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• Virtual Growing Child 5-Dimensional Functional Models for Treating Respiratory Anomalies (dMRI-VGC)

  Objectives We are sharing a database of dynamic magnetic resonance imaging (dMRI) scans of normal children, which can serve as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The database can also be useful to advance future AI-based research on image-based object segmentation and analysis. Background In pediatric patients with respiratory abnormalities, it is important to understand the alterations in regional dynamics of the lungs and other thoracoabdominal components, which in turn requires a quantitative understanding of what is considered as normal in healthy children. Currently, such a normative database of regional respiratory structure and function in healthy children does not exist. Participants 200 normal children (ages 6-18 years) participated in our research study related to this dataset. DesignThe shared open-source normative database is from our ongoing virtual growing child (VGC) project, which includes 4D dMRI images representing one breathing cycle for each normal child and also segmentations of 10 objects at end expiration (EE) and end inspiration (EI) phases of the respiratory cycle in the 4D image. The lung volumes at EE and EI as well as the excursion volumes of chest wall and diaphragm from EE to EI, left and right sides separately, are also reported. The database has thus 4,000 3D segmentations from 200 normal children in total. The database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. All dMRI scans are acquired from normal children during free-breathing. The dMRI acquisition protocol was as follows: 3T MRI scanner (Verio, Siemens, Erlangen, Germany), true-FISP bright-blood sequence, TR=3.82 ms, TE=1.91 ms, voxel size ~1×1×6 mm3, 320×320 matrix, bandwidth 258 Hz, and flip angle 76o. With recent advances, for each sagittal location across the thorax and abdomen, we acquired 40 2D slices over several tidal breathing cycles at ~480 ms/slice. On average, 35 sagittal locations are imaged, yielding a total of ~1400 2D MRI slices, with a resulting total scan time of 11-13 minutes for any particular study participant.The collected dMRI scan data then went through the procedure of 4D image construction, image processing, object segmentation, and volumetric measurements from segmentations. 4D image construction: For the acquired dMRI scans, we utilized an automated 4D image construction approach to form one 4D image over one breathing cycle (consisting of typically 5-8 respiratory phases) from each acquired dMRI scan to represent the whole dynamic thoraco-abdominal body region. The algorithm selects 175-280 slices (35 sagittal locations × 5-8 respiratory phases) from the 1400 acquired slices in an optimal manner using an optical flux method. Image processing: Intensity standardization is performed on every time point/3D volume of the 4D image so that image values have the same tissue-specific meaning across all subjects. Object segmentation: For each subject, there are 10 objects segmented at both EE and EI time points in this database. They include the thoracoabdominal skin outer boundary, left and right lungs, liver, spleen, left and right kidneys, diaphragm, and left and right hemi-diaphragms. All dMRI scans utilize large field of view images, which include the full thorax and abdomen to the inferior aspect of the kidneys in the sagittal plane. We used a pretrained U-Net based deep learning network to first segment all objects, and then all auto-segmentation results were visually checked and manually refined as needed, under the supervision of a radiologist with over 25 years of expertise in MRI and thoracoabdominal radiology. Manual segmentations have been performed for all objects in all datasets. Volumetric measurements based on object segmentations for lung volumes (left and right separately) at EE and EI, as well as for chest wall and diaphragm excursion volumes (left and right separately) are reported. ConclusionsThe provided database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters of volumes for normal children. The database has 4,000 3D segmentations from 200 normal children, which to our knowledge is the largest and only such dMRI dataset to date. All images and object segmentations are saved in DICOM. All DICOM files (176,574 in total) have been anonymized, and PHI has been removed. The database can be used as a reference standard to quantify regional respiratory abnormalities in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The large amount of object segmentations can potentially benefit AI-based research on image-based object segmentation and analysis.

