Multi-omics Technologies Research Articles

Multi-omics analyses of Bacillus amyloliquefaciens treated mice infected with Schistosoma japonicum reveal dynamics change of intestinal microbiome and its associations with host metabolism.

Schistosomiasis japonica is a serious threat to human health. It causes damage to the intestine and liver. Probiotic therapy has been shown to be effective in alleviating intestinal diseases and improving host health. Previous studies have found that Bacillus amyloliquefaciens could alleviate the pathological symptoms of schistosomiasis japonica, but the regulatory mechanism of alleviating schistosomiasis japonica is still unknown. This study analyzed the dynamic changes of intestinal microbiome in mice infected with Schistosoma japonicum after the intervention of B. amyloliquefaciens and its connection to host metabolism by multi-omics sequencing technology. B. amyloliquefaciens was found to significantly regulate the homeostasis of intestinal microbiota by promoting the growth of beneficial bacteria and inhibiting potential pathogenic bacteria and protect the number of core microbes. Meanwhile, the genes related to the metabolism of glycerophospholipids and amino acid from intestinal microbiome changed significantly, and were shown to be significantly positively correlated with the associated metabolites of microbial origin. Moreover, host metabolism (lipid metabolism and steroid hormone biosynthesis) was also found to be significantly regulated. The recovery of intestinal microbial homeostasis and the regulation of host metabolism revealed the potential probiotic properties of B. amyloliquefaciens, which also provided new ideas for the prevention and adjuvant treatment of schistosomiasis japonica.

Multi-omics approaches for abiotic stress tolerance in rice (Oryza sativa L.)

Rice, one of the world's staple crops, faces significant challenges due to abiotic stresses such as drought, salinity and extreme temperatures, which threaten global food security. Traditional breeding methods have limitations in developing stress-tolerant rice varieties within a short time frame. Thus, there is a growing interest in employing multi-omics approaches, integrating genomics, transcriptomics, proteomics, metabolomics and epigenomics, to unravel the complex molecular mechanisms underlying abiotic stress tolerance in rice. In contrast to a single-omics method, this combination of multi-dimensional approaches provides an extensive understanding of cellular dynamics under abiotic stress conditions. This review discusses recent advances in multi-omics technologies and their applications in dissecting the molecular responses of rice to abiotic stresses. It highlights the integration of multi-omics data to identify critical genes, pathways and regulatory networks involved in stress responses and tolerance mechanisms.Furthermore, it explores the potential of multi-omics-assisted breeding strategies for developing stress-tolerant rice varieties with improved agronomic traits. The challenges and future perspectives in utilizing multi-omics approaches to enhance rice's abiotic stress tolerance are also discussed. Overall, multi-omics approaches offer a comprehensive platform to understand the molecular basis of stress tolerance in rice and accelerate the development of resilient varieties to ensure global food security.

Ouabain Ameliorates Alzheimer’s Disease-Associated Neuropathology and Cognitive Impairment in FAD4T Mice

Background: Alzheimer’s disease (AD) is a common clinical neurodegenerative disorder, primarily characterized by progressive cognitive decline and behavioral abnormalities. The hallmark pathological changes of AD include widespread neuronal degeneration, plaques formed by the deposition of amyloid β-protein (Aβ), and neurofibrillary tangles (NFTs). With the acceleration of global aging, the incidence of AD is rising year by year, making it a major global public health concern. Due to the complex pathology of AD, finding effective interventions has become a key focus of research. Ouabain (OUA), a cardiac glycoside, is well-known for its efficacy in treating heart disease. Recent studies have also indicated its potential in AD therapy, although its exact mechanism of action remains unclear. Methods: This study integrates bioinformatics, multi-omics technologies, and in vivo and in vitro experiments to investigate the effects of OUA on the pathophysiological changes of AD and its underlying molecular mechanisms. Results: This study analyzed the expression of the triggering receptor expressed on myeloid cells 2 (TREM2) across different stages of AD using bioinformatics. Serum samples from patients were used to validate soluble TREM2 (sTREM2) levels. Using an Aβ1-42-induced microglial cell model, we confirmed that OUA enhances the PI3K/AKT signaling pathway activation by upregulating TREM2, which reduces neuroinflammation and promotes the transition of microglia from an M1 proinflammatory state to an M2 anti-inflammatory state. To evaluate the in vivo effects of OUA, we assessed the learning and memory capacity of FAD4T transgenic mice using the Morris water maze and contextual fear conditioning tests. We used real-time quantitative PCR, immunohistochemistry, and Western blotting to measure the expression of inflammation-associated cytokines and to assess microglia polarization. OUA enhances cognitive function in FAD4T mice and has been confirmed to modulate microglial M1/M2 phenotypes both in vitro and in vivo. Furthermore, through bioinformatics analysis, molecular docking, and experimental validation, TREM2 was identified as a potential target for OUA. It regulates PI3K/Akt signaling pathway activation, playing a crucial role in OUA-mediated M2 microglial polarization and its anti-inflammatory effects in models involving Aβ1-42-stimulated BV-2 cells and FAD4T mice. Conclusions: These findings indicate that OUA exerts anti-neuroinflammatory effects by regulating microglial polarization, reducing the production of inflammatory mediators, and activating the PI3K/Akt signaling pathway. Given its natural origin and dual effects on microglial polarization and neuroinflammation, OUA emerges as a promising therapeutic candidate for neuroinflammatory diseases such as AD.

