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Abstract Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.

Abstract 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.

Abstract 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.

Abstract Background Copy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome. Results While consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395). Conclusions NGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools.

Abstract Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Abstract 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.

Abstract 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.

Abstract Background Recent state-of-the-art sequencing technologies enable the investigation of challenging regions in the human genome and expand the scope of variant benchmarking datasets. Herein, we sequence a Chinese Quartet, comprising two monozygotic twin daughters and their biological parents, using four short and long sequencing platforms (Illumina, BGI, PacBio, and Oxford Nanopore Technology). Results The long reads from the monozygotic twin daughters are phased into paternal and maternal haplotypes using the parent–child genetic map and for each haplotype. We also use long reads to generate haplotype-resolved whole-genome assemblies with completeness and continuity exceeding that of GRCh38. Using this Quartet, we comprehensively catalogue the human variant landscape, generating a dataset of 3,962,453 SNVs, 886,648 indels (

Abstract 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.

Defects in genes involved in the DNA damage response cause homologous recombination repair deficiency (HRD). HRD is found in a subgroup of cancer patients for several tumor types, and it has a clinical relevance to cancer prevention and therapies. Accumulating evidence has identified HRD as a biomarker for assessing the therapeutic response of tumor cells to poly(ADP-ribose) polymerase inhibitors and platinum-based chemotherapies. Nevertheless, the biology of HRD is complex, and its applications and the benefits of different HRD biomarker assays are controversial. This is primarily due to inconsistencies in HRD assessments and definitions (gene-level tests, genomic scars, mutational signatures, or a combination of these methods) and difficulties in assessing the contribution of each genomic event. Therefore, we aim to review the biological rationale and clinical evidence of HRD as a biomarker. This review provides a blueprint for the standardization and harmonization of HRD assessments.

Abstract 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.

Abstract 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.

Abstract Due to the tolerance of mismatches between gRNA and targeting sequence, base editors frequently induce unwanted Cas9-dependent off-target mutations. Here, to develop models to predict such off-targets, we design gRNA-off- target pairs for adenine base editors (ABEs) and cytosine base editors (CBEs) and stably integrate them into the human cells. After five days of editing, we obtain valid efficiency datasets of 54,663 and 55,727 off-targets for ABEs and CBEs, respectively. We use the datasets to train deep learning models, resulting in ABEdeepoff and CBEdeepoff, which can predict off-target sites. We use these tools to predict off-targets for a panel of endogenous loci and achieve Spearman correlation values varying from 0.710 to 0.859. Finally, we develop an integrated tool that is freely accessible via an online web server http://www.deephf.com/#/bedeep/bedeepoff . These tools could facilitate minimizing the off-target effects of base editing.

Infection by the Schistosoma japonicum can result in acute, chronic and late-stage manifestations. The latter often presents with severe organ failures and premature death. Importantly, infection can also produce autoimmune phenomena reflected by the development of autoantibodies. We wished to explore and profile the presence of autoantibodies in sera of patients with different stages of S. japonicum infection with the added aim of providing a reference assisting diagnosis. Blood samples from 55 patients with chronic and 20 patients with late-stage schistosomiasis japonica together, with a control group of 50 healthy people were randomly investigated against a microarray of 121 different autoantigens. In addition, the frequency of antibodies against Schistosoma egg antigen (SEA) was examined. In the sera from patients with chronic schistosomiasis japonica, 14 different highly expressed autoantibodies were detected, while patients with late-stage schistosomiasis were found to express as many as 43 autoantibody specificities together with a significantly higher frequency of antibodies against SEA compared to the control group. The findings presented suggest that autoantibody-based classification of schistosomiasis japonica represents a promising approach for the elucidation of subtypes of the disease. This approach may reflect differential disease mechanisms, which could ultimately lead to better treatment.

