The recurrent architecture of tumour initiation, progression and drug sensitivity

Multistate Gene Cluster Switches Determine the Adaptive Mitochondrial and Metabolic Landscape of Breast Cancer

Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution. Metabolic adaptations represent important therapeutic targets in tumors, highlighting the need to characterize the full spectrum, characteristics, and regulation of the metabolic switches. To investigate the hypothesis that metabolic switches associated with specific metabolic states can be recognized by locating large alternating gene expression patterns, we developed a method to identify interspersed gene sets by massive correlated biclustering and to predict their metabolic wiring. Testing the method on breast cancer transcriptome datasets revealed a series of gene sets with switch-like behavior that could be used to predict mitochondrial content, metabolic activity, and central carbon flux in tumors. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labeled substrates. The metabolic switch positions also distinguished between cellular states, correlating with tumor pathology, prognosis, and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Significance: A method for identifying the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation stratifies breast cancer into metabolic subtypes, predicting their biology, architecture, and clinical outcome.

Circular RNA in cancer

Over the past decade, circular RNA (circRNA) research has evolved into a bona fide research field shedding light on the functional consequence of this unique family of RNA molecules in cancer. Although the method of formation and the abundance of circRNAs can differ from their cognate linear mRNA, the spectrum of interacting partners and their resultant cellular functions in oncogenesis are analogous. However, with 10 times more diversity in circRNA variants compared with linear RNA variants, combined with their hyperstability in the cell, circRNAs are equipped to influence every stage of oncogenesis. This is an opportune time to address the breadth of circRNA in cancer focused on their spatiotemporal expression, mutations in biogenesis factors and contemporary functions through each stage of cancer. In this Review, we highlight examples of functional circRNAs in specific cancers, which satisfy critical criteria, including their physical co-association with the target and circRNA abundance at stoichiometrically valid quantities. These considerations are essential to develop strategies for the therapeutic exploitation of circRNAs as biomarkers and targeted anticancer agents.

Unravelling the mosaic: Epigenetic diversity in glioblastoma

Glioblastoma is the most common primary malignant brain tumour. Despite decades of intensive research in the disease, its prognosis remains poor, with an average survival of only 14 months after diagnosis. The remarkable level of intra- and interpatient heterogeneity is certainly contributing to the lack of progress in tackling this tumour. Epigenetic dysregulation plays an important role in glioblastoma biology and significantly contributes to intratumour heterogeneity. However, it is becoming increasingly clear that it also contributes to intertumour heterogeneity, which historically had mainly been linked to diverse genetic events occurring in different patients. In this review, we explore how DNA methylation, chromatin remodelling, microRNA (miRNA) dysregulation, and long noncoding RNA (lncRNA) alterations contribute to intertumour heterogeneity in glioblastoma, including its implications for advanced tumour stratification, which is the essential first step for developing more effective patient-specific therapeutic approaches.

Current understanding of functional peptides encoded by lncRNA in cancer

Dysregulated gene expression and imbalance of transcriptional regulation are typical features of cancer. RNA always plays a key role in these processes. Human transcripts contain many RNAs without long open reading frames (ORF, > 100 aa) and that are more than 200 bp in length. They are usually regarded as long non-coding RNA (lncRNA) which play an important role in cancer regulation, including chromatin remodeling, transcriptional regulation, translational regulation and as miRNA sponges. With the advancement of ribosome profiling and sequencing technologies, increasing research evidence revealed that some ORFs in lncRNA can also encode peptides and participate in the regulation of multiple organ tumors, which undoubtedly opens a new chapter in the field of lncRNA and oncology research. In this review, we discuss the biological function of lncRNA in tumors, the current methods to evaluate their coding potential and the role of functional small peptides encoded by lncRNA in cancers. Investigating the small peptides encoded by lncRNA and understanding the regulatory mechanisms of these functional peptides may contribute to a deeper understanding of cancer and the development of new targeted anticancer therapies.

Ras-dependent activation of BMAL2 regulates hypoxic metabolism in pancreatic cancer

To identify drivers of malignancy in human pancreatic ductal adenocarcinoma (PDAC), we performed regulatory network analysis on a large collection of expression profiles from laser capture microdissected samples of PDAC and benign precursors. We discovered that BMAL2 plays a role in the initiation, progression, post resection survival, and KRAS activity in PDAC. Functional analysis of BMAL2 target genes led us to hypothesize that it plays a role in regulating the response to hypoxia, a critical but poorly understood feature of PDAC physiology. Knockout of BMAL2 in multiple human PDAC cell lines revealed effects on viability and invasion, particularly under hypoxic conditions. Loss of BMAL2 also affected glycolysis and other metabolic processes. We found that BMAL2 directly regulates hypoxia-responsive target genes. We also found that BMAL2 is necessary for the stabilization of HIF1A upon exposure to hypoxia, but destabilizes HIF2A under hypoxia. These data demonstrate that BMAL2 is a master transcriptional regulator of hypoxia responses in PDAC and may serve as a long-sought molecular switch that distinguishes HIF1A- and HIF2A-dependent modes of hypoxic metabolism.

How Artificial Intelligence Unravels the Complex Web of Cancer Drug Response

The intersection of precision medicine and artificial intelligence (AI) holds profound implications for cancer treatment, with the potential to significantly advance our understanding of drug responses based on the intricate architecture of tumor cells. A recent study by Park and colleagues titled "A Deep Learning Model of Tumor Cell Architecture Elucidates Response and Resistance to CDK4/6 Inhibitors" epitomizes this intersection by leveraging an interpretable deep learning model grounded in a comprehensive map of multiprotein assemblies in cancer, known as Nested Systems in Tumors. This study not only elucidates mechanisms underlying the response to CDK4/6 inhibitors in breast cancer therapy but also highlights the critical role of model interpretability leading to new mechanistic insights.

