A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium.

With the widespread RNA-seq applications of different sequencing platforms in biomedical science research in recent years, a systematic evaluation of RNA-seq data quality is crucial and timely. The Sequencing Quality Control (SEQC) project is a large-scale community effort for assessing the performance of RNA-seq technology across different platforms and multiple laboratories, where reference RNA samples with multiple replicates were sequenced at 12 laboratories using 3 sequencing platforms. Different from the SEQC project, we performed an independent and comprehensive analysis of RNA-seq data of the SEQC project to assess sequencing reproducibility across platforms, sequencing sites, sample replicates, and FlowCells, respectively. With the employment of graphical tools and statistical models, our systemic analysis supports a distinctive conclusion that reproducibility across platforms and sequencing sites are not acceptable, whereas reproducibility across sample replicates and FlowCells are acceptable.

To Tattoo or Not: That is the Question

To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline’s performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

Whole-transcriptome sequencing (‘RNA-Seq’) has been drastically changing the scale and scope of genomic research. In order to fully understand the power and limitations of this technology, the US Food and Drug Administration (FDA) launched the third phase of the MicroArray Quality Control (MAQC-III) project, also known as the SEquencing Quality Control (SEQC) project. Using two well-established human reference RNA samples from the first phase of the MAQC project, three sequencing platforms were tested across more than ten sites with built-in truths including spike-in of external RNA controls (ERCC), titration data and qPCR verification. The SEQC project generated over 30 billion sequence reads representing the largest RNA-Seq data ever generated by a single project on individual RNA samples. This extraordinarily ultradeep transcriptomic data set and the known truths built into the study design provide many opportunities for further research and development to advance the improvement and application of RNA-Seq.

The rapid advancement of emerging genomics technologies and their application for assessing safety and efficacy of FDA-regulated products require a high standard of reliability and robustness supporting regulatory decision-making in the FDA. To facilitate the regulatory application, the FDA implemented a novel data submission program, Voluntary Genomics Data Submission (VGDS), and also to engage the stakeholders. As part of the endeavor, for the past 10 years, the FDA has led an international consortium of regulatory agencies, academia, pharmaceutical companies, and genomics platform providers, which was named MicroArray Quality Control Consortium (MAQC), to address issues such as reproducibility, precision, specificity/sensitivity, and data interpretation. Three projects have been completed so far assessing these genomics technologies: gene expression microarrays, whole genome genotyping arrays, and whole transcriptome sequencing (i.e., RNA-seq). The resultant studies provide the basic parameters for fit-for-purpose application of these new data streams in regulatory environments, and the solutions have been made available to the public through peer-reviewed publications. The latest MAQC project is also called the SEquencing Quality Control (SEQC) project focused on next-generation sequencing. Using reference samples with built-in controls, SEQC studies have demonstrated that relative gene expression can be measured accurately and reliably across laboratories and RNA-seq platforms. Besides prediction performance comparable to microarrays in clinical settings and safety assessments, RNA-seq is shown to have better sensitivity for low expression and reveal novel transcriptomic features. Future effort of MAQC will be focused on quality control of whole genome sequencing and targeted sequencing.