Glioma undergoes adaptive changes, leading to poor prognosis and resistance to treatment. CD99 influences the migration and invasion of glioma cells and plays an oncogene role. However, whether CD99 can affect the adaptiveness of gliomas is still lacking in research, making its clinical value underestimated. Here, we enrolled our in-house and public multiomics datasets for bioinformatic analysis and conducted immunohistochemistry staining to investigate the role of CD99 in glioma adaptive response and its clinical implications.CD99 is expressed in more adaptative glioma subtypes and cell states. Under hypoxic conditions, CD99 is upregulated in glioma cells and is associated with angiogenesis and metabolic adaptations. Gliomas with over-expressed CD99 also increased the immunosuppressive tumor-associated macrophages. The relevance with tumor adaptiveness of CD99 presented clinical significance. We discovered that CD99 overexpression is associated with short-time recurrence and validated its prognostic value. Additionally, Glioma patients with high expression of CD99 were resistant to chemotherapy and radiotherapy. The CD99 expression was also related to anti-angiogenic and immune checkpoint inhibitor therapy response. Inhibitors of the PI3K-AKT pathway have therapeutic potential against CD99-overexpressing gliomas.Our study identified CD99 as a biomarker characterizing the adaptive response in glioma. Gliomas with high CD99 expression are highly tolerant to stress conditions such as hypoxia and antitumor immunity, making treatment responses dimmer and tumor progression. Therefore, for patients with CD99-overexpressing gliomas, tumor adaptiveness should be fully considered during treatment to avoid drug resistance, and closer clinical monitoring should be carried out to improve the prognosis.

Summary: Background: Ground-glass opacity (GGO)-like lung adenocarcinoma (LUAD) has been detected increasingly in the clinic and its inert property and superior survival indicate unique biological characteristics. However, we do not know much about them, which hampers identification of key reasons for the inert property of GGO-like LUAD. Methods: Using whole-exome sequencing and RNA sequencing, taking into account both radiological and pathological classifications of the same 197 patients concomitantly, we systematically interrogate genes driving the progression from GGO to solid nodule and potential reasons for the inertia of GGO. Using flow cytometry and IHC, we validated the abundance of immune cells and activity of cell proliferation. Findings: Identifying the differences between GGO and solid nodule, we found adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) and GGO-like LUAD exhibited lower TP53 mutation frequency and less active cell proliferation-related pathways than solid nodule in LUAD. Identifying the differences in GGO between AIS/MIA and LUAD, we noticed that EGFR mutation frequency and CNV load were significantly higher in LUAD than in AIS/MIA. Regulatory T cell was also higher in LUAD, while CD8+ T cell decreased from AIS/MIA to LUAD. Finally, we constructed a transcriptomic signature to quantify the development from GGO to solid nodule, which was an independent predictor of patients’ prognosis in 11 external LUAD datasets. Interpretation: Our results provide deeper insights into the indolent nature of GGO and provide a molecular basis for the treatment of GGO-like LUAD. Funding: This study was supported in part by the National Natural Science Foundation of China (32170657), the National Natural Science Foundation of China (82203037), and Shanghai Sailing Program (22YF1408900).

Measurement(s) RNA expression profiling • whole-exome sequencing (WES) • somatic mutations • copy number alterations (CNAs) Technology Type(s) RNA sequencing • DNA sequencing • OncoScan CNV assay Factor Type(s) Intervention or procedure Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location China

Abstract Background Clinical laboratories routinely use formalin-fixed paraffin-embedded (FFPE) tissue or cell block cytology samples in oncology panel sequencing to identify mutations that can predict patient response to targeted therapy. To understand the technical error due to FFPE processing, a robustly characterized diploid cell line was used to create FFPE samples with four different pre-tissue processing formalin fixation times. A total of 96 FFPE sections were then distributed to different laboratories for targeted sequencing analysis by four oncopanels, and variants resulting from technical error were identified. Results Tissue sections that fail more frequently show low cellularity, lower than recommended library preparation DNA input, or target sequencing depth. Importantly, sections from block surfaces are more likely to show FFPE-specific errors, akin to “edge effects” seen in histology, while the inner samples display no quality degradation related to fixation time. Conclusions To assure reliable results, we recommend avoiding the block surface portion and restricting mutation detection to genomic regions of high confidence.

Measurement(s) miRNA expression profiling Technology Type(s) Next Generation Sequencing Factor Type(s) organ • age • sex Sample Characteristic - Organism Rattus norvegicus

Abstract 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.

