the processing of exogenous compounds exhibit great diversity and complexity, and the phenotypes studied in pharmacogenomics are also more complex than typical genetic studies. In this chapter, we review basic concepts in pharmacogenomic study designs, data generation techniques, statistical analysis approaches, and commonly used methods and briefly discuss the ultimate translation of findings to clinical care.

Protein prediction for trait mapping in diverse populations

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.

Genome-Wide Association Study Statistical Models: A Review

Statistical models are at the core of the genome-wide association study (GWAS). In this chapter, we provide an overview of single- and multilocus statistical models, Bayesian, and machine learning approaches for association studies in plants. These models are discussed based on their basic methodology, cofactors adjustment accounted for, statistical power and computational efficiency. New statistical models and machine learning algorithms are both showing improved performance in detecting missed signals, rare mutations and prioritizing causal genetic variants; nevertheless, further optimization and validation studies are required to maximize the power of GWAS.