False Discovery Rate Threshold Research Articles

MYC amplification segment-size mutual exclusivity analysis with other oncogenic events in a pan-cancer sequencing cohort.

3073 Background: MYC gene alterations resulting in increased copy number are recurrent oncogenic alterations across human malignancies. Presently no clear data exists to aid in interpreting MYC amplification characteristics for identifying MYC-dependent tumors. Methods: Analysis was conducted on a center-wide cohort of 68,920 tumor-normal matched samples from pediatric and adult patients sequenced with an FDA-authorized targeted sequencing panel (MSK-IMPACT). To ascertain tumor content, ploidy and allelic specific gene copy number, tumor-normal samples were analyzed with FACETS. Samples with tumor purity <20% were excluded. MYC amplification was defined as total copy number (TCN) minus ploidy ≥ 4. All samples were queried for presence of concurrent oncogenic or likely-oncogenic alterations based on OncoKb annotation. Co-occurrence (Co) and mutual-exclusivity (Me) analysis between MYC amplification according to segment size and other oncogenic alterations was performed with DISCOVER (false discovery rate threshold of 0.01) for each tumor type. Results: After removing low-quality samples (n=19,010/68,920), 49,910 samples were included in the analysis. 3,219 (6.4%) samples harbored MYC amplification, with segment size ranging from less than 100Kb to more than 100Mb, with 38% samples displaying focal amplifications shorter than 10 Mb and 62% broad amplifications longer than 10 Mb. TCN was inversely correlated with segment size (R= -0.34, p=2.2e-16). Triple-negative breast cancer (13.3%), hepatocellular carcinoma (8.7%), neuroendocrine tumors of the rectum (8.3%), high-grade serous ovarian cancer (7.6%) and large cell neuroendocrine carcinomas of the lung (6.7%) had the highest prevalence of MYC amplification without concurrent potentially driver oncogene mutations, amplifications or fusions. CoMe analysis demonstrated mutual exclusivity of MYC gene amplification regardless of segment size with most major tumor-type specific oncogenic events. All MYC amplifications were mutually exclusive with MET splice variants and mutations (q< 0.001) and KRAS mutations (q< 0.001) in lung adenocarcinoma (LUAD), FGFR3 mutations (q< 0.001) and fusions (q< 0.001) in bladder cancer, PTEN mutations (q< 0.001) in hormone receptor-positive breast cancer, KRAS mutations (q< 0.001) in colorectal cancer and CDK4 amplifications (q< 0.001) in sarcomas. Broad MYC amplifications were mutually exclusive with RET, ROS1 and ALK fusions in LUAD, while focal ones co-occurred with RET (q=0.007) and ALK fusions (q< 0.001) or secondary mutations (q< 0.001). Conclusions: This dataset defines a subgroup of MYC amplified cancers without co-occurring drivers and further subdivided by segment size, providing information that may help better identify patients for trials of novel MYC-directed therapies.

Aberrantly Expressed Hsa_circ_0060762 and CSE1L as Potential Peripheral Blood Biomarkers for ALS.

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive adult-onset neurodegenerative disease that is often diagnosed with a delay due to initial non-specific symptoms. Therefore, reliable and easy-to-obtain biomarkers are an absolute necessity for earlier and more accurate diagnostics. Circular RNAs (circRNAs) have already been proposed as potential biomarkers for several neurodegenerative diseases. In this study, we further investigated the usefulness of circRNAs as potential biomarkers for ALS. We first performed a microarray analysis of circRNAs on peripheral blood mononuclear cells of a subset of ALS patients and controls. Among the differently expressed circRNA by microarray analysis, we selected only the ones with a host gene that harbors the highest level of conservation and genetic constraints. This selection was based on the hypothesis that genes under selective pressure and genetic constraints could have a major role in determining a trait or disease. Then we performed a linear regression between ALS cases and controls using each circRNA as a predictor variable. With a False Discovery Rate (FDR) threshold of 0.1, only six circRNAs passed the filtering and only one of them remained statistically significant after Bonferroni correction: hsa_circ_0060762 and its host gene CSE1L. Finally, we observed a significant difference in expression levels between larger sets of patients and healthy controls for both hsa_circ_0060762 and CSE1L. CSE1L is a member of the importin β family and mediates inhibition of TDP-43 aggregation; the central pathogenicity in ALS and hsa_circ_0060762 has binding sites for several miRNAs that have been already proposed as biomarkers for ALS. In addition, receiver operating characteristics curve analysis showed diagnostic potential for CSE1L and hsa_circ_0060762. Hsa_circ_0060762 and CSE1L thus represent novel potential peripheral blood biomarkers and therapeutic targets for ALS.

