Novel drug targets and molecular mechanisms for sarcopenia based on systems biology.

Sarcopenia is a major public health concern among older adults, leading to disabilities, falls, fractures, and mortality. This study aimed to elucidate the pathophysiological mechanisms of sarcopenia and identify potential therapeutic targets using systems biology approaches. RNA-seq data from muscle biopsies of 24 sarcopenic and 29 healthy individuals from a previous cohort were analysed. Differential expression, gene set enrichment, gene co-expression network, and topology analyses were conducted to identify target genes implicated in sarcopenia pathogenesis, resulting in the selection of 6 hub genes (PDHX, AGL, SEMA6C, CASQ1, MYORG, and CCDC69). A drug repurposing approach was then employed to identify new pharmacological treatment options for sarcopenia (clofibric-acid, troglitazone, withaferin-a, palbociclib, MG-132, bortezomib). Finally, validation experiments in muscle cell line (C2C12) revealed MG-132 and troglitazone as promising candidates for sarcopenia treatment. Our approach, based on systems biology and drug repositioning, provides insight into the molecular mechanisms of sarcopenia and offers potential new treatment options using existing drugs.

GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions.

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.

Identification of Ferroptosis-Inflammation Related Hub Genes and the Disease Subtypes in Idiopathic Pulmonary Fibrosis via System Biology Approaches.

We aim to screen and analyze the ferroptosis inflammation-related hub genes associated with idiopathic pulmonary fibrosis (IPF). The GSE52463 and GSE110147 datasets were obtained from the GEO database and merged. The DEGs were selected by differential analysis and intersected with inflammation-related genes and ferroptosis-related genes to acquire the ferroptosis-related differentially expressed genes (FRDEGs). GO, KEGG, GSEA, and GSVA were performed to investigate the features of FRDEGs. The key module genes were selected by WGCNA and employed to generate the PPI network using Cytoscape. Subsequently, the hub genes were identified using cytoHubba and validated by ROC curves generated by survivalROC. Finally, the correlations of hub genes were analyzed through Spearman and the subtypes of IPF were constructed using ConsensusClusterPlus. A total of 1814 DEGs were screened out and 18 FRDEGs were acquired from the intersection of DEGs, ferroptosis-related genes, and inflammation-related genes. GO and KEGG analysis revealed that FRDEGs were primarily involved in bacterial-origin molecular, response infectious disease, and iron ion transport. GSEA results suggested a predominant association with autoimmune diseases and GSVA identified ten different pathways between PF and control. Through WGCNA, three highly correlated modules were identified and ten key module genes were obtained by intersecting genes in the three modules with FRDEGs. Finally, employing three algorithms within the cytoHubba led to the identification of eight hub genes: CCND1, TP53, STAT3, CTNNB1 CDH1, ESR1, HSP90AA1, and EP300. Eventually, two distinct subtypes of IPF were identified. The present research successfully identified the hub genes associated with ferroptosis and inflammation and their biological effects on IPF. Furthermore, two disease subtypes of IPF were constructed.

System biology approaches for drug repurposing.

Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.

Metabolomics in Children Cow's Milk Protein Allergy: Possible Contribution from a System Biology Approach?

One of the most frequent triggers of food anaphylaxis in pediatric age but also among the most common, early, and complex causes of childhood food allergy is cow's milk protein allergy (CMPA). The diagnostic course and management of this allergy is defined in a complex clinical picture due to several factors. First of all, the epidemiological data are not uniform, mainly as a consequence of the diagnostic methodology used in the various studies and the different age ranges covered. In addition, there is the complexity of terminology, since although CMPA traditionally refers to immune-mediated reactions to cow's milk, it is a term encompassing numerous clinical features with different symptoms and the requirement for specific treatments. Moreover, the differential diagnosis with other very frequent diseases, especially in the first year of life, such as gastro-esophageal reflux disease or colic, is still complex. This can result in misdiagnosis and incorrect treatment, with harmful health consequences and significant economic repercussions. In this context, the combination of several omics sciences together, which have already proved useful in clarifying the allergenicity of cow's milk proteins with greater precision, could improve the diagnostic tests currently in use through the identification of new, more specific, and precise biomarkers that make it possible to improve diagnostic accuracy and predict the patient's response to the various available treatments for the recovery of tolerance.

Strategic pathway analysis for dual management of epilepsy and comorbid depression: a systems biology perspective.

