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.

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.

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.

Abstract C023: A microbiome-produced metabolite drives immunostimulatory macrophages and boosts response to immune checkpoint inhibitors in pancreatic cancer

The composition of the gut microbiome controls innate and adaptive immunity and has emerged as a key regulator of tumor growth and the success of immune checkpoint blockade (ICB) therapy. However, the underlying mechanisms remain unclear. Pancreatic ductal adenocarcinoma (PDAC) tends to be refractory to therapy, including ICB. We found that the gut microbe-derived metabolite trimethylamine N-oxide (TMAO) enhances anti-tumor immunity to PDAC. Delivery of TMAO given intraperitoneally or via dietary choline supplement to PDAC-bearing mice reduces tumor growth and is associated with an immunostimulatory tumor-associated macrophage (TAM) phenotype and activated effector T cell response in the tumor microenvironment. Mechanistically, TMAO signals through potentiating type-I interferon (IFN) pathway and confers anti-tumor effects in a type-I IFN dependent manner. Notably, delivering TMAO-primed macrophages alone produced similar anti-tumor effects. Combining TMAO with ICB (anti-PD1 and/or anti-Tim3) significantly reduced tumor burden and improved survival beyond TMAO or ICB alone. Finally, the levels of trimethylamine (TMA)-producing bacteria and of CutC gene expression correlate with improved survivorship and response to anti-PD1 in cancer patients. Together, our study identifies the gut microbial metabolite TMAO as an important driver of anti-tumor immunity and lays the groundwork for new therapeutic strategies.

Citation Format: Gauri Mirji, Alison Worth, Sajad Bhat, Mohamed Sayed, Toshitha Kannan, Aaron Goldman, Hsin-Yao Tang, Qin Liu, Noam Auslander, Chi Dang, Mohamed Abdel-Mohsen, Andrew Kossenkov, Ben Stanger, Rahul Shinde. A microbiome-produced metabolite drives immunostimulatory macrophages and boosts response to immune checkpoint inhibitors in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr C023.

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.

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.

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”.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00905-y.

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.

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.

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.

The GENDULF algorithm: mining transcriptomics to uncover modifier genes for monogenic diseases

Modifier genes are believed to account for the clinical variability observed in many Mendelian disorders, but their identification remains challenging due to the limited availability of genomics data from large patient cohorts. Here, we present GENDULF (GENetic moDULators identiFication), one of the first methods to facilitate prediction of disease modifiers using healthy and diseased tissue gene expression data. GENDULF is designed for monogenic diseases in which the mechanism is loss of function leading to reduced expression of the mutated gene. When applied to cystic fibrosis, GENDULF successfully identifies multiple, previously established disease modifiers, including EHF, SLC6A14, and CLCA1. It is then utilized in spinal muscular atrophy (SMA) and predicts U2AF1 as a modifier whose low expression correlates with higher SMN2 pre-mRNA exon 7 retention. Indeed, knockdown of U2AF1 in SMA patient-derived cells leads to increased full-length SMN2 transcript and SMN protein expression. Taking advantage of the increasing availability of transcriptomic data, GENDULF is a novel addition to existing strategies for prediction of genetic disease modifiers, providing insights into disease pathogenesis and uncovering novel therapeutic targets.

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.

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

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.

Single-Cell-Derived Primary Rectal Carcinoma Cell Lines Reflect Intratumor Heterogeneity Associated with Treatment Response

PURPOSE

The standard treatment of patients with locally advanced rectal cancer consists of preoperative chemoradiotherapy (CRT) followed by surgery. However, the response of individual tumors to CRT is extremely diverse, presenting a clinical dilemma. This broad variability in treatment response is likely attributable to intratumor heterogeneity (ITH).

EXPERIMENTAL DESIGN

We addressed the impact of ITH on response to CRT by establishing single-cell-derived cell lines (SCDCL) from a treatment-naïve rectal cancer biopsy after xenografting.

RESULTS

Individual SCDCLs derived from the same tumor responded profoundly different to CRT in vitro. Clonal reconstruction of the tumor and derived cell lines based on whole-exome sequencing revealed nine separate clusters with distinct proportions in the SCDCLs. Missense mutations in SV2A and ZWINT were clonal in the resistant SCDCL, but not detected in the sensitive SCDCL. Single-cell genetic analysis by multiplex FISH revealed the expansion of a clone with a loss of PIK3CA in the resistant SCDCL. Gene expression profiling by tRNA-sequencing identified the activation of the Wnt, Akt, and Hedgehog signaling pathways in the resistant SCDCLs. Wnt pathway activation in the resistant SCDCLs was confirmed using a reporter assay.

