A census of pathway maps in cancer systems biology

The RNF214-TEAD-YAP signaling axis promotes hepatocellular carcinoma progression via TEAD ubiquitylation

RNF214 is an understudied ubiquitin ligase with little knowledge of its biological functions or protein substrates. Here we show that the TEAD transcription factors in the Hippo pathway are substrates of RNF214. RNF214 induces non-proteolytic ubiquitylation at a conserved lysine residue of TEADs, enhances interactions between TEADs and YAP, and promotes transactivation of the downstream genes of the Hippo signaling. Moreover, YAP and TAZ could bind polyubiquitin chains, implying the underlying mechanisms by which RNF214 regulates the Hippo pathway. Furthermore, RNF214 is overexpressed in hepatocellular carcinoma (HCC) and inversely correlates with differentiation status and patient survival. Consistently, RNF214 promotes tumor cell proliferation, migration, and invasion, and HCC tumorigenesis in mice. Collectively, our data reveal RNF214 as a critical component in the Hippo pathway by forming a signaling axis of RNF214-TEAD-YAP and suggest that RNF214 is an oncogene of HCC and could be a potential drug target of HCC therapy.

Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3D in vitro tissue models of disease

Rapid advances in tissue engineering have resulted in more complex and physiologically relevant 3D in vitro tissue models with applications in fundamental biology and therapeutic development. However, the complexity provided by these models is often not leveraged fully due to the reductionist methods used to analyze them. Computational and mathematical models developed in the field of systems biology can address this issue. Yet, traditional systems biology has been mostly applied to simpler in vitro models with little physiological relevance and limited cellular complexity. Therefore, integrating these two inherently interdisciplinary fields can result in new insights and move both disciplines forward. In this review, we provide a systematic overview of how systems biology has been integrated with 3D in vitro tissue models and discuss key application areas where the synergies between both fields have led to important advances with potential translational impact. We then outline key directions for future research and discuss a framework for further integration between fields.

HMGB1-mediated transcriptional activation of circadian gene TIMELESS contributes to endometrial cancer progression through Wnt-β-catenin pathway

TIMELESS (TIM) is a circadian gene which is implicated in the regulation of daily rhythm, DNA replication and repair, and cancer initiation and progression. Nevertheless, the role of TIM in endometrial cancer (EC) development is largely unknown. Bioinformatics analysis showed that TIM was aberrantly up-regulated in EC tissues and positively correlated with clinical or histological grade of EC. Functional studies showed that TIM knockdown reduced EC cell viability and restrained EC cell migration in vitro, as well as blocked xenograft tumor growth in vivo. Mechanistically, HMGB1 transcriptionally up-regulated TIM expression in EC cells. In addition, TIM could activate the transcription of the canonical Wnt ligand WNT8B, and TIM depletion could reduce the malignant potential of EC cells largely by targeting and down-regulating WNT8B. As a conclusion, HMGB1/TIM/WNT8B signal cascade was identified in this study for the first time. HMGB1 exerted its oncogenic role by activating the transcription of TIM, leading to the activation of Wnt signaling and EC progression.

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.

Network pharmacology and metabolomics elucidate the underlying mechanisms of Venenum Bufonis in the treatment of colorectal cancer

ETHNOPHARMACOLOGICAL RELEVANCE

The present study aims to evaluate the efficacy of Venenum Bufonis (VBF), a traditional Chinese medicine derived from the dried secretions of the Chinese toad, in treating colorectal cancer (CRC). The comprehensive roles of VBF in CRC through systems biology and metabolomics approaches have been rarely investigated.

AIMS OF THE STUDY

The study sought to uncover the potential underlying mechanisms of VBF's anti-cancer effects by investigating the impact of VBF on cellular metabolic balance.

MATERIALS AND METHODS

An integrative approach combining biological network analysis, molecular docking and multi-dose metabolomics was used to predict the effects and mechanisms of VBF in CRC treatment. The prediction was verified by cell viability assay, EdU assay and flow cytometry.

RESULTS

The results of the study indicate that VBF presents anti-CRC effects and impacts cellular metabolic balance through its impact on cell cycle-regulating proteins, such as MTOR, CDK1, and TOP2A. The results of the multi-dose metabolomics analysis suggest a dose-dependent reduction of metabolites related to DNA synthesis after VBF treatment, while the EdU and flow cytometry results indicate that VBF inhibits cell proliferation and arrests the cell cycle at the S and G2/M phases.

CONCLUSIONS

These findings suggest that VBF disrupts purine and pyrimidine pathways in CRC cancer cells, leading to cell cycle arrest. This proposed workflow integrating molecular docking, multi-dose metabolomics, and biological validation, which contented EdU assay, cell cycle assay, provides a valuable framework for future similar studies.

Single-cell CRISPR screens in vivo map T cell fate regulomes in cancer

CD8+ cytotoxic T cells (CTLs) orchestrate antitumour immunity and exhibit inherent heterogeneity1,2, with precursor exhausted T (Tpex) cells but not terminally exhausted T (Tex) cells capable of responding to existing immunotherapies3-7. The gene regulatory network that underlies CTL differentiation and whether Tex cell responses can be functionally reinvigorated are incompletely understood. Here we systematically mapped causal gene regulatory networks using single-cell CRISPR screens in vivo and discovered checkpoints for CTL differentiation. First, the exit from quiescence of Tpex cells initiated successive differentiation into intermediate Tex cells. This process is differentially regulated by IKAROS and ETS1, the deficiencies of which dampened and increased mTORC1-associated metabolic activities, respectively. IKAROS-deficient cells accumulated as a metabolically quiescent Tpex cell population with limited differentiation potential following immune checkpoint blockade (ICB). Conversely, targeting ETS1 improved antitumour immunity and ICB efficacy by boosting differentiation of Tpex to intermediate Tex cells and metabolic rewiring. Mechanistically, TCF-1 and BATF are the targets for IKAROS and ETS1, respectively. Second, the RBPJ-IRF1 axis promoted differentiation of intermediate Tex to terminal Tex cells. Accordingly, targeting RBPJ enhanced functional and epigenetic reprogramming of Tex cells towards the proliferative state and improved therapeutic effects and ICB efficacy. Collectively, our study reveals that promoting the exit from quiescence of Tpex cells and enriching the proliferative Tex cell state act as key modalities for antitumour effects and provides a systemic framework to integrate cell fate regulomes and reprogrammable functional determinants for cancer immunity.