Interactive visualization is an effective way to promote the reproducibility of results presented in biomedical publications and to facilitate additional exploration of the reported data. However, there is a lack of convenient tools that balance reproducibility with ease of use. To address this problem, we develop BioVisReport, a lightweight solution for the rapid generation of an interactive website based on a user-defined Markdown file, which acts as a text markup language without requiring users to master complex syntax and allows them to preview the results in real-time. Interactive websites generated by the tool can help readers conveniently reproduce research findings and perform further in-depth analyses beyond those reported in the original peer-reviewed publications. Currently, BioVisReport offers 17 basic types of plots for visualizing published data. In addition, the extensibility of BioVisReport supports flexible integration of user-developed Python plugins with multiple programming languages. BioVisReport is freely available at https://biovis.report/.

Sericinus montelus (Lepidoptera, Papilionidae, Parnassiinae) is a high-value ornamental swallowtail butterfly species widely distributed in Northern and Central China, Japan, Korea, and Russia. The larval stage of this species feeds exclusively on Aristolochia plants. The Aristolochia species is well known for its high levels of aristolochic acids (AAs), which have been found to be carcinogenic for numerous animals. The swallowtail butterfly is among the few that can feed on these toxic host plants. However, the genetic adaptation of S. montelus to confer new abilities for AA tolerance has not yet been well explored, largely due to the limited genomic resources of this species. This study aimed to present a chromosome-level reference genome for S. montelus using the Oxford Nanopore long-read sequencing, Illumina short-read sequencing, and Hi-C technology. The final assembly was composed of 581.44 Mb with an expected genome size of 619.27 Mb. Further, 99.98% of the bases could be anchored onto 30 chromosomes. The N50 of contigs and scaffolds was 5.74 and 19.12 Mb, respectively. Approximately 48.86% of the assembled genome was suggested to be repeat elements, and 13,720 protein-coding genes were predicted in the current assembly. The phylogenetic analysis indicated that S. montelus diverged from the common ancestor of swallowtails about 58.57–80.46 million years ago. Compared with related species, S. montelus showed a significant expansion of P450 gene family members, and positive selections on eloa, heatr1, and aph1a resulted in the AA tolerance for S. montelus larva. The de novo assembly of a high-quality reference genome for S. montelus provided a fundamental genomic tool for future research on evolution, genome genetics, and toxicology of the swallowtail butterflies.

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.

Abstract Background Genomic structural variations (SV) are important determinants of genotypic and phenotypic changes in many organisms. However, the detection of SV from next-generation sequencing data remains challenging. Results In this study, DNA from a Chinese family quartet is sequenced at three different sequencing centers in triplicate. A total of 288 derivative data sets are generated utilizing different analysis pipelines and compared to identify sources of analytical variability. Mapping methods provide the major contribution to variability, followed by sequencing centers and replicates. Interestingly, SV supported by only one center or replicate often represent true positives with 47.02% and 45.44% overlapping the long-read SV call set, respectively. This is consistent with an overall higher false negative rate for SV calling in centers and replicates compared to mappers (15.72%). Finally, we observe that the SV calling variability also persists in a genotyping approach, indicating the impact of the underlying sequencing and preparation approaches. Conclusions This study provides the first detailed insights into the sources of variability in SV identification from next-generation sequencing and highlights remaining challenges in SV calling for large cohorts. We further give recommendations on how to reduce SV calling variability and the choice of alignment methodology.

Measurement(s) Somatic Mutation Analysis Technology Type(s) whole genome sequencing • Whole Exome Sequencing Factor Type(s) sequencing platform • sample prepration • library preparation • bioinformatics method Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.16713655

Personalised medicine (PM) presents a great opportunity to improve the future of individualised healthcare. Recent advances in -omics technologies have led to unprecedented efforts characterising the biology and molecular mechanisms that underlie the development and progression of a wide array of complex human diseases, supporting further development of PM. This article reflects the outcome of the 2021 EATRIS-Plus Multi-omics Stakeholder Group workshop organised to 1) outline a global overview of common promises and challenges that key European stakeholders are facing in the field of multi-omics research, 2) assess the potential of new technologies, such as artificial intelligence (AI), and 3) establish an initial dialogue between key initiatives in this space. Our focus is on the alignment of agendas of European initiatives in multi-omics research and the centrality of patients in designing solutions that have the potential to advance PM in long-term healthcare strategies.