Schizophrenia and co-morbidity risk: Evidence from a data driven phenomewide association study

Schizophrenia is a chronic debilitating psychiatric disorder with significant morbidity and mortality. In this study, we used information from 337,484 UK Biobank participants and performed PheWAS using schizophrenia genetic risk score on 1135 disease outcomes. Signals that passed the false discovery rate threshold were further analyzed for evidence on the causality of the association. We extended the analysis to 30 serum, four urine, and six neuroimaging biomarkers to identify biomarkers that could be affected by schizophrenia. Schizophrenia GRS was associated with 54 (39 distinct) disease outcomes including schizophrenia in the PheWAS analysis. Of these, a causal association were found with 10 distinct diseases in the MR analysis. Schizophrenia causally linked with higher odds of anxiety (OR = 1.41, 95%CI 1.12 to 1.21), bipolar disorder (OR = 1.52, 95%CI 1.36 to 1.70), major depressive disorder (OR = 1.12, 95%CI 1.08 to 1.16) and suicidal ideation (OR = 1.30, 95%CI 1.19 to 1.42). Lower odds were found for several diseases including type 1 diabetes, coronary atherosclerosis and some musculoskeletal disorders. In analyses using biomarkers, schizophrenia was associated with lower serum 25(OH)D, gamma glutamyltransferase, cystatin C, serum creatinine. However, we did not find association with any of the brain imaging markers. Our analyses confirmed the co-existence of schizophrenia with other mental health disorders but did not otherwise suggest strong effects on disease risk. Biomarker analyses reflected associations which could be explained by unhealthy lifestyles, suggesting patients with schizophrenia may benefit from screening for and managing broader health aspects.

Abstract 6770: Spatial characterization of immune microenvironment from early onset metastatic colorectal cancer (EOmCRC) showed a dual prognostic role for IDO1 expression

Abstract Background: Epidemiological studies demonstrate that the incidence of CRC in young adults is continually rising in both Europe and the USA. The differences between the spatial distribution of immune related markers (IRM) and their prognostic role in EOmCRC compared to late onset (LOmCRC) remains to be elucidated. Methods: Forty paraffin embedded formalin fixed unstained sections from patients (pts) diagnosed with either EOmCRC (≤45) or (≥50) LOmCRC, matched for chemotherapy (chx) and molecular features, all with microsatellite stable disease were included. All tissue samples were obtained from pts enrolled in SPECTAcolor (EORTC-40CRC-GITCG) platform trial. The expression of IRM was studied using the GeoMX Digital Spatial Profiler (DSP). Fifty-seven IRMs were used and three morphology markers (MMs) anti-CD45, pan-cytokeratin and SYTO 13. Three 300 μm regions of interest (ROIs) were selected within the core of the tumour and further 3 ROIs within the active margin of the tumour. Each ROI was segmented based on MMs to obtain a tumour microenvironment (TME) and a tumour (T) mask. The analysis used the GeoMX DSP Advanced Analysis Suite software, R and IBM SPSS. Control for multiple comparisons use a false discovery rate (FDR) threshold of 0.05. Results: Samples from 40 pts were collected; 19 (47.5%) were EOmCRCs; 16 (40%) were female; 23 (57.5%) with pathological stage IV. The majority being, 23 (57.5%) grade ≥ 2, left sided 26 (65%), oxaliplatin based first line chx 23 (57.5%). Ten (25%) were KRAS mutant (mt), 1 (2.5%) NRAS mt and 1 (2.5%) BRAF mt. The median overall (OS) and progression free survival (PFS) for the entire cohort was 36.6 months (m) 95%CI (33.5 - 39.7) and 11.7 m (8.1 - 15.3), respectively.The <0.05 FDR differential expression analysis based on tissue segmentation uncovered 9 hits when comparing the TME between different age groups and 2 hits when comparing T segments, all overexpressed in LOmCRC. Indoleamine 2,3-dioxygenase 1 (IDO1) was differentially overexpressed in TME (adjusted p=0.043) and T (adjusted p=0.042) for LOmCRC.In the EOmCRC group IDO1 relative high expression in T was an independent marker associated with worse survival outcomes after multivariate cox regression corrected for the rest of significant markers (MVCC) for OS, HR: 7.01*105 95%CI (9.08*105-5.41*1010), p=0.003 and PFS, HR:7.7*105 95%CI (1.96-30.3*106), p=0.029. However, interestingly high IDO1 in TME was an independent marker associated with better OS, HR: 0.02 95%CI (0.01-0.46), p=0.015. Conclusion: Differential expression analysis of IRMs found IDO1 as differentially expressed in both segments (T and TME). Furthermore, high IDO1 expression seemed to have a dual prognostic role depending on the segment analysed, showing worse OS when found in the tumour but better OS when found in tumour microenvironment for early onset mCRC. Citation Format: Raghavendar Thyagaraja Nagaraju, Jakub Franczak, Markus Möhler, Elisa Fontana, Anne Giraut, Lieve Dirix, Jose Casas-Martin, Beatrice Borelli, Florian Lordick, Elizabeth Smith, Sabine Tejpar, Gunnar Folprecht, Jorge Barriuso. Spatial characterization of immune microenvironment from early onset metastatic colorectal cancer (EOmCRC) showed a dual prognostic role for IDO1 expression. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6770.

