Pan-cancer transcriptional signatures predictive of oncogenic mutations reveal that Fbw7 regulates cancer cell oxidative metabolism

Population-specific Mutation Patterns in Breast Tumors from African American, European American, and Kenyan Patients

Women of African descent have the highest breast cancer mortality in the United States and are more likely than women from other population groups to develop an aggressive disease. It remains uncertain to what extent breast cancer in Africa is reminiscent of breast cancer in African American or European American patients. Here, we performed whole-exome sequencing of genomic DNA from 191 breast tumor and non-cancerous adjacent tissue pairs obtained from 97 African American, 69 European American, 2 Asian American, and 23 Kenyan patients. Our analysis of the sequencing data revealed an elevated tumor mutational burden in both Kenyan and African American patients, when compared with European American patients. TP53 mutations were most prevalent, particularly in African American patients, followed by PIK3CA mutations, which showed similar frequencies in European American, African American, and the Kenyan patients. Mutations targeting TBX3 were confined to European Americans and those targeting the FBXW7 tumor suppressor to African American patients whereas mutations in the ARID1A gene that are known to confer resistance to endocrine therapy were distinctively enriched among Kenyan patients. A Kyoto Encyclopedia of Genes and Genomes pathway analysis could link FBXW7 mutations to an increased mitochondrial oxidative phosphorylation capacity in tumors carrying these mutations. Finally, Catalogue of Somatic Mutations in Cancer (COSMIC) mutational signatures in tumors correlated with the occurrence of driver mutations, immune cell profiles, and neighborhood deprivation with associations ranging from being mostly modest to occasionally robust. To conclude, we found mutational profiles that were different between these patient groups. The differences concentrated among genes with low mutation frequencies in breast cancer.

SIGNIFICANCE

The study describes differences in tumor mutational profiles between African American, European American, and Kenyan breast cancer patients. It also investigates how these profiles may relate to the tumor immune environment and the neighborhood environment in which the patients had residence. Finally, it describes an overrepresentation of ARID1A gene mutations in breast tumors of the Kenyan patients.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

Biallelic FBXW7 knockout induces AKAP8-mediated DNA damage in neighbouring wildtype cells

Colorectal cancer possesses marked intratumoral heterogeneity. While subclonal interactions between Vogelstein driver mutations have been extensively studied, less is known about competitive or cooperative effects between subclonal populations with other cancer driver mutations. FBXW7 is a cancer driver mutation which is present in close to 17% of colorectal cancer cells. In this study, we generated isogenic FBXW7 mutant cells using CRISPR-Cas9. We identified an upregulation of oxidative phosphorylation and DNA damage in FBXW7 mutant cells, which surprisingly proliferated at a decreased rate compared to wildtype cells. To determine subclonal interactions, wildtype and mutant FBXW7 cells were cocultured using a Transwell system. Wildtype cells cocultured with FBXW7 mutant cells similarly developed DNA damage which was not observed when wildtype cells were co-cultured with other wildtype cells, suggesting that FBXW7 mutant cells were inducing DNA damage in neighbouring wildtype cells. Using mass spectrometry, we identified AKAP8 as being secreted by FBXW7 mutant cells into the coculture media. Furthermore, overexpression of AKAP8 in wildtype cells recapitulated the DNA damage phenotype observed during coculture, while co-culture of wildtype cells with double mutant FBXW7^-/-/AKAP8^-/- cells abrogated the DNA damage phenotype. Here, we describe a hitherto unknown phenomenon of AKAP8-mediated DNA damage from FBXW7 mutant to neighbouring wildtype cells. Our findings demonstrate the importance of elucidating the local effect of cancer driver mutations between subclonal populations.

Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors

Artificial intelligence (AI), also known as machine intelligence, is widely utilized in the medical field, promoting medical advances. Malignant tumors are the critical focus of medical research and improvement of clinical diagnosis and treatment. Mediastinal malignancy is an important tumor that attracts increasing attention today due to the difficulties in treatment. Combined with artificial intelligence, challenges from drug discovery to survival improvement are constantly being overcome. This article reviews the progress of the use of AI in the diagnosis, treatment, and prognostic prospects of mediastinal malignant tumors based on current literature findings.

Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.