Background: Next-generation sequencing (NGS) is amenable to a multitude of clinical applications by virtue of its automated and highly parallelized analysis of nucleic acid templates. However, prior studies have identified non-systematic biases introduced during preparation of NGS libraries as the primary source of technical variation preventing immediate application for measuring nucleic acid abundance in the clinical setting. We reasoned that a PCR-based NGS library preparation protocol that incorporated competitive internal amplification control (IAC) mixtures (i.e. internal standards) would control for the majority of bias introduced during NGS library preparation, enabling clinical laboratories to offer cost effective moderately complex diagnostic panels from quantitative NGS data. Methods: In order to test this approach, we obtained reference material RNA titration pools used in the FDA-sponsored Sequencing Quality Control (SEQC) project that have been characterized for nucleic acid abundance by multiple qPCR, Microarray and NGS platforms. Using Multiplex-PCR with primers and competitive IAC for 150-gene targets we prepared NGS libraries from: 1) gDNA to test general analytical performance, and 2) cDNA from reverse transcribed SEQC project reference material to determine accuracy in detecting fold change. Using gDNA mixed with serially titrated IAC mixtures as input, we observed a linear dynamic range over 106 orders of magnitude, with an average R2 = 0.995 (0.993 – 0.997; 95% CI). There was a high correlation coefficient (R2= 0.96) between measured values for copies of nucleic acid abundance in two separate library preparations (separate reverse transcriptions and Multiplex PCR-based library preparations) from the same reference RNA material (FDA SEQC project Sample A). Because the SEQC project RNA Samples C and D represent a known cross titration between SEQC project RNA Samples A and B, by comparing measured to expected values for expression of each gene in Samples C and D it is possible to determine accuracy of the method. In preliminary studies, the correlation coefficient of expected versus observed for Sample C was R2 = 0.96, with an ROC curve-determined accuracy to detect a 3-fold change of 97% (95 – 99%; 95% CI). Inter-platform and inter-laboratory comparisons are ongoing. Conclusion: The approach described here overcomes key sources of non-systematic bias introduced during NGS library preparation. This should enable reproducible inter-laboratory and inter-platform quantitative NGS results, and a clear path to regulatory approval for clinical diagnostic applications. Citation Format: Thomas Blomquist, Erin L. Crawford, James C. Willey. Quantitative sequencing following PCR-driven library preparation with internal standard mixtures has improved analytical performance and lower cost. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4150. doi:10.1158/1538-7445.AM2013-4150

BackgroundAztreonam-avibactam (ATM-AVI) was recently approved by the United States (US) Food and Drug Administration (FDA) for the treatment of intra-abdominal infections. ATM-AVI has shown potent activity against multidrug-resistant (MDR) Enterobacterales, including metallo-β-lactamase (MBL) producers. We evaluated the antimicrobial susceptibility of Enterobacterales and P. aeruginosa (PSA) of immunosuppressed patients from US medical centers.Antimicrobial susceptibility of Enterobacterales and resistant subsets of isolates from immunosuppressed patientsa Carbapenemase-producing CRE isolates.Abbreviations: CLSI, Clinical and Laboratory Standards Institute; US FDA, United States Food and Drug Administration; MDR, multidrug-resistant; CRE, carbapenem-resistant Enterobacterales; CBase, carbapenemase.Antimicrobial susceptibility of selected species collected from immunosuppressed patientsa % inhibited at ≤8 mg/L, the CLSI breakpoint for aztreonam. b Not tested or no breakpoint published by US FDA.Abbreviations: CLSI, Clinical and Laboratory Standards Institute; US FDA, United States Food and Drug AdministrationMethodsBacterial isolates were consecutively collected (1/patient) from 75 US medical centers in 2019-2024 and susceptibility tested by broth microdilution. Enterobacterales and PSA from patients hospitalized in hematology, oncology, and transplant units were evaluated. Carbapenem-resistant Enterobacterales (CRE; isolates with MIC ≥ 4 mg/L for meropenem and/or imipenem) were screened for β-lactamase by whole genome sequencing.ResultsEnterobacterales were mainly from bloodstream infection (BSI; 53.6%) and urinary tract infection (UTI; 19.9%) and PSA were mainly from BSI (37.9%) and pneumonia (35.0%). ATM-AVI, ceftazidime-avibactam (CAZ-AVI), and meropenem-vaborbactam (MEM-VAB) were highly active against Enterobacterales (99.9-99.4% susceptible [S]), including MDR isolates (99.6-98.1% S; Table 1), ATM-AVI retained potent activity against CRE isolates (95.8% S). Ceftolozane-tazobactam (TOL-TAZ) showed good activity against E. coli (95.7% S), and K. pneumoniae (92.8% S), but limited activity against E. cloacae species complex (75.9% S; Table 2). All (100.0%) carbapenemase (CBase)-producing CRE isolates were ATM-AVI-S while 77.4% were CAZ-AVI-S and 67.7% were MEM-VAB-S. The most common CBases were KPC (61.3%), NDM (16.1%), and OXA-48 types (16.1%). MBL represented 19.4% of CBases. The most active agents against PSA were CAZ-AVI (95.7% S), TOL-TAZ (94.8% S), and tobramycin (91.5% S). PIP-TAZ and meropenem were active against 81.4% and 82.5% of PSA, respectively, and ATM-AVI inhibited 78.6% of PSA at ≤8 mg/L.ConclusionATM-AVI demonstrated almost complete activity (99.9% S) against Enterobacterales, including 100.0% of CBase producers, and both CAZ-AVI and TOL-TAZ were highly active against PSA from immunosuppressed patients.DisclosuresHelio Sader, United States Food and Drug Administration: FDA Contract Number: 75F40123C00140 Mariana Castanheira, PhD, Melinta Therapeutics: Advisor/Consultant|Melinta Therapeutics: Grant/Research Support Rodrigo E. Mendes, PhD, GSK: Grant/Research Support|Shionogi & Co., Ltd.: Grant/Research Support|United States Food and Drug Administration: FDA Contract Number: 75F40123C00140