Co-targeting FAK and Gli1 inhibits the tumor-associated macrophages-released CCL22-mediated esophageal squamous cell carcinoma malignancy

Esophageal squamous cell carcinoma (ESCC) is a frequently seen esophageal tumor type in China. Activation of signaling proteins and relevant molecular mechanisms in ESCC are partially explored, impairing the antitumor efficiency of targeted therapy in ESCC treatment. Tumor-associated macrophages (TAMs)-released C-C motif chemokine 22 (CCL22) can activate intratumoral focal adhesion kinase (FAK), thus promoting the progression of ESCC. Here, we demonstrated that highly secreted CCL22 by TAMs (CCL22-positive TAMs) induced ESCC cell stemness and invasion through facilitating transcriptional activity of intratumoral glioma-associated oncogene 1 (Gli1), a downstream effector for Hedgehog (HH) pathway. Mechanistically, FAK-activated protein kinase B (AKT) mediated Gli1 phosphorylation at its Ser112/Thr115/Ser116 sites and released Gli1 from suppressor of fused homolog, the endogenous inhibitor of Gli1 to activate downstream stemness-associated factors, such as SRY-box transcription factor 2 (SOX2), Nanog homeobox (Nanog), or POU class 5 homeobox (OCT4). Furthermore, inhibition of FAK activity by VS-4718, the FAK inhibitor, enhanced antitumor effect of GDC-0449, the HH inhibitor, both in xenografted models and in vitro assays. Clinically, CCL22/Gli1 axis is used to evaluate ESCC prognosis. Overall, our study establishes the communication of FAK with HH pathway and offers the novel mechanism related to Gli1 activation independent of Smoothened as well as the rationale for the anti-ESCC combination treatment.

An integrative analysis to predict the active compounds and explore polypharmacological mechanisms of Orthosiphon stamineus Benth

BACKGROUND

Orthosiphon stamineus Benth is a dietary supplement and traditional Chinese herb with widespread clinical applications, but a comprehensive understanding of its active compounds and polypharmacological mechanisms is lacking. This study aimed to systematically investigate the natural compounds and molecular mechanisms of O. stamineus via network pharmacology.

METHODS

Information on compounds from O. stamineus was collected via literature retrieval, while physicochemical properties and drug-likeness were evaluated using SwissADME. Protein targets were screened using SwissTargetPrediction, while the compound-target networks were constructed and analyzed via Cytoscape with CytoHubba for seed compounds and core targets. Enrichment analysis and disease ontology analysis were then carried out, generating target-function and compound-target-disease networks to intuitively explore potential pharmacological mechanisms. Lastly, the relationship between active compounds and targets was confirmed via molecular docking and dynamics simulation.

RESULTS

A total of 22 key active compounds and 65 targets were identified and the main polypharmacological mechanisms of O. stamineus were addressed. The molecular docking results suggested that nearly all core compounds and their targets possess good binding affinity. In addition, the separation of receptor and ligands was not observed in all dynamics simulation processes, whereas complexes of orthosiphol Z-AR and Y-AR performed best in simulations of molecular dynamics.

CONCLUSION

This study successfully identified the polypharmacological mechanisms of the main compounds in O. stamineus, and predicted five seed compounds along with 10 core targets. Moreover, orthosiphol Z, orthosiphol Y, and their derivatives can be utilized as lead compounds for further research and development. The findings here provide improved guidance for subsequent experiments, and we identified potential active compounds for drug discovery or health promotion.

The RNA interactome in the Hallmarks of Cancer

Ribonucleic acid (RNA) molecules are indispensable for cellular homeostasis in healthy and malignant cells. However, the functions of RNA extend well beyond that of a protein-coding template. Rather, both coding and non-coding RNA molecules function through critical interactions with a plethora of cellular molecules, including other RNAs, DNA, and proteins. Deconvoluting this RNA interactome, including the interacting partners, the nature of the interaction, and dynamic changes of these interactions in malignancies has yielded fundamental advances in knowledge and are emerging as a novel therapeutic strategy in cancer. Here, we present an RNA-centric review of recent advances in the field of RNA-RNA, RNA-protein, and RNA-DNA interactomic network analysis and their impact across the Hallmarks of Cancer. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.

Learning from cancer to address COVID-19

Patients with cancer have been disproportionately affected by the novel coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Knowledge collected during the last three decades of cancer research has helped the medical research community worldwide to respond to many of the challenges raised by COVID-19, during the pandemic. The review, briefly summarizes the underlying biology and risk factors of COVID-19 and cancer, and aims to present recent evidence on cellular and molecular relationship between the two diseases, with a focus on those that are related to the hallmarks of cancer and uncovered in the first less than three years of the pandemic (2020-2022). This may not only help answer the question "Why cancer patients are considered to be at a particularly high risk of developing severe COVID-19 illness?", but also helped treatments of patients during the COVID-19 pandemic. The last session highlights the pioneering mRNA studies and the breakthrough discovery on nucleoside-modifications of mRNA by Katalin Karikó, which led to the innovation and development of the mRNA-based SARSCoV-2 vaccines saving lives of millions and also opened the door for a new era of vaccines and a new class of therapeutics.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.