Explore the mechanism of yishenjiangya formula in the treatment of senile hypertension based on multi-omics technology

Ethnopharmacological relevanceThe Yishenjiangya formula (YSJ) is a traditional Chinese medicine (TCM) primarily composed of qi-tonifying components. This classic formula is commonly utilized to treat kidney qi deficiency in elderly patients with hypertension. According to TCM, maintaining a balance between qi and blood is crucial for stable blood pressure. Kidney qi deficiency can disrupt this balance, altering fluid shear force and, ultimately, leading to hypertension, particularly in elderly populations. Despite YSJ's efficacy in treating hypertension, its specific anti-hypertensive mechanisms remain unclear. Aim of the studyYSJ is commonly prescribed for elderly patients with hypertension. Earlier metabolomics studies demonstrated that YSJ exerts antihypertensive effects by influencing four key pathways: linoleic acid metabolism, glycerol phospholipid metabolism, arginine and proline metabolism, and steroid hormone biosynthesis. This study aims to combine metabolomic and proteomic analyses to thoroughly understand the molecular biological mechanisms responsible for YSJ's anti-hypertensive properties. MethodsUltra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) metabolomics, combined with Label-Free Quantitation (LFQ) proteomics, was employed to analyze serum samples from elderly individuals with and without hypertension pre- and post-YSJ intervention. Serum levels of candidate proteins were assessed using enzyme-linked immunosorbent assay, and receiver operating characteristic curves were used to evaluate the diagnostic performance of the target proteins. ResultsEight differentially expressed metabolites and three differentially expressed proteins were identified as potential therapeutic targets of YSJ. These substances are primarily involved in unsaturated fatty acid metabolism, fluid shear stress and atherosclerosis pathway, primary bile acid biosynthesis, proline metabolism, apoptosis, and endoplasmic reticulum stress. YSJ exerts its therapeutic effects on hypertension in the elderly by modulating these pathways. ConclusionsYSJ effectively treats senile hypertension. By analyzing the correlation between therapeutic targets and pathways, YSJ's anti-hypertensive effect was achieved by inhibiting lipid peroxidation and matrix degeneration. Combining metabolomics and proteomics provides an effective method for uncovering YSJ's anti-hypertensive mechanisms.

Investigation of the polysaccharides synthesis enhancement mechanism from Agaricus bitoquis (Quél.) Sacc Chaidam and its fermentation regulation technology system based on multi-omics technologies

