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Background Ataxia-telangiectasia (A-T) is a rare autosomal recessive disorder caused by mutations in the ataxia telangiectasia mutated (ATM) gene. A-T patients manifest considerable variability in clinical and immunological features, suggesting the presence of genetic modifying factors. A striking heterogeneity has been observed in class switching recombination (CSR) in A-T patients which cannot be explained by the severity of ATM mutations. Methods To investigate the cause of variable CSR in A-T patients, we applied whole-exome sequencing (WES) in 20 A-T patients consisting of 10 cases with CSR defect (CSR-D) and 10 controls with normal CSR (CSR-N). Comparative analyses on modifier variants found in the exomes of these two groups of patients were performed. Results For the first time, we identified some variants in the exomes of the CSR-D group that were significantly associated with antigen processing and presentation pathway. Moreover, in this group of patients, the variants in four genes involved in DNA double-strand breaks (DSB) repair signaling, in particular, XRCC3 were observed, suggesting an association with CSR defect. Conclusion Additional impact of certain variants, along with ATM mutations, may explain the heterogeneity in CSR defect phenotype among A-T patients. It can be concluded that genetic modulators play an important role in the course of A-T disease and its clinical severity.

Profiling of circulating tumor DNA (ctDNA) in the bloodstream shows promise for noninvasive cancer detection. Chromatin fragmentation features have previously been explored to infer gene expression profiles from cell-free DNA (cfDNA), but current fragmentomic methods require high concentrations of tumor-derived DNA and provide limited resolution. Here we describe promoter fragmentation entropy as an epigenomic cfDNA feature that predicts RNA expression levels at individual genes. We developed 'epigenetic expression inference from cell-free DNA-sequencing' (EPIC-seq), a method that uses targeted sequencing of promoters of genes of interest. Profiling 329 blood samples from 201 patients with cancer and 87 healthy adults, we demonstrate classification of subtypes of lung carcinoma and diffuse large B cell lymphoma. Applying EPIC-seq to serial blood samples from patients treated with PD-(L)1 immune-checkpoint inhibitors, we show that gene expression profiles inferred by EPIC-seq are correlated with clinical response. Our results indicate that EPIC-seq could enable noninvasive, high-throughput tissue-of-origin characterization with diagnostic, prognostic and therapeutic potential.

Ataxia-telangiectasia (A-T) is a rare autosomal recessive disorder caused by mutations in the ataxia telangiectasia mutated (ATM) gene. A-T patients manifest considerable variability in clinical and immunological features, suggesting the presence of genetic modifying factors. A striking heterogeneity has been observed in class switching recombination (CSR) in A-T patients which cannot be explained by the severity of ATM mutations.To investigate the cause of variable CSR in A-T patients, we applied whole-exome sequencing (WES) in 20 A-T patients consisting of 10 cases with CSR defect (CSR-D) and 10 controls with normal CSR (CSR-N). Comparative analyses on modifier variants found in the exomes of these two groups of patients were performed.For the first time, we identified some variants in the exomes of the CSR-D group that were significantly associated with antigen processing and presentation pathway. Moreover, in this group of patients, the variants in four genes involved in DNA double-strand breaks (DSB) repair signaling, in particular, XRCC3 were observed, suggesting an association with CSR defect.Additional impact of certain variants, along with ATM mutations, may explain the heterogeneity in CSR defect phenotype among A-T patients. It can be concluded that genetic modulators play an important role in the course of A-T disease and its clinical severity.

In the past decade, assays that profile different aspects of the epigenome have grown exponentially in number and variation. However, standard guidelines for researchers to choose between available tools depending on their needs are lacking. Here, we introduce a comprehensive collection of the most commonly used bulk and single-cell epigenomic assays and compare and contrast their strengths and weaknesses. We summarize some of the most important technical and experimental parameters that should be considered for making an appropriate decision when designing epigenomic experiments.

BACKGROUND Ataxia-telangiectasia (A-T) is a rare genetic disorder characterized by a distinct range of clinical manifestations, including progressive ataxia, immunodeficiency, and radiosensitivity. METHODS Clinical data, laboratory results, and genetic data were collected from forty-three A-T patients. Whole-exome sequencing and Sanger sequencing were done for the patients clinically diagnosed as suffering from A-T. Based on the phenotype severity of the disease, patients were divided into severe and mild subgroups. RESULTS The median (IQR) age of diagnosis in this cohort was 5 (3-7) years, and various types of clinical manifestations, including fever (P =.005), lower respiratory tract infection (P = .033), diarrhea (P = .014), and hepatosplenomegaly (P = .032), were significantly higher among patients diagnosed with the severe phenotype. Our results showed a correlation between phenotype severity and mutation type. The chance of having severe phenotype in patients who have severe mutations, including frameshift and nonsense, was 7.3 times higher than in patients who were categorized in the mild genotype group (odds ratio = 7.3, P = .006). Thirty-four types of mutations including 9 novel mutations were observed in our study. CONCLUSION Molecular analysis provides the opportunity for accurate diagnosis and timely management in A-T patients with chronic progressive disease, especially infections and the risk of malignancies. This study characterizes for the first time the broad spectrum of mutations and phenotypes in Iranian A-T patients, which is required for carrier detection and reducing the burden of disease in the future using the patients' families and for the public healthcare system.