Activation of the integrated stress response is a vulnerability for multidrug-resistant FBXW7-deficient cells

FBXW7 is one of the most frequently mutated tumor suppressors, deficiency of which has been associated with resistance to some anticancer therapies. Through bioinformatics and genome‐wide CRISPR screens, we here reveal that FBXW7 deficiency leads to multidrug resistance (MDR). Proteomic analyses found an upregulation of mitochondrial factors as a hallmark of FBXW7 deficiency, which has been previously linked to chemotherapy resistance. Despite this increased expression of mitochondrial factors, functional analyses revealed that mitochondria are under stress, and genetic or chemical targeting of mitochondria is preferentially toxic for FBXW7‐deficient cells. Mechanistically, the toxicity of therapies targeting mitochondrial translation such as the antibiotic tigecycline relates to the activation of the integrated stress response (ISR) in a GCN2 kinase‐dependent manner. Furthermore, the discovery of additional drugs that are toxic for FBXW7‐deficient cells showed that all of them unexpectedly activate a GCN2‐dependent ISR regardless of their accepted mechanism of action. Our study reveals that while one of the most frequent mutations in cancer reduces the sensitivity to the vast majority of available therapies, it renders cells vulnerable to ISR‐activating drugs.

Association between germline variants and somatic mutations in colorectal cancer

Colorectal cancer (CRC) is a heterogeneous disease with evidence of distinct tumor types that develop through different somatically altered pathways. To better understand the impact of the host genome on somatically mutated genes and pathways, we assessed associations of germline variations with somatic events via two complementary approaches. We first analyzed the association between individual germline genetic variants and the presence of non-silent somatic mutations in genes in 1375 CRC cases with genome-wide SNPs data and a tumor sequencing panel targeting 205 genes. In the second analysis, we tested if germline variants located within previously identified regions of somatic allelic imbalance were associated with overall CRC risk using summary statistics from a recent large scale GWAS (n≃125 k CRC cases and controls). The first analysis revealed that a variant (rs78963230) located within a CNA region associated with TLR3 was also associated with a non-silent mutation within gene FBXW7. In the secondary analysis, the variant rs2302274 located in CDX1/PDGFRB frequently gained/lost in colorectal tumors was associated with overall CRC risk (OR = 0.96, p = 7.50e-7). In summary, we demonstrate that an integrative analysis of somatic and germline variation can lead to new insights about CRC.

Identification of metabolic pathways underlying FGF1 and CHIR99021-mediated cardioprotection

Acute myocardial infarction is a leading cause of death worldwide. We have previously identified two cardioprotective molecules — FGF1 and CHIR99021— that confer cardioprotection in mouse and pig models of acute myocardial infarction. Here, we aimed to determine if improved myocardial metabolism contributes to this cardioprotection. Nanofibers loaded with FGF1 and CHIR99021 were intramyocardially injected to ischemic myocardium of adult mice immediately following surgically induced myocardial infarction. Animals were euthanized 3 and 7 days later. Our data suggested that FGF1/CHIR99021 nanofibers enhanced the heart’s capacity to utilize glycolysis as an energy source and reduced the accumulation of branched-chain amino acids in ischemic myocardium. The impact of FGF1/CHIR99021 on metabolism was more obvious in the first three days post myocardial infarction. Taken together, these findings suggest that FGF1/CHIR99021 protects the heart against ischemic injury via improving myocardial metabolism which may be exploited for treatment of acute myocardial infarction in humans.

•

FGF1/CHIR confer cardioprotection in myocardial infarction animals

•

FGF1/CHIR enhance the capability of ischemic hearts to produce energy via glycolysis

•

FGF1/CHIR reduce the abundance of branched chain amino acids in ischemic hearts

•

This study reveals a novel approach to correct metabolic disorders in ischemic hearts

FBXW7 and the Hallmarks of Cancer: Underlying Mechanisms and Prospective Strategies

FBXW7, a member of the F-box protein family within the ubiquitin–proteasome system, performs an indispensable role in orchestrating cellular processes through ubiquitination and degradation of its substrates, such as c-MYC, mTOR, MCL-1, Notch, and cyclin E. Mainly functioning as a tumor suppressor, inactivation of FBXW7 induces the aberrations of its downstream pathway, resulting in the occurrence of diseases especially tumorigenesis. Here, we decipher the relationship between FBXW7 and the hallmarks of cancer and discuss the underlying mechanisms. Considering the interplay of cancer hallmarks, we propose several prospective strategies for circumventing the deficits of therapeutic resistance and complete cure of cancer patients.

Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations

The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field. Cumulative advances across the science are bringing this promise to realization, including refinement of machine learning- and deep learning algorithms; expansion in the depth and variety of databases, including multiomics; and the decreased cost of massively parallelized computational power. Examples of successful clinical applications of AI can be found throughout the cancer continuum and in multidisciplinary practice, with computer vision-assisted image analysis in particular having several U.S. Food and Drug Administration-approved uses. Techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, natural language processing to infer health trajectories from medical notes, and advanced clinical decision support systems that combine genomics and clinomics. Substantial issues have delayed broad adoption, with data transparency and interpretability suffering from AI's "black box" mechanism, and intrinsic bias against underrepresented persons limiting the reproducibility of AI models and perpetuating health care disparities. Midfuture projections of AI maturation involve increasing a model's complexity by using multimodal data elements to better approximate an organic system. Far-future positing includes living databases that accumulate all aspects of a person's health into discrete data elements; this will fuel highly convoluted modeling that can tailor treatment selection, dose determination, surveillance modality and schedule, and more. The field of AI has had a historical dichotomy between its proponents and detractors. The successful development of recent applications, and continued investment in prospective validation that defines their impact on multilevel outcomes, has established a momentum of accelerated progress.

Global and context-specific transcriptional consequences of oncogenic Fbw7 mutations

The Fbw7 ubiquitin ligase targets many proteins for proteasomal degradation, which include oncogenic transcription factors (TFs) (e.g., c-Myc, c-Jun, and Notch). Fbw7 is a tumor suppressor and tumors often contain mutations in FBXW7, the gene that encodes Fbw7. The complexity of its substrate network has obscured the mechanisms of Fbw7-associated tumorigenesis, yet this understanding is needed for developing therapies. We used an integrated approach employing RNA-Seq and high-resolution mapping (cleavage under target and release using nuclease) of histone modifications and TF occupancy (c-Jun and c-Myc) to examine the combinatorial effects of misregulated Fbw7 substrates in colorectal cancer (CRC) cells with engineered tumor-associated FBXW7 null or missense mutations. Both Fbw7 mutations caused widespread transcriptional changes associated with active chromatin and altered TF occupancy: some were common to both Fbw7 mutant cell lines, whereas others were mutation specific. We identified loci where both Jun and Myc were coregulated by Fbw7, suggesting that substrates may have synergistic effects. One coregulated gene was CIITA, the master regulator of MHC Class II gene expression. Fbw7 loss increased MHC Class II expression and Fbw7 mutations were correlated with increased CIITA expression in TCGA colorectal tumors and cell lines, which may have immunotherapeutic implications for Fbw7-associated cancers. Analogous studies in neural stem cells in which FBXW7 had been acutely deleted closely mirrored the results in CRC cells. Gene set enrichment analyses revealed Fbw7-associated pathways that were conserved across both cell types that may reflect fundamental Fbw7 functions. These analyses provide a framework for understanding normal and neoplastic context-specific Fbw7 functions.

Loss of FBXW7 Correlates with Increased IDH1 Expression in Glioma and Enhances IDH1-Mutant Cancer Cell Sensitivity to Radiation

F-box and WD repeat domain containing 7 (FBXW7) is a substrate receptor of the ubiquitin ligase SKP1-Cullin1-F-box complex and a potent tumor suppressor that prevents unregulated cell growth and tumorigenesis. However, little is known about FBXW7-mediated control of cell metabolism and related functions in cancer therapy. Here, we report that FBXW7 expression inversely correlates with the expression levels of the key metabolic enzyme isocitrate dehydrogenase 1 (IDH1) in patients with glioma and public glioma datasets. Deletion of FBXW7 significantly increased both wild-type (WT) and mutant IDH1 expression, which was mediated by blocking degradation of sterol regulatory element binding protein 1 (SREBP1). The upregulation of neomorphic mutant IDH1 by FBXW7 deletion stimulated production of the oncometabolite 2-hydroxyglutarate at the expense of increasing pentose phosphate pathway activity and NADPH consumption, limiting the buffering ability against radiation-induced oxidative stress. In addition, FBXW7 knockout and IDH1 mutations induced nonhomologous end joining and homologous recombination defects, respectively. In vitro and in vivo, loss of FBXW7 dramatically enhanced the efficacy of radiation treatment in IDH1-mutant cancer cells. Taken together, this work identifies FBXW7 deficiency as a potential biomarker representing both DNA repair and metabolic vulnerabilities that sensitizes IDH1-mutant cancers to radiotherapy. SIGNIFICANCE: Deficiency of FBXW7 causes defects in DNA repair and disrupts NADPH homeostasis in IDH1-mutant glioma cells, conferring high sensitivity to radiotherapy.