Abstract 3168: DE-meta: Revealing tumor gene expression by meta-analysis of RNASeq and proteomics datasets

Abstract Detecting differentially expressed genes under different biological conditions is crucial to characterize mechanisms of cancer development and identifying determinants of patient outcome. High throughput technologies such as RNA sequencing and mass spectrometry-based proteomics have been widely used to identify differentially expressed genes (DEG), on a transcript and on a peptide basis, respectively. However, both transcription and translation of genes provide information about gene expression. Thus, leveraging both RNA and protein expression data could potentially produce more accurate results. Various statistical tools have been developed to tackle the differential expression problem for a single platform, such as edgeR, DESeq2, etc. However, a tool that integrates both transcriptome and proteomics data for differential expression analysis has not yet been developed. Meta-analysis can potentially increase statistical power and generate a more consensus conclusion by combining multiple datasets. Here we present DE-Meta, a new tool developed using R, which performs combined meta-analysis of RNA-seq and MS-based proteomics on matched tumor specimens. To characterize the performance of our method using real world data, we utilized a published lung cancer dataset and set a truth standard of DEG between known two expression subtypes: terminal respiratory unit (TRU) and proximal proliferative (PP). Briefly, we restricted to 7,458 common genes detected by RNA-seq and MS-based proteomics. We then, randomly selected tumors as the truth sample set, n=20 TRU and n=20 PP tumors. With this truth sample set, DEGs were calculated separately by RNA or by protein, using DESeq2 and regression models. The union of these DEGs defined the truth DEG set. Using independent tumors (n=10 TRU and n=10 PP), we applied DE-meta to detect DEGs by RNA, by protein or joint RNA and protein data. Through receiver operating characteristic curve analysis, we show that the DE-meta joint model performed better than the single platform models (joint AUC=0.651, RNA AUC=0.610, protein AUC=0.623). Focusing genes relevant to tumor biology, we found that the DE-meta joint model called an 169 cancer-relevant DEGs, such as MSH2, CD4, WDR3, PIK3R1, and RPS6KA3. which were not detected by the RNA or protein models, at the same false discovery rate threshold of 0.05 (Benjamini-Hochberg procedure). In summary, our new DE-meta tool, by joint analysis of RNA-seq and MS-based proteomics datasets, yields more accurate and biologically-relevant results. The views expressed in this abstract are solely of the authors and do not reflect the official policy of the Departments of Army/Navy/Air Force, Department of Defense, USUHS, HJF, or U.S. Government. Citation Format: Xijun Zhang, Clifton L. Dalgard, Matthew D. Wilkerson. DE-meta: Revealing tumor gene expression by meta-analysis of RNASeq and proteomics datasets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3168.

Abstract HER2-01: HER2-01 Clinical and Molecular Characteristics of HER2-low/zero Early Stage Triple-Negative Breast Cancer

Abstract Background: In the metastatic setting, low HER2 expression is associated with clinical benefit from trastuzumab deruxtecan, a HER2-targeting antibody drug conjugates. However, little is known about the biological significance of low HER2 expression in patients with early stage triple-negative breast cancer (TNBC) receiving neoadjuvant therapy (NAT). Methods: Out of 595 patients with stage I-III TNBC enrolled on the prospective ARTEMIS trial (NCT02276443) from 2015-2021, we identified 367 patients with available HER2 immunohistochemistry (IHC) results on pre-NAT tumor tissue (HER2-zero: n=218; HER2-low [IHC 1+, 2+]: n=149). All patients were treated with anthracycline-based NAT. In cases where sufficient pre-NAT tumor tissue were available, additional IHC and/or RNAseq were performed. Differential gene expression (DGE) and pathway analysis were performed using DEseq2. Gene set enrichment analysis (GSEA) was performed using the Hallmark gene sets. Deconvolution analyses were performed using CIBERSORT. We controlled for multiple hypothesis using a false discovery rate (FDR) threshold with the Benjamini-Hochberg method, accepting as significant genes with at least a 2-fold change and < 5% FDR. Results: Table 1 summarizes baseline clinicopathological features of the 367 patients. Compared to HER2-zero tumors, HER2-low tumors were less likely of metaplastic histology (p=0.001), associated with lower Ki67 (p=0.017) and were more likely to be androgen receptor (AR)-positive (p=0.01). There were no significant differences in tumor-infiltrating lymphocytes (TILs) infiltration and PD-L1 expression between HER2-zero and HER2-low tumors. Among the 226 patients with sufficient pre-NAT tissue for RNAseq, DGE analyses demonstrated upregulation of genes involved in fatty acid metabolism (ACSM1) and steroid hormone metabolism (DHRS2, UGT2B28) in HER2-low tumors compared with HER2-zero tumors. Deconvolution analyses revealed no significant differences between predicted proportions of immune cell subpopulations between HER2-low and HER2-zero tumors. Although rates of pCR were not significantly different between patients with HER2-zero (46%) and HER2-low tumors (40%) (p=0.34), non-pCR in patients with HER2-low tumors was associated with increased expression of EREG, which encodes an EGFR ligand, while non-pCR in patients with HER2-zero tumors was associated with downregulation in genes involved in immune response pathways. GSEA further identified the Hallmark allograft rejection (FDR q=0.001), interferon gamma response (FDR q=0.002), and interferon alpha response pathways (FDR q=0.007) as the 3 most significantly downregulated pathways in HER2-zero tumors from patients experiencing a non-pCR relative to HER2-zero tumors from patients experiencing a pCR. Conclusion: In early stage TNBC, low HER2 expression is associated with increased AR expression and upregulation of genes associated with fatty acid and steroid hormone metabolism. Gene expression analyses suggest that drivers of resistance to NAT differ between HER2-low and HER2-zero tumors. Biological differences between HER2-zero and HER2-low tumors exist and may influence future personalized treatment for patients with early stage TNBC. Citation Format: Clinton Yam, Ziyi Li, Anil Korkut, Wencai Ma, Elisabeth Kong, Holly A. Hill, Hussein Abbas, Sausan Abouharb, Beatriz Adrada, Banu K. Arun, Carlos H. Barcenas, Ajit Bisen, Daniel Booser, Aman Buzdar, Rosalind Candelaria, Junjie Chen, Alyson Clayborn, Senthil Damodaran, Qingqing Ding, Haven Garber, Gabriel N. Hortobagyi, Kelly K. Hunt, Nuhad K. Ibrahim, Adaeze Iheme, Meghan S. Karuturi, Kimberly Koenig, Rachel M. Layman, Jangsoon Lee, Jennifer K. Litton, Melissa Mitchell, Giancarlo Moscol, Jason Mouabbi, Rashmi K. Murthy, Oluchi Oke, Paula Pohlmann, David Ramirez, Elizabeth Ravenberg, Sadia Saleem, Mediget Teshome, Vicente Valero, Jason White, Madison Williams, Wendy Woodward, Chasity Yajima, Naoto T. Ueno, Ken Chen, Gaiane Rauch, Lei Huo, Debu Tripathy. HER2-01 Clinical and Molecular Characteristics of HER2-low/zero Early Stage Triple-Negative Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr HER2-01.

