A novel approach toward optimal workflow selection for DNA methylation biomarker discovery

DNA methylation is a major epigenetic modification involved in many physiological processes. Normal methylation patterns are disrupted in many diseases and methylation-based biomarkers have shown promise in several contexts. Marker discovery typically involves the analysis of publicly available DNA methylation data from high-throughput assays. Numerous methods for identification of differentially methylated biomarkers have been developed, making the need for best practices guidelines and context-specific analyses workflows exceedingly high. To this end, here we propose TASA, a novel method for simulating methylation array data in various scenarios. We then comprehensively assess different data analysis workflows using real and simulated data and suggest optimal start-to-finish analysis workflows. Our study demonstrates that the choice of analysis pipeline for DNA methylation-based marker discovery is crucial and different across different contexts.

Inferring gene expression from cell-free DNA fragmentation profiles

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.

Genetic Risk Variants for Class Switching Recombination Defects in Ataxia-Telangiectasia Patients

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10875-021-01147-8.

The spectrum of ATM gene mutations in Iranian patients with ataxia-telangiectasia

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.

A Comparative Overview of Epigenomic Profiling Methods

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.

Molecular features that predict the response to antimetabolite chemotherapies

BACKGROUND

Antimetabolite chemotherapeutic agents that target cellular metabolism are widely used in the clinic and are thought to exert their anti-cancer effects mainly through non-specific cytotoxic effects. However, patients vary dramatically with respect to treatment outcome, and the sources of heterogeneity remain largely unknown.

METHODS

Here, we introduce a computational method for identifying gene expression signatures of response to chemotherapies and apply it to human tumors and cancer cell lines. Furthermore, we characterize a set of 17 antimetabolite agents in various contexts to investigate determinants of sensitivity to these agents.

RESULTS

We identify distinct favorable and unfavorable metabolic expression signatures for 5-FU and Gemcitabine. Importantly, we find that metabolic pathways targeted by each of these antimetabolites are specifically enriched in its expression signatures. We provide evidence against the common notion about non-specific cytotoxic functions of antimetabolite drugs.

CONCLUSIONS

This study demonstrates through unbiased analyses that the activities of metabolic pathways likely contribute to therapeutic response.

A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product

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.

RRmix: A method for simultaneous batch effect correction and analysis of metabolomics data in the absence of internal standards

With the surge of interest in metabolism and the appreciation of its diverse roles in numerous biomedical contexts, the number of metabolomics studies using liquid chromatography coupled to mass spectrometry (LC-MS) approaches has increased dramatically in recent years. However, variation that occurs independently of biological signal and noise (i.e. batch effects) in metabolomics data can be substantial. Standard protocols for data normalization that allow for cross-study comparisons are lacking. Here, we investigate a number of algorithms for batch effect correction and differential abundance analysis, and compare their performance. We show that linear mixed effects models, which account for latent (i.e. not directly measurable) factors, produce satisfactory results in the presence of batch effects without the need for internal controls or prior knowledge about the nature and sources of unwanted variation in metabolomics data. We further introduce an algorithm-RRmix-within the family of latent factor models and illustrate its suitability for differential abundance analysis in the presence of strong batch effects. Together this analysis provides a framework for systematically standardizing metabolomics data.

Designing a broad-spectrum integrative approach for cancer prevention and treatment

Targeted therapies and the consequent adoption of "personalized" oncology have achieved notable successes in some cancers; however, significant problems remain with this approach. Many targeted therapies are highly toxic, costs are extremely high, and most patients experience relapse after a few disease-free months. Relapses arise from genetic heterogeneity in tumors, which harbor therapy-resistant immortalized cells that have adopted alternate and compensatory pathways (i.e., pathways that are not reliant upon the same mechanisms as those which have been targeted). To address these limitations, an international 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 aspects of relevant cancer biology, interdisciplinary teams reviewed each hallmark area and nominated a wide range of high-priority targets (74 in total) that could be modified to improve patient outcomes. For these targets, corresponding low-toxicity therapeutic approaches were then suggested, many of which were phytochemicals. Proposed actions on each target and all of the approaches were further reviewed for known effects on other hallmark areas and the tumor microenvironment. Potential contrary or procarcinogenic effects were found for 3.9% of the relationships between targets and hallmarks, and mixed evidence of complementary and contrary relationships was found for 7.1%. Approximately 67% of the relationships 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. This novel approach has potential to be relatively inexpensive, it should help us address stages and types of cancer that lack conventional treatment, and it may reduce relapse risks. A proposed agenda for future research is offered.

Targeting One Carbon Metabolism with an Antimetabolite Disrupts Pyrimidine Homeostasis and Induces Nucleotide Overflow

Antimetabolites 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.

Histone Methylation Dynamics and Gene Regulation Occur through the Sensing of One-Carbon Metabolism

S-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.

Context dependent utilization of serine in cancer

Serine and glycine have diverse biological functions but the general and context-dependent utilization of these nutrients in cancer is poorly understood. Our recent work integrates genomics data and isotope tracing using computational tools to study serine utilization across multiple cancer and normal human samples.

Characterization of the usage of the serine metabolic network in human cancer

The serine, glycine, one-carbon (SGOC) metabolic network is implicated in cancer pathogenesis, but its general functions are unknown. We carried out a computational reconstruction of the SGOC network and then characterized its expression across thousands of cancer tissues. Pathways including methylation and redox metabolism exhibited heterogeneous expression indicating a strong context dependency of their usage in tumors. From an analysis of coexpression, simultaneous up- or downregulation of nucleotide synthesis, NADPH, and glutathione synthesis was found to be a common occurrence in all cancers. Finally, we developed a method to trace the metabolic fate of serine using stable isotopes, high-resolution mass spectrometry, and a mathematical model. Although the expression of single genes didn't appear indicative of flux, the collective expression of several genes in a given pathway allowed for successful flux prediction. Altogether, these findings identify expansive and heterogeneous functions for the SGOC metabolic network in human cancer.