Machine intelligence in non-invasive endocrine cancer diagnostics

Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.

Anti-miR-135/SPOCK1 axis antagonizes the influence of metabolism on drug response in intestinal/colon tumour organoids

Little is known about the role of microRNAs (miRNAs) in rewiring the metabolism within tumours and adjacent non-tumour bearing normal tissue and their potential in cancer therapy. This study aimed to investigate the relationship between deregulated miRNAs and metabolic components in murine duodenal polyps and non-polyp-derived organoids (mPOs and mNPOs) from a double-mutant Apc^MinFbxw7^∆G mouse model of intestinal/colorectal cancer (CRC). We analysed the expression of 373 miRNAs and 12 deregulated metabolic genes in mPOs and mNPOs. Our findings revealed miR-135b might target Spock1. Upregulation of SPOCK1 correlated with advanced stages of CRCs. Knockdown of miR-135b decreased the expression level of SPOCK1, glucose consumption and lactic secretion in CRC patient-derived tumours organoids (CRC tPDOs). Increased SPOCK1 induced by miR-135b overexpression promoted the Warburg effect and consequently antitumour effect of 5-fluorouracil. Thus, combination with miR-135b antisense nucleotides may represent a novel strategy to sensitise CRC to the chemo-reagent based treatment.

Ubiquitination regulation of aerobic glycolysis in cancer

Aerobic glycolysis, or the Warburg effect, is regarded as a critical part of metabolic reprogramming and plays a crucial role in the occurrence and development of tumours. Ubiquitination and deubiquitination, essential post-translational modifications, have attracted increasing attention with regards to the regulation of metabolic reprogramming in cancer. However, the mechanism of ubiquitination in glycolysis remains unclear. In this review, we discuss the roles of ubiquitination and deubiquitination in regulating glycolysis, and their involvement in regulating important signalling pathways, enzymes, and transcription factors. Focusing on potential mechanisms may provide novel strategies for cancer treatment.

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti-cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI-powered cancer care.

Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

Background

Genes implicated in tumorigenesis often exhibit diverse sets of genomic variants in the tumor cohorts within which they are frequently mutated. For many genes, neither the transcriptomic effects of these variants nor their relationship to one another in cancer processes have been well-characterized. We sought to identify the downstream expression effects of these mutations and to determine whether this heterogeneity at the genomic level is reflected in a corresponding heterogeneity at the transcriptomic level.

Results

By applying a novel hierarchical framework for organizing the mutations present in a cohort along with machine learning pipelines trained on samples’ expression profiles we systematically interrogated the signatures associated with combinations of mutations recurrent in cancer. This allowed us to catalogue the mutations with discernible downstream expression effects across a number of tumor cohorts as well as to uncover and characterize over a hundred cases where subsets of a gene’s mutations are clearly divergent in their function from the remaining mutations of the gene. These findings successfully replicated across a number of disease contexts and were found to have clear implications for the delineation of cancer processes and for clinical decisions.

Conclusions

The results of cataloguing the downstream effects of mutation subgroupings across cancer cohorts underline the importance of incorporating the diversity present within oncogenes in models designed to capture the downstream effects of their mutations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04147-y.

Phosphorylation regulates cullin-based ubiquitination in tumorigenesis

Cullin-RING ligases (CRLs) recognize and interact with substrates for ubiquitination and degradation, and can be targeted for disease treatment when the abnormal expression of substrates involves pathologic processes. Phosphorylation, either of substrates or receptors of CRLs, can alter their interaction. Phosphorylation-dependent ubiquitination and proteasome degradation influence various cellular processes and can contribute to the occurrence of various diseases, most often tumorigenesis. These processes have the potential to be used for tumor intervention through the regulation of the activities of related kinases, along with the regulation of the stability of specific oncoproteins and tumor suppressors. This review describes the mechanisms and biological functions of crosstalk between phosphorylation and ubiquitination, and most importantly its influence on tumorigenesis, to provide new directions and strategies for tumor therapy.

Regulation of metabolic reprogramming by tumor suppressor genes in pancreatic cancer

Background

Pancreatic cancer continues to be one of the most aggressive malignant tumors. Work in recent years in cancer molecular biology has revealed that metabolic reprogramming is an additional hallmark of cancer that is involved in the pathogenesis of cancers, and is intricately linked to gene mutations.