A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

Identification and Pharmacological Targeting of Treatment-Resistant, Stem-like Breast Cancer Cells for Combination Therapy

Tumors frequently harbor isogenic yet epigenetically distinct subpopulations of multi-potent cells with high tumor-initiating potential-often called Cancer Stem-Like Cells (CSLCs). These can display preferential resistance to standard-of-care chemotherapy. Single-cell analyses can help elucidate Master Regulator (MR) proteins responsible for governing the transcriptional state of these cells, thus revealing complementary dependencies that may be leveraged via combination therapy. Interrogation of single-cell RNA sequencing profiles from seven metastatic breast cancer patients, using perturbational profiles of clinically relevant drugs, identified drugs predicted to invert the activity of MR proteins governing the transcriptional state of chemoresistant CSLCs, which were then validated by CROP-seq assays. The top drug, the anthelmintic albendazole, depleted this subpopulation in vivo without noticeable cytotoxicity. Moreover, sequential cycles of albendazole and paclitaxel-a commonly used chemotherapeutic -displayed significant synergy in a patient-derived xenograft (PDX) from a TNBC patient, suggesting that network-based approaches can help develop mechanism-based combinatorial therapies targeting complementary subpopulations.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Subtype-selective prenylated isoflavonoids disrupt regulatory drivers of MYCN-amplified cancers

Transcription factors have proven difficult to target with small molecules because they lack pockets necessary for potent binding. Disruption of protein expression can suppress targets and enable therapeutic intervention. To this end, we developed a drug discovery workflow that incorporates cell-line-selective screening and high-throughput expression profiling followed by regulatory network analysis to identify compounds that suppress regulatory drivers of disease. Applying this approach to neuroblastoma (NBL), we screened bioactive molecules in cell lines representing its MYC-dependent (MYCNA) and mesenchymal (MES) subtypes to identify selective compounds, followed by PLATESeq profiling of treated cells. This revealed compounds that disrupt a sub-network of MYCNA-specific regulatory proteins, resulting in MYCN degradation in vivo. The top hit was isopomiferin, a prenylated isoflavonoid that inhibited casein kinase 2 (CK2) in cells. Isopomiferin and its structural analogs inhibited MYC and MYCN in NBL and lung cancer cells, highlighting the general MYC-inhibiting potential of this unique scaffold.

Elucidation and Pharmacologic Targeting of Master Regulator Dependencies in Coexisting Diffuse Midline Glioma Subpopulations

Diffuse Midline Gliomas (DMGs) are universally fatal, primarily pediatric malignancies affecting the midline structures of the central nervous system. Despite decades of clinical trials, treatment remains limited to palliative radiation therapy. A major challenge is the coexistence of molecularly distinct malignant cell states with potentially orthogonal drug sensitivities. To address this challenge, we leveraged established network-based methodologies to elucidate Master Regulator (MR) proteins representing mechanistic, non-oncogene dependencies of seven coexisting subpopulations identified by single-cell analysis-whose enrichment in essential genes was validated by pooled CRISPR/Cas9 screens. Perturbational profiles of 372 clinically relevant drugs helped identify those able to invert the activity of subpopulation-specific MRs for follow-up in vivo validation. While individual drugs predicted to target individual subpopulations-including avapritinib, larotrectinib, and ruxolitinib-produced only modest tumor growth reduction in orthotopic models, systemic co-administration induced significant survival extension, making this approach a valuable contribution to the rational design of combination therapy.

Quantified pathway mutations associate epithelial-mesenchymal transition and immune escape with poor prognosis and immunotherapy resistance of head and neck squamous cell carcinoma

BACKGROUND

Pathway mutations have been calculated to predict the poor prognosis and immunotherapy resistance in head and neck squamous cell carcinoma (HNSCC). To uncover the unique markers predicting prognosis and immune therapy response, the accurate quantification of pathway mutations are required to evaluate epithelial-mesenchymal transition (EMT) and immune escape. Yet, there is a lack of score to accurately quantify pathway mutations.

MATERIAL AND METHODS

Firstly, we proposed Individualized Weighted Hallmark Gene Set Mutation Burden (IWHMB, https://github.com/YuHongHuang-lab/IWHMB ) which integrated pathway structure information and eliminated the interference of global Tumor Mutation Burden to accurately quantify pathway mutations. Subsequently, to further elucidate the association of IWHMB with EMT and immune escape, support vector machine regression model was used to identify IWHMB-related transcriptomic features (IRG), while Adversarially Regularized Graph Autoencoder (ARVGA) was used to further resolve IRG network features. Finally, Random walk with restart algorithm was used to identify biomarkers for predicting ICI response.

RESULTS

We quantified the HNSCC pathway mutation signatures and identified pathway mutation subtypes using IWHMB. The IWHMB-related transcriptomic features (IRG) identified by support vector machine regression were divided into 5 communities by ARVGA, among which the Community 1 enriching malignant mesenchymal components promoted EMT dynamically and regulated immune patterns associated with ICI responses. Bridge Hub Gene (BHG) identified by random walk with restart was key to IWHMB in EMT and immune escape, thus, more predictive for ICI response than other 70 public signatures.

CONCLUSION

In summary, the novel pathway mutation scoring-IWHMB suggested that the elevated malignancy mediated by pathway mutations is a major cause of poor prognosis and immunotherapy failure in HNSCC, and is capable of identifying novel biomarkers to predict immunotherapy response.

CRISPR-Based Therapies: Revolutionizing Drug Development and Precision Medicine

With the discovery of CRISPR-Cas9, drug development and precision medicine have undergone a major change. This review article looks at the new ways that CRISPR-based therapies are being used and how they are changing the way medicine is done. CRISPR technology's ability to precisely and flexibly edit genes has opened up new ways to find, validate, and develop drug targets. Also, it has made way for personalized gene therapies, precise gene editing, and advanced screening techniques, all of which hold great promise for treating a wide range of diseases. In this article, we look at the latest research and clinical trials that show how CRISPR could be used to treat genetic diseases, cancer, infectious diseases, and other hard-to-treat conditions. However, ethical issues and problems with regulations are also discussed in relation to CRISPR-based therapies, which shows how important it is to use them safely and responsibly. As CRISPR continues to change how drugs are made and used, this review shines a light on the amazing things that have been done and what the future might hold in this rapidly changing field.

Emerging Treatment Approaches for COVID-19 Infection: A Critical Review

In the present scenario, the SARS-CoV-2 virus has imposed enormous damage on human survival and the global financial system. It has been estimated that around 111 million people all around the world have been infected, and about 2.47 million people died due to this pandemic. The major symptoms were sneezing, coughing, cold, difficulty breathing, pneumonia, and multi-organ failure associated 1with SARS-CoV-2. Currently, two key problems, namely insufficient attempts to develop drugs against SARSCoV-2 and the lack of any biological regulating process, are mostly responsible for the havoc caused by this virus. Henceforth, developing a few novel drugs is urgently required to cure this pandemic. It has been noticed that the pathogenesis of COVID-19 is caused by two main events: infection and immune deficiency, that occur during the pathological process. Antiviral medication can treat both the virus and the host cells. Therefore, in the present review, the major approaches for the treatment have been divided into "target virus" and "target host" groups. These two mechanisms primarily rely on drug repositioning, novel approaches, and possible targets. Initially, we discussed the traditional drugs per the physicians' recommendations. Moreover, such therapeutics have no potential to fight against COVID-19. After that, detailed investigation and analysis were conducted to find some novel vaccines and monoclonal antibodies and conduct a few clinical trials to check their effectiveness against SARSCoV- 2 and mutant strains. Additionally, this study presents the most successful methods for its treatment, including combinatorial therapy. Nanotechnology was studied to build efficient nanocarriers to overcome the traditional constraints of antiviral and biological therapies.

