Getting the most out of RNA-seq data analysis

Introduction: Colon cancer is the fourth most common cancer in the United States and the third leading cause of cancer-related death. High throughput genomic sequencing has led to a number of significant advances in tumor biology and in identifying novel signaling molecules such as long non-coding RNA (lncRNA). The aim of this study was to identify differentially expressed lncRNA in colon cancer from a large RNA-sequencing (RNA-seq) data set. Methods: The raw RNA-seq files of 398 patients with colon cancer were downloaded from The Cancer Genome Atlas (TCGA). Sequencing files were aligned using STAR (Spliced Transcripts Alignment to a Reference) to the most recent genome annotation. Using a subset of patients with paired colon cancer and normal colon epithelium RNA-seq data (n=40), an exploratory binomial regression model was used to calculate differential RNA expression. The most differentially expressed lncRNA were identified from the exploratory analysis and verified by comparing the larger colon cancer RNA-seq data set (n=358) with the normal colon epithelium RNA-seq data set (n=40). Results: 33,514 genes were identified from the comparative analysis, and using differential expression cut off values of > +1.5 or < -1.5 log fold change and a false discovery rate of <0.05; 543 were upregulated and 1822 were downregulated. Within this dysregulated group, 60 lncRNAs were identified. Using the larger data set (n=358 vs n=40), 41/60 lncRNA remained differentially expressed, of which 15 were downregulated and 26 were upregulated. Twenty-four of these lncRNAs have not previously been described in colon cancer, 12 of which are upregulated and 12 of which are downregulated (Table 1). Conclusions: This analysis of RNA-seq data from TCGA has identified dysregulated lncRNAs which have not previously been described in human colon cancer. These lncRNAs may have significant roles in colon cancer tumor signaling. Table 1.Differential expression of lncRNA not described in colon cancerlncRNALog Fold ChangeP-valueDescription in other cancersKRT16P14.1041.36E-21Increased in lung squamous cell carcinomaBOK-AS13.7784.98E-09Increased in oral squamous cell carcinomaCELP3.1151.30E-09Not availableCLDN10-AS12.3944.61E-12Not availablePOU6F2-AS12.2678.25E-09Not availableSLC7A11-AS12.1931.60E-13Decreased in gastric adenocarcinomaCSAG42.1083.59E-10Not availableLUCAT12.0224.21E-11Increased in Ovarian cancer, renal clear cell carcinoma, head and neck squamous cell carcinoma, Non-small cell cancer, glioma, osteosarcoma, esophageal squamous cell carcinomaVPS9D1-AS11.9795.27E-21Decreased in gastric adenocarcinoma, Increased in non-small cell lung cancerLEF1-AS11.8464.48E-13Increased in glioblastomaCASC81.6485.64E-06Single nucleotide polymorphisms in colorectal adenocarcinomaLINC004911.5903.11E-04Not availableLINC00648-1.5021.02E-15Not availableLINC00163-1.6751.03E-22Not availableSATB2-AS1-1.8074.18E-09Increased in osteosarcomaLINC00702-2.1424.81E-18Not availableTRHDE-AS1-2.2623.44E-24Not availableLINC00908-2.3082.76E-103Not availableLINC00483-2.4012.87E-39Increased in gastric adenocarcinomaLINC00461-2.4401.42E-41Increased in glioma, multiple myelomaFENDRR-2.6872.12E-58Decreased in breast cancer, prostate cancer, osteosarcoma, gastric adenocarcinoma,ARHGEF26-AS1-2.7148.55E-32Not availableADAMTS9-AS2-2.9041.78E-74Increased in lung cancer salivary adenoid cystic carcinoma, Decreased in glioma, gastric adenocarcinomaMT1JP-4.4601.08E-114Decreased in retinoblastoma, Decreased in gastric adenocarcinoma Citation Format: Stephen J. O'Brien, Theodore Kalbfleisch, Sudhir Srivastava, Shesh Rai, Susan Galandiuk. Differential expression of long non-coding RNA in colon adenocarcinoma RNA-sequence data set [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1817.

