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Abstract Background Advances in whole genome profiling have revolutionized the cancer research field, but at the same time have raised new bioinformatics challenges. For next generation sequencing (NGS), these include data storage, computational costs, sequence processing and alignment, delineating appropriate statistical measures, and data visualization. Currently there is a lack of workflows for efficient analysis of large, MethylCap-seq datasets containing multiple sample groups. Methods The NGS application MethylCap-seq involves the in vitro capture of methylated DNA and subsequent analysis of enriched fragments by massively parallel sequencing. The workflow we describe performs MethylCap-seq experimental Quality Control (QC), sequence file processing and alignment, differential methylation analysis of multiple biological groups, hierarchical clustering, assessment of genome-wide methylation patterns, and preparation of files for data visualization. Results Here, we present a scalable, flexible workflow for MethylCap-seq QC, secondary data analysis, tertiary analysis of multiple experimental groups, and data visualization. We demonstrate the experimental QC procedure with results from a large ovarian cancer study dataset and propose parameters which can identify problematic experiments. Promoter methylation profiling and hierarchical clustering analyses are demonstrated for four groups of acute myeloid leukemia (AML) patients. We propose a Global Methylation Indicator (GMI) function to assess genome-wide changes in methylation patterns between experimental groups. We also show how the workflow facilitates data visualization in a web browser with the application Anno-J. Conclusions This workflow and its suite of features will assist biologists in conducting methylation profiling projects and facilitate meaningful biological interpretation.

Acute myeloid leukemia (AML) is prevalent in both adult and pediatric patients. Despite advances in patient categorization, the heterogeneity of AML remains a challenge. Recent studies have explored the use of gene expression data to enhance AML diagnosis and prognosis, however, alternative approaches rooted in physics and chemistry may provide another level of insight into AML transformation. Utilizing publicly available databases, we analyze 884 human and mouse blood and bone marrow samples. We employ a personalized medicine strategy, combining state-transition theory and surprisal analysis, to assess the RNA transcriptome of individual patients. The transcriptome is transformed into physical parameters that represent each sample's steady state and the free energy change (FEC) from that steady state, which is the state with the lowest free energy.We found the transcriptome steady state was invariant across normal and AML samples. FEC, representing active molecular processes, varied significantly between samples and was used to create patient-specific barcodes to characterize the biology of the disease. We discovered that AML samples that were in a transition state had the highest FEC. This disease state may be characterized as the most unstable and hence the most therapeutically targetable since a change in free energy is a thermodynamic requirement for disease progression. We also found that distinct sets of ongoing processes may be at the root of otherwise similar clinical phenotypes, implying that our integrated analysis of transcriptome profiles may facilitate a personalized medicine approach to cure AML and restore a steady state in each patient., (© 2024. The Author(s).)

Trastuzumab therapy in HER2+ breast cancer patients has mixed success owing to acquired resistance to therapy. A detailed understanding of downstream molecular cascades resulting from trastuzumab resistance is yet to emerge. In this study, we investigate the cellular mechanisms underlying acquired resistance using trastuzumab-sensitive and -resistant cancer cells (BT474 and BT474R) treated with endogenous ligands EGF and HRG across time. We probe early receptor organization through microscopy and signaling events through multiomics measurements and assess the bioenergetic state through mitochondrial measurements. Integrative analyses of our measurements reveal significant alterations in EGF-treated BT474 HER2 membrane dynamics and robust downstream activation of PI3K/AKT/mTORC1 signaling. EGF-treated BT474R shows a sustained interferon-independent activation of the IRF1/STAT1 cascade, potentially contributing to trastuzumab resistance. Both cell lines exhibit temporally divergent metabolic demands and HIF1A-mediated stress responses. BT474R demonstrates inherently increased mitochondrial activity. HRG treatment in BT474R leads to a pronounced reduction in AR expression, affecting downstream lipid metabolism with implications for treatment response. Our results provide novel insights into mechanistic changes underlying ligand treatment in BT474 and BT474R and emphasize the pivotal role of endogenous ligands. These results can serve as a framework for furthering the understanding of trastuzumab resistance, with therapeutic implications for women with acquired resistance.