Abstract Background Lung adenocarcinoma (LUAD) remains one of the world’s most known aggressive malignancies with a high mortality rate. Molecular biological analysis and bioinformatics are of great importance as they have recently occupied a large area in the studies related to the identification of various biomarkers to predict survival for LUAD patients. In our study, we attempted to identify a new prognostic model by developing a new algorithm to calculate the allele frequency deviation (AFD), which in turn may assist in the early diagnosis and prediction of clinical outcomes in LUAD. Method First, a new algorithm was developed to calculate AFD using the whole-exome sequencing (WES) dataset. Then, AFD was measured for 102 patients, and the predictive power of AFD was assessed using Kaplan–Meier analysis, receiver operating characteristic (ROC) curves, and area under the curve (AUC). Finally, multivariable cox regression analyses were conducted to evaluate the independence of AFD as an independent prognostic tool. Result The Kaplan–Meier analysis showed that AFD effectively segregated patients with LUAD into high-AFD-value and low-AFD-value risk groups (hazard ratio HR = 1.125, 95% confidence interval CI 1.001–1.26, p = 0.04) in the training group. Moreover, the overall survival (OS) of patients who belong to the high-AFD-value group was significantly shorter than that of patients who belong to the low-AFD-value group with 42.8% higher risk and 10% lower risk of death for both groups respectively (HR for death = 1.10; 95% CI 1.01–1.2, p = 0.03) in the training group. Similar results were obtained in the validation group (HR = 4.62, 95% CI 1.22–17.4, p = 0.02) with 41.6%, and 5.5% risk of death for patients who belong to the high and low-AFD-value groups respectively. Univariate and multivariable cox regression analyses demonstrated that AFD is an independent prognostic model for patients with LUAD. The AUC for 5-year survival were 0.712 and 0.86 in the training and validation groups, respectively. Conclusion AFD was identified as a new independent prognostic model that could provide a prognostic tool for physicians and contribute to treatment decisions.

BackgroundThe overall 5-year survival of lung cancer was reported to be only ~15%, with lung adenocarcinoma (LUAD) as the main pathological subtype. Before developing into invasive stages, LUAD undergoes pre-invasive stages of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA), where surgical resection gives an excellent 5-year survival rate. Given the dramatic decline of prognosis from pre-invasive to invasive stages, a deeper understanding of key molecular changes driving the progression of LUAD is highly needed.MethodsIn this study, we performed whole-exome sequencing and RNA sequencing on surgically resected 24 AIS, 74 MIA, 99 LUAD specimens, and their adjacent paired normal tissues. Survival data were obtained by follow-up after surgery. Key molecular events were found by comparing the gene expression profiles of tumors with different stages. Finally, to measure the level of imbalance between tumor intrinsic growth potential and immune microenvironment, a tumor progressive (TP) index was developed to predict tumor progression and patients’ survival outcome and validated by external datasets.ResultsAs tumors progressed to more invasive stages, they acquired higher growth potential, mutational frequency of tumor suppressor genes, somatic copy number alterations, and tumor mutation burden, along with suppressed immune function. To better predict tumor progression and patients’ outcome, TP index were built to measure the imbalance between tumor intrinsic growth potential and immune microenvironment. Patients with a higher TP index had significantly worse recurrence-free survival [Hazard ratio (HR), 10.47; 95% CI, 3.21–34.14; p < 0.0001] and overall survival (OS) [Hazard ratio (HR), 4.83e8; 95% CI, 0–Inf; p = 0.0013]. We used The Cancer Genome Atlas (TCGA)-LUAD dataset for validation and found that patients with a higher TP index had significantly worse OS (HR, 1.10; 95% CI, 0.83–1.45; p = 0.048), demonstrating the prognostic value of the TP index for patients with LUAD.ConclusionsThe imbalance of tumor intrinsic growth potential and immune function orchestrate the progression of LUAD, which can be measured by TP index. Our study provided new insights into predicting survival of patients with LUAD and new target discovery for LUAD through assessing the imbalance between tumor intrinsic growth potential and immune function.

