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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.

Due to low success rates and long cycles of traditional drug development, the clinical tendency is to apply omics techniques to reveal patient-level disease characteristics and individualized responses to treatment. However, the heterogeneous form of data and uneven distribution of targets make drug discovery and precision medicine a non-trivial task. This study takes pyroptosis therapy for triple-negative breast cancer (TNBC) as a paradigm and uses data mining of a large TNBC cohort and drug databases to establish a biofactor-regulated neural network for rapidly screening and optimizing compound pyroptosis drug pairs. Subsequently, biomimetic nanococrystals are prepared using the preferred combination of mitoxantrone and gambogic acid for rational drug delivery. The unique mechanism of obtained nanococrystals regulating pyroptosis genes through ribosomal stress and triggering pyroptosis cascade immune effects are revealed in TNBC models. In this work, a target omics-based intelligent compound drug discovery framework explores an innovative drug development paradigm, which repurposes existing drugs and enables precise treatment of refractory diseases. Cancer-targeted drug discovery can be achieved by transcriptomics screening on patients. Here this group reports a drug target screening model built upon triple-negative breast cancer (TNBC) cohort and drug database with the selected drug pair exhibiting effective pyroptosis induction and TNBC tumor growth inhibition.

Translating RNA-seq into clinical diagnostics requires ensuring the reliability and cross-laboratory consistency of detecting clinically relevant subtle differential expressions, such as those between different disease subtypes or stages. As part of the Quartet project, we present an RNA-seq benchmarking study across 45 laboratories using the Quartet and MAQC reference samples spiked with ERCC controls. Based on multiple types of ‘ground truth’, we systematically assess the real-world RNA-seq performance and investigate the influencing factors involved in 26 experimental processes and 140 bioinformatics pipelines. Here we show greater inter-laboratory variations in detecting subtle differential expressions among the Quartet samples. Experimental factors including mRNA enrichment and strandedness, and each bioinformatics step, emerge as primary sources of variations in gene expression. We underscore the profound influence of experimental execution, and provide best practice recommendations for experimental designs, strategies for filtering low-expression genes, and the optimal gene annotation and analysis pipelines. In summary, this study lays the foundation for developing and quality control of RNA-seq for clinical diagnostic purposes. Here the authors report on an RNA-seq benchmarking study that demonstrates greater inter-lab variations in detecting subtle differential expression. The study reveals the impact of experimental execution, experimental designs, low-expression gene filtering, and analysis tool selection.

The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop “distribution-collection-evaluation-integration” workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.

The heterogeneity of triple‐negative breast cancers (TNBC) remains challenging for various treatments. Ferroptosis, a recently identified form of cell death resulting from the unrestrained peroxidation of phospholipids, represents a potential vulnerability in TNBC. In this study, a high intensity focused ultrasound (HIFU)‐driven nanomotor is developed for effective therapy of TNBC through induction of ferroptosis. Through bioinformatics analysis of typical ferroptosis‐associated genes in the FUSCCTNBC dataset, gambogic acid is identified as a promising ferroptosis drug and loaded it into the nanomotor. It is found that the rapid motion of nanomotors propelled by HIFU significantly enhanced tumor accumulation and penetration. More importantly, HIFU not only actuated nanomotors to trigger effective ferroptosis of TNBC cells, but also drove nanomotors to activate ferroptosis‐mediated antitumor immunity in primary and metastatic TNBC models, resulting in effective tumor regression and prevention of metastases. Overall, HIFU‐driven nanomotors show great potential for ferroptosis‐immunotherapy of TNBC. Triple‐negative breast cancer (TNBC) poses a significant treatment challenge due to its heterogeneous nature. Researchers have developed a nanomotor powered by high intensity focused ultrasound (HIFU) to deliver a ferroptosis drug, which is identified through bioinformatics analysis. This HIFU‐responsive nanomotor is able to trigger ferroptosis, activate the immune system, reduce tumor growth, and prevent metastasis in TNBC models.