The polysaccharides (PS) of Agaricus bitoquis (Quél.) Sacc. Chaidam (ABSC) had attracted the attention of researchers due to their strong anti hypoxia, antioxidant, and anti fatigue activities. Therefore, this article focuses on studying the regulatory channels of exogenous additives-Lycium ruthenicum Murr. Anthocyanin (LRA) to delay fungus cell aging and promote the incremental synthesis of PS from ABSC, and optimizing the regulation system for pilot fermentation experiments. The joint analysis of Ultra High Performance Liquid Chromatography-Quadrupole-Time of Flight Mass Spectrometry (UHPLC-Q-TOFMS) and RNA sequencing (RNA-seq) database elucidates that at the molecular level, the crude extract of LRA inhibits the Telecommunications Association (TCA) cycle, transferring carbon metabolism to the synthesis of polysaccharide precursors (UDP-glucose, UDP-galactose, etc.), promoting the synthesis of PS, promoting the formation of key antioxidant substances (including flavonoids such as quercetin and alginate), improving cell membrane fluidity (saturated fatty acid to unsaturated conversion) and enhancing the degradation of toxic substances (oxidized glutathione), thereby promoting the anti-aging activity and PS synthesis ability of mycelial cells; LRA-adding could enhance the activity in the pilot fermentation of ABSC, which make the mycelia biomass, EPS and IPS production were significantly increased (89.04 %, 65.46 % and 37.64 % respectively) compared with the untreatment control. Based on the optimal fermentation system of PS synthesized in mycelium by adding LRA, Logistic, Gauss, and Asymptotic kinetic equations were used to describe the cell growth, product synthesis and regulatory substrate consumption from ABSC. The correlation coefficients of three models were all above 0.95, indicating a good fit between the experimental values and predicted value, this indicated ABSC fermentation kinetics model could better simulate the actual production process and provide theoretical data for the industrial production of polysaccharides. The results demonstrated that the present proposed LRA intensification approach could be useful to boost up the fermentation of ABSC, which possibly applied to yield increase and fermentation product acquisition of macrofungi.

Gut microbiota, serum metabolites, and lipids related to blood glucose control and type 1 diabetes.

The composition and function of gut microbiota, lipids, and metabolites in patients with type 1 diabetes (T1D) or its association with glycemic control remains unknown. We aimed to use multi-omics sequencing technology and machine learning (ML) approaches to investigate potential function and relationships among the gut microbiota, lipids, and metabolites in T1D patients at varied glycemic levels. We conducted a multi-omics analysis of the gut microbiome from fecal samples, metabolites, and lipids obtained from serum samples, collected from a cohort of 72 T1D patients. The patients were divided into two groups based on their hemoglobin A1c (HbA1c) levels. 16S rRNA sequencing, and metabolomics methods were applied to analyze changes in composition and function of gut microbiota, metabolites, and lipids. The linear discriminant analysis, Shapley additive explanations (SHAP) algorithm, and ML algorithms revealed the enrichment of Bacteroides_nordii, Bacteroides_cellulosilyticus in the glycemic control (GC) group, while Bacteroides_coprocola and Sutterella_wadsworthensis were enriched in the poor glycemic control (PGC) group. Several metabolic enrichment sets like fatty acid biosynthesis and glycerol phosphate shuttle metabolism were different between two groups. Bacteroides_nordii exhibited a negative association with D-fructose, a component involved in the starch and sucrose metabolism pathway, as well as with monoglycerides (16:0) involved in the glycerolipid metabolism pathway. We identified distinct characteristics of gut microbiota, metabolites, and lipids in T1D patients exhibiting different levels of glycemic control. Through comprehensive analysis, microbiota (Bacteroides_nordii, Bacteroides_coprocola), metabolites (D-fructose), and lipids (Monoglycerides) may serve as potential mediators that communicated the interaction between the gut, circulatory systems, and glucose fluctuations in T1D patients.

ScDRMAE: integrating masked autoencoder with residual attention networks to leverage omics feature dependencies for accurate cell clustering.

Cell clustering is foundational for analyzing the heterogeneity of biological tissues using single-cell sequencing data. With the maturation of single-cell multi-omics sequencing technologies, we can integrate multiple omics data to perform cell clustering, thereby overcoming the limitations of insufficient information from single omics data. Existing methods for cell clustering often only consider the differences in data patterns during the analysis of multi-omics data, but the dependencies between omics features of different cell types also significantly influence cell clustering. Moreover, the high dropout rates in scRNA-seq and scATAC-seq data can impact the performance of cell clustering. We propose a cell clustering model based on a masked autoencoder, scDRMAE. Utilizing a masking mechanism, scDRMAE effectively learns the relationships between different features and imputes false zeros caused by dropout events. To differentiate the importance of various omics data in cell clustering, we dynamically adjust the weights of different omics data through an attention mechanism. Finally, we use the K-means algorithm for cluster analysis of the fused multi-omics data. On commonly used sets of 15 multi-omics datasets, our method demonstrates superior cell clustering performance on multiple metrics compared to other computational methods. In addition, when datasets exhibit varying degrees of dropout noise, our method shows better performance and stronger stability on multiple metrics compared to other methods. Moreover, by analyzing the cell clusters classified by scDRMAE, we identified several biologically significant biomarkers that have been validated, further confirming the effectiveness of scDRMAE in cell clustering from a biological perspective.