Systematic identification of gene combinations to target in innate immune cells to enhance T cell activation

Genetic engineering of immune cells has opened new avenues for improving their functionality but it remains a challenge to pinpoint which genes or combination of genes are the most beneficial to target. Here, we conduct High Multiplicity of Perturbations and Cellular Indexing of Transcriptomes and Epitopes (HMPCITE-seq) to find combinations of genes whose joint targeting improves antigen-presenting cell activity and enhances their ability to activate T cells. Specifically, we perform two genome-wide CRISPR screens in bone marrow dendritic cells and identify negative regulators of CD86, that participate in the co-stimulation programs, including Chd4, Stat5b, Egr2, Med12, and positive regulators of PD-L1, that participate in the co-inhibitory programs, including Sptlc2, Nckap1l, and Pi4kb. To identify the genetic interactions between top-ranked genes and find superior combinations to target, we perform high-order Perturb-Seq experiments and we show that targeting both Cebpb and Med12 results in a better phenotype compared to the single perturbations or other combinations of perturbations.

Master regulator activity QTL protocol to implicate regulatory pathways potentially mediating GWAS signals using eQTL data

Here, we present a protocol to identify transcriptional regulators potentially mediating downstream biological effects of germline variants associated with complex traits of interest, which enables functional hypothesis generation independent of colocalizing expression quantitative trait loci (eQTLs). We describe steps for tissue-/cell-type-specific co-expression network modeling, expression regulator activity inference, and identification of representative phenotypic master regulators. Finally, we detail activity QTL and eQTL analyses. This protocol requires genotype, expression, and relevant covariables and phenotype data from existing eQTL datasets. For complete details on the use and execution of this protocol, please refer to Hoskins et al.1.

Pancancer network analysis reveals key master regulators for cancer invasiveness

BACKGROUND

Tumor invasiveness reflects numerous biological changes, including tumorigenesis, progression, and metastasis. To decipher the role of transcriptional regulators (TR) involved in tumor invasiveness, we performed a systematic network-based pan-cancer assessment of master regulators of cancer invasiveness.

MATERIALS AND METHODS

We stratified patients in The Cancer Genome Atlas (TCGA) into invasiveness high (INV-H) and low (INV-L) groups using consensus clustering based on an established robust 24-gene signature to determine the prognostic association of invasiveness with overall survival (OS) across 32 different cancers. We devise a network-based protocol to identify TRs as master regulators (MRs) unique to INV-H and INV-L phenotypes. We validated the activity of MRs coherently associated with INV-H phenotype and worse OS across cancers in TCGA on a series of additional datasets in the Prediction of Clinical Outcomes from the Genomic Profiles (PRECOG) repository.

RESULTS

Based on the 24-gene signature, we defined the invasiveness score for each patient sample and stratified patients into INV-H and INV-L clusters. We observed that invasiveness was associated with worse survival outcomes in almost all cancers and had a significant association with OS in ten out of 32 cancers. Our network-based framework identified common invasiveness-associated MRs specific to INV-H and INV-L groups across the ten prognostic cancers, including COL1A1, which is also part of the 24-gene signature, thus acting as a positive control. Downstream pathway analysis of MRs specific to INV-H phenotype resulted in the identification of several enriched pathways, including Epithelial into Mesenchymal Transition, TGF-β signaling pathway, regulation of Toll-like receptors, cytokines, and inflammatory response, and selective expression of chemokine receptors during T-cell polarization. Most of these pathways have connotations of inflammatory immune response and feasibility for metastasis.

CONCLUSION

Our pan-cancer study provides a comprehensive master regulator analysis of tumor invasiveness and can suggest more precise therapeutic strategies by targeting the identified MRs and downstream enriched pathways for patients across multiple cancers.

Hepatocellular carcinoma cell differentiation trajectory predicts immunotherapy, potential therapeutic drugs, and prognosis of patients

The aim of this study is to explore a novel classification and investigate the clinical significance of hepatocellular carcinoma (HCC) cells. We analyzed integrated single-cell RNA sequencing and bulk RNA-seq data obtained from HCC samples. Cell trajectory analysis divided HCC cells into three subgroups with different differentiation states: state 1 was closely related to phosphoric ester hydrolase activity, state 2 was involved in eukaryotic initiation factor 4E binding, translation regulator activity and ribosome, and state 3 was associated with oxidoreductase activity and metabolism. Three molecular classes based on HCC differentiation-related genes (HDRGs) from HCC samples were identified, which revealed immune checkpoint gene expression and overall survival (OS) of HCC patients. Moreover, a prognostic risk scoring (RS) model was generated based on eight HDRGs, and the results showed that the OS of the high-risk group was worse than that of the low-risk group. Further, potential therapeutic drugs were screened out based on eight prognostic RS-HDRGs. This study highlights the importance of HCC cell differentiation in immunotherapy, clinical prognosis, and potential molecular-targeted drugs for HCC patients, and proposes a direction for the development of individualized treatments for HCC.

Exportin XPO7 acts as an oncogenic factor in prostate cancer via upregulation of TCF3

PURPOSE

As a nuclear transport protein, XPO7 has been observed to show abnormal expression in various types of human cancers. However, the role of XPO7 in PCa remains elusive.

METHODS

Here, in this study, immunohistochemistry and bioinformatics were used to determine the expression pattern and prognostic significance of XPO7. To investigate the functions of XPO7 in vitro and in vivo, we knocked down XPO7 in PCa cell lines and established xenograft mice models. Then, we used multiple experiments to determine the cell proliferation, migration, invasion, cell cycle and EMT in PCa cells after XPO7 modulation. Mechanistically, we conducted RNA-seq and identified the regulating effect of XPO7 on cell cycle-related and PI3K-AKT pathways. Furthermore, we assessed the regulating correlation between XPO7 and TCF3 and verified by a series of rescue experiments.

