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KRAS gene mutations are predictive markers of non-response to anti-epidermal growth factor receptor. An increasing number of techniques are being developed to detect KRAS mutations. To obtain consistent and comparable results, a traceable reference material (RM) is necessary for validation the routinely used method. However, a lack of reference methods is a main impediment for deriving traceability and measurement comparability. In this study, droplet digital PCR (ddPCR) and next generation sequencing (NGS) were evaluated. No cross- reactivity was detected with any of the probe by ddPCR. The measured fraction of KRAS mutant allele by ddPCR and NGS agreed with the prepared value by gravimetrical dilution (concordance ( k ) >0.95 and >0.93 for ddPCR and NGS, respectively). The reliable limit of quantification (LOQ) was 0.1% and 1% for ddPCR and NGS, respectively. In conclusion, the validated ddPCR and NGS are suitable to characterize the KRAS RM due to the high specificity and accuracy. Verification of the LOD of three commercial kits by using the NIM-KRAS-8 RM showed that the LOD was inconsistent with the claimed LOD of the kits (1%) for some assays. This indicates a traceable RM was important for setting up the criteria regarding the LOD for the commercial kit.

Digital polymerase chain reaction (dPCR) is a unique approach to measurement of the absolute copy number of target DNA without using external standards. However, the comparability of different dPCR platforms with respect to measurement of DNA copy number must be addressed before dPCR can be classified fundamentally as an absolute quantification technique. The comparability of four dPCR platforms with respect to accuracy and measurement uncertainty was investigated by using a certified plasmid reference material. Plasmid conformation was found to have a significant effect on droplet-based dPCR (QX100 and RainDrop) not shared with chip-based QuantStudio 12k or BioMark. The relative uncertainty of partition volume was determined to be 0.7%, 0.8%, 2.3% and 2.9% for BioMark, QX100, QuantStudio 12k and RainDrop, respectively. The measurements of the certified pNIM-001 plasmid made using the four dPCR platforms were corrected for partition volume and closely consistent with the certified value within the expended uncertainty. This demonstrated that the four dPCR platforms are of comparable effectiveness in quantifying DNA copy number. These findings provide an independent assessment of this method of determining DNA copy number when using different dPCR platforms and underline important factors that should be taken into consideration in the design of dPCR experiments.

Abstract Digital PCR (dPCR) has developed considerably since the publication of the Minimum Information for Publication of Digital PCR Experiments (dMIQE) guidelines in 2013, with advances in instrumentation, software, applications, and our understanding of its technological potential. Yet these developments also have associated challenges; data analysis steps, including threshold setting, can be difficult and preanalytical steps required to purify, concentrate, and modify nucleic acids can lead to measurement error. To assist independent corroboration of conclusions, comprehensive disclosure of all relevant experimental details is required. To support the community and reflect the growing use of dPCR, we present an update to dMIQE, dMIQE2020, including a simplified dMIQE table format to assist researchers in providing key experimental information and understanding of the associated experimental process. Adoption of dMIQE2020 by the scientific community will assist in standardizing experimental protocols, maximize efficient utilization of resources, and further enhance the impact of this powerful technology.

Background Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios. Results As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies. Conclusions Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

The precise quantification of KRAS single nucleotide variant (SNV) is critical for the treatment and prognosis of lung and colorectal cancer. Validation of digital PCR (dPCR) as a method for accurate quantification of KRAS SNV has great clinical importance. An international co-validation on absolute quantification of KRAS SNV by dPCR was conducted among three national measurement institutes (NMIs) from China (NIM), South Korea (KRISS), and Japan (NMIJ). A candidate reference material (RM) was provided by NIM and three measurands were reported: copy number concentration (Tc) of KRAS G12A mutation and wild type and KRAS G12A fractional abundance (FA). Homogeneity and stability assessment showed that the study materials provided by NIM were sufficiently homogeneous and stable during the study period. En number performance statistics was used to evaluate equivalence of the study among the three NMIs. All En values for both Tc and KRAS G12A FA≤1 showed good agreement and consistency with the reference value within the expanded uncertainty. This indicates that dPCR with full uncertainty evaluation can serve as a candidate primary reference measurement procedure (PRMP) for the KRAS SNV measurement and value assignment of reference materials.

Background Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets. Results As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC–MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing “ground truth” for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols. Conclusions Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies.

Background/Aims: Circulating tumor DNA (ctDNA) is becoming a valuable cancer biomarker for clinical decision-making. Nevertheless, the lack of quality control materials to assess the reliability of test results remains a challenge. This study aimed to establish digital PCR (dPCR) assays for detecting TP53 variants (R175H and R248W) and develop a preparation method for ctDNA reference materials to improve detection reliability. Methods: Two dPCR assays targeting TP53-R175H and TP53-R248W variants were developed and validated for repeatability, sensitivity, and linearity. Additionally, a ctDNA reference material preparation protocol was developed by digesting nucleosomes from cultured cancer cell lines with micrococcal nuclease, followed by magnetic beads purification. The size distribution and quality of the generated ctDNA fragments was analyzed, and the developed dPCR assays were applied to detect the variants in the ctDNA samples. Results: The dPCR assays demonstrated high repeatability (RSD of 0.16% to 7.65%) and excellent linearity (R2 values of 1.0000 and 0.9981) across variant allele frequencies of 50%–0.1%. The limits of detection (LOD) and quantification (LOQ) were 0.143% (R175H) and 0.092% (R248W). The ctDNA reference materials exhibited single dominant peaks at 128 bp (R175H) and 143 bp (R248W). The dPCR assays successfully detected variants in these reference materials, confirming their applicability for ctDNA samples. onclusion: Firstly, accurate measurement procedures for TP53-R175H and TP53-R248W variants based on dPCR were established in this study. Furthermore, a protocol for preparing ctDNA reference material was established here. By digesting nucleosomal DNA derived from cancer cell lines with micrococcal nuclease, this method can closely mimic the properties of clinical ctDNA. The dPCR method and ctDNA reference material preparation approach established here could be used in ctDNA detection and for improving its reliability.

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory ‘ground truth’. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings. A new RNA reference set improves detection of differential expression in clinical settings.

Background Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome. Results We established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data. Conclusions The Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.

Background Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS. Results To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×. Conclusions Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.