Background: Ataxia-telangiectasia (A-T) is a rare genetic disorder characterized by a distinct range of clinical manifestations, including progressive ataxia, immunodeficiency, and radiosensitivity. Methods: Clinical data, laboratory results, and genetic data were collected from forty-three A-T patients. Whole exome sequencing and Sanger sequencing were done for the patients clinically diagnosed as suffering from A-T. Based on the phenotype severity of the disease, patients were divided into severe and mild sub-groups. Results: The median (IQR) age of diagnosis in this cohort was 5 (3-7) years and various types of clinical manifestations, including fever (p= 0.005), lower respiratory tract infection (p= 0.033), diarrhea (p= 0.014), and hepatosplenomegaly (p= 0.032) were significantly higher amongst patients diagnosed with the severe phenotype. Our results showed a strong correlation between phenotype severity and mutation type. The chance of having severe phenotype in patients who have severe mutations, including frameshift and nonsense, was 7.3 times higher compared to patients who were categorized in the mild genotype group (odds ratio= 7.3, p= 0.006). Thirty-four types of mutations including 9 novel mutations, were observed in our study. Conclusion: Molecular analysis provides the opportunity for accurate diagnosis and timely management in A-T patients with chronic progressive disease, especially infections and the risk of malignancies. This study characterizes for the first time, the broad spectrum of mutations and phenotypes in Iranian A-T patients which are required for carrier detection and reducing the burden of disease in future using the patients’ families and for the public health care system. Keywords: Ataxia-telangiectasia (A-T), ATM, Whole-exome sequencing, Class switching recombination (CSR), phenotype severity.

Background Diffuse large B-cell lymphoma (DLBCL) is a genetically and clinically heterogeneous disease. The cell-of-origin (COO) classification subdivides DLBCL into the transcriptionally defined activated B-cell (ABC) and germinal center B-cell (GCB) subtypes. While RNA based methods are considered the gold standard to determine COO, they are rarely used in clinical routine due to logistical and methodological challenges. Alternatives include the immunohistochemistry-based Hans algorithm and classifiers to infer the COO subtype from DNA sequencing [Esfahani et al., Blood 2019]. Despite the undisputed value of these methods, the concordance with the gold standard RNA and their prognostic implication are limited. We have recently shown that expression of individual genes can be inferred from cfDNA fragmentation patterns using a method called EPIC-Seq (EPigenetic expression Inference from Cell-free DNA Sequencing) [Esfahani et al., Cancer Res. 2020]. We therefore reasoned that EPIC-Seq may improve COO classification compared to other non-RNA methods. Methods A gene expression model, using cfDNA fragmentation patterns, was trained using leukocyte RNA-sequencing and deep whole genome profiling of the plasma cell-free DNA of an individual with no evidence of circulating disease. The trained model takes two features into account to infer gene expression: 1. promoter fragmentation entropy (PFE), and 2. normalized coverage at the nucleosome-depleted region of a given transcription start site (TSS). We then used a capture panel targeting TSS specifically designed for EPIC-Seq. We selected genes based on their power to discriminate COO subtypes in tumor RNA sequencing [Schmitz et al., NEJM 2018]. We first developed a classifier to distinguish DLBCL cases from healthy plasma using the inferred gene expression. Moreover, we defined GCB and ABC signature scores as the average inferred expression of a set of 'GCB genes' (n=34) and 'ABC genes' (n=34), respectively. Finally, we defined the COO score as the difference between the GCB and ABC scores. To validate our assay and method, we profiled 71 plasma samples from 68 healthy individuals and 90 pretreatment plasma samples from patients with large B-cell lymphomas using EPIC-Seq. Results We first evaluated the performance of the EPIC-Seq classifier in distinguishing DLBCL cases from controls and achieved an AUC of 0.92 in a cross-validation setting (Fig 1a). We then compared the result of EPIC-Seq COO classifier with the genotype-based method previously developed in our group. We observed epigenetic scores to be significantly correlated with previously described mutation-based GCB scores (r=0.75, P=1E-5, Fig. 1b). When comparing to the Hans classification algorithm, we observed significantly higher GCB scores in cases classified as GCB by Hans as compared with non-GCB cases (Wilcox P=0.001, Fig. 1c). Comparing the prognostic power of epigenetic and mutation-based COO labels in previously untreated patients using univariate Cox regressions, the EPIC-Seq classifier better stratified event-free survival (EFS) with higher GCB scores being associated with favorable outcomes (n=70, EPIC-Seq: HR=0.13, P=0.033 vs CAPP-Seq: HR=0.95, P=0.62). Importantly, when binarizing patients into GCB and non-GCB cases by the median, patients with tumors classified as GCB had significantly longer EFS than non-GCB counterparts (log-rank P=0.013, Fig. 1d). The Hans algorithm, in contrast, failed to stratify patients for EFS, among patients analyzed by both immunohistochemistry and DNA genotyping (Fig. 1e). Finally, we profiled n=12 additional DLBCL cases by both RNA-sequencing and EPIC-Seq. Strikingly, we found EPIC-COO scores to be significantly correlated with RNA based GCB scores (r=0.84, P=6E-4, Fig. 1f) underscoring the concordance of EPIC-Seq based COO classification with a gold standard scoring system. Conclusions We here apply EPIC-Seq, a method to infer expression of individual genes from cfDNA fragmentation patterns, to classify DLBCL patients into COO subtypes. COO classification by EPIC-Seq outperformed both Hans and mutation-based methods with regards to outcome stratification and correlated well with RNA-based methods. Overall, these results suggest that EPIC-Seq has utility for noninvasive classification of DLBCL cell-of-origin subtypes and may help to overcome logistical and methodological challenges of RNA-based methods. Figure 1 Figure 1. Disclosures Shahrokh Esfahani: Foresight Diagnostics: Current holder of stock options in a privately-held company. Kurtz: Genentech: Consultancy; Foresight Diagnostics: Consultancy, Current holder of stock options in a privately-held company; Roche: Consultancy. Diehn: Foresight Diagnostics: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; CiberMed: Current holder of stock options in a privately-held company, Patents & Royalties; Illumina: Research Funding; Varian Medical Systems: Research Funding; BioNTech: Consultancy; RefleXion: Consultancy; AstraZeneca: Consultancy; Roche: Consultancy. Alizadeh: Gilead: Consultancy; Bristol Myers Squibb: Research Funding; Janssen Oncology: Honoraria; Celgene: Consultancy, Research Funding; Forty Seven: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; CAPP Medical: Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Roche: Consultancy, Honoraria; Foresight Diagnostics: Consultancy, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company; Cibermed: Consultancy, Current holder of individual stocks in a privately-held company, Current holder of stock options in a privately-held company.