RESULTS

We found a higher XPO7 expression in prostate cancer tissues and predicted a poorer prognosis of prostate cancer. Then, we further revealed that the ectopic expression of XPO7 in PCa cells facilitated cells proliferation, migration, cell cycle progression and EMT in vitro and promoted tumor growth in vivo. Mechanistically, we conducted RNA-seq and identified the regulating effect of XPO7 on cell cycle-related and PI3K-AKT pathways. Furthermore, a significantly positive correlation was discovered between the expression of XPO7 and TCF3. In addition, XPO7 may regulate PCa through mediating TCF3 expression. TCF3 depletion could alleviate the influence of XPO7 overexpression on malignant phenotypes of PCa cells.

CONCLUSIONS

These findings indicate that XPO7 promotes PCa initiation and progression and that targeting XPO7 might be therapeutically beneficial to patients with PCa.

Construction of molecular typing in LIHC microenvironment based on lipid metabolism-related genes

Consensus on the stage of liver hepatocellular carcinoma (LIHC) in patients is difficult, which restricts the diagnosis and treatment of liver cancer. Molecular typing based on genes related to the lipid metabolism pathways can reflect deeper characteristics of liver cancer and complement the deficiency of the clinical staging system. In this study, we constructed and verified two cell subtypes: C1 and C2 in LIHC, based on six lipid metabolic pathway-associated genes identified in two independent external validation cohorts comprising single-cell RNA-sequencing technology (scRNA-Seq) data and bulk RNA-seq data downloaded from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The C2 subtype showed poorer prognosis, higher immune scores, and greater correlation with pathways associated with tumor progression as compared to the C1 subtype. Moreover, the sensitivity of many tested targeted drugs in C1 was relative to C2. Furthermore, Gene Set Enrichment Analysis (GSEA) revealed several significantly enriched oncological signatures and metabolic processes, which might help elucidate the underlying molecular mechanisms. At the same time, we identified there were significantly different metabolites in C1 and C2 subtypes using 11 LIHC tissue samples. In conclusion, we constructed two molecular subtypes based on the lipid metabolism-associated genes, which may provide valuable information to further study the pathogenesis and devise clinical management strategies for LIHC.

A Transcriptome-Based Precision Oncology Platform for Patient-Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies

Predicting in vivo response to antineoplastics remains an elusive challenge. We performed a first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism of action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor-prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual master regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyperconnected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing precision cancer medicine approaches, as also illustrated by a case study.

SIGNIFICANCE

Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo. OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials. This article is highlighted in the In This Issue feature, p. 1275.

Structure Learning for Gene Regulatory Networks

Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput "omics" data typically available. To overcome this challenge, often referred to as the "small n, large p problem," we exploit known organizing principles of biological networks that are sparse, modular, and likely share a large portion of their underlying architecture. We present SHINE-Structure Learning for Hierarchical Networks-a framework for defining data-driven structural constraints and incorporating a shared learning paradigm for efficiently learning multiple Markov networks from high-dimensional data at large p/n ratios not previously feasible. We evaluated SHINE on Pan-Cancer data comprising 23 tumor types, and found that learned tumor-specific networks exhibit expected graph properties of real biological networks, recapture previously validated interactions, and recapitulate findings in literature. Application of SHINE to the analysis of subtype-specific breast cancer networks identified key genes and biological processes for tumor maintenance and survival as well as potential therapeutic targets for modulating known breast cancer disease genes.

Systematic elucidation and pharmacological targeting of tumor-infiltrating regulatory T cell master regulators

Due to their immunosuppressive role, tumor-infiltrating regulatory T cells (TI-Tregs) represent attractive immuno-oncology targets. Analysis of TI vs. peripheral Tregs (P-Tregs) from 36 patients, across four malignancies, identified 17 candidate master regulators (MRs) as mechanistic determinants of TI-Treg transcriptional state. Pooled CRISPR-Cas9 screening in vivo, using a chimeric hematopoietic stem cell transplant model, confirmed the essentiality of eight MRs in TI-Treg recruitment and/or retention without affecting other T cell subtypes, and targeting one of the most significant MRs (Trps1) by CRISPR KO significantly reduced ectopic tumor growth. Analysis of drugs capable of inverting TI-Treg MR activity identified low-dose gemcitabine as the top prediction. Indeed, gemcitabine treatment inhibited tumor growth in immunocompetent but not immunocompromised allografts, increased anti-PD-1 efficacy, and depleted MR-expressing TI-Tregs in vivo. This study provides key insight into Treg signaling, specifically in the context of cancer, and a generalizable strategy to systematically elucidate and target MR proteins in immunosuppressive subpopulations.

NetBID2 provides comprehensive hidden driver analysis

Many signaling and other genes known as "hidden" drivers may not be genetically or epigenetically altered or differentially expressed at the mRNA or protein levels, but, rather, drive a phenotype such as tumorigenesis via post-translational modification or other mechanisms. However, conventional approaches based on genomics or differential expression are limited in exposing such hidden drivers. Here, we present a comprehensive algorithm and toolkit NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), which reverse-engineers context-specific interactomes and integrates network activity inferred from large-scale multi-omics data, empowering the identification of hidden drivers that could not be detected by traditional analyses. NetBID2 has substantially re-engineered the previous prototype version by providing versatile data visualization and sophisticated statistical analyses, which strongly facilitate researchers for result interpretation through end-to-end multi-omics data analysis. We demonstrate the power of NetBID2 using three hidden driver examples. We deploy NetBID2 Viewer, Runner, and Cloud apps with 145 context-specific gene regulatory and signaling networks across normal tissues and paediatric and adult cancers to facilitate end-to-end analysis, real-time interactive visualization and cloud-based data sharing. NetBID2 is freely available at https://jyyulab.github.io/NetBID .

Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer

Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions.

SIGNIFICANCE

The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.

Critical transition and reversion of tumorigenesis

Cancer is caused by the accumulation of genetic alterations and therefore has been historically considered to be irreversible. Intriguingly, several studies have reported that cancer cells can be reversed to be normal cells under certain circumstances. Despite these experimental observations, conceptual and theoretical frameworks that explain these phenomena and enable their exploration in a systematic way are lacking. In this review, we provide an overview of cancer reversion studies and describe recent advancements in systems biological approaches based on attractor landscape analysis. We suggest that the critical transition in tumorigenesis is an important clue for achieving cancer reversion. During tumorigenesis, a critical transition may occur at a tipping point, where cells undergo abrupt changes and reach a new equilibrium state that is determined by complex intracellular regulatory events. We introduce a conceptual framework based on attractor landscapes through which we can investigate the critical transition in tumorigenesis and induce its reversion by combining intracellular molecular perturbation and extracellular signaling controls. Finally, we present a cancer reversion therapy approach that may be a paradigm-changing alternative to current cancer cell-killing therapies.