Multi-omics profiling the dynamic variations of flavor and nutritional components during pearl gentian grouper soup processing

Flavor and nutritional value are essential components of fish soup, as highlighted by both producers and consumers. This study systematically examined the color, aroma, taste, and nutritional composition of pearl gentian grouper soup (PGGS) samples processed over varying durations, employing multi-omics technologies, including sensomics, volatilomics, lipidomics, and metabolomics. A comprehensive identification of 58 volatile aroma compounds, 407 lipids, 203 metabolites, and 47 taste components was achieved. The findings indicated that as processing time increased, the brightness of PGGS diminished, resulting in a thicker consistency. The aroma profile of PGGS was predominantly characterized by meaty and fatty notes, which were closely associated with carbonyl compounds (nonanal, dodecanal, (E)-2-octenal, and 2-undecanone) that developed due to glyceride oxidation during prolonged thermal processing. The predominant taste profile of PGGS was identified as umami, likely resulting from the synergistic effects of umami-enhancing amino acids (glutamic acid, aspartic acid) and 5ʹ-nucleotides (adenosine monophosphate, inosine monophosphate, guanosine monophosphate). Furthermore, PGGS may represent a significant source of taurine, with its taurine content accounting for over 60% of the total free amino acids (FAAs). Considering the variations in color, flavor, and nutritional content throughout the processing of PGGS, as well as the associated time costs, a processing duration of 2 h was optimal.

Vitessce: integrative visualization of multimodal and spatially resolved single-cell data.

Multiomics technologies with single-cell and spatial resolution make it possible to measure thousands of features across millions of cells. However, visual analysis of high-dimensional transcriptomic, proteomic, genome-mapped and imaging data types simultaneously remains a challenge. Here we describe Vitessce, an interactive web-based visualization framework for exploration of multimodal and spatially resolved single-cell data. We demonstrate integrative visualization of millions of data points, including cell-type annotations, gene expression quantities, spatially resolved transcripts and cell segmentations, across multiple coordinated views. The open-source software is available at http://vitessce.io .

Benchmarking algorithms for single-cell multi-omics prediction and integration.

The development of single-cell multi-omics technology has greatly enhanced our understanding of biology, and in parallel, numerous algorithms have been proposed to predict the protein abundance and/or chromatin accessibility of cells from single-cell transcriptomic information and to integrate various types of single-cell multi-omics data. However, few studies have systematically compared and evaluated the performance of these algorithms. Here, we present a benchmark study of 14 protein abundance/chromatin accessibility prediction algorithms and 18 single-cell multi-omics integration algorithms using 47 single-cell multi-omics datasets. Our benchmark study showed overall totalVI and scArches outperformed the other algorithms for predicting protein abundance, and LS_Lab was the top-performing algorithm for the prediction of chromatin accessibility in most cases. Seurat, MOJITOO and scAI emerge as leading algorithms for vertical integration, whereas totalVI and UINMF excel beyond their counterparts in both horizontal and mosaic integration scenarios. Additionally, we provide a pipeline to assist researchers in selecting the optimal multi-omics prediction and integration algorithm.

Recent progress and applications of single-cell sequencing technology in breast cancer.

Single-cell RNA sequencing (scRNA-seq) technology enables the precise analysis of individual cell transcripts with high sensitivity and throughput. When integrated with multiomics technologies, scRNA-seq significantly enhances the understanding of cellular diversity, particularly within the tumor microenvironment. Similarly, single-cell DNA sequencing has emerged as a powerful tool in cancer research, offering unparalleled insights into the genetic heterogeneity and evolution of tumors. In the context of breast cancer, this technology holds substantial promise for decoding the intricate genomic landscape that drives disease progression, treatment resistance, and metastasis. By unraveling the complexities of tumor biology at a granular level, single-cell DNA sequencing provides a pathway to advancing our comprehension of breast cancer and improving patient outcomes through personalized therapeutic interventions. As single-cell sequencing technology continues to evolve and integrate into clinical practice, its application is poised to revolutionize the diagnosis, prognosis, and treatment strategies for breast cancer. This review explores the potential of single-cell sequencing technology to deepen our understanding of breast cancer, highlighting key approaches, recent advancements, and the role of the tumor microenvironment in disease plasticity. Additionally, the review discusses the impact of single-cell sequencing in paving the way for the development of personalized therapies.