The mechanisms underlying the transformation of chronic myeloid leukemia (CML) from chronic phase (CP) to blast crisis (BC) are not fully elucidated. Here, we show lower levels of miR-142 in CD34 + CD38 - blasts from BC CML patients than in those from CP CML patients, suggesting that miR-142 deficit is implicated in BC evolution. Thus, we create miR-142 knockout CML (i.e., miR-142 -/- BCR-ABL) mice, which develop BC and die sooner than miR-142 wt CML (i.e., miR-142 +/+ BCR-ABL) mice, which instead remain in CP CML. Leukemic stem cells (LSCs) from miR-142 -/- BCR-ABL mice recapitulate the BC phenotype in congenic recipients, supporting LSC transformation by miR-142 deficit. State-transition and mutual information analyses of "bulk" and single cell RNA-seq data, metabolomic profiling and functional metabolic assays identify enhanced fatty acid β-oxidation, oxidative phosphorylation and mitochondrial fusion in LSCs as key steps in miR-142-driven BC evolution. A synthetic CpG-miR-142 mimic oligodeoxynucleotide rescues the BC phenotype in miR-142 -/- BCR-ABL mice and patient-derived xenografts., (© 2023. Springer Nature Limited.)

Cell fate commitment is driven by dynamic changes in chromatin architecture and activity of lineage-specific transcription factors (TFs). The chromatin assembly factor-1 (CAF-1) is a histone chaperone that regulates chromatin architecture by facilitating nucleosome assembly during DNA replication. Accumulating evidence supports a substantial role of CAF-1 in cell fate maintenance, but the mechanisms by which CAF-1 restricts lineage choice remain poorly understood. Here, we investigate how CAF-1 influences chromatin dynamics and TF activity during lineage differentiation. We show that CAF-1 suppression triggers rapid differentiation of myeloid stem and progenitor cells into a mixed lineage state. We find that CAF-1 sustains lineage fidelity by controlling chromatin accessibility at specific loci, and limiting the binding of ELF1 TF at newly-accessible diverging regulatory elements. Together, our findings decipher key traits of chromatin accessibility that sustain lineage integrity and point to a powerful strategy for dissecting transcriptional circuits central to cell fate commitment., (© 2022. The Author(s).)

The role of plasticity and epigenetics in shaping cancer evolution and response to therapy has taken center stage with recent technological advances including single cell sequencing. This roadmap article is focused on state-of-the-art mathematical and experimental approaches to interrogate plasticity in cancer, and addresses the following themes and questions: is there a formal overarching framework that encompasses both non-genetic plasticity and mutation-driven somatic evolution? How do we measure and model the role of the microenvironment in influencing/controlling non-genetic plasticity? How can we experimentally study non-genetic plasticity? Which mathematical techniques are required or best suited? What are the clinical and practical applications and implications of these concepts?, (Creative Commons Attribution license.)

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit.

Background: Inadvertent sample swaps are a real threat to data quality in any medium to large scale omics studies. While matches between samples from the same individual can in principle be identified from a few well characterized single nucleotide polymorphisms (SNPs), omics data types often only provide low to moderate coverage, thus requiring integration of evidence from a large number of SNPs to determine if two samples derive from the same individual or not., Methods: We select about six thousand SNPs in the human genome and develop a Bayesian framework that is able to robustly identify sample matches between next generation sequencing data sets., Results: We validate our approach on a variety of data sets. Most importantly, we show that our approach can establish identity between different omics data types such as Exome, RNA-Seq, and MethylCap-Seq. We demonstrate how identity detection degrades with sample quality and read coverage, but show that twenty million reads of a fairly low quality RNA-Seq sample are still sufficient for reliable sample identification., Conclusion: Our tool, SMASH, is able to identify sample mismatches in next generation sequencing data sets between different sequencing modalities and for low quality sequencing data.

