A selective S-acyltransferase inhibitor suppresses tumor growth.

S-acyltransferases play integral roles in essential physiological processes including regulation of oncogenic signaling pathways. While discovered over 40 years ago the field still lacks specific S-acylation inhibitors thus the potential benefit of pharmacologically targeting S-acyltransferases for human disease is still unknown. Here we report the identification of an orally bioavailable acyltransferase inhibitor SD-066-4 that inhibits the acyltransferase ZDHHC20. We identified a specific alanine residue that accommodates the methyl group of SD-066-4, thus providing isoform selectivity. SD-066-4 stably reduces EGFR S-acylation in Kras mutant cells and blocks the growth of Kras mutant lung tumors extending overall survival. We find that lung cancer patients harboring deletions in ZDHHC20 or ZDHHC14 concurrent with Kras alterations have a significant survival benefit, underscoring the translational importance of these enzymes.

Targeting LxCxE cleft pocket of retinoblastoma protein in M2 macrophages inhibits ovarian cancer progression.

Ovarian cancer remains a major health threat with limited treatment options available. It is characterized by immunosuppressive tumor microenvironment (TME) maintained by tumor- associated macrophages (TAMs) hindering anti-tumor responses and immunotherapy efficacy. Here we show that targeting retinoblastoma protein (Rb) by disruption of its LxCxE cleft pocket, causes cell death in TAMs by induction of ER stress, p53 and mitochondria-related cell death pathways. A reduction of pro-tumor Rb high M2-type macrophages from TME in vivo enhanced T cell infiltration and inhibited cancer progression. We demonstrate an increased Rb expression in TAMs in women with ovarian cancer is associated with poorer prognosis. Ex vivo, we show analogous cell death induction by therapeutic Rb targeting in TAMs in post-surgery ascites from ovarian cancer patients. Overall, our data elucidates therapeutic targeting of the Rb LxCxE cleft pocket as a novel promising approach for ovarian cancer treatment through depletion of TAMs and re-shaping TME immune landscape. Statement of significance: Currently, targeting immunosuppressive myeloid cells in ovarian cancer microenvironment is the first priority need to enable successful immunotherapy, but no effective solutions are clinically available. We show that targeting LxCxE cleft pocket of Retinoblastoma protein unexpectedly induces preferential cell death in M2 tumor-associated macrophages. Depletion of immunosuppressive M2 tumor-associated macrophages reshapes tumor microenvironment, enhances anti-tumor T cell responses, and inhibits ovarian cancer. Thus, we identify a novel paradoxical function of Retinoblastoma protein in regulating macrophage viability as well as a promising target to enhance immunotherapy efficacy in ovarian cancer.

Multiple sclerosis patient-derived spontaneous B cells have distinct EBV and host gene expression profiles in active disease.

Epstein-Barr virus (EBV) is an aetiologic risk factor for the development of multiple sclerosis (MS). However, the role of EBV-infected B cells in the immunopathology of MS is not well understood. Here we characterized spontaneous lymphoblastoid cell lines (SLCLs) isolated from MS patients and healthy controls (HC) ex vivo to study EBV and host gene expression in the context of an individual's endogenous EBV. SLCLs derived from MS patient B cells during active disease had higher EBV lytic gene expression than SLCLs from MS patients with stable disease or HCs. Host gene expression analysis revealed activation of pathways associated with hypercytokinemia and interferon signalling in MS SLCLs and upregulation of forkhead box protein 1 (FOXP1), which contributes to EBV lytic gene expression. We demonstrate that antiviral approaches targeting EBV replication decreased cytokine production and autologous CD4+ T cell responses in this ex vivo model. These data suggest that dysregulation of intrinsic B cell control of EBV gene expression drives a pro-inflammatory, pathogenic B cell phenotype that can be attenuated by suppressing EBV lytic gene expression.

Microbial gene expression analysis of healthy and cancerous esophagus uncovers bacterial biomarkers of clinical outcomes.