Multi-omics analysis provides new insights into mechanism of didymin on non-alcoholic fatty liver disease in rats

BackgroundNon-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases accompanied by lipid and glucose metabolism disorder. Didymin has been reported to have various hepatoprotective effects, however, its potential effects and mechanisms on NAFLD remain unclear from the perspective of the whole. PurposeTo investigate the underlying mechanism of didymin against NAFLD using multi-omics technologies. MethodsRats were fed with a high-fat diet (HFD) for 8 weeks to induce NAFLD, followed by didymin treatment for 8 weeks. Next, biochemical analysis and histopathological examinations were performed to evaluate the effects of didymin. The key regulating pathways were predicted using transcriptomics, metabolomics and proteomics, and the target pathways were then verified by detecting the key genes/proteins using various experiments. ResultsDidymin markedly mitigated liver injury and excessive lipid droplet accretion. An integrative multi-omics analysis suggested that the PPAR signaling cascade and insulin signaling pathway might serve as pivotal mechanisms underlying the modulation of lipid and glucose homeostasis by didymin. Further dissection identified five pivotal genes (PPARα, PPARβ, FABP4, ANGPTL4, and PLIN2) and four genes (HK1, HK3, GCK, and PTPN1) as potential hubs within these pathways. Subsequent validation experiments, including qPCR and Western blot, demonstrated upregulated expression of PPARα and PPARβ, indicating the activation of the PPAR pathway by didymin. Concurrently, didymin appeared to modulate the insulin signaling pathway, as evidenced by the upregulated expression of HK1 and downregulated expression of PTPN1. Notably, the manipulation of PPARα, PPARβ, and PTPN1 expression in LO2 cells through silence or overexpression confirmed that didymin significantly reduced lipid accumulation, with its molecular targets likely being the PPAR and insulin pathways. ConclusionsOur findings demonstrate that didymin has a protective effect on NAFLD, and its underlying mechanism may be associated with the regulation of the PPAR and insulin signaling pathways.

Distinct Immune Homeostasis Remodeling Patterns after HLA-Matched and Haploidentical Transplantation.

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a widely used treatment for a variety of hematopoietic disorders, and also provides a valuable platform for investigating the development of donor-derived immune cells in recipients post-HSCT. The immune system remodels from the donor to the recipient during allo-HSCT. However, little is known about the cell profile alterations as donor homeostasis rebalances to recipient homeostasis following HSCT. Here, multi-omics technology is applied at both the singlecell and bulk sample levels, as well as spectrum flow cytometry and fluorescent transgenic mouse models, to dissect the dynamics of the rebalanced homeostatic immune system in recipients after allo-HSCT. The data reveal that all immune subpopulations observed in donors are successfully restored in recipients, though with varying levels of abundance. The remodeling of immune homeostasis exhibits different patterns in HLA-matched and haploidentical HSCT, highlighting distinct biases in T cell reconstitution from the central and peripheral pathways. Furthermore, ZNF683 is critical for maintaining the persistence and quiescence of CD8 T-cell in haploidentical HSCT. The research can serve as a foundation for developing novel strategies to induce immune tolerance.

Network pharmacology for traditional Chinese medicine in era of artificial intelligence

AbstractTraditional Chinese Medicine Network Pharmacology (TCM-NP) is an interdisciplinary discipline that integrates information science, systems biology, network science and pharmacology, providing a systematic research methodology for TCM studies. With the development of artificial intelligence (AI) and multi-omics technologies, TCM-NP has entered a new era and can incorporate multimodal and high-dimensional data in the context of big data to enhance both theoretical foundations and technical capabilities. Despite its advancement, TCM-NP still faces challenges, particularly in ensuring the quality of data and research, as well as achieving more profound scientific discoveries. The field needs further innovation to obtain more precise and biomedically meaningful results. Overall research progress in TCM-NP depends on developing more accurate algorithms together with utilizing higher-quality and larger-scale data. This paper gives a perspective on the trends and characteristics of TCM-NP development and application in the era of AI.

Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine.

