Enhancer activation from transposable elements in extrachromosomal DNA.

Extrachromosomal DNA (ecDNA) is a hallmark of aggressive cancer, contributing to both oncogene amplification and tumor heterogeneity. Here, we used Hi-C, super-resolution imaging, and long-read sequencing to explore the nuclear architecture of MYC-amplified ecDNA in colorectal cancer cells. Intriguingly, we observed frequent spatial proximity between ecDNA and 68 repetitive elements which we called ecDNA-interacting elements or EIEs. To characterize a potential regulatory role of EIEs, we focused on a fragment of the L1M4a1#LINE/L1 which we found to be co-amplified with MYC on ecDNA, gaining enhancer-associated chromatin marks in contrast to its normally silenced state. This EIE, in particular, existed as a naturally occurring structural variant upstream of MYC, gaining oncogenic potential in the transcriptionally permissive ecDNA environment. This EIE sequence is sufficient to enhance MYC expression and is required for cancer cell fitness. These findings suggest that silent repetitive genomic elements can be reactivated on ecDNA, leading to functional cooption and amplification. Repeat element activation on ecDNA represents a mechanism of accelerated evolution and tumor heterogeneity and may have diagnostic and therapeutic potential.

Extraction of Unstructured Electronic Health Records to Evaluate Glioblastoma Treatment Patterns.

PURPOSE: Data on lines of therapy (LOTs) for cancer treatment are important for clinical oncology research, but LOTs are not explicitly recorded in electronic health records (EHRs). We present an efficient approach for clinical data abstraction and a flexible algorithm to derive LOTs from EHR-based medication data on patients with glioblastoma multiforme (GBM). METHODS: Nonclinicians were trained to abstract the diagnosis of GBM from EHRs, and their accuracy was compared with abstraction performed by clinicians. The resulting data were used to build a cohort of patients with confirmed GBM diagnosis. An algorithm was developed to derive LOTs using structured medication data, accounting for the addition and discontinuation of therapies and drug class. Descriptive statistics were calculated and time-to-next-treatment (TTNT) analysis was performed using the Kaplan-Meier method. RESULTS: Treating clinicians as the gold standard, nonclinicians abstracted GBM diagnosis with a sensitivity of 0.98, specificity 1.00, positive predictive value 1.00, and negative predictive value 0.90, suggesting that nonclinician abstraction of GBM diagnosis was comparable with clinician abstraction. Of 693 patients with a confirmed diagnosis of GBM, 246 patients contained structured information about the types of medications received. Of them, 165 (67.1%) received a first-line therapy (1L) of temozolomide, and the median TTNT from the start of 1L was 179 days. CONCLUSION: We described a workflow for extracting diagnosis of GBM and LOT from EHR data that combines nonclinician abstraction with algorithmic processing, demonstrating comparable accuracy with clinician abstraction and highlighting the potential for scalable and efficient EHR-based oncology research.

Selective prediction for extracting unstructured clinical data.

OBJECTIVE: While there are currently approaches to handle unstructured clinical data, such as manual abstraction and structured proxy variables, these methods may be time-consuming, not scalable, and imprecise. This article aims to determine whether selective prediction, which gives a model the option to abstain from generating a prediction, can improve the accuracy and efficiency of unstructured clinical data abstraction. MATERIALS AND METHODS: We trained selective classifiers (logistic regression, random forest, support vector machine) to extract 5 variables from clinical notes: depression (n = 1563), glioblastoma (GBM, n = 659), rectal adenocarcinoma (DRA, n = 601), and abdominoperineal resection (APR, n = 601) and low anterior resection (LAR, n = 601) of adenocarcinoma. We varied the cost of false positives (FP), false negatives (FN), and abstained notes and measured total misclassification cost. RESULTS: The depression selective classifiers abstained on anywhere from 0% to 97% of notes, and the change in total misclassification cost ranged from -58% to 9%. Selective classifiers abstained on 5%-43% of notes across the GBM and colorectal cancer models. The GBM selective classifier abstained on 43% of notes, which led to improvements in sensitivity (0.94 to 0.96), specificity (0.79 to 0.96), PPV (0.89 to 0.98), and NPV (0.88 to 0.91) when compared to a non-selective classifier and when compared to structured proxy variables. DISCUSSION: We showed that selective classifiers outperformed both non-selective classifiers and structured proxy variables for extracting data from unstructured clinical notes. CONCLUSION: Selective prediction should be considered when abstaining is preferable to making an incorrect prediction.