[EN] Transcriptomics is one of the most important and relevant areas of bioinformatics. It allows detecting the genes that are expressed at a particular moment in time to explore the relation between genotype and phenotype. Transcriptomic analysis has been historically performed using microarrays until 2008 when high-throughput RNA sequencing (RNA-Seq) was launched on the market, replacing the old technique. However, despite the clear advantages over microarrays, it was necessary to understand factors such as the quality of the data, reproducibility and replicability of the analyses and potential biases.
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\nThe first section of the thesis covers these studies. First, an R package called NOISeq was developed and published in the public repository "Bioconductor", which includes a set of tools to better understand the quality of RNA-Seq data, minimise the impact of noise in any posterior analyses and implements two new methodologies (NOISeq and NOISeqBio) to overcome the difficulties of comparing two different groups of samples (differential expression). Second, I show our contribution to the Sequencing Quality Control (SEQC) project, a continuation of the Microarray Quality Control (MAQC) project led by the US Food and Drug Administration (FDA, United States) that aims to assess the reproducibility and replicability of any RNA-Seq analysis.
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\nOne of the most effective approaches to understand the different factors that influence the regulation of gene expression, such as the synergic effect of transcription factors, methylation events and chromatin accessibility, is the integration of transcriptomic with other omics data. To this aim, a file that contains the chromosomal position where the events take place is required. For this reason, in the second chapter, we present a new and easy to customise tool (RGmatch) to associate chromosomal positions to the exons, transcripts or genes that could regulate the events.
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\nAnother aspect of great interest is the study of non-coding genes, especially long non-coding RNAs (lncRNAs). Not long ago, these regions were thought not to play a relevant role and were only considered as transcriptional noise. However, they represent a high percentage of the human genes and it was recently shown that they actually play an important role in gene regulation. Due to these motivations, in the last chapter we focus, first, in trying to find a methodology to find out the generic functions of every lncRNA using publicly available data and, second, we develop a new tool (spongeScan) to predict the lncRNAs that could be involved in the sequestration of micro-RNAs (miRNAs) and therefore altering their regulation task.