RNA-Seq is increasingly being used for differential gene expression analysis which was dominated by the microarray technology in the past decade. However, inferring differential gene expression based on the observed difference of RNA-Seq read counts has unique challenges that were not present in microarray-based analysis. The differential expression estimation may be biased against low read count values such that the differential expression of genes with high read counts is more easily detected. The estimation bias may further propagate in downstream analyses at the systems biology level if it is not corrected. To obtain a better inference of differential gene expression, we propose a new efficient algorithm based on a markov random field (MRF) model, called MRFSeq, that uses additional gene coexpression data to enhance the prediction power. Our main technical contribution is the careful selection of the clique potential functions in the MRF so its maximum a posteriori (MAP) estimation can be reduced to the well-known maximum flow problem and thus solved in polynomial time. Our extensive experiments on simulated and real RNA-Seq datasets demonstrate that MRFSeq is more accurate and less biased against genes with low read counts than the existing methods based on RNA-Seq data alone. For example, on the well-studied MAQC dataset, MRFSeq improved the sensitivity from 11.6% to 38.8% for genes with low read counts. MRFSeq is implemented in C++ and available at http://www.cs.ucr.edu/~yyang027/mrfseq.htm.

The study of differential gene expression patterns through RNA-Seq comprises a routine task in the daily lives of molecular bioscientists, who produce vast amounts of data requiring proper management and analysis. Despite widespread use, there are still no widely accepted golden standards for the normalization and statistical analysis of RNA-Seq data, and critical biases, such as gene lengths and problems in the detection of certain types of molecules, remain largely unaddressed. Stimulated by these unmet needs and the lack of in-depth research into the potential of combinatorial methods to enhance the analysis of differential gene expression, we had previously introduced the PANDORA P-value combination algorithm while presenting evidence for PANDORA's superior performance in optimizing the tradeoff between precision and sensitivity. In this article, we present the next generation of the algorithm along with a more in-depth investigation of its capabilities to effectively analyze RNA-Seq data. In particular, we show that PANDORA-reported lists of differentially expressed genes are unaffected by biases introduced by different normalization methods, while, at the same time, they comprise a reliable input option for downstream pathway analysis. Additionally, PANDORA outperforms other methods in detecting differential expression patterns in certain transcript types, including long non-coding RNAs.

BackgroundRNA sequencing (RNA-seq) is a powerful tool for genome-wide expression profiling of biological samples with the advantage of high-throughput and high resolution. There are many existing algorithms nowadays for quantifying expression levels and detecting differential gene expression, but none of them takes the misaligned reads that are mapped to non-exonic regions into account. We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes.ResultsWe implemented our novel XBSeq algorithm and evaluated it by using a set of simulated expression datasets under different conditions, using a combination of negative binomial and Poisson distributions with parameters derived from real RNA-seq data. We compared the performance of our method with other commonly used differential expression analysis algorithms. We also evaluated the changes in true and false positive rates with variations in biological replicates, differential fold changes, and expression levels in non-exonic regions. We also tested the algorithm on a set of real RNA-seq data where the common and different detection results from different algorithms were reported.ConclusionsIn this paper, we proposed a novel XBSeq, a differential expression analysis algorithm for RNA-seq data that takes non-exonic mapped reads into consideration. When background noise is at baseline level, the performance of XBSeq and DESeq are mostly equivalent. However, our method surpasses DESeq and other algorithms with the increase of non-exonic mapped reads. Only in very low read count condition XBSeq had a slightly higher false discovery rate, which may be improved by adjusting the background noise effect in this situation. Taken together, by considering non-exonic mapped reads, XBSeq can provide accurate expression measurement and thus detect differential expressed genes even in noisy conditions.