RNA-Seq is the leading technology for analyzing gene expression on a global scale across a broad spectrum of sample types. However, due to chemical modifications by fixation or degradation due to collection methods, samples often contain an abundance of RNA that is no longer intact, and the capability of current RNA-Seq protocols to accurately quantify such samples is often limited. We have developed an RNA-Seq protocol to address these key issues as well as quantify gene expression from the whole transcriptome. Furthermore, for compatibility with improved sequencing platforms, we use restructured adapter sequences to generate libraries for Illumina HiSeq, MiSeq, and NextSeq platforms. Our protocol utilizes duplex-specific nuclease (DSN) to remove abundant ribosomal RNA sequences while retaining other types of RNA for superior transcriptome profiling from low quantity input. We employ the Illumina sequencing platform, but this method is described in sufficient detail to adapt to other platforms.

Receptor-interacting protein kinase-3 (RIP3 or RIPK3) is an essential part of the cellular machinery that executes "programmed" or "regulated" necrosis. Here we show that programmed necrosis is activated in response to many chemotherapeutic agents and contributes to chemotherapy-induced cell death. However, we show that RIP3 expression is often silenced in cancer cells due to genomic methylation near its transcriptional start site, thus RIP3-dependent activation of MLKL and downstream programmed necrosis during chemotherapeutic death is largely repressed. Nevertheless, treatment with hypomethylating agents restores RIP3 expression, and thereby promotes sensitivity to chemotherapeutics in a RIP3-dependent manner. RIP3 expression is reduced in tumors compared to normal tissue in 85% of breast cancer patients, suggesting that RIP3 deficiency is positively selected during tumor growth/development. Since hypomethylating agents are reasonably well-tolerated in patients, we propose that RIP3-deficient cancer patients may benefit from receiving hypomethylating agents to induce RIP3 expression prior to treatment with conventional chemotherapeutics.

Purpose: Molecular risk stratification of acute myeloid leukemia (AML) is largely based on genetic markers. However, epigenetic changes, including DNA methylation, deregulate gene expression and may also have prognostic impact. We evaluated the clinical relevance of integrating DNA methylation and genetic information in AML., Methods: Next-generation sequencing analysis of methylated DNA identified differentially methylated regions (DMRs) associated with prognostic mutations in older (≥ 60 years) cytogenetically normal (CN) patients with AML (n = 134). Genes with promoter DMRs and expression levels significantly associated with outcome were used to compute a prognostic gene expression weighted summary score that was tested and validated in four independent patient sets (n = 355)., Results: In the training set, we identified seven genes (CD34, RHOC, SCRN1, F2RL1, FAM92A1, MIR155HG, and VWA8) with promoter DMRs and expression associated with overall survival (OS; P ≤ .001). Each gene had high DMR methylation and lower expression, which were associated with better outcome. A weighted summary expression score of the seven gene expression levels was computed. A low score was associated with a higher complete remission (CR) rate and longer disease-free survival and OS (P < .001 for all end points). This was validated in multivariable models and in two younger (< 60 years) and two older independent sets of patients with CN-AML. Considering the seven genes individually, the fewer the genes with high expression, the better the outcome. Younger and older patients with no genes or one gene with high expression had the best outcomes (CR rate, 94% and 87%, respectively; 3-year OS, 80% and 42%, respectively)., Conclusion: A seven-gene score encompassing epigenetic and genetic prognostic information identifies novel AML subsets that are meaningful for treatment guidance.

The outcome of older (≥ 60 years) acute myeloid leukemia (AML) patients is poor, and novel treatments are needed. In a phase 2 trial for older AML patients, low-dose (20 mg/m(2) per day for 10 days) decitabine, a DNA hypomethylating azanucleoside, produced 47% complete response rate with an excellent toxicity profile. To assess the genome-wide activity of decitabine, we profiled pretreatment and post treatment (day 25/course 1) methylomes of marrow samples from patients (n = 16) participating in the trial using deep-sequencing analysis of methylated DNA captured by methyl-binding protein (MBD2). Decitabine significantly reduced global methylation compared with pretreatment baseline (P = .001). Percent marrow blasts did not correlate with global methylation levels, suggesting that hypomethylation was related to the activity of decitabine rather than to a mere decrease in leukemia burden. Hypomethylation occurred predominantly in CpG islands and CpG island-associated regions (P ranged from .03 to .04) A significant concentration (P < .001) of the hypomehtylated CpG islands was found in chromosome subtelomeric regions, suggesting a differential activity of decitabine in distinct chromosome regions. Hypermethylation occurred much less frequently than hypomethylation and was associated with low CpG content regions. Decitabine-related methylation changes were concordant with those previously reported in distinct genes. In summary, our study supports the feasibility of methylome analyses as a pharmacodynamic endpoint for hypomethylating therapies.