Vitamin D and Systems Biology

The biological actions of the vitamin D receptor (VDR) have been investigated intensively for over 100 years and has led to the identification of significant insights into the repertoire of its biological actions. These were initially established to be centered on the regulation of calcium transport in the colon and deposition in bone. Beyond these well-known calcemic roles, other roles have emerged in the regulation of cell differentiation processes and have an impact on metabolism. The purpose of the current review is to consider where applying systems biology (SB) approaches may begin to generate a more precise understanding of where the VDR is, and is not, biologically impactful. Two SB approaches have been developed and begun to reveal insight into VDR biological functions. In a top-down SB approach genome-wide scale data are statistically analyzed, and from which a role for the VDR emerges in terms of being a hub in a biological network. Such approaches have confirmed significant roles, for example, in myeloid differentiation and the control of inflammation and innate immunity. In a bottom-up SB approach, current biological understanding is built into a kinetic model which is then applied to existing biological data to explain the function and identify unknown behavior. To date, this has not been applied to the VDR, but has to the related ERα and identified previously unknown mechanisms of control. One arena where applying top-down and bottom-up SB approaches may be informative is in the setting of prostate cancer health disparities.

Hub Genes in Non-Small Cell Lung Cancer Regulatory Networks

There are currently no accurate biomarkers for optimal treatment selection in early-stage non-small cell lung cancer (NSCLC). Novel therapeutic targets are needed to improve NSCLC survival outcomes. This study systematically evaluated the association between genome-scale regulatory network centralities and NSCLC tumorigenesis, proliferation, and survival in early-stage NSCLC patients. Boolean implication networks were used to construct multimodal networks using patient DNA copy number variation, mRNA, and protein expression profiles. T statistics of differential gene/protein expression in tumors versus non-cancerous adjacent tissues, dependency scores in in vitro CRISPR-Cas9/RNA interference (RNAi) screening of human NSCLC cell lines, and hazard ratios in univariate Cox modeling of the Cancer Genome Atlas (TCGA) NSCLC patients were correlated with graph theory centrality metrics. Hub genes in multi-omics networks involving gene/protein expression were associated with oncogenic, proliferative potentials and poor patient survival outcomes (p < 0.05, Pearson's correlation). Immunotherapy targets PD1, PDL1, CTLA4, and CD27 were ranked as top hub genes within the 10th percentile in most constructed multi-omics networks. BUB3, DNM1L, EIF2S1, KPNB1, NMT1, PGAM1, and STRAP were discovered as important hub genes in NSCLC proliferation with oncogenic potential. These results support the importance of hub genes in NSCLC tumorigenesis, proliferation, and prognosis, with implications in prioritizing therapeutic targets to improve patient survival outcomes.

A New View of Activating Mutations in Cancer

A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.

Machine learning based identification of hub genes in renal clear cell carcinoma using multi-omics data

Renal cell carcinoma is one of the most universal urinary system cancers in the world. The most common renal cell carcinoma subtype is renal clear cell carcinoma. It is usually associated with high rates of metastasis and mortality. Therefore, finding effective therapeutic targets and prognostic molecular markers is of great significance to improve the early diagnosis rate and prognostic accuracy of renal clear cell carcinoma. In this work, we successfully identified six hub genes that are closely related to the occurrence, development and prognosis of renal clear cell carcinoma and proposed three new potential prognostic markers, namely ATP4B, AC144831.1 and Tfcp2l1 through differentially expressed genes (DEGs) analysis, GO functional enrichment and KEGG pathway analysis, WGCNA analysis, and survival analysis. In addition, we established machine learning models to predict the occurrence of tumors through the gene expression data of patients. It is expected that the results of this study can provide reference value for the treatment of renal clear cell carcinoma.

Impact of Elevated LDH on Cystatin C-Based Glomerular Filtration Rate Estimates in Patients with Cancer

Background: The determination of renal function is crucial for the clinical management of patients with cancer. The glomerular filtration rate (GFR) serves as a key parameter, estimated by creatinine clearance determination in 24-h collected urine (CrCl) as well as equation-based approaches (eGFR) relying on serum creatinine (eGFR CKD EPIcrea) or serum cystatin C (eGFR cystatin C). Serum creatinine and serum cystatin C levels differentially depend on muscle and tumor mass, respectively. Although muscle and tumor mass may thus represent confounding factors, comparative studies for eGFR estimate approaches in cancer patients are lacking. Methods: The present study retrospectively analyzed GFR estimates based on equations of creatinine (eGFRcr), cystatin C (eGFRcys) and combined creatinine-cystatin C levels (eGFRcr-cys) in a subset of patients. The associations of LDH with cystatin C or LDH with eGFRcr, eGFRcys and GFRcr-cys were explored. Results: The laboratory values of 123 consecutive patients were included. The median age was 59 (24−87) and 47.2% were female. There was a statistically significant difference in the mean of CKD EPIcrea (85.17 ± 21.63 mL/min/1.73 m2), CKD EPIcys (61.16 ± 26.03 mL/min/1.73 m2) and CKD EPIcrea-cys (70.42 ± 23.89 mL/min/1.73 m2) (p < 0.0001). Spearman’s correlation analysis revealed a significant correlation of elevated plasma LDH >1.5 UNV and cystatin C values (r = 0.270, p < 0.01, n = 123). LDH values >1.5 UNV were associated with significantly lower CKD EPIcys (r = 0.184, p < 0.01) or CKD EPIcrea-cys (r = 0.226, p < 0.05) estimates compared to CKD EPIcrea. Conclusions: The inclusion of cystatin C as a biomarker led to a lower eGFR estimates compared to creatinine alone or in a combination of both cystatin C and creatinine. The level of cystatin C correlated with the level of LDH, suggesting that the use of cystatin C-based calculations of GFR in cancer patients with elevated LDH should be used with caution.