The International Coalition of Medicines Regulatory Authorities recently announced a collaboration to address the expanded use of Real-World Data (RWD) to support regulatory decision-making.1 Drug safety and comparative effectiveness studies using electronic health records and medical claims databases are being used increasingly because compared with randomized clinical trials (RCTs) they can offer larger populations and be completed more quickly at a fraction of the cost. While database studies deliver results with impressive efficiency, this efficiency can come at a scientific cost. Instead of designing data collection to meet the scientific needs of a particular study, database studies analyze (secondary) data not designed to support scientific research. Consequently, database studies are more vulnerable than RCTs to biases arising from inaccurate data. A challenge in using database studies to guide health policy decisions, therefore, is ensuring that database studies are not only efficient, but also accurate. One potential source of error in database studies is outcome misclassification. Diagnoses recorded in health encounter databases are influenced by factors such as variation in patient care-seeking behavior, access to care, quality of care, clinical challenges of differential diagnosis, and diagnoses that can indicate suspected or rule-out conditions. To address uncertainties in identifying health outcomes, database researchers often identify outcomes using combinations of diagnoses, procedures, treatments, medical specialties, place of service, and other factors in the form of an algorithm (also called a computable phenotype or operational definition).2, 3 Owing to limitations in source data, however, even the best algorithms are unlikely to identify every case of interest (false negative errors) and can identify as cases individuals who do not have the outcome (false positive errors). Because errors in identifying outcomes can distort study results, it has been proposed that "validation of algorithms is essential to avoid misclassification bias."4 A recent FDA Guidance for RWD advises that "FDA expects validation of the outcome variable to minimize outcome misclassification."5 Regulators impose high scientific standards and even though validation is not widely practiced in epidemiology, validation is routinely requested for studies submitted to regulators. Many public health researchers receive little formal training in validation, however, and validation studies are not covered thoroughly in texts.6 Consequently, many researchers are guided by convention and validation tends to be conducted perfunctorily. As regulators contemplate wider use of database studies and despite routine use of validation, the impact of outcome misclassification bias in these studies is rarely assessed and remains largely unknown. Analytic methods to adjust results for misclassification have been described but have not been widely adopted.7-17 In this Commentary, we highlight shortcomings in popular validation methods and strategies for reducing misclassification bias. Basic concepts of validation have been described recently for epidemiology4, 6 and specifically as practiced in pharmacoepidemiology.3 In brief, outcome validation entails comparing outcome classification in the database with outcome classification in a gold (or reference) standard (e.g., medical records or disease registry data). Because the gold standard is typically more expensive and time- consuming to obtain than the information in the database used for the main study, the gold standard is usually used for a sample of the study population to assess the performance of the case identifying algorithm.2, 3, 16 Algorithm performance is assessed by parameters such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity. The conventional strategy is to report the proportion of potential cases that was confirmed by the gold standard, or PPV (Table 1).2-4, 16-18 A high PPV means the outcome of interest is confirmed for a large proportion of potential cases and is taken to suggest the algorithm is fit for purpose, with a PPV of 70%–80% or greater considered to indicate a high-performing algorithm.3, 16, 18 For a couple of reasons, the conventional practice of reporting an effect estimate and a PPV does not avoid or minimize bias from outcome misclassification. First, a PPV indicates the proportion of people identified as cases who are noncases (false positive errors), but does not address cases the algorithm failed to identify (false negative errors). While the importance of each kind error varies by setting, each type of error can bias study results.7-17 Second, to understand the impact of misclassification on study results requires parameters that estimate false positive (specificity or PPV) and false negative (sensitivity or NPV) errors in each of the comparison groups.7-17 Differences between comparison groups is readily seen as important in the context of confounding bias, but less easily recognized for misclassification bias. Even in the presence of a high PPV, small differences between comparison groups in misclassification errors can introduce important bias and lead to incorrect conclusions.9, 16 A simulation study demonstrated that a result showing a negative association (RR = 0.87 (95% CI 0.76, 0.99)) with an outcome PPV of 93% could be the result of outcome misclassification masking an even stronger association (RR = 0.70 (95% CI 0.60, 0.81)).16 Alternatively, the negative result could be entirely spurious due to misclassification bias creating an association when in fact there was none (RR = 1.0 (95% CI 0.88, 1.13)).16 Simply put, reporting an effect estimate accompanied by a high PPV does not indicate the