SummaryAntimetabolites that affect nucleotide metabolism are frontline chemotherapy agents in several cancers and often successfully target one carbon metabolism. However, the precise mechanisms and resulting determinants of their therapeutic value are unknown. We show that 5-fluorouracil (5-FU), a commonly used antimetabolite therapeutic with varying efficacy, induces specific alterations to nucleotide metabolism by disrupting pyrimidine homeostasis. An integrative metabolomics analysis of the cellular response to 5-FU reveals intracellular uracil accumulation, whereas deoxyuridine levels exhibited increased flux into the extracellular space, resulting in an induction of overflow metabolism. Subsequent analysis from mice bearing colorectal tumors treated with 5-FU show specific secretion of metabolites in tumor-bearing mice into serum that results from alterations in nucleotide flux and reduction in overflow metabolism. Together, these findings identify a determinant of an antimetabolite response that may be exploited to more precisely define the tumors that could respond to targeting cancer metabolism.

Cancer is a disease characterized by unrestrained cellular proliferation. In order to sustain growth, cancer cells undergo a complex metabolic rearrangement characterized by changes in metabolic pathways involved in energy production and biosynthetic processes. The relevance of the metabolic transformation of cancer cells has been recently included in the updated version of the review “Hallmarks of Cancer”, where dysregulation of cellular metabolism was included as an emerging hallmark. While several lines of evidence suggest that metabolic rewiring is orchestrated by the concerted action of oncogenes and tumor suppressor genes, in some circumstances altered metabolism can play a primary role in oncogenesis. Recently, mutations of cytosolic and mitochondrial enzymes involved in key metabolic pathways have been associated with hereditary and sporadic forms of cancer. Together, these results demonstrate that aberrant metabolism, once seen just as an epiphenomenon of oncogenic reprogramming, plays a key role in oncogenesis with the power to control both genetic and epigenetic events in cells. In this review, we discuss the relationship between metabolism and cancer, as part of a larger effort to identify a broad-spectrum of therapeutic approaches. We focus on major alterations in nutrient metabolism and the emerging link between metabolism and epigenetics. Finally, we discuss potential strategies to manipulate metabolism in cancer and tradeoffs that should be considered. More research on the suite of metabolic alterations in cancer holds the potential to discover novel approaches to treat it.

Supplementary Figures. Figure S1. Relationship between target enzyme expression and response to Gemcitabine in TCGA pancreatic cancer. A) Kaplan-Meier plot compares progression free survival in high-RRM1 expression vs. low-RRM1 expression subgroups of TCGA PAAD patients. B) Kaplan-Meier plot compares progression free survival in high-RRM2 expression vs. low-RRM2 expression subgroups TCGA PAAD patients. Figure S2. Relationship between target enzyme expression and survival in an independent pancreatic cancer cohort. A) Kaplan-Meier plot compares overall survival in high-RRM1 expression vs. low-RRM1 expression subgroups of patients. B) Kaplan-Meier plot compares overall survival in high-RRM2 expression vs. low-RRM2 expression subgroups of patients. C) Kaplan-Meier plot compares overall survival in subgroups of patients divided based on our gene signature (see Methods). Figure S3. Identifying gene expression signatures of sensitivity to Gemcitabine in pancreatic cancer cell lines. A) Schematic of the step-wise filtering used for gene selection in pancreatic cancer (COSMIC PAAD). B) Hierarchical clustering heatmap of the discretized gene favorability scores. Columns represent genes and rows represent individuals. Favorable scores are shown by the color red (F=1), unfavorable by blue (F= -1), and neutral by yellow (F=0) (see Methods). C) Box-plots comparing the resistance to Gemcitabine (log IC-50 values) between the two cell line subgroups identified in part B (error bars show the range of the data points in each group). (DOCX 225Â kb)

Altered DNA methylation is common in cancer and often considered an early event in tumorigenesis. However, the sources of heterogeneity of DNA methylation among tumours remain poorly defined. Here we capitalize on the availability of multi-platform data on thousands of human tumours to build integrative models of DNA methylation. We quantify the contribution of clinical and molecular factors in explaining intertumoral variability in DNA methylation. We show that the levels of a set of metabolic genes involved in the methionine cycle is predictive of several features of DNA methylation in tumours, including the methylation of cancer genes. Finally, we demonstrate that patients whose DNA methylation can be predicted from the methionine cycle exhibited improved survival over cases where this regulation is disrupted. This study represents a comprehensive analysis of the determinants of methylation and demonstrates the surprisingly large interaction between metabolism and DNA methylation variation. Together, our results quantify links between tumour metabolism and epigenetics and outline clinical implications., Altered DNA methylation is a feature of cancer and between-patient variability is prevalent. Here, the authors integrate data on thousands of human tumours, and find that expression levels of methionine metabolism genes are predictive of methylation features, and that the breakdown of this relationship is a negative prognostic marker.