A stable mode of bookmarking by TBP recruits RNA polymerase II to mitotic chromosomes.

Maintenance of transcription programs is challenged during mitosis when chromatin becomes condensed and transcription is silenced. How do the daughter cells re-establish the original transcription program? Here, we report that the TATA-binding protein (TBP), a key component of the core transcriptional machinery, remains bound globally to active promoters in mouse embryonic stem cells during mitosis. Using live-cell single-molecule imaging, we observed that TBP mitotic binding is highly stable, with an average residence time of minutes, in stark contrast to typical TFs with residence times of seconds. To test the functional effect of mitotic TBP binding, we used a drug-inducible degron system and found that TBP promotes the association of RNA Polymerase II with mitotic chromosomes, and facilitates transcriptional reactivation following mitosis. These results suggest that the core transcriptional machinery promotes efficient transcription maintenance globally.

Engineering Intracellularly Retained Gaussia Luciferase Reporters for Improved Biosensing and Molecular Imaging Applications.

Gaussia luciferase (GLUC) is a bioluminescent reporter protein of increasing importance. As a secretory protein, it has increased sensitivity in vitro and in vivo (∼20 000-fold, and ∼1000-fold, respectively) over its competitor, secreted alkaline phosphatase. Unfortunately, this same advantageous secretory nature of GLUC limits its usefulness for many other possible intracellular applications, e.g., imaging signaling pathways in intact cells, in vivo imaging, and in developing molecular imaging biosensors to study protein-protein interactions and protein folding. Hence, to widen the research applications of GLUC, we developed engineered variants that increase its intracellular retention both by modifying the N-terminal secretory signal peptide and by tagging additional sequences to its C-terminal region. We found that when GLUC was expressed in mammalian cells, its N-terminal secretory signal peptide comprising amino acids 1-16 was essential for GLUC folding and functional activity in addition to its inherent secretory property. Modification of the C-terminus of GLUC by tagging a four amino acid (KDEL) endoplasmic reticulum targeting peptide in multiple repeats significantly improved its intracellular retention, with little impact on its folding and enzymatic activity. We used stable cells expressing this engineered GLUC with KDEL repeats to monitor chemically induced endoplasmic reticulum stress on cells. Additionally, we engineered an apoptotic sensor using modified variants of GLUC containing a four amino acid caspase substrate peptide (DEVD) between the GLUC protein and the KDEL repeats. Its use in cell culture resulted in increased GLUC secretion in the growth medium when cells were treated with the chemotherapeutic drugs doxorubicin, paclitaxel, and carboplatin. We thus successfully engineered a new variant GLUC protein that is retained inside cells rather than secreted extracellularly. We validated this novel reporter by incorporating it in biosensors for detection of cellular endoplasmic reticulum stress and caspase activation. This new molecularly engineered enzymatic reporter has the potential for widespread applications in biological research.

Orlistat and antisense-miRNA-loaded PLGA-PEG nanoparticles for enhanced triple negative breast cancer therapy.

BACKGROUND: This study explores the use of hydrophilic poly(ethylene glycol)-conjugated poly(lactic-co-glycolic acid) nanoparticles (PLGA-PEG-NPs) as delivery system to improve the antitumor effect of antiobesity drug orlistat for triple-negative breast cancer (TNBC) therapy by improving its bioavailability. MATERIALS & METHODS: PLGA-PEG-NPs were synthesized by emulsion-diffusion-evaporation method, and the experiments were conducted in vitro in MDA-MB-231 and SKBr3 TNBC and normal breast fibroblast cells. RESULTS: Delivery of orlistat via PLGA-PEG-NPs reduced its IC50 compared with free orlistat. Combined treatment of orlistat-loaded NPs and doxorubicin or antisense-miR-21-loaded NPs significantly enhanced apoptotic effect compared with independent doxorubicin, anti-miR-21-loaded NPs, orlistat-loaded NPs or free orlistat treatments. CONCLUSION: We demonstrate that orlistat in combination with antisense-miR-21 or current chemotherapy holds great promise as a novel and versatile treatment agent for TNBC.