presence, direction or magnitude of outcome misclassification bias. FDA recommends including a quantitative bias analysis to demonstrate whether and how outcome misclassification might affect study results. Using validation to inform bias analyses has implications for the design of validation studies.6 The conventional approach to validation in database studies is to calculate one PPV, whereas to support bias analysis validation studies would need to stratify by exposure status in calculating a PPV for each comparison group.6-17 Although conceptually straightforward, stratifying validation samples by exposure status would necessitate larger validation samples to maintain precision. Second, unless there are sound reasons to disregard false negative errors, estimates of sensitivity or NPV are also needed.7-17 Quantitative bias analyses are usually presented using estimates of sensitivity and specificity which can be challenging to estimate in database studies. Bias analysis methods have also been developed for predictive values,6-12, 16, 17 but while estimates of PPV are commonplace, estimates of NPV in studies of rare outcomes can require large sample sizes and are reported rarely. It is sometimes thought that PPV is the only bias parameter that can be estimated in database studies.16 Brenner and Gefeller8 developed a simple bias analysis that uses PPVs in each comparison group so is well-suited to correct for outcome misclassification bias in pharmacoepidemiology.8 Using only PPVs requires that sensitivity be nondifferential, however, which is a tenuous assumption rarely supported by data.19-23 They also present an equation for a simple bias analysis that replaces the nondifferentiality assumption with estimates of PPV and sensitivity in each comparison group.8, 17 Estimating sensitivity in a database study is not as straightforward as estimating PPV. To estimate sensitivity, we need as a denominator a sample of true cases. When studying rare outcomes, a random sample of the study population would need to be quite large to identify a useful sample of true cases. By excluding noncases using conditional sampling, however, it may be possible to target a sample that is enriched in people more likely to be cases.24 For instance, one could design a "screening" algorithm aimed at capturing all (or nearly all) of the cases so that it has near-perfect sensitivity, while still being specific enough to exclude many noncases.25 Validating a random sample of people identified by such as screening algorithm could generate sufficient cases to be used to estimate sensitivity of algorithms designed to have high PPVs (Table 1). Suppose one were interested in occurrence of serious acute nonfatal myocardial infarction (AMI). A primary algorithm might require a primary hospital discharge diagnosis of AMI.26, 27 To assess sensitivity of this algorithm in the study population, a more sensitive screening algorithm could be developed by relaxing the requirement that the AMI diagnosis be in the primary position thus including a discharge diagnosis of AMI in any position.28 One might also broaden the list of diagnoses to include diagnoses associated with AMI, such as unstable angina. Such a screening algorithm might identify all AMI cases of interest and include a larger proportion of AMI cases than a simple random sample of the population. One could then obtain gold standard information on AMI diagnosis for a random sample of people identified by the screening algorithm. Because the population identified by the screening algorithm is enriched in AMI cases, a much smaller sample of people who meet the screening algorithm is needed to obtain a desired number of AMI cases than a sample of the entire study population. After outcome classification using the gold standard, the confirmed AMI cases could then serve as the denominator for estimating the sensitivity of the primary algorithm (e.g., a primary discharge diagnosis of AMI). By designing the validation sample to include people from each comparison group, one could estimate values of PPV and sensitivity of the primary algorithm in each comparison group. These bias parameter estimates could then be used in a bias analysis to correct the RR estimate for outcome misclassification bias to the accuracy of the gold standard.8, 17 Conventional practice in safety and effectiveness studies using validation is to report an effect estimate along with a PPV to assess algorithm performance. A problem with this approach is that we cannot infer from a PPV the impact of outcome misclassification errors on study results. A high PPV does not mean outcome misclassification bias is negligible, and a low PPV does not mean that misclassification bias is important. A preferable approach is to use validation in RWD to inform quantitative bias analysis that corrects effect estimates for misclassification bias to the accuracy of the gold standard. Such a strategy requires larger validation samples and additional resources to complete database studies but would not substantially affect the efficiency advantage of database studies compared with RCTs. Validation for RWD studies is common only for studies submitted to regulators mainly because regulators request it. The proposed approach of using validation to inform bias analysis also is likely to be adopted only if regulators request it. We recommend that regulators strongly consider requesting validation studies to inform quantitative bias analyses. Studies that use quantitative bias analysis to correct results for misclassification bias would strengthen the validity of RWD studies so that they can be used more confidently to support societal and personal health policy decisions. The authors declare no conflict of interest.