Aging is a fundamental biological process characterized by a progressive decline in physiological functions and an increased susceptibility to diseases. Understanding aging at the molecular level is crucial for developing interventions that could delay or reverse its effects. This review explores the integration of machine learning (ML) with multi-omics technologies-including genomics, transcriptomics, epigenomics, proteomics, and metabolomics-in studying the molecular hallmarks of aging to develop personalized medicine interventions. These hallmarks include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. Using ML to analyze big and complex datasets helps uncover detailed molecular interactions and pathways that play a role in aging. The advances of ML can facilitate the discovery of biomarkers and therapeutic targets, offering insights into personalized anti-aging strategies. With these developments, the future points toward a better understanding of the aging process, aiming ultimately to promote healthy aging and extend life expectancy.

Unlocking the Complexity: Exploration of Acute Lymphoblastic Leukemia at the Single Cell Level.

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. ALL originates from precursor lymphocytes that acquire multiple genomic changes over time, including chromosomal rearrangements and point mutations. While a large variety of genomic defects was identified and characterized in ALL over the past 30 years, it was only in recent years that the clonal heterogeneity was recognized. Thanks to the latest advancements in single-cell sequencing techniques, which have evolved from the analysis of a few hundred cells to the analysis of thousands of cells simultaneously, the study of tumor heterogeneity now becomes possible. Different modalities can be explored at the single-cell level: DNA, RNA, epigenetic modifications, and intracellular and cell surface proteins. In this review, we describe these techniques and highlight their advantages and limitations in the study of ALL biology. Moreover, multiomics technologies and the incorporation of the spatial dimension can provide insight into intercellular communication. We describe how the different single-cell sequencing technologies help to unravel the molecular complexity of ALL, shedding light on its development, its heterogeneity, its interaction with the leukemia microenvironment and possible relapse mechanisms.

Advances in spatial multi-omics in tumors.

Single-cell techniques have convincingly demonstrated that tumor tissue usually contains multiple genetically defined cell subclones with different gene mutation sets as well as various transcriptional profiles, but the spatial heterogeneity of the microenvironment and the macrobiological characteristics of the tumor ecosystem have not been described. For the past few years, spatial multi-omics technologies have revealed the cellular interactions, microenvironment, and even systemic tumor-host interactions in the tumor ecosystem at the spatial level, which can not only improve classical therapies such as surgery, radiotherapy, and chemotherapy but also promote the development of emerging targeted therapies in immunotherapy. Here, we review some emerging spatial omics techniques in cancer research and therapeutic applications and propose prospects for their future development.

Charting the Molecular Terrain of Exercise: The Power of Multi-Omic Mapping.

Physical activity plays a fundamental role in human health and disease. Exercise has been shown to improve a wide variety of disease states, and the scientific community is committed to understanding the precise molecular mechanisms that underlie the exquisite benefits. This review provides an overview of molecular responses to acute exercise and chronic training, particularly energy mobilization and generation, structural adaptation, inflammation, and immune regulation. Further it offers a detailed discussion on known molecular signals and systemic regulators activated during various forms of exercise and their role in orchestrating health benefits. Critically, the increasing use of multi-omic technologies is explored with an emphasis on how multi-omic and multi-tissue studies contribute to a more profound understanding of exercise biology. These data inform anticipated future advancement in the field and highlight the prospect of integrating exercise with pharmacology for personalized disease prevention and treatment.

Progress of multi-omics technology in precision treatment of chronic rhinosinusitis

Progress of multi-omics technology in precision treatment of chronic rhinosinusitis

Precision Cancer Medicine 2.0-Oncology in the postgenomic era.

Genomic medicine has transformed the lives of patients with cancer byenabling individualised and evidence-based clinical decision-making. Despite this progress, the implementation of precision cancer medicine is limited by its dependence on isolated biomarkers. The development of bulk and single-cell multiomic technologies has revealed the enormous complexity of the cancer ecosystem. Beyond the cancer cell, the tumour microenvironment, macroenvironment and host factors, including the microbiome, profoundly influence the cancer phenotype, and accounting for these enhances the resolution of precision medicine. The advent of robust multiomic profiling and interpretable machine learning algorithms mark the dawn of a new postgenomic era of personalised cancer medicine. In Precision Cancer Medicine 2.0, high-resolution personalised clinical decision-making is informed by the comprehensive multiomic profiling of tumour and host, integrated using artificial intelligence.