NaRnEA: An Information Theoretic Framework for Gene Set Analysis

Gene sets are being increasingly leveraged to make high-level biological inferences from transcriptomic data; however, existing gene set analysis methods rely on overly conservative, heuristic approaches for quantifying the statistical significance of gene set enrichment. We created Nonparametric analytical-Rank-based Enrichment Analysis (NaRnEA) to facilitate accurate and robust gene set analysis with an optimal null model derived using the information theoretic Principle of Maximum Entropy. By measuring the differential activity of ~2500 transcriptional regulatory proteins based on the differential expression of each protein's transcriptional targets between primary tumors and normal tissue samples in three cohorts from The Cancer Genome Atlas (TCGA), we demonstrate that NaRnEA critically improves in two widely used gene set analysis methods: Gene Set Enrichment Analysis (GSEA) and analytical-Rank-based Enrichment Analysis (aREA). We show that the NaRnEA-inferred differential protein activity is significantly correlated with differential protein abundance inferred from independent, phenotype-matched mass spectrometry data in the Clinical Proteomic Tumor Analysis Consortium (CPTAC), confirming the statistical and biological accuracy of our approach. Additionally, our analysis crucially demonstrates that the sample-shuffling empirical null models leveraged by GSEA and aREA for gene set analysis are overly conservative, a shortcoming that is avoided by the newly developed Maximum Entropy analytical null model employed by NaRnEA.

Genomics-Driven Precision Medicine in Pediatric Solid Tumors

Over the past decades, several study programs have conducted genetic testing in cancer patients to identify potential genetic targets for the development of precision therapeutic strategies. These biomarker-driven trials have demonstrated improved clinical outcomes and progression-free survival rates in various types of cancers, especially for adult malignancies. However, similar progress in pediatric cancers has been slow due to their distinguished mutation profiles compared to adults and the low frequency of recurrent genomic alterations. Recently, increased efforts to develop precision medicine for childhood malignancies have led to the identification of genomic alterations and transcriptomic profiles of pediatric patients which presents promising opportunities to study rare and difficult-to-access neoplasms. This review summarizes the current state of known and potential genetic markers for pediatric solid tumors and provides perspectives on precise therapeutic strategies that warrant further investigations.

Precision immunotherapy

A mechanistic approach to overcoming treatment resistance reveals new targets.

TEAD4 is a master regulator of high-risk nasopharyngeal carcinoma

The molecular basis underlying nasopharyngeal carcinoma (NPC) remains unclear. Recent progress in transcriptional regulatory network analysis helps identify the master regulator (MR) proteins that transcriptionally define malignant tumor phenotypes. Here, we investigated transcription factor-target interactions and identified TEA domain transcription factor 4 (TEAD4) as an MR of high-risk NPC. Precisely, TEAD4 promoted NPC migration, invasion and cisplatin resistance, depending on its autopalmitoylation. Mechanistically, YTHDF2 (YTH domain family 2) recognized WTAP (Wilms tumor 1-associating protein)-mediated TEAD4 m⁶A methylation to facilitate its stability and led to aberrant up-regulation of TEAD4. Up-regulated TEAD4 further drove NPC progression by transcriptionally activating BZW2 (basic leucine zipper and W2 domains 2) to induce the oncogenic AKT pathway. Moreover, the transcriptional activity of TEAD4 was independent of its canonical coactivators YAP/TAZ. Clinically, TEAD4 serves as an independent predictor of unfavorable prognosis and cisplatin response in NPC. Our data revealed the crucial role of TEAD4 in driving tumor malignancy, thus, may provide therapeutic vulnerability in NPC.

Progress and perspectives of platinum nanozyme in cancer therapy

Malignant tumors, one of the worst-case scenarios within human health problems, are now posing an increasing threat to the well-being of the global population. At present, the treatment of malignant tumors mainly includes surgery, radiotherapy, chemotherapy, immunotherapy, etc. Radiotherapy and chemotherapy are often applied to inoperable tumors, and some other tumors after surgery as important adjuvant therapies. Nonetheless, both radiotherapy and chemotherapy have a series of side effects, such as radiation-induced lung injury, and chemotherapy-induced bone marrow suppression. In addition, the positioning accuracy of radiotherapy and chemotherapy is not assured and satisfactory, and the possibility of tumor cells not being sensitive to radiation and chemotherapy drugs is also problematic. Nanozymes are nanomaterials that display natural enzyme activities, and their applications to tumor therapy have made great progress recently. The most studied one, platinum nanozyme, has been shown to possess a significant correlation with radiotherapy sensitization of tumors as well as photodynamic therapy. However, there are still several issues that limited the usage of platinum-based nanozymes in vivo. In this review, we briefly summarize the representative studies regarding platinum nanozymes, and especially emphasize on the current challenges and the directions of future development for platinum nanozymes therapy.

Validation of a non-oncogene encoded vulnerability to exportin 1 inhibition in pediatric renal tumors

BACKGROUND

Malignant rhabdoid tumors (MRTs) and Wilms' tumors (WTs) are rare and aggressive renal tumors of infants and young children comprising ∼5% of all pediatric cancers. MRTs are among the most genomically stable cancers, and although WTs are genomically heterogeneous, both generally lack therapeutically targetable genetic mutations.

METHODS

Comparative protein activity analysis of MRTs (n = 68) and WTs (n = 132) across TCGA and TARGET cohorts, using metaVIPER, revealed elevated exportin 1 (XPO1) inferred activity. In vitro studies were performed on a panel of MRT and WT cell lines to evaluate effects on proliferation and cell-cycle progression following treatment with the selective XPO1 inhibitor selinexor. In vivo anti-tumor activity was assessed in patient-derived xenograft (PDX) models of MRTs and WTs.

FINDINGS

metaVIPER analysis identified markedly aberrant activation of XPO1 in MRTs and WTs compared with other tumor types. All MRT and most WT cell lines demonstrated baseline, aberrant XPO1 activity with in vitro sensitivity to selinexor via cell-cycle arrest and induction of apoptosis. In vivo, XPO1 inhibitors significantly abrogated tumor growth in PDX models, inducing effective disease control with sustained treatment. Corroborating human relevance, we present a case report of a child with multiply relapsed WTs with prolonged disease control on selinexor.

CONCLUSIONS

We report on a novel systems-biology-based comparative framework to identify non-genetically encoded vulnerabilities in genomically quiescent pediatric cancers. These results have provided preclinical rationale for investigation of XPO1 inhibitors in an upcoming investigator-initiated clinical trial of selinexor in children with MRTs and WTs and offer opportunities for exploration of inferred XPO1 activity as a potential predictive biomarker for response.