Abstract Background Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients. Methods Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature. Results A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P

Abstract We identified a rare missense germline mutation in BARD1 (c.403G>A or p.Asp135Asn) as pathogenic using integrated genomics and transcriptomics profiling of germline and tumor samples from an early-onset triple-negative breast cancer patient who later was administrated with a PARP inhibitor for 2 months. We demonstrated in cell and mouse models that, compared to the wild-type, (1) c.403G>A mutant cell lines were more sensitive to irradiation, a DNA damage agent, and a PARP inhibitor; (2) c.403G>A mutation inhibited interaction between BARD1 and RAD51 (but not BRCA1); and (3) c.403G>A mutant mice were hypersensitive to ionizing radiation. Our study shed lights on the clinical interpretation of rare germline mutations of BARD1.

Abstract Background Triple-negative breast cancer (TNBC) is a heterogeneous disease and we have previously shown that rapid relapse of TNBC is associated with distinct sociodemographic features. We hypothesized that rapid versus late relapse in TNBC is also defined by distinct clinical and genomic features of primary tumors. Methods Using three publicly-available datasets, we identified 453 patients diagnosed with primary TNBC with adequate follow-up to be characterized as ‘rapid relapse’ (rrTNBC; distant relapse or death ≤2 years of diagnosis), ‘late relapse’ (lrTNBC; > 2 years) or ‘no relapse’ (nrTNBC: > 5 years no relapse/death). We explored basic clinical and primary tumor multi-omic data, including whole transcriptome (n = 453), and whole genome copy number and mutation data for 171 cancer-related genes (n = 317). Association of rapid relapse with clinical and genomic features were assessed using Pearson chi-squared tests, t-tests, ANOVA, and Fisher exact tests. We evaluated logistic regression models of clinical features with subtype versus two models that integrated significant genomic features. Results Relative to nrTNBC, both rrTNBC and lrTNBC had significantly lower immune signatures and immune signatures were highly correlated to anti-tumor CD8 T-cell, M1 macrophage, and gamma-delta T-cell CIBERSORT inferred immune subsets. Intriguingly, lrTNBCs were enriched for luminal signatures. There was no difference in tumor mutation burden or percent genome altered across groups. Logistic regression mModels that incorporate genomic features significantly outperformed standard clinical/subtype models in training (n = 63 patients), testing (n = 63) and independent validation (n = 34) cohorts, although performance of all models were overall modest. Conclusions We identify clinical and genomic features associated with rapid relapse TNBC for further study of this aggressive TNBC subset.

Abstract Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

Abstract Background Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing. Results All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5–20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden. Conclusion This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.

Abstract Background Esophageal cancer (EC) is considered as one of the deadliest malignancies with respect to incidence and mortality rate, and numerous risk factors may affect the prognosis of EC patients. For better understanding of the risk factors associated with the onset and prognosis of this malignancy, we develop an interactive web-based tool for the convenient analysis of clinical and survival characteristics of EC patients. Methods The clinical data were obtained from The Surveillance, Epidemiology, and End Results (SEER) database. Seven analysis and visualization modules were built with Shiny. Results The Esophageal Cancer Clinical Data Interactive Analysis (ECCDIA, http://webapps.3steps.cn/ECCDIA/ ) was developed to provide basic data analysis, visualization, survival analysis, and nomogram of the overall group and subgroups of 77,273 EC patients recorded in SEER. The basic data analysis modules contained distribution analysis of clinical factor ratios, Sankey plot analysis for relationships between clinical factors, and a map for visualizing the distribution of clinical factors. The survival analysis included Kaplan-Meier (K-M) analysis and Cox analysis for different subgroups of EC patients. The nomogram module enabled clinicians to precisely predict the survival probability of different subgroups of EC patients. Conclusion ECCDIA provides clinicians with an interactive prediction and visualization tool for visualizing invaluable clinical and prognostic information of individual EC patients, further providing useful information for better understanding of esophageal cancer.