Multi-Omics Integration-Based Prioritisation of Competing Endogenous RNA Regulation Networks in Small Cell Lung Cancer: Molecular Characteristics and Drug Candidates

Background

The competing endogenous RNA (ceRNA) network-mediated regulatory mechanisms in small cell lung cancer (SCLC) remain largely unknown. This study aimed to integrate multi-omics profiles, including the transcriptome, regulome, genome and pharmacogenome profiles, to elucidate prioritised ceRNA characteristics, pathways and drug candidates in SCLC.

Method

We determined the plasma messenger RNA (mRNA), microRNA (miRNA), long noncoding RNA (lncRNA) and circular RNA (circRNA) expression levels using whole-transcriptome sequencing technology in our SCLC plasma cohort. Significantly expressed plasma mRNAs were then overlapped with the Gene Expression Omnibus (GEO) tissue mRNA data (GSE 40275, SCLC tissue cohort). Next, we applied a multistep multi-omics (transcriptome, regulome, genome and pharmacogenome) integration analysis to first construct the network and then to identify the lncRNA/circRNA-miRNA-mRNA ceRNA characteristics, genomic alterations, pathways and drug candidates in SCLC.

Results

The multi-omics integration-based prioritisation of SCLC ceRNA regulatory networks consisted of downregulated mRNAs (CSF3R/GAA), lncRNAs (AC005005.4-201/DLX6-AS1-201/NEAT1-203) and circRNAs (hsa_HLA-B_1/hsa_VEGFC_8) as well as upregulated miRNAs (hsa-miR-4525/hsa-miR-6747-3p). lncRNAs (lncRNA-AC005005.4-201 and NEAT1-203) and circRNAs (circRNA-hsa_HLA-B_1 and hsa_VEGFC_8) may regulate the inhibited effects of hsa-miR-6747-3p for CSF3R expression in SCLC, while lncRNA-DLX6-AS1-201 or circRNA-hsa_HLA-B_1 may neutralise the negative regulation of hsa-miR-4525 for GAA in SCLC. CSF3R and GAA were present in the genomic alteration, and further identified as targets of FavId and Trastuzumab deruxtecan, respectively. In the SCLC-associated pathway analysis, CSF3R was involved in the autophagy pathways, while GAA was involved in the glucose metabolism pathways.

Conclusions

We identified potential lncRNA/cirRNA-miRNA-mRNA ceRNA regulatory mechanisms, pathways and promising drug candidates in SCLC, providing novel potential diagnostics and therapeutic targets in SCLC.

Cystatin SN promotes epithelial-mesenchymal transition and serves as a prognostic biomarker in lung adenocarcinoma

Background

Cystatins are a class of proteins that can inhibit cysteine protease and are widely distributed in human bodily fluids and secretions. Cystatin SN (CST1), a member of the CST superfamily, is abnormally expressed in a variety of tumors. However, its effect on the occurrence and development of lung adenocarcinoma (LUAD) remains unclear.

Methods

We obtained transcriptome analysis data of CST1 from The Cancer Genome Atlas (TCGA) and GSE31210 databases. The association of CST1 expression with prognosis, gene mutations and tumor immune microenvironment was analyzed using public databases. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed to investigate the potential mechanisms of CST1.

Results

In this study, we found that CST1 was highly expressed in lung adenocarcinoma and was associated with prognosis and tumor immune microenvironment. Genetic mutations of CST1 were shown to be related to disease-free survival (DFS) by using the c-BioPortal tool. Potential proteins binding to CST1 were identified by constructing a protein-protein interaction (PPI) network. Gene set enrichment analysis (GSEA) of CST1 revealed that CST1 was notably enriched in epithelial-mesenchymal transition (EMT). Cell experiments confirmed that overexpression of CST1 promoted lung adenocarcinoma cells migration and invasion, while knockdown of CST1 significantly inhibited lung adenocarcinoma cells migration and invasion.

Conclusions

Our comprehensive bioinformatics analyses revealed that CST1 may be a novel prognostic biomarker in LUAD. Experiments confirmed that CST1 promotes epithelial-mesenchymal transition in LUAD cells. These findings will help to better understand the distinct role of CST1 in LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09685-z.

CancerVar: An artificial intelligence-empowered platform for clinical interpretation of somatic mutations in cancer

Several knowledgebases are manually curated to support clinical interpretations of thousands of hotspot somatic mutations in cancer. However, discrepancies or even conflicting interpretations are observed among these databases. Furthermore, many previously undocumented mutations may have clinical or functional impacts on cancer but are not systematically interpreted by existing knowledgebases. To address these challenges, we developed CancerVar to facilitate automated and standardized interpretations for 13 million somatic mutations based on the AMP/ASCO/CAP 2017 guidelines. We further introduced a deep learning framework to predict oncogenicity for these variants using both functional and clinical features. CancerVar achieved satisfactory performance when compared to several independent knowledgebases and, using clinically curated datasets, demonstrated practical utility in classifying somatic variants. In summary, by integrating clinical guidelines with a deep learning framework, CancerVar facilitates clinical interpretation of somatic variants, reduces manual work, improves consistency in variant classification, and promotes implementation of the guidelines.

