A system of reporters for comparative investigation of EJC-independent and EJC-enhanced nonsense-mediated mRNA decay

Nonsense-mediated mRNA decay (NMD) is a network of pathways that degrades transcripts that undergo premature translation termination. In mammals, NMD can be divided into the exon junction complex (EJC)-enhanced and EJC-independent branches. Fluorescence- and luminescence-based reporters have long been effective tools to investigate NMD, yet existing reporters largely focus on the EJC-enhanced pathway. Here, we present a system of reporters for comparative studies of EJC-independent and EJC-enhanced NMD. This system also enables the study of NMD-associated outcomes such as premature termination codon (PTC) readthrough and truncated protein degradation. These reporters are compatible with fluorescence or luminescence-based readouts via transient transfection or stable integration. Using this reporter system, we show that EJC-enhanced NMD RNA levels are reduced by 2- or 9-fold and protein levels are reduced by 7- or 12-fold compared to EJC-independent NMD, depending on the reporter gene used. Additionally, the extent of readthrough induced by G418 and an NMD inhibitor (SMG1i), alone and in combination, varies across NMD substrates. When combined, G418 and SMG1i increase readthrough product levels in an additive manner for EJC-independent reporters, while EJC-enhanced reporters show a synergistic effect. We present these reporters as a valuable toolkit to deepen our understanding of NMD and its associated mechanisms.

Tumor and immune cell types interact to produce heterogeneous phenotypes of pediatric high-grade glioma

BACKGROUND

Pediatric high-grade gliomas (PHGG) are aggressive brain tumors with 5-year survival rates ranging from <2% to 20% depending upon subtype. PHGG presents differently from patient to patient and is intratumorally heterogeneous, posing challenges in designing therapies. We hypothesized that heterogeneity occurs because PHGG comprises multiple distinct tumor and immune cell types in varying proportions, each of which may influence tumor characteristics.

METHODS

We obtained 19 PHGG samples from our institution's pediatric brain tumor bank. We constructed a comprehensive transcriptomic dataset at the single-cell level using single-cell RNA-Seq (scRNA-Seq), identified known glial and immune cell types, and performed differential gene expression and gene set enrichment analysis. We conducted multi-channel immunofluorescence (IF) staining to confirm the transcriptomic results.

RESULTS

Our PHGG samples included 3 principal predicted tumor cell types: astrocytes, oligodendrocyte progenitors (OPCs), and mesenchymal-like cells (Mes). These cell types differed in their gene expression profiles, pathway enrichment, and mesenchymal character. We identified a macrophage population enriched in mesenchymal and inflammatory gene expression as a possible source of mesenchymal tumor characteristics. We found evidence of T-cell exhaustion and suppression.

CONCLUSIONS

PHGG comprises multiple distinct proliferating tumor cell types. Microglia-derived macrophages may drive mesenchymal gene expression in PHGG. The predicted Mes tumor cell population likely derives from OPCs. The variable tumor cell populations rely on different oncogenic pathways and are thus likely to vary in their responses to therapy.

Thyroid Cancer Polygenic Risk Score Improves Classification of Thyroid Nodules as Benign or Malignant

CONTEXT

Thyroid nodule ultrasound-based risk stratification schemas rely on the presence of high-risk sonographic features. However, some malignant thyroid nodules have benign appearance on thyroid ultrasound. New methods for thyroid nodule risk assessment are needed.

OBJECTIVE

We investigated polygenic risk score (PRS) accounting for inherited thyroid cancer risk combined with ultrasound-based analysis for improved thyroid nodule risk assessment.

METHODS

The convolutional neural network classifier was trained on thyroid ultrasound still images and cine clips from 621 thyroid nodules. Phenome-wide association study (PheWAS) and PRS PheWAS were used to optimize PRS for distinguishing benign and malignant nodules. PRS was evaluated in 73 346 participants in the Colorado Center for Personalized Medicine Biobank.

RESULTS

When the deep learning model output was combined with thyroid cancer PRS and genetic ancestry estimates, the area under the receiver operating characteristic curve (AUROC) of the benign vs malignant thyroid nodule classifier increased from 0.83 to 0.89 (DeLong, P value = .007). The combined deep learning and genetic classifier achieved a clinically relevant sensitivity of 0.95, 95% CI [0.88-0.99], specificity of 0.63 [0.55-0.70], and positive and negative predictive values of 0.47 [0.41-0.58] and 0.97 [0.92-0.99], respectively. AUROC improvement was consistent in European ancestry-stratified analysis (0.83 and 0.87 for deep learning and deep learning combined with PRS classifiers, respectively). Elevated PRS was associated with a greater risk of thyroid cancer structural disease recurrence (ordinal logistic regression, P value = .002).