CONCLUSIONS

Our model system of patient-derived SCDCLs provides evidence for the critical role of ITH for treatment response in patients with rectal cancer and shows that distinct genetic aberration profiles are associated with treatment response. We identified specific pathways as the molecular basis of treatment response of individual clones, which could be targeted in resistant subclones of a heterogenous tumor.

Interplay between DNA damage repair and apoptosis shapes cancer evolution through aneuploidy and microsatellite instability

Driver mutations and chromosomal aneuploidy are major determinants of tumorigenesis that exhibit complex relationships. Here, we identify associations between driver mutations and chromosomal aberrations that define two tumor clusters, with distinct regimes of tumor evolution underpinned by unique sets of mutations in different components of DNA damage response. Gastrointestinal and endometrial tumors comprise a separate cluster for which chromosomal-arm aneuploidy and driver mutations are mutually exclusive. The landscape of driver mutations in these tumors is dominated by mutations in DNA repair genes that are further linked to microsatellite instability. The rest of the cancer types show a positive association between driver mutations and aneuploidy, and a characteristic set of mutations that involves primarily genes for components of the apoptotic machinery. The distinct sets of mutated genes derived here show substantial prognostic power and suggest specific vulnerabilities of different cancers that might have therapeutic potential.

Functional Genomic Complexity Defines Intratumor Heterogeneity and Tumor Aggressiveness in Liver Cancer

Chronic inflammation and chromosome aneuploidy are major traits of primary liver cancer (PLC), which represent the second most common cause of cancer-related death worldwide. Increased cancer fitness and aggressiveness of PLC may be achieved by enhancing tumoral genomic complexity that alters tumor biology. Here, we developed a scoring method, namely functional genomic complexity (FGC), to determine the degree of molecular heterogeneity among 580 liver tumors with diverse ethnicities and etiologies by assessing integrated genomic and transcriptomic data. We found that tumors with higher FGC scores are associated with chromosome instability and TP53 mutations, and a worse prognosis, while tumors with lower FGC scores have elevated infiltrating lymphocytes and a better prognosis. These results indicate that FGC scores may serve as a surrogate to define genomic heterogeneity of PLC linked to chromosomal instability and evasion of immune surveillance. Our findings demonstrate an ability to define genomic heterogeneity and corresponding tumor biology of liver cancer based only on bulk genomic and transcriptomic data. Our data also provide a rationale for applying this approach to survey liver tumor immunity and to stratify patients for immune-based therapy.

A unique insert in the genomes of high-risk human papillomaviruses with a predicted dual role in conferring oncogenic risk

The differences between high risk and low risk human papillomaviruses (HR-HPV and LR-HPV, respectively) that contribute to the tumorigenic potential of HR-HPV are not well understood but can be expected to involve the HPV oncoproteins, E6 and E7. We combine genome comparison and machine learning techniques to identify a previously unnoticed insert near the 3’-end of the E6 oncoprotein gene that is unique to HR-HPV. Analysis of the insert sequence suggests that it exerts a dual effect, by creating a PDZ domain-binding motif at the C-terminus of E6, as well as eliminating the overlap between the E6 and E7 coding regions in HR-HPV. We show that, as a result, the insert might enable coupled termination-reinitiation of the E6 and E7 genes, supported by motifs complementary to the human 18S rRNA. We hypothesize that the added functionality of E6 and positive regulation of E7 expression jointly account for the tumorigenic potential of HR-HPV.

Predicting Complete Remission of Acute Myeloid Leukemia: Machine Learning Applied to Gene Expression

Machine learning (ML) is a useful tool for advancing our understanding of the patterns and significance of biomedical data. Given the growing trend on the application of ML techniques in precision medicine, here we present an ML technique which predicts the likelihood of complete remission (CR) in patients diagnosed with acute myeloid leukemia (AML). In this study, we explored the question of whether ML algorithms designed to analyze gene-expression patterns obtained through RNA sequencing (RNA-seq) can be used to accurately predict the likelihood of CR in pediatric AML patients who have received induction therapy. We employed tests of statistical significance to determine which genes were differentially expressed in the samples derived from patients who achieved CR after 2 courses of treatment and the samples taken from patients who did not benefit. We tuned classifier hyperparameters to optimize performance and used multiple methods to guide our feature selection as well as our assessment of algorithm performance. To identify the model which performed best within the context of this study, we plotted receiver operating characteristic (ROC) curves. Using the top 75 genes from the k-nearest neighbors algorithm (K-NN) model (K = 27) yielded the best area-under-the-curve (AUC) score that we obtained: 0.84. When we finally tested the previously unseen test data set, the top 50 genes yielded the best AUC = 0.81. Pathway enrichment analysis for these 50 genes showed that the guanosine diphosphate fucose (GDP-fucose) biosynthesis pathway is the most significant with an adjusted P value = .0092, which may suggest the vital role of N-glycosylation in AML.