Semisupervised Machine Learning for Sensitive Open Modification Spectral Library Searching

A key analysis task in mass spectrometry proteomics is matching the acquired tandem mass spectra to their originating peptides by sequence database searching or spectral library searching. Machine learning is an increasingly popular postprocessing approach to maximize the number of confident spectrum identifications that can be obtained at a given false discovery rate threshold. Here, we have integrated semisupervised machine learning in the ANN-SoLo tool, an efficient spectral library search engine that is optimized for open modification searching to identify peptides with any type of post-translational modification. We show that machine learning rescoring boosts the number of spectra that can be identified for both standard searching and open searching, and we provide insights into relevant spectrum characteristics harnessed by the machine learning model. The semisupervised machine learning functionality has now been fully integrated into ANN-SoLo, which is available as open source under the permissive Apache 2.0 license on GitHub at https://github.com/bittremieux/ANN-SoLo.

Genome-wide association study of ear tip barrenness in waxy maize.

Ear tip-barrenness (ETB), which results from aborted kernels or infertile florets at the ear tip, is an undesirable factor affecting the yield and quality of waxy maize. To uncover the genetic basis of ETB, a genome-wide association study (GWAS) was conducted using the genotype with 27,354 SNPs and phenotype with three environments. Five SNPs that distributed on chromosomes 1, 3 and 6, were identified to be significantly associated with ETB based on the threshold of false discovery rate (FDR) at 0.05. Among these significant loci, three SNPs were clustered together and colocalized with genomic regions previously reported. The average length of ETB decreased almost linearly from the inbred lines containing no favorable alleles across the three loci (1.75 cm) to those with one (1.18 cm), two (0.94 cm) and three (0.65 cm) favorable alleles. Moreover, three important genes, Zm00001d030028, Zm00001d041510 and Zm00001d038676 were predicted for three significant QTLs, respectively. These results promote the understanding genetic basis for ETB and will be useful for breeding waxy maize varieties with high-quality and high-yield.

Whole-blood methylation signatures are associated with and accurately classify multiple sclerosis disease severity

BackgroundThe variation in multiple sclerosis (MS) disease severity is incompletely explained by genetics, suggesting genetic and environmental interactions are involved. Moreover, the lack of prognostic biomarkers makes it difficult for clinicians to optimise care. DNA methylation is one epigenetic mechanism by which gene–environment interactions can be assessed. Here, we aimed to identify DNA methylation patterns associated with mild and severe relapse-onset MS (RMS) and to test the utility of methylation as a predictive biomarker.MethodsWe conducted an epigenome-wide association study between 235 females with mild (n = 119) or severe (n = 116) with RMS. Methylation was measured with the Illumina methylationEPIC array and analysed using logistic regression. To generate hypotheses about the functional consequence of differential methylation, we conducted gene set enrichment analysis using ToppGene. We compared the accuracy of three machine learning models in classifying disease severity: (1) clinical data available at baseline (age at onset and first symptoms) built using elastic net (EN) regression, (2) methylation data using EN regression and (3) a weighted methylation risk score of differentially methylated positions (DMPs) from the main analysis using logistic regression. We used a conservative 70:30 test:train split for classification modelling. A false discovery rate threshold of 0.05 was used to assess statistical significance.ResultsFemales with mild or severe RMS had 1472 DMPs in whole blood (839 hypermethylated, 633 hypomethylated in the severe group). Differential methylation was enriched in genes related to neuronal cellular compartments and processes, and B-cell receptor signalling. Whole-blood methylation levels at 1708 correlated CpG sites classified disease severity more accurately (machine learning model 2, AUC = 0.91) than clinical data (model 1, AUC = 0.74) or the wMRS (model 3, AUC = 0.77). Of the 1708 selected CpGs, 100 overlapped with DMPs from the main analysis at the gene level. These overlapping genes were enriched in neuron projection and dendrite extension, lending support to our finding that neuronal processes, rather than immune processes, are implicated in disease severity.ConclusionRMS disease severity is associated with whole-blood methylation at genes related to neuronal structure and function. Moreover, correlated whole-blood methylation patterns can assign disease severity in females with RMS more accurately than clinical data available at diagnosis.