Motivation: High-throughput sequencing enables expression analysis at the level of individual transcripts. The analysis of transcriptome expression levels and differential expression (DE) estimation requires a probabilistic approach to properly account for ambiguity caused by shared exons and finite read sampling as well as the intrinsic biological variance of transcript expression.Results: We present Bayesian inference of transcripts from sequencing data (BitSeq), a Bayesian approach for estimation of transcript expression level from RNA-seq experiments. Inferred relative expression is represented by Markov chain Monte Carlo samples from the posterior probability distribution of a generative model of the read data. We propose a novel method for DE analysis across replicates which propagates uncertainty from the sample-level model while modelling biological variance using an expression-level-dependent prior. We demonstrate the advantages of our method using simulated data as well as an RNA-seq dataset with technical and biological replication for both studied conditions.Availability: The implementation of the transcriptome expression estimation and differential expression analysis, BitSeq, has been written in C++ and Python. The software is available online from http://code.google.com/p/bitseq/, version 0.4 was used for generating results presented in this article.Contact: glaus@cs.man.ac.uk, antti.honkela@hiit.fi or m.rattray@sheffield.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

The precise wiring patterns between neurons are crucial for the proper formation and maintenance of neuronal circuits. Synaptic adhesion molecules are thought to play an important role in the regulation of synaptic connection specificity. The immunoglobulin-like subfamily of synaptic adhesion molecules, the 21-member DIPs and 11-member Dprs have been hypothesized to represent the two families of neuronal recognition proteins in the Drosophila melanogaster model organism. The protein expression previously characterized in the outer layers of the optic lobe has shown that some of the Dprs are broadly expressed in the lamina neurons and their cognate DIPs are expressed in their synaptic target medulla neurons. Here, we investigate the differential gene expression patterns of DIPs and Dprs within the optic glomeruli of the three Lobular Columnar cell types: LC4, LPLC1, and LPLC2. Using a publicly available RNA-seq dataset, we develop and compare multiple analyses pipelines to predict the variation in gene expression levels of each DIP and Dpr across cell types within the visual system and their expression state in a specific cell population. Dpr1/DIP-eta were identified as being more highly expressed in LPLC2, and Dpr9/DIP-beta and Dpr12/DIP-delta in LPLC1. To validate these predictions, we investigate the protein expression patterns of the identified DIPs and Dprs. Endogenous labeling of the DIPs and Dprs shows that the differential gene expression patterns match the protein expression patterns for DIP-eta while the Dprs were not differentially expressed across the optic glomeruli of the three LC cell types. We then sought to identify the DIPs and Dprs that may be involved in synaptic partner recognition during development in the three LC cell types. A differential gene expression analysis identified Dpr1 that is differentially expressed between LC4 and LPLC1 and Dpr13, Dpr20 and DIP-eta between LPLC2 and LPLC1. This work provides a better understanding of the differential expression of the DIPs and Dprs within cell types in the visual system and their potential role in synaptic partner recognition.