Publisher Correction: Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma

In the version of this article originally published, there was an error in the URL linked to by an accession code in the data availability section of the methods. The erroneous URL was: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100351 . The correct URL is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115821 . The error has been corrected in the HTML and PDF versions of this article.

Abstract A69: Mutagenicity of urea cycle dysregulation and its implications for cancer immunotherapy

Immune checkpoint therapy leads to durable clinical responses in many cancer patients, but fails in others. To improve the clinical response to immunotherapy, it is highly important to identify predictive biomarkers. While checkpoint genes’ expression levels, tumor neo-antigen load and microsatellite instability (MSI) have been associated with enhanced response to checkpoint immunotherapies, they yet provide only a modest predictive signal and hence there is a need to identify additional predictive factors. Specifically, while there is growing evidence that metabolic alterations can affect the tumor and modulate the immune response, the potential effects of altered cancer metabolism on tumor mutagenesis and immunotherapy remain unexplored.

The urea cycle (UC) converts excess nitrogen derived from the breakdown of nitrogen-containing molecules (e.g., ammonia) to urea, a relatively non-toxic and disposable nitrogenous compound. We and others have shown that silencing of the UC enzyme ASS1 promotes cancer proliferation by diverting its substrate aspartate toward CAD enzyme, which mediates the first three reactions in the pyrimidine synthesis pathway. We now demonstrate, by analysis of the TCGA data, tumor samples and cancer cell line experiments, that UC dysregulation (UCD) is a much wider common metabolic phenomenon that maximizes nitrogen utilization in cancer, favoring pyrimidine synthesis over urea disposal. Of note, while UCD is significantly associated with decreased cancer patient survival, the overall mutational load is not.

Remarkably, we find that the UCD changes the 1:1 purine (R)-to-pyrimidine (Y) ratio in favor of pyrimidine in cancer cells. Moreover, in analysis of both TCGA data and UC perturbed cancer cells we find that: (a) UCD is significantly associated with a novel and unique pattern of purine-to-pyrimidine transversion mutational bias across many cancer types at the DNA coding (sense) strand, and (b) this trend becomes stronger and more significant at both the mRNA and protein levels, testifying to its functional implications. Notably, the overall mutational load in cancer is negatively correlated with UCD, testifying to their independence.

To test whether the mutational bias is associated with better immunotherapy response, we analyze published data of three large melanoma cohorts. We find that responders of both anti-PD1 and anti-CTLA4 therapy exhibit significantly higher UCD and R->Y mutational bias than non-responders. We further observe that the peptides carrying transverse R->Y mutations are preferentially presented as neoantigens in responders independent of mutational load, and this trend becomes significant for more clonal neoantigens, promoting UCD as a potential biomarker for the success of immunotherapy.

Finally, as nitrogen metabolites are excreted in the urine, we hypothesize that these changes may be detectable in urine of UCD-cancers. We observe increased levels of pyrimidine derived metabolites in the urine of mice bearing colon tumors associated with UCD in the tumors compared to normal intestine. In an analogous manner, we find significantly higher levels pyrimidine derived metabolites in the urine of human patients with prostate cancer compared to controls.

Collectively, these results support our hypothesis that UCD is a prevalent metabolic phenomenon in cancer, generating mutational biased neo-peptides, worsening patients’ prognosis and yet enhancing the response to immune therapy independent of mutational load and MSI. Taken together, our findings point to the important role of UCD in a broad spectrum of cancers, to the potential use of UCD related metabolites as cancer biomarkers, and last but not least, to the role of UCD in predicting response to immune check point therapy. Broadly, our results suggest future therapeutic interventions aiming to increase UCD levels to enhance the coverage and efficiency of cancer immunotherapy.