  Study phs004002

• Identification of biomarkers of response to preoperative talazoparib monotherapy in treatment naïve gBRCA+ breast cancers

  Germline mutations in BRCA1 or BRCA2 exist in ~2-7% of breast cancer patients, which has led to the approval of PARP inhibitors in the advanced setting. We have previously reported a phase II neoadjuvant trial of single agent talazoparib for patients with germline BRCA pathogenic variants with a pathologic complete response (pCR) rate of 53%. As nearly half of the patients treated did not have pCR, better strategies are needed to overcome treatment resistance. To this end, we conducted multi-omic analysis of 13 treatment naïve breast cancer tumors from patients that went on to receive single-agent neoadjuvant talazoparib. We looked for biomarkers that were predictive of response (assessed by residual cancer burden) after 6 months of therapy. We found that all resistant tumors exhibited either the loss of SHLD2, expression of a hypoxia signature, or expression of a stem cell signature. These results indicate that the deep analysis of pre-treatment tumors can identify biomarkers that are predictive of response to talazoparib and potentially other PARP inhibitors, and provides a framework that will allow for better selection of patients for treatment, as well as a roadmap for the development of novel combination therapies to prevent emergence of resistance.

  Study EGAS00001005676

• Transcriptional characterization of human innate lymphoid cells (ILCs) and natural killer (NK) cells from fresh umbilical cord blood

  The heterogenicity of cord blood ILCs (ILC1, ILC2, ILC3) and NK cells in humans has so far not been assessed. In this study, we sorted from fresh cord blood the respective cell types from 3 donors and performed single-cell transcriptome analysis. To achieve high resoluation, each sorted population was labelled individually via multiplexing antibodies and subsequent Abseq staining before pooling. To determine single-cell transcriptomes the BD Rhapsody pipeline was performed. This is the first data set to characterize human ILCs and NK cells at such depth.

  Study EGAS50000001204

• Panbody_nanoseq

  Different cell types within the human body are subject to different endogenous and exogenous exposures which may result in distinct patterns of mutagenesis. In non-clonal tissues, it is difficult to assess these mutational patterns, including the rate at which they occur. This study will examine the mutational rates and signatures operating across a wide range of normal tissues using the nanoseq technology to reliably call variants, irrespective of their clonal composition. Each cell type will be enriched for using laser capture microdissection. This will serve as a reference against which future work on benign and malignant disease entities can be compared.

  Study EGAS00001005521

• AML FLT3 TCR study

  We identified a T-cell receptor (TCR) reactive to the recurrent FLT3 D835Y mutation in the tyrosine-kinase domain frequently expressed in acute myeloid leukemia (AML). To validate the TCRs' elimination efficacy of leukemic cells, we transplanted human AML cells with FLT3 D835Y mutation into NSG-SGM3 mice and treated either with TCR FLT3 D835Y redirected T cells, or control TCR (TCR 1G4). After treatment, we performed flow sorting of human AML cells from the mice (mCD45-hCD45+hCD3-hCD19-) and compared to primary AML blasts (hCD3-hCD19-) and primary T cells (hCD3+hCD8+orhCD4+hCD19-hCD33-) by whole-exome sequencing.

  Study EGAS00001007467

• Transcriptional profiling of ovarian tumours and cell lines

  Purpose: Endogenous retroviruses (ERVs) play a role in a variety of biological processes, including embryogenesis and cancer. DNA methyltransferase inhibitors (DNMTi)-induced ERV expression triggers interferon responses in ovarian cancer cells via the viral sensing machinery. Baseline expression of ERVs also occurs in cancer cells, though this process is poorly understood and previously unexplored in epithelial ovarian cancer (EOC). Here, the prognostic and immunomodulatory consequences of baseline ERV expression was assessed in EOC. Experimental Design: ERV expression was assessed using EOC transcriptional data from TCGA and from an independent cohort (HH), as well as from untreated or DNMTi-treated EOC cell lines. LASSO logistic regression defined an ERV expression score to predict patient prognosis. Immunohistochemistry (IHC) was conducted on the HH cohort. Combination of DNMTi treatment with γδ T cells was tested in vitro, using EOC cell lines and patient-derived tumor cells. Results: ERV expression was found to define clinically relevant subsets of EOC patients. An ERV prognostic score was successfully generated in TCGA and validated in the independent cohort. In EOC patients from this cohort, a high ERV score was associated with better survival (log-rank p=0.0009) and correlated with infiltration of CD8+PD1+ T cells (r=0.46, p=0.0001). In vitro, baseline ERV expression dictates the level of ERV induction in response to DNMTi. Manipulation of ERV expression by DNMTi resulted in improved EOC cell killing by cytotoxic immune cells. Conclusions: These findings uncover the potential for baseline ERV expression to robustly inform EOC patient prognosis, influence tumor immune infiltration and affect anti-tumor immunity.