Associations of Genetically Predicted Vitamin B12 Status across the Phenome.

Variation in vitamin B12 levels has been associated with a range of diseases across the life-course, the causal nature of which remains elusive. We aimed to interrogate genetically predicted vitamin B12 status in relation to a plethora of clinical outcomes available in the UK Biobank. Genome-wide association study (GWAS) summary data obtained from a Danish and Icelandic cohort of 45,576 individuals were used to identify 8 genetic variants associated with vitamin B12 levels, serving as genetic instruments for vitamin B12 status in subsequent analyses. We conducted a Mendelian randomisation (MR)-phenome-wide association study (PheWAS) of vitamin B12 status with 945 distinct phenotypes in 439,738 individuals from the UK Biobank using these 8 genetic instruments to proxy alterations in vitamin B12 status. We used external GWAS summary statistics for replication of significant findings. Correction for multiple testing was taken into consideration using a 5% false discovery rate (FDR) threshold. MR analysis identified an association between higher genetically predicted vitamin B12 status and lower risk of vitamin B deficiency (including all B vitamin deficiencies), serving as a positive control outcome. We further identified associations between higher genetically predicted vitamin B12 status and a reduced risk of megaloblastic anaemia (OR = 0.35, 95% CI: 0.20-0.50) and pernicious anaemia (0.29, 0.19-0.45), which was supported in replication analyses. Our study highlights that higher genetically predicted vitamin B12 status is potentially protective of risk of vitamin B12 deficiency associated with pernicious anaemia diagnosis, and reduces risk of megaloblastic anaemia. The potential use of genetically predicted vitamin B12 status in disease diagnosis, progression and management remains to be investigated.

Differential expression of MDGA1 in major depressive disorder

The identification of gene expression-based biomarkers for major depressive disorder (MDD) continues to be an important challenge. In order to identify candidate biomarkers and mechanisms, we apply statistical and machine learning feature selection to an RNA-Seq gene expression dataset of 78 unmedicated individuals with MDD and 79 healthy controls. We identify 49 genes by LASSO penalized logistic regression and 45 genes at the false discovery rate threshold 0.188. The MDGA1 gene has the lowest P-value (4.9e-5) and is expressed in the developing brain, involved in axon guidance, and associated with related mood disorders in previous studies of bipolar disorder (BD) and schizophrenia (SCZ). The expression of MDGA1 is associated with age and sex, but its association with MDD remains significant when adjusted for covariates. MDGA1 is in a co-expression cluster with another top gene, ATXN7L2 (ataxin 7 like 2), which was associated with MDD in a recent GWAS. The LASSO classification model of MDD includes MDGA1, and the model has a cross-validation accuracy of 79%. Another noteworthy top gene, IRF2BPL, is in a close co-expression cluster with MDGA1 and may be related to microglial inflammatory states in MDD. Future exploration of MDGA1 and its gene interactions may provide insights into mechanisms and heterogeneity of MDD.

Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using proteomics profiling

Abstract Background Hypertrophic cardiomyopathy (HCM) is one of the most common genetic cardiac disorders and affects 1 in 200–500 individuals. HCM is known to be heterogeneous. Approximately 20–30% of patients with HCM develop atrial fibrillation (AF), which can lead to stroke and worsening of heart failure symptoms. As AF increases the risk of stroke by 8-fold, AF in HCM is a Class 1 indication for anticoagulation. Despite its high prevalence and clinical importance of early AF detection, there are no risk stratification tools available to predict new-onset AF in patients with HCM. Furthermore, it is still unknown which signaling pathways mediate AF in HCM. Proteomics profiling can determine concentrations of thousands of proteins and potentially reveal underlying molecular mechanisms of disease progression. Purpose To develop plasma proteomics-based model to predict new-onset AF in patients with HCM and to determine signaling pathways dysregulated in those who subsequently develop AF. Methods In this prospective, multi-center cohort study, we conducted plasma proteomics profiling of 5,032 proteins on 397 patients with HCM. We developed a proteomics-based random forest machine learning model to predict new-onset AF using samples from one institution (training set, n=278). We tested the predictive ability of the model using independent samples from the other institution (test set, n=119). We estimated the hazard ratio for new-onset AF using a Cox proportional hazards model comparing high- and low-risk groups as determined by the proteomics-based model. We also performed pathway analysis of proteins significantly (i.e., univariable P<0.05) associated with new-onset AF using a false discovery rate (FDR) threshold of 0.05. Results A total of 15 patients in the training set (5.4%) and 7 in the test set (5.9%) developed new-onset AF. Using the proteomics-based model developed in the training set, the area under the receiver-operating characteristic (ROC) curve to predict new-onset AF was 0.87 (95% confidence interval [CI] 0.77–0.98; Figure) in the test set. The sensitivity was 0.86 (95% CI 0.42–0.99) and the specificity was 0.77 (95% CI 0.68–0.84). In the test set, patients categorized as high-risk based on the proteomics model had a significantly higher rate of developing new-onset AF (hazard ratio 8.18; 95% CI 1.55–43.20; P=0.01). Pathway analysis revealed that the Ras-MAPK pathway was dysregulated in patients who subsequently developed AF (FDR=0.01; Table). Pathways involved in inflammation were also dysregulated. Conclusions This study serves as the first to demonstrate the ability of proteomics profiling to predict new-onset AF in patients with HCM, exhibiting dysregulation of both novel (e.g., Ras-MAPK) and known pathways in patients who subsequently experience AF. These results not only exhibit the utility of proteomics profiling for clinical risk stratification but also suggest mechanisms underlying the development of AF in HCM. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH/NHLBI