Citation Format: Joo Sang Lee, Narin Carmel, Hiren Karathia, Noam Auslander, Shiran Rabinovich, Rom Keshet, Noa Stettner, Alon Silberman, Lilach Agemy, Daniel Helbling, Raya Eilam, Qin Sun, Alexander Brandis, Hila Weiss, David Dimmock, Noam Stern-Ginossar, Avigdor Scherz, Igor Ulitsky, Sandesh CS Nagamani, Ronit Elhasid, Sridhar Hannenhalli, Eytan Ruppin, Ayelet Erez. Mutagenicity of urea cycle dysregulation and its implications for cancer immunotherapy [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2017 Oct 1-4; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2018;6(9 Suppl):Abstract nr A69.

Urea Cycle Dysregulation Generates Clinically Relevant Genomic and Biochemical Signatures

The urea cycle (UC) is the main pathway by which mammals dispose of waste nitrogen. We find that specific alterations in the expression of most UC enzymes occur in many tumors, leading to a general metabolic hallmark termed "UC dysregulation" (UCD). UCD elicits nitrogen diversion toward carbamoyl-phosphate synthetase2, aspartate transcarbamylase, and dihydrooratase (CAD) activation and enhances pyrimidine synthesis, resulting in detectable changes in nitrogen metabolites in both patient tumors and their bio-fluids. The accompanying excess of pyrimidine versus purine nucleotides results in a genomic signature consisting of transversion mutations at the DNA, RNA, and protein levels. This mutational bias is associated with increased numbers of hydrophobic tumor antigens and a better response to immune checkpoint inhibitors independent of mutational load. Taken together, our findings demonstrate that UCD is a common feature of tumors that profoundly affects carcinogenesis, mutagenesis, and immunotherapy response.

Harnessing synthetic lethality to predict the response to cancer treatment

While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi's utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients' drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.

An integrated computational and experimental study uncovers FUT9 as a metabolic driver of colorectal cancer

Metabolic alterations play an important role in cancer and yet, few metabolic cancer driver genes are known. Here we perform a combined genomic and metabolic modeling analysis searching for metabolic drivers of colorectal cancer. Our analysis predicts FUT9, which catalyzes the biosynthesis of Ley glycolipids, as a driver of advanced-stage colon cancer. Experimental testing reveals FUT9's complex dual role; while its knockdown enhances proliferation and migration in monolayers, it suppresses colon cancer cells expansion in tumorspheres and inhibits tumor development in a mouse xenograft models. These results suggest that FUT9's inhibition may attenuate tumor-initiating cells (TICs) that are known to dominate tumorspheres and early tumor growth, but promote bulk tumor cells. In agreement, we find that FUT9 silencing decreases the expression of the colorectal cancer TIC marker CD44 and the level of the OCT4 transcription factor, which is known to support cancer stemness. Beyond its current application, this work presents a novel genomic and metabolic modeling computational approach that can facilitate the systematic discovery of metabolic driver genes in other types of cancer.

Co-targeting the tumor endothelium and P-selectin-expressing glioblastoma cells leads to a remarkable therapeutic outcome

Glioblastoma is a highly aggressive brain tumor. Current standard-of-care results in a marginal therapeutic outcome, partly due to acquirement of resistance and insufficient blood-brain barrier (BBB) penetration of chemotherapeutics. To circumvent these limitations, we conjugated the chemotherapy paclitaxel (PTX) to a dendritic polyglycerol sulfate (dPGS) nanocarrier. dPGS is able to cross the BBB, bind to P/L-selectins and accumulate selectively in intracranial tumors. We show that dPGS has dual targeting properties, as we found that P-selectin is not only expressed on tumor endothelium but also on glioblastoma cells. We delivered dPGS-PTX in combination with a peptidomimetic of the anti-angiogenic protein thrombospondin-1 (TSP-1 PM). This combination resulted in a remarkable synergistic anticancer effect on intracranial human and murine glioblastoma via induction of Fas and Fas-L, with no side effects compared to free PTX or temozolomide. This study shows that our unique therapeutic approach offers a viable alternative for the treatment of glioblastoma.