Main text

However, though oncogenes such as KRAS and c-Myc play important roles in the process, and have been extensively studied, no substantial improvements in the prognosis of pancreatic cancer have seen. Therefore, some scientists have tried to explain the mechanisms of abnormal cancer metabolism from the perspective of tumor suppressor genes. In this paper, we reviewed researches about how metabolic reprogramming was regulated by tumor suppressor genes in pancreatic cancer and their clinical implications.

Conclusion

Abnormal metabolism and genetic mutations are mutually causal and complementary in tumor initiation and development. A clear understanding of how metabolic reprogramming is regulated by the mutated genes would provide important insights into the pathogenesis and ultimately treatment of pancreatic cancer.

Comprehensive Isotopic Targeted Mass Spectrometry: Reliable Metabolic Flux Analysis with Broad Coverage

Metabolic flux analysis (MFA) is highly relevant to understanding metabolic mechanisms of various biological processes. While the pace of methodology development in MFA has been rapid, a major challenge the field continues to witness is limited metabolite coverage, often restricted to a small to moderate number of well-known compounds. In addition, isotopic peaks from an enriched metabolite tend to have low abundances, which makes liquid chromatography tandem mass spectrometry (LC-MS/MS) highly useful in MFA due to its high sensitivity and specificity. Previously we have built large-scale LC-MS/MS approaches that can be routinely used for measurement of up to ∼1,900 metabolite/feature levels [Gu et al. Anal. Chem. 2015, 87, 12355-12362. Shi et al. Anal. Chem. 2019, 91, 13737-13745.]. In this study, we aim to expand our previous studies focused on metabolite level measurements to flux analysis and establish a novel comprehensive isotopic targeted mass spectrometry (CIT-MS) method for reliable MFA analysis with broad coverage. As a proof-of-principle, we have applied CIT-MS to compare the steady-state enrichment of metabolites between Myc(oncogene)-On and Myc-Off Tet21N human neuroblastoma cells cultured with U-13C6-glucose medium. CIT-MS is operationalized using multiple reaction monitoring (MRM) mode and is able to perform MFA of 310 identified metabolites (142 reliably detected, 46 kinetically profiled) selected from >35 metabolic pathways of strong biological significance. Further, we developed a novel concept of relative flux, which eliminates the requirement of absolute quantitation in traditional MFA and thus enables comparative MFA under the pseudosteady state. As a result, CIT-MS was shown to possess the advantages of broad coverage, easy implementation, fast throughput, and more importantly, high fidelity and accuracy in MFA. In principle, CIT-MS can be easily adapted to track the flux of other labeled tracers (such as 15N-tracers) in any metabolite detectable by LC-MS/MS and in various biological models (such as mice). Therefore, CIT-MS has great potential to bring new insights to both basic and clinical metabolism research.

FBXW7 Triggers Degradation of KMT2D to Favor Growth of Diffuse Large B-cell Lymphoma Cells

Mature B-cell neoplasms are the fifth most common neoplasm. Due to significant heterogeneity at the clinical and genetic levels, current therapies for these cancers fail to provide long-term cures. The clinical success of proteasome inhibition for the treatment of multiple myeloma and B-cell lymphomas has made the ubiquitin pathway an important emerging therapeutic target. In this study, we assessed the role of the E3 ligase FBXW7 in mature B-cell neoplasms. FBXW7 targeted the frequently inactivated tumor suppressor KMT2D for protein degradation, subsequently regulating gene expression signatures related to oxidative phosphorylation (OxPhos). Loss of FBXW7 inhibited diffuse large B-cell lymphoma cell growth and further sensitized cells to OxPhos inhibition. These data elucidate a novel mechanism of regulation of KMT2D levels by the ubiquitin pathway and uncover a role of FBXW7 in regulating oxidative phosphorylation in B-cell malignancies. SIGNIFICANCE: These findings characterize FBXW7 as a prosurvival factor in B-cell lymphoma via degradation of the chromatin modifier KMT2D.

Artificial intelligence in oncology

Artificial intelligence (AI) has contributed substantially to the resolution of a variety of biomedical problems, including cancer, over the past decade. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extraction, is increasingly being applied in various areas of both basic and clinical cancer research. In this review, we describe numerous recent examples of the application of AI in oncology, including cases in which deep learning has efficiently solved problems that were previously thought to be unsolvable, and we address obstacles that must be overcome before such application can become more widespread. We also highlight resources and datasets that can help harness the power of AI for cancer research. The development of innovative approaches to and applications of AI will yield important insights in oncology in the coming decade.