Background Aztreonam-avibactam (ATM-AVI) was approved by the United States (US) Food and Drug Administration (FDA) in February 2025. ATM-AVI has shown potent activity against multidrug-resistant (MDR) Enterobacterales, including metallo-β-lactamase (MBL) producers. We evaluated activity of ATM-AVI and comparators against isolates nonsusceptible (NS) to ceftazidime-avibactam (CAZ-AVI) and/or meropenem-vaborbactam (MEM-VAB) and against MBL producers.Table 1.Antimicrobial susceptibility of resistant subsetsAbbreviations: CAZ-AVI, ceftazidime-avibactam; NS, nonsusceptible; CRE, carbapenem-resistant Enterobacterales; MBL, metallo-β-lactamase; MEM-VAB, meropenem-vaborbactam; ATM-AVI, aztreonam-avibactam; IMI-REL, imipenem-relebactam.Finlandogram showing the activity of aztreonam-avibactam against selected resistant subsets. Methods 80,927 Enterobacterales isolates were consecutively collected (1/patient) in 2016–2024 from 103 US medical centers. Among these isolates, 194 (0.24%) were NS to CAZ-AVI or MEM-VAB, 115 (0.14%) were NS to both CAZ-AVI and MEM-VAB, and 832 isolates were carbapenem-resistant (CRE). Isolates were susceptibility tested by broth microdilution and screened for carbapenemase (CBase) genes by whole genome sequencing. Cefiderocol was tested in iron-depleted media. US FDA breakpoints were applied for ATM-AVI.Distribution of carbapenemase (CBase) typesAbbreviations: NS, nonsusceptible; CAZ-AVI, ceftazidime-avibactam; MEM-VAB, meropenem-vaborbactam; CRE, carbapenem-resistant Enterobacterales. Results ATM-AVI was active (MIC ≤4 mg/L) against 91.9% of CAZ-AVI-NS, 94.0% of MEM-VAB-NS, 98.1% of CRE and 98.3% of MBL-producing isolates (Table 1 and Figure 1). Cefiderocol retained activity against 78.8% of CAZ-AVI-NS, 83.2% of MEM-VAB-NS, 94.7% of CRE and 83.2% of MBL-producing isolates. Amikacin was the most active compound among non-β-lactams, it inhibited 63.1% of CAZ-AVI-NS and 61.1% of CRE isolates at CLSI susceptible breakpoint. A CBase was identified in 154 (79.4%) of isolates NS to CAZ-AVI or MEM-VAB and 673 (80.9%) of CREs. NDM (n=110; 56.7% of isolates) was the most common CBase type among isolates NS to CAZ-AVI or MEM-VAB, whereas KPC (n=525; 63.1% of CREs) and NDM (n=109; 13.1%) were the most common CBase types among CREs (Figure 2). Notably, a MBL was identified in 34.2% (69/202) of CREs collected in 2023-2024. Conclusion ATM-AVI demonstrated potent activity against isolates NS to CAZ-AVI and/or MEM-VAB as well as against CRE isolates, including MBL producers. The activities of other β-lactamase inhibitor combinations and cefiderocol were compromised by the increasing occurrence of MBL producers in US medical centers. Disclosures Helio Sader, United States Food and Drug Administration: FDA Contract Number: 75F40123C00140 Rodrigo E. Mendes, PhD, GSK: Grant/Research Support|Shionogi & Co., Ltd.: Grant/Research Support|United States Food and Drug Administration: FDA Contract Number: 75F40123C00140 Mariana Castanheira, PhD, Melinta Therapeutics: Advisor/Consultant|Melinta Therapeutics: Grant/Research Support