Chemoradiotherapy Resistance in Colorectal Cancer Cells is Mediated by Wnt/β-catenin Signaling

Activation of Wnt/β-catenin signaling plays a central role in the development and progression of colorectal cancer. The Wnt-transcription factor, TCF7L2, is overexpressed in primary rectal cancers that are resistant to chemoradiotherapy and TCF7L2 mediates resistance to chemoradiotherapy. However, it is unclear whether the resistance is mediated by a TCF7L2 inherent mechanism or Wnt/β-catenin signaling in general. Here, inhibition of β-catenin by siRNAs or a small-molecule inhibitor (XAV-939) resulted in sensitization of colorectal cancer cells to chemoradiotherapy. To investigate the potential role of Wnt/β-catenin signaling in controlling therapeutic responsiveness, nontumorigenic RPE-1 cells were stimulated with Wnt-3a, a physiologic ligand of Frizzled receptors, which increased resistance to chemoradiotherapy. This effect could be recapitulated by overexpression of a degradation-resistant mutant of β-catenin (S33Y), also boosting resistance of RPE-1 cells to chemoradiotherapy, which was, conversely, abrogated by siRNA-mediated silencing of β-catenin. Consistent with these findings, higher expression levels of active β-catenin were observed as well as increased TCF/LEF reporter activity in SW1463 cells that evolved radiation resistance due to repeated radiation treatment. Global gene expression profiling identified several altered pathways, including PPAR signaling and other metabolic pathways, associated with cellular response to radiation. In summary, aberrant activation of Wnt/β-catenin signaling not only regulates the development and progression of colorectal cancer, but also mediates resistance of rectal cancers to chemoradiotherapy.Implications: Targeting Wnt/β-catenin signaling or one of the downstream pathways represents a promising strategy to increase response to chemoradiotherapy. Mol Cancer Res; 15(11); 1481-90. ©2017 AACR.

Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction

Altered cellular metabolism is an important characteristic and driver of cancer. Surprisingly, however, we find here that aggregating individual gene expression using canonical metabolic pathways fails to enhance the classification of noncancerous vs. cancerous tissues and the prediction of cancer patient survival. This supports the notion that metabolic alterations in cancer rewire cellular metabolism through unconventional pathways. Here we present MCF (Metabolic classifier and feature generator), which incorporates gene expression measurements into a human metabolic network to infer new cancer-mediated pathway compositions that enhance cancer vs. adjacent noncancerous tissue classification across five different cancer types. MCF outperforms standard classifiers based on individual gene expression and on canonical human curated metabolic pathways. It successfully builds robust classifiers integrating different datasets of the same cancer type. Reassuringly, the MCF pathways identified lead to metabolites known to be associated with the pertaining specific cancer types. Aggregating gene expression through MCF pathways leads to markedly better predictions of breast cancer patients’ survival in an independent cohort than using the canonical human metabolic pathways (C-index = 0.69 vs. 0.52, respectively). Notably, the survival predictive power of individual MCF pathways strongly correlates with their power in predicting cancer vs. noncancerous samples. The more predictive composite pathways identified via MCF are hence more likely to capture key metabolic alterations occurring in cancer than the canonical pathways characterizing healthy human metabolism.

Cancer proliferating cells adapt their metabolism to support the conversion of available nutrients into biomass, which often involves an increased rate of specific metabolic pathways, such as glycolysis. Surprisingly, however, we observe that aggregating individual gene expression using canonical human metabolic pathways frequently fails to enhance the classification of noncancerous vs. cancerous tissues and in the task of predicting cancer patient survival. This supports the notion that metabolic alterations in cancer rewire cellular metabolism through unconventional pathways. Here we introduce a novel algorithm (MCF) that aims to identify these cancer-mediated ‘composite’ metabolic pathways by identifying those that best differentiate between cancerous vs. non-cancerous tissues gene expression. Remarkably, MCF successfully builds robust classifiers integrating different datasets of the same cancer type. We further show that the data-driven pathways identified by MCF, in contrast to the canonical literature-based pathways, successfully generate clinically relevant features that are predictive of breast cancer patients’ survival in an independent dataset. Our findings thus suggest that cancer metabolism may be rewired via non-standard composite pathways.

A joint analysis of transcriptomic and metabolomic data uncovers enhanced enzyme-metabolite coupling in breast cancer

Disrupted regulation of cellular processes is considered one of the hallmarks of cancer. We analyze metabolomic and transcriptomic profiles jointly collected from breast cancer and hepatocellular carcinoma patients to explore the associations between the expression of metabolic enzymes and the levels of the metabolites participating in the reactions they catalyze. Surprisingly, both breast cancer and hepatocellular tumors exhibit an increase in their gene-metabolites associations compared to noncancerous adjacent tissues. Following, we build predictors of metabolite levels from the expression of the enzyme genes catalyzing them. Applying these predictors to a large cohort of breast cancer samples we find that depleted levels of key cancer-related metabolites including glucose, glycine, serine and acetate are significantly associated with improved patient survival. Thus, we show that the levels of a wide range of metabolites in breast cancer can be successfully predicted from the transcriptome, going beyond the limited set of those measured.