Identification of co-expression hub genes for ferroptosis in kidney renal clear cell carcinoma based on weighted gene co-expression network analysis and The Cancer Genome Atlas clinical data

Renal clear cell carcinoma (KIRC) is one of the most common tumors worldwide and has a high mortality rate. Ferroptosis is a major mechanism of tumor occurrence and development, as well as important for prognosis and treatment of KIRC. Here, we conducted bioinformatics analysis to identify KIRC hub genes that target ferroptosis. By Weighted gene co-expression network analysis (WGCNA), 11 co-expression-related genes were screened out. According to Kaplan Meier's survival analysis of the data from the gene expression profile interactive analysis database, it was identified that the expression levels of two genes, PROM2 and PLIN2, are respectively related to prognosis. In conclusion, our findings indicate that PROM2 and PLIN2 may be effective new targets for the treatment and prognosis of KIRC.

Identifying individualized risk subpathways reveals pan-cancer molecular classification based on multi-omics data

Cancer is a highly heterogeneous disease with different functional disorders among individuals. The initiation and progression of cancer is usually related to dysregulation of local regions within pathways. Identification of individualized risk pathways is crucial for revealing the mechanisms of tumorigenesis and heterogeneity. However, approach that focused on mining patient-specific risk subpathway regions is still lacking. Here, we developed an individualized cancer risk subpathway identification method that was referred as InCRiS by integrating multi-omics data. Then, the method was applied to nearly 3000 samples across 9 TCGA cancer types and its robustness and reliability were evaluated. Dissecting dysregulated subpathways in these tumor samples revealed several key regions which may play oncogenic roles in cancer. The construction of risk subpathway dysregulation profile of pan-cancers revealed 11 pan-cancer molecular classification (InCRiS subtypes) with significantly different clinical outcomes. Moreover, subpathway regions that tend to be disordered in individuals of specific subtypes were examined for understanding the pathogenesis of tumor and some key genes such as CTNNB1, EP300 and PRKDC were nominated in different subtypes. In summary, the proposed method and resulting data presented useful resources to study the mechanism of tumor heterogeneity and to discovery novel therapeutic targets for precise treatment of cancer.

DNA damage response proteins synergistically affect the cancer prognosis and resistance

Amplification of oxidative stress can be utilized as a strategy to attenuate cancer progression by instigating apoptosis. However, the duration of positive response to such therapies is limited, as cancer cells eventually develop resistance. The underlying molecular mechanisms of cancer cells to escape apoptosis under oxidative stress is unknown. Employing big data, and its integration with transcriptome, proteome and network analysis in six cancer types revealed system-level interactions between DNA damage response (DDR) proteins, including; DNA damage repair, cell cycle checkpoints and anti-apoptotic proteins. Cancer system biology is used to elucidate mechanisms for cancer progression, but networks defining mechanisms causing resistance is less explored. Using system biology, we identified DDR hubs between G1-S and M phases that were associated with bad prognosis. The increased expression of DDR network was involved in resistance under high oxidative stress. We validated our findings by combining H₂O₂ induced oxidative stress and DDR inhibitors in human lung cancer cells to conclude the necessity of targeting a 'disease-causing network'. Collectively, our work provides insights toward designing strategies for network pharmacology to combat resistance in cancer research.

Artificial intelligence, molecular subtyping, biomarkers, and precision oncology

A targeted cancer therapy is only useful if there is a way to accurately identify the tumors that are susceptible to that therapy. Thus rapid expansion in the number of available targeted cancer treatments has been accompanied by a robust effort to subdivide the traditional histological and anatomical tumor classifications into molecularly defined subtypes. This review highlights the history of the paired evolution of targeted therapies and biomarkers, reviews currently used methods for subtype identification, and discusses challenges to the implementation of precision oncology as well as possible solutions.

The Origins of Phenotypic Heterogeneity in Cancer

Heterogeneity is a pervasive feature of cancer, and understanding the sources and regulatory mechanisms underlying heterogeneity could provide key insights to help improve the diagnosis and treatment of cancer. In this review, we discuss the origin of heterogeneity in the phenotype of individual cancer cells. Genotype-phenotype (G-P) maps are widely used in evolutionary biology to represent the complex interactions of genes and the environment that lead to phenotypes that impact fitness. Here, we present the rationale of an extended G-P (eG-P) map with a cone structure in cancer. The eG-P cone is formed by cells that are similar at the genome layer but gradually increase variability in the epigenome, transcriptome, proteome, metabolome and signalome layers to produce large variability at the phenome layer. Experimental evidence from single-cell -omics analyses supporting the cancer eG-P cone concept is presented, and the impact of epimutations and the interaction of cancer and tumor microenvironmental eG-P cones are integrated with the current understanding of cancer biology. The eG-P cone concept uncovers potential therapeutic strategies to reduce cancer evolution and improve cancer treatment. More methods to study phenotypes in single cells will be key to better understand cancer cell fitness in tumor biology and therapeutics.

An emergent Wnt5a/YAP/TAZ regulatory circuit and its possible role in cancer

Wnt5a is a ligand that plays several roles in development, homeostasis, and disease. A growing body of evidence indicates that Wnt5a is involved in cancer progression. Despite extensive research in this field, our knowledge about how Wnt5a is precisely involved in cancer is still incomplete. It is usually thought that certain combinations of Frizzled receptors and co-receptors might explain the observed effects of Wnt5a either as a tumor suppressor or by promoting migration and invasion. While accepting this 'receptor context' model, this review proposes that Wnt5a is integrated within a larger regulatory circuit involving β-catenin, YAP/TAZ, and LATS1/2. Remarkably, WNT5A and YAP1 are transcriptionally regulated by the Hippo and Wnt pathways, respectively, and might form a regulatory circuit acting through LATS kinases and secreted Wnt/β-catenin inhibitors, including Wnt5a itself. Therefore, understanding the precise role of Wnt5a and YAP in cancer requires a systems biology perspective.