Depression is a common psychiatric comorbidity among patients with epilepsy (PWE), affecting more than a third of PWE. Management of depression may improve quality of life of epileptic patients. Unfortunately, available antidepressants worsen epilepsy by reducing the seizure threshold. This situation demands search of new safer target for combined directorate of epilepsy and comorbid depression. A system biology approach may be useful to find novel pathways/markers for the cure of both epilepsy and associated depression via analyzing available genomic and proteomic information. Hence, the system biology approach using curated 64 seed genes involved in temporal lobe epilepsy and mental depression was applied. The interplay of 600 potential proteins was revealed by the Disease Module Detection (DIAMOnD) Algorithm for the treatment of both epilepsy and comorbid depression using these seed genes. The gene enrichment analysis of seed and diamond genes through DAVID suggested 95 pathways. Selected pathways were refined based on their syn or anti role in epilepsy and depression. In conclusion, total 8 pathways and 27 DIAMOnD genes/proteins were finally deduced as potential new targets for modulation of selected pathways to manage epilepsy and comorbid depression. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00208-1.

Loss of 24-hydroxylated catabolism increases calcitriol and fibroblast growth factor 23 and alters calcium and phosphate metabolism in fetal mice.

Calcitriol circulates at low levels in normal human and rodent fetuses, in part due to increased 24-hydroxylation of calcitriol and 25-hydroxyvitamin D by 24-hydroxylase (CYP24A1). Inactivating mutations of CYP24A1 cause high postnatal levels of calcitriol and the human condition of infantile hypercalcemia type 1, but whether the fetus is disturbed by the loss of CYP24A1 is unknown. We hypothesized that loss of Cyp24a1 in fetal mice will cause high calcitriol, hypercalcemia, and increased placental calcium transport. The Cyp24a1+/- mice were mated to create pregnancies with wildtype, Cyp24a1+/-, and Cyp24a1 null fetuses. The null fetuses were hypercalcemic, modestly hypophosphatemic (compared to Cyp24a1+/- fetuses only), with 3.5-fold increased calcitriol, 4-fold increased fibroblast growth factor 23 (FGF23), and unchanged parathyroid hormone. The quantitative RT-PCR confirmed the absence of Cyp24a1 and 2-fold increases in S100g, sodium-calcium exchanger type 1, and calcium-sensing receptor in null placentas but not in fetal kidneys; these changes predicted an increase in placental calcium transport. However, placental 45Ca and 32P transport were unchanged in null fetuses. Fetal ash weight and mineral content, placental weight, crown-rump length, and skeletal morphology did not differ among the genotypes. Serum procollagen 1 intact N-terminal propeptide and bone expression of sclerostin and Blgap were reduced while calcitonin receptor was increased in nulls. In conclusion, loss of Cyp24a1 in fetal mice causes hypercalcemia, modest hypophosphatemia, and increased FGF23, but no alteration in skeletal development. Reduced incorporation of calcium into bone may contribute to the hypercalcemia without causing a detectable decrease in the skeletal mineral content. The results predict that human fetuses bearing homozygous or compound heterozygous inactivating mutations of CYP24A1 will also be hypercalcemic in utero but with normal skeletal development.

Integrative systems biology of wheat susceptibility to Fusarium graminearum uncovers a conserved gene regulatory network and identifies master regulators targeted by fungal core effectors.

BACKGROUND: Plant diseases are driven by an intricate set of defense mechanisms counterbalanced by the expression of host susceptibility factors promoted through the action of pathogen effectors. In spite of their central role in the establishment of the pathology, the primary components of plant susceptibility are still poorly understood and challenging to trace especially in plant-fungal interactions such as in Fusarium head blight (FHB) of bread wheat. Designing a system-level transcriptomics approach, we leveraged the analysis of wheat responses from a susceptible cultivar facing Fusarium graminearum strains of different aggressiveness and examined their constancy in four other wheat cultivars also developing FHB. RESULTS: In this study, we describe unexpected differential expression of a conserved set of transcription factors and an original subset of master regulators were evidenced using a regulation network approach. The dual-integration with the expression data of pathogen effector genes combined with database mining, demonstrated robust connections with the plant molecular regulators and identified relevant candidate genes involved in plant susceptibility, mostly able to suppress plant defense mechanisms. Furthermore, taking advantage of wheat cultivars of contrasting susceptibility levels, a refined list of 142 conserved susceptibility gene candidates was proposed to be necessary host's determinants for the establishment of a compatible interaction. CONCLUSIONS: Our findings emphasized major FHB determinants potentially controlling a set of conserved responses associated with susceptibility in bread wheat. They provide new clues for improving FHB control in wheat and also could conceivably leverage further original researches dealing with a broader spectrum of plant pathogens.