Abstract Pseudomonas aeruginosa is an opportunistic pathogen that thrives in diverse environments and causes a variety of human infections. Pseudomonas aeruginosa AG1 (PaeAG1) is a high-risk sequence type 111 (ST-111) strain isolated from a Costa Rican hospital in 2010. PaeAG1 has both blaVIM-2 and blaIMP-18 genes encoding for metallo-β-lactamases, and it is resistant to β-lactams (including carbapenems), aminoglycosides, and fluoroquinolones. Ciprofloxacin (CIP) is an antibiotic commonly used to treat P. aeruginosa infections, and it is known to produce DNA damage, triggering a complex molecular response. In order to evaluate the effects of a sub-inhibitory CIP concentration on PaeAG1, growth curves using increasing CIP concentrations were compared. We then measured gene expression using RNA-Seq at three time points (0, 2.5 and 5 h) after CIP exposure to identify the transcriptomic determinants of the response (i.e. hub genes, gene clusters and enriched pathways). Changes in expression were determined using differential expression analysis and network analysis using a top–down systems biology approach. A hybrid model using database-based and co-expression analysis approaches was implemented to predict gene–gene interactions. We observed a reduction of the growth curve rate as the sub-inhibitory CIP concentrations were increased. In the transcriptomic analysis, we detected that over time CIP treatment resulted in the differential expression of 518 genes, showing a complex impact at the molecular level. The transcriptomic determinants were 14 hub genes, multiple gene clusters at different levels (associated to hub genes or as co-expression modules) and 15 enriched pathways. Down-regulation of genes implicated in several metabolism pathways, virulence elements and ribosomal activity was observed. In contrast, amino acid catabolism, RpoS factor, proteases, and phenazines genes were up-regulated. Remarkably, > 80 resident-phage genes were up-regulated after CIP treatment, which was validated at phenomic level using a phage plaque assay. Thus, reduction of the growth curve rate and increasing phage induction was evidenced as the CIP concentrations were increased. In summary, transcriptomic and network analyses, as well as the growth curves and phage plaque assays provide evidence that PaeAG1 presents a complex, concentration-dependent response to sub-inhibitory CIP exposure, showing pleiotropic effects at the systems level. Manipulation of these determinants, such as phage genes, could be used to gain more insights about the regulation of responses in PaeAG1 as well as the identification of possible therapeutic targets. To our knowledge, this is the first report of the transcriptomic analysis of CIP response in a ST-111 high-risk P. aeruginosa strain, in particular using a top-down systems biology approach.

Background: Mutations in genes encoding sarcomere and cytoskeletal proteins are major causes of primary dilated cardiomyopathy (DCM). Likewise, ischemic myocardial injury is a major cause of secondary cardiac remodeling, which, in a subset, is severe and resembles DCM. The latter is referred to as ischemic dilated cardiomyopathy (IDCM). We postulated the presence of pathogenic and likely pathogenic variants (PVs and LPVs, respectively) in genes known to cause primary DCM might predispose the heart to severe cardiac dilatation and dysfunction post myocardial ischemic injury, i.e., IDCM.Methods: We performed whole-exome sequencing in 1,041 patients with primary DCM, 215 patients with IDCM, and 414 healthy controls. Indices of cardiac size and function were similar between those with primary and ischemic DCM. PVs and LPVs, including the truncating variants in 36 genes known to cause primary DCM were identified and compared among the three groups.Results: Pathogenic variants and LPVs were detected in 266 individuals, comprised of 215/1,041 (20.7%) patients with DCM, 27/215 (12.6%) patients with IDCM, and 24/414 (5.8%) control individuals. PVs and LPVs in the TTN gene were the most common and detected in 130/1,041 (12.5%) of patients with DCM, 15/215 (7.0%) of cases with IDCM, and 10/414 (2.4%) control individuals. Of 135 TTNtv, 118 involved exons that were >90% spliced in. These variants were found in 120/1,041 (11.5%) of DCM patients, 6/215 (2.8%) of IDCM cases, and only in 1/414 (0.2%) of the control population (p < 0.001 among the three groups).Conclusions: Pathogenic variants and LPVs in genes known to cause primary DCM are enriched in patients with IDCM, suggesting that such variants function as susceptibility alleles for cardiac dilatation and dysfunction in post myocardial ischemic injury. Thus, IDCM shares a partial genetic etiology with the primary DCM.

MYCN amplification is common in neuroblastomas. Here, the authors identify a long noncoding RNA, lncNB1 in these cancers and show that it promotes tumorigenesis by binding to ribosomal protein, RPL35 to enhance E2F1 and DEPDC1B protein synthesis, which phosphorylates ERK to stabilise N-Myc.