Dissecting intercellular and intracellular signaling networks with barcoded genetic tools

The power of next-generation sequencing has stimulated the development of many analysis techniques for transcriptomics and genomics. More recently, the concept of 'molecular barcoding' has broadened the spectrum of sequencing-based applications to dissect different aspects of intracellular and intercellular signaling. In these assay formats, barcode reporters replace standard reporter genes. The virtually infinitive number of expressed barcode sequences allows high levels of multiplexing, hence accelerating experimental progress. Furthermore, reporter barcodes are used to quantitatively monitor a variety of biological events in living cells which has already provided much insight into complex cellular signaling and will further increase our knowledge in the future.

Significance of CD8+ T cell infiltration-related biomarkers and the corresponding prediction model for the prognosis of kidney renal clear cell carcinoma

Cytotoxic T cells expressing cell surface CD8 played a key role in anti-cancer immunotherapy, including kidney renal clear cell carcinoma (KIRC). Here we set out to comprehensively analyze and evaluate the significance of CD8⁺ T cell-related markers for patients with KIRC. We checked immune cell response in KIRC and identified cell type-specific markers and related pathways in the tumor-infiltrating CD8⁺ T (TIL-CD8T) cells. We used these markers to explore their prognostic signatures in TIL-CD8⁺ T by evaluating their prognostic efficacy and group differences at various levels. Through pan-cancer analysis, 12 of 63 up-regulated and 162 of 396 down-regulated genes in CD8+ T cells were found to be significantly correlated with the survival prognosis. Based on our highly integrated multi-platform analyses across multiple datasets, we constructed a 6-gene risk scoring model specific to TIL-CD8T. In this model, high TIL-CD8 sig score was corresponding to a higher incidence frequency of copy number variation and drug sensitivity to sorafenib. Moreover, the prognosis of patients with the same or similar immune checkpoint gene levels could be distinguished from each other by TIL-CD8 sig score.

Interpretation of cancer mutations using a multiscale map of protein systems

A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges—how to comprehensively map such systems and how to identify which are under mutational selection—have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales.

The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence

Comprehensive analysis of omics data, such as genome, transcriptome, proteome, metabolome, and interactome, is a crucial technique for elucidating the complex mechanism of cancer onset and progression. Recently, a variety of new findings have been reported based on multi-omics analysis in combination with various clinical information. However, integrated analysis of multi-omics data is extremely labor intensive, making the development of new analysis technology indispensable. Artificial intelligence (AI), which has been under development in recent years, is quickly becoming an effective approach to reduce the labor involved in analyzing large amounts of complex data and to obtain valuable information that is often overlooked in manual analysis and experiments. The use of AI, such as machine learning approaches and deep learning systems, allows for the efficient analysis of massive omics data combined with accurate clinical information and can lead to comprehensive predictive models that will be desirable for further developing individual treatment strategies of immunotherapy and molecular target therapy. Here, we aim to review the potential of AI in the integrated analysis of omics data and clinical information with a special focus on recent advances in the discovery of new biomarkers and the future direction of personalized medicine in non-small lung cancer.

k-core genes underpin structural features of breast cancer

Gene co-expression networks (GCNs) have been developed as relevant analytical tools for the study of the gene expression patterns behind complex phenotypes. Determining the association between structure and function in GCNs is a current challenge in biomedical research. Several structural differences between GCNs of breast cancer and healthy phenotypes have been reported. In a previous study, using co-expression multilayer networks, we have shown that there are abrupt differences in the connectivity patterns of the GCN of basal-like breast cancer between top co-expressed gene-pairs and the remaining gene-pairs. Here, we compared the top-100,000 interactions networks for the four breast cancer phenotypes (Luminal-A, Luminal-B, Her2+ and Basal), in terms of structural properties. For this purpose, we used the graph-theoretical k-core of a network (maximal sub-network with nodes of degree at least k). We developed a comprehensive analysis of the network k-core ([Formula: see text]) structures in cancer, and its relationship with biological functions. We found that in the Top-100,000-edges networks, the majority of interactions in breast cancer networks are intra-chromosome, meanwhile inter-chromosome interactions serve as connecting bridges between clusters. Moreover, core genes in the healthy network are strongly associated with processes such as metabolism and cell cycle. In breast cancer, only the core of Luminal A is related to those processes, and genes in its core are over-expressed. The intersection of the core nodes in all subtypes of cancer is composed only by genes in the chr8q24.3 region. This region has been observed to be highly amplified in several cancers before, and its appearance in the intersection of the four breast cancer k-cores, may suggest that local co-expression is a conserved phenomenon in cancer. Considering the many intricacies associated with these phenomena and the vast amount of research in epigenomic regulation which is currently undergoing, there is a need for further research on the epigenomic effects on the structure and function of gene co-expression networks in cancer.

Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas

Colorectal cancer (CRC) is one of the most common tumors worldwide and is associated with high mortality. Here we performed bioinformatics analysis, which we validated using immunohistochemistry in order to search for hub genes that might serve as biomarkers or therapeutic targets in CRC. Based on data from The Cancer Genome Atlas (TCGA), we identified 4832 genes differentially expressed between CRC and normal samples (1562 up-regulated and 3270 down-regulated in CRC). Gene ontology (GO) analysis showed that up-regulated genes were enriched mainly in organelle fission, cell cycle regulation, and DNA replication; down-regulated genes were enriched primarily in the regulation of ion transmembrane transport and ion homeostasis. Weighted gene co-expression network analysis (WGCNA) identified eight gene modules that were associated with clinical characteristics of CRC patients, including brown and blue modules that were associated with cancer onset. Analysis of the latter two hub modules revealed the following six hub genes: adhesion G protein-coupled receptor B3 (BAI3, also known as ADGRB3), cyclin F (CCNF), cytoskeleton-associated protein 2 like (CKAP2L), diaphanous-related formin 3 (DIAPH3), oxysterol binding protein-like 3 (OSBPL3), and RERG-like protein (RERGL). Expression levels of these hub genes were associated with prognosis, based on Kaplan–Meier survival analysis of data from the Gene Expression Profiling Interactive Analysis database. Immunohistochemistry of CRC tumor tissues confirmed that OSBPL3 is up-regulated in CRC. Our findings suggest that CCNF, DIAPH3, OSBPL3, and RERGL may be useful as therapeutic targets against CRC. BAI3 and CKAP2L may be novel biomarkers of the disease.