Summary Targeted cancer therapies that use genetics are successful, but principles for selectively targeting tumor metabolism that is also dependent on the environment remain unknown. We now show that differences in rate-controlling enzymes during the Warburg effect (WE), the most prominent hallmark of cancer cell metabolism, can be used to predict a response to targeting glucose metabolism. We establish a natural product, koningic acid (KA), to be a selective inhibitor of GAPDH, an enzyme we characterize to have differential control properties over metabolism during the WE. With machine learning and integrated pharmacogenomics and metabolomics, we demonstrate that KA efficacy is not determined by the status of individual genes, but by the quantitative extent of the WE, leading to a therapeutic window in vivo. Thus, the basis of targeting the WE can be encoded by molecular principles that extend beyond the status of individual genes.

DNA methylation varies across genomic regions, tissues and individuals in a population. Altered DNA methylation is common in cancer and often considered an early event in tumorigenesis. However, the sources of heterogeneity of DNA methylation among tumors remain poorly defined. Here, we capitalize on the availability of multi-platform data on thousands of molecularly-and clinically-annotated human tumors to build integrative models that identify the determinants of DNA methylation. We quantify the relative contribution of clinical and molecular factors in explaining within-cancer (inter-individual) variability in DNA methylation. We show that the levels of a set of metabolic genes involved in the methionine cycle that are constituents of one-carbon metabolism are predictive of several features of DNA methylation status in tumors including the methylation of genes that are known to drive oncogenesis. Finally, we demonstrate that patients whose DNA methylation status can be predicted from the genes in one-carbon metabolism exhibited improved survival over cases where this regulation is disrupted. To our knowledge, this study is the first comprehensive analysis of the determinants of methylation and demonstrates the surprisingly large contribution of metabolism in explaining epigenetic variation among individual tumors of the same cancer type. Together, our results illustrate links between tumor metabolism and epigenetics and outline future clinical implications.

