Deconvolution of the tumor-educated platelet transcriptome reveals activated platelet and inflammatory cell transcript signatures.

Tumor-educated platelets (TEPs) are a potential method of liquid biopsy for the diagnosis and monitoring of cancer. However, the mechanism underlying tumor education of platelets is not known, and transcripts associated with TEPs are often not tumor-associated transcripts. We demonstrated that direct tumor transfer of transcripts to circulating platelets is an unlikely source of the TEP signal. We used CDSeq, a latent Dirichlet allocation algorithm, to deconvolute the TEP signal in blood samples from patients with glioblastoma. We demonstrated that a substantial proportion of transcripts in the platelet transcriptome are derived from non-platelet cells, and the use of this algorithm allows the removal of contaminant transcripts. Furthermore, we used the results of this algorithm to demonstrate that TEPs represent a subset of more activated platelets, which also contain transcripts normally associated with non-platelet inflammatory cells, suggesting that these inflammatory cells, possibly in the tumor microenvironment, transfer transcripts to platelets that are then found in circulation. Our analysis suggests a useful and efficient method of processing TEP transcriptomic data to enable the isolation of a unique TEP signal associated with specific tumors.

Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants.

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

Molecular Dynamics Study of Structure, Folding, and Aggregation of Poly-PR and Poly-GR Proteins.

Poly-proline-arginine (poly-PR) and poly-glycine-arginine (poly-GR) proteins are believed to be the most toxic dipeptide repeat (DPR) proteins that are expressed by the hexanucleotide repeat expansion mutation in C9ORF72, which are associated with amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) diseases. Their structural information and mechanisms of toxicity remain incomplete, however. Using molecular dynamics simulation and all-atom model of proteins, we study folding and aggregation of both poly-PR and poly-GR. The results indicate formation of double-helix structure during the aggregation of poly-PR into dimers, whereas no stable aggregate is formed during the aggregation of poly-GR; the latter only folds into α-helix and double-helix structures that are similar to those formed in the folding of poly-glycine-alanine (poly-GA) protein. Our findings are consistent with the experimental data indicating that poly-PR and poly-GR are less likely to aggregate because of the hydrophilic arginine residues within their structures. Such characteristics could, however, in some respect facilitate migration of the DPR proteins between and within cells and, at the same time, give proline residues the benefits of activating the receptors that regulate ionotropic effect in neurons, resulting in death or malfunction of neurons because of the abnormal increase or decrease of the ion transmission. This may explain the neurotoxicities of poly-PR and poly-GR associated with many neurodegenerative diseases. To our knowledge, this is the first molecular dynamics simulation of the phenomena involving poly-PR and poly-GR proteins.

Molecular dynamics study of structure, folding, and aggregation of poly-glycine-alanine (Poly-GA).

Poly-glycine-alanine (poly-GA) proteins are widely believed to be one of the main toxic dipeptide repeat molecules associated with amyotrophic lateral sclerosis (ALS) and frontotemporal dementia diseases. Using discontinuous molecular dynamics simulation and an all-atom model of the proteins, we study folding, stability, and aggregation of poly-GA. The results demonstrate that poly-GA is an aggregation-prone protein that, after a long enough time, forms ß-sheet-rich aggregates that match recent experiment data and that two unique helical structures are formed very frequently, namely, ß-helix and double-helix. The details of the two structures are analyzed. The analysis indicates that such helical structures are stable and share the characteristics of both α-helices and ß-sheets. Molecular simulations indicate that identical phenomena also occur in the aggregation of poly-glycine-arginine (poly-GR). Therefore, we hypothesize that proteins of type (GX)n in which X may be any non-glycine amino acid and n is the repeat length may share the same folding structures of ß-helix and double-helix and that it is the glycine in the repeat that contributes the most to this characteristic. Molecular dynamics simulation with continuous interaction potentials and explicit water molecules as the solvent supports the hypothesis. To our knowledge, this is the first molecular dynamics simulation of the phenomena involving poly-GA and poly-GR proteins.