CONCLUSION

Augmenting ultrasound-based risk assessment with PRS improves diagnostic accuracy.

Monitoring environmental microbiomes: Alignment of microbiology and computational biology competencies within a culturally integrated curriculum and research framework

We have developed a flexible undergraduate curriculum that leverages the place-based research of environmental microbiomes to increase the number of Indigenous researchers in microbiology, data science and scientific computing. Monitoring Environmental Microbiomes (MEM) provides a curriculum and research framework designed to integrate an Indigenous approach when conducting authentic scientific research and to build interest and confidence at the undergraduate level. MEM has been successfully implemented as a short summer workshop to introduce computing practices in microbiome analysis. Based on self-assessed student knowledge of topics and skills, increased scientific confidence and interest in genomics careers were observed. We propose MEM be incorporated in a scalable course-based research experience for undergraduate institutions, including tribal colleges and universities, community colleges and other minority serving institutions. This coupled curricular and research framework explicitly considers cultural perspectives, access and equity to train a diverse future workforce that is more informed to engage in microbiome research and to translate microbiome science to benefit community and environmental health.

The PARP inhibitor Rucaparib synergizes with radiation to attenuate atypical teratoid rhabdoid tumor growth

Background

Atypical teratoid rhabdoid tumors (ATRT) are highly aggressive pediatric brain tumors. The available treatments rely on toxic chemotherapy and radiotherapy, which themselves can cause poor outcomes in young patients. Poly (ADP-ribose) polymerases (PARP), multifunctional enzymes which play an important role in DNA damage repair and genome stability have emerged as a new target in cancer therapy. An FDA-approved drug screen revealed that Rucaparib, a PARP inhibitor, is important for ATRT cell growth. This study aims to investigate the effect of Rucaparib treatment in ATRT.

Methods

This study utilized cell viability, colony formation, flow cytometry, western blot, immunofluorescence, and immunohistochemistry assays to investigate Rucaparib's effectiveness in BT16 and MAF737 ATRT cell lines. In vivo, intracranial orthotopic xenograft model of ATRT was used. BT16 cell line was transduced with a luciferase-expressing vector and injected into the cerebellum of athymic nude mice. Animals were treated with Rucaparib by oral gavaging and irradiated with 2 Gy of radiation for 3 consecutive days. Tumor growth was monitored using In Vivo Imaging System.

Results

Rucaparib treatment decreased ATRT cell growth, inhibited clonogenic potential of ATRT cells, induced cell cycle arrest and apoptosis, and led to DNA damage accumulation as shown by increased expression of γH2AX. In vivo, Rucaparib treatment decreased tumor growth, sensitized ATRT cells to radiation and significantly increased mice survival.

Conclusion

We demonstrated that Rucaparib has potential to be a new therapeutic strategy for ATRT as seen by its ability to decrease ATRT tumor growth both in vitro and in vivo.

Population-based analysis of radiation-induced gliomas after cranial radiotherapy for childhood cancers

Background

Cranial radiotherapy (RT) used for pediatric CNS cancers and leukemias carries a risk of secondary CNS malignancies, including radiation-induced gliomas (RIG). Our aim was to characterize the epidemiology of RIG.

Methods

This retrospective study used SEER data (1975–2016). Cohort 1 included patients diagnosed with glioma as a second malignancy ≥2 years after receiving treatment for a first malignancy diagnosed at 0–19 years, either a primary CNS tumor (1a, n = 57) or leukemia (1b, n = 20). Cohort 2 included patients who received RT for a pediatric CNS tumor and died of presumed progressive disease >7 years after diagnosis, since previous studies have documented many missed RIGs in this group (n = 296). Controls (n = 10 687) included all other patients ages 0–19 years who received RT for a first CNS tumor or leukemia.

Results

For Cohort 1, 0.77% of patients receiving cranial RT developed RIG. 3.39% of patients receiving cranial RT for primary CNS tumors fell in cohort 2. Median latency to RIG diagnosis was 11.1 years and was significantly shorter for cohort 1b than 1a. Median OS for cohort 1 was 9.0 months. Receiving surgery, radiation, or chemotherapy were all associated with a nonstatistically significant improvement in OS (P .1–.2). A total of 1.8% of all brain tumor deaths fell in cohort 1, with 7.9% in cohort 2.