Under a Creative Commons license.-- Review.-- et al., Targeted therapies and the consequent adoption of >personalized> oncology have achieved notablesuccesses in some cancers; however, significant problems remain with this approach. Many targetedtherapies are highly toxic, costs are extremely high, and most patients experience relapse after a fewdisease-free months. Relapses arise from genetic heterogeneity in tumors, which harbor therapy-resistantimmortalized cells that have adopted alternate and compensatory pathways (i.e., pathways that are notreliant upon the same mechanisms as those which have been targeted). To address these limitations, aninternational task force of 180 scientists was assembled to explore the concept of a low-toxicity >broad-spectrum> therapeutic approach that could simultaneously target many key pathways and mechanisms. Using cancer hallmark phenotypes and the tumor microenvironment to account for the various aspectsof relevant cancer biology, interdisciplinary teams reviewed each hallmark area and nominated a widerange of high-priority targets (74 in total) that could be modified to improve patient outcomes. For thesetargets, corresponding low-toxicity therapeutic approaches were then suggested, many of which werephytochemicals. Proposed actions on each target and all of the approaches were further reviewed forknown effects on other hallmark areas and the tumor microenvironment. Potential contrary or procar-cinogenic effects were found for 3.9% of the relationships between targets and hallmarks, and mixedevidence of complementary and contrary relationships was found for 7.1%. Approximately 67% of therelationships revealed potentially complementary effects, and the remainder had no known relationship. Among the approaches, 1.1% had contrary, 2.8% had mixed and 62.1% had complementary relationships. These results suggest that a broad-spectrum approach should be feasible from a safety standpoint. Thisnovel approach has potential to be relatively inexpensive, it should help us address stages and types ofcancer that lack conventional treatment, and it may reduce relapse risks. A proposed agenda for futureresearch is offered., Amr Amin was funded by Terry Fox Foundation Grant # TF-13-20 and UAEU Program for Advanced Research (UPAR) #31S118; Jack Arbiser was funded by NIHAR47901; Alexandra Arreola was funded by NIH NRSA Grant F31CA154080; Alla Arzumanyan was funded by NIH (NIAID) R01: Combination therapies for chronic HBV, liver disease, and cancer (AI076535); Work in the lab of Asfar S. Azmi is supported by NIH R21CA188818 as well as from Sky Foundation Inc. Michigan; Fabian Benencia was supported by NIH Grant R15 CA137499-01; Alan Bilsland was supported by the University of Glasgow, Beatson Oncology Centre Fund, CRUK (www.cancerresearchuk.org) Grant C301/A14762; Amancio Carnero was supported by grants from the Spanish Ministry of Economy and Competitivity, ISCIII (Fis: PI12/00137, RTICC: RD12/0036/0028) co-funded by FEDER from Regional Development European Funds (European Union), Consejeria de Ciencia e Innovacion (CTS-6844 and CTS-1848) and Consejeria de Salud of the Junta de Andalucia (PI-0135-2010 and PI-0306-2012). His work on this project has also been made possible thanks to the Grant PIE13/0004 co-funded by the ISCIII and FEDER funds; Stephanie C. Casey was supported by NIH Grant F32CA177139; Mrinmay Chakrabarti was supported by the United Soybean Board; Rupesh Chaturvedi was supported by an NIH NCCAM Grant (K01AT007324); Georgia Zhuo Chen was supported by an NIH NCI Grant (R33 CA161873-02); Helen Chen acknowledges financial support from the Michael Cuccione Childhood Cancer Foundation Graduate Studentship; Sophie Chen acknowledges financial support from the Ovarian and Prostate Cancer Research Trust, UK; Yi Charlie Chen acknowledges financial support from the West Virginia Higher Education Policy Commission/Division of Science Research, his research was also supported by NIH grants (P20RR016477 and P20GM103434) from the National Institutes of Health awarded to the West Virginia IDeA Network of Biomedical Research Excellence; Maria Rosa Ciriolo was partially supported by the Italian Association for Cancer Research (AIRC) Grants #IG10636 and #15403; Helen M. Coley acknowledges financial support from the GRACE Charity, UK and the Breast Cancer Campaign, UK; Marisa Connell was supported by a Michael Cuccione Childhood Cancer Foundation Postdoctoral Fellowship; Sarah Crawford was supported by a research grant from Connecticut State University; Charlotta Dabrosin acknowledges financial support from the Swedish Research Council and the Swedish Research Society; Giovanna Damia gratefully acknowledges the generous contributions of The Italian Association for Cancer Research (IG14536 to G.D.), Santanu Dasgupta gratefully acknowledges the support of the University of Texas Health Science Centre at Tyler, Elsa U. Pardee Foundation; William K. Decker was supported in part by CPRIT, the Cancer Prevention and Research Institute of Texas; Anna Mae E. Diehl was supported by NIH National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), the NIH National Institute on Alcohol Abuse and Alcoholism (NIAAA), Gilead and Shire Pharmaceuticals; Q. Ping Dou was partially supported by NIH/NCI (1R01CA20009, 5R01CA127258-05 and R21CA184788), and NIH P30 CA22453 (to Karmanos Cancer Institute); Janice E. Drew was supported by the Scottish Government's Rural and Environment Science and Analytical Services Division; Eyad Elkord thanks the National Research Foundation, United Arab Emirates University and the Terry Fox Foundation for supporting research projects in his lab; Bassel El-Rayes was supported by Novartis Pharmaceutical, Aveo Pharmaceutical, Roche, Bristol Myers Squibb, Bayer Pharmaceutical, Pfizer, and Kyowa Kirin; Mark A. Feitelson was supported by NIH/NIAID Grant AI076535, Dean W. Felsher was supported by NIH grants (R01CA170378, U54CA149145, and U54CA143907); Lynnette R Ferguson was financially supported by the Auckland Cancer Society and the Cancer Society of New Zealand; Gary L. Firestone was supported by NIH Public Service Grant CA164095 awarded from the National Cancer Institute; Christian Frezza “would like to acknowledge funding from a Medical Research Council CCU-Program Grant on cancer metabolism, and a unique applicant AICR project grant”; Mark M. Fuster was supported by NIH Grant R01-HL107652; Alexandros G. Georgakilas was supported by an EU Marie Curie Reintegration Grant MC-CIG-303514, Greek National funds through the Operational Program ‘Educational and Lifelong Learning of the National Strategic Reference Framework (NSRF)-Research Funding Program THALES (Grant number MIS 379346) and COST Action CM1201 ‘Biomimetic Radical Chemistry’; Michelle F. Green was supported by a Duke University Molecular Cancer Biology T32 Training Grant; Brendan Grue was supported by a National Sciences Engineering and Research Council Undergraduate Student Research Award in Canada; Dorota Halicka was supported by by NIH NCI grant NCI RO1 28704; Petr Heneberg was supported by the Charles University in Prague projects UNCE 204015 and PRVOUK P31/2012, by the Czech Science Foundation projects 15-03834Y and P301/12/1686, by the Czech Health Research Council AZV project 15-32432A, and by the Internal Grant Agency of the Ministry of Health of the Czech Republic project NT13663-3/2012; Matthew D. Hirschey wishes to acknowledge Duke University Institutional Support, the Duke Pepper Older Americans Independence Center (OAIC) Program in Aging Research supported by the National Institute of Aging (P30AG028716-01) and NIH/NCI training grants to Duke University (T32-CA059365-19 and 5T32-CA059365), Lorne J. Hofseth was supported by NIH grants (1R01CA151304, 1R03CA1711326, and 1P01AT003961); Kanya Honoki was supported in part by the grant from the Ministry of Education, Culture, Sports, Science and Technology, Japan (No. 24590493); Hsue-Yin Hsu was supported in part by grants from the Ministry of Health and Welfare (CCMP101-RD-031 and CCMP102-RD-112) and Tzu-Chi University (61040055-10) of Taiwan; Lasse D. Jensen was supported by Svenska Sallskapet for Medicinsk Forskning, Gosta Fraenkels Stiftelse, Ak.e Wibergs Stiftelse, Ollie och Elof Ericssons Stiftelse, Linkopings Universitet and the Karolinska Institute, Sweden; Wen G. Jiang wishes to acknowledge the support by Cancer Research Wales, the Albert Hung Foundation, the Fong Family Foundation, and Welsh Government A4B scheme; Lee W. Jones was supported in part by grants from the NIH NCI; W Nicol Keith was supported by the University of Glasgow, Beatson Oncology Centre Fund, CRUK (www.cancerresearchuk.org) Grant C301/A14762; Sid P. Kerkar was supported by the NIH Intramural Research Program; Rob J. Kulathinal was supported by the National Science Foundation, and the American Cancer Society; Byoung S. Kwon was supported in part by National Cancer Center (NCC-1310430-2) and National Research Foundation (NRF-2005-0093837); Anne Le was supported by Sol Goldman Pancreatic Cancer Research Fund Grant 80028595, a Lustgarten Fund Grant 90049125 and Grant NIHR21CA169757 (to Anne Le); Michael A. Lea was funded by the The Alma Toorock Memorial for Cancer Research; Ho-Young Lee., This work was supported by grants from the National Research Foundation of Korea (NRF), the Ministry of Science, ICT & Future Planning (MSIP), Republic of Korea (Nos. 2011-0017639 and 2011-0030001) and by a NIH Grant R01 CA100816; Liang-Tzung Lin was supported in part by a grant from the Ministry of Education of Taiwan (TMUTOP103005-4); Jason W. Locasale acknowledges support from NIH awards (CA168997 and AI110613) and the International Life Sciences Institute; Bal L. Lokeshwar was supported in part by United States’ Public Health Services Grants: NIH R01CA156776 and VA-BLR&D Merit Review Grant No. 5I01-BX001517-02; Valter D. Longo acknowledges support from NIH awards (P01AG034906 and R01AG020642) and from the V Foundation; Costas A. Lyssiotis was funded in part by the Pancreatic Cancer Action Network as a Pathway to Leadership Fellow and through a Dale F. Frey Breakthrough award from the Damon Runyon Cancer Research Foundation; Karen L. MacKenzie wishes to acknowledge the support from the Children's Cancer Institute Australia (affiliated with the University of New South Wales, Australia and the Sydney Children's Hospital Network); Maria Marino was supported by grant from University Roma Tre to M.M. (CLA 2013) and by the Italian Association for Cancer Research (AIRC-Grant #IG15221), Ander Matheu is funded by Carlos III Health Institute (AM: CP10/00539), Basque Foundation for Science (IKERBASQUE) and Marie Curie CIG Grant (AM: 2012/712404); Christopher Maxwell was supported by funding from the Canadian Institutes of Health Research, in partnership with the Avon Foundation for Women (OBC-134038) and the Canadian Institutes of Health Research New Investigator Salary Award (MSH-136647); Eoin McDonnell received Duke University Institutional Support; Kapil Mehta was supported by Bayer Healthcare System G4T (Grants4Targets); Gregory A. Michelotti received support from NIH NIDDK, NIH NIAAA, and Shire Pharmaceuticals; Vinayak Muralidhar was supported by the Harvard-MIT Health Sciences and Technology Research Assistantship Award; Elena Niccolai was supported by the Italian Ministry of University and the University of Italy; Virginia R. Parslow gratefully acknowledges the financial support of the Auckland Cancer Society Research Centre (ACSRC); Graham Pawelec was supported by the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF) Grant number 16SV5536K, and by the European Commission (FP7 259679 “IDEAL”); Peter L. Pedersen was supported by NIH Grant CA-10951; Brad Poore was supported by Sol Goldman Pancreatic Cancer Research Fund Grant 80028595, the Lustgarten Fund Grant 90049125, and Grant NIHR21CA169757 (to Anne Le); Satya Prakash was supported by a Canadian Institutes of Health Research Grant (MOP 64308); Lizzia Raffaghello was supported by an NIH Grant (P01AG034906-01A1) and Cinque per Mille dell’IRPEF–Finanziamento della Ricerca Sanitaria; Jeffrey C. Rathmell was supported by an NIH Grant (R01HL108006); Swapan K. Ray was supported by the United Soybean Board; Domenico Ribatti received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant agreement n°278570; Luigi Ricciardiello was supported by the AIRC Investigator Grants 10216 and 13837, and the European Community's Seventh Framework Program FP7/2007–2013 under Grant agreement 311876; Francis Rodier acknowledges the support of the Canadian Institute for Health Research (FR: MOP114962, MOP125857), Fonds de Recherche Québec Santé (FR: 22624), and the Terry Fox Research Institute (FR: 1030), Gian Luigi Russo contributed to this effort while participating in the Fulbright Research Scholar Program 2013–14; Isidro Sanchez-Garcia is partially supported by FEDER and by MICINN (SAF2012-32810), by NIH Grant (R01 CA109335-04A1), by Junta de Castilla y León (BIO/SA06/13) and by the ARIMMORA project (FP7-ENV-2011, European Union Seventh Framework Program). Isidro Sanchez-Garcia's lab is also a member of the EuroSyStem and the DECIDE Network funded by the European Union under the FP7 program; Andrew J. Sanders wishes to acknowledge the support by Cancer Research Wales, the Albert Hung Foundation, the Fong Family Foundation, and Welsh Government A4B scheme; Neeraj K. Saxena was supported by grant funding from NIH NIDDK (K01DK077137, R03DK089130); Dipali Sharma was partially funded by NIH NCI grants (R01CA131294, R21 CA155686), the Avon Foundation and a Breast Cancer Research Foundation Grant (90047965); Markus David Siegelin received funding from National Institute of Health, NINDS Grant K08NS083732, and the 2013 AACR-National Brain Tumor Society Career Development Award for Translational Brain Tumor Research, Grant Number 13-20-23-SIEG; Neetu Singh was supported by funds from the Department of Science and Technology (SR/FT/LS-063/2008), New Delhi, India; Carl Smythe was supported by Yorkshire Cancer Research and The Wellcome Trust, UK; Carmela Spagnuolo was supported by funding from Project C.I.S.I.A., act n. 191/2009 from the Italian Ministry of Economy and Finance Project CAMPUS-QUARC, within program FESR Campania Region 2007/2013, objectives 2.1, 2.2; Diana M. Stafforini was supported by grants from the National Cancer Institute (5P01CA073992), IDEA Award W81XWH-12-1-0515 from the Department of Defense, and by the Huntsman Cancer Foundation; John Stagg was supported by the Canadian Institutes of Health Research; Pochi R. Subbarayan was supported by the University of Miami Clinical and Translational Science Institute (CTSI) Pilot Research Grant (CTSI-2013-P03) and SEEDS You Choose Awards; Phuoc T. Tran was funded by the DoD (W81XWH-11-1-0272 and W81XWH-13-1-0182), a Kimmel Translational Science Award (SKF-13-021), an ACS Scholar award (122688-RSG-12-196-01-TBG) and the NIH (R01CA166348); Kathryn E. Wellen receives funding from the National Cancer Institute, Pancreatic Cancer Action Network, Pew Charitable Trusts, American Diabetes Association, and Elsa U. Pardee Foundation; Huanjie Yang was partially supported by the Scientific Research Foundation for the Returned Oversea Scholars, State Education Ministry and Scientific and Technological Innovation Project, Harbin (2012RFLXS011), Paul Yaswen was supported by funding from the United States National Institutes of Health (ES019458) and the California Breast Cancer Research Program (17UB-8708); Clement Yedjou was supported by a grant from the National Institutes of Health (Grant # G1200MD007581), through the RCMI-Center for Environmental Health; Xin Yin was supported by NIH/National Heart, Lung, and Blood Institute Training Grant T32HL098062.; Jiyue Zhu was supported by NIH Grant R01GM071725; Massimo Zollo was supported by the European FP7-TuMIC HEALTH-F2-2008-201662, the Italian Association for Cancer research (AIRC) Grant IG # 11963 and the Regione Campania L.R:N.5, the European National Funds PON01-02388/1 2007-2013.