The genomic and immune landscape of pre-invasive lung adenocarcinoma is poorly understood. Here, the authors perform exome and transcriptome sequencing on precursor legions and invasive lung adenocarcinomas, identifying recurrently mutated genes in pre/minimally invasive cases, and arm level alteration events linked to immune infiltration.

Application of highly specific Cas9 variants can be restricted by the design of the guide RNA. Here the authors present DeepHF, a gRNA activity prediction tool built from genome-scale screens of 50,000 guides covering 20,000 genes.

Abstract Background Reference genome selection is a prerequisite for successful analysis of next generation sequencing (NGS) data. Current practice employs one of the two most recent human reference genome versions: HG19 or HG38. To date, the impact of genome version on SNV identification has not been rigorously assessed. Methods We conducted analysis comparing the SNVs identified based on HG19 vs HG38, leveraging whole genome sequencing (WGS) data from the genome-in-a-bottle (GIAB) project. First, SNVs were called using 26 different bioinformatics pipelines with either HG19 or HG38. Next, two tools were used to convert the called SNVs between HG19 and HG38. Lastly we calculated conversion rates, analyzed discordant rates between SNVs called with HG19 or HG38, and characterized the discordant SNVs. Results The conversion rates from HG38 to HG19 (average 95%) were lower than the conversion rates from HG19 to HG38 (average 99%). The conversion rates varied slightly among the various calling pipelines. Around 1.5% SNVs were discordantly converted between HG19 or HG38. The conversions from HG38 to HG19 had more SNVs which failed conversion and more discordant SNVs than the opposite conversion (HG19 to HG38). Most of the discordant SNVs had low read depth, were low confidence SNVs as defined by GIAB, and/or were predominated by G/C alleles (52% observed versus 42% expected). Conclusion A significant number of SNVs could not be converted between HG19 and HG38. Based on careful review of our comparisons, we recommend HG38 (the newer version) for NGS SNV analysis. To summarize, our findings suggest caution when translating identified SNVs between different versions of the human reference genome.

Background: Mutations in the TTN gene are the most common causes of dilated cardiomyopathy (DCM). The clinical significance of TTN gene variants remains inadequately understood.Methods: Whole-exome sequencing and phenotypic characterisation were performed, and patients were followed up for a median of 44 months.Results: We analyzed the association of the TTN variants with the clinical outcomes in a prospective study of 1,041 patients with sporadic DCM. TTN truncating variants (tTTN) were detected in 120 (11.5%) patients as compared with 2.4/10,000 East Asian populations in the Genome Aggregation Database (GnomAD; p < 0.0001). Pathogenic TTN missense variants were also enriched in DCM as compared with the GnomAD populations (27.6 vs. 5.9%, p < 0.0001). DCM patients with tTTN had a lower left ventricular ejection fraction (28.89 ± 8.72 vs. 31.81 ± 9.97, p = 0.002) and a lower frequency of the left bundle branch block (3.3 vs. 11.3%, p = 0.011) than those without or with mutations in other known causal genes (OCG). However, tTTN were not associated with the composite primary endpoint of cardiac death and heart transplantation during the follow-up period [adjusted hazard ratio (HR): 0.912; 95% confidence interval: 0.464–1.793; p = 0.790]. There was also no sex-dependent effect. Concomitant tTTN and pathogenic variants in OCG were present in only eight DCM patients and did not affect the outcome.Conclusion: The phenotype of DCM caused by tTTN, major causes of sporadic DCM, is not distinctly different from those caused by other causal genes for DCM.

Abstract Background Neuroblastoma is the most common pediatric malignancy with heterogeneous clinical behaviors, ranging from spontaneous regression to aggressive progression. Many studies have identified aberrations related to the pathogenesis and prognosis, broadly classifying neuroblastoma patients into high- and low-risk groups, but predicting tumor progression and clinical management of high-risk patients remains a big challenge. Results We integrate gene-level expression, array-based comparative genomic hybridization and functional gene-interaction network of 145 neuroblastoma patients to detect potential driver genes. The drivers are summarized into a driver-gene score (DGscore) for each patient, and we then validate its clinical relevance in terms of association with patient survival. Focusing on a subset of 48 clinically defined high-risk patients, we identify 193 recurrent regions of copy number alterations (CNAs), resulting in 274 altered genes whose copy-number gain or loss have parallel impact on the gene expression. Using a network enrichment analysis, we detect four common driver genes, ERCC6, HECTD2, KIAA1279, EMX2, and 66 patient-specific driver genes. Patients with high DGscore, thus carrying more copy-number-altered genes with correspondingly up- or down-regulated expression and functional implications, have worse survival than those with low DGscore (P = 0.006). Furthermore, Cox proportional-hazards regression analysis shows that, adjusted for age, tumor stage and MYCN amplification, DGscore is the only significant prognostic factor for high-risk neuroblastoma patients (P = 0.008). Conclusions Integration of genomic copy number alteration, expression and functional interaction-network data reveals clinically relevant and prognostic putative driver genes in high-risk neuroblastoma patients. The identified putative drivers are potential drug targets for individualized therapy. Reviewers This article was reviewed by Armand Valsesia, Susmita Datta and Aleksandra Gruca.