Background: Breast cancer is the most common female malignancy in the US, and radiotherapy is a routine part of multi-disciplinary breast cancer care. Radiation treatment (RT) tailored to target radiation response pathways could maximize treatment benefit in radiation resistant tumors, while minimizing unnecessary toxicity and cost in more radiation responsive breast tumor subtypes. Here we report on genome-wide analysis, gene expression profiles and biological pathways associated with RT response in breast cancer patients. Methods: Between 2010-2013, a total of 32 breast cancer patients were enrolled at Duke University Medical Center on an IRB approved Phase I clinical trial testing preoperative radiotherapy. Paired pre and post-RT FFPE biopsy samples were available for N = 26. Genome wide-analysis was performed using two distinct techniques: i) Microarray analysis using the Affymetrix GeneChip ® Human Transcriptome Array 2.0 (HTA 2.0); ii) RNA-Seq using TruSeq ®RNA Access Library kit (Illumina). All libraries were sequenced on the Illumina HiSeq 2000 generating 50 bp single reads at a sequencing depth of 40 million reads. The RNA-Seq data was processed using the TrimGalore toolkit. For the paired pre and post-RT differential gene expression analysis we used the linear model. Genes that had an FDR ≤ 5% in the RNA-Seq experiment were compared with all microarray probesets that had an FDR ≤ 5%. If at least one probeset that belonged to a gene in the RNA-Seq list was significant and showed the same direction of change between pre and post-RT samples, they were identified as consistent. Gene Set Enrichment Analysis (GSEA) and Database for Annotation, Visualization and Integrated Discovery (DAVID) were used to identify significant pathways and gene ontologies that were differentially regulated between the conditions. Results: 2648 genes showed the same significant results in response to RT in microarray and RNA-Seq including: i) a total of 1401 genes that had significantly higher expression levels, and ii) a total of 1247 genes that had significantly lower expression levels following RT. Principal Components Analysis (PCA) demonstrated distinct separation of pre and post-RT gene expression signatures. Differentially expressed genes were involved in major biological pathways such as cell cycle checkpoint, DNA replication and DNA repair pathways. A subset of these genes also demonstrated differential expression in radiation responsive vs. non-responsive breast cancer cell lines. Conclusion: New laboratory techniques have become available which provide the opportunity to use FFPE samples for microarray and RNA-seq analysis. Using these new techniques and our rare pre and post-RT FFPE breast tumor samples, we have been able to demonstrate distinct differences in gene expression profiles and modulation of biological pathways in response to RT in human breast tumors. Citation Format: Sharareh Siamakpour-Reihani, Wei Chen, David Corcoran, Chen-Ting Lee, Ying Zhou, Mark W. Dewhirst, Jen-Tsan A. Chi, Janet K. Horton. Radiation response genome-wide analysis using paired pre and post-radiation FFPE human breast tumor samples. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1647.

In a companion article we have reported the prevalence, in an older, defined population, of traditional assessments (intermittent claudication and abnormal pulse examination) of peripheral arterial disease (PAD) as compared with the results of highly accurate noninvasive testing. In this article we report the sensitivity, specificity, and positive and negative predictive values for claudication and abnormal pulses for the diagnosis of large-vessel and small-vessel PAD as determined by noninvasive testing. Claudication and abnormal pulses were completely unrelated to isolated small-vessel PAD. In contrast, both claudication and abnormal pulses were significantly correlated with large-vessel PAD. Claudication and an abnormal femoral pulse showed a high specificity and positive predictive value but a low sensitivity for large-vessel PAD. Conversely, an abnormal dorsalis pedis pulse showed a good sensitivity but low specificity and positive predictive value. The best single discriminator was an abnormal posterior tibial pulse, which had high sensitivity, specificity, and positive predictive value. Various combinations of claudication and pulse abnormalities revealed a good sensitivity for broader criteria but at the expense of specificity, whereas stricter criteria had a good specificity and positive predictive value but a poor sensitivity. No combination was superior to an abnormal posterior tibial pulse alone.(ABSTRACT TRUNCATED AT 250 WORDS)

BackgroundPost-FDA approval immune therapy for cancer becomes widely used for locally advanced or metastatic urothelial carcinomas. There are patients that may suffer severe side effects from these immune checkpoint blockades...