Conclusion

A total of 1%–4% of patients undergoing cranial RT for pediatric cancers later developed RIG, which can occur 3–35 years after RT. Given the substantial and likely underestimated impact on overall CNS tumor mortality, RIG is deserving of increased attention in preclinical and clinical studies.

Transcription factor-nucleosome dynamics from plasma cfDNA identifies ER-driven states in breast cancer

Genome-wide binding profiles of estrogen receptor (ER) and FOXA1 reflect cancer state in ER+ breast cancer. However, routine profiling of tumor transcription factor (TF) binding is impractical in the clinic. Here, we show that plasma cell-free DNA (cfDNA) contains high-resolution ER and FOXA1 tumor binding profiles for breast cancer. Enrichment of TF footprints in plasma reflects the binding strength of the TF in originating tissue. We defined pure in vivo tumor TF signatures in plasma using ER+ breast cancer xenografts, which can distinguish xenografts with distinct ER states. Furthermore, state-specific ER-binding signatures can partition human breast tumors into groups with significantly different ER expression and mortality. Last, TF footprints in human plasma samples can identify the presence of ER+ breast cancer. Thus, plasma TF footprints enable minimally invasive mapping of the regulatory landscape of breast cancer in humans and open vast possibilities for clinical applications across multiple tumor types.

Compositional Data Analysis using Kernels in mass cytometry data

MOTIVATION

Cell-type abundance data arising from mass cytometry experiments are compositional in nature. Classical association tests do not apply to the compositional data due to their non-Euclidean nature. Existing methods for analysis of cell type abundance data suffer from several limitations for high-dimensional mass cytometry data, especially when the sample size is small.

RESULTS

We proposed a new multivariate statistical learning methodology, Compositional Data Analysis using Kernels (CODAK), based on the kernel distance covariance (KDC) framework to test the association of the cell type compositions with important predictors (categorical or continuous) such as disease status. CODAK scales well for high-dimensional data and provides satisfactory performance for small sample sizes (n < 25). We conducted simulation studies to compare the performance of the method with existing methods of analyzing cell type abundance data from mass cytometry studies. The method is also applied to a high-dimensional dataset containing different subgroups of populations including Systemic Lupus Erythematosus (SLE) patients and healthy control subjects.

AVAILABILITY AND IMPLEMENTATION

CODAK is implemented using R. The codes and the data used in this manuscript are available on the web at http://github.com/GhoshLab/CODAK/.

CONTACT

prudra@okstate.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Advances online.

Ethnic Disparities in Imaging Utilization at Diagnosis of Non-Small Cell Lung Cancer

BACKGROUND

Prior research demonstrated statistically significant racial disparities related to lung cancer treatment and outcomes. We examined differences in initial imaging and survival between blacks, Hispanics, and non-Hispanic whites.

METHODS

The linked Surveillance, Epidemiology, and End Results-Medicare database between 2007 and 2015 was used to compare initial imaging modality for patients with lung cancer. Participants included 28 881 non-Hispanic whites, 3123 black, and 1907 Hispanics, patients age 66 years and older who were enrolled in Medicare fee-for-service and diagnosed with lung cancer. The primary outcome was comparison of positron emission tomography (PET) imaging with computerized tomography (CT) imaging use between groups. A secondary outcome was 12-month cancer-specific survival. Information on stage, treatment, and treatment facility was included in the analysis. Chi-square test and logistic regression were used to evaluate factors associated with imaging use. Kaplan-Meier method and Cox proportional hazards regression were used to calculate adjusted hazard ratios and survival. All statistical tests were two-sided.

RESULTS

After adjusting for demographic, community, and facility characteristics, blacks were less likely to undergo PET or CT imaging at diagnosis compared with non-Hispanic whites odds ratio (OR) = 0.54 (95% confidence interval [CI] = 0.50 to 0.59; P < .001). Hispanics were also less likely to receive PET with CT imaging (OR = 0.72, 95% CI = 0.65 to 0.81; P < .001). PET with CT was associated with improved survival (HR = 0.61, 95% CI = 0.57 to 0.65; P < .001).

CONCLUSIONS

Blacks and Hispanics are less likely to undergo guideline-recommended PET with CT imaging at diagnosis of lung cancer, which may partially explain differences in survival. Awareness of this issue will allow for future interventions aimed at reducing this disparity.