Developmental and therapeutic implications of IL4ra expression for rhabdomyosarcoma.

Rhabdomyosarcoma (RMS) is a solid tumor whose metastatic progression can be accelerated through interleukin-4 receptor alpha (Il4ra) mediated interaction with normal muscle stem cells (satellite cells). To understand the function of Il4ra in this tumor initiation phase of RMS, we conditionally deleted Il4ra in genetically-engineered RMS mouse models. Nullizygosity of Il4ra altered the latency, site and/or stage distribution of RMS tumors compared to IL4RA intact models. Primary tumor cell cultures taken from the genetically-engineered models then used in orthotopic allografts further defined the interaction of satellite cells and RMS tumor cells in the context of tumor initiation: in alveolar rhabdomyosarcoma (ARMS), satellite cell co-injection was necessary for Il4ra null tumor cells engraftment, whereas in embryonal rhabdomyosarcoma (ERMS), satellite cell co-injection decreased latency of engraftment of Il4ra wildtype tumor cells but not Il4ra null tumor cells. When refocusing on Il4ra wildtype tumors by single cell sequencing and cytokine studies, we have uncovered a putative signaling interplay of Il4 from T-lymphocytes being received by Il4ra + rhabdomyosarcoma tumor cells, which in turn express Ccl2, the ligand for Ccr2 and Ccr5. Taken together, these results suggest that mutations imposed during tumor initiation have different effects than genetic or therapeutic intervention imposed once tumors are already formed. We also propose that CCL2 and its cognate receptors CCR2 and/or CCR5 are potential therapeutic targets in Il4ra mediated RMS progression.

An atlas of causal and mechanistic drivers of interpatient heterogeneity in glioma.

Grade IV glioma, formerly known as glioblastoma multiforme (GBM) is the most aggressive and lethal type of brain tumor, and its treatment remains challenging in part due to extensive interpatient heterogeneity in disease driving mechanisms and lack of prognostic and predictive biomarkers. Using mechanistic inference of node-edge relationship (MINER), we have analyzed multiomics profiles from 516 patients and constructed an atlas of causal and mechanistic drivers of interpatient heterogeneity in GBM (gbmMINER). The atlas has delineated how 30 driver mutations act in a combinatorial scheme to causally influence a network of regulators (306 transcription factors and 73 miRNAs) of 179 transcriptional "programs", influencing disease progression in patients across 23 disease states. Through extensive testing on independent patient cohorts, we share evidence that a machine learning model trained on activity profiles of programs within gbmMINER significantly augments risk stratification, identifying patients who are super-responders to standard of care and those that would benefit from 2 nd line treatments. In addition to providing mechanistic hypotheses regarding disease prognosis, the activity of programs containing targets of 2 nd line treatments accurately predicted efficacy of 28 drugs in killing glioma stem-like cells from 43 patients. Our findings demonstrate that interpatient heterogeneity manifests from differential activities of transcriptional programs, providing actionable strategies for mechanistically characterizing GBM from a systems perspective and developing better prognostic and predictive biomarkers for personalized medicine.

A single-cell based precision medicine approach using glioblastoma patient-specific models.

Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single-cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that modulate cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches to an individual patient in a concurrent, adjuvant, or recurrent setting.

Functional genomic analysis of epithelioid sarcoma reveals distinct proximal and distal subtype biology.

BACKGROUND: Metastatic epithelioid sarcoma (EPS) remains a largely unmet clinical need in children, adolescents and young adults despite the advent of EZH2 inhibitor tazemetostat. METHODS: In order to realise consistently effective drug therapies, a functional genomics approach was used to identify key signalling pathway vulnerabilities in a spectrum of EPS patient samples. EPS biopsies/surgical resections and cell lines were studied by next-generation DNA exome and RNA deep sequencing, then EPS cell cultures were tested against a panel of chemical probes to discover signalling pathway targets with the most significant contributions to EPS tumour cell maintenance. RESULTS: Other biologically inspired functional interrogations of EPS cultures using gene knockdown or chemical probes demonstrated only limited to modest efficacy in vitro. However, our molecular studies uncovered distinguishing features (including retained dysfunctional SMARCB1 expression and elevated GLI3, FYN and CXCL12 expression) of distal, paediatric/young adult-associated EPS versus proximal, adult-associated EPS. CONCLUSIONS: Overall results highlight the complexity of the disease and a limited chemical space for therapeutic advancement. However, subtle differences between the two EPS subtypes highlight the biological disparities between younger and older EPS patients and emphasise the need to approach the two subtypes as molecularly and clinically distinct diseases.