YAP1 is a Prognostic Biomarker and Correlated with Immune Cell Infiltration in Pancreatic Cancer

Yes-associated protein-1 (YAP1) is an important effector of the Hippo pathway and has crosstalk with other cancer signaling pathways. It induces an immunosuppressive tumor microenvironment by activating pathways in several cellular components. However, the mechanisms by which it drives immune infiltration in pancreatic cancer remain poorly understood. We analyzed the expression of YAP1 as well as its prognostic value and correlations with immune infiltrates in various cancers, with a focus on pancreatic cancer. In particular, using the Oncomine database and Gene Expression Profiling Interactive Analysis (GEPIA) database, we found that YAP1 is differentially expressed between tumor tissues and control tissues in a number of cancers and in particular, is elevated in pancreatic cancer. Using the Kaplan–Meier plotter, GEPIA, and Long-term Outcome and Gene Expression Profiling database of pan-cancers (LOGpc), we further established the prognostic value of YAP1. We found that YAP1 expression was significantly related to outcomes in multiple types of cancer based on data from The Cancer Genome Atlas, particularly in pancreatic cancer. Correlations between YAP1 and immune cell infiltration and immune cell marker expression were examined using Tumor Immune Estimation Resource and GEPIA. High expression levels of YAP1 were significantly associated with a variety of immune markers and immune cell subsets in pancreatic cancer. These results suggest that YAP1 is correlated with patient outcomes and tumor immune cell infiltration in multiple cancer types and is a valuable prognostic biomarker in pancreatic cancer.

From Oncogenic Signaling Pathways to Single-Cell Sequencing of Immune Cells: Changing the Landscape of Cancer Immunotherapy

Over the past decade, there have been remarkable advances in understanding the signaling pathways involved in cancer development. It is well-established that cancer is caused by the dysregulation of cellular pathways involved in proliferation, cell cycle, apoptosis, cell metabolism, migration, cell polarity, and differentiation. Besides, growing evidence indicates that extracellular matrix signaling, cell surface proteoglycans, and angiogenesis can contribute to cancer development. Given the genetic instability and vast intra-tumoral heterogeneity revealed by the single-cell sequencing of tumoral cells, the current approaches cannot eliminate the mutating cancer cells. Besides, the polyclonal expansion of tumor-infiltrated lymphocytes in response to tumoral neoantigens cannot elicit anti-tumoral immune responses due to the immunosuppressive tumor microenvironment. Nevertheless, the data from the single-cell sequencing of immune cells can provide valuable insights regarding the expression of inhibitory immune checkpoints/related signaling factors in immune cells, which can be used to select immune checkpoint inhibitors and adjust their dosage. Indeed, the integration of the data obtained from the single-cell sequencing of immune cells with immune checkpoint inhibitors can increase the response rate of immune checkpoint inhibitors, decrease the immune-related adverse events, and facilitate tumoral cell elimination. This study aims to review key pathways involved in tumor development and shed light on single-cell sequencing. It also intends to address the shortcomings of immune checkpoint inhibitors, i.e., their varied response rates among cancer patients and increased risk of autoimmunity development, via applying the data from the single-cell sequencing of immune cells.

Hippo pathway effectors YAP and TAZ and their association with skeletal muscle ageing

Skeletal muscle atrophy commonly occurs during ageing, thus pathways that regulate muscle mass may represent a potential therapeutic avenue for interventions. In this review, we explored the Hippo signalling pathway which plays an essential role in human oncogenesis and the pathway's influence on myogenesis and satellite cell functions, on supporting cells such as fibroblasts, and autophagy. YAP/TAZ was found to regulate both myoblast proliferation and differentiation, albeit with unique roles. Additionally, YAP/TAZ has different functions depending on the expressing cell type, making simple inference of their effects difficult. Studies in cancers have shown that the Hippo pathway influenced the autophagy pathway, although with mixed results. Most of the present researches on YAP/TAZ are focused on its oncogenicity and further studies are needed to translate these findings to physiological ageing. Taken together, the modulation of YAP/TAZ or the Hippo pathway in general may offer potential new strategies for the prevention or treatment of ageing.