High-quality RNA is crucial for obtaining reliable RNA sequencing (RNA-seq) data, with metrics like RNA Integrity Number (RIN). However, these metrics, while effective for evaluating RNA integrity, do not always correlate with RNA-seq data quality, especially at the transcript level. This gap is particularly evident in total RNA-seq, where existing measures such as the coefficient of variation (CV) for read coverage fail to fully capture data quality and are influenced by confounding factors like read coverage depth. To address this, we developed a novel method to assess RNA-seq data quality by quantifying nonuniformity in read coverage while minimizing the influence of read coverage depth. In this study we invented new matric called windowCV (wCV) and applied to a diverse range of RNA-seq datasets, including fresh frozen (FF) and FFPE total RNA-seq data, as well as poly(A)-enriched mRNA-seq data. In mRNA-seq, our method captured 3' read coverage bias, a hallmark of RNA degradation, particularly in longer transcripts. For total RNA-seq data, we identified noisy coverage patterns associated with poor data quality, even in samples with high RIN values. By fitting regression lines between wCV and mean coverage depth (MCD) and calculating the area under the curve (wCVAUC), we refined the assessment to account for RNA quality variability. Using the TCGA pilot study and our own datasets, we demonstrated that wCVAUC reliably identified low quality RNA-seq data and highlighted its impact on downstream analyses, including gene expression quantification and clustering. Importantly, we observed that low RIN values do not always predict poor RNA-seq data quality, as some samples with RIN values below 7 exhibited high-quality RNA-seq data based on wCVAUC. It means that our analyses showed that wCVAUC effectively distinguished high-quality from low-quality samples, including cases where traditional metrics like RIN and CV were insufficient. Additionally, our investigation into the relationships between nonuniformity of read coverage, exon GC content, and RNA localization revealed that the transcript-level RNA-seq data quality of lncRNA genes in FFPE samples is influenced by low exon GC content and nuclear localization. In conclusion, our method provides robust, transcript-level metrics for assessing RNA-seq data quality across platforms, enabling more accurate identification of low-quality data and minimizing biases in downstream analyses. This approach offers a new standard for integrating RNA-seq data quality with sample variability, particularly for challenging datasets such as FFPE and total RNA-seq. Citation Format: Wonyoung Choi, Miyeon Yeon, Jay Lee, Hyo Young Choi, David Neil Hayes. The new approach for measuring nonuniformity of read coverages reveals the quality of RNA-seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2489.

Background:Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values to facilitate meaningful recommendations for echocardiography.Methods:Using 2 232 130 ECGs linked to electronic health records and echocardiography reports from 484 765 adults between 1984 to 2021, we trained machine learning models to predict the presence or absence of any of 7 echocardiography-confirmed diseases within 1 year. This composite label included the following: moderate or severe valvular disease (aortic/mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction <50%, or interventricular septal thickness >15 mm. We tested various combinations of input features (demographics, laboratory values, structured ECG data, ECG traces) and evaluated model performance using 5-fold cross-validation, multisite validation trained on 1 site and tested on 10 independent sites, and simulated retrospective deployment trained on pre-2010 data and deployed in 2010.Results:Our composite rECHOmmend model used age, sex, and ECG traces and had a 0.91 area under the receiver operating characteristic curve and a 42% positive predictive value at 90% sensitivity, with a composite label prevalence of 17.9%. Individual disease models had area under the receiver operating characteristic curves from 0.86 to 0.93 and lower positive predictive values from 1% to 31%. Area under the receiver operating characteristic curves for models using different input features ranged from 0.80 to 0.93, increasing with additional features. Multisite validation showed similar results to cross-validation, with an aggregate area under the receiver operating characteristic curve of 0.91 across our independent test set of 10 clinical sites after training on a separate site. Our simulated retrospective deployment showed that for ECGs acquired in patients without preexisting structural heart disease in the year 2010, 11% were classified as high risk and 41% (4.5% of total patients) developed true echocardiography-confirmed disease within 1 year.Conclusions:An ECG-based machine learning model using a composite end point can identify a high-risk population for having undiagnosed, clinically significant structural heart disease while outperforming single-disease models and improving practical utility with higher positive predictive values. This approach can facilitate targeted screening with echocardiography to improve underdiagnosis of structural heart disease.