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 Patients with adenocarcinoma in situ (AIS) and minimally invasive (MIA) lung adenocarcinoma (LUAD) are curable by surgery, whereas 20% stage I patients die within five years after surgery. We hypothesize that poor-prognosis stage I patients may exhibit key molecular characteristics deviating from AIS/MIA. Therefore, we tried to reveal molecularly and prognostically distinct subtypes of stage I LUAD by applying key molecular alterations from AIS/MIA to invasive LUAD progression. Methods The RNA and whole-exome sequencing data of 197 tumor-normal matched samples from patients with AIS, MIA, and invasive LUAD were analyzed. ddPCR quantified 202 samples from 182 patients at the absolute expression level. Immunohistochemical quantified the protein expression levels of ACTA2. RNA-seq data from 954 LUAD patients, including 541 stage I patients, along with 12 published datasets comprising 1,331 stage I LUAD patients, were used to validate our findings. Results Focal adhesion (FA) was identified as the only pathway significantly perturbed at both genomic and transcriptomic levels by comparing 98 AIS/MIA and 99 LUAD. Then, two FA genes (COL11A1 and THBS2) were found strongly upregulated from AIS/MIA to stage I while steadily expressed from normal to AIS/MIA. Furthermore, unsupervised clustering separated stage I patients into two molecularly and prognostically distinct subtypes (S1 and S2) based on COL11A1 and THBS2 expressions (FA2). Subtype S1 resembled AIS/MIA, whereas S2 exhibited more somatic alterations and activated cancer-associated fibroblast. Immunohistochemistry on 73 samples also observed that CAF was more active in S2 compared to S1 and AIS/MIA. The prognostic value of these two genes identified from our knowledge-driven process was confirmed by 541 stage I patients in a prospective dataset, ddPCR and 12 published datasets. Conclusions We successfully revealed two molecularly and prognostically distinct subtypes of stage I LUAD by applying key molecular alterations from AIS/MIA to invasive LUAD progression. Our model may help reliably identify high-risk stage I patients for more intensive post-surgery treatment.

The lack of quantitative methylation reference datasets (ground truth) and cross-laboratory reproducibility assessment hinders clinical translation of epigenome-wide sequencing technologies. Using certified Quartet DNA reference materials, here we generate 108 epigenome-sequencing datasets across three mainstream protocols (whole-genome bisulfite sequencing, enzymatic methyl-seq, and TET-assisted pyridine borane sequencing) with triplicates per sample across laboratories. We observe strand-specific methylation biases across all protocols and libraries. Cross-laboratory reproducibility analyses reveal high quantitative methylation levels agreement (mean Pearson correlation coefficient (PCC) = 0.96) but low detection concordance (mean Jaccard index = 0.36). Using consensus voting, we construct genome-wide quantitative methylation reference datasets serving as ground truth for proficiency testing. Key technical parameters–including mean CpG depth, coverage, and strand consistency–correlate strongly with reference-dependent quality metrics (recall, PCC, and RMSE). Collectively, these resources establish foundational standards for benchmarking emerging epigenomic technologies and analytical pipelines, enabling robust, standardized quality control in research and clinical applications. Quality control for epigenomic datasets requires robust ground truths. Here, authors generate genome-wide quantitative methylation reference datasets from the publicly available Quartet DNA reference materials, which could serve as a resource for the standardised benchmarking of emerging technologies.

Introduction: Next-generation sequencing (NGS) technologies have been widely used in clinical genomic testing for drug response phenotypes. However, the inherent limitations of short reads make accurate inference of diplotypes still challenging, which may reduce the effectiveness of genotype-guided drug therapy. Methods: An automated Pharmacogenomics Annotation tool (PAnno) was implemented, which reports prescribing recommendations and phenotypes by parsing the germline variant call format (VCF) file from NGS and the population to which the individual belongs. Results: A ranking model dedicated to inferring diplotypes, developed based on the allele (haplotype) definition and population allele frequency, was introduced in PAnno. The predictive performance was validated in comparison with four similar tools using the consensus diplotype data of the Genetic Testing Reference Materials Coordination Program (GeT-RM) as ground truth. An annotation method was proposed to summarize prescribing recommendations and classify drugs into avoid use, use with caution, and routine use, following the recommendations of the Clinical Pharmacogenetics Implementation Consortium (CPIC), etc. It further predicts phenotypes of specific drugs in terms of toxicity, dosage, efficacy, and metabolism by integrating the high-confidence clinical annotations in the Pharmacogenomics Knowledgebase (PharmGKB). PAnno is available at https://github.com/PreMedKB/PAnno . Discussion: PAnno provides an end-to-end clinical pharmacogenomics decision support solution by resolving, annotating, and reporting germline variants.