Abstract Recent genome-sequencing studies have revealed dozens of genes frequently mutated in esophageal squamous cell carcinoma, but few genes are associated with patients’ clinical outcomes. Novel prognostic biomarkers are urgently needed in the clinic. We collected both somatic mutations and clinical information of 442 Chinese esophageal squamous cell carcinoma patients from four published studies. Survival analysis was performed to reveal the clinical significance of the mutated genes. Dysregulation of the mutated genes was observed from public gene-expression data sets and its effects on cell migration and invasion were investigated with siRNA-mediated silencing. Our integrated analysis revealed 26 genes significantly and frequently mutated in esophageal squamous cell carcinoma. Importantly, mutations in ZFHX4, SPHKAP, NRXN1, KIAA1109, DNAH5 and KCNH7 were associated with poor survival. In addition, ZFHX4 was overexpressed in tumor tissues compared to normal controls, and knockdown of ZFHX4 in vitro significantly inhibited cell migration and invasion. Mutations in ZFHX4 were strongly associated with poor prognosis and the down-regulation of ZFHX4 inhibits the progression of esophageal squamous cell carcinoma. Further investigation is warranted to confirm the prognostic values of ZFHX4 in a prospective study.

Abstract The mouse has been widely used as a model organism for studying human diseases and for evaluating drug safety and efficacy. Many diseases and drug effects exhibit tissue specificity that may be reflected by tissue-specific gene-expression profiles. Here we construct a comprehensive mouse transcriptomic BodyMap across 17 tissues of six-weeks old C57BL/6JJcl mice using RNA-seq. We find different expression patterns between protein-coding and non-coding genes. Liver expressed the least complex transcriptomes, that is, the smallest number of genes detected in liver across all 17 tissues, whereas testis and ovary harbor more complex transcriptomes than other tissues. We report a comprehensive list of tissue-specific genes across 17 tissues, along with a list of 4,781 housekeeping genes in mouse. In addition, we propose a list of 27 consistently and highly expressed genes that can be used as reference controls in expression-profiling analysis. Our study provides a unique resource of mouse gene-expression profiles, which is helpful for further biomedical research.

Abstract In contrast to protein-coding genes, long-noncoding RNAs (lncRNAs) are much less well understood, despite increasing evidence indicating a wide range of their biological functions, and possible roles in various cancers. Based on public RNA-seq datasets of four solid cancer types, we here utilize Weighted Correlation Network Analysis (WGCNA) to propose a strategy for exploring the functions of lncRNAs altered in more than two cancer types, which we call onco-lncRNAs. Results indicate that cancer-expressed lncRNAs show high tissue specificity and are weakly expressed, more so than protein-coding genes. Most of the 236 onco-lncRNAs we identified have not been reported to have associations with cancers before. Our analysis exploits co-expression network to reveal that onco-lncRNAs likely play key roles in the multistep development of human cancers, covering a wide range of functions in genome stability maintenance, signaling, cell adhesion and motility, morphogenesis, cell cycle, immune and inflammatory response. These observations contribute to a more comprehensive understanding of cancer-associated lncRNAs, while demonstrating a novel and efficient strategy for subsequent functional studies of lncRNAs.

For reproduction, the human parasiteSchistosoma japonicumrelies on a complex and incompletely understood interplay between female and male schistosomes. Here the authors sequence the transcriptome of female and male schistosomes across eight time points during sexual development.

In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis — that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.

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