A case study: Assessing the efficacy of the revised dosage regimen via prediction model for recurrent event rate using biomarker data.

In recently conducted phase III trials in a rare disease area, patients received monthly treatment at a high dose of the drug, which targets to lower a specific biomarker level, closely associated with the efficacy endpoint, to around 10% across patients. Although this high dose demonstrated strong efficacy, treatments were withheld due to the reports of serious adverse events. Dosing in these studies were later resumed at a reduced dosage which targets to lower the biomarker level to 15%-35% across patients. Two questions arose after this disruption. The first is whether the efficacy of this revised regimen as measured by the reduction in annualized event rate is adequate to support the continuation of the development and the second is whether the potential bias due to the loss of patients during this dosing gap process can be gauged. To address these questions, we built a prediction model that quantitatively characterizes biomarker vs. endpoint relationship and predicts efficacy at the 15%-35% range of the biomarker level using the available data from the original high dose. This model predicts favorable event rate in the target biomarker level and shows that the bias due to the loss of patients is limited. These results support the continued development of the revised regimen, however, given the limitation of the data available, this prediction is planned to be validated further when data under the revised regimen become available.

BiFET: sequencing Bias-free transcription factor Footprint Enrichment Test.

Transcription factor (TF) footprinting uncovers putative protein-DNA binding via combined analyses of chromatin accessibility patterns and their underlying TF sequence motifs. TF footprints are frequently used to identify TFs that regulate activities of cell/condition-specific genomic regions (target loci) in comparison to control regions (background loci) using standard enrichment tests. However, there is a strong association between the chromatin accessibility level and the GC content of a locus and the number and types of TF footprints that can be detected at this site. Traditional enrichment tests (e.g. hypergeometric) do not account for this bias and inflate false positive associations. Therefore, we developed a novel post-processing method, Bias-free Footprint Enrichment Test (BiFET), that corrects for the biases arising from the differences in chromatin accessibility levels and GC contents between target and background loci in footprint enrichment analyses. We applied BiFET on TF footprint calls obtained from EndoC-ßH1 ATAC-seq samples using three different algorithms (CENTIPEDE, HINT-BC and PIQ) and showed BiFET's ability to increase power and reduce false positive rate when compared to hypergeometric test. Furthermore, we used BiFET to study TF footprints from human PBMC and pancreatic islet ATAC-seq samples to show its utility to identify putative TFs associated with cell-type-specific loci.

Association Between a Single General Anesthesia Exposure Before Age 36 Months and Neurocognitive Outcomes in Later Childhood.

IMPORTANCE: Exposure of young animals to commonly used anesthetics causes neurotoxicity including impaired neurocognitive function and abnormal behavior. The potential neurocognitive and behavioral effects of anesthesia exposure in young children are thus important to understand. OBJECTIVE: To examine if a single anesthesia exposure in otherwise healthy young children was associated with impaired neurocognitive development and abnormal behavior in later childhood. DESIGN, SETTING, AND PARTICIPANTS: Sibling-matched cohort study conducted between May 2009 and April 2015 at 4 university-based US pediatric tertiary care hospitals. The study cohort included sibling pairs within 36 months in age and currently 8 to 15 years old. The exposed siblings were healthy at surgery/anesthesia. Neurocognitive and behavior outcomes were prospectively assessed with retrospectively documented anesthesia exposure data. EXPOSURES: A single exposure to general anesthesia during inguinal hernia surgery in the exposed sibling and no anesthesia exposure in the unexposed sibling, before age 36 months. MAIN OUTCOMES AND MEASURES: The primary outcome was global cognitive function (IQ). Secondary outcomes included domain-specific neurocognitive functions and behavior. A detailed neuropsychological battery assessed IQ and domain-specific neurocognitive functions. Parents completed validated, standardized reports of behavior. RESULTS: Among the 105 sibling pairs, the exposed siblings (mean age, 17.3 months at surgery/anesthesia; 9.5% female) and the unexposed siblings (44% female) had IQ testing at mean ages of 10.6 and 10.9 years, respectively. All exposed children received inhaled anesthetic agents, and anesthesia duration ranged from 20 to 240 minutes, with a median duration of 80 minutes. Mean IQ scores between exposed siblings (scores: full scale = 111; performance = 108; verbal = 111) and unexposed siblings (scores: full scale = 111; performance = 107; verbal = 111) were not statistically significantly different. Differences in mean IQ scores between sibling pairs were: full scale = -0.2 (95% CI, -2.6 to 2.9); performance = 0.5 (95% CI, -2.7 to 3.7); and verbal = -0.5 (95% CI, -3.2 to 2.2). No statistically significant differences in mean scores were found between sibling pairs in memory/learning, motor/processing speed, visuospatial function, attention, executive function, language, or behavior. CONCLUSIONS AND RELEVANCE: Among healthy children with a single anesthesia exposure before age 36 months, compared with healthy siblings with no anesthesia exposure, there were no statistically significant differences in IQ scores in later childhood. Further study of repeated exposure, prolonged exposure, and vulnerable subgroups is needed.