FUNDING

This work was funded by CureSearch for Children's Cancer, Alan B. Slifka Foundation, NIH (U01 CA217858, S10 OD012351, and S10 OD021764), Michael's Miracle Cure, Hyundai Hope on Wheels, Cannonball Kids Cancer, Conquer Cancer the ASCO Foundation, Cycle for Survival, Paulie Strong Foundation, and the Grayson Fund.

A transcriptional network of cell cycle dysregulation in noninvasive papillary urothelial carcinoma

Human cancers display a restricted set of expression profiles, despite diverse mutational drivers. This has led to the hypothesis that select sets of transcription factors act on similar target genes as an integrated network, buffering a tumor's transcriptional state. Noninvasive papillary urothelial carcinoma (NIPUC) with higher cell cycle activity has higher risk of recurrence and progression. In this paper, we describe a transcriptional network of cell cycle dysregulation in NIPUC, which was delineated using the ARACNe algorithm applied to expression data from a new cohort (n = 81, RNA sequencing), and two previously published cohorts. The transcriptional network comprised 121 transcription factors, including the pluripotency factors SOX2 and SALL4, the sex hormone binding receptors ESR1 and PGR, and multiple homeobox factors. Of these 121 transcription factors, 65 and 56 were more active in tumors with greater and less cell cycle activity, respectively. When clustered by activity of these transcription factors, tumors divided into High Cell Cycle versus Low Cell Cycle groups. Tumors in the High Cell Cycle group demonstrated greater mutational burden and copy number instability. A putative mutational driver of cell cycle dysregulation, such as homozygous loss of CDKN2A, was found in only 50% of High Cell Cycle NIPUC, suggesting a prominent role of transcription factor activity in driving cell cycle dysregulation. Activity of the 121 transcription factors strongly associated with expression of EZH2 and other members of the PRC2 complex, suggesting regulation by this complex influences expression of the transcription factors in this network. Activity of transcription factors in this network also associated with signatures of pluripotency and epithelial-to-mesenchymal transition (EMT), suggesting they play a role in driving evolution to invasive carcinoma. Consistent with this, these transcription factors differed in activity between NIPUC and invasive urothelial carcinoma.

A Patient-to-Model-to-Patient (PMP) Cancer Drug Discovery Protocol for Identifying and Validating Therapeutic Agents Targeting Tumor Regulatory Architecture

The current Achilles heel of cancer drug discovery is the inability to forge precise and predictive connections among mechanistic drivers of the cancer cell state, therapeutically significant molecular targets, effective drugs, and responsive patient subgroups. Although advances in molecular biology have helped identify molecular markers and stratify patients into molecular subtypes, these associational strategies typically fail to provide a mechanistic rationale to identify cancer vulnerabilities. Recently, integrative systems biology methodologies have been used to reverse engineer cellular networks and identify master regulators (MRs), proteins whose activity is both necessary and sufficient to implement phenotypic states under physiological and pathological conditions, which are organized into highly interconnected regulatory modules called tumor checkpoints. Because of their functional relevance, MRs represent ideal pharmacological targets and biomarkers. Here, we present a six-step patient-to-model-to-patient protocol that employs computational and experimental methodologies to reconstruct and interrogate the regulatory logic of human cancer cells for identifying and therapeutically targeting the tumor checkpoint with novel as well as existing pharmacological agents. This protocol systematically identifies, from specific patient tumor samples, the MRs that comprise the tumor checkpoint. Then, it identifies in vitro and in vivo models that, by recapitulating the patient's tumor checkpoint, constitute the appropriate cell lines and xenografts to further elucidate the tissue context-specific drug mechanism of action (MOA) and permit precise, biomarker-based preclinical validations of drug efficacy. The combination of determination of a drug's context-specific MOA and precise identification of patients' tumor checkpoints provides a personalized, mechanism-based biomarker to enrich prospective clinical trials with patients likely to respond. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

A model for network-based identification and pharmacological targeting of aberrant, replication-permissive transcriptional programs induced by viral infection

SARS-CoV-2 hijacks the host cell transcriptional machinery to induce a phenotypic state amenable to its replication. Here we show that analysis of Master Regulator proteins representing mechanistic determinants of the gene expression signature induced by SARS-CoV-2 in infected cells revealed coordinated inactivation of Master Regulators enriched in physical interactions with SARS-CoV-2 proteins, suggesting their mechanistic role in maintaining a host cell state refractory to virus replication. To test their functional relevance, we measured SARS-CoV-2 replication in epithelial cells treated with drugs predicted to activate the entire repertoire of repressed Master Regulators, based on their experimentally elucidated, context-specific mechanism of action. Overall, 15 of the 18 drugs predicted to be effective by this methodology induced significant reduction of SARS-CoV-2 replication, without affecting cell viability. This model for host-directed pharmacological therapy is fully generalizable and can be deployed to identify drugs targeting host cell-based Master Regulator signatures induced by virtually any pathogen.

Role of germline variants in the metastasis of breast carcinomas

Most cancer-related deaths in breast cancer patients are associated with metastasis, a multistep, intricate process that requires the cooperation of tumour cells, tumour microenvironment and metastasis target tissues. It is accepted that metastasis does not depend on the tumour characteristics but the host’s genetic makeup. However, there has been limited success in determining the germline genetic variants that influence metastasis development, mainly because of the limitations of traditional genome-wide association studies to detect the relevant genetic polymorphisms underlying complex phenotypes. In this work, we leveraged the extreme discordant phenotypes approach and the epistasis networks to analyse the genotypes of 97 breast cancer patients. We found that the host’s genetic makeup facilitates metastases by the dysregulation of gene expression that can promote the dispersion of metastatic seeds and help establish the metastatic niche—providing a congenial soil for the metastatic seeds.

Therapy resistance: opportunities created by adaptive responses to targeted therapies in cancer

Normal cells explore multiple states to survive stresses encountered during development and self-renewal as well as environmental stresses such as starvation, DNA damage, toxins or infection. Cancer cells co-opt normal stress mitigation pathways to survive stresses that accompany tumour initiation, progression, metastasis and immune evasion. Cancer therapies accentuate cancer cell stresses and invoke rapid non-genomic stress mitigation processes that maintain cell viability and thus represent key targetable resistance mechanisms. In this Review, we describe mechanisms by which tumour ecosystems, including cancer cells, immune cells and stroma, adapt to therapeutic stresses and describe three different approaches to exploit stress mitigation processes: (1) interdict stress mitigation to induce cell death; (2) increase stress to induce cellular catastrophe; and (3) exploit emergent vulnerabilities in cancer cells and cells of the tumour microenvironment. We review challenges associated with tumour heterogeneity, prioritizing actionable adaptive responses for optimal therapeutic outcomes, and development of an integrative framework to identify and target vulnerabilities that arise from adaptive responses and engagement of stress mitigation pathways. Finally, we discuss the need to monitor adaptive responses across multiple scales and translation of combination therapies designed to take advantage of adaptive responses and stress mitigation pathways to the clinic.