Improving Peptide-Level Mass Spectrometry Analysis via Double Competition.

The analysis of shotgun proteomics data often involves generating lists of inferred peptide-spectrum matches (PSMs) and/or of peptides. The canonical approach for generating these discovery lists is by controlling the false discovery rate (FDR), most commonly through target-decoy competition (TDC). At the PSM level, TDC is implemented by competing each spectrum's best-scoring target (real) peptide match with its best match against a decoy database. This PSM-level procedure can be adapted to the peptide level by selecting the top-scoring PSM per peptide prior to FDR estimation. Here, we first highlight and empirically augment a little known previous work by He et al., which showed that TDC-based PSM-level FDR estimates can be liberally biased. We thus propose that researchers instead focus on peptide-level analysis. We then investigate three ways to carry out peptide-level TDC and show that the most common method ("PSM-only") offers the lowest statistical power in practice. An alternative approach that carries out a double competition, first at the PSM and then at the peptide level ("PSM-and-peptide"), is the most powerful method, yielding an average increase of 17% more discovered peptides at 1% FDR threshold relative to the PSM-only method.

Genetic Variants Associated with Elevated Plasma Ceramides in Individuals with Metabolic Syndrome.

Metabolic syndrome (MetS) is a complex condition of metabolic disorders and shows a steady onset globally. Ceramides are known as intracellular signaling molecules that influence key metabolism through various pathways such as MetS and insulin resistance. Therefore, it is important to identify novel genetic factors related to increased plasma ceramides in subjects with MetS. Here we first measured plasma ceramides levels in 37 subjects with MetS and in 38 healthy subjects by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Specifically, levels of C16 ceramide (Cer-16), C18 ceramide (Cer-18), C20 ceramide (Cer-20), C18 dihydroceramide (DhCer-18), C24 dihydroceramide (DhCer-24), and C24:1 dihydroceramide (DhCer-24:1) were significantly increased in MetS group (p < 5.0 × 10−2). We then performed single nucleotide polymorphism (SNP) genotyping to identify variants associated with elevated plasma ceramides in MetS group using Axiom® Korea Biobank Array v1.1 chip. We also performed linear regression analysis on genetic variants involved in ceramide synthesis and significantly elevated plasma ceramides and dihydroceramides. Ten variants (rs75397325, rs4246316, rs80165332, rs62106618, rs12358192, rs11006229, rs10826014, rs149162405, rs6109681, and rs3906631) across six genes (ACER1, CERS3, CERS6, SGMS1, SPTLC2, and SPTLC3) functionally involved in ceramide biosynthesis showed significant associations with the elevated levels of at least one of the ceramide species in MetS group at a statistically significant threshold of false discovery rate (FDR)-adjusted p < 5.0 × 10−2. Our findings suggest that the variants may be genetic determinants associated with increased plasma ceramides in individuals with MetS.

Impact of Multidomain Lifestyle Intervention on Cerebral Cortical Thickness and Serum Brain-Derived Neurotrophic Factor: the SUPERBRAIN Exploratory Sub-study

In the SoUth Korean study to PrEvent cognitive impaiRment and protect BRAIN health through lifestyle intervention in at-risk elderly people (SUPERBRAIN), we evaluated the impact of a 24-week facility-based multidomain intervention (FMI) and home-based MI (HMI) on cortical thickness, brain volume, and the serum brain-derived neurotrophic factor (BDNF). Totally, 152 participants, aged 60–79 years without dementia but with ≥ 1 modifiable dementia risk factor, were randomly assigned to the FMI, HMI, or control groups. Among them, 55 participants (20 FMI, 19 HMI, and 16 controls) underwent brain MRI at baseline and 24 weeks. We compared changes in global/regional mean cortical thickness at the region-of-interest (ROI) between the intervention and control groups. The changes in the total cortical gray matter volume and global mean cortical thickness were compared using analysis of covariance with age, sex, and education as covariates. ComBat site harmonization was applied for cortical thickness values across the scanners. ROI-based analysis was controlled for multiple comparisons, with a false discovery rate threshold of p < 0.05. Serum BDNF levels were significantly higher in the FMI group than in the control group (p = 0.029). Compared with the control group, the mean global cortical thickness increased in the FMI group (0.033 ± 0.070 vs. − 0.003 ± 0.040, p = 0.013); particularly, cortical thickness of the bilateral frontotemporal lobes, cingulate gyri, and insula increased. The increase in cortical thickness and serum BDNF in the FMI group suggests that group preventive strategies at the facility may be beneficial through structural neuroplastic changes in brain areas, which facilitates learning and neurotrophic factors.