Identification of high-risk monomorphic post-transplant lymphoproliferative disorder following solid organ transplantation.

Monomorphic post-transplant lymphoproliferative disorder (M-PTLD) occurring after solid organ transplant histologically resembles aggressive non-Hodgkin lymphomas, with diffuse large B-cell lymphoma being the most common. In a cohort of 40 patients with DLBCL-type M-PTLD, inferior progression free survival (PFS) was observed for Revised International Prognostic Index (R-IPI) >2 (p = 0.01) and high-risk pathologic features (p = 0.02), defined by double expressor lymphoma, MYC rearrangement, or increased copy number of either MYC or BCL2. Overall survival (OS) was inferior in R-IPI >2 (p = 0.002) and high-risk pathologic features (p = 0.003). Combining both R-IPI >2 and high-risk pathologic features resulted in well-delineated good, intermediate, and poor risk groups of DLBCL-type M-PTLD with respect to both PFS and OS (p < 0.001). Our results demonstrate a prognostic role for both the R-IPI score and presence of high-risk pathologic features in DLBCL-type M-PTLD.

Comparative Analysis of Brain and Fat Body Gene Splicing Patterns in the Honey Bee, Apis mellifera.

RNA-seq has proven to be a powerful tool to unravel various aspects of the transcriptome, especially the quantification of alternative splicing (AS) that leads to isoform diversity. The honey bee (Apis mellifera) is an important model organism for studying the molecular underpinnings of behavioral plasticity and social behavior, and recent RNA-seq studies of honey bees have revealed AS patterns and their regulation by DNA methylation. However, tissue-specific AS patterns have not been fully explored. In this paper, we characterized AS patterns in two different honey bee tissue types, and also explored their conservation and regulation. We used the RNA-seq data from brain and fat body to improve the existing models of honey bee genes and identified tissue-specific AS patterns. We found that AS genes show high conservation between honey bee and Drosophila melanogaster We also confirmed and extended previous findings of a correlation between gene body DNA methylation and AS patterns, providing further support for the role of DNA methylation in regulating AS. In addition, our analysis suggests distinct functional roles for tissue-specific alternatively spliced genes. Taken together, our work provides new insights into the conservation and dynamics of AS patterns across different tissue types.

Tumor Thrombus From Urothelial Carcinoma of Renal Pelvis.

We present a case report of an unfortunate 67-year-old man. He developed symptoms of thromboembolic disease in the immediate post-operative period after nephroureterectomy with recurrent pulmonary embolism in spite of being on several different anticoagulation regimens. He was later found to have tumor thrombus in the pulmonary artery secondary to urothelial tumor extension.

Spirometry in Hospitalized Patients with Acute Exacerbation of COPD Accurately Predicts Post Discharge Airflow Obstruction.

Purpose: Objective documentation of airflow obstruction is often lacking inhospitalized patients treated for acute exacerbation of chronic obstructive pulmonary disease (AECOPD). The utility of spirometry performed in hospitalized patients to identify airflow obstruction, and thus a diagnosis of COPD, is unclear. Our aim was to compare inpatient spirometry, performed during an AECOPD, with outpatient spirometry. Methods: A retrospective analysis of data from patients enrolled in an AECOPD care plan was performed. As part of the plan, patients underwent inpatient spirometry to establish a COPD diagnosis and outpatient clinic spirometry within 4 weeks of hospital discharge to confirm it. Data analyzed included forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), slow vital capacity (SVC) and FEV1/ vital capacity (VC). Obstruction was defined by FEV1/VC<0.70. Results: A total of 159 patients (mean age 63.2 +/- 10.5 years) had corresponding in- and outpatient spirometry. The median days between inpatient and outpatient spirometry was 12 (interquartile range [IQR] 9-16). Inpatient spirometry had a sensitivity of 94%, specificity of 24%, positive predictive value of 83% and negative predictive value of 53% for predicting outpatient obstruction. The area under curve for using inpatient spirometry was 0.82. The mean difference between inpatient and outpatient FEV1 was 0.44 +/- 0.03 liters or 17.3 +/- 1.13 % predicted (p<0.0001) for FEV1. Conclusions: Inpatient spirometry accurately predicts outpatient airflow obstruction, thus providing an opportunity to identify patients admitted with suspected AECOPD who have no prior spirometric documentation.

Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform.

Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop.