Targeting circadian transcriptional programs in triple negative breast cancer through a cis-regulatory mechanism.

Circadian clock genes are emerging targets in many types of cancer, but their mechanistic contributions to tumor progression are still largely unknown. This makes it challenging to stratify patient populations and develop corresponding treatments. In this work, we show that in breast cancer, the disrupted expression of circadian genes has the potential to serve as biomarkers. We also show that the master circadian transcription factors (TFs) BMAL1 and CLOCK are required for the proliferation of metastatic mesenchymal stem-like (mMSL) triple-negative breast cancer (TNBC) cells. Using currently available small molecule modulators, we found that a stabilizer of cryptochrome 2 (CRY2), the direct repressor of BMAL1 and CLOCK transcriptional activity, synergizes with inhibitors of proteasome, which is required for BMAL1 and CLOCK function, to repress a transcriptional program comprising circadian cycling genes in mMSL TNBC cells. Omics analyses on drug-treated cells implied that this repression of transcription is mediated by the transcription factor binding sites (TFBSs) features in the cis-regulatory elements (CRE) of clock-controlled genes. Through a massive parallel reporter assay, we defined a set of CRE features that are potentially repressed by the specific drug combination. The identification of cis -element enrichment may serve as a new way of defining and targeting tumor types through the modulation of cis -regulatory programs, and ultimately provide a new paradigm of therapy design for cancer types with unclear drivers like TNBC.

Functional characterization of age-dependent p16 epimutation reveals biological drivers and therapeutic targets for colorectal cancer.

BACKGROUND: Methylation of the p16 promoter resulting in epigenetic gene silencing-known as p16 epimutation-is frequently found in human colorectal cancer and is also common in normal-appearing colonic mucosa of aging individuals. Thus, to improve clinical care of colorectal cancer (CRC) patients, we explored the role of age-related p16 epimutation in intestinal tumorigenesis. METHODS: We established a mouse model that replicates two common genetic and epigenetic events observed in human CRCs: Apc mutation and p16 epimutation. We conducted long-term survival and histological analysis of tumor development and progression. Colonic epithelial cells and tumors were collected from mice and analyzed by RNA sequencing (RNA-seq), quantitative PCR, and flow cytometry. We performed single-cell RNA sequencing (scRNA-seq) to characterize tumor-infiltrating immune cells throughout tumor progression. We tested whether anti-PD-L1 immunotherapy affects overall survival of tumor-bearing mice and whether inhibition of both epigenetic regulation and immune checkpoint is more efficacious. RESULTS: Mice carrying combined Apc mutation and p16 epimutation had significantly shortened survival and increased tumor growth compared to those with Apc mutation only. Intriguingly, colon tumors with p16 epimutation exhibited an activated interferon pathway, increased expression of programmed death-ligand 1 (Pdl1), and enhanced infiltration of immune cells. scRNA-seq further revealed the presence of Foxp3+ Tregs and γδT17 cells, which contribute to an immunosuppressive tumor microenvironment (TME). Furthermore, we showed that a combined therapy using an inhibitor of DNA methylation and a PD-L1 immune checkpoint inhibitor is more effective for improving survival in tumor-bearing mice than blockade of either pathway alone. CONCLUSIONS: Our study demonstrated that age-dependent p16 epimutation creates a permissive microenvironment for malignant transformation of polyps to colon cancer. Our findings provide a mechanistic rationale for future targeted therapy in patients with p16 epimutation.

METTL3-Mediated m6A Modification Controls Splicing Factor Abundance and Contributes to Aggressive CLL.

RNA splicing dysregulation underlies the onset and progression of cancers. In chronic lymphocytic leukemia (CLL), spliceosome mutations leading to aberrant splicing occur in ∼20% of patients. However, the mechanism for splicing defects in spliceosome-unmutated CLL cases remains elusive. Through an integrative transcriptomic and proteomic analysis, we discover that proteins involved in RNA splicing are posttranscriptionally upregulated in CLL cells, resulting in splicing dysregulation. The abundance of splicing complexes is an independent risk factor for poor prognosis. Moreover, increased splicing factor expression is highly correlated with the abundance of METTL3, an RNA methyltransferase that deposits N6-methyladenosine (m6A) on mRNA. METTL3 is essential for cell growth in vitro and in vivo and controls splicing factor protein expression in a methyltransferase-dependent manner through m6A modification-mediated ribosome recycling and decoding. Our results uncover METTL3-mediated m6A modification as a novel regulatory axis in driving splicing dysregulation and contributing to aggressive CLL. SIGNIFICANCE: METTL3 controls widespread splicing factor abundance via translational control of m6A-modified mRNA, contributes to RNA splicing dysregulation and disease progression in CLL, and serves as a potential therapeutic target in aggressive CLL. See related commentary by Janin and Esteller, p. 176. This article is highlighted in the In This Issue feature, p. 171.

Clipper: p-value-free FDR control on high-throughput data from two conditions.

High-throughput biological data analysis commonly involves identifying features such as genes, genomic regions, and proteins, whose values differ between two conditions, from numerous features measured simultaneously. The most widely used criterion to ensure the analysis reliability is the false discovery rate (FDR), which is primarily controlled based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions. Clipper is a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper outperforms existing methods for a broad range of applications in high-throughput data analysis.

The concurrence of DNA methylation and demethylation is associated with transcription regulation.

The mammalian DNA methylome is formed by two antagonizing processes, methylation by DNA methyltransferases (DNMT) and demethylation by ten-eleven translocation (TET) dioxygenases. Although the dynamics of either methylation or demethylation have been intensively studied in the past decade, the direct effects of their interaction on gene expression remain elusive. Here, we quantify the concurrence of DNA methylation and demethylation by the percentage of unmethylated CpGs within a partially methylated read from bisulfite sequencing. After verifying 'methylation concurrence' by its strong association with the co-localization of DNMT and TET enzymes, we observe that methylation concurrence is strongly correlated with gene expression. Notably, elevated methylation concurrence in tumors is associated with the repression of 40~60% of tumor suppressor genes, which cannot be explained by promoter hypermethylation alone. Furthermore, methylation concurrence can be used to stratify large undermethylated regions with negligible differences in average methylation into two subgroups with distinct chromatin accessibility and gene regulation patterns. Together, methylation concurrence represents a unique methylation metric important for transcription regulation and is distinct from conventional metrics, such as average methylation and methylation variation.

mbImpute: an accurate and robust imputation method for microbiome data.

A critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiome data-mbImpute-to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. We demonstrate that mbImpute improves the power of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer, and mbImpute preserves non-zero distributions of taxa abundances.

Identifying the combinatorial control of signal-dependent transcription factors.

The effectiveness of immune responses depends on the precision of stimulus-responsive gene expression programs. Cells specify which genes to express by activating stimulus-specific combinations of stimulus-induced transcription factors (TFs). Their activities are decoded by a gene regulatory strategy (GRS) associated with each response gene. Here, we examined whether the GRSs of target genes may be inferred from stimulus-response (input-output) datasets, which remains an unresolved model-identifiability challenge. We developed a mechanistic modeling framework and computational workflow to determine the identifiability of all possible combinations of synergistic (AND) or non-synergistic (OR) GRSs involving three transcription factors. Considering different sets of perturbations for stimulus-response studies, we found that two thirds of GRSs are easily distinguishable but that substantially more quantitative data is required to distinguish the remaining third. To enhance the accuracy of the inference with timecourse experimental data, we developed an advanced error model that avoids error overestimates by distinguishing between value and temporal error. Incorporating this error model into a Bayesian framework, we show that GRS models can be identified for individual genes by considering multiple datasets. Our analysis rationalizes the allocation of experimental resources by identifying most informative TF stimulation conditions. Applying this computational workflow to experimental data of immune response genes in macrophages, we found that a much greater fraction of genes are combinatorially controlled than previously reported by considering compensation among transcription factors. Specifically, we revealed that a group of known NFκB target genes may also be regulated by IRF3, which is supported by chromatin immuno-precipitation analysis. Our study provides a computational workflow for designing and interpreting stimulus-response gene expression studies to identify underlying gene regulatory strategies and further a mechanistic understanding.

Postoperative Admission of Adult Craniotomy Patients to the Neuroscience Ward Reduces Length of Stay and Cost.

BACKGROUND: The neurointensive care unit (NICU) has traditionally been the default recovery unit after elective craniotomies. OBJECTIVE: To assess whether admitting adult patients without significant comorbidities to the neuroscience ward (NW) instead of NICU for recovery resulted in similar clinical outcome while reducing length of stay (LOS) and hospitalization cost. METHODS: We retrospectively analyzed the clinical and cost data of adult patients undergoing supratentorial craniotomy at a university hospital within a 5-yr period who had a LOS less than 7 d. We compared those admitted to the NICU for 1 night of recovery versus those directly admitted to the NW. RESULTS: The NICU and NW groups included 340 and 209 patients, respectively, and were comparable in terms of age, ethnicity, overall health, and expected LOS. NW admissions had shorter LOS (3.046 vs 3.586 d, P < .001), and independently predicted shorter LOS in multivariate analysis. While the NICU group had longer surgeries (6.8 vs 6.4 h), there was no statistically significant difference in the cost of surgery. The NW group was associated with reduced hospitalization cost by $3193 per admission on average (P < .001). Clinically, there were no statistically significant differences in the rate of return to Operating Room, Emergency Department readmission, or hospital readmission within 30 d. CONCLUSION: Admitting adult craniotomy patients without significant comorbidities, who are expected to have short LOS, to NW was associated with reduced LOS and total cost of admission, without significant differences in postoperative clinical outcome.

A flexible model-free prediction-based framework for feature ranking.

Despite the availability of numerous statistical and machine learning tools for joint feature modeling, many scientists investigate features marginally, i.e., one feature at a time. This is partly due to training and convention but also roots in scientists' strong interests in simple visualization and interpretability. As such, marginal feature ranking for some predictive tasks, e.g., prediction of cancer driver genes, is widely practiced in the process of scientific discoveries. In this work, we focus on marginal ranking for binary classification, one of the most common predictive tasks. We argue that the most widely used marginal ranking criteria, including the Pearson correlation, the two-sample t test, and two-sample Wilcoxon rank-sum test, do not fully take feature distributions and prediction objectives into account. To address this gap in practice, we propose two ranking criteria corresponding to two prediction objectives: the classical criterion (CC) and the Neyman-Pearson criterion (NPC), both of which use model-free nonparametric implementations to accommodate diverse feature distributions. Theoretically, we show that under regularity conditions, both criteria achieve sample-level ranking that is consistent with their population-level counterpart with high probability. Moreover, NPC is robust to sampling bias when the two class proportions in a sample deviate from those in the population. This property endows NPC good potential in biomedical research where sampling biases are ubiquitous. We demonstrate the use and relative advantages of CC and NPC in simulation and real data studies. Our model-free objective-based ranking idea is extendable to ranking feature subsets and generalizable to other prediction tasks and learning objectives.

DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features.

Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes.

Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines.

Making binary decisions is a common data analytical task in scientific research and industrial applications. In data sciences, there are two related but distinct strategies: hypothesis testing and binary classification. In practice, how to choose between these two strategies can be unclear and rather confusing. Here, we summarize key distinctions between these two strategies in three aspects and list five practical guidelines for data analysts to choose the appropriate strategy for specific analysis needs. We demonstrate the use of those guidelines in a cancer driver gene prediction example.

Integrin-Src-YAP1 signaling mediates the melanoma acquired resistance to MAPK and PI3K/mTOR dual targeted therapy.

Activation of PI3K/AKT pathway is one of the most recurrent resistant mechanisms for BRAF-targeted therapy, and the combination of MAPK and PI3K/AKT inhibitors becomes one of the most promising regimens for BRAF-targeted relapsed melanoma patients. Although the potent drug efficacy was observed in preclinical experiments and early clinical trials, the dual-drug resistance is inevitable observed. In this study, we systematically explored the mechanisms of dual-drug resistance to MAPKi and PI3K/mTORi in melanoma. With transcriptomic dissection of dual-drug resistant models, we identified that the drug tolerance was mediated by ECM-integrins α3ß1 and α11ß1 signaling. Upon binding ECM, the integrins activated downstream kinase Src rather than FAK, WNT, or TGFß. Knockdown of integrins α3, α11, and ß1 significantly inhibited the proliferation of dual-drug resistant sublines while with trivial effects on parental cells. Although Src inhibition suppressed the phosphorylation of AKT, c-JUN, and p38, none of inhibitors targeting these kinases reversed the dual-drug resistance in model cells. Notably, Src inhibitor promoted the phosphorylations of LATS1 and YAP1, subsequently, re-localized YAP1 from nucleus to cytosol facilitating further degradation. Both small molecule inhibitors and shRNAs targeting YAP1 or Src overcame the MAPKi and PI3K/mTORi dual-drug resistance. In conclusion, our data not only illuminated an integrin-Src-YAP1 pathway mediated MAPKi and PI3K/mTORi dual-drug resistant mechanism but also provided a potential combinatorial regimen for the drug-relapsed melanoma patients.

Spliceosome Profiling Visualizes Operations of a Dynamic RNP at Nucleotide Resolution.

Tools to understand how the spliceosome functions in vivo have lagged behind advances in the structural biology of the spliceosome. Here, methods are described to globally profile spliceosome-bound pre-mRNA, intermediates, and spliced mRNA at nucleotide resolution. These tools are applied to three yeast species that span 600 million years of evolution. The sensitivity of the approach enables the detection of canonical and non-canonical events, including interrupted, recursive, and nested splicing. This application of statistical modeling uncovers independent roles for the size and position of the intron and the number of introns per transcript in substrate progression through the two catalytic stages. These include species-specific inputs suggestive of spliceosome-transcriptome coevolution. Further investigations reveal the ATP-dependent discard of numerous endogenous substrates after spliceosome assembly in vivo and connect this discard to intron retention, a form of splicing regulation. Spliceosome profiling is a quantitative, generalizable global technology used to investigate an RNP central to eukaryotic gene expression.