Genetic Architecture of Powdery Mildew Resistance Revealed by a Genome-Wide Association Study of a Worldwide Collection of Flax (Linum usitatissimum L.)

Powdery mildew is one of the most important diseases of flax and is particularly prejudicial to its yield and oil or fiber quality. This disease, caused by the obligate biotrophic ascomycete Oïdium lini, is progressing in France. Genetic resistance of varieties is critical for the control of this disease, but very few resistance genes have been identified so far. It is therefore necessary to identify new resistance genes to powdery mildew suitable to the local context of pathogenicity. For this purpose, we studied a worldwide diversity panel composed of 311 flax genotypes both phenotyped for resistance to powdery mildew resistance over 2 years of field trials in France and resequenced. Sequence reads were mapped on the CDC Bethune reference genome revealing 1,693,910 high-quality SNPs, further used for both population structure analysis and genome-wide association studies (GWASs). A number of four major genetic groups were identified, separating oil flax accessions from America or Europe and those from Asia or Middle-East and fiber flax accessions originating from Eastern Europe and those from Western Europe. A number of eight QTLs were detected at the false discovery rate threshold of 5%, located on chromosomes 1, 2, 4, 13, and 14. Taking advantage of the moderate linkage disequilibrium present in the flax panel, and using the available genome annotation, we identified potential candidate genes. Our study shows the existence of new resistance alleles against powdery mildew in our diversity panel, of high interest for flax breeding program.

Gut dysbiosis in cutaneous T-cell lymphoma is characterized by shifts in relative abundances of specific bacterial taxa and decreased diversity in more advanced disease.

BackgroundCutaneous T‐cell lymphoma (CTCL) patients often suffer from recurrent skin infections and profound immune dysregulation in advanced disease. The gut microbiome has been recognized to influence cancers and cutaneous conditions; however, it has not yet been studied in CTCL.ObjectivesTo investigate the gut microbiome in patients with CTCL and in healthy controls.MethodsA case‐control study was conducted between January 2019 and November 2020 at Northwestern’s busy multidisciplinary CTCL clinic (Chicago, Illinois, USA) utilizing 16S ribosomal RNA gene amplicon sequencing and bioinformatics analyses to characterize the microbiota present in fecal samples of CTCL patients (n = 38) and age‐matched healthy controls (n = 13) from the same geographical region.ResultsGut microbial α‐diversity trended lower in patients with CTCL and was significantly lower in patients with advanced CTCL relative to controls (P = 0.015). No differences in β‐diversity were identified. Specific taxa were significantly reduced in patient samples; significance was determined using adjusted P‐values (q‐values) that accounted for a false discovery rate threshold of 0.05. Significantly reduced taxa in patient samples included the phylum Actinobacteria (q = 0.0002), classes Coriobacteriia (q = 0.002) and Actinobacteria (q = 0.03), order Coriobacteriales (q = 0.003), and genus Anaerotruncus (q = 0.01). The families Eggerthellaceae (q = 0.0007) and Lactobacillaceae (q = 0.02) were significantly reduced in patients with high skin disease burden.ConclusionsGut dysbiosis can be seen in patients with CTCL compared to healthy controls and is pronounced in more advanced CTCL. The taxonomic shifts associated with CTCL are similar to those previously reported in atopic dermatitis and opposite those of psoriasis, suggesting microbial parallels to the immune profile and skin barrier differences between these conditions. These findings may suggest new microbial disease biomarkers and reveal a new angle for intervention.

Single-Nuclear RNA-Sequencing Identifies Cell-Specific Transcriptional Programs in Cardiac Allograft Vasculopathy

<h3>Purpose</h3> Cardiac allograft vasculopathy (CAV) is the leading cause of late graft failure and mortality after heart transplantation (HT). Current therapies have limited efficacy in preventing or stalling the progression of CAV. Improved understanding of CAV biology is needed to identify novel therapeutics. <h3>Methods</h3> We performed single-nuclear RNA sequencing (snRNA-seq) on human cardiac tissue obtained by endomyocardial biopsy from the right ventricle of 3 HT recipients without CAV and at the time of heart explant in 3 HT recipients with severe CAV. Differentially expressed genes (DEGs) between cohorts were identified with DESeq2 using a 5% false discovery rate threshold for significant genes. <h3>Results</h3> Unsupervised clustering of 38,211 nuclei identified 15 major cell types (Figure A). Relative to cells from non-CAV hearts, endothelial cells and fibroblasts from hearts with severe CAV showed enrichment for genes and pathways associated with inflammation, angiogenesis, and extracellular matrix organization (Figure B and C; endothelial cells). Intersecting these DEGs with the druggable genome identified novel cell-specific drug targets. <h3>Conclusion</h3> We show feasibility of performing snRNA-seq from human endomyocardial biopsy samples and identify cell-specific transcriptional programs that are unique to patients with severe CAV. These data lay the groundwork for larger-scale studies that can accelerate mechanistic understanding of CAV development.