Background: DNA methylation is an important epigenetic mark and dysregulation of DNA methylation is associated with many diseases including cancer. Advances in next-generation sequencing now allow unbiased methylome profiling of entire patient cohorts, greatly facilitating biomarker discovery and presenting new opportunities to understand the biological mechanisms by which changes in methylation contribute to disease. Enrichment-based sequencing assays such as MethylCap-seq are a cost effective solution for genome-wide determination of methylation status, but the technical reliability of methylation reconstruction from raw sequencing data has not been well characterized., Methods: We analyze three MethylCap-seq data sets and perform two different analyses to assess data quality. First, we investigate how data quality is affected by excluding samples that do not meet quality control cutoff requirements. Second, we consider the effect of additional reads on enrichment score, saturation, and coverage. Lastly, we verify a method for the determination of the global amount of methylation from MethylCap-seq data by comparing to a spiked-in control DNA of known methylation status., Results: We show that rejection of samples based on our quality control parameters leads to a significant improvement of methylation calling. Additional reads beyond ~13 million unique aligned reads improved coverage, modestly improved saturation, and did not impact enrichment score. Lastly, we find that a global methylation indicator calculated from MethylCap-seq data correlates well with the global methylation level of a sample as obtained from a spike-in DNA of known methylation level., Conclusions: We show that with appropriate quality control MethylCap-seq is a reliable tool, suitable for cohorts of hundreds of patients, that provides reproducible methylation information on a feature by feature basis as well as information about the global level of methylation.

RNA editing describes the process in which individual or short stretches of nucleotides in a messenger or structural RNA are inserted, deleted, or substituted. A high level of RNA editing has been observed in the mitochondrial genome of Physarum polycephalum. The most frequent editing type in Physarum is the insertion of individual Cs. RNA editing is extremely accurate in Physarum; however, little is known about its mechanism. Here, we demonstrate how analyzing two organisms from the Myxomycetes, namely Physarum polycephalum and Didymium iridis, allows us to test hypotheses about the editing mechanism that can not be tested from a single organism alone. First, we show that using the recently determined full transcriptome information of Physarum dramatically improves the accuracy of computational editing site prediction in Didymium. We use this approach to predict genes in the mitochondrial genome of Didymium and identify six new edited genes as well as one new gene that appears unedited. Next we investigate sequence conservation in the vicinity of editing sites between the two organisms in order to identify sites that harbor the information for the location of editing sites based on increased conservation. Our results imply that the information contained within only nine or ten nucleotides on either side of the editing site (a distance previously suggested through experiments) is not enough to locate the editing sites. Finally, we show that the codon position bias in C insertional RNA editing of these two organisms is correlated with the selection pressure on the respective genes thereby directly testing an evolutionary theory on the origin of this codon bias. Beyond revealing interesting properties of insertional RNA editing in Myxomycetes, our work suggests possible approaches to be used when finding sequence motifs for any biological process fails.

Advances in whole genome profiling have revolutionized the cancer research field, but at the same time have raised new bioinformatics challenges. For next generation sequencing (NGS), these include data storage, computational costs, sequence processing and alignment, delineating appropriate statistical measures, and data visualization. The NGS application MethylCap-seq involves the in vitro capture of methylated DNA and subsequent analysis of enriched fragments by massively parallel sequencing. Here, we present a scalable, flexible workflow for MethylCap-seq Quality Control, secondary data analysis, tertiary analysis of multiple experimental groups, and data visualization. This workflow and its suite of features will assist biologists in conducting methylation profiling projects and facilitate meaningful biological interpretation.