Local microbiome shifts are implicated in the development and progression of gastrointestinal cancers, and in particular, esophageal carcinoma (ESCA), which is among the most aggressive malignancies. Short-read RNA sequencing (RNAseq) is currently the leading technology to study gene expression changes in cancer. However, using RNAseq to study microbial gene expression is challenging. Here, we establish a new tool to efficiently detect viral and bacterial expression in human tissues through RNAseq. This approach employs a neural network to predict reads of likely microbial origin, which are targeted for assembly into longer contigs, improving identification of microbial species and genes. This approach is applied to perform a systematic comparison of bacterial expression in ESCA and healthy esophagi. We uncover bacterial genera that are over or underabundant in ESCA vs healthy esophagi both before and after correction for possible covariates, including patient metadata. However, we find that bacterial taxonomies are not significantly associated with clinical outcomes. Strikingly, in contrast, dozens of microbial proteins were significantly associated with poor patient outcomes and in particular, proteins that perform mitochondrial functions and iron-sulfur coordination. We further demonstrate associations between these microbial proteins and dysregulated host pathways in ESCA patients. Overall, these results suggest possible influences of bacteria on the development of ESCA and uncover new prognostic biomarkers based on microbial genes. In addition, this study provides a framework for the analysis of other human malignancies whose development may be driven by pathogens.

An African-Specific Variant of TP53 Reveals PADI4 as a Regulator of p53-Mediated Tumor Suppression.

TP53 is the most frequently mutated gene in cancer, yet key target genes for p53-mediated tumor suppression remain unidentified. Here, we characterize a rare, African-specific germline variant of TP53 in the DNA-binding domain Tyr107His (Y107H). Nuclear magnetic resonance and crystal structures reveal that Y107H is structurally similar to wild-type p53. Consistent with this, we find that Y107H can suppress tumor colony formation and is impaired for the transactivation of only a small subset of p53 target genes; this includes the epigenetic modifier PADI4, which deiminates arginine to the nonnatural amino acid citrulline. Surprisingly, we show that Y107H mice develop spontaneous cancers and metastases and that Y107H shows impaired tumor suppression in two other models. We show that PADI4 is itself tumor suppressive and that it requires an intact immune system for tumor suppression. We identify a p53-PADI4 gene signature that is predictive of survival and the efficacy of immune-checkpoint inhibitors. SIGNIFICANCE: We analyze the African-centric Y107H hypomorphic variant and show that it confers increased cancer risk; we use Y107H in order to identify PADI4 as a key tumor-suppressive p53 target gene that contributes to an immune modulation signature and that is predictive of cancer survival and the success of immunotherapy. See related commentary by Bhatta and Cooks, p. 1518. This article is highlighted in the In This Issue feature, p. 1501.

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.

Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

Unstable EBV latency drives inflammation in multiple sclerosis patient derived spontaneous B cells.

Epidemiological studies have demonstrated that Epstein-Barr virus (EBV) is a known etiologic risk factor, and perhaps prerequisite, for the development of MS. EBV establishes life-long latent infection in a subpopulation of memory B cells. Although the role of memory B cells in the pathobiology of MS is well established, studies characterizing EBV-associated mechanisms of B cell inflammation and disease pathogenesis in EBV (+) B cells from MS patients are limited. Accordingly, we analyzed spontaneous lymphoblastoid cell lines (SLCLs) from multiple sclerosis patients and healthy controls to study host-virus interactions in B cells, in the context of an individual's endogenous EBV. We identify differences in EBV gene expression and regulation of both viral and cellular genes in SLCLs. Our data suggest that EBV latency is dysregulated in MS SLCLs with increased lytic gene expression observed in MS patient B cells, especially those generated from samples obtained during "active" disease. Moreover, we show increased inflammatory gene expression and cytokine production in MS patient SLCLs and demonstrate that tenofovir alafenamide, an antiviral that targets EBV replication, decreases EBV viral loads, EBV lytic gene expression, and EBV-mediated inflammation in both SLCLs and in a mixed lymphocyte assay. Collectively, these data suggest that dysregulation of EBV latency in MS drives a pro-inflammatory, pathogenic phenotype in memory B cells and that this response can be attenuated by suppressing EBV lytic activation. This study provides further support for the development of antiviral agents that target EBV-infection for use in MS.

A deep learning approach reveals unexplored landscape of viral expression in cancer.

About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we develop viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We utilize viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA). Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer and disclose human endogenous viruses whose expression is associated with poor overall survival. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions.