Activation of the tumor suppressive Hippo pathway by triptonide as a new strategy to potently inhibit aggressive melanoma cell metastasis

Metastatic melanoma has a very high mortality rate despite the availability of chemotherapy, radiotherapy, and immunotherapy; therefore, more effective therapeutics are needed. The Hippo pathway plays an inhibitory role in melanoma progression, but the tumor suppressors Salvador homolog-1 (SAV1) and large tumor suppressor 1 (LATS1) in this pathway are down-regulated in melanoma. As a result, the downstream oncogenic Yes-associated protein (YAP) is active, resulting in uncontrolled melanoma growth and metastasis. Therapeutics for remedying SAV1 and LATS1 deficiency in melanoma have not yet been reported in the literature. Here, we show that the small molecule triptonide (MW 358 Da) robustly suppressed melanoma cell tumorigenicity, migration, and invasion. Furthermore, triptonide markedly reduced tumor growth and melanoma lung metastasis in tumor-bearing mice with low toxicity. Molecular mechanistic studies revealed that triptonide promoted SAV1 and LATS1 expression, strongly activated the tumor-suppressive Hippo pathway, degraded oncogenic YAP via the lysosomal pathway, and reduced levels of tumorigenic microphthalmia-associated transcription factor (MITF) in melanoma cells. Triptonide also strongly inhibited activation of AKT, a SAV1-binding signaling protein. Collectively, our results conceptually demonstrate that induction of SAV1 and LATS1 expression and activation of the tumor-suppressive Hippo pathway by triptonide potently inhibits aggressive melanoma cell growth and metastasis. These findings suggest a new strategy for developing therapeutics to treat metastatic melanoma and highlight a novel drug candidate against aggressive melanoma.

Folic Acid-Terminated Poly(2-Diethyl Amino Ethyl Methacrylate) Brush-Gated Magnetic Mesoporous Nanoparticles as a Smart Drug Delivery System

Currently, chemotherapy is an important method for the treatment of various cancers. Nevertheless, it has many limitations, such as poor tumour selectivity and multi-drug resistance. It is necessary to improve this treatment method by incorporating a targeted drug delivery system aimed to reduce side effects and drug resistance. The present work aims to develop pH-sensitive nanocarriers containing magnetic mesoporous silica nanoparticles (MMSNs) coated with pH-responsive polymers for tumour-targeted drug delivery via the folate receptor. 2-Diethyl amino ethyl methacrylate (DEAEMA) was successfully grafted on MMSNs via surface initiated ARGET atom transfer radical polymerization (ATRP), with an average particle size of 180 nm. The end groups of poly (2-(diethylamino)ethyl methacrylate) (PDEAEMA) brushes were converted to amines, followed by a covalent bond with folic acid (FA) as a targeting agent. FA conjugated to the nanoparticle surface was confirmed by X-ray photoelectron spectroscopy (XPS). pH-Responsive behavior of PDEAEMA brushes was investigated by Dynamic Light Scattering (DLS). The nanoparticles average diameters ranged from ca. 350 nm in basic media to ca. 650 in acidic solution. Multifunctional pH-sensitive magnetic mesoporous nanoparticles were loaded with an anti-cancer drug (Doxorubicin) to investigate their capacity and long-circulation time. In a cumulative release pattern, doxorubicin (DOX) release from nano-systems was ca. 20% when the particle exposed to acidic media, compared to ca. 5% in basic media. The nano-systems have excellent biocompatibility and are minimally toxic when exposed to MCF-7, and -MCF-7 ADR cells.

A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

Accumulating studies have confirmed the crucial role of long non-coding RNAs (ncRNAs) as favorable biomarkers for cancer diagnosis, therapy, and prognosis prediction. In our recent study, we established a robust model which is based on multi-gene signature to predict the therapeutic efficacy and prognosis in glioblastoma (GBM), based on Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. lncRNA-seq data of GBM from TCGA and CGGA datasets were used to identify differentially expressed genes (DEGs) compared to normal brain tissues. The DEGs were then used for survival analysis by univariate and multivariate COX regression. Then we established a risk score model, depending on the gene signature of multiple survival-associated DEGs. Subsequently, Kaplan-Meier analysis was used for estimating the prognostic and predictive role of the model. Gene set enrichment analysis (GSEA) was applied to investigate the potential pathways associated to high-risk score by the R package “cluster profile” and Wiki-pathway. And five survival associated lncRNAs of GBM were identified: LNC01545, WDR11-AS1, NDUFA6-DT, FRY-AS1, TBX5-AS1. Then the risk score model was established and shows a desirable function for predicting overall survival (OS) in the GBM patients, which means the high-risk score significantly correlated with lower OS both in TCGA and CGGA cohort. GSEA showed that the high-risk score was enriched with PI3K-Akt, VEGFA-VEGFR2, TGF-beta, Notch, T-Cell pathways. Collectively, the five-lncRNAs signature-derived risk score presented satisfactory efficacies in predicting the therapeutic efficacy and prognosis in GBM and will be significant for guiding therapeutic strategies and research direction for GBM.

Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells

Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. We address these challenges by developing DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states in cellular subsystems that are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accurate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to the design of synergistic drug combinations, which we validate systematically by combinatorial CRISPR, drug-drug screening in vitro, and patient-derived xenografts. DrugCell provides a blueprint for constructing interpretable models for predictive medicine.

A compendium of mutational cancer driver genes

A fundamental goal in cancer research is to understand the mechanisms of cell transformation. This is key to developing more efficient cancer detection methods and therapeutic approaches. One milestone towards this objective is the identification of all the genes with mutations capable of driving tumours. Since the 1970s, the list of cancer genes has been growing steadily. Because cancer driver genes are under positive selection in tumorigenesis, their observed patterns of somatic mutations across tumours in a cohort deviate from those expected from neutral mutagenesis. These deviations, which constitute signals of positive selection, may be detected by carefully designed bioinformatics methods, which have become the state of the art in the identification of driver genes. A systematic approach combining several of these signals could lead to a compendium of mutational cancer genes. In this Review, we present the Integrative OncoGenomics (IntOGen) pipeline, an implementation of such an approach to obtain the compendium of mutational cancer drivers. Its application to somatic mutations of more than 28,000 tumours of 66 cancer types reveals 568 cancer genes and points towards their mechanisms of tumorigenesis. The application of this approach to the ever-growing datasets of somatic tumour mutations will support the continuous refinement of our knowledge of the genetic basis of cancer.