Background Diffuse large B-cell lymphomas (DLBCL) can be divided into subtypes that relate to their cell-of-origin: germinal center B-cell (GCB) and activated B-cell (ABC). These clinically distinct subtypes were originally discovered based on their unique transcriptional signatures, but they also harbor distinct genetic aberrations and epigenetic profiles. We previously showed the utility of genetic mutations in circulating tumor DNA (ctDNA) for DLBCL subtyping using cancer personalized profiling by deep sequencing (CAPP-Seq) [Scherer et al., Sci Transl Med 2016, Newman et al., Nat Med 2014]. Here, we assess epigenetic information encoded in cell-free DNA (cfDNA) for addressing this distinction. Methods We extended recent observations revealing nucleosome depletion at transcription start sites (TSS) of highly expressed genes within cfDNA [Snyder et al. 2016 Cell and Ulz et al. 2016 Nat Genetics]. Specifically, to overcome the inherent sensitivity limitations of cfDNA whole genome sequencing (WGS), we focused on 32 genes differentially expressed between DLBCL subtypes (as well as 70 control genes). We targeted 2 kb regions flanking each TSS for these genes by CAPP-Seq [Newman et al. 2014 Nature Med]. Plasma samples were collected from 41 individuals and cfDNA subjected to ultra-deep sequencing (~4000x) by CAPP-Seq. DLBCL cases were labeled as either ABC or GCB based on gene expression in tumor biopsies. Results We observed significant heterogeneity in the TSS profiles of individual genes. Despite this variation, many genes in our targeted panel exhibited discriminatory profiles. For example, CD20 (MS4A1), reliably distinguished DLBCL cases from control subjects in nucleosome depletion at the TSS as expected (p= 1.5e-05, Fig1a). Remarkably, when considering 32 genes differentially expressed between DLBCL subtypes, we observed significant differences in their TSS profiles in plasma from patients with GCB vs ABC DLBCL (p=0.0002, Fig 1b). For the majority of the subtype-specific genes, the pattern of nucleosome depletion at the TSS was consistent with expected expression (Fig 1b). Specifically, genes typically over-expressed in GCB DLBCL (e.g., CD10/MME, LMO2, SERPINA9), had significantly more TSS nucleosome depletion in cfDNA from GCB-like DLBCL patients. Conversely, we observed significantly more nucleosome depletion in cfDNA of ABC DLBCL patients at the TSS of genes known to be transcriptionally more active in ABC DLBCL (e.g., IRF4, PIM1, CCND2 and IL16). When aggregating the epigenetic profiles across these genes into a single cell-of-origin score, we observed significant stratification of patients with distinct DLBCL subtypes (Fig 1c, p=0.008). Across the cohort, this epigenetic cell-of-origin score from cfDNA showed significant correlation (Spearman rho=0.67, p=0.05) with a score derived from somatic mutations genotyped from ctDNA [Scherer et al. 2016 Sci Transl Med]. Conclusions Lymphoma subtypes exhibit distinct chromatin accessibility profiles that can be captured by targeted cell free DNA profiling, and that can serve as surrogates for expected gene expression differences. These epigenetic features can be used to noninvasively classify DLBCL molecular subtypes using blood plasma and should be readily extensible to other cancer classification problems. Extension of this approach to other epigenetic features could serve to further refine potential applications. Figure legend. Figure 1. (A) Sequencing coverage around the TSS of the B-cell antigen CD20 (MS4A1) is shown for three representative DLBCL patients (red) and three normal controls (gray). The boxplot shows summary of normalized depth across all samples (n=10 DLBCL, n=31 control). (B) The y-axis shows 32 genes with known ABC-favoring (blue) and GCB-favoring (orange) expression. The x-axis shows a delta score defined as log2( mean normalized depth across GCB samples - mean normalized depth across GCB samples). (C) Boxplot of the GCBness score obtained by subtracting the overall depth around the TSSs of a set of GCB-genes from the same metric for a set of ABC-genes. Disclosures No relevant conflicts of interest to declare.