Introduction to Genomic Network Reconstruction for Cancer Research

High-throughput genomic technologies have revolutionized the study of cancer. Current research in oncology is now limited more for the capacity of analyzing and interpreting data, rather than the availability of data itself. Integrative approaches to obtain functional information from data are at the core of the disciplines gathered under the systems biology banner. In this context, network models have been used to study cancer, from the identification of key molecules involved in the disease to the discovery of functional alterations associated with specific manifestations of the disease.In this chapter, we describe the state of the art of network reconstruction from genomic data, with an emphasis in gene expression experiments. We explore the strengths and limitations of correlation, Bayesian, and information theoretic approaches to network reconstruction. We present tools that leverage the flexibility of network science to gain a deeper understanding of cancer biology.

SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes

Single-cell RNA-Sequencing has the potential to provide deep biological insights by revealing complex regulatory interactions across diverse cell phenotypes at single-cell resolution. However, current single-cell gene regulatory network inference methods produce a single regulatory network per input dataset, limiting their capability to uncover complex regulatory relationships across related cell phenotypes. We present SimiC, a single-cell gene regulatory inference framework that overcomes this limitation by jointly inferring distinct, but related, gene regulatory dynamics per phenotype. We show that SimiC uncovers key regulatory dynamics missed by previously proposed methods across a range of systems, both model and non-model alike. In particular, SimiC was able to uncover CAR T cell dynamics after tumor recognition and key regulatory patterns on a regenerating liver, and was able to implicate glial cells in the generation of distinct behavioral states in honeybees. SimiC hence establishes a new approach to quantitating regulatory architectures between distinct cellular phenotypes, with far-reaching implications for systems biology.

RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling

Changes in a few key transcriptional regulators can lead to different biological states. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights. Most current tools perform pathway/GO enrichment analysis to identify key genes and regulators but tend to overlook the gene/protein regulatory interactions. Here we present RegEnrich, an open-source Bioconductor R package, which combines differential expression analysis, data-driven gene regulatory network inference, enrichment analysis, and gene regulator ranking to identify key regulators using gene/protein expression profiling data. By benchmarking using multiple gene expression datasets of gene silencing studies, we found that RegEnrich using the GSEA method to rank the regulators performed the best. Further, RegEnrich was applied to 21 publicly available datasets on in vitro interferon-stimulation of different cell types. Collectively, RegEnrich can accurately identify key gene regulators from the cells under different biological states, which can be valuable in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and ultimately drug development.

Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach

BACKGROUND

Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patients with breast cancer, particularly in relation with the current molecular breast cancer (BRCA) classification. In this view, we developed a new computational method to infer cell-surface protein activities from transcriptomics data, termed 'SURFACER'.

METHODS

Gene expression data from GTEx were used to build a normal breast network model as input to infer differential cell-surface proteins activity in BRCA tissue samples retrieved from TCGA versus normal samples. Data were stratified according to the PAM50 transcriptional subtypes (Luminal A, Luminal B, HER2 and Basal), while unsupervised clustering techniques were applied to define BRCA subtypes according to cell-surface proteins activity.

RESULTS

Our approach led to the identification of 213 PAM50 subtypes-specific deregulated surface genes and the definition of five BRCA subtypes, whose prognostic value was assessed by survival analysis, identifying a cell-surface activity configuration at increased risk. The value of the SURFACER method in BRCA genotyping was tested by evaluating the performance of 11 different machine learning classification algorithms.

CONCLUSIONS

BRCA patients can be stratified into five surface activity-specific groups having the potential to identify subtype-specific actionable targets to design tailored targeted therapies or for diagnostic purposes. SURFACER-defined subtypes show also a prognostic value, identifying surface-activity profiles at higher risk.

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.

Integrative multi-omics identifies high risk multiple myeloma subgroup associated with significant DNA loss and dysregulated DNA repair and cell cycle pathways

Background

Despite significant therapeutic advances in improving lives of multiple myeloma (MM) patients, it remains mostly incurable, with patients ultimately becoming refractory to therapies. MM is a genetically heterogeneous disease and therapeutic resistance is driven by a complex interplay of disease pathobiology and mechanisms of drug resistance. We applied a multi-omics strategy using tumor-derived gene expression, single nucleotide variant, copy number variant, and structural variant profiles to investigate molecular subgroups in 514 newly diagnosed MM (NDMM) samples and identified 12 molecularly defined MM subgroups (MDMS1-12) with distinct genomic and transcriptomic features.

Results

Our integrative approach let us identify NDMM subgroups with transversal profiles to previously described ones, based on single data types, which shows the impact of this approach for disease stratification. One key novel subgroup is our MDMS8, associated with poor clinical outcome [median overall survival, 38 months (global log-rank p-value < 1 × 10⁻⁶)], which uniquely presents a broad genomic loss (> 9% of entire genome, t-test p value < 1e−5) driving dysregulation of various transcriptional programs affecting DNA repair and cell cycle/mitotic processes. This subgroup was validated on multiple independent datasets, and a master regulator analyses identified transcription factors controlling MDMS8 transcriptomic profile, including CKS1B and PRKDC among others, which are regulators of the DNA repair and cell cycle pathways.

Conclusion

Using multi-omics unsupervised clustering we were able to discover a new high-risk multiple myeloma patient segment. This high-risk group presents diverse previously known genetic markers, but also a new characteristic defined by accumulation of genomic loss which seems to drive transcriptional dysregulation of cell cycle, DNA repair and DNA damage. Finally, our work identified various master regulators, including E2F2 and CKS1B as the genes controlling these key biological pathways.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01140-5.