BackgroundAztreonam-avibactam (ATM-AVI) was recently approved by the United States (US) Food and Drug Administration (FDA) for the treatment of intra-abdominal infections. Difficult-to-treat resistant (DTR) isolates, defined as bacterial isolates expressing nonsusceptibility to all first-line agents, is a major problem worldwide. We evaluated the activity of ATM-AVI and comparators against DTR Enterobacterales from US medical centers.Table 1.Antimicrobial susceptibility of selected resistant subsetsa Includes only DTR isolates. Abbreviations: DTR, difficult-to-treat resistant; CAZ-AVI, ceftazidime-avibactam; NS, nonsuscpetible; MEM-VAB, meropenem-vaborbactam; CRE, carbapenem-resistant Enterobacterales.Distribution of carbapenemase (CBase) types among difficult-to-treat (DTR) and carbapenem-resistant (CRE) isolatesMethods42,295 Enterobacterales isolates were consecutively collected (1/patient) from 85 US medical centers in 2020-2024 and susceptibility tested by CLSI broth microdilution. The collection included 450 carbapenem-resistant (CRE; defined as resistant [R] to meropenem or imipenem) and 307 DTR (defined as a fluoroquinolone-R CRE) isolates; which were screened for β-lactamases by whole genome sequencing.ResultsATM-AVI was active (MIC ≤ 4 mg/L) against 98.0% of DTR (MIC50/90, 0.25/1 mg/L) and 97.1% of CRE (MIC50/90, 0.25/1 mg/L) isolates, and retained potent activity against DTR isolates nonsusceptible (NS) to ceftazidime-avibactam (CAZ-AVI; 95.5% susceptible [S]; MIC50/90, 0.25/1 mg/L), meropenem-vaborbactam (MEM-VAB; 96.2% S; MIC50/90, 0.25/1 mg/L), and/or cefiderocol (90.0% S; MIC50/90, 0.5/4 mg/L; Table 1). Cefiderocol was active against 93.5% of DTR isolates, whereas CAZ-AV, MEM-VAB, IMI-REL and the aminoglycosides exhibited limited activity against these organisms. ATM-AVI (MIC50/90, 0.12/0.5 mg/L and cefiderocol (MIC50/90, 2/8 mg/L) were active against 97.7% and 86.4% of MBL producers, respectively. The most common carbapenemase (CBase) gene identified among DTR isolates were blaKPC (53.1% of isolates) and blaNDM (25.7%). DTR and CRE isolates exhibited similar frequencies of CBase types. An MBL gene was observed in 27.4% of DTR and 24.2% of CRE isolates (Figure 1).ConclusionATM-AVI retained potent activity against DTR Enterobacterales from US medical centers and its activity was not adversely affected by clinically relevant CBases or resistance to agents used to treat multidrug-resistant Enterobacterales. The activities of other β-lactamase inhibitor combinations and cefiderocol were compromised by the increased occurrence of MBL producers among DTR and CRE isolates.DisclosuresHelio Sader, United States Food and Drug Administration: FDA Contract Number: 75F40123C00140 Rodrigo E. Mendes, PhD, GSK: Grant/Research Support|Shionogi & Co., Ltd.: Grant/Research Support|United States Food and Drug Administration: FDA Contract Number: 75F40123C00140 Marisa Winkler, MD, PhD, Basilea: Advisor/Consultant|Basilea: Grant/Research Support|GSK: Advisor/Consultant|GSK: Grant/Research Support|Melinta Therapeutics: Advisor/Consultant|Melinta Therapeutics: Grant/Research Support|Mundipharma: Advisor/Consultant|Mundipharma: Grant/Research Support|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Pulmocide: Advisor/Consultant|Pulmocide: Grant/Research Support Mariana Castanheira, PhD, Melinta Therapeutics: Advisor/Consultant|Melinta Therapeutics: Grant/Research Support

Old Drugs, New Concerns

621 POSTER Should the primary tumour be resected in patients with colorectal cancer and non-resectable synchronous metastases?

Lessons learned from independent central review

As a powerful tool for genome-wide gene expression analysis, DNA microarray technology is widely used in biomedical research. One important application of microarrays is to identify differentially expressed genes (DEGs) between two distinct biological conditions, e.g. disease versus normal or treatment versus control, so that the underlying molecular mechanism differentiating the two conditions maybe revealed. Mechanistic interpretation of microarray results requires the identification of reproducible and reliable lists of DEGs, because irreproducible lists of DEGs may lead to different biological conclusions. Many vendors are providing microarray platforms of different characteristics for gene expression analysis, and the widely publicized apparent lack of intra- and cross-platform concordance in DEGs from microarray analysis of the same sets of study samples has been of great concerns to the scientific community and regulatory agencies like the US Food and Drug Administration (FDA). In this chapter, we describe the study design of and the main findings from the FDA-led MicroArray Quality Control (MAQC) project that aims to objectively assess the performance of different microarray platforms and the advantages and limitations of various competing statistical methods in identifying DEGs from microarray data. Using large data sets generated on two human reference RNA samples established by the MAQC project, we show that the levels of concordance in inter-laboratory and cross-platform comparisons are generally high. Furthermore, the levels of concordance largely depend on the statistical criteria used for ranking and selecting DEGs, irrespective of the chosen platforms or test sites. Importantly, a straightforward method combining fold-change ranking with a non-stringent P-value cutoff produces more reproducible lists of DEGs than those by t-test P-value ranking. Similar conclusions are reached when microarray data sets from a rat toxicogenomics study are analyzed. The availability of the MAQC reference RNA samples and the large reference data sets provides a unique resource for the gene expression community to reach consensus on the “best practices” for the generation, analysis, and applications of microarray data in drug development and personalized medicine.

Microarrays