Using passenger mutations to estimate the timing of driver mutations and identify mutator alterations.

BACKGROUND: Recent developments in high-throughput genomic technologies make it possible to have a comprehensive view of genomic alterations in tumors on a whole genome scale. Only a small number of somatic alterations detected in tumor genomes are driver alterations which drive tumorigenesis. Most of the somatic alterations are passengers that are neutral to tumor cell selection. Although most research efforts are focused on analyzing driver alterations, the passenger alterations also provide valuable information about the history of tumor development. RESULTS: In this paper, we develop a method for estimating the age of the tumor lineage and the timing of the driver alterations based on the number of passenger alterations. This method also identifies mutator genes which increase genomic instability when they are altered and provides estimates of the increased rate of alterations caused by each mutator gene. We applied this method to copy number data and DNA sequencing data for ovarian and lung tumors. We identified well known mutators such as TP53, PRKDC, BRCA1/2 as well as new mutator candidates PPP2R2A and the chromosomal region 22q13.33. We found that most mutator genes alter early during tumorigenesis and were able to estimate the age of individual tumor lineage in cell generations. CONCLUSIONS: This is the first computational method to identify mutator genes and to take into account the increase of the alteration rate by mutator genes, providing more accurate estimates of the tumor age and the timing of driver alterations.

Estimating the order of mutations during tumorigenesis from tumor genome sequencing data.

MOTIVATION: Tumors are thought to develop and evolve through a sequence of genetic and epigenetic somatic alterations to progenitor cells. Early stages of human tumorigenesis are hidden from view. Here, we develop a method for inferring some aspects of the order of mutational events during tumorigenesis based on genome sequencing data for a set of tumors. This method does not assume that the sequence of driver alterations is the same for each tumor, but enables the degree of similarity or difference in the sequence to be evaluated. RESULTS: To evaluate the new method, we applied it to colon cancer tumor sequencing data and the results are consistent with the multi-step tumorigenesis model previously developed based on comparing stages of cancer. We then applied the new method to DNA sequencing data for a set of lung cancers. The model may be a useful tool for better understanding the process of tumorigenesis. AVAILABILITY: The software is available at: http://linus.nci.nih.gov/Data/YounA/OrderMutation.zip.

Transcriptional activation of Jun and Fos members of the AP-1 complex is a conserved signature of immune aging that contributes to inflammaging.

Diverse mouse strains have different health and life spans, mimicking the diversity among humans. To capture conserved aging signatures, we studied long-lived C57BL/6J and short-lived NZO/HILtJ mouse strains by profiling transcriptomes and epigenomes of immune cells from peripheral blood and the spleen from young and old mice. Transcriptional activation of the AP-1 transcription factor complex, particularly Fos, Junb, and Jun genes, was the most significant and conserved aging signature across tissues and strains. ATAC-seq data analyses showed that the chromatin around these genes was more accessible with age and there were significantly more binding sites for these TFs with age across all studied tissues, targeting pro-inflammatory molecules including Il6. Age-related increases in binding sites of JUN and FOS factors were also conserved in human peripheral blood ATAC-seq data. Single-cell RNA-seq data from the mouse aging cell atlas Tabula Muris Senis showed that the expression of these genes increased with age in B, T, NK cells, and macrophages, with macrophages from old mice expressing these molecules more abundantly than other cells. Functional data showed that upon myeloid cell activation via poly(I:C), the levels of JUN protein and its binding activity increased more significantly in spleen cells from old compared to young mice. In addition, upon activation, old cells produced more IL6 compared to young cells. In sum, we showed that the aging-related transcriptional activation of Jun and Fos family members in AP-1 complex is conserved across immune tissues and long- and short-living mouse strains, possibly contributing to increased inflammation with age.

Identifying cancer driver genes in tumor genome sequencing studies.

MOTIVATION: Major tumor sequencing projects have been conducted in the past few years to identify genes that contain 'driver' somatic mutations in tumor samples. These genes have been defined as those for which the non-silent mutation rate is significantly greater than a background mutation rate estimated from silent mutations. Several methods have been used for estimating the background mutation rate. RESULTS: We propose a new method for identifying cancer driver genes, which we believe provides improved accuracy. The new method accounts for the functional impact of mutations on proteins, variation in background mutation rate among tumors and the redundancy of the genetic code. We reanalyzed sequence data for 623 candidate genes in 188 non-small cell lung tumors using the new method. We found several important genes like PTEN, which were not deemed significant by the previous method. At the same time, we determined that some genes previously reported as drivers were not significant by the new analysis because mutations in these genes occurred mainly in tumors with large background mutation rates. AVAILABILITY: The software is available at: http://linus.nci.nih.gov/Data/YounA/software.zip.

Learning transcriptional networks from the integration of ChIP-chip and expression data in a non-parametric model.

RESULTS: We have developed LeTICE (Learning Transcriptional networks from the Integration of ChIP-chip and Expression data), an algorithm for learning a transcriptional network from ChIP-chip and expression data. The network is specified by a binary matrix of transcription factor (TF)-gene interactions partitioning genes into modules and a background of genes that are not involved in the transcriptional regulation. We define a likelihood of a network, and then search for the network optimizing the likelihood. We applied LeTICE to the location and expression data from yeast cells grown in rich media to learn the transcriptional network specific to the yeast cell cycle. It found 12 condition-specific TFs and 15 modules each of which is highly represented with functions related to particular phases of cell-cycle regulation. AVAILABILITY: Our algorithm is available at http://linus.nci.nih.gov/Data/YounA/LeTICE.zip

The MiAge Calculator: a DNA methylation-based mitotic age calculator of human tissue types.

Cell division is important in human aging and cancer. The estimation of the number of cell divisions (mitotic age) of a given tissue type in individuals is of great interest as it allows not only the study of biological aging (using a new molecular aging target) but also the stratification of prospective cancer risk. Here, we introduce the MiAge Calculator, a mitotic age calculator based on a novel statistical framework, the MiAge model. MiAge is designed to quantitatively estimate mitotic age (total number of lifetime cell divisions) of a tissue using the stochastic replication errors accumulated in the epigenetic inheritance process during cell divisions. With the MiAge model, the MiAge Calculator was built using the training data of DNA methylation measures of 4,020 tumor and adjacent normal tissue samples from eight TCGA cancer types and was tested using the testing data of DNA methylation measures of 2,221 tumor and adjacent normal tissue samples of five other TCGA cancer types. We showed that within each of the thirteen cancer types studied, the estimated mitotic age is universally accelerated in tumor tissues compared to adjacent normal tissues. Across the thirteen cancer types, we showed that worse cancer survivals are associated with more accelerated mitotic age in tumor tissues. Importantly, we demonstrated the utility of mitotic age by showing that the integration of mitotic age and clinical information leads to improved survival prediction in six out of the thirteen cancer types studied. The MiAge Calculator is available at http://www.columbia.edu/∼sw2206/softwares.htm .

A pan-cancer analysis of driver gene mutations, DNA methylation and gene expressions reveals that chromatin remodeling is a major mechanism inducing global changes in cancer epigenomes.

BACKGROUND: Recent large-scale cancer sequencing studies have discovered many novel cancer driver genes (CDGs) in human cancers. Some studies also suggest that CDG mutations contribute to cancer-associated epigenomic and transcriptomic alterations across many cancer types. Here we aim to improve our understanding of the connections between CDG mutations and altered cancer cell epigenomes and transcriptomes on pan-cancer level and how these connections contribute to the known association between epigenome and transcriptome. METHOD: Using multi-omics data including somatic mutation, DNA methylation, and gene expression data of 20 cancer types from The Cancer Genome Atlas (TCGA) project, we conducted a pan-cancer analysis to identify CDGs, when mutated, have strong associations with genome-wide methylation or expression changes across cancer types, which we refer as methylation driver genes (MDGs) or expression driver genes (EDGs), respectively. RESULTS: We identified 32 MDGs, among which, eight are known chromatin modification or remodeling genes. Many of the remaining 24 MDGs are connected to chromatin regulators through either regulating their transcription or physically interacting with them as potential co-factors. We identified 29 EDGs, 26 of which are also MDGs. Further investigation on target genes' promoters methylation and expression alteration patterns of these 26 overlapping driver genes shows that hyper-methylation of target genes' promoters are significantly associated with down-regulation of the same target genes and hypo-methylation of target genes' promoters are significantly associated with up-regulation of the same target genes. CONCLUSION: This finding suggests a pivotal role for genetically driven changes in chromatin remodeling in shaping DNA methylation and gene expression patterns during tumor development.