BACH2 inhibition reverses β cell failure in type 2 diabetes models

Type 2 diabetes (T2D) is associated with defective insulin secretion and reduced β cell mass. Available treatments provide a temporary reprieve, but secondary failure rates are high, making insulin supplementation necessary. Reversibility of β cell failure is a key translational question. Here, we reverse engineered and interrogated pancreatic islet-specific regulatory networks to discover T2D-specific subpopulations characterized by metabolic inflexibility and endocrine progenitor/stem cell features. Single-cell gain- and loss-of-function and glucose-induced Ca2+ flux analyses of top candidate master regulatory (MR) proteins in islet cells validated transcription factor BACH2 and associated epigenetic effectors as key drivers of T2D cell states. BACH2 knockout in T2D islets reversed cellular features of the disease, restoring a nondiabetic phenotype. BACH2-immunoreactive islet cells increased approximately 4-fold in diabetic patients, confirming the algorithmic prediction of clinically relevant subpopulations. Treatment with a BACH inhibitor lowered glycemia and increased plasma insulin levels in diabetic mice, and restored insulin secretion in diabetic mice and human islets. The findings suggest that T2D-specific populations of failing β cells can be reversed and indicate pathways for pharmacological intervention, including via BACH2 inhibition.

Cancer gene mutation frequencies for the U.S. population

Mutations play a fundamental role in the development of cancer, and many create targetable vulnerabilities. There are both public health and basic science benefits from the determination of the proportion of all cancer cases within a population that include a mutant form of a gene. Here, we provide the first such estimates by combining genomic and epidemiological data. We estimate KRAS is mutated in only 11% of all cancers, which is less than PIK3CA (13%) and marginally higher than BRAF (8%). TP53 is the most commonly mutated gene (35%), and KMT2C, KMT2D, and ARID1A are among the ten most commonly mutated driver genes, highlighting the role of epigenetic dysregulation in cancer. Analysis of major cancer subclassifications highlighted varying dependencies upon individual cancer drivers. Overall, we find that cancer genetics is less dominated by high-frequency, high-profile cancer driver genes than studies limited to a subset of cancer types have suggested.

Understanding the frequency of gene mutations in cancer could be important for generating targeted therapeutics. Here, the authors use SEER data and cancer genomics data from TCGA to estimate the gene mutation frequencies in the US cancer population.

PTEN loss promotes oncogenic function of STMN1 via PI3K/AKT pathway in lung cancer

Among all cancer types, lung cancer has already become the leading cause of cancer-related death around the world. The molecular mechanism understanding this development is still needed to be improved to treat lung cancer. Stathmin (STMN1) was initially identified as a cytoplasmic protein phosphorylated responding to cell signal and controlled cell physiological processes. The dysregulation of STMN1 is found in various kinds of tumors. However, the molecular mechanism of STMN1 regulating lung cancer is still unclear. Here, we found that STMN1 was overexpressed in lung cancer tissues and associated with worse survival rates of lung cancer patients. Inhibition of STMN1 suppressed lung cancer cell growth, migration and invasion, and promoted drug sensitivity. Moreover, PTEN loss promoted STMN1 expression via PI3K/AKT pathway. PTEN loss ameliorated the inhibition of cell growth, migration and invasion, and drug sensitivity induced by STMN1 knockdown in lung cancer. The high expression of STMN1 was negatively correlated with the low expression of PTEN in lung cancer specimens. Overall, our work demonstrated that PTEN regulated the oncogenic function of STMN1 in lung cancer.

A CRISPR/Cas9-Engineered ARID1A-Deficient Human Gastric Cancer Organoid Model Reveals Essential and Nonessential Modes of Oncogenic Transformation

Mutations in ARID1A rank among the most common molecular aberrations in human cancer. However, oncogenic consequences of ARID1A mutation in human cells remain poorly defined due to lack of forward genetic models. Here, CRISPR/Cas9-mediated ARID1A knockout (KO) in primary TP53-/- human gastric organoids induced morphologic dysplasia, tumorigenicity, and mucinous differentiation. Genetic WNT/β-catenin activation rescued mucinous differentiation, but not hyperproliferation, suggesting alternative pathways of ARID1A KO-mediated transformation. ARID1A mutation induced transcriptional regulatory modules characteristic of microsatellite instability and Epstein-Barr virus-associated subtype human gastric cancer, including FOXM1-associated mitotic genes and BIRC5/survivin. Convergently, high-throughput compound screening indicated selective vulnerability of ARID1A-deficient organoids to inhibition of BIRC5/survivin, functionally implicating this pathway as an essential mediator of ARID1A KO-dependent early-stage gastric tumorigenesis. Overall, we define distinct pathways downstream of oncogenic ARID1A mutation, with nonessential WNT-inhibited mucinous differentiation in parallel with essential transcriptional FOXM1/BIRC5-stimulated proliferation, illustrating the general utility of organoid-based forward genetic cancer analysis in human cells. SIGNIFICANCE: We establish the first human forward genetic modeling of a commonly mutated tumor suppressor gene, ARID1A. Our study integrates diverse modalities including CRISPR/Cas9 genome editing, organoid culture, systems biology, and small-molecule screening to derive novel insights into early transformation mechanisms of ARID1A-deficient gastric cancers.See related commentary by Zafra and Dow, p. 1327.This article is highlighted in the In This Issue feature, p. 1307.

Network-based identification of key master regulators associated with an immune-silent cancer phenotype

A cancer immune phenotype characterized by an active T-helper 1 (Th1)/cytotoxic response is associated with responsiveness to immunotherapy and favorable prognosis across different tumors. However, in some cancers, such an intratumoral immune activation does not confer protection from progression or relapse. Defining mechanisms associated with immune evasion is imperative to refine stratification algorithms, to guide treatment decisions and to identify candidates for immune-targeted therapy. Molecular alterations governing mechanisms for immune exclusion are still largely unknown. The availability of large genomic datasets offers an opportunity to ascertain key determinants of differential intratumoral immune response. We follow a network-based protocol to identify transcription regulators (TRs) associated with poor immunologic antitumor activity. We use a consensus of four different pipelines consisting of two state-of-the-art gene regulatory network inference techniques, regularized gradient boosting machines and ARACNE to determine TR regulons, and three separate enrichment techniques, including fast gene set enrichment analysis, gene set variation analysis and virtual inference of protein activity by enriched regulon analysis to identify the most important TRs affecting immunologic antitumor activity. These TRs, referred to as master regulators (MRs), are unique to immune-silent and immune-active tumors, respectively. We validated the MRs coherently associated with the immune-silent phenotype across cancers in The Cancer Genome Atlas and a series of additional datasets in the Prediction of Clinical Outcomes from Genomic Profiles repository. A downstream analysis of MRs specific to the immune-silent phenotype resulted in the identification of several enriched candidate pathways, including NOTCH1, TGF-$\beta $, Interleukin-1 and TNF-$\alpha $ signaling pathways. TGFB1I1 emerged as one of the main negative immune modulators preventing the favorable effects of a Th1/cytotoxic response.