Nanoclustered Cascaded Enzymes for Targeted Tumor Starvation and Deoxygenation-Activated Chemotherapy without Systemic Toxicity

Intratumoral glucose depletion-induced cancer starvation represents an important strategy for anticancer therapy, but it is often limited by systemic toxicity, nonspecificity, and adaptive development of parallel energy supplies. Herein, we introduce a concept of cascaded catalytic nanomedicine by combining targeted tumor starvation and deoxygenation-activated chemotherapy for an efficient cancer treatment with reduced systemic toxicity. Briefly, nanoclustered cascaded enzymes were synthesized by covalently cross-linking glucose oxidase (GOx) and catalase (CAT) via a pH-responsive polymer. The release of the enzymes can be first triggered by the mildly acidic tumor microenvironment and then be self-accelerated by the subsequent generation of gluconic acid. Once released, GOx can rapidly deplete glucose and molecular oxygen in tumor cells while the toxic side product, i.e., H₂O₂, can be readily decomposed by CAT for site-specific and low-toxicity tumor starvation. Furthermore, the enzymatic cascades also created a local hypoxia with the oxygen consumption and reductase-activated prodrugs for an additional chemotherapy. The current report represents a promising combinatorial approach using cascaded catalytic nanomedicine to reach concurrent selectivity and efficiency of cancer therapeutics.

Smooth muscle cell-specific knockout of FBW7 exacerbates intracranial atherosclerotic stenosis

Intracranial atherosclerotic stenosis (ICAS), the most common cause of stroke worldwide, is associated with high risk of recurrent ischemic stroke. F-box and WD repeat domain containing protein 7 (FBW7), an ubiquitin E3 ligase, is recently suggested to be involved in atherogenesis. However, whether FBW7 affects cerebrovascular remodeling during ICAS remains unknowns. We found that the expression of FBW7 was decreased in mouse brain microvessels from high-fat diet (HFD)-fed atherosclerotic mice. The reduced FBW7 expression was negatively associated with the remodeling of middle cerebral artery (MCA). Specific loss of FBW7 in smooth muscle cells (SMCs) markedly potentiated brain vascular SMC (VSMC) proliferation, migration and subsequent MCA remodeling in atherosclerotic mice. The increase of total reactive oxygen species (ROS) generation and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity in brain microvessels and VSMCs were enhanced after knockout of FBW7, while the mitochondria-derived ROS was unchanged. Analysis of several key subunits of NADPH oxidase revealed that FBW7 deficiency augmented HFD-induced the increase of Nox1 expression, but had no effect on p47phox and p67phox phosphorylation as well as p22phox expression. Both NADPH oxidase specific inhibitor and Nox1 downregulation abrogated the effects of FBW7 deficiency on MCA remodeling. Immunoprecipitation assay identified that FBW7 interacted with Nox1. FBW7 knockout increased Nox1 protein stability by inhibiting ubiquitin-mediated degradation. Collectively, our study demonstrates that SMC-specific deficiency of FBW7 exacerbates ICAS by facilitating Nox1-derived ROS generation, VSMC proliferation and cerebrovascular remodeling.

The Tumor Microenvironment in Colorectal Cancer Therapy

The current standard-of-care for metastatic colorectal cancer (mCRC) includes chemotherapy and anti-angiogenic or anti-epidermal growth factor receptor (EGFR) monoclonal antibodies, even though the addition of anti-angiogenic agents to backbone chemotherapy provides little benefit for overall survival. Since the approval of anti-angiogenic monoclonal antibodies bevacizumab and aflibercept, for the management of mCRC over a decade ago, extensive efforts have been devoted to discovering predictive factors of the anti-angiogenic response, unsuccessfully. Recent evidence has suggested a potential correlation between angiogenesis and immune phenotypes associated with colorectal cancer. Here, we review evidence of interactions between tumor angiogenesis, the immune microenvironment, and metabolic reprogramming. More specifically, we will highlight such interactions as inferred from our novel immune-metabolic (IM) signature, which groups mCRC into three distinct clusters, namely inflamed-stromal-dependent (IM Cluster 1), inflamed-non stromal-dependent (IM Cluster 2), and non-inflamed or cold (IM Cluster 3), and discuss the merits of the IM classification as a guide to new immune-metabolic combinatorial therapeutic strategies in mCRC.

Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives

In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. The existing solutions of AI and their limitations in cancer genetic testing and diagnostics such as variant calling and interpretation are critically analyzed. Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.