The host transcriptional response to superinfection by influenza A virus and Streptococcus pneumoniae.

Secondary bacterial challenges during influenza virus infection "superinfection") cause excessive mortality and hospitalization. Here, we present a longitudinal study of bulk gene expression changes in murine lungs during superinfection, with an initial influenza A virus infection and a subsequent Streptococcus pneumoniae infection. In addition to the well-characterized impairment of the host response, we identified superinfection-specific alterations in the global transcriptional program that are linked to the host's ability to resist the pathogens. Particularly, whereas superinfected mice manifested an excessive rapid induction of the resistance-to-infection program, there was a substantial tissue-level rewiring of this program: upon superinfection, interferon-regulated genes were switched from positive to negative correlations with the host's resistance state, whereas genes of fatty acid metabolism switched from negative to positive correlations with resistance states. Thus, the transcriptional resistance state in superinfection is reprogrammed toward repressed interferon signaling and induced fatty acid metabolism. Our findings suggest new insights into a tissue-level remodeling of the host defense upon superinfection, providing promising targets for future therapeutic interventions. IMPORTANCE: Secondary bacterial infections are the most frequent complications during influenza A virus (IAV) pandemic outbreaks, contributing to excessive morbidity and mortality in the human population. Most IAV-related deaths are attributed to Streptococcus pneumoniae (SP) infections, which usually begin within the first week of IAV infection in the respiratory tracts. Here, we focused on longitudinal transcriptional responses during a superinfection model consisting of an SP infection that follows an initial IAV infection, comparing superinfection to an IAV-only infection, an SP-only infection, and control treatments. Our longitudinal data allowed a fine analysis of gene expression changes during superinfection. For instance, we found that superinfected mice exhibited rapid gene expression induction or reduction within the first 12 h after encountering the second pathogen. Cell proliferation and immune response activation processes were upregulated, while endothelial processes, vasculogenesis, and angiogenesis were downregulated, providing promising targets for future therapeutic interventions. We further analyzed the longitudinal transcriptional responses in the context of a previously defined spectrum of the host's resistance state, revealing superinfection-specific reprogramming of resistance states, such as reprogramming of fatty acid metabolism and interferon signaling. The reprogrammed functions are compelling new targets for switching the pathogenic superinfection state into a single-infection state.

Integrative Multiomics Approach to Skin: The Sinergy between Individualised Medicine and Futuristic Precision Skin Care?

The skin is a complex ecosystem colonized by millions of microorganisms, the skin microbiota, which are crucial in regulating not only the physiological functions of the skin but also the metabolic changes underlying the onset of skin diseases. The high microbial colonization together with a low diversity at the phylum level and a high diversity at the species level of the skin is very similar to that of the gastrointestinal tract. Moreover, there is an important communication pathway along the gut-brain-skin axis, especially associated with the modulation of neurotransmitters by the microbiota. Therefore, it is evident that the high complexity of the skin system, due not only to the genetics of the host but also to the interaction of the host with resident microbes and between microbe and microbe, requires a multi-omics approach to be deeply understood. Therefore, an integrated analysis, with high-throughput technologies, of the consequences of microbial interaction with the host through the study of gene expression (genomics and metagenomics), transcription (transcriptomics and meta-transcriptomics), and protein production (proteomics and meta-proteomics) and metabolite formation (metabolomics and lipidomics) would be useful. Although to date very few studies have integrated skin metabolomics data with at least one other 'omics' technology, in the future, this approach will be able to provide simple and fast tests that can be routinely applied in both clinical and cosmetic settings for the identification of numerous skin diseases and conditions. It will also be possible to create large archives of multi-omics data that can predict individual responses to pharmacological treatments and the efficacy of different cosmetic products on individual subjects by means of specific allotypes, with a view to increasingly tailor-made medicine. In this review, after analyzing the complexity of the skin ecosystem, we have highlighted the usefulness of this emerging integrated omics approach for the analysis of skin problems, starting with one of the latest 'omics' sciences, metabolomics, which can photograph the expression of the genome during its interaction with the environment.

System biology approaches to identify hub genes linked with ECM organization and inflammatory signaling pathways in schizophrenia pathogenesis.