The microbiome-derived metabolite TMAO drives immune activation and boosts responses to immune checkpoint blockade in pancreatic cancer.

The composition of the gut microbiome can control innate and adaptive immunity and has emerged as a key regulator of tumor growth, especially in the context of immune checkpoint blockade (ICB) therapy. However, the underlying mechanisms for how the microbiome affects tumor growth remain unclear. Pancreatic ductal adenocarcinoma (PDAC) tends to be refractory to therapy, including ICB. Using a nontargeted, liquid chromatography-tandem mass spectrometry-based metabolomic screen, we identified the gut microbe-derived metabolite trimethylamine N-oxide (TMAO), which enhanced antitumor immunity to PDAC. Delivery of TMAO intraperitoneally or via a dietary choline supplement to orthotopic PDAC-bearing mice reduced tumor growth, associated with an immunostimulatory tumor-associated macrophage (TAM) phenotype, and activated effector T cell response in the tumor microenvironment. Mechanistically, TMAO potentiated the type I interferon (IFN) pathway and conferred antitumor effects in a type I IFN-dependent manner. Delivering TMAO-primed macrophages intravenously produced similar antitumor effects. Combining TMAO with ICB (anti-PD1 and/or anti-Tim3) in a mouse model of PDAC significantly reduced tumor burden and improved survival beyond TMAO or ICB alone. Last, the levels of bacteria containing CutC (an enzyme that generates trimethylamine, the TMAO precursor) correlated with long-term survival in patients with PDAC and improved response to anti-PD1 in patients with melanoma. Together, our study identifies the gut microbial metabolite TMAO as a driver of antitumor immunity and lays the groundwork for potential therapeutic strategies targeting TMAO.

Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma.

Immune Checkpoint Inhibitor (ICI) therapy has revolutionized treatment for advanced melanoma; however, only a subset of patients benefit from this treatment. Despite considerable efforts, the Tumor Mutation Burden (TMB) is the only FDA-approved biomarker in melanoma. However, the mechanisms underlying TMB association with prolonged ICI survival are not entirely understood and may depend on numerous confounding factors. To identify more interpretable ICI response biomarkers based on tumor mutations, we train classifiers using mutations within distinct biological processes. We evaluate a variety of feature selection and classification methods and identify key mutated biological processes that provide improved predictive capability compared to the TMB. The top mutated processes we identify are leukocyte and T-cell proliferation regulation, which demonstrate stable predictive performance across different data cohorts of melanoma patients treated with ICI. This study provides biologically interpretable genomic predictors of ICI response with substantially improved predictive performance over the TMB.

Gene-expression profiles of pretreatment biopsies predict complete response of rectal cancer patients to preoperative chemoradiotherapy.

PURPOSE: Preoperative (neoadjuvant) chemoradiotherapy (CRT) and total mesorectal excision is the standard treatment for rectal cancer patients (UICC stage II/III). Up to one-third of patients treated with CRT achieve a pathological complete response (pCR). These patients could be spared from surgery and its associated morbidity and mortality, and assigned to a "watch and wait" strategy. However, reliably identifying pCR based on clinical or imaging parameters remains challenging. EXPERIMENTAL DESIGN: We generated gene-expression profiles of 175 patients with locally advanced rectal cancer enrolled in the CAO/ARO/AIO-94 and -04 trials. One hundred and sixty-one samples were used for building, training and validating a predictor of pCR using a machine learning algorithm. The performance of the classifier was validated in three independent cohorts, comprising 76 patients from (i) the CAO/ARO/AIO-94 and -04 trials (n = 14), (ii) a publicly available dataset (n = 38) and (iii) in 24 prospectively collected samples from the TransValid A trial. RESULTS: A 21-transcript signature yielded the best classification of pCR in 161 patients (Sensitivity: 0.31; AUC: 0.81), when not allowing misclassification of non-complete-responders (False-positive rate = 0). The classifier remained robust when applied to three independent datasets (n = 76). CONCLUSION: The classifier can identify >1/3 of rectal cancer patients with a pCR while never classifying patients with an incomplete response as having pCR. Importantly, we could validate this finding in three independent datasets, including a prospectively collected cohort. Therefore, this classifier could help select rectal cancer patients for a "watch and wait" strategy. TRANSLATIONAL RELEVANCE: Forgoing surgery with its associated side effects could be an option for rectal cancer patients if the prediction of a pathological complete response (pCR) after preoperative chemoradiotherapy would be possible. Based on gene-expression profiles of 161 patients a classifier was developed and validated in three independent datasets (n = 76), identifying over 1/3 of patients with pCR, while never misclassifying a non-complete-responder. Therefore, the classifier can identify patients suited for "watch and wait".