  Study EGAS00001004814

• High Sensitivity ctDNA Analysis Using a Novel Panel and NOIR-SS Technology for Monitoring Advanced Urothelial Carcinoma

  This dataset contains sequencing data generated by targeted amplicon analysis using the NOIR-SS method in 15 patients with advanced urothelial carcinoma. Formalin-fixed paraffin-embedded (FFPE) tumor tissues (15 samples) and plasma cfDNA (50 samples) were analyzed. Tumor tissues were subjected to targeted panel sequencing to identify mutations in TP53, FGFR3, TERT, and KRAS/HRAS. Plasma cfDNA was analyzed using a tumor-informed approach focusing on the mutations identified in matched tumor tissues. Sequencing was performed on the Ion PGM or Ion S5 XL platform. Raw sequencing data are provided in FASTQ format under controlled access.

  Study JGAS000836

• Identification of the cause of juvenile parkinsonism in a case_SYNJ1

  This study aimed to elucidate the genetic causes underlying the Juvenile Parkinsonism diagnosed in a girl using whole-exome sequencing, analysis of CAG repeats, RNA sequencing analysis on fibroblasts, and metabolite identification. As a result, a homozygous missense mutation SNP T/C (rs2254562) in SYNJ1 and alteration in metabolism were identified.

  Study EGAS00001007686

• RNAseq Iron-Treated iPSC-derived Microglia

  Iron accumulation in microglia has been observed in Alzheimer’s disease and other neurodegenerative disorders and is thought to contribute to disease progression through various mechanisms including neuroinflammation. To study the interaction between iron accumulation and inflammation, we treated human induced pluripotent stem cell-derived microglia (iPSC-MG) with an increasing concentration of iron, in combination with inflammatory stimuli such as interferon gamma and amyloid β, and performed RNA sequencing.

  Study EGAS00001006112

• Differential Presence of Exons in Cell-Free DNA Reveals Different Patterns in Colorectal Cancer Between Metastatic and Non-Metastatic Patients

  Tumor genotyping during disease follow-up is gaining an increasing interest for monitoring and clinical management in colorectal cancer (CRC). Growing evidences suggest that single biopsies are inefficient for molecular profiling, due to clonal evolution and intratumoral heterogeneity of primary tumors and/or metastasis. Circulating free DNA (cfDNA) has emerged as an alternative source of genetic material that seems to be representative of the continuously changing tumor molecular features. Thus, the so-called 'liquid biopsy' also offers the additional advantages of accessibility and non-invasiveness for the patient. Novel strategies based on next-generation sequencing (NGS) of cfDNA are being currently developed, with most studies focusing on targeted deep sequencing panels of potential clinically actionable genes. Here we describe an alternative approach, consisting of exome-sequencing in cfDNA at a relatively shallower depth to provide a more general overview of the circulating genome. In this study, two groups of colorectal cancer patients, with either disseminated (M) or localized disease (N), were compared using whole-exome sequencing and a RNA-seq bioinformatics analysis pipeline. Thus, a set of 379 exons present at different levels in cfDNA from both groups were identified, giving rise to a completely new concept in this field, termed as 'Differential Presence of Exons'. Differentially present exons (DPEs) were subsequently used for M and N patients clustering and classification, allowing for the design of a predictive algorithm with encouraging preliminary results in a small subset of patients that could not be initially classified attending to the selection criteria (U group, unassignable).

  Study EGAS00001002687