Schizophrenia (SZ) is a chronic and devastating mental illness that affects around 20 million individuals worldwide. Cognitive deficits and structural and functional changes of the brain, abnormalities of brain ECM components, chronic neuroinflammation, and devastating clinical manifestation during SZ are likely etiological factors shown by affected individuals. However, the pathophysiological events associated with multiple regulatory pathways involved in the brain of this complex disorder are still unclear. This study aimed to develop a pipeline based on bioinformatics and systems biology approaches for identifying potential therapeutic targets involving possible biological mechanisms from SZ patients and healthy volunteers. About 420 overlapping differentially expressed genes (DEGs) from three RNA-seq datasets were identified. Gene ontology (GO), and pathways analysis showed several biological mechanisms enriched by the commonly shared DEGs, including extracellular matrix organization (ECM) organization, collagen fibril organization, integrin signaling pathway, inflammation mediated by chemokines and cytokines signaling pathway, and GABA-B receptor II and IL4 mediated signaling. Besides, 15 hub genes (FN1, COL1A1, COL3A1, COL1A2, COL5A1, COL2A1, COL6A2, COL6A3, MMP2, THBS1, DCN, LUM, HLA-A, HLA-C, and FBN1) were discovered by comprehensive analysis, which was mainly involved in the ECM organization and inflammatory signaling pathway. Furthermore, the miRNA target of the hub genes was analyzed with the random-forest-based approach software miRTarBase. In addition, the transcriptional factors and protein kinases regulating overlapping DEGs in SZ, namely, SUZ12, EZH2, TRIM28, TP53, EGR1, CSNK2A1, GSK3B, CDK1, and MAPK14, were also identified. The results point to a new understanding that the hub genes (fibronectin 1, collagen, matrix metalloproteinase-2, and lumican) in the ECM organization and inflammatory signaling pathways may be involved in the SZ occurrence and pathogenesis.

Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease.

Hirschsprung's disease (HSCR) is a rare developmental disorder in which enteric ganglia are missing along a portion of the intestine. HSCR has a complex inheritance, with RET as the major disease-causing gene. However, the pathogenesis of HSCR is still not completely understood. Therefore, we applied a computational approach based on multi-omics network characterization and clustering analysis for HSCR-related gene/miRNA identification and biomarker discovery. Protein-protein interaction (PPI) and miRNA-target interaction (MTI) networks were analyzed by DPClusO and BiClusO, respectively, and finally, the biomarker potential of miRNAs was computationally screened by miRNA-BD. In this study, a total of 55 significant gene-disease modules were identified, allowing us to propose 178 new HSCR candidate genes and two biological pathways. Moreover, we identified 12 key miRNAs with biomarker potential among 137 predicted HSCR-associated miRNAs. Functional analysis of new candidates showed that enrichment terms related to gene ontology (GO) and pathways were associated with HSCR. In conclusion, this approach has allowed us to decipher new clues of the etiopathogenesis of HSCR, although molecular experiments are further needed for clinical validations.

The Rise of Hypothesis-Driven Artificial Intelligence in Oncology.

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.

Disulfidptosis status influences prognosis and therapeutic response in clear cell renal cell carcinoma.

Disulfidptosis is a recently identified type of programmed cell death. It is characterized by aberrant accumulation of intracellular disulfides. The clinical implications of disulfidptosis in clear cell renal cell carcinoma (ccRCC) remain unclear. A series of bioinformatics approaches were employed to analyze ten disulfidptosis-related molecules. Firstly, the expression patterns of the disulfidptosis-related molecules were different between normal and ccRCC tissues. A comprehensive cohort of patients with ccRCC was then assembled from three public databases and subjected to cluster analysis based on disulfidptosis-related molecules. Consensus cluster analysis revealed three distinct disulfidptosis clusters. We then conducted weighted gene co-expression network analysis (WGCNA) to identify highly correlated genes. 267 hub genes were screened out through WGCNA, and three gene clusters were then determined. Finally, we identified 87 genes with prognostic value and then used them to develop a disulfidptosis scoring (DSscore) system, which was proven to independently predict survival in ccRCC. Patients in the high-DSscore group exhibited a significant survival advantage and better immunotherapeutic responses compared with those in the low-DSscore group. However, the patients in the low-DSscore group exhibited a greater degree of chemotherapeutic response. In addition, the expression of disulfidptosis-related molecules was validated by qRT-PCR, and the potential of disulfidptosis-related molecules to indicate distinct cell subtypes were validated by single-cell RNA-sequencing. In conclusion, DSscore is a promising index for predicting the prognosis and efficacy of immunotherapy in patients with ccRCC and may provide a basis for novel strategies for future studies.