Identification of combinations of somatic mutations that predict cancer survival and immunotherapy benefit.

Cancer evolves through the accumulation of somatic mutations over time. Although several methods have been developed to characterize mutational processes in cancers, these have not been specifically designed to identify mutational patterns that predict patient prognosis. Here we present CLICnet, a method that utilizes mutational data to cluster patients by survival rate. CLICnet employs Restricted Boltzmann Machines, a type of generative neural network, which allows for the capture of complex mutational patterns associated with patient survival in different cancer types. For some cancer types, clustering produced by CLICnet also predicts benefit from anti-PD1 immune checkpoint blockade therapy, whereas for other cancer types, the mutational processes associated with survival are different from those associated with the improved anti-PD1 survival benefit. Thus, CLICnet has the ability to systematically identify and catalogue combinations of mutations that predict cancer survival, unveiling intricate associations between mutations, survival, and immunotherapy benefit.

Hard wiring of normal tissue-specific chromosome-wide gene expression levels is an additional factor driving cancer type-specific aneuploidies.

BACKGROUND: Many carcinomas have recurrent chromosomal aneuploidies specific to the tissue of tumor origin. The reason for this specificity is not completely understood. METHODS: In this study, we looked at the frequency of chromosomal arm gains and losses in different cancer types from the The Cancer Genome Atlas (TCGA) and compared them to the mean gene expression of each chromosome arm in corresponding normal tissues of origin from the Genotype-Tissue Expression (GTEx) database, in addition to the distribution of tissue-specific oncogenes and tumor suppressors on different chromosome arms. RESULTS: This analysis revealed a complex picture of factors driving tumor karyotype evolution in which some recurrent chromosomal copy number reflect the chromosome arm-wide gene expression levels of the their normal tissue of tumor origin. CONCLUSIONS: We conclude that the cancer type-specific distribution of chromosomal arm gains and losses is potentially "hardwiring" gene expression levels characteristic of the normal tissue of tumor origin, in addition to broadly modulating the expression of tissue-specific tumor driver genes.

Incorporating Machine Learning into Established Bioinformatics Frameworks.

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

Prediction of the incubation period for COVID-19 and future virus disease outbreaks.

BACKGROUND: A crucial factor in mitigating respiratory viral outbreaks is early determination of the duration of the incubation period and, accordingly, the required quarantine time for potentially exposed individuals. At the time of the COVID-19 pandemic, optimization of quarantine regimes becomes paramount for public health, societal well-being, and global economy. However, biological factors that determine the duration of the virus incubation period remain poorly understood. RESULTS: We demonstrate a strong positive correlation between the length of the incubation period and disease severity for a wide range of human pathogenic viruses. Using a machine learning approach, we develop a predictive model that accurately estimates, solely from several virus genome features, in particular, the number of protein-coding genes and the GC content, the incubation time ranges for diverse human pathogenic RNA viruses including SARS-CoV-2. The predictive approach described here can directly help in establishing the appropriate quarantine durations and thus facilitate controlling future outbreaks. CONCLUSIONS: The length of the incubation period in viral diseases strongly correlates with disease severity, emphasizing the biological and epidemiological importance of the incubation period. Perhaps, surprisingly, incubation times of pathogenic RNA viruses can be accurately predicted solely from generic features of virus genomes. Elucidation of the biological underpinnings of the connections between these features and disease progression can be expected to reveal key aspects of virus pathogenesis.

Seeker: alignment-free identification of bacteriophage genomes by deep learning.