Pharmacometabonomic association of cyclophosphamide 4-hydroxylation in hematopoietic cell transplant recipients.

The widely used alkylating agent cyclophosphamide (CY) has substantive interpatient variability in the area under the curve (AUC) of it and its metabolites. Numerous factors may influence the drug‐metabolizing enzymes that metabolize CY to 4‐hydroxycyclophosphamide (4HCY), the principal precursor to CY’s cytotoxic metabolite. We sought to identify endogenous metabolomics compounds (EMCs) associated with 4HCY formation clearance (ratio of 4HCY/CY AUC) using global metabolomics. Patients who undergo hematopoietic cell transplantation receiving post‐transplant CY (PT‐CY) were enrolled, cohort 1 (n = 26) and cohort 2 (n = 25) donating longitudinal blood samples before they started HCT (pre‐HCT), before infusion of the donor allograft (pre‐graft), before the first dose of PT‐CY (pre‐CY), and 24 h after the first dose of PT‐CY (24‐h post‐CY), which is also immediately before the second dose of CY. A total of 512 and 498 EMCs were quantitated in two cohorts, respectively. Both univariate linear regression with false discovery rate (FDR), and pathway enrichment analyses using a global association test were performed. At the pre‐CY time point, no EMCs were associated at FDR less than 0.1. At pre‐HCT, cohort 1 had one EMC (levoglucosan) survive the FDR threshold. At pre‐graft, cohort 1 and cohort 2 had 20 and 13 EMCs, respectively, exhibiting unadjusted p values less than 0.05, with the only EMCs having an FDR less than 0.1 being two unknown EMCs. At 24‐h post‐CY, there were three EMCs, two ketones, and threitol, at FDR less than 0.1 in cohort 2. These results demonstrate the potential of pharmacometabonomics, but future studies in larger samples are needed to optimize CY.

Psychiatric symptoms of frontotemporal dementia and subcortical (co-)pathology burden: new insights.

Three subtypes of distinct pathological proteins accumulate throughout multiple brain regions and shape the heterogeneous clinical presentation of frontotemporal lobar degeneration (FTLD). Besides the main pathological subtypes, co-occurring pathologies are common in FTLD brain donors. The objective of this study was to investigate how the location and burden of (co-)pathology correlate to early psychiatric and behavioural symptoms of FTLD. Eighty-seven brain donors from The Netherlands Brain Bank cohort (2008-2017) diagnosed with FTLD were included: 46 FTLD-TAR DNA-binding protein 43 (FTLD-TDP), 34 FTLD-tau, and seven FTLD-fused-in-sarcoma (FTLD-FUS). Post-mortem brain tissue was dissected into 20 standard regions and stained for phosphorylated TDP-43, phosphorylated tau, FUS, amyloid-β, and α-synuclein. The burden of each pathological protein in each brain region was assessed with a semi-quantitative score. Clinical records were reviewed for early psychiatric and behavioural symptoms. Whole-brain clinico-pathological partial correlations were calculated (local false discovery rate threshold = 0.01). Elaborating on the results, we validated one finding using a quantitative assessment of TDP-43 pathology in the granular layer of the hippocampus in FTLD-TDP brain donors with (n = 15) and without (n = 15) hallucinations. In subcortical regions, the presence of psychiatric symptoms showed positive correlations with increased hippocampal pathology burden: hallucinations with TDP-43 in the granular layer (R = 0.33), mania with TDP-43 in CA1 (R = 0.35), depression with TDP-43 in CA3 and with parahippocampal tau (R = 0.30 and R = 0.23), and delusions with CA3 tau (R = 0.26) and subicular amyloid-β (R = 0.25). Behavioural disinhibition showed positive correlations with tau burden in the thalamus (R = 0.29) and with both TDP-43 and amyloid-β burden in the subthalamus (R = 0.23 and R = 0.24). In the brainstem, the presence of α-synuclein co-pathology in the substantia nigra correlated with disinhibition (R = 0.24), tau pathology in the substantia nigra correlated with depression (R = 0.25) and in the locus coeruleus with both depression and perseverative/compulsive behaviour (R = 0.26 and R = 0.32). The quantitative assessment of TDP-43 in the granular layer validated the higher burden of TDP-43 pathology in brain donors with hallucinations compared to those without hallucinations (P = 0.007). Our results show that psychiatric symptoms of FTLD are linked to subcortical pathology burden in the hippocampus, and hallucinations are linked to a higher burden of TDP-43 in the granular layer. Co-occurring non-FTLD pathologies in subcortical regions could contribute to configuring the clinical phenotype of FTLD.