Type 2 Diabetes-Associated Genetic Variants Regulate Chromatin Accessibility in Human Islets.

Type 2 diabetes (T2D) is a complex disorder in which both genetic and environmental risk factors contribute to islet dysfunction and failure. Genome-wide association studies (GWAS) have linked single nucleotide polymorphisms (SNPs), most of which are noncoding, in >200 loci to islet dysfunction and T2D. Identification of the putative causal variants and their target genes and whether they lead to gain or loss of function remains challenging. Here, we profiled chromatin accessibility in pancreatic islet samples from 19 genotyped individuals and identified 2,949 SNPs associated with in vivo cis-regulatory element use (i.e., chromatin accessibility quantitative trait loci [caQTL]). Among the caQTLs tested (n = 13) using luciferase reporter assays in MIN6 ß-cells, more than half exhibited effects on enhancer activity that were consistent with in vivo chromatin accessibility changes. Importantly, islet caQTL analysis nominated putative causal SNPs in 13 T2D-associated GWAS loci, linking 7 and 6 T2D risk alleles, respectively, to gain or loss of in vivo chromatin accessibility. By investigating the effect of genetic variants on chromatin accessibility in islets, this study is an important step forward in translating T2D-associated GWAS SNP into functional molecular consequences.

Alpha TC1 and Beta-TC-6 genomic profiling uncovers both shared and distinct transcriptional regulatory features with their primary islet counterparts.

Alpha TC1 (αTC1) and Beta-TC-6 (ßTC6) mouse islet cell lines are cellular models of islet (dys)function and type 2 diabetes (T2D). However, genomic characteristics of these cells, and their similarities to primary islet alpha and beta cells, are undefined. Here, we report the epigenomic (ATAC-seq) and transcriptomic (RNA-seq) landscapes of αTC1 and ßTC6 cells. Each cell type exhibits hallmarks of its primary islet cell counterpart including cell-specific expression of beta (e.g., Pdx1) and alpha (e.g., Arx) cell transcription factors (TFs), and enrichment of binding motifs for these TFs in αTC1/ßTC6 cis-regulatory elements. αTC1/ßTC6 transcriptomes overlap significantly with the transcriptomes of primary mouse/human alpha and beta cells. Our data further indicate that ATAC-seq detects cell-specific regulatory elements for cell types comprising ≥ 20% of a mixed cell population. We identified αTC1/ßTC6 cis-regulatory elements orthologous to those containing type 2 diabetes (T2D)-associated SNPs in human islets for 33 loci, suggesting these cells' utility to dissect T2D molecular genetics in these regions. Together, these maps provide important insights into the conserved regulatory architecture between αTC1/ßTC6 and primary islet cells that can be leveraged in functional (epi)genomic approaches to dissect the genetic and molecular factors controlling islet cell identity and function.

Randomized Trial of Oral Cyclophosphamide and Veliparib in High-Grade Serous Ovarian, Primary Peritoneal, or Fallopian Tube Cancers, or BRCA-Mutant Ovarian Cancer.

PURPOSE: Veliparib, a PARP inhibitor, demonstrated clinical activity in combination with oral cyclophosphamide in patients with BRCA-mutant solid tumors in a phase I trial. To define the relative contribution of PARP inhibition to the observed clinical activity, we conducted a randomized phase II trial to determine the response rate of veliparib in combination with cyclophosphamide compared with cyclophosphamide alone in patients with pretreated BRCA-mutant ovarian cancer or in patients with pretreated primary peritoneal, fallopian tube, or high-grade serous ovarian cancers (HGSOC). EXPERIMENTAL DESIGN: Adult patients were randomized to receive cyclophosphamide alone (50 mg orally once daily) or with veliparib (60 mg orally once daily) in 21-day cycles. Crossover to the combination was allowed at disease progression. RESULTS: Seventy-five patients were enrolled and 72 were evaluable for response; 38 received cyclophosphamide alone and 37 the combination as their initial treatment regimen. Treatment was well tolerated. One complete response was observed in each arm, with three partial responses (PR) in the combination arm and six PRs in the cyclophosphamide alone arm. Genetic sequence and expression analyses were performed for 211 genes involved in DNA repair; none of the detected genetic alterations were significantly associated with treatment benefit. CONCLUSION: This is the first trial that evaluated single-agent, low-dose cyclophosphamide in HGSOC, peritoneal, fallopian tube, and BRCA-mutant ovarian cancers. It was well tolerated and clinical activity was observed; the addition of veliparib at 60 mg daily did not improve either the response rate or the median progression-free survival.