Recent advances in metagenomic sequencing have enabled discovery of diverse, distinct microbes and viruses. Bacteriophages, the most abundant biological entity on Earth, evolve rapidly, and therefore, detection of unknown bacteriophages in sequence datasets is a challenge. Most of the existing detection methods rely on sequence similarity to known bacteriophage sequences, impeding the identification and characterization of distinct, highly divergent bacteriophage families. Here we present Seeker, a deep-learning tool for alignment-free identification of phage sequences. Seeker allows rapid detection of phages in sequence datasets and differentiation of phage sequences from bacterial ones, even when those phages exhibit little sequence similarity to established phage families. We comprehensively validate Seeker's ability to identify previously unidentified phages, and employ this method to detect unknown phages, some of which are highly divergent from the known phage families. We provide a web portal (seeker.pythonanywhere.com) and a user-friendly Python package (github.com/gussow/seeker) allowing researchers to easily apply Seeker in metagenomic studies, for the detection of diverse unknown bacteriophages.

Pyrvinium Pamoate Induces Death of Triple-Negative Breast Cancer Stem-Like Cells and Reduces Metastases through Effects on Lipid Anabolism.

Cancer stem-like cells (CSC) induce aggressive tumor phenotypes such as metastasis formation, which is associated with poor prognosis in triple-negative breast cancer (TNBC). Repurposing of FDA-approved drugs that can eradicate the CSC subcompartment in primary tumors may prevent metastatic disease, thus representing an effective strategy to improve the prognosis of TNBC. Here, we investigated spheroid-forming cells in a metastatic TNBC model. This strategy enabled us to specifically study a population of long-lived tumor cells enriched in CSCs, which show stem-like characteristics and induce metastases. To repurpose FDA-approved drugs potentially toxic for CSCs, we focused on pyrvinium pamoate (PP), an anthelmintic drug with documented anticancer activity in preclinical models. PP induced cytotoxic effects in CSCs and prevented metastasis formation. Mechanistically, the cell killing effects of PP were a result of inhibition of lipid anabolism and, more specifically, the impairment of anabolic flux from glucose to cholesterol and fatty acids. CSCs were strongly dependent upon activation of lipid biosynthetic pathways; activation of these pathways exhibited an unfavorable prognostic value in a cohort of breast cancer patients, where it predicted high probability of metastatic dissemination and tumor relapse. Overall, this work describes a new approach to target aggressive CSCs that may substantially improve clinical outcomes for patients with TNBC, who currently lack effective targeted therapeutic options. SIGNIFICANCE: These findings provide preclinical evidence that a drug repurposing approach to prevent metastatic disease in TNBC exploits lipid anabolism as a metabolic vulnerability against CSCs in primary tumors.

Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses an immediate, major threat to public health across the globe. Here we report an in-depth molecular analysis to reconstruct the evolutionary origins of the enhanced pathogenicity of SARS-CoV-2 and other coronaviruses that are severe human pathogens. Using integrated comparative genomics and machine learning techniques, we identify key genomic features that differentiate SARS-CoV-2 and the viruses behind the two previous deadly coronavirus outbreaks, SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), from less pathogenic coronaviruses. These features include enhancement of the nuclear localization signals in the nucleocapsid protein and distinct inserts in the spike glycoprotein that appear to be associated with high case fatality rate of these coronaviruses as well as the host switch from animals to humans. The identified features could be crucial contributors to coronavirus pathogenicity and possible targets for diagnostics, prognostication, and interventions.

Genomic determinants of pathogenicity in SARS-CoV-2 and other human coronaviruses.

SARS-CoV-2 poses an immediate, major threat to public health across the globe. Here we report an in-depth molecular analysis to reconstruct the evolutionary origins of the enhanced pathogenicity of SARS-CoV-2 and other coronaviruses that are severe human pathogens. Using integrated comparative genomics and machine learning techniques, we identify key genomic features that differentiate SARS-CoV-2 and the viruses behind the two previous deadly coronavirus outbreaks, SARS-CoV and MERS-CoV, from less pathogenic coronaviruses. These features include enhancement of the nuclear localization signals in the nucleocapsid protein and distinct inserts in the spike glycoprotein that appear to be associated with high case fatality rate of these coronaviruses as well as the host switch from animals to humans. The identified features could be crucial elements of coronavirus pathogenicity and possible targets for diagnostics, prognostication and interventions.