Cancer cells undergo numerous adaptive processes to sustain growth and survival. One notable mechanism is by rewiring metabolism, most prominently through a phenomenon known as the Warburg effect (WE). The WE is defined by increased glucose consumption and lactate excretion in the presence or absence of oxygen. Although the WE has been extensively studied, efforts to develop successful glycolytic inhibitors have been largely unsuccessful. Targeting cancer metabolism has remained a challenge due to the lack of obvious metabolic biomarkers and difficulties achieving full enzyme inhibition without inducing toxicity in normal tissue. Although targeted cancer therapies that use genetics have been largely successful, principles for selectively targeting tumor metabolism that also depend on the environment remain unknown. In the present study, we employ metabolic control analysis to reveal that glyceraldehyde-3-phosphate dehydrogenase (GAPDH), the sixth enzyme in glycolysis, exhibits differential control properties during the WE and can be used to predict response to targeting glucose metabolism. Using high-performance liquid chromatography coupled to high-resolution mass spectrometry (HPLC-HRMS), we conducted comparative metabolomics to establish a natural product produced by Trichoderma fungi, koningic acid (KA), as a selective inhibitor of GAPDH. We expressed a fungal-derived resistant-GAPDH allele in human cells to show that KA is highly specific for GAPDH. With machine learning, integrated pharmacogenomics, and metabolomics, we demonstrate that KA efficacy is not determined by the status of individual genes, but by the quantitative extent of the WE, leading to a therapeutic window in vivo. Thus, the basis of targeting the WE can be encoded by molecular principles that extend beyond genetic status. Current work focuses on elucidating acquired resistance mechanisms of KA in cancer cells undergoing the WE. Together, these data importantly demonstrate that a complete understanding of pharmacogenomics for cancer therapy likely requires information encoded at the metabolic level. Citation Format: Maria V. Liberti, Ziwei Dai, Suzanne E. Wardell, Joshua A. Baccile, Xiaojing Liu, Xia Gao, Robert Baldi, Mahya Mehrmohamadi, Marc O. Johnson, Neel S. Madhukar, Alexander Shestov, Iok I. C. Chio, Olivier Elemento, Jeffrey C. Rathmell, Frank C. Schroeder, Donald P. McDonnell, Jason W. Locasale. A predictive model for selective targeting of the Warburg effect through GAPDH inhibition with a natural product [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5496.