Unraveling dedifferentiation and metastasis traces in pancreatic ductal adenocarcinoma ductal cells: Insights from single-cell RNA sequencing analysis of ITGB4 and C19orf33.

Pancreatic Ductal Adenocarcinoma (PDAC) ranks among the most prevalent gastrointestinal malignancies, with risk factors including smoking, alcohol abuse, diabetes mellitus, obesity, age, family history, and genetic predisposition. Extensive research has focused on unraveling biomarkers and molecular intricacies associated with PDAC. Leveraging data from the Gene Expression Omnibus microarray and single-cell RNA sequencing datasets, our study identified ITGB4 and C19orf33 as potentially differentially expressed genes in PDAC samples when contrasted with non-malignant tissues. Notably, these genes exhibited a strong correlative expression pattern, primarily within ductal cells. Gene Expression Profiling Interactive Analysis corroborated our findings, further confirming the correlation between ITGB4 and C19orf33. Additionally, we conducted experiments involving two pivotal PDAC-related cell lines, MIA PaCa-2 and PANC-1, treated with oxaliplatin and 5-Fluorouracil. We also assessed the expression of these candidate genes in PDAC samples in comparison to adjacent normal tissues. Our findings revealed that C19orf33 is upregulated in PDAC samples, and treatment of PDAC cells with chemotherapeutic agents led to a correlated decrease in the expression of both ITGB4 and C19orf33. These co-expressed and correlated genes are implicated in relevant signaling pathways, suggesting shared biological activities that may contribute to the promotion of metastasis within malignant ductal cells. This study identifies ITGB4 and C19orf33 as key genes potentially shedding light on the molecular mechanisms driving tumorigenesis and metastasis in PDAC. These genes hold promise as potential diagnostic and therapeutic targets, offering valuable insights into the management of this challenging disease.

Toward a predictive understanding of epithelial cell death.

Epithelial cell death is highly prevalent during development and tissue homeostasis. While we have a rather good understanding of the molecular regulators of programmed cell death, especially for apoptosis, we still fail to predict when, where, how many and which specific cells will die in a tissue. This likely relies on the much more complex picture of apoptosis regulation in a tissular and epithelial context, which entails cell autonomous but also non-cell autonomous factors, diverse feedback and multiple layers of regulation of the commitment to apoptosis. In this review, we illustrate this complexity of epithelial apoptosis regulation by describing these different layers of control, all demonstrating that local cell death probability is a complex emerging feature. We first focus on non-cell autonomous factors that can locally modulate the rate of cell death, including cell competition, mechanical input and geometry as well as systemic effects. We then describe the multiple feedback mechanisms generated by cell death itself. We also outline the multiple layers of regulation of epithelial cell death, including the coordination of extrusion and regulation occurring downstream of effector caspases. Eventually, we propose a roadmap to reach a more predictive understanding of cell death regulation in an epithelial context.

A method to improve the prediction performance of cancer-gene association by screening negative training samples through gene network data.

This work focuses on data sampling in cancer-gene association prediction. Currently, researchers are using machine learning methods to predict genes that are more likely to produce cancer-causing mutations. To improve the performance of machine learning models, methods have been proposed, one of which is to improve the quality of the training data. Existing methods focus mainly on positive data, i.e. cancer driver genes, for screening selection. This paper proposes a low-cancer-related gene screening method based on gene network and graph theory algorithms to improve the negative samples selection. Genetic data with low cancer correlation is used as negative training samples. After experimental verification, using the negative samples screened by this method to train the cancer gene classification model can improve prediction performance. The biggest advantage of this method is that it can be easily combined with other methods that focus on enhancing the quality of positive training samples. It has been demonstrated that significant improvement is achieved by combining this method with three state-of-the-arts cancer gene prediction methods.

Application progress of proteomics in dry eye research / 国际眼科杂志(Guoji Yanke Zazhi)

The complex pathological mechanism of dry eye involves multiple pathways, such as immunity and inflammation, and requires an integral research program to control the whole picture. Various histological techniques can elucidate the complex physio-pathological state of organisms from a holistic and global perspective, thus providing more comprehensive biological information. Mass spectrometry can sensitively detect the changes of protein content in tear samples, providing convenience for proteomics research of dry eye. At present, proteomics has demonstrated its application in the identification of dry eye types, severity grading, and therapeutic effect evaluation. In addition, proteomics combined with metabolomics and microbiomics can more comprehensively explain the pathogenesis of dry eye. In the future, proteomics is expected to provide more powerful support for the precise diagnosis and treatment of dry eye, taking an advantage in targeted therapy.