5012 Background: Accurate detection of prostate cancer is imperative to patient management, yet standard imaging methods perform poorly in accurately detecting mPC. 18F-DCFPyL is a novel PET imaging agent that selectively binds to prostate-specific membrane antigen, a recognized target for prostate cancer. OSPREY was a prospective, multicenter study in pts with either newly diagnosed high-risk prostate cancer (cohort A), or known or suspected mPC (cohort B). Here we focus on Cohort B. Methods: 117 men planned for biopsy of recurrent or mPC received 18F-DCFPyL. Pts underwent image-guided biopsy. Sensitivity, positive predictive value (PPV), and safety of 18F-DCFPyL PET/CT were the key endpoints for Cohort B. 18F-DCFPyL PET/CT scans were evaluated by three independent, blinded central readers; and results were compared to histopathology as the truth standard. Results: The sensitivity and PPV of 18F-DCFPyL PET/CT as compared to histopathology ranged from 92.9-98.6% (lower bound of 95% CI: 84.0-91.6%) and 81.2-87.8%, respectively. Diagnostic performance by anatomic location showed high sensitivity and high PPV in all sites of disease (Table). Only two (1.7%) cohort B pts experienced ≥1 drug-related AE (dysgeusia and generalized rash), both were mild (Grade 1) in severity. Conclusions: 18F-DCFPyL PET/CT was well tolerated and demonstrated high sensitivity and PPV in accurately detecting nodal, bone, and visceral/soft tissue metastases. A positive 18F-DCFPyL PET/CT scan is highly likely to represent pathologically proven distant disease, demonstrating the potential of 18F-DCFPyL as a PET imaging agent to favorably influence treatment planning. Clinical trial information: NCT02981368. [Table: see text]

BackgroundRNA-Sequencing (RNA-Seq) experiments have been optimized for library preparation, mapping, and gene expression estimation. These methods, however, have revealed weaknesses in the next stages of analysis of differential expression, with results sensitive to systematic sample stratification or, in more extreme cases, to outliers. Further, a method to assess normalization and adjustment measures imposed on the data is lacking.ResultsTo address these issues, we utilize previously published eQTLs as a novel gold standard at the center of a framework that integrates DNA genotypes and RNA-Seq data to optimize analysis and aid in the understanding of genetic variation and gene expression. After detecting sample contamination and sequencing outliers in RNA-Seq data, a set of previously published brain eQTLs was used to determine if sample outlier removal was appropriate. Improved replication of known eQTLs supported removal of these samples in downstream analyses. eQTL replication was further employed to assess normalization methods, covariate inclusion, and gene annotation. This method was validated in an independent RNA-Seq blood data set from the GTEx project and a tissue-appropriate set of eQTLs. eQTL replication in both data sets highlights the necessity of accounting for unknown covariates in RNA-Seq data analysis.ConclusionAs each RNA-Seq experiment is unique with its own experiment-specific limitations, we offer an easily-implementable method that uses the replication of known eQTLs to guide each step in one’s data analysis pipeline. In the two data sets presented herein, we highlight not only the necessity of careful outlier detection but also the need to account for unknown covariates in RNA-Seq experiments.

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