SummaryS-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) link one-carbon metabolism to methylation status. However, it is unknown whether regulation of SAM and SAH by nutrient availability can be directly sensed to alter the kinetics of key histone methylation marks. We provide evidence that the status of methionine metabolism is sufficient to determine levels of histone methylation by modulating SAM and SAH. This dynamic interaction led to rapid changes in H3K4me3, altered gene transcription, provided feedback regulation to one-carbon metabolism, and could be fully recovered upon restoration of methionine. Modulation of methionine in diet led to changes in metabolism and histone methylation in the liver. In humans, methionine variability in fasting serum was commensurate with concentrations needed for these dynamics and could be partly explained by diet. Together these findings demonstrate that flux through methionine metabolism and the sensing of methionine availability may allow direct communication to the chromatin state in cells.

The ingestion and digestion of food is a universal aspect of animal growth and survival and is important for our understanding of obesity and diabetes. All animals have a digestive system that comprises of an upper gastrointestinal tract mainly for the ingestion and storage of food, and a lower gastrointestinal tract or the gut for digestion and absorption. When food is ingested, peristaltic contraction is initiated and directs the bolus through the esophagus toward the stomach or an equivalent structure1. In addition to serving as the major site for digestion, the stomach acts as a reservoir to store food. This adaptation may have significance for the survival of many animals in which the immediate access to the infrequent availability of food necessitates large meal consumptions that can then be slowly digested or utilized at a later time. Once the food is triturated in the stomach, it is released into the lower gut where the